A Smart Bear

Strategic choices: When both options are good

Jason Cohen — Sun, 22 Feb 2026 00:00:00 +0000

![I put them all in one basket on purpose. I like to live dangerously.](https://public.asmartbear.com/books/anthology/chapters/strategic-choices/d6573print-simple.png) All day, everyone at the company makes decisions, independently: How to respond to this customer. How much effort to put into this feature. What we build next. How we position a weakness on the website. Where next month's budget goes. Which marketing channel we bet on. What ideas we even bother to consider. Everyone had *better* be making independent decisions. That's how you [scale](https://longform.asmartbear.com/scale/)--whether three people or three hundred; even if it's just you. It's how you [manage autonomous teams](https://longform.asmartbear.com/in-command/), pushing power down to the individual (but [not too far](https://longform.asmartbear.com/tension-autonomy-admin/)). But even perfect individual decisions can easily be inconsistent between teams: decisions can be locally rational yet contradict decisions made elsewhere. If everyone is moving in different directions, the company goes nowhere, even if everyone is productive and efficient. Even autonomous teams require alignment. Not alignment on tactics (those change weekly), nor on platitudes (those change nothing), but alignment on a small set of macro-level decisions. These are the **strategic choices**.[^1] [^1]: A term popularized by Roger L. Martin, who has much more to say in his 2013 book _Playing to Win: How Strategy Really Works_ and in [articles like this](https://rogermartin.medium.com/strategic-choice-chartering-3d56d8962ec3). Indeed, he defines the whole concept of strategy as a set of choices: "Strategy is an integrated set of choices that uniquely positions the firm in its industry so as to create sustainable advantage and superior value relative to the competition." When the daily decisions conform to the strategic ones, "more people" actually can produce "more results." Customers stop seeing dissonance between the promises marketing made on the homepage and what engineering built. Decisions made independently across the company reinforce each other, which means they compound instead of diluting each other. Most "strategy" documents fail because they repeat platitudes instead of making decisions that require trade-offs and sacrifice. They read like moral aspirations: "Customer-first." "Move fast over bureaucracy." "Quality over shortcuts." "Great design over mediocrity." Those aren't wrong per se; they're just non-decisions. No one says, "Our strategy is to have low quality and ugly design," yet mediocre products exist. Why? Because companies chose *other* priorities--price, speed, extensibility, power, complexity--and the trade-offs showed up as low quality or uninspired design. These underlying choices--saying "no" even to things that are universally desirable like quality and design--are the hallmarks of real strategy. Your unique set of choices makes you "the best" according to your [target market](https://longform.asmartbear.com/icp-ideal-customer-persona/). A strategy that never says "no" is not a strategy. It should say "no" most of the time; that's a signal that you are actually [focusing](https://longform.asmartbear.com/focus/). [Great strategies](https://longform.asmartbear.com/great-strategy/) include strategic choices having these properties: 1. **Real decisions:** Chooses A over B even though B is also desirable. 2. **Accepts consequences:** Chooses A *and* all of A's consequences, including the unfortunate ones. 3. **Reinforces each other:** A set of choices that reinforce each other, rather than choices that are smart in isolation but conflict together. Next I'll explain each property, share a few facilitation tips, and end with a table of examples to spark your own discussions. ## Real strategic choices ### 1. Both sides of the choice are smart "Good design" isn't a real choice because "bad design" was never a smart alternative. "All-in-one" *is* a real choice, because an alternative is "A small core, extremely extensible"; both are smart. You know this because **many successful companies, with great products, have made each choice**. Notably, **"both" is not a sensible choice.** Either the product is self-contained or it isn't. Either you design the UX as a complete experience, or you design it to accept third-party UX in ways the original designers couldn't predict. Either you sell "the only thing you'll ever need to buy," or you sell "the platform with an ecosystem for everything." Either it "does a few things very well," or it "does everything but you piece it together." Either support can answer questions about the whole product, or support has to route you to plugin vendors who own the pieces you're asking about. You know you've made a real, strategic choice when there's a long list of second-order effects that permeates the entire company. This is another form of [the Opposite Test](https://longform.asmartbear.com/opposite-test/). Once again, ensuring that "the opposite" isn't trivial or nonsense, is how we know we're saying something real and important. It's true of marketing too. ### 2. Choices have consequences, both desirable and unfortunate A platitude feels good because it has no downside. How could "great design" be bad? Customers love it, developers are proud of their work, and it reduces support. If it's all good news, it isn't a real choice, or you aren't grappling with the actual consequences. "Great design" comes with costs. You need talented designers, which means a larger team. You need engineers who are excited to craft the UX with as much care as they scale the database and want to chase down every last UI bug before adding the next shiny new feature. You invest in design systems. Features and changes take longer because they are required not only to work, but to be well-designed. You say "no" to features customers want if it means the design would worsen. Even then, not all customers agree on what "great design" means, so you thrill some people while irritating others. A strategic choice is a package deal: you don't get advantages without weaknesses. When you list the consequences, writing them into the strategy, the decision becomes usable by the whole company. You reduce individual interpretation and you help everyone align their daily decisions. This also helps you ensure the entire strategy is internally consistent. ### 3. Not just consistent: mutually reinforcing Use the list of choice-consequences to check for contradictions. Conflicts often hide in the second-order effects. For example, "small team" and "feature-rich" might look unrelated, but keeping a team small usually means keeping the product small and simple, because the team has to build it, maintain it, support it, and evolve it. That's in conflict with "feature-rich." But perhaps they're not in conflict after all if we make yet another strategic choice: a small team *can* maintain a small core that is designed for third-party extensions. Then, an ecosystem of plugins and APIs makes the entire product feature-rich after all! Now the choices interlock: "small team," "small core," and "extensible ecosystem." Each drags along its own implications, which surface more choices, and so on. This exploration is how you arrive at a coherent set of decisions. Even better than "not conflicting" is "reinforcing." "Extensible" helps the product be "feature-rich." A small team forces the core to stay small, maintaining space for the ecosystem to thrive. Each decision makes the next easier and stronger. When strong choices reinforce each other, the whole company starts to feel like it has a personality--an opinion about How The World Should Be. Some customers won't share that opinion, but the ones who do will love you all the more. [Love is how you win](https://longform.asmartbear.com/willingness-to-pay/). And everyone at the company can move faster. You stop re-litigating trade-offs. Marketing and sales stay aligned with the product. People have more fun, because coherence feels like competence--and competence feels good. ## Converting weak choices into strong choices When you're facilitating the creation of strategic choices, you will find that people constantly write weak pseudo-choices that fail the Opposite Test. They already know which side they prefer, so they'd rather make the other side obviously bad--so their choice "wins"--instead of engaging in the struggle of making firm, clear decisions. ![Man at desk has 'In' and 'Out' baskets, and another overflowing basket labeled 'Dither.'](https://public.asmartbear.com/books/anthology/chapters/strategic-choices/d6978print-simple.png) There's probably a valid perspective hiding under that weakly-worded choice, so your job is to extract the underlying choice that captures that perspective. This can help: > What are some successful and beloved companies that chose the "bad" side? > > What is the underlying reason why people love that company despite that flaw? What is the thing they love so much, that even though it has this bad consequence, they still love that product? That "thing they love" is *actually* the other side of the strategic choice. For example, "well-designed UX, not bad UX" is a fake choice. So ask: what are some reasons that some beloved products have a poor UX? We looked at one reason already: the product is a small core with an ecosystem of plugins and extensions. That flexibility lets third-party software "do anything," which lets customers "do anything," and that's genuinely wonderful... but at the cost of UX coherence. A real-world example of this is note-taking apps. All-in-one apps like Apple Notes and Bear are beautifully cohesive in part because they control the entire experience--no plugins, not even an API. Meanwhile, competitors like Obsidian offer an incredible array of downloadable functionality. A common complaint is that the UX becomes a hodgepodge, but people love the expansive choice of features and the ability to build their own, custom, ideal information management system. By asking "Why is Obsidian so popular and beloved despite its UX being so messy," you arrive at the real strategic choice, which is "all-in-one vs infinitely extensible," in which design is one important consequence. ## Why not both? Sometimes both? This time both? Everyone will "cheat" eventually. Since both sides are desirable, we are tempted to start pulling in facets of the other side. You'll build an all-in-one product, but customers love that a competitor has plugins, so you'll start exploring what it would mean for you to add plugins. Or: "We should raise the price; we're leaving money on the table." Or: "We should slow down and polish this; it's embarrassing." Those temptations feel compelling precisely because the other choice is also smart. Strategy exists so that anyone in the company can confidently say: *Yes, that's a good idea in the abstract, but no, it doesn't fit the bundle of trade-offs we have chosen.* Still, sometimes it's reasonable to do both. Suppose you've chosen "lowest price," [which often implies lower quality](https://longform.asmartbear.com/never-compete-on-price/), because low prices mandate cost constraints. But sometimes you can improve quality without increasing cost--better design choices, better suppliers, advantages of scale, great employees. Shouldn't you take those wins? Yes. Take them. The rule is: **When in conflict, adhere to the strategic choice. When not in conflict, do whatever is best.** When quality and cost don't conflict, take both. But usually they do conflict. In those moments, you can't satisfy everyone. Some customers want one side of the trade-off; others want the other. If you don't decide consistently, you land in the only truly losing position: not enough quality and design to earn delight and loyalty, but also not enough power and breadth to win complex use cases. You lose both markets, because you claimed "it's a balance" and created something no one is excited to use. The [Agile Manifesto](https://agilemanifesto.org/) epitomizes this attitude. "Working software over comprehensive documentation" doesn't claim documentation is bad; it says that when time is limited (and it always is), you invest more in one than the other. That's not dogma; it's a mechanism for consistency under pressure. They even underline their intent: "While there is value in the items on the right, we value the items on the left more." Use the same pattern for strategy. If your strategy is low price, you don't introduce a high-price offering "to get some of those customers." But if your low-price strategy implies lower quality, you happily take higher quality whenever it doesn't threaten the low price decision. It remains a strict choice, [not a balance](https://longform.asmartbear.com/conflicting-choices/). ## Strategic choice examples You could incorporate a few of the following into your strategy, and use them to prompt even better ideas: | **Choice** | **Advantages** | **Weaknesses** | |:----|:----|:----| | Lowest price over premium price | • Largest possible market, positioned on access and affordability
• Many competitors can never win, just because of price | • Can't afford advertising and sales; word-of-mouth must carry you
• Cost caps materials, performance, and "nice-to-haves," so quality and features hit a ceiling | | Premium price over lowest price | • High margins let you outspend competitors on brand, polish, and distribution
• Status signaling becomes a feature: "no one ever got fired for buying this" | • Smaller market where the seller has no power because customers can buy from anyone
• You must continuously fund the brand (marketing, prestige, pay celebs to carry the handbag), or the premium collapses | | Cohesive all-in-one UX over extensibility | • "Designed" experience earns delight, trust, and habit--customers feel calm using it
• Support, docs, and onboarding stay simple because there's one blessed path | • You personally own feature breadth; every "just add" request becomes your problem
• Power users leave when they need plugins, scripting, or weird workflows you won't build | | Extensible ecosystem over cohesive UX | • Ecosystem creates long-tail capability you'd never staff, and customers love "it does anything"
• Partners build your distribution: integrations and plugins become marketing channels | • UX coherence degrades because standards vary; the product feels like a quilt
• Security, compatibility, and support are a supply-chain problem you don't control | | Simple UX over powerful capability | • Short time-to-value drives activation and self-serve growth; users feel smart immediately
• Lower cognitive load reduces churn and support because fewer people get stuck | • You lose complex workflows, governance, and customization deals by design
• You get outmatched on checklists and RFPs where buyers equate "more" with "better" | | Powerful capability over simple UX | • You win deep, high-stakes workflows where capability beats elegance, earning high revenue per customer
• High retention because configuration, training, and process integration create switching costs | • Onboarding requires training, setup, and specialists, slowing adoption and expansion
• Support/services become mandatory, and the product becomes something customers "operate" | | Minimal support over high-touch support | • Fewer support reps means margins are higher and the team focuses on product instead of tickets
• Forces ruthless usability and docs, so the product scales without human glue | • You can't sell into buyers who require phone numbers, SLAs, and hand-holding
• When users get stuck, they churn silently; you lose the feedback loop that would fix it | | High-touch support over minimal support | • Humans bridge gaps competitors can't, creating loyalty and differentiation
• High-touch support becomes a product discovery engine: you learn faster because you hear the truth daily | • Expensive and hard to scale; load rises with customers unless you redesign the model
• Creates 24/7 expectations and a dependency you can't remove without breaking retention | | Small-team simplicity over enterprise complexity | • Shorter sales cycles: fewer committees, pilots, and procurement rituals
• Low implementation burden means customers succeed without consultants and long projects | • You lose enterprise deals that require governance, audits, and corporate integrations
• Constant pressure for "just one more thing" slowly rots the simplicity you sell | | Enterprise complexity over small-team simplicity | • You pass procurement gates and unlock big budgets once embedded into org processes
• Integrations, permissions, and governance create real lock-in and retention | • Long sales cycles require cash, patience, and a sales machine
• Product and UX bloat spreads everywhere because every screen must honor roles and policies | | Cutting-edge speed over rock-solid stability | • Novelty differentiates; you ship what competitors can't (yet) and earn attention
• Attracts builders and early adopters who want the frontier | • Bugs, breakage, and confusion become part of the brand
• Customers pay change-management tax, so conservative use cases churn and avoid you | | Rock-solid stability over cutting-edge speed | • Trust becomes the moat for critical workflows; customers fear switching away
• Lower support load and lower churn because "it never breaks" beats "it's new" | • You are slow; visible novelty wins attention and budget
• You miss frontier opportunities, and might even become obsolete | | Open source over closed source | • Lower trust barrier: prospects can try, inspect, and adopt without begging permission
• Community accelerates features and integrations you wouldn't fund internally | • Monetization is hard because the core feels "free" and competitors have the same product
• Governance and maintenance become political and exhausting, and expectations rise because flaws stay visible | | Closed source over open source | • Full control of roadmap, UX coherence, licensing, packaging, and pricing power
• Simpler governance: no community factions, forks, or public roadmap brawls | • Adoption slows in segments that demand transparency or fear lock-in
• You fund velocity yourself; you lose community leverage and must manufacture trust signals | | Fewer, better features over more features | • Polish and coherence create delight, retention, and word-of-mouth
• Lower complexity means fewer bugs, fewer docs, fewer tickets, and faster iteration | • You lose on checklists and RFPs where feature count masquerades as value
• You under-serve edge cases that matter deeply to certain segments, and they churn loudly | | More features over fewer, better features | • You win procurement comparisons by checking more boxes out of the gate
• Broader coverage expands market and creates more upsell paths | • Quality becomes uneven because breadth steals time from polish and reliability
• UX coherence collapses as features accrete, and support/docs explode with new failure modes | --- The weaknesses of your choices are painful to accept. The advantages of the opposite choices sound wonderful. Welcome to strategy, where you make hard choices. If the choice wasn't hard to make, it wasn't actually a choice. Make the hard choices now, relieving everyone else of that burden, and allowing them to make [smart decisions independently](https://longform.asmartbear.com/in-command/).

Creating space

Jason Cohen — Sun, 04 Jan 2026 00:00:00 +0000

![With this humble tennis ball, Phil, we shall forever change the status quo.](https://public.asmartbear.com/books/anthology/chapters/focus/d8415print-simple.png) A single word of advice, given to all startups, and to CEOs of large companies, and they in turn to their teams: **"Focus!"** But what does that mean, precisely? And anyway isn't quantity sometimes in fact better than quality? Don't we need to test a bunch of stuff instead of trying to make one thing work? Only when we stop doing most things, do we have the time and energy to fully, deeply execute the most important things. And the important things are the difference between thriving and perishing. --- > "I saw myself sitting in the crotch of this fig tree, starving to death, just because I couldn't make up my mind which of the figs I would choose. I wanted each and every one of them, but choosing one meant losing all the rest, and, as I sat there, unable to decide, the figs began to wrinkle and go black, and, one by one, they plopped to the ground at my feet." > --Sylvia Plath, *[The Bell Jar](https://bookfinder.com/isbn/9780060837020)* --- When you stop trying to sell people who [aren't a good fit](https://longform.asmartbear.com/purgatory/), you create space for 3x more sales pitches, each with 3x the close-rate. When you [help draining customers exit the business](https://www.youtube.com/watch?v=g9-TVWNu4Aw&list=PLgZuj5WdK69GiFlqdgo04LvV7RdiEVg_F), you create space to spend time with easy, happy ones, who want to spend more every year while giving you positive energy. When you stop trying to win over everyone, you create space to win over [the right ones](https://longform.asmartbear.com/icp-ideal-customer-persona/). When you stop trying to shore up every weakness, you create space to [leverage](https://longform.asmartbear.com/leverage/) your strengths. When you stop trying to schedule all work, you create space for [impactful work](https://longform.asmartbear.com/rocks-pebbles-sand/). When you stop trying to do everything in a novel way (UX, coding, pricing, org structure), you create space for a few truly impactful modes of differentiation. When you stop using lazy, generic words in marketing, you create space on the page to communicate [what you actually do](https://longform.asmartbear.com/specificity/), or [who you actually are](https://longform.asmartbear.com/authentic-is-dead/), or [why anyone should care](https://longform.asmartbear.com/mission-vision/). When you end toxic relationships, you create space for healthy ones. Personal or professional. For you and the teams around you. When you stop trying to control every last detail, you create space for [empowered teams to flourish](https://longform.asmartbear.com/in-command/), and for you to work on things that only you can do, or only you ought to do. When you stop reading garbage, you create space to read something meaningful, or useful, or enjoyable, or inspiring, or refreshing. When you stop chasing every metric, you create space to attack the [one metric that will transform the company](https://longform.asmartbear.com/one-priority/). When you stop trying to improve everything by 1%, you create space to [improve the biggest thing by 30%](https://longform.asmartbear.com/double-your-productivity/). When you stop trying to please every ill-fitting customer, you create space to convert "satisfied" customers into "[fanatics](https://longform.asmartbear.com/customer-love/)" who increase word-of-mouth growth, leave positive reviews, and never cancel even if something bad happens. When you stop checking email and social media every 11 minutes, you create space to become enveloped in the flow of creativity and productivity. When you stop trying to [maximize every metric](https://longform.asmartbear.com/maximizing/), you create space to maximize the [few metrics that matter most](https://longform.asmartbear.com/product-metrics/). When you stop pursuing every opportunity, you create space to fully capitalize on the one with the [thickest intersection](https://longform.asmartbear.com/investment/) of upside potential and ability to execute. When you stop creating superficial relationships with every stranger on social media, you create space to create deep, mutual, caring relationships. When you stop trying to win every battle with every competitor, you create space to win the ones that matter most to the people in your ideal market. When you stop trying to "find the balance" in everything, you create space to [maximize the one choice](https://longform.asmartbear.com/conflicting-choices/) that creates clarity and strength. When you stop trying to "[have it all](https://longform.asmartbear.com/two-big-things/)," you create the space to deeply experience the few things that are most important. --- We don't have unlimited time or energy, but we can [better spend the limited time that we have](https://longform.asmartbear.com/prioritization/). That's why: *Focus.* --- > Decisions are easy when you have only one priority. > Your destination is a huge mountain peak on the horizon. > You can see it from everywhere. > Yes to that mountain, and no to everything else. > You'll always know where you're going, and what you're doing next. > All paths go either towards that mountain or away from it. > > Because of this perspective, problems won't deter you. > Most people look down at the ground, upset by every obstacle. > With your eyes on the horizon, you'll step over obstacles, > undeterred. > --Derek Sivers, _[Hell Yeah or No](https://sive.rs/n)_

Mission, Vision, poTAYto, poTAHto

Jason Cohen — Sun, 09 Nov 2025 00:00:00 +0000

![You're right, it's not much of a mission statement](https://public.asmartbear.com/books/anthology/chapters/mission-vision/d2714print-simple.png) ## Who cares? Mission, vision, purpose, BHAG, North Star. Are they useful, or academic nonsense? For many of us, these are highfalutin' terms that have no role in early stage startups, because we're too busy making stuff and realizing that customers actually wanted something else. By the time the company is large, there are teams of people--probably in marketing--carefully sculpting these things as sentence fragments in large serif fonts on "About Us" pages that no one reads and [no one believes](https://www.forbes.com/sites/jeroenkraaijenbrink/2021/04/13/why-your-mission-and-vision-statements-dont-work-and-what-to-do-about-it/?sh=302415d934e2). And by no one, I mean not employees, not customers, and not investors. Phrases that sound grand but are just grandiose. How can these terms matter, when pundits can't even agree on their definition? Take "mission." One interpretation is like "missionary"--our higher purpose, something bigger than ourselves, which we are helping to bring about. So [Patagonia's mission](https://www.patagonia.com.hk/pages/our-mission) is "to save our home planet," though what it _does_ is sell outdoor clothing. Or [Tesla's mission](https://www.zeta.org/members/tesla) is "to accelerate the world's transition to sustainable energy," though what it _does_ is sell cars, followed by selling batteries and solar panels. Or [Coca-Cola's mission](https://www.leadingforlife.org/apps/pages/index.jsp?uREC_ID=215122&type=d&termREC_ID=&pREC_ID=622328) is "to refresh the world in mind, body, and spirit," but what it _does_ is sell barely-potable chemicals and containers of said chemicals embedded in carbonated water. Well, two of those three companies are at least fulfilling their mission. ![bear planting a tree](https://public.asmartbear.com/books/anthology/chapters/mission-vision/bear-planting-simple.png) The other definition of "mission" is nearly the opposite: Your current execution goal. An army battalion has a mission to conquer and defend a region. A starship has a five-year mission to explore new worlds while not interfering _(wink wink)_ with new life and new civilizations. [McDonald's mission](https://www.mcdonalds.com/content/dam/sites/uk/nfl/pdf/corporate-governance-framework-2020.pdf) is "to be our customers' favorite place and way to eat and drink," which is about themselves and how customers regard them rather than something bigger than themselves. [FedEx's mission](https://businessmodelanalyst.com/fedex-mission-statement/) is "To produce superior financial returns for its shareowners [sic] by providing high value-added logistics, transportation and related business services," which is unabashedly about stock price and summarizing the service they provide, not a higher purpose. Points for honesty, but uninspiring. So, it's unclear what "mission" or "vision" ought to mean, and unclear whether they have utility. And yet, some of the greatest and most well-respected and most admired companies in the world are serious about their words and have been from the start. [Khan Academy](https://www.khanacademy.org/about)--"Our mission is to provide a free, world‑class education for anyone, anywhere"--and they have. [TOMS shoes](https://www.toms.com/en-us/about-toms?srsltid=AfmBOorbmAZgcpmDNb-CaomWWqBynd1mIXcTrpyzyYJXlq8m-QJ2LecU)--"We're in business to improve lives"--and they have done so for [many millions of people](https://www.toms.com/en-us/supply-chain-transparency?srsltid=AfmBOoq2xrheJn8FqrUd0o6IgsaVRrpxtm-zlucJOal2MET0bVAabi4m). [DuckDuckGo](https://duckduckgo.com/about)--"Showing the world that protecting privacy is simple"--prioritizing not just the privacy of their own users but advocating for privacy rights online. And the aforementioned Patagonia, where the founder placed the company into a trust to ensure that their history of protecting the planet would also forever be their future. ![Simon Sinek's Golden Circle: Why, How, What](https://public.asmartbear.com/books/anthology/chapters/mission-vision/image-simple.png) Is it smart to adopt this "bigger than ourselves" definition of "mission," and to genuinely have one? This question has been explored at length, most famously by Simon Sinek in _Start with Why_.[^1] He argues that having a higher purpose makes companies better on every front; here's some of his arguments and those of other researchers, in my words: [^1]: Sinek, S. (2011). _Start with Why_. Penguin. Although with 67 million views, more people have received his ideas through his [TED talk](https://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action?language=en). * **Loyal, vocal customers** spreading the word, even with high prices and some missing features, because the company is meaningful to them, even part of their identity. (See ["Willingness-to-pay"](https://longform.asmartbear.com/willingness-to-pay/) for more.) * **Passionate employees** with less turnover, because they're working for a purpose, not for a paycheck only. * **Differentiation** in a market full of only-profit-driven companies that exploit employees and customers, which consumers hate (today even more so and more vocally than ever). * **Resilience** during economic downturns and negative publicity as "purpose" transcends price and occasional missteps. * **Consistency** in messaging, prioritization, and goals, which is useful for every company, but creates a special clarity and justification. * **Raising money** from angel investors, who realize you'll probably fail, but who are at a phase of their own lives where they wish to make a difference. * **Higher profits**[^2] as a result of the factors above, which yields (perhaps ironically) a greater return to shareholders. * **[Legacy](https://longform.asmartbear.com/legacy/)** that outlasts any one person or product, providing meaning and fulfillment in a world where typically "work is just work." [^2]: There is little empirical evidence to support this common claim. There are plenty of anecdotes, including some from this article, but almost none of the largest 1000 companies in the world are mission-driven, and [a study showed](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2840005) that while "purpose + execution" resulted in superior returns, "purpose alone" did not. There is [weak evidence](https://www.munich-business-school.de/fileadmin/user_upload/forschung/working_papers/MBS-WP-2022-05_Antonio_Schmidkonz_Kraft_Purpose_Washing.pdf) that "purpose-washing" (claiming to be purpose-driven, when you are not) results in lower performance, however this correlation might suffer from hindsight bias. You don't have to be a mission-driven company to succeed. In fact, most successful companies aren't. They tacitly adopt the executional definition of "mission" and ignore it anyway. You can do that too. Maybe you should; you just want to make a dollar and get out of here. Even if so, I think you can be mission-*derived*, rather than mission-*driven*. Just because it's wasn't your origin story, doesn't mean you don't care about having a positive impact on the world, which you can declare and intentionally advance. More on that later. ## Purpose, N-year Vision, Next Milestone Because "mission" and "vision" and other such words are untethered to consistent definitions, I prefer words that are closer to their intended meaning. The following is what I like. ### Purpose *The transformation in the world that is bigger than ourselves, that we hope everyone involved is excited to help bring about.* This is like Khan's "Everyone deserves a free, world‑class education" or Tesla's "Accelerate the world's transition to sustainable energy." We will never fully achieve it, therefore it is not a "goal in our operational plan," but rather it is answers: "Why are we doing this at all? Why should anyone else care?" The Purpose is not about us, but about others. It doesn't mention us or what we do. It is important to us and to others, even this company didn't exist. Transitioning to sustainable energy is a Purpose no matter which companies come and go. If others adopt the same purpose, we are happy. That isn't competition, that's an ally.[^3] Exactly because the Purpose is bigger and more important than ourselves, we want others to join our Purpose. [^3]: Examples: Khan Academy gives away its AI teaching assistant to help other educators build curriculum. TOMS shoes shares its R&D on sustainable materials with other shoe manufacturers. Tesla [open-sourced the patents](https://www.scirp.org/journal/paperinformation?paperid=101900) to its core battery technology, even though "better batteries" is one of their fundamental inventions. Netlify coined the term "[jamstack](https://jamstack.org/)" to promote a new server architecture that rivals not only adopted, but eclipsed Netlify in revenue. ### N-year Vision *Our desired future state: What we will have achieved?* Here "N" should be smaller for new companies, larger for established ones. The reason: It must detail a situation that is different from today, so that it articulates an evolution, but not so far into the future that it isn't actionable today. Rule of thumb: Divide the age of the company by 3, and round up. It must include: #### What did we build? The product or service. Tesla [started with](https://www.tesla.com/secret-master-plan) the first battery-powered luxury sports car. Patagonia [started with](https://www.patagonia.com/company-history/) hand-build equipment for climbers; its initial clothing line was just T-shirts. Amazon started with a vision of "Earth's Biggest Bookstore." #### For whom? What is the subset of customers that we will be serving? Can be larger than[the ICP](https://longform.asmartbear.com/icp-ideal-customer-persona/), but cannot be "everyone." Tesla started with rich Americans, Patagonia with serious American rock-climbers, Amazon with only 2,000 titles. #### The insight What is the special thing that differentiates the company, either in the resulting product or in how it will achieve it? This is the critical thing that makes this company "this company." Tesla's insight was that a new kind of battery could result in performance that matched the fastest gas-guzzling cars. Patagonia's insight was that climbers are also nature-lovers, and would appreciate not only their commitment to that cause, but want products with minimal impact on the environment, and durable to minimize landfill contributions. Amazon's insight was operational: to eliminate both physical stores and hold almost no inventory, allowing them to scale faster and have lower prices than physical bookstores. Famously: "Your margin is my opportunity." Because it must be brief (one or two sentences) it must necessarily be reductive--so over-simplified that it lacks precision. Therefore, it is the starting point for a fuller explanation. The strategy document is where you specify those details. The short summary aids in comprehension and communication. And so, in my own words: * Telsa: Create the first high-performance luxury sports car for Americans that is powered only by batteries. * Patagonia: Build the most durable, low-environmental-impact equipment for serious rock-climbers. * Amazon: Create the largest and most affordable bookstore on Earth, by selling exclusively online with just-in-time inventory. The Vision should be clear, concise, declarative, and describe a specific future. Be bold, but plausible. ![I'm a doer! I get results! I make things happen! I'm intentionally vague!](https://public.asmartbear.com/books/anthology/chapters/mission-vision/d7462print-simple.png) ### Next Milestone *The most important thing to achieve next, and how we'll know when we've arrived.* Generally you can identify the next milestone by working backwards from the N-Year Vision. It looks something like this: > If we're going to achieve [final result] by [far-future-date], we will have to accomplish [1-5 critical projects or results]. So, right now what we need accomplish next is [near-term project], and we'll know we've achieved it when [objective observable]. Some examples, with companies at different stages and founders with different goals: * I want to raise $1M in 10 months, so I can hire my previous team who is ready to join up. So, I will need to begin raising in 6 months, and to convince investors (and myself!), I will need to have proven that people want to buy our software, and that the market is large and growing. If I can get 200 paying customers by then, I'll have proved that people want it. But today I have only beta-testers (although they are happy!). Therefore, the next milestone is to get 20 paying customers--enough that it's not a fluke, but going from even zero to one will take real work. * I've reached $5M in ARR but growth has stalled. I've [analyzed why it stalled](https://longform.asmartbear.com/growth-asymptote/): cancellations are too high. Because customers cancel at 5%/mo and my marketing is already good, I [mathematically cannot grow](https://longform.asmartbear.com/max-mrr/) without reducing cancellation. I'm discovered that some customer cohorts have only 2% cancellation, and also pay 5x as much! Therefore, I need to evolve the company to target those types of customers with both marketing and product. I'll keep a lower-priced version, but the mixture of customers will change. Today the mixture is 10/90 in the wrong direction. We can't change that over night, so our next milestone is to shift _new customers_ to a 35/65 mixture. That will represent significant progress, and after that we'll be a lot smarter about what to do next. ![bear slumped over papers](https://public.asmartbear.com/books/anthology/chapters/mission-vision/bear-exhausted-slumped-simple.png) * It's been 5 years and I'm [burned out](https://longform.asmartbear.com/fulfillment/). I don't want to leave, because [this is my life](https://longform.asmartbear.com/identity-selling-sadness/), and I don't want to do anything else. But I can't go on like this. The company needs the leader to be healthy, and also needs someone who has lead a company from my current stage to the next one; I'm not that person, neither in skills nor in emotional desire. In two years I want to have tripled revenue and tripled profit, but I want someone else to help get there. Therefore, my next milestone is to hire a new CEO and manage the transition process with her[^4] and the rest of the company, as well as my own, so I can stay for another ten years without burning out. * Our business has matured into a sustainable level of growth, but despite many efforts, we can't grow revenue faster than it is. Our unit economics are great--[CAC is low](https://longform.asmartbear.com/bootstrapped-cpc/), cancellations are low, NPS is high, and so on. So, we need to [expand into an adjacent space](https://longform.asmartbear.com/adjacency/) to accelerate growth. We don't yet know how to expand, though, so we're going to brainstorm some ideas from the article just referenced, then run experiments to figure out the best course of action, before committing millions of dollars and thousands of person-hours to the effort. The next milestone is to have run five experiments and selected the one or two things we're going to do. * We've bootstrapped our indie game studio for three years and released two moderately successful titles. We're profitable but it's still hand-to-mouth, and we're tiring of the constant pressure to ship. Our vision is to have enough runway to take 18 months to build our dream game--something ambitious that could break out and define us. To get there, we need $600K in the bank. Our current games generate about $12K/month in profit, so we need to either boost that to $35K/month or find another revenue stream. Therefore, our next milestone is to create a small, high-margin DLC for our most popular game that can ship in 4 months and boost monthly profits to at least $20K--enough progress to make the bigger goal feel achievable. * Our agency has grown to 35 people over the past decade, with solid enterprise clients and consistent 15% year-over-year growth. But I've watched SaaS founders build massive value while we're still trading hours for dollars. I want to transform part of our agency into a productized service with recurring revenue that could eventually become a software product. In three years, I want recurring revenue to be 40% of our business. But I also fully realize why [this is so hard to do](https://longform.asmartbear.com/consulting-company-accounting/). My next milestone is to identify our three most repeatable, high-margin service offerings, package one as a fixed-price monthly subscription, and sign our first 10 recurring customers even if it means giving them a steep discount to pioneer this model with us. * Our open-source project has gained significant traction with developers (50,000 GitHub stars), but we're still working on it nights and weekends while holding down day jobs. We want to make this our full-time focus by creating a sustainable business around the project without alienating the community. In two years, we want to have a team of 5 full-time maintainers funded entirely by the project. The next milestone is to launch a hosted version with premium features that don't exist in the open source version, sign up 50 paying customers, and reach $10K MRR--enough to justify two of us quitting our jobs to work on it full-time. [^4]: For those of you irked by my use of the feminine pronoun, I was [personally in this position](https://longform.asmartbear.com/fulfillment/) at WP Engine, and her name is Heather Brunner. I call her our "late-joining co-founder," because there's no better description for what has unfolded over the past ten years. ![bear at laptop](https://public.asmartbear.com/books/anthology/chapters/mission-vision/bear-laptop-tools-simple.png) * I acquired a 15-year-old traditional manufacturing business with a reliable customer base but antiquated operations and virtually no online presence. My vision is to double revenue in three years by modernizing operations and expanding sales channels. The first order of business is cost reduction through operational efficiency--we're leaving 20% of potential profit on the table through waste and outdated processes. Therefore, my next milestone is implementing a modern ERP system, retraining staff, and reducing our COGS by 12% within six months, which will generate the cash needed to fund our expansion into e-commerce. * We're a Series B [AI startup](https://longform.asmartbear.com/ai-startups/) that grew rapidly by providing custom solutions to Fortune 500 companies. We're at $12M ARR with 90% gross margins, but building custom solutions isn't scalable and our growth is plateauing. Our investors are pushing for a more standardized product approach that could reach $100M ARR. Our next milestone is to extract the common patterns from our custom work, build a self-serve platform that addresses 80% of use cases without customization, and get 3 existing customers to successfully migrate to it--proving both that our platform approach works and that we can make the business model transition without losing our current base. Note that in all cases, there was something specific to do, and some objective way of knowing whether it was done. Often the "objective way" is a [metric](https://longform.asmartbear.com/product-metrics/), but not all important things are numbers. Unlike the Purpose and Vision, the Milestone is where you get specific, tactical, operational, decisive, and either metrics-driven, or at least metrics-influenced. To fully operationalize the Milestone, use [this practical quarterly strategic planning method](https://longform.asmartbear.com/strategic-planning/), then use [this version of Rocks/Pebbles/Sand](https://longform.asmartbear.com/rocks-pebbles-sand/) to prioritize work, and track progress using a [sensible set of metrics](https://longform.asmartbear.com/product-metrics/). ![I don't get it! We've got a mission statement, a credo, and a mantra!](https://public.asmartbear.com/books/anthology/chapters/mission-vision/d5966print-simple.png) ## Purpose-derived You might be purpose-*driven*, or purpose-*derived*. Purpose-driven means the origin and true number-one goal of the company is to fulfill its Purpose, evidenced by behavior where you advance the Purpose even when it means lower sales, lower market share, lower profits, and activity that other companies would label "a distraction." Purpose-derived means you have a Purpose, but it is not your singular motivation. It *is* good for others, it *is* bigger than ourselves, but it is a by-product of our success, rather than the driver of it. I believe most companies fall into this category (when they don't fall into the category of simply being a net-negative in the world). Take Smart Bear. I did not start that company with a higher purpose. I had an idea for a tool that would "data-mine" the history of a software project. It wasn't a very good idea, but it lead to a very good idea, which was a tool to help software developers review each others' work.[^5] But it did have a great derived Purpose: To increase software quality. The proof of this derived Purpose is in its evolution in the more than 16 years since [I sold](https://longform.asmartbear.com/rich-vs-king-sold-company/) and left the business: It accumulated nearly a dozen tools relating to software quality, many open-source, all beloved by their customers. Even at the time, I had written a free book all about how to do reviews well, none of which required buying our software. This is a good Purpose. Or take WP Engine. I did not start that company with a higher purpose. My blog kept crashing when I got on the front page of link-sharing site Hackernews, so I reached out to other bloggers to find out how they handled it. They said: "I don't know, but if you find it, tell me, because I need that!"[^6] But it did have a great derived Purpose: To enable everyone to run a superior website. After all, if you don't have a website, you're invisible. And websites have to be fast (or Google won't rank you high enough) and scalable (or you'll crash when you get that awesome PR hit) and secure (or the bad people will hack your site). But 99.9% of business owners (large and small) and individuals can not and ought not become technology experts! And some people are trying to have a voice in unsafe places, speaking truth to power. They need someone to trust, so they can thrive online, so they have the freedom to create. This isn't just a nice story I tell myself to believe what we do makes a difference--we've been told this, unprompted, more times than I can count. This is a good Purpose. [^5]: [Here's more of that story.](https://longform.asmartbear.com/predict-the-future/) [^6]: [Here's more of that story](https://longform.asmartbear.com/product-market-fit-formula/), and [here's the method I used](https://longform.asmartbear.com/customer-development/) to validate whether that initial signal was truly validation. Apple started out selling computers to hobbyists. It was 21 years before they developed the "Think Different" campaign, establishing that they cared about (in their own words), "The crazy ones. The misfits. The rebels. The troublemakers. The round pegs in the square holes. The ones who see things differently. ... And while some may see them as the crazy ones, we see genius." This was Purpose-derived, but it's what we remember, and it's referenced repeatedly by Simon Sinek himself in _Start with Why_. Apple did not, in fact, start with why. But it got there. --- > "True happiness is not attained through self-gratification, but through fidelity to a worthy purpose." > --Helen Keller Slapping words you don't mean onto an "About Us" page does not create a Purpose. What does create a Purpose, is figuring out the positive difference you can make in the world, then writing that succinctly, then incorporating it into your strategy and positioning and goals, so that you gain most of the listed benefits above, and so that on your deathbed you can honestly say: "What I did, mattered." Or best of all, you can be driven by a higher Purpose from the start, and build a sustainable organization that brings about some of that change. It's one of the most wonderful things you can do with the precious few hours you have on this Earth. We live in an era where it's possible to do good as well as become rich. I wish you luck in both.

Your target market isn't demographic

Jason Cohen — Sun, 07 Sep 2025 00:00:00 +0000

![Every year there are naughty children who don't get any toys. That's a vastly underserved niche I think we can capitalize on.](https://public.asmartbear.com/books/anthology/chapters/target-market/d7423print-simple.png) I don't like the traditional target market definition that focuses on demographics and firmographics: * We sell to small businesses in the UK. * Our fast-casual restaurant appeals to college students and young professionals. * We target outdoor enthusiasts in the Pacific Northwest * Our TV show is targeted at females, 19-29 years old. Sometimes this sort of description warranted. If you sell back-office software specifically for dentist offices under Norwegian law, then a demographic description is accurate. But my experience is that, for most companies, these dimensions incorrectly describe the market. Take the TV show above. Despite the stated demographic, most females 19-29 don't watch that show. Why not--what differentiates those who watch from those who don't? Conversely, many people who are *not* females 19-29 *do* watch it. Why? Your actual "target market" is described by whatever characteristics are shared by the watchers, and are distinct from the non-watchers. Even if you're roughly correct that the named demographic accurately describes many viewers, you're missing the deeper value and desirability of your own show, and therefore targeting too many people (the females 19-29 who aren't actually your target) and also targeting too few (everyone else who is). You've avoided naming the actual "target." What will the advertisement for that show say? "Hey, if you're a female 19-29, watch this!" No, it will appeal to something else--people who like that genre, people who like those actors, people who enjoy that aesthetic, people who like that writing style, people who want to be seen as the type of person who would watch a show like this. Whatever _that_ is, is the target market, and should inform the advertisement. The same lesson applies to business products. Consider an email client. It might be used personally or at work. It might be used at a small company or a large company. It's not language-dependent. So, traditional market dimensions are irrelevant: "B2B or B2C", company-size, or geography. Rather, the target market depends on the use-cases and characteristics of the users. It may be optimized for people who get hundreds or thousands of emails every day and therefore need special processing workflows. Or for people who have assistants that process mail on their behalf. Or for people who get only a few emails per day. Or for people who worry about security. Or for people who do systematic outbound campaigns. Or for a shared support desk. These attributes have to do with the use-cases of the customer, not demographics nor firmographics. Let's make this concrete with an apparently-inexplicable scenario that most B2B software companies will relate to. ## Why Google also buys "SMB" software In the early days of my company WP Engine, our website design (Figure 1) belied the fact that we were a small company selling to SMBs (Small and Mid-sized Businesses) who had a successful WordPress[^1] site. We were not positioned to be impressive to even [mid-sized companies](https://longform.asmartbear.com/mid-market/), to say nothing of large enterprises. [^1]: WordPress is by far the most popular site-building software, [used by nearly half of all websites globally](https://w3techs.com/technologies/history_overview/content_management/all/y). ![WP Engine's early website design screaming "We're a small business selling to small businesses."](https://public.asmartbear.com/books/anthology/chapters/target-market/image-3-simple.png) And yet, mid-sized and large companies bought too. Less in the first two years, but substantially in the next two years. We wouldn't land "Enterprise-sized" multi-million-dollar contracts, but rather they bought the same product that the small customers bought. What did it mean? Maybe it's a sign that we should start selling directly to the Enterprise! Many startups experience this pattern. Twitter is full of founders with good, simple software, shocked to find that Google, Facebook, Salesforce, and Oracle have become customers, asking the melee-that-is-social-media whether this is evidence that they should "go up-market" with a sales team. The short answer is: No. The full answer is: You are quite understandably under the delusion that "company size" is a good way to characterize your target market. Occasionally that is a good way, but in fact software designed for small businesses is almost guaranteed to be purchased by big businesses also. The reason is that _individual teams_ at large companies also buy things. Not everything at Google is purchased top-down and forced upon the roughly 27,000 engineers that work there. (If your software _is_ purchased top-down and forced, _then_ you are indeed selling to the enterprise, and that should be included in the definition of your target market.) Those teams are buying for the same reason the one (and only) team at a small business is buying, and _that reason_ defines your target market. In turn, it also defines your advertising, positioning, pricing, and features. In my case at WP Engine, lots of people have WordPress sites, for lots of reasons. For a small business it might be their main website; at a large enterprise, it might be part of a marketing campaign, or a community project, or a "labs" team, or all sorts of reasons: * One of the largest banks in the world used us to allow bank branches to publish their own content. * One of the largest software companies in the world used us for their developer-relations portal. * One of the largest software development tools companies used us for their public technical documentation. * One of the largest fast-food chains in the world used us to publish social content in small countries, where local agencies could get all the details right (culture, language, color, etc). This happened because our target market wasn't "small businesses and individuals with WordPress." It was people who used WordPress who value: * Enterprise-_grade_ quality (e.g. speed, scale, security, 24/7 high-quality tech support). * A flexible, open-source, inexpensive-to-build platform where it was easy to hire employees or agencies anywhere in the world. * Pricing that was 10x or 100x larger than "cheap hosting" but `$\frac{1}{10}$` or `$\frac{1}{100}$` as much as "enterprise CMS solutions". These are characteristics needed by a mid-sized company's home page, or one of the use-cases for an Enterprise. These are the common threads; these are pieces of the definition of the "target market." You can find more pieces of that definition by continuing to ask "why do they value those things?" They value "Enterprise-grade" because they valued the website (as opposed to throw-away websites, or websites just for fun where price really is an issue). They value flexibility because they wanted to be able to change it themselves, or because they wanted to outsource anywhere in the world. Some wanted WordPress specifically because individual marketers were often already familiar with WordPress because of personal projects, and therefore could build and manage WordPress-based websites at work. The result of this chain-of-whys is internal clarity, and the path to effective marketing. For example, if your segment were "mid-sized companies," what would we say on the home page? "WordPress for mid-sized companies?" Great... soulless, boring, and what exactly the definition of "mid-sized" anyway? But now consider something as simple as "Enterprise-Grade WordPress." Or perhaps "Affordable Enterprise-Grade WordPress." You could make it even more [authentic](https://longform.asmartbear.com/authentic-is-dead/) and interesting and better, but at least it's being [specific](https://longform.asmartbear.com/specificity/) about its strengths (i.e. affordable, reliable, high-quality). It's already an improvement. ## How to identify your actual target market ### Techniques and frameworks This is part of what you're doing when you [find](https://longform.asmartbear.com/find-customers-to-interview/) and then [interview customers](https://longform.asmartbear.com/customer-development/). You're trying to get at their view of the problem you solve, how they describe it, what constraints they have, what would compel them to buy. You use this information to [identify your ideal customer profile](https://longform.asmartbear.com/icp-ideal-customer-persona/) and target everything for only them. Don't add company size, or industry, or geography, unless that is inextricably tied to the nature of the product, where it truly cannot be sold or used outside of that boundary. You use the [Needs Stack](https://longform.asmartbear.com/needs-stack/) framework to define where you fit in into customers' "Maslow Hierarchy of Needs." You select the dimensions of [Love and Utility](https://longform.asmartbear.com/willingness-to-pay/) at the intersection of your personal motivation and what will delight customers. These intersections are your target market. In short, this isn't fundamentally different from existing techniques for understanding customers and markets. You're just seeing that there are more important characteristics than demographics and firmographics. ![We're transitioning away from small and medium businesses, and focusing instead on tall and grande.](https://public.asmartbear.com/books/anthology/chapters/target-market/d7583print-simple.png) ### The testimonial test The way you can check whether a certain customer profile [B] matches the target market of your existing customer profile [A] is by asking this question: > Would customer [B] be swayed by a genuine, effusive testimonial given by [A]? That is, would they say: Oh wow, yeah that person's situation is just like mine, so alright, I'm interested, I'll give it a try. Sometimes proactively thinking about this question helps you to define the target market in the first place. It can even be used to directly test it--by seeing whether testimonials in sales calls light up people's eyes, or whether inserting them on landing pages increases conversions. In particular: Gather a list of your best customers, where "best" is defined by high retention, propensity to upgrade, reports "10" NPS, proactively leaves positive reviews and advocates for you on social media, engages with beta-testing or ideation for new features. Their shared characteristics help you define your target market, as would asking "what types of organizations and people would care about _their_ testimonials?" George Moore said it best in _Crossing the Chasm_ back in 1991, when he defined "a market" as: > • a set of actual or potential customers > • for a given set of products or services > • who have a common set of needs or wants, and > • who reference each other when making a buying decision > > People intuitively understand every part of this definition except the last. Unfortunately, getting the last part--... the tendency of [a market] to reference each other when marking buying decisions--is absolutely key to successful high-tech marketing. Notice that he, too, did not mention demographics[^2] or firmographics, but rather a "common set of needs or wants" and that they see each other as references. [^2]: Contrary to my formulation, he goes on to say that if two people buy the same product, even for the same reason, but don't actually communicate with each other, they are not in the same market. He gives the example of an oscilloscope for monitoring heart beats, saying that a doctor in Boston and one in Zaire have "no reasonable basis for communicating with each other," and therefore they are different markets. I believe this is less true in today's connected world (see the next section), and especially in software, I like the slightly less restrictive version that I gave above. ### Market segmentation dimensions for the modern age Historically, some market dimensions were forced upon us due to technical and societal constraints that no longer exist. It used to be that the only thing TV shows could know about an audience are things like gender and age, mediated by only a few firms like Nielsen, but that's no longer the case. It used to be extremely difficult to accept credit card payments or transact in many countries and many currencies, complying with local taxes and trade laws, but companies like Stripe, PayPal, Paddle, and FastSpring have made that accessible to everyone, breaking down the traditional constraints of geography and currency. AI tech support and translations might soon break down language barriers. We've already seen some examples of what dimensions we might use instead. Here's a list of ideas; most will not be applicable, but they'll get the thoughts flowing: | Dimension | Notes | |:----|:----| | Individual vs
team | Sometimes can be "both," e.g. if a free single-player-mode gets people hooked, but teams is where the money is, e.g. project management like Asana or Notion. | | Integrations | Nowadays, most software interacts with other software, whether part of a workflow, or because of the addition of AI, to-do system, project management, or chat. | | Other software used daily | What else do they use every day, whether that suggests integration or just helps to characterize what customers expect and experience | | Processes | What other processes and workflows do they use, or aspire to use, particularly as those intersect your product? | | JTBD success | What objective numbers are they held to? Which are vital vs operational? Which are satisfied vs maximized? What number, changing how, would get them promoted? Fired? | | Personal success | What fulfills them? What makes them happy? What is so compelling, they will put up with other things they don't like, or advocate for you within the company? (Their [Needs Stack](https://longform.asmartbear.com/needs-stack/).) | | Professional success | How are they evaluated? How do they get promoted? How are they seen as a success in their field? Early-career professionals have different priorities than mid-career or late-career individuals, regardless of company size. | | Corporate goals | Is the primary goal of their company revenue growth rate? Revenue size? Profit dollars? Profit percentage? Market-share? Rule-of-40? Cost-savings? Increased GPM? | | Role inside company | Are they customer-facing or inward-facing? Do they interact with other departments or keep to themselves? What do others expect of them? | | Title | What titles does this person with this role have? Probably a variety, especially if the product should span companies in different industries, or of different sizes. | | Problem ownership | Who "owns" the problem you're solving? Is it centralized in one role or distributed across a team or organization? Is the problem-owner and solution-owner the same person? | | Cultural
attributes | Does their organization prioritize productivity or work-life balance? Do they teeter over ethical lines or stay firmly in or out of bounds? Are they focused on external innovation or internal efficiency? | | "[Crossing the Chasm](https://www.hightechstrategies.com/crossing-the-chasm-summary)"
lifecycle phase | What best describes the customer:
• _innovators_--tinkerers; not used in production.
• _early adopters_--leading-edge; takes risks on new technology only if it creates a competitive advantage.
• _early majority_--wary; only well-known vendors; requires not just product but customer service, integrations, implementers, or other support.
• _late majority_--scared; dragged kicking and screaming into this decade by investors or market pressures. | | Work style | Do they work from home, only in office, or hybrid? Are they separated from co-workers by time zones? How many hours do they work per day? Do they work synchronously or asynchronously? | | Technical
acumen | Where are they on the spectrum from "engineers who will argue with you about algorithms" to "technophobes reluctantly using devices they hate"? | | Risk tolerance | Organizations analyze risk and accept risk differently. For example, creating new markets requires customers who accept risk. Risk-attitude is uncorrelated with company size. | | Tech stack philosophy | Do they prefer open-source, proprietary, cloud-native, or on-premise solutions? Often stems from values, not just technical needs. | | Update cadence | Do they prefer frequent incremental improvements or infrequent but substantial updates? Shapes product development rhythm. | | Communication culture | Are they email-heavy, Slack-dependent, or meeting-oriented? Affects how they'll use and integrate your product. | | Organization size | SMB is often bought by larger, but not vice-versa. Best when selling top-down, or when the organization itself is relevant, e.g. HR software. | | Organization growth trajectory | Is the customer in hyper-growth, steady state, or declining? Each stage creates different priorities and constraints. | | Regulatory environment | How heavily regulated is their industry or function? What regulations? Affects feature needs, compliance requirements, and risk tolerance. | | Budget type | Does the buyer have a fixed/annual budget, flexible budget, or "find money when needed" approach? Is there a [clear budget threshold](https://longform.asmartbear.com/buy-both/) where the sale is significantly different? | | Sales process | Is this a top-down sale or bottom-up freemium / PLG? If the former, that might also be correlated with company size; the latter often isn't. | | Decision cycle | Some make decisions in hours, others in weeks, others in quarters. | | Emergency vs deliberate | Is the decision made because of an urgent condition (e.g. security breach) or is it made deliberately, with research, comparisons, or trials? Do they run pilots and A/B tests or make all-or-nothing commitments? | | Maker vs administrator | Is the primary user a person who is creating, making, delivering, or someone who is managing, monitoring, analyzing, reporting? | | Business model | Subscription, one-time, transaction-based, usage-based, freemium, ad-supported, etc? Can dictate what metrics they care about most, how they can pay, and how they see the product fitting into their value-chain or cost structure. | | Learning style | Do they prefer video tutorials, documentation, 1:1 training, or figuring things out themselves? Shapes marketing, onboarding, and support. | Most of your answers will be "don't know" or "don't care," and you should invent new dimensions, but hopefully this gets you moving on the path to a better target market definition. --- When in doubt, go back to your current, best customers. Your target market is the set of potential customers that your best customers self-identify with, in the sense that they would view each other's testimonials as compelling. They themselves might not know exactly why; it's your job to figure out what characteristics define that set. But your best customers hold those answers. Through analyzing the problems they face, the way they wish to solve them, and the rest of their context, through observation and interrogation, you can develop a crisper list of characteristics that you can then target in marketing, identify in sales, and optimize for in product. Discovering what you were always meant to be, and now becoming that, on purpose.

Max MRR: Your growth ceiling

Jason Cohen — Sun, 27 Jul 2025 00:00:00 +0000

![If by profits you mean an excess of revenues over outlays and expenses in a business enterprise over a given period of time, then yes, it doesn't look good.](https://public.asmartbear.com/books/anthology/chapters/max-mrr/d7790print-simple.png) For years I've battled the same, tired misconceptions: * "7% cancellation is fine, especially for consumer businesses." * "As long as I keep adding $300/mo of new MRR every month, I'll build a real business." * "I'll keep scaling past a few million in ARR by [doing what I've always done](https://longform.asmartbear.com/explore-execute/)." I've [given real-world data](https://longform.asmartbear.com/product-market-fit/) to disprove these notions, but it's still hard to internalize and to apply it to your own companies. Rather than try to convince you again, I'm going to do something far more constructive: Give you a metric called **Max MRR**, that not only dispels false-justifications for high cancellations but is also **predictive** and **updates monthly**, which means it's the [kind of metric](https://longform.asmartbear.com/product-metrics/) you can target, track, and iterate on. Because it's a visceral, tangible number--not some abstract financial ratio--it cuts through the veil of willful ignorance. Here's how it works, along with a real-world example. ## Buffer [Buffer](https://buffer.com/) is a [widely beloved](https://buffer.com/resources/buffer-values/) values-first bootstrapped company with one of the most popular tools for scheduling social media posts. Because [their financial data is public](https://buffer.com/metrics) (in another example of their special, enviable culture), and because they've gone through several strikingly different phases of growth, they are a perfect case-study for SaaS metrics. As we'll see, our subject metric of "Max MRR" accurately characterizes their business better than other commonly-used metrics. Buffer started growing modestly, and then quickly, as [most successful companies do](https://longform.asmartbear.com/product-market-fit/). After seven years of healthy growth, revenue topped out (before COVID) and then shrunk (during COVID), reaching a new, lower plateau by 2023. Then, after rebooting product, pricing, and their target market during 2023, revenue started going up again: ![Buffer's MRR over time, exhibiting the Elephant Curve until a strategic shift in 2023 reignited growth.](https://public.asmartbear.com/books/anthology/chapters/max-mrr/image-simple.png) What was happening during those years when growth stopped? Was the customer base static? Not at all (Figure 2). During Buffer's period of stagnation and shrinkage, it was experiencing more new customers and upgrades than ever before... but also even more cancellations and downgrades than ever before. Extremely dynamic, yet nevertheless flat overall. ![Buffer's period of stagnation and shrinkage contained "more of everything" -- new, cancels, upgrades, and downgrades.](https://public.asmartbear.com/books/anthology/chapters/max-mrr/buffercomponents2025-simple.png) The "Max MRR" metric is able to predict this capricious variation in revenue, months ahead of time. We'll first construct a toy example to see how it works, and then apply it to Buffer. ## The Max MRR Metric Let's explore a simple, hypothetical SaaS company, with stable growth metrics, as follows: * Starting at $0 MRR * +$1,000 new MRR each month from marketing and sales * 5% cancellation each month from existing customers Tracking MRR over time, it starts out growing nicely, reaching $10k MRR in 15 months, but then growth levels out, and it never reaches $20k MRR, even after 60 months: ![Hypothetical SaaS company levels out at $20k MRR](https://public.asmartbear.com/books/anthology/chapters/max-mrr/mrr-flat-marketing-flat-cancellation-rate-simple.png) The reason this happens is that new MRR is chugging along at a constant pace (+$1000/mo), whereas cancellation is non-linear; it is proportional to how large the company is. The larger the company, the more *absolute dollars* of churn there is, even though the _rate_ of churn is steady at 5%. As churn dollars grow, growth slows. Eventually cancellation-dollars are just as large as new-dollars, and the company stops growing, reaching a [steady-state](https://longform.asmartbear.com/ssebitda/). What is this maximum size? Mathematically it's trivial to compute; we just convert the previous sentence into numbers, where `$c$` is the monthly cancellation rate: `$$\begin{aligned} \text{MRR}_\text{max} \times c &= \text{MRR}_\text{new} \\ \text{MRR}_\text{max} &= \dfrac{\text{MRR}_\text{new}}{c} \end{aligned}$$` In our toy example, `$\text{MRR}_\text{new} = \$1000$` and `$c = 0.05$`, so the maximum total revenue is `$\frac{\$1000}{0.05} = \$20{,}000$`, which is exactly what the revenue is approaching in our chart: ![Hypothetical SaaS company levels out at $20k MRR](https://public.asmartbear.com/books/anthology/chapters/max-mrr/mrr-flat-marketing-flat-cancellation-rate-1-2-simple.png) This model also explains the common phenomenon seen in new startups: Fast initial growth that tapers off sooner than the founders expected, given that the target market is still many orders of magnitude larger than the company. The chart shows why. When "current MRR" is far away from "Max MRR," MRR will grow quickly; without many existing customers, there's not much churn. As customers accumulate, churn grows, and then it's the _churn_ that's slowing growth, not the _market_. As MRR approaches Max MRR, churn becomes the dominant effect on growth. Thus the Max MRR metric indicates not only the maximum size of the company, but the size at which growth will start slowing. Obviously we want this metric to increase over time, as we increase new revenue or decrease cancellations. How can we know where we should invest our time? ### Max MRR as cancellation rate improves In real companies, neither new MRR nor cancellation rate is constant, so Max MRR changes over time. How does that look? Varying cancellation even by small amount yields a large difference, because cancellation is a nonlinear--exponential in fact--so small changes are magnified. Let's take the same company, still with a fixed $1000/mo in New MRR, starting with 7%/mo cancellation for the first 20 months, then improving to 4% over the next 10 months, then holding at 4%: ![Hypothetical SaaS company with improving cancellation rate](https://public.asmartbear.com/books/anthology/chapters/max-mrr/mrr-flat-marketing-improving-cancellation-3-simple.png) Notice that although cancellation changed relatively quickly, revenue doesn't react quickly, looking mostly linear but "wavy." It is therefore difficult to tell, looking at revenue, that we've made a massive improvement to the business model. In contrast, let's also plot the "Max MRR" metric: ![Hypothetical SaaS company with improving cancellation rate and max-growth level](https://public.asmartbear.com/books/anthology/chapters/max-mrr/improve-simple.png) Notice how the "Max MRR" line shoots up as soon as cancellation rate starts improving, while MRR lags behind. When MRR gets near that ceiling, the cancellation rate dominates the (slowing) growth; when we lift the ceiling, "New MRR" becomes the dominant factor again. "Max MRR" is therefore a *leading* indicator of long-term growth, because it shoots up quickly when conditions change, whereas "Current MRR" is a _lagging_ indicator. ### Max MRR as new MRR accelerates Now let's swap which variable is improving; we'll fix cancellation rate back at 5%, and allow "New MRR" to increase. We already know that if we have a steady input from marketing, growth levels out quickly. What if marketing improves? Suppose in the first month we add $100 in MRR, in the second month we add $200, and so on, adding $100 more each month than the month before. This is _accelerating_ revenue--growing faster and faster. And we'll let it accelerate for 60 straight months: ![Hypothetical SaaS company with accelerating revenue](https://public.asmartbear.com/books/anthology/chapters/max-mrr/accel-marketing-simple.png) The first thing we notice is that growth doesn't stop. "Max MRR" also grows alongside actual MRR. But before we celebrate too much, realize that MRR is _not accelerating_. MRR is growing at more or less a constant pace--it looks like a line for most of this time frame. Yet this is a company that is _accelerating_ their marketing output! So this is disappointing. *New MRR* is accelerating, yet *MRR* is growing only linearly. This is once again because cancellations are exponential, which means cancellations are accelerating too. The acceleration from cancellation cancels out the acceleration from New MRR. Thus, even companies with accelerating new growth still typically grow revenue linearly, as [analyzed here with many real-world examples](https://longform.asmartbear.com/exponential-growth/). Furthermore, this scenario is still too optimistic; you cannot grow a marketing channel forever. In fact, channels not only top out, but tend to decline once mature (for reasons [again detailed here](https://longform.asmartbear.com/exponential-growth/)). In a more typical situation, the company unlocks a marketing channel but then reaches the channel's capacity for delivering additional sales every month. This results in initially-accelerating growth, followed by the ceiling that we've now come to expect. Once again the Max MRR curve presages all this, and therefore is a useful leading indicator: ![marketing growth followed by stable marketing](https://public.asmartbear.com/books/anthology/chapters/max-mrr/marketing-to-cap-simple.png) We also see the emergence of the classic "S-curve" revenue line. Growth accelerates during the combination of "not many customers yet who could cancel" and "new marketing channel is still growing." When marketing effectiveness levels off, the curve switches into deceleration. This has been observed thousands of times across companies in all industries and decades. The Max MRR curve predicts this, months or even years before it happens. This is its utility. Now that you have a good sense of how everything works with toy models, let's apply this in the real world. ## Max MRR at Buffer Figure 9 shows their MRR alongside Max MRR. Notice how the Max MRR line reacts immediately and ahead of revenue, anticipating both the good news and bad news. ![Buffer MRR and Max MRR](https://public.asmartbear.com/books/anthology/chapters/max-mrr/buffermaxmrr2025-simple.png) Buffer went through a few phases: | Timeframe | New MRR[^1] &
Cancels[^2] | Discussion | |:----|:----|:----| | 2012-
2014 | MRR
$3k → $40k

Cancel
8% → 6% | As they found Product/Market Fit, they got cancellations under control and accelerated new MRR, resulting in Max MRR increasing and revenue starting to accelerate. | | 2014-
2020 | MRR
$40k → $140k

Cancel
6% | Although cancellations never improved, they accelerated new MRR significantly, with a few clear "found a new channel... channel became saturated" events visible where Max MRR briefly plateaued. This maintained linear growth, but never accelerated growth, because cancellations were still high. | | 2020-
2024 | MRR
$140k → $90k

Cancel
7% | Revenue sharply declines and struggles for years; cancellation pops up another percentage, causing the dip. New MRR is still large, but because cancellation is also large, Max MRR dips below revenue, so revenue falls. | | 2024- | MRR
$100k → $150k

Cancel
6% | New MRR substantially increased, with cancellation slightly improving, so Max MRR shoots up and growth reignites. Imagine what would happen with cancellation of 4%! | [^1]: Includes new customer MRR, upgrade MRR, and reactivating previously-cancelled customers. [^2]: Includes customer cancellations and downgrades. Notice how the Max MRR line reacts much quicker, and with much larger magnitude, than any other metric. Anticipating both the good news and bad news. Looking at the components of Max MRR, the most impactful culprit is the high cancellation rate. 6% is precarious, requiring forever-accelerating new MRR, and quickly tanking revenue as soon as new MRR didn't keep up, which of course it inevitably cannot. What if Buffer had focused on customer retention, shifting the cancellation rate over five years from 8% in 2014 to 3% by 2019? Then revenue would have doubled: ![Buffer MRR at 3% cancellation](https://public.asmartbear.com/books/anthology/chapters/max-mrr/buffer3p2025-simple.png) Of course this is easy to predict knowing how Max MRR works. If you halve the cancellation rate, you double Max MRR. In 2023, Buffer founder and CEO [Joel Gascoigne](https://x.com/joelgascoigne?lang=en) [wrote a strategy letter](https://buffer.com/shareholders/2023) saying the same thing. He shifted the corporate strategy back to their [ideal customer](https://longform.asmartbear.com/icp-ideal-customer-persona/) inside their best [target market](https://longform.asmartbear.com/target-market/) (entrepreneurs and small businesses), back to a pricing model that worked better for them ([freemium](https://longform.asmartbear.com/freemium/)), who therefore retain at higher rates. And it worked--we can see cancellation diminish, MRR increase, and Max MRR increase ahead of MRR. ## Max MRR vs NRR `NRR` is Net Revenue Retention, which answers the question: If no new customers were added, how would revenue change over the course of a year, as a percentage of the ARR from the start of that year? Specifically, and converting from MRR to ARR: `$$\text{NRR} = \dfrac{\text{Upgrade} - \text{Downgrade} - \text{Cancel}}{\text{MRR}} \times 12$$` When upgrades exceed cancellations and downgrades, revenue grows every month, even when you're not adding new customers. At what point would revenue top out in that scenario? If the market were infinite, you would never stop growing! Therefore "Max MRR" ceases to have meaning. Of course, markets are not infinite, just as marketing channels are not infinite, so you will top out anyway, but not because of the factors that go into Max MRR. Rather, it will be because you'll have reached saturation in your target market. This demonstrates a simple point: After high retention, the most powerful growth lever is to have NRR ≥ 100%. Indeed, almost all public SaaS companies have NRR ≥ 100%, exactly because if they didn't, their Max NRR ceiling would be too low for their lofty revenue goals: ![Average NRR at IPO for SaaS companies is 119%; all were above 100%. Average ARR at IPO is $255M. (Year 2022)](https://public.asmartbear.com/books/anthology/chapters/max-mrr/nrr-simple.png) This rule isn't important only for heavily-funded VC-backed companies gunning for hundreds of millions of ARR. It's just as valid for the solopreneur, because NRR creates growth even with a limited budget for marketing. It also means your customers are growing with you, becoming more successful, receiving more value, and [happy to share some of that with you](https://longform.asmartbear.com/willingness-to-pay/) in the form of price. This is a wonderful signal and a wonderful business model regardless of your long-term financial goals. ## Max MRR vs Quick Ratio The SaaS Quick Ratio[^3] is sometimes used in a similar way to Max MRR, measuring how much cancellations and downgrades are dragging down growth from new customers and upgrades: [^3]: Easily confused with the [financial Quick Ratio](https://en.wikipedia.org/wiki/Quick_ratio), which is the ratio of ready-assets to liabilities, used to measure how easily a company will be able to cover its financial obligations. `$$\text{Quick Ratio} = \dfrac{\text{New} + \text{Upgrade}}{\text{Cancel} + \text{Downgrade}}$$` A high-growth, early-scale company should have a Quick Ratio of 3-4; a company at scale with healthy growth should be 1.5-2. Once it reaches 1, the company isn't growing, and below 1, it is contracting. This is similar to Max MRR. However, useful as it is, it does not forecast revenue growth at all, as is evident from Buffer's Quick Ratio: ![Buffer quick ratio](https://public.asmartbear.com/books/anthology/chapters/max-mrr/bufferquick2025-simple.png) Buffer's Quick Ratio drops precipitously during Buffer's multi-year period of healthy growth. It continues its downward trajectory even when growth stalls and reverses. It starts recovering before the company actually made positive changes in 2023, and still doesn't seem very strong in 2025 when clearly growth has dramatically changed (and Max MRR does dramatically increase). This doesn't invalidate Quick Ratio as a useful metric, it just isn't a predictor of revenue growth. Max MRR is. Also, Max MRR is easier to understand and get excited about. It feels tangible to have a "revenue ceiling" that you are trying to increase. It's harder to get excited about a goal like: "Let's get our Quick Ratio from 1.4 to 1.6." --- No metric tells the entire story on its own; Max MRR is no exception. But, it's a fun, simple, explanatory, predictive, and concrete number that is applicable for companies of all stages and sizes. Now go get your cancellation rate under control, and then switch your focus to NRR.

Ruthless prioritization while the dog pees on the floor

Jason Cohen — Sun, 22 Jun 2025 00:00:00 +0000

![Remember that thing we thought was a thing? It's not a thing. But here's the thing, that other thing? Totally a thing!](https://public.asmartbear.com/books/anthology/chapters/prioritization/d7053print-simple.png) ## The prioritization mandate Time is a zero-sum resource: An hour spent on one thing necessarily means _not_ spending an hour on the entire universe of alternative things. Every minute is a choice. Every choice is a trade-off. Time is a hard limit. We can maximize *productive* time through better habits: good sleep, sensible diet, reasonable exercise, restorative rest, reading books instead of doom-scrolling Twitter, sleeker task-management, fewer meetings, shorter meetings. But even here we're prioritizing within 24 hours each day, choosing how we labor and rest to maximize our effectiveness. There [isn't enough time for more than two big things](https://longform.asmartbear.com/two-big-things/) in your life. > "All we have to decide is what to do with the time that is given us." > --Gandalf the Wizard We need more time. Not 30% more, but 3000% more. Every company proves that statement with a data repository called "the issue tracker". We accumulate thousands of valid items over the years: Little ideas that would be nice to do; big features that would [create differentiation](https://longform.asmartbear.com/talk-vs-walk/); bugs that paying customers actually experience; [design tweaks](https://longform.asmartbear.com/design/) that make the team proud of their craft. You will never complete all these; completing even 10% would be a miracle, the ratio worsening every week as more items are added than removed. The better you listen to customers and the more creative your teams, the worse the ratio becomes. You hire more people to get more done, but new talent have new ideas, and the ratio worsens again. Inspiring, and maddening. So, time is a fixed constraint that limits us to 1%-10% of what we "need" to execute. Unintuitively, the fraction diminishes with scale; we will never defeat it. The inescapable conclusion is the trite statement that "We must prioritize"--intelligently determining which precious few things we will actually do. In fact we can't help but prioritize, even if mindlessly. Since we can only do one thing at a time, whatever we're doing now is definitionally our "highest priority." Reading this sentence is currently your highest priority. While "prioritizing" doom-scrolling is obviously faulty decision-making, there's the more insidious cases of prioritizing things that are fun but only somewhat useful, or of prioritizing things due to necessity but not importance (e.g. paying taxes on time). Indeed, a common complaint of prioritization is that we're only doing things that happen to have deadlines, instead of things that matter. "Urgent, but not important" tends to win over "Important, but not urgent," if we're not paying attention. Prioritization is a choice, and more often than we'd like to admit, the choice was thoughtless. We've all lost a few hours to doom-scrolling or YouTube or TV (if you were born before 1990). Maybe that activity was useful for "restorative rest." Maybe it was just a poor choice. Maybe we should give ourselves grace. Maybe. ### Prior art There are, of course, myriad prioritization frameworks. On this site alone I've detailed many of my own: * [Fermi ROI](https://longform.asmartbear.com/roi-rubric/): Replacing rubrics, especially "ROI"-style * [Binstack](https://longform.asmartbear.com/maximized-decision/): Making significant choices with incomparable dimensions * [Adjacency Matrix](https://longform.asmartbear.com/adjacency/): How to expand an existing product * [Investment Criteria](https://longform.asmartbear.com/investment/): When to invest significant time and money * [Rocks, Pebbles, Sand](https://longform.asmartbear.com/rocks-pebbles-sand/): Analyzing and prioritizing three _sizes_ of work * [Satisficing vs Maximizing](https://longform.asmartbear.com/maximizing/): Prioritizing [some things](https://longform.asmartbear.com/product-metrics/) as "good enough," others as "never good enough" * [Cleaving](https://longform.asmartbear.com/complex-decisions/): Separating upside from downside, treating each differently * [Fairytale Quarterly Planning](https://longform.asmartbear.com/strategic-planning/): Prioritizing work against strategic objectives and the obstacles that are preventing us from winning * [JIT streams](https://longform.asmartbear.com/jit-backlogs/): Handling multiple, incomparable inputs, separating prioritization from work-planning I don't like anything with a rubric or a computed score ([this is why](https://longform.asmartbear.com/roi-rubric/)). I don't like anything with a "confidence level" (because you [can't discuss it accurately](https://longform.asmartbear.com/probability-words/)). I don't like anything that is [built to produce symmetry](https://longform.asmartbear.com/models/) for consultants' slides rather than reflecting the messiness of the real world. I don't like anything that purports to compare incomparable things (e.g. scoring "more growth" with the same made-up number as we score "don't run out of money" or "make employees happy"). Beyond the built-for-purpose frameworks above, there's a simple overarching framework that applies to every type of prioritization, foisted upon us through the observation that we have time for less than 10% of what we'd like to accomplish. Here is that framework. ## 10x / 0.1x Prioritization ### 10x tasks Despite how precious time is, some tasks are so valuable, so impactful, that the return on your investment is an order of magnitude more than what you put in, even if you valued your time at (say) [$1000/hour](https://longform.asmartbear.com/value-time/). You must seek out these "[10x things](https://longform.asmartbear.com/10x/)" that can transform the company. Examples: * The few features that win the majority of sales, whether through pure [delight](https://longform.asmartbear.com/willingness-to-pay/) or because of a combination of utility and uniqueness among the competition. Rule of thumb: Impactful features are actively used by at least 40% of customers, or are a critical reason-to-buy or reason-to-stay for at least 15%. * Finding the ideal [marketing positioning](https://longform.asmartbear.com/more-value-not-save-money/) and [wording](https://longform.asmartbear.com/authentic-is-dead/) where advertisements convert 2x higher and people landing on the home page buy 2x more often. (Not 10% higher--[you can't actually measure that](https://longform.asmartbear.com/p-hacking/).) * Finding the [pricing model](https://longform.asmartbear.com/pricing-determines-your-business-model/) that maximizes profitable growth while maintaining fairness for customers. * Taking the time to [correctly identify the next strategic objective or biggest obstacle](https://longform.asmartbear.com/strategic-planning/), so everyone can prioritize their own time towards this most-important thing; working on the wrong thing is a 100% waste of time, even if efficiently- and perfectly-executed. * Hiring the next critical employee who dramatically increases the company's throughput and work-quality and decision-making-quality, while adding a skill-set that was previously missing, a skill-set needed to overcome the current obstacle or the next strategic objective. * Applying energy to an area where even small changes have a large impact on growth or profitability. * Addressing the [single biggest drag on growth](https://longform.asmartbear.com/growth-asymptote/). * Deciding how to [expand the business into the next adjacency](https://longform.asmartbear.com/adjacency/). If you don't know what one or two 10x tasks you should be working on, then *identifying that is your highest priority*. Otherwise you are certainly not prioritizing properly; the entire company is misusing their time. [This is my method](https://longform.asmartbear.com/strategic-planning/) for determining what those things are. If you have too many 10x choices, the [Binstack prioritization framework](https://longform.asmartbear.com/maximized-decision/) is designed for this type of decision, where the inputs are incomparable, and you want to maximal impact. It's rare, however, to actually have too many 10x possibilities. If you think you do, it's likely that you are being too generous in declaring things "10x". They need to literally "10x" a key metric, or be the difference between survival or bankruptcy. Their upside must be so massive, that even when it inevitably takes 2x longer to implement and is `$\frac{1}{2}$` as impactful as you thought, it was still worth it. To take that 4x hit and still have a great outcome requires starting at 10x. To further refine your thinking, treat these as *investments*--i.e. applying significant time, expecting an out-sized return, but with uncertainty. [Here is a guide for making good investments](https://longform.asmartbear.com/investment/). ### 0.1x tasks Most activities are worth far less than $1000/hour, even if they are mandatory. These are the "0.1x tasks." You must minimize these through several strategies: * Eliminate them completely by structuring your life, product, [target-customer-selection](https://longform.asmartbear.com/icp-ideal-customer-persona/), or [company strategy](https://longform.asmartbear.com/great-strategy/) to avoid them. [Here is a guide](https://longform.asmartbear.com/pivot-points/) for inventing ways to avoid them. * [Delegate](https://longform.asmartbear.com/delegation/), even if the result is worse than what you'd do yourself (e.g. grocery delivery being both more expensive, and not picking the same apples you would have picked). It's not worth the time to do everything (your definition of) "perfect." You need to be [in command instead of in control](https://longform.asmartbear.com/in-command/). * [Batch or automate](https://longform.asmartbear.com/stop-chopping-to-pieces/), accepting minor penalties (e.g. paying bills only once per month and risking occasional late fees, or batching security patches, since you can't ignore them forever but each one is unlikely to be exploited in the next few weeks). * Archive loose tickets that are older than 100 days. Because of the rule that more than 90% of our ideas will never be done, realize that these are already among that 90%; deleting them will help you prioritize the remainder. If something is truly important, it will come up again in future. (Hence "archive", not "delete".) It's tempting to assume that small, easy tasks are automatically 0.1x tasks, but that is a fallacy of conflating "impact" with "effort". Easy things can have a large impact (making them 10x almost definitionally), and complex things can have no impact on revenue or employee happiness. In some regimes, many small tasks add up to a 10x impact; I call this "life by a thousand sparks".[^1] A common example is "great design". While [amazing design is not required for success](https://longform.asmartbear.com/design/), there are many examples of products winning primarily because of beloved design. Great design is not "one thing," however. There are macro-scale architectural decisions, but also it emerges from thousand details: subtle color choices, pixel-perfect layout, font and word selection, aspect ratios, play of white space, satisfyingly snappy interactivity, a design system for comprehensive consistency, fixing every last bug that is aesthetic rather than functional, and harmony between the website, the product, the emails, and the material for marketing, sales, and support. These myriad tasks should not be dismissed as individually 0.1x, *if* they are specifically in service to a 10x concept that is also the primary way you win customers in a competitive market. [^1]: A facetious inverse of "death by a thousand cuts". ![I am multi-tasking! I've done this report three or four times already!](https://public.asmartbear.com/books/anthology/chapters/prioritization/d3501print-simple.png) ### What if there were no 1x tasks? Prioritization isn't a dichotomy, but I encourage you to act is if it were, forcing yourself to make clearer decisions. Idea-abundance is beautiful, but we must be ruthless, final, and precious with our time. You will inevitably label things as "10x" which are really 1x. You will spend too much time on 0.1x things; after all, there are so many. Forcing this binary choice is reductive, but helps us be ruthless. Of course in reality there is a spectrum of ideas, sizes, impacts, risks, and confidence. Our ability to [measure any of that even _post facto_](https://longform.asmartbear.com/unmeasurable-metrics/) is laughably poor, and [our ability to predict any of them](https://longform.asmartbear.com/confidence/) is even more pitiful. If you insist on the existence of 1x tasks,[^2] use the [Rocks, Pebbles, Sand Framework](https://longform.asmartbear.com/rocks-pebbles-sand/) which explains how to segment by size, how to prioritize each size differently, and how to resolve the common conflicts that arise as you schedule work in the messy real world. [^2]: Another exponent of 10x / 1x / 0.1x is the great [Product Management](https://longform.asmartbear.com/great-product-manager/) teacher [Shreyas Doshi](https://linktr.ee/shreyasdoshi) with his excellent [LNO framework](https://twitter.com/shreyas/status/1223816226918453253?s=20). ## Dealing with the fallout: Dogs peeing on the floor In a well-lit living room, a dog is peeing on the floor while a man sits in a chair, reading a book. The man doesn't react. You're watching from a window, concluding that this man must be ignorant, crazy, or at least a poor decision-maker. Put down the book and take that dog for a walk, idiot! Except, you don't know the full story. In one hour, the man has the most important meeting of his life. His performance in this meeting will dictate the next ten years of his career. Everything he needs to know to be successful in this meeting, is in that book. Yes the dog should have been taken for a walk, but the penalty of having to clean up the pee is worth it, because the call is *that* important. The decision is rational. Yet the observer sees only irrationality. This is caused by two things: 1. The observer doesn't know the complete story. 2. The observer has trouble accepting that something bad/stupid can nevertheless be the right decision, because the alternative is even worse. This inevitably happens at your company when you ruthlessly prioritize. Because you're so [focused on the _most_ important thing](https://longform.asmartbear.com/focus/), other things lay fallow. Important things. Fires burning that you're intentionally ignoring because although they _are_ fires, they still aren't as important as the Most Important Thing. Dogs peeing on the floor. On purpose. ![bear working while fire burns](https://public.asmartbear.com/books/anthology/chapters/prioritization/bear-working-while-fire-cropped-simple.png) But others see the fires burning, the dogs peeing, for months, for years, and then lose faith in leadership. They complain--understandably, and accurately--and lose morale. Worse, they start believing that The Deciders must be crazy, just as the window-peeker believed. Loss of trust and respect leads to talent leaving, which leads to the death of an organization. "They never listen to my ideas," the most prolific idea-creators complain. They're right, too; after all, mathematically you could never implement more than a few percent of those ideas. Not because you're "not listening," but because of inexorable math. You have to focus on the peeing dogs, and you can't do most other things. The way you combat this natural progression is to address the two points above. You have to transmit the complete story, not just the few priorities, but also *why* they are the top ones. You have to acknowledge the twenty things other that also deserve attention, explaining why we're intentionally letting those fires burn, those dogs pee, because the top priorities are even more important. This is why the "not doing" list is such an important part of the plan. And you can retell the story of the dog peeing on the floor, acknowledging that it feels bad watching the dogs pee, especially when you know how to prevent it. I don't remember the origin of this particular parable, but I've retold it many times at WP Engine, and it sticks. It works. This is what prioritization actually looks like. The full picture, not just the social media admonitions that you "[have to focus!](https://longform.asmartbear.com/focus/)". The full story requires constant, repetitive communication. Because it _is_ bad to see the pee; we can rationalize it only when we see that the pee buys us time to do the most important things--the things that leverage our precious, woefully limited time, for out-sized results. No one said it would be easy. Good luck.

More or less

Jason Cohen — Sun, 01 Jun 2025 00:00:00 +0000

![fabled sword magic free cupcake](https://public.asmartbear.com/books/anthology/chapters/more-or-less/d8797print-simple.png) There are three viable strategies in the interplay between value and price: #### More for more Get the best, with a price to match. _Luxury. Advantage._ #### More for less Everything you actually need, at a reasonable price. _High-ROI. Practical._ #### Less for less It's not much, but it's incredibly affordable. _A steal. Accessible._ You see this pattern across all industries, in both consumer and business markets. ![Miu Miu Cardigan](https://public.asmartbear.com/books/anthology/chapters/more-or-less/cardigan-simple.png) In clothing, for example, a cashmere sweater embodies **More for more**--expensive, but deliciously soft (enjoyment) and warm (functional). A high-end brand has a function beyond high-quality materials: Status is bestowed upon the owner, due to it being overpriced beyond its quality. Buyers are not budget-constrained, which enables high prices and correspondingly high profit margins, but customers also have their pick of options, where every other product on the planet is a competitor. The customer is in control. Hence the importance of being "the best" according to some subset of the market. ![LLBean sweater](https://public.asmartbear.com/books/anthology/chapters/more-or-less/llbean-simple.png) **More for less** describes the practical decision-making that governs most purchases: Highly functional articles that will last for years, at one-fifth or even one-tenth the price of the same luxury item. Most people have a budget and therefore are seeking "The best that I can afford." Thus price is the primary constraint, inside which they prioritize features according to personal preference; they can't have everything, but they can prioritize everything. The product's goal is not to be the best along every dimension, but rather to offer a set of trade-offs that enough customers prefer over other sets. ![Tamu hoodie](https://public.asmartbear.com/books/anthology/chapters/more-or-less/hoodie-simple.png) Fast-fashion vendors provide **Less for less**, where you'll find the article disintegrated in the washing machine by the end of the season, but hey, it was cheaper than lunch. Consumers aren't choosing between trade-offs, but rather between having a sweater, or having no sweater at all. All negatives are accepted, as there is no alternative. The vendor enjoys a massive market, but rock-bottom prices mandate extreme cost-efficiency, requiring almost non-existent marketing and sales costs, and even in the best case yields barely any profit even at scale. A competitor with a better cost structure can decimate the business. [Don't worry about it](https://www.youtube.com/watch?v=MKTN2OiR2R8&ab_channel=SaturdayNightLive). --- All three approaches are valid and, [like all strategies](https://longform.asmartbear.com/great-strategy/), each drags along a set of consequences, some of which you despise, others you love. You must pick one approach, and accept its batch of consequences. If you don't incorporate those consequences into the rest of your strategy, you'll have a confusing market message, a Frankenstein product, and the wrong pricing model. ## More for more ("The best") Building "the best" is wonderful for both profit and enjoyment of life. It feels good to have pride in your craft, to have customers who love what you do and agree with your superlative self-assessment. "The best" commands a high price, which makes profit easier, even while self-funding further investment. This is a beautiful place to be, but it comes with consequences. "The best" is not the average of a subset of things that already exist. The best is an outlier. The best is special. The best is different. Your ideas need to be substantially different from what's already available, or it's definitionally not true that you're the best. So, the first question is: The best at what? It could be anything, but it has to be specific, and customers have to agree it's worth the higher price tag. It could be the best design--the simplest, the most pleasant to use, the most beautiful. It could be quality--the peace of mind and enjoyment of a product always "just works". It could be a superior workflow, could be that it integrates with all the tools the customer uses, it could be personal tech support, and more. This article on ["Willingness to pay"](https://longform.asmartbear.com/willingness-to-pay/) lists more ways of being "the best". ![Bear in cardigan](https://public.asmartbear.com/books/anthology/chapters/more-or-less/bear-cardigan-simple.png) Coupled with "best at what" is "best for whom?" There are billions of people on the planet who absolutely do not agree what "best" means in clothes, software, food, or articles about pricing strategy. This is where founders get fouled up; they think that just because *they* love it, and they "built it for myself" and therefore "I am the customer," and therefore other people will also to pay to solve that problem, in that way. They rarely do. The fallacies with that assumption (and some truths), are [detailed here](https://longform.asmartbear.com/scratched-my-own-itch/). Instead, use your initial spark of insight as the starting point, your motivation, knowing that half of it is indeed correct but half isn't, and so you go [find potential customers to interview](https://longform.asmartbear.com/find-customers-to-interview/), and use a [structured process to fix your hypotheses](https://longform.asmartbear.com/customer-development/) to morph your definition of "best" into alignment with what some [specific ideal customer](https://longform.asmartbear.com/icp-ideal-customer-persona/). Customers in this category care about something even more than they care about price. What kinds of things are like that? In business, an example is so-called "mission-critical" things, i.e. a failure or low quality causes outsized damage in internal cost, or far worse, damage to their reputation, in the worst case causing customers to leave.[^1] Something that is an urgent, top priority for them right now, whether due to emergency or strategic mandate.[^2] In the consumer world, it could be what Google calls a "toothbrush product"--something vital that is used at least twice a day, or ideally all day,[^3] or tied to personal identity.[^4] [^1]: Our company WP Engine is an example, because we run websites like eCommerce (all of the customers' revenue) or media (merely being slow means they lose traffic, which means they lose ad revenue) or corporate home pages (downtime costs lost leads and lower reputation). This can also be [CYA](https://en.wikipedia.org/wiki/Cover_your_ass), i.e. "no one ever got fired for buying ______". [^2]: [See this article](https://longform.asmartbear.com/purgatory/) for many examples of how this arises, and how to define your target customer and product to match. [^3]: Examples: to-do apps for busy people, email for people who spend more than an hour per day there, calendar apps for people whose calendars look like they only barely lost a game of Tetris, note-taking apps for people with 1000s of notes [^4]: Like luxury brands, where being "far too expensive" is the point, like a ram with ridiculous horns. Being "the best" isn't just about the product or the customer-fit; all other decisions must also be aligned with that positioning. Your marketing will focus on excellence and exclusivity rather than affordability. Your team will likely be smaller with higher-caliber individuals--passionate artisans who take pride in creating excellence and celebrate perfectionism. When hiring, you'll focus on finding the top 1% of talent, often paying premium salaries. Your customer support response-time isn't measured in hours but in seconds, often with white-glove, personalized service and dedicated account representatives. Your product development cycles may be slower, emphasizing perfection before launch, developing features without cutting corners regardless of cost. Because the number of customers in the market is small and limited, market-share matters, and upgrading existing customers is vital for growth at scale.[^5] [^5]: Many public enterprise software companies get the _majority_ of their revenue growth from up-selling existing customers, rather than adding new ones. Fortunately, the high price tag means you can invest in these things. And if you pull it off, it's the best for yourself because you're fulfilled when you're exercising your best qualities and you're validated when others agree on Twitter and say it to your face. You can recruit great employees, because who doesn't want to work at a company whose customers actually love them? Most products are not "the best". But for those who can deliver it, it creates a virtuous cycle of excellence, satisfaction, and profitability that's hard to beat. ## More for less ("High ROI") Efficiency is beautiful. "More for less" means receiving outsized value for a given cost. It is the right value proposition for most things in life, especially in business where you need to get a job done as cheaply as possible, but not cheaper. ![Bear in sweater](https://public.asmartbear.com/books/anthology/chapters/more-or-less/bear-sweater-simple.png) The global apparel industry exemplifies the claim that this is the most popular category. The "more for less" category generates $1,200B of the industry's total $1,600B [annual revenue](https://straitsresearch.com/report/apparel-market), with [luxury at $250B](https://www.imarcgroup.com/luxury-fashion-market) and [fast-fashion at $150B](https://www.researchandmarkets.com/reports/5939667/fast-fashion-market-report). With profit margins of 5%-15%,[^6] they are unsurprisingly more profitable than fast-fashion (under 5%)[^7] and less than luxury (20%-30%).[^8] "More for less" software companies can have much better profit margins than apparel; coupled with reasonable prices and addressing the largest market segment, this is a great business model. [^6]: L.L. Bean, makers of the sweater above, is private and doesn't report profit, so this range is based on their 10% annual spend on [bonuses and give-back activity](https://www.llbean.com/llb/shop/518872?page=llbean-announces-2023-year-end-results-and-employee-contributions) and data from other retailers. [^7]: The outlier-most-profitable fast-fashion brand stands at 9%, but [almost all are under 5%](https://newfrontierfunding.com/understanding-cost-based-pricing-apparel-business/). [^8]: For example Prada, which makes the Miu Miu sweater above, [generates](https://www.pradagroup.com/content/dam/pradagroup/documents/investors/FY-2024/Prada_Group_FY24_PR.pdf) 23% net profit. Which types of product benefit from high-efficiency, but doesn't require "the best?" Perhaps almost anything. Back-end systems that operate the company but are invisible to customer are a good start, because no competitive advantage is conveyed to the company for getting "the best." Even more ideally, areas where downtime isn't terribly disruptive to their real-time operations; a reporting system can be down for an hour without hurting their business, but a customer support tool cannot. Or an area where it's OK if not every desired feature is present, again like reporting software where the lack of exactly the right chart-type is not a deal-breaker, and won't prevent them from seeing the data. Even better when the product just needs to "tick a box" to "say that we have it," because then requirements won't be stringent. It might sound negative that your product isn't "critical to their success", but being non-real-time, non-essential, also means you have far more leniency in quality or lack of features. This leniency in requirements is also why you will make 100x more sales than you expect, even though you still target the "perfect" ideal customer with marketing and product features. **For your ideal customer, [you are in fact "the best,"](https://www.youtube.com/watch?v=dVSXNkWUq88&ab_channel=JasonCohen)** and they cannot believe they're getting the "More for more" product for the "More for less" price. That attitude [defines them as the ideal customer](https://longform.asmartbear.com/target-market/); if you can't win _them_ over from your home page, you're not doing your job. But, for every one of those, there's a hundred others where you're the right choice. You have most of what they want, you don't have any deal-breakers, and the price is right, so you've made the sale. So, you gain the power of specificity, clarity, and emotional connection from talking to your ideal customer, while winning all the "More for less" customers anyway. [This article on ICPs](https://longform.asmartbear.com/icp-ideal-customer-persona/) explains this dynamic in detail, with examples. Although the main value here is economy, "save money" is weak positioning. Position by showing how much value you're generating rather than how much money you're saving as described in [this article on positioning for value](https://longform.asmartbear.com/more-value-not-save-money/). Because, even if this is your pricing strategy, that doesn't mean it's your marketing material. Your marketing position still needs to be as strong as possible, emphasizing that it really is *more* for less. As with the other pricing strategies, the rest of your decisions must be aligned. Marketing will make the customer feel like this is the "smart choice," neither wasting money on useless branding nor falling prey to the mistake of "If you think this is expensive, wait until you see how expensive 'cheap' is." Marketing must make the trade-offs clear, not try to claim it's the best for everyone, which quietly alienates everyone; comparison charts shouldn't be a solid column of green checkmarks, but rather elucidate where you are strong, and where you are not the right solution. Product features must not be too difficult to maintain, as the price tag doesn't allow for special exceptions or for brittle integrations that only 5% of the customers use. Rather, build only those features used by at least 50% of customers, or where at least 15% of customers buy you specifically for that reason. Hire team members who excel at identifying and eliminating waste without sacrificing core value. Your whole business must reflect the "More for less" mentality; your internal costs and decisions match your external promises. Pundits have long eschewed the "More for less" strategy as a poor compromise between being either "the best" or "the cheapest." As Michael Porter famously wrote in _Competitive Strategy_ (1980), there are only three strategies: (a) Differentiated Best; (b) Low-Cost Leader; (c) Niche Leader. Later thinkers pointed out that (a) and (c) are the same, differing only in market-scope. This leaves the Porter model with what I am calling "More for more" and "Less for less." However, the customers have spoken, and in most consumer and business industries, it is in fact the "More for less" category that wins most of the customers and most of the revenue and most of the profits. We must not fall prey to the [incorrect attitude that this is "a balance"](https://longform.asmartbear.com/conflicting-choices/) between value and cost; we should instead recognize that "More for less" is a third, independent strategy, and leverage its strengths to the fullest. ## Less for less ("Incredible deal") They say ["never compete on price"](https://longform.asmartbear.com/never-compete-on-price/), but as the linked article demonstrates, that statement is incorrect. There are many wildly successful companies, from solo bootstrappers to some of the largest and most-profitable companies in the world,[^9] who became successful due to absolute rock-bottom prices, even though those products come with severe trade-offs that many consumers would not accept. Sometimes, "cheapest" *is* "best". It may seem like "less for less" couldn't be a good strategy, because neither employees nor consumers ought to be happy with "less," and low prices means low revenue, which in turns means little money for marketing, sales, development, support, product, design, or fun company-culture stuff around the office.[^10] All that is true, and are among the consequences we accept when we adopt this strategy, but the hundreds of billions of dollars of profit (to say nothing of revenue) generated by the many "less for less" successes prove that these trade-offs are sometimes the right ones. [^9]: Examples: Amazon, Walmart, Vanguard, Costco, Dollar General, IKEA, Southwest Airlines, H&M, Zara, Xiaomi, ALDI [^10]: Or whatever virtual-office-substitute we're pretending has the same effect as human beings being beings together. When something is surprisingly cheap, customers are not surprised that they have to compromise on most dimensions, such as quality, design, convenience, features, service, appearance, or longevity. Sure, it's missing some obvious features. Sure, it doesn't integrate with Enterprise systems. Sure, customer service is going to be a set of knowledge-base articles. Sure, it sometimes crashes. What did you expect for $5 per month? There are billions of people who will buy $5 clothes that barely survive the season. If I pay $2/month for web hosting and I get email and a website, it's okay if the email gets some spam and the website is not always fast or even always working. And I don't expect the clothing retailer to have customer service or accept returns, and I don't expect tech support from that web host to know anything beyond helping me reset my password. ![Bear in hoodie](https://public.asmartbear.com/books/anthology/chapters/more-or-less/bear-hoodie-simple.png) The fundamental reason a customer will accept a wide range of trade-offs is that **the alternative is to not have the product at all.** That is, the choice is between having any hoodie versus having no hoodie. The product is "binary" in this sense--having versus not-having--thanks to an accessible price tag. One does not quibble over "features" or "quality" or "service" when the alternative is "nothing". This is at once empowering--supplying goods to people who otherwise had no access--and depressing--that so many people cannot afford the comforts invented through modern ingenuity. And so we generate the worst versions of those items for most of the world to "enjoy." Which viewpoint is correct? The answer can often be found in the [mission and purpose behind the company](https://longform.asmartbear.com/mission-vision/)--the real one, not the one written by the marketing department on the "About Us" page--where the fundamental driver of the company is either one of empowerment and impact, or one that exploits desperation. Regardless, the company should extol the positive aspects in branding and positioning: Emphasizing democratization, participation, simplicity, affordability, and accessibility. The hardest part of executing this strategy is being profitable, since there's so little revenue to be profitable with. The costs have to be extraordinarily, unconventionally low. Some of that can come with scale, either directly (e.g. bulk-purchases from suppliers) or structurally (e.g. tech support per customer is lower at 100,000 customers than at 100, simply because most of them don't have a question in any given month). Still, it takes a lot of $2/mo customers before you can afford to hire even a single tech support person. Or if winning a customer means winning $2/mo and maybe $50 across the entire lifetime of that customer, you can't afford to [pay very much to acquire that customer](https://longform.asmartbear.com/bootstrapped-cpc/), whether that's in direct advertising spend, or sharing revenue with an influencer,[^11] or indirect so-called "organic" marketing,[^12] and you certainly can't have a sales team. You must rely on word-of-mouth and reviews from customers; you'll need to encourage it. [^11]: Affiliates are paid only after you are paid, which means this is an affordable form of marketing from the start. [^12]: While harder to [get customers at first](https://longform.asmartbear.com/low-budget-marketing/), when pages aren't ranked in Google and social media accounts aren't followed by anyone, those assets do scale over time. ![and for connoisseur on budget we have lovely tuesday vintage at buck fifty](https://public.asmartbear.com/books/anthology/chapters/more-or-less/d0305print-simple.png) Given that revenue will significantly lag costs, is it mandatory that such a company raise money? The company will certainly consume cash for a while, whether that's in the form of someone's savings, friends-and-family, a loan, or indeed raising money. [Annual billing can help](https://longform.asmartbear.com/annual-prepay/) quite a bit. This is why pundits (including me) are always telling bootstrappers to "raise their prices" or, since you now know that prices are coupled with every other strategic decision in the company: "switch from a 'less for less' strategy to either 'more for less' or 'more for more.'" If you don't want to raise money, don't build a business that burns money on purpose. However, it's not true that such business need to raise an exorbitant amount of money. In fact, in [the same article above](https://longform.asmartbear.com/never-compete-on-price/), every one of the four case studies (which each grew to multiple-billion dollars in revenue) raised only a small amount of money. Those stories are detailed in the article. When the strategic decisions are fully aligned to a low-cost operation, you only need enough starter cash to get going. This is where VC-funded companies often get it wrong; they assume "we can work the costs out later," but when "later" comes, they realize their processes, technology, and culture has solidified around whatever worked for acquiring customers, and change is either impossible or dramatically painful, like layoffs, rewriting the code that works, and trying to force unnatural marketing and sales motions. There are also examples of bootstrapped companies using this strategy who never raised money; they typically have some extreme cost advantage, such as being located in a country with extremely low costs, yet selling to the United States and Europe. Being physically located in a low-cost zone is a competitive advantage that companies in The West cannot match. As usual, what doesn't work is having a low price without the rest of the strategic decisions. This constantly happens with inexperienced founders who think: "My product sucks, so I'm not comfortable charging a lot." That's not a good reason, and you shouldn't just build crappy product. Instead, the question should be: How do I build a [simple yet lovable product](https://longform.asmartbear.com/slc/) that some [specific ideal customer](https://longform.asmartbear.com/icp-ideal-customer-persona/) would pay 10x more than I'm currently charging, even though it doesn't have a lot of features, and sometimes doesn't even work? That's exactly [what I did with WP Engine](https://longform.asmartbear.com/product-market-fit-formula/). While the hardest intrinsic challenge is to be profitable, the hardest external challenge is that a competitor can beat you with lower prices alone. And this is all too easy, because there's so many reasons why a competitor might win: 1. Newer technology means they can build it for less. 2. Different location or other cost-structure. 3. Larger company uses a "loss-leader" to bury you, since they don't need it to make money at all.[^13] The antidote is to constantly increase your cost-efficiency, and possibly proactively lower prices. This is what Amazon did in retail and later did in the cloud.[^14] [^13]: A particularly ironic example: Microsoft killed Netscape by putting over a thousand developers on Internet Explorer and releasing it for free; more than a decade later, Google killed Explorer by putting over a thousand developers on Chrome and releasing it for free. [^14]: Amazon knew from their "your margin is my opportunity" strategy in retail, they should proactively reduce prices as their cloud operations scaled, to prevent people from looking for alternatives. Later, when Microsoft and Google were established as the only two serious competitors, [they all stopped lowering prices simultaneously](https://longform.asmartbear.com/moats/). Surely a coincidence? "Less for less" is brutal, because of relentless competition and having no extra money to work with. But, it means accessing the largest number of potential customers, and in the best case, you are truly enriching the world by bringing goods and services to people who otherwise could not afford to enjoy them. --- All three pricing strategies are valid--each boasts wildly successful companies of all sizes, in every country. Ideally, one of these strategies jumps out at you as the smartest one, or at least the one you'd be the most excited and proud to implement. Another might jump out as being dumb, or at least too difficult, not worth the consequences, not where you want to spend your [precious](https://longform.asmartbear.com/prioritization/), irreplaceable [time on this Earth](https://longform.asmartbear.com/legacy/). Listen to your gut. Because all are, in fact, smart strategies, you should pick the one that best matches your [personal strengths and proclivities](https://longform.asmartbear.com/pivot-points/), because that's the one you will execute the best while being [most fulfilled](https://longform.asmartbear.com/success-guaranteed/). This is another choice that increases your [chance of success](https://longform.asmartbear.com/startup-drake-equation/). The only wrong choice is to not choose at all, and thus be confusing to customers and misaligned with the rest of your strategy. Then you will make internally-inconsistent choices, working against yourself, and your customers won't know how to think about your product, which means they won't be comfortable buying it. And then your product is the right choice for no one.

When customers buy your competitor's product… and then buy yours

Jason Cohen — Sun, 18 May 2025 00:00:00 +0000

![Buy. Sell. Sell. Buy. Bark. Woof. Sorry. Buy.](https://public.asmartbear.com/books/anthology/chapters/buy-both/d7939print-simple.png) Why would someone pay millions of dollars for software, and then also pay a bootstrapped company $700 for software that does the same thing? How can you win those deals too? --- "It's not a matter of life or death," most software developers say to excuse their bugs. But we were in a hospital. A modern hospital where everything is digitized, where it is not an exaggeration to say that if the systems failed, patients could die. When Gerry and I walked into the hospital's network operations center, our faces lit up with an appropriate level of awe. On sales calls, the customer's jokes are always funny, and the customer's workspace fills you with awe. Compared to all the other activity at the hospital, our product might appear insignificant. At ITWatchDogs our web-enabled hardware[^1] measured indoor weather--the environment of server rooms, factory floors, or hospital rooms--things like temperature, humidity, whether the door was open, whether power was flowing, whether there was a problem with the air conditioning. If it gets too hot in a server room, components literally melt, causing downtime and significant replacement expense and the discovery that "backup" doesn't always include "restore". We were selling physical devices that monitored those factors and alerted when an air conditioner needs to be serviced ahead of an actual failure, or respond to a failure in minutes rather than hours. [^1]: This was in the early 2000s, when "web-enabled hardware" was rare and interesting. The NOC (Network Operations Center) looks like NASA Mission Control--monitors everywhere, with numbers and graphs and kaleidoscopic colors that only long-tenured employees could pattern-match between "normal" and "concerning". Relatively few people monitor thousands of devices in the data center and strewn about the hospital--terminals and pagers and medical devices and patient records and the front desk and back-end systems, and who knows what else. A hospital is the classic domain of the "Enterprise Software Buyer," and indeed they had "Enterprise Software" doing the monitoring. In particular they used HP OpenView--a multi-million dollar software extravaganza from Hewlett-Packard, in case you thought all that company knows how to do is charge too much for printer ink. Well, it turns out they charge too much for printer ink *and* they charge too much to monitor hospital IT systems. This is a company that can [focus](https://longform.asmartbear.com/focus/) on more than one thing at a time! We were hoping they would buy our weather gear, link it via HP OpenView to the Shuttle-Launch monitors, and save lives, or at least, not lose them due to IT failures. But we noticed something else that surprised us. Something that turned into an instructive observation for anyone building a start-up and [competing against incumbents](https://longform.asmartbear.com/startup-beats-incumbent/) including Big Enterprise Brands. They were also using **What's Up Gold**. ![You've never seen a more hideous logo.](https://public.asmartbear.com/books/anthology/chapters/buy-both/image-simple.png) Your first question should be: How could a hospital--that buys enterprise software--use a product with such a silly name? It wasn't because of the logo--that was even more embarrassing. Modern designers are sitting here, mouth agape, stunned. Why is it a ransom note? Why did the ransomer not have access to an apostrophe for "What's"? [Does design not matter at all?](https://longform.asmartbear.com/design/) This is an existential crisis; let's give the designers space to begin breathing normally again. Your second question should be: What does that product do? And the answer is at once surprising and not surprising. It's not surprising because it monitors IT devices; hence "What's Up?" And when you bought the more expensive version, you got "What's Up?"... Gold.[^2] It was a simpler time. [^2]: And you thought product names matter when selling to the enterprise? Maybe you also forgot our company was called ITWatchDogs and our product was the "Weather Goose." And a few years later I founded Smart Bear and [sold almost exclusively to the enterprise](https://longform.asmartbear.com/bad-advice/) with more animalia. Do what you know! ![Can you believe this was the website for a product that costs $700? These were the days when website design truly didn't matter.](https://public.asmartbear.com/books/anthology/chapters/buy-both/image-2-simple.png) It was surprising because they had bought and were also using HP OpenView. So why also buy What's Up Gold? What's Up Gold was a small bootstrapped company with shrink-wrapped software. How did they successfully sell a product when HP OpenView is sitting right there, at a cost of a million dollars? ![It's easy to forget how ugly the internet used to be. HP was worth billions of dollars yet decided on this design.](https://public.asmartbear.com/books/anthology/chapters/buy-both/image-4-simple.png) So we asked them. This is already a lesson. You've got to [proactively seek out potential customers](https://longform.asmartbear.com/find-customers-to-interview/) and then [talk to them](https://longform.asmartbear.com/customer-development/), to find out what's up. Whenever you see something surprising, that's a signal that there's something to learn. Dig in and learn it. "Well," the guy said: > "What's Up Gold has a couple of features we like. There's this thing we do many times per day, and it's really quick with What's Up Gold. And it's only like five hundred dollars or something. We can just run that on our credit card without asking permission. So we just bought it." There's a couple of things to notice. First, they bought two products to solve the same problem. We'll unpack that next. Second, the pricing comment--it wasn't just that the pricing was *inexpensive*, it was that the pricing was *something that they could procure with neither effort nor consequence*. You probably think about pricing in terms of the psychology of the buyer ("Does $49.99 feel much cheaper than $50?"). But when you're selling to businesses--especially mid-sized to large businesses--it's less about the psychology of the buyer but rather how much of a pain in the ass it is for the buyer. When you understand [pain-in-the-ass procurement](https://longform.asmartbear.com/startups-emotionally-draining/), you gain a new understanding of pricing. And you understand that $500 is not the same as $499.99; it's the same as $0. It's like voting theory. Suppose there are four members of a board, with voting shares of 7%, 16%, 17%, and 60%. How much voting power does everyone have? The answer is: 0%, 0%, 0%, and 100%, because the last person always wins; the other percentages are irrelevant. Same with this customer: $500 is the same as $0, but $1,000 might be the same as $60,000. And now you can see why they would buy a product to solve a problem that was (theoretically) already solved. Because from their perspective, What's Up Gold is free, so if it adds even a little incremental value, they'll buy it.[^3] [^3]: This is sadly not always the case; [here's an in-depth discussion](https://longform.asmartbear.com/purgatory/), including the alternate cases. Now consider this from What's Up Gold's perspective. Do they sell to the enterprise? Yes, in the sense that this customer is "an Enterprise". But they're not "selling to the Enterprise" in the sense that they were competing directly with HP OpenView. Other products competed directly with HP OpenView (e.g IBM's Tivoli, CA's Unicenter, BMC's Patrol--see, [you are required to use acronyms](https://longform.asmartbear.com/bad-advice/) and a "serious" product name to sell to The Enterprise!). What's Up Gold would never win against that line-up. But it doesn't matter--they sold their product to the same customer anyway. And now imagine that they could do that to every hospital--indeed every data center--in the world. --- What's Up Gold thrived with: 1. A product with a few [unique](https://longform.asmartbear.com/leverage/) and [delightful](https://longform.asmartbear.com/willingness-to-pay/) features. 2. A price that solved for procurement. (Notably _not_ "price to value" nor perception tricks.) 3. A [target market](https://longform.asmartbear.com/target-market/) defined by the needs of an individual IT person, not "SMB" vs "Enterprise." 4. A way to [get the attention](https://longform.asmartbear.com/first-marketing-channel/) of the right person (i.e. "distribution"). On that last point: We were curious how What's Up Gold managed to get in front of this IT person, especially since they'd already bought a solution. So we asked about that too.[^4] "Oh," the guy said, gesturing towards a table smothered in manuals, assorted wires, mangled Ethernet jacks, and loose CD-ROMs, "I might have seen it in one of those magazines, I don't know. I probably saw an ad in the back of one of the ones in the bathroom." [^4]: Sub-lesson: [Always ask](https://longform.asmartbear.com/customer-development/) about adjacent products and behaviors. Wherever they found What's Up Gold, maybe they could have found us too. ![IT bear](https://public.asmartbear.com/books/anthology/chapters/buy-both/bear-it-admin-simple.png) So while you're worried about SEO optimization and AdWords positioning, it's easy to overlook the magazine you take into the stall for 20 minutes of solace. And yes, we later went to the bathroom to see which magazines were there. And then we advertised in them. And it worked. The things we do for [success](https://longform.asmartbear.com/success-guaranteed/). Oddly, we learned a lot about the customer even though it wasn't questions about our product. We learned things when we asked about other products, as long as we were learning about the **life of the customer**. Maybe the customer was more honest than usual, exactly because it wasn't about us. We learned about a magazine we needed to advertise in. We learned customers would buy two pieces of software to solve the same problem. And we learned about the credit card limit. So we changed the sales pitch on the fly. We came in expecting to pitch a $10,000 package. Instead, we said: "You could just buy $500 worth of stuff from us today. See whether you like it." Would they give it a try [if it were free](https://longform.asmartbear.com/purgatory/)? Yes they would. Each month they bought a little more. They were hooked--the product was good!--so they bought in a manner that was convenient for them. It turned out that hospital was part of a larger hospital group. Eventually, we got orders for tens of thousands of dollars from their global IT center, because after it worked well in that one location, word spread inside the company, and then they really did go through procurement and make a big order. That's how Enterprise sales often works. It's not always a large top-down deal; it can start small, start accessible, start simple, start affordable, and then expand outward once you're a proven quantity. "Land and expand," goes the insipid but accurate saying. That was 20 years ago, and SaaS software is different, and markets are different, and customer expectations are different, and budgets are different, and [AI is different](https://longform.asmartbear.com/ai-startups/), but what remains the same are the fundamental lessons of learning from your customers, and making things they want to buy. Twice.

Product Purgatory: When they love it but still don't buy

Jason Cohen — Sun, 04 May 2025 00:00:00 +0000

![sales graphs](https://public.asmartbear.com/books/anthology/chapters/purgatory/d8671print-simple.png) Most people say nice things when you pitch your product. "Oh, that looks really great! I like it, but we're not buying right now. Maybe call back later in the year?" It's hard to tell you that your baby is ugly. This creates a trap for new founders who are (rightly!) infatuated with their ideas. They yearn so desperately for validation--nominally product validation but actually personal validation--that they interpret these kindly social white lies as confirmation. But sometimes the potential customer _really does_ love it. They *really do* wish they could buy it. And yet, they're still not buying, or at least "not yet." You have the distinct feeling they weren't just being nice--they were genuine. And yet, the purchase never happens. This is Product Purgatory. ## Not even with a magic wand It's amazing how many products fail an even simpler test of desirability and viability. I call it the Magic Wand Test, and it works like this: > I have a magic wand, and when I wave it, our product will be fully implemented in your company. Everyone is trained. Other tools are integrated. Processes are changed. Security requirements are satisfied. And best of all, it is *completely free*! > > So! Do you want me to wave this magic wand? Shockingly, the answer is often: **"No."** ![magician bear](https://public.asmartbear.com/books/anthology/chapters/purgatory/bear-magician-simple.png) How could they say "No!?" If the product delivers any value whatsoever, you might as well take it, right? No, because all products not only create friction in implementation, but risk and time and money, often on-going, and the Magic Wand Test proves it. The value of the product must _significantly exceed_ this activation energy. A great example of this was an Austin-based startup twenty years ago. There were a spate of news stories about employees leaking secret corporate documents via email.[^1] So this startup created a solution: All corporate email would flow through the startup's servers, where the content is scanned for sensitive material. Questionable things could be returned to the sender or otherwise flagged.[^2] [^1]: This risk continues to the present day. In 2017 a Boeing employee [accidentally emailed his spouse](https://www.mimecast.com/blog/insider-threat-examples/) a spreadsheet with personal information of 36,000 coworkers. Boeing offering two years of free credit monitoring to affected employees at an estimated cost of $7,000,000. [^2]: In another fascinating example of how companies solve this problem on their own, in 2008 Tesla faced a situation where confidential company information was being leaked to the press. Tesla [implemented a clever method](https://www.ibtimes.co.uk/elon-used-simple-email-trick-catch-employee-leaking-tesla-data-press-1724419) to identify the leaker: Send a unique email to each employee, differing only in things like white space and punctuation, so that when the press reprinted that information using copy/paste, those details would identify the source. Despite the very real problem they solved, they couldn't get anyone to buy it. The reason wasn't that people didn't care about leaks; the news stories were real. The reason was that, even if you could have it for free, there is just too much risk in putting a brand new startup in the middle of all your email. There's the obvious security issue of a probably-hackable startup, and someone else's employees potentially having access to all your email. Also bugs. If this new company has bugs--and doesn't all new software have bugs?--that would compromise email, which is catastrophic. So although there was some value, and a real problem, the value did not significantly exceed the penalty of using the product. In the real world the Magic Wand doesn't exist; even if the price is fair, there are real costs and disruption of implementation and training and integration, and people simply getting used to a new user interface and processes and therefore being unproductive for a while. Product inventors revel in all the value they generate, but don't see the penalties that they incur. The customers, however, see. Furthermore, it's not good enough for value to merely exceed penalty; the value must be *far larger* than the penalty. If the value is only slightly larger, it's not worth engaging, not worth the money, not worth the risk. [Change is difficult](https://longform.asmartbear.com/change/) and [time is limited](https://longform.asmartbear.com/prioritization/); customers cannot spend their time on things that have only minor impact. This challenge looms even larger for new companies, because you have neither brand nor track record. That's even more risk, including that you're out of business in two years or that your product can't handle real-world use-cases. Even for free, many types of customer are not willing to take those risks. I don't want to use "the newest code library" for anything except fun throw-away projects, because I don't want to rip and replace in a few years, and I want thousands of bugs to have already been found and eradicated, and I want to be able to hire people who already have experience with the library. It's surprisingly hard to pass the Magic Wand Test, and this is sometimes the answer to the original puzzle of why the customer appears to like it but does not buy. But now let's go further and consider the product that really does pass the Magic Wand test, but still isn't bought. What else is missing? ![I like it, but I'm looking for more of a status symbol. Any way you can double the price?](https://public.asmartbear.com/books/anthology/chapters/purgatory/d3220print-simple.png) ## Motivation & Urgency: Why is today the day? The sales call goes well. They're engaged, asking great questions. They explain how they would use this in real life, how they would integrate this into their processes, how it would save them time or [save them money](https://longform.asmartbear.com/more-value-not-save-money/) or create new opportunities for them. They think the price is fair. They don't muse wistfully about competitors, and they hate what they currently use. In short, your product clearly passes the Magic Wand Test. It's not your imagination or desperation seeing interest where there is none. And yet... they still don't buy. Because they're genuinely interested, they give you genuine excuses: "We can't implement this right now but call back in nine months." Their excuse points to the answer. At all times the person with buying power has one, two, or three top-priority things on their plate. These are the things in [their quarterly plan](https://longform.asmartbear.com/strategic-planning/), or there is a pressing emergency that has overridden their plan. They're working on those things currently, or they're up next. Your thing is not one of those three priorities. Maybe you're priority seven or ten. You're on the list--that's what's different between this case and failing the Magic Wand Test where you're not even on the list. ![bear making a sale](https://public.asmartbear.com/books/anthology/chapters/purgatory/bear-sales-simple.png) They won't displace a top-three priority for you. Fair enough; you don't expect them to. And since you're not number four either, you're not coming up in the immediate future. This is why they're putting you off for a year--by then, perhaps they've addressed the top-priority things of the moment, so they might have time for you. This is the source of Product Purgatory. You [got so many things right](https://longform.asmartbear.com/problem/)--the problem exists, the customer knows it, the customer has budget, the customer has the desire... but there's no urgency, no reason it has to be done now. **Urgency is the missing ingredient.** A good example of this in the world of website development is "accessibility"--ensuring websites work well for people with diverse abilities and disabilities. This includes support for screen readers, resizable text, semantic navigation, high contrast modes, and alt-tags for images. Having an accessible website passes the Magic Wand Test. At our company WP Engine, we made both our website and user portal accessible, which not only generated appreciative messages but also sales, as those customers chose us over competitors. This wasn't just the right thing to do--it resulted in sales! However, the number of people needing accommodations are relatively few, and implementation requires ongoing work. The harsh truth is that there are probably five other website projects that would drive more sales than adding accessibility. For nearly everyone who has a website, while "accessibility" might be priority number eight, it's not number one. "For nearly everyone" suggests a solution. For products in purgatory, the key question isn't whether customers see value, but rather _which_ customers are ready to buy *now*. They're only ready when it's a top three priority, whether driven by emergencies, strategic decisions, or company culture (as in our case at WP Engine). So: When does "accessibility" become a major blocker for a company's revenue? Many government contracts require accessibly from their vendors, so if a company were to create a strategic mandate to [expand](https://longform.asmartbear.com/adjacency/) their sales into the government sector, "accessibility" suddenly becomes a top-three priority, because it is required to execute that strategy. Or, companies being sued over accessibility, those selling to audiences with accessibility requirements, or those integrating with major platforms like Walmart or Amazon that mandate accessibility. In general, you solve Purgatory by further narrowing your target market with a condition of "...and needs it right now." You then target all marketing, sales, and new product features to that [ideal customer profile](https://longform.asmartbear.com/icp-ideal-customer-persona/). These guiding questions will help you locate pockets of urgency: #### For whom is this critical to their strategy, especially if it's directly tied to revenue? Examples: Regulatory compliance, industry certifications, public declarations of strategy, entering a new market, buying another company, dealing with a competitive shift in their market, preparing for or just having closed new fundraising (especially IPO), modernizing legacy systems, customers publicly demanding new requirements #### What kind of emergency would force a company to buy something? Examples: Getting sued, executive turnover, a PR debacle, a negative analyst report, a competitor with a loud, large announcement, a significant employee leaving, a public security breach, regulatory investigation, market-share decline, supply-chain disruption, manufacturing defect, scaling issues #### What deeply personal or high-impact life changes would trigger action? Examples: Becoming a parent, a child leaving for college, a serious health diagnosis, inheriting money, losing a job, moving to a new city, retiring, starting a business, divorce, a significant accident, reaching a milestone birthday, selling a business #### What competitive pressures create urgency? Examples: new competitors entering their market, competitors gaining market share, competitors announcing major new features or initiatives, trade publications emphasizing things they lack, technology disruptions in their market, negative viral attention, public criticism from a high-profile customer, geographic or economic shifts #### What financial pressures create urgency? Examples: Budgets closing at a specific time of year, needing to "use up" budget, new fundraising creating new expectations together with new budget, new teams with new expectations, a change in profitability, a public change in their financial goals #### How would you locate customers in this condition? Examples: Official announcements, CEOs and founders on podcasts, consistent customer complaints in reviews and social media, a shift in messaging, purchasing another company, [changes in employment openings](https://x.com/alxberman/status/1914328520861286705?s=12), trends in employee comments on Glassdoor, industry analyst reports, trends in trade publications, venture capital allocations, regulatory change notifications, recent leadership changes, adding new departments or teams, life events like a birth, death, marriage, divorce, changed jobs, changed homes There's no such thing as a product that is always valuable or always risky or always a top priority. There are market segments where those things are true, and segments where they are not. The answer might be bad news: What if there isn't a target segment, or it's too small, or it's too hard to find them, or they have a disqualification like no budget or being locked into another vendor. Well then, it might not be workable. But this is the critical question to escape Purgatory. Even if you think the universe of "perfect customers" is small, take heart: For every customers who is in the center of that bullseye, there are 100x more who will find it useful enough to buy anyway. The reason for this is not obvious; it is explained in [this article](https://longform.asmartbear.com/icp-ideal-customer-persona/). It is scary to focus your laser on such a small surface area, but it works. In any case you have no choice, if you want to exit Purgatory.

SSEBITDA⁠--A steady-state profit metric for SaaS companies

Jason Cohen — Sun, 20 Apr 2025 00:00:00 +0000

![i understand that part but then where do storks come from](https://public.asmartbear.com/books/anthology/chapters/ssebitda/d1202print-simple.png) Your company is unprofitable because you're "spending to grow"--pumping money into sales and marketing faster than you're collecting revenue, resulting in accelerated but unprofitable growth. This is what you're _supposed_ to do--so they say--to build a large enterprise. Is this smart, or are you just creating a permanently-unprofitable company? How do you objectively measure whether you're strategically trading dollars-today for profit-tomorrow, or whether your business model is broken? ## EBITDASM: Rackspace's attempt When [Rackspace](https://en.wikipedia.org/wiki/Rackspace_Technology) was rapidly scaling, they answered this question using a metric they called EBITDASM. Here was their logic: > Rackspace is efficient at acquiring customers. Specifically, our CAC[^1] is small compared to our LTV.[^2] > > Therefore, we spend as much as possible on growth. We take market share cost-effectively; we burn cash in the short-run, but we're profitable in the long-run. > > However, we know other companies _claim_ this was true for them, but they never actually get profitable! We don't want to become one of those. What can we _objectively_ measure that would tell us whether we're being smart? > > Well, growth primarily comes from S&M[^3] spend. So, what if we stopped S&M spend? We'd stop growing, but what would happen to the rest of our finances? > > Specifically: Would we be profitable? If the answer is "no," then our core business model isn't profitable. But if the answer is "yes," then we really are justified in saying that our underlying business is profitable, and our additional spent beyond that is indeed "to grow." They called the resulting metric **EBITDASM**--an extension of the typical American accounting metric of EBITDA,[^4] where in addition to the usual exclusions, we also exclude Sales and Marketing. [^1]: CAC is the Cost to Acquire a Customer, defined as all Sales and Marketing expenses, divided by the number of customers yielded by those activities. [^2]: LTV is the LifeTime Value of a customer, meaning the total gross revenue the customer will generate over the years it remains a customer. Side-note: [I don't believe in LTV](https://longform.asmartbear.com/ltv/). [^3]: S&M is Sales and Marketing. Why, what did _you_ think that stood for? [^4]: [EBITDA](https://en.wikipedia.org/wiki/Earnings_before_interest,_taxes,_depreciation_and_amortization) is Earnings Before Interest, Taxes, Depreciation, and Amortization. It measures a company's normal business operations by stripping away non-operational expenses. It's useful for comparing operational profitability across companies with different capital structures, tax situations, and accounting practices. Computing using the usual metrics: `EBITDASM = EBITDA + SM` For small software companies who don't have purchases to amortize, don't have assets to depreciate, and don't have interest on loans, an even simpler version is just: `EBITDASM = [your definition of "profit"] + SM` I like this model, and ten years ago we were using it at WP Engine. ## Two flaws in EBITDASM While EBITDASM is a good start, it suffers from two flaws: #### (1) Halting Sales & Marketing does not mean revenue remains flat. An assumption above was: If we stop S&M spend, the company will be static, with revenue neither growing nor shrinking. But that's not true, because revenue will fall (due to cancellations and downgrades) or grow (due to upgrades). The intent was correct--measuring profitability assuming the company is neither growing nor shrinking[^5]--so we need a metric that actually measures that. #### (2) What if marketing and sales isn't efficient? Another assumption was: Because we're efficient at acquiring customers, it's logical to spend as much as possible. But what about companies where that assumption is false or unclear? How efficient does customer-acquisition have to be[^6] before we're allowed to use this metric? We shouldn't have to debate that; the metric should apply to all companies. Of course Sales and Marketing cost-effectiveness is important! But it's a separate metric which, by the way, you can already compute. Let's not cram multiple ideas into a single metric. [^5]: To see why: Consider a company that's "profitable" in the sense that revenue is greater than costs, but it's shrinking every month. That's a company that will soon be dead, and sooner will be unprofitable. This is not what we mean by "profitable." [^6]: There is prior art on this question, e.g. some say the threshold is an LTV/CAC of 3, because of [one blog post by David Skok](https://www.forentrepreneurs.com/saas-metrics-2/) more than ten years ago. But others say 5 while others say 1, and [I say that LTV isn't the right way to think about it anyway](https://longform.asmartbear.com/ltv/), and that you ought to use "payback period" instead. Is there a way of measuring whether the company is "fundamentally profitable" while avoiding these two flaws? Long-time readers might at this moment have the flash of realization that I had when first considering these flaws--it involves the same set of SaaS metrics that come together neatly in the definition of [COC (Cost of Cancellation)](https://longform.asmartbear.com/coc/).[^7] It turns out that **COC is the key** to this metric of "underlying profitability." [^7]: Briefly: COC is defined as the cost of replacing customers that cancel, and thus the cost to remain at this "steady-state." Most simply it is: `CAC ✕ C`, where `C` is the number of customers that cancel in a month. See the article for the formula, derivation, and discussion. ## SSEBITDA: Steady-State profitability Reframing the question leads us to a simple conclusion. Let's define a metric closer to our original intent: "Steady-state profitability," which I abbreviate as **SSEBITDA**. Longer: *How profitable would we be, if we were spending only enough to maintain the current state of the company, neither growing nor shrinking?* Having read the previous article on COC, the formula is simple: `SSEBITDA = EBITDA + SM − COC` In short, including the S&M costs needed to replace cancelled customers, but no additional S&M costs. This solves both flaws because: 1. We've added back the cost of maintaining flat revenue ("steady-state"). 2. COC includes sales-efficiency, so it works for all companies. Thus it is the formula for "profit if we maintain the company as it exists today." ## Corollary: Profitable Growth Rate Another interesting metric falls directly out of SSEBITDA--"Profitable Growth Rate," i.e. the rate of revenue growth the company can self-fund while still being profitable. Where `p` is the cost of a dollar of revenue (also defined in the [COC article](https://longform.asmartbear.com/coc/)): `Profitable Growth Rate = SSEBITDA / p` Justification: If we recycle 100% of the funds from steady-state profitability back into growth, we spend `p` dollars to earn each dollar of new recurring revenue. Any growth larger than that will require being unprofitable, by a known amount. This is handy, because **now we can justify "spend to grow" with precision**. For example, if SSEBITDA is 12% and `p` is 4, the company can grow at 3%/mo using its own money. Supposing the company is willing and able to spend more to grow at 8%/mo, it will be unprofitable by 20%.[^8] A company in exactly this situation is indeed "spending to grow" in a responsible manner, and will result a profitable business once the dust settles. [^8]: Because 8% is 5% more than we could grow based on our own profits, and that 5% is bought at a cost of `p`=4 dollars to earn every 1 dollar of revenue, so 5% × 4 = 20%. ## Additional thoughts on SSEBITDA Having used SSEBITDA for some years now, here are some assorted additional thoughts: #### Negative SSEBITDA A negative SSEBITDA isn't necessarily a problem. It depends on the context and goals of the company. For example, an early-stage high-tech SaaS company will be spending much more in R&D *as a percentage of revenue* than a later-stage company. Or a mid-stage company (like WP Engine) might have higher G&A spend (office space, finance, legal, HR) *as a percentage of revenue,* until it grows into those services. Those are expenses that eat into EBITDA and thus SSEBITDA, but that doesn't mean the company won't be profitable with larger scale. #### Removing other costs for a more precise "steady-state" Or: Why _not_ to do that. It's tempting to point out that not all engineers are working on maintenance; perhaps in steady-state we wouldn't need to add so many new features, and thus engineering expenses would be lower too. And we might do away with some of that overhead expense. This seems logical, but I don't think it's actually correct, nor worth your time to calculate. First, it's a lot of work to figure out in detail, and this is supposed to be quick and helpful. Second, it's not really true. A product that never adds a new feature is _not_ in a "steady-state," because the market is changing around it--customer needs, competitive pressures, the rise of new technologies and trends [like AI](https://longform.asmartbear.com/ai-startups/). So, exactly how much new-feature development is needed for "steady-state?" No one knows, and it's not worth your time to try to invent an answer. #### Actionable idea arise from components of SSEBITDA. If SSEBITDA is negative because GPM is low or COC is high, that's an unprofitable model, and you need to address the root causes. If it's especially low due to cancellations or high CAC, those are actionable. If you have intentionally high R&D costs because you're investing in product, and a bump in G&A because you just moved into a new office space, you know those will correct themselves over time. You might even calculate an "expected SSEBITDA after scale" where e.g R&D plus G&A costs total 30% of revenue and see how you're doing. The overall number is useful as a sort of combined north-star metric, but all the insights and actions come from backing out of the inputs to the number, seeing which of those are worth changing, and acting on those inputs. #### Watch it directionally more than absolutely. This is good advice for [most metrics](https://longform.asmartbear.com/product-metrics/). At WP Engine we watched it move month over month from negative to positive and then continue to grow. While you're seeing a positive trend, not just in the overall metric but in the component inputs, and when you have a roadmap designed to continue to improve those metrics, that's a healthy path regardless of the absolute value of the metric today. After all, your goal is not to actually be in a steady state! #### Once positive, growing *as a percentage of revenue* could be less important. Consider that if SSEBITDA is steady 10% of revenue, *and the company is growing*, then in absolute dollars SSEBITDA is growing. Of course it's always great to see improvement in this metric as a percentage of revenue, but certainly it's logical for a company to invest some of it back into the business for de-risking and further growth rather than maniacally increasing this metric. --- Now, go get profitable. (Eventually!)

Sometimes never compete on price

Jason Cohen — Sun, 06 Apr 2025 00:00:00 +0000

![Due to recent economic conditions, picture worth has dropped to an all time low of 842 words.](https://public.asmartbear.com/books/anthology/chapters/never-compete-on-price/d3502print-simple.png) *"Never compete on price."* So we are told. But customers want low prices, and it worked for Costco, Southwest Airlines, Vanguard, IKEA, Amazon, Walmart, McDonald's, H&M, Dollar General, TJ Maxx, and many others. So why are we told not to use a low-prices to win? When two products are fundamentally different--in features, integrations, complexity--customers often have no real choice between them. A large enterprise must use software with single-sign-on, security controls, compliance features, and the ability to scale to tens of thousands of users with roles and permissions and integrations with corporate systems. For a small business, those features make the software harder to use. And no one wants to pay for things they don't need. So, Enterprise- and SMB-targeted products differentiate on capabilities and user-experience, not on price. ![bear with loss](https://public.asmartbear.com/books/anthology/chapters/never-compete-on-price/image-simple.png) But consider two products that solve the same problem for the same customer segment in largely the same way, with largely the same features. The sales team highlights minor differences that matter to maybe 10% of users. Each founder is certain that theirs is the Only True Way, but everyone else has a hard time distinguishing them. Price, then, is the last remaining thing they can compete on. They are two gas stations across the street from one another; the main difference is the number on the big sign. So, one company drops its price by 20%, and starts winning market share. The other, having no other way to compete, does the same. This continues through "special offers" and permanent cuts and "branding" and kick-backs to influencers until there's no profit left. Both companies end up poor, and in case your response is "Good! Companies are evil and don't deserve profits!", remember this also means they're unable to invest in product development or customer service; thus the products and companies stagnate and then deteriorate, which in turn ruins the customers' experience. Low price comes at a price, to consumer and company alike. The math is even worse. A 20% price cut needs a 25% increase in number-of-customers to maintain the same amount revenue.[^1] It's worse again with profit; even a seemingly-minor 10% price reduction can easily mean a 50% profit reduction[^2]--terrible! [^1]: Charging 80% of the price at 120% purchase volume multiplies to 96% of the revenue you had. Percentage losses always require even-larger-percentage gains just to get back to where you were. This asymmetric effect also appears in [the math of becoming more productive](https://longform.asmartbear.com/double-your-productivity/). [^2]: Suppose the company charges $100 for a product and makes $20 profit after all costs. Reducing the price to $90 (a 10% price reduction) reduces profit to $10 (a 50% reduction from $20). This is exactly why [established companies cannot compete on price](https://longform.asmartbear.com/compete-on-profit/) on their main product, and thus is an Achilles' heel for [startups to exploit](https://longform.asmartbear.com/startup-beats-incumbent/). This math is why pundits (including me) constantly admonish bootstrapped founders to "raise your prices." Low prices leave room for neither reinvestment nor profit. New founders price their products low for the wrong reason: They don't know how to set prices, and they know their initial product isn't very good, so instead of making it [simple and lovable](https://longform.asmartbear.com/slc/) and winning a reasonable price from their [ideal customers](https://longform.asmartbear.com/icp-ideal-customer-persona/) who would be [happy to pay it](https://longform.asmartbear.com/willingness-to-pay/), they sheepishly charge a low price, attracting the worst kinds of customers (the ones who can't afford a proper solution, but occupy the greatest amount of time in tech support), and dramatically increasing the number of customers they must accumulate before they can quit their day job, all with no extra money for marketing or sales or design or product development. This is the wrong decision, made in fear and ignorance. So, is it true? "Never compete on price?" Well, no. Of *course* it can be an excellent strategy to have the lowest prices. There are many examples of wonderful companies, with amazing products and happy customers, and even happy shareholders, with profits, growth, and longevity, where "low prices" is a critical strategic component. Jeff Bezos famously quipped: "Your margin is my opportunity." Meaning: While a competitor is extracting profit from some product, Amazon will sell the product cheaper, which either means stealing market share (if that competitor stubbornly maintains its price) or destroying the competitor's profit (if that competitor matches Amazon's price). But why doesn't this run into the problems we just outlined? Why is this smart for Amazon, but dumb in our hypothetical commoditized-market example? ## Low price as a complete strategy The difference is that successful low-price strategies treat price as **just one component of a comprehensive strategy to win**. A strategy with unique, interlocking, self-reinforcing decisions, where "lowest price" was an *outcome* of the *decisions*, not a tactic thrust upon them by the competition, not their last-ditch effort to win a sale. Those decisions created **negative consequences** also, that **many potential customers hate**. Great strategy requires strong decisions, which means trade-offs. They created greatness in some areas _by leveraging specific weaknesses_ in others. In doing so, they created a strategy that others wouldn't (and didn't) copy, not because no one wanted to copy their strengths, but because they weren't willing to copy their weaknesses, which enabled those strengths. These unique trade-offs resulted in a unique product, and although some consumers hate it, many love it. For some, it is the ideal product; for 100x more, it is not ideal, but it is a better set of trade-offs than the alternative. When you dare to trade off, you dare to sell to a subset of the market, but then you win, and [you win more than the small "ideal" subset](https://longform.asmartbear.com/icp-ideal-customer-persona/) that you targeted. Let's examine some examples of interlocking, weakness-leading-to-special-strength low-price strategies. ![Costco logo](https://public.asmartbear.com/books/anthology/chapters/never-compete-on-price/image-2-simple.png) #### Costco By coupling higher quality products together with higher quantities in each purchasable package, Costco's _unit price_ is lower than grocery stores. Consumers are thrilled to get a deal for something "this good." A store that caters to bulk-buying can also get away with a "warehouse" feel, which means spending less on the interior. Bulk-buying also means they don't need to stock all the goods that a daily grocery store must; this gives them inventory flexibility. These lower costs yield lower prices while also yielding profit. Because other grocery stores can't match these qualities, Costco stores remain unique. Notice the drawbacks: Unappealing interior, limited scope of inventory, and on top of that, they require an annual membership fee! The fee, by the way, is pure profit, and increases how often a consumer comes back to the store. Furthermore, to maintain their brand as the low-cost leader, the store has a strict 14% margin cap on all products. This is a clear financial penalty, especially when customers would be willing to pay more--but again this interlocks with their brand promise. Defying more industry "wisdom," Costco is well-known for treating employees particularly well, including an average $24/hr wage that is typically double what grocery and retail pay in the same cities. This decreases turnover and increases productivity, which again contributes to profits and a great customer experience. If only all grocery stores would realize the wisdom of this strategy. Thus, interlocking decisions about employment, quality, package-sizes, inventory, store-constructions, and membership, yields "low prices" as an outcome. ![Southwest logo](https://public.asmartbear.com/books/anthology/chapters/never-compete-on-price/image-3-simple.png) #### Southwest Airlines Southwest Airlines has been a low-fare powerhouse for 50 years, yet remained profitable even during the US terrorists attacks of September 2001 and the recession of 2008. Every other major US airline filed for bankruptcy at least once, but not Southwest. Yet Southwest consistently has extremely cheap tickets. Like Costco, they made interlocking product and operational decisions that generated this result. They shuttle back and forth many times per day along the same few, short routes. Already there's a negative trade-off--no long hauls, no international--but also positive ones--frequent flights means consumers have better schedules and it's easier to "catch the next flight" if needed. They don't interconnect with other airlines, won't transfer your bags, and they fly into secondary airports where major carriers don't operate--more negative trade-offs, but secondary airports are cheaper, so fares can be lower. Other airlines have many kinds of airplanes to support many kinds of routes, but Southwest uses only one type of airplane; this gives them pricing power over the supplier (due to large orders), and is cheaper to maintain (mechanics need only a single set of tools, single set of spare parts, and single training program), reducing costs again. They have no amenities--no 1st-class seating, no meals, no support for travel agencies--which again is a negative trade-off for some consumers, but lowers costs and therefore supports lower prices. This strategy was so effective--and so well-studied--that it gave rise to a whole sub-category--the so-called "low-cost carriers" that includes JetBlue, Spirit, and Frontier. None were as successful, two of the three went bankrupt, and none impinged on Southwest's profits, demonstrating that great strategy works even in the face of competition that is expressly "copying you" to replicate your success. ![Vanguard logo](https://public.asmartbear.com/books/anthology/chapters/never-compete-on-price/image-4-simple.png) #### Vanguard Funds Every large mutual fund conglomerate in the late 1970s included hefty operational fees that paid the salaries of fund managers and analysts who researched stocks and decided when to buy and sell. Consumers paid around 2% per year to have these managers vigilantly generating insights and taking actions on their behalf. If the fund increased in value by 5%, the consumer would receive only a 3% increase. It really hurts if the fund decreases in value. Most funds still operate this way today; even VC and PE funds typically have a 2% annual management fee. Vanguard realized that many people might want a completely different product: One that simply tracks indexes like the S&P 500, or automatically (i.e. without human judgment) tracks well-defined sets of stocks like "Large American companies." Without teams of human beings, they could eliminate the 2% management fees and expense ratios, replacing it with _de minimus_ fees from automatic trades. For consumers who believe that fund managers outperform the market, this new product was silly. But for consumers who believe that managers don't out-perform the market in the long run--especially after removing the compounding effect of fees--the Vanguard funds were uniquely low-cost. Again, "low cost" was an outcome of a unique product, that made different trade-offs, that was appealing only to a subset of the market. ![IKEA logo](https://public.asmartbear.com/books/anthology/chapters/never-compete-on-price/image-5-simple.png) #### IKEA IKEA revolutionized furniture retail with a comprehensive strategy where every decision reinforces low prices. The foundation: Forcing customers to assemble their own furniture. Often poorly, frustrated, and complaining. This seemingly simple decision created a cascade of advantages--flat-pack furniture dramatically reduces shipping and storage costs, allows for more efficient store layouts, and minimizes damage during transport. Their unique showroom-to-warehouse flow, where customers navigate a single path through inspirational room setups before collecting their own items, both enhances the shopping experience and reduces staffing costs. The globally standardized product line means they're not creating custom products for each market. These decisions come with clear trade-offs--furniture that isn't heirloom quality, the notorious assembly experience, limited selection within categories--but for price-conscious consumers furnishing their first apartment or dorm room or spare bedroom, IKEA's system delivers remarkable value. The low price isn't a tactic; it's the natural outcome of a thoroughly designed system. ## Low-cost strategies don't always require significant financing The modern SaaS playbook dictates that "low price" strategies require raising tens or hundreds of millions in venture capital. The playbook is logical: Low-price business models are profitable only at scale--when revenue finally outgrows the overhead of people, suppliers start providing bulk discounts, sales and marketing costs amortize over enough customers, and so on. Logical though it may be, it is not the only playbook. All four of these iconic, multi-billion-dollar low-price leaders thrived with remarkably modest initial financing. Costco's first warehouse opened in 1983 at a cost of $5M, and went public in 1985 with a small $30M offering. Costco's model was profitable almost immediately--the membership fee revenue covered most of the fixed costs, allowing them to operate on razor-thin product-margins from the beginning. Their expansion was largely funded through operational cash flow rather than massive external financing rounds. Southwest's initial financing was remarkably modest for an airline. Founded in 1967, they started with $560,000 in initial capital. They began operations in 1971 with just three aircraft serving three cities. Southwest's ability to turn a profit quickly allowed it to fund most of its expansion through earnings rather than heavy external financing. Vanguard was created in 1975 with an initial capitalization around $2M. Their unique "client-owned" ownership structure meant that it didn't need to satisfy external investors seeking high returns. The index fund strategy required minimal operational overhead from the beginning--no teams of analysts, no expensive trading operations. Vanguard's growth was organic and didn't require substantial external financing rounds. ![bear scale](https://public.asmartbear.com/books/anthology/chapters/never-compete-on-price/bear-scales-simple.png) IKEA was almost entirely self-funded through operational cash flow. Ingvar Kamprad started with a small loan from his father to launch a mail-order business in 1943. The first furniture showroom opened in 1953, funded entirely from the profits of the mail-order business. IKEA's rapid expansion globally was financed primarily through reinvested profits. Remember, these are physical companies with materials, supply chains, inventory, and buildings. Software companies should be able to do the same with no more investment than this. --- The common themes which allowed these companies to beat competitors for many decades in heavily-contested, commoditized markets, with increasing profits, with little financing, all with the lowest prices: 1. Their strategy including trade-offs that many customers find distasteful or a "deal-breaker." By targeting a subset of the market, accepting that for many customers the trade-offs were wrong, they dominated that subset. 2. The weaknesses in their strategy were critical to preventing copycats, whether from existing competitors or new entrants. Others want to copy strengths, but won't copy weaknesses. Because the weaknesses were mandatory to produce their strengths, they were never replicated. 3. Their business models were profitable after just one store or with few products, not only once at scale.
_(In SaaS, we would say: Profitable unit economics, with GPM above 70%, with a [CAC payback period](https://longform.asmartbear.com/bootstrapped-cpc/) of less than a year.)_ 4. They reinvested profits into expansion, not extracted as dividends.
_(This is an area where bootstrapped companies often diverge from funded ones, or founders with ambition to create a larger enterprise.)_ 5. They innovated in operations--not just in customer-facing products and features--to achieve their unique advantages and cost-effectiveness that enabled profit despite having lower prices. 6. They started with narrower offerings in narrow geographic footprints and [expanded](https://longform.asmartbear.com/adjacency/) gradually. 7. They had a long-term vision and held [inviolable values](https://longform.asmartbear.com/emptiness/), never taking short-term short-cuts or compromises. This should give hope to bootstrappers everywhere, or to folks wanting to raise sustainably-small rounds from strategic investors and advisors. --- When the only differentiation a product can boast is that "it's cheaper," that's not a strategy at all. The result is commoditization and a race to the bottom--if it gets off the ground at all. That's why you mustn't compete _only_ on price. That's being _cheap_, not _affordable_. That's a lack of vision, not a comprehensive strategy. When lower prices are _a result_ of strategy--business structure, cost structure, product trade-offs (that [ICPs](https://longform.asmartbear.com/icp-ideal-customer-persona/) love but others hate), and other decisions that competitors can't or won't make, then low prices are a powerful, winning strategy.

All pretty models are wrong, but some ugly models are useful

Jason Cohen — Sun, 23 Mar 2025 00:00:00 +0000

!["Study hard what interests you the most in the most undisciplined, irreverent and original manner possible!" --Richard Feynman](https://public.asmartbear.com/books/anthology/chapters/models/image-3-simple.png) ## Wrong, but useful Contrary to our intuition, a theory of physics can be **useful**--making accurate predictions--and yet simultaneously **incorrect**--not accurately describing how the universe actually works. The Mayans' conception of the Earth, moon, sun, planets, and stars, was as ludicrous as every other ancient civilization, yet their priests routinely predicted the timing of eclipses with impressive accuracy. The priests leveraged this accuracy as evidence that their religion was correct, and that their exalted position in society was justified. Their religion--and therefore their explanation of how the universe worked--is laughable to the modern reader: The Earth in the center (of course), with thirteen tiers of heaven whirling above and nine levels of underworld threatening from below. Eclipses are not caused by a physical object blocking the light of the sun, but rather spiritual beings temporarily consuming the sun or moon (Figure 1). Even the most fervently religious person today would classify these ideas as fanciful mythology, though the Mayans were no less certain of the veracity of their religion than modern-day humans are of theirs. ![Recolorized segment of the Dresden Codex, a Mayan text from around 1200 AD which, among other things, predicts eclipses to within a few days.](https://public.asmartbear.com/books/anthology/chapters/models/mayan-simple.png) Nevertheless, they were careful observers and meticulous calculators. They understood that eclipses happened roughly every 173 days, adjusted by a 405-month cycle and additional smaller corrections. They tracked these cycles and updated their model over the centuries, and as a result **their theory yielded accurate predictions, even though the theory's explanation of _why_ was entirely incorrect**. This is a striking example of the common bromide: All models are wrong, but some models are useful. Their model was _wrong_ in describing the universe, but _useful_ in predicting eclipses. --- A thousand years later, modern physicists still wrestle with the question of whether their models explain how the universe actually works, or are merely useful. Physicist Richard Feynman illustrated this point using the Mayan example above. Quantum Mechanics (QM) is so weird and counter-intuitive, Feynman says, that "nobody understands quantum mechanics."[^1] And this is from someone who won a Nobel Prize for creating an accurate and useful model for how some of it works.[^2] Feynman (jointly) won the prize for modeling how particles interact with each other. The model sounds as crazy as Mayan mythology: It says that *all possible interactions happen simultaneously*[^3] (yes, "everything, all the time"), with every interaction-possibility reinforcing or cancelling-out other possibilities, and with each interaction weighted by the probability of its occurrence. When we make a measurement in the lab, the cosmic dice are thrown, and one of those interactions is observed to have occurred in actual fact. [^1]: From _The Character of Physical Law_. This is sometimes erroneous quoted as: "If you think you understand quantum mechanics, you don't understand quantum mechanics." That's a cute paraphrase, but he didn't postulate that someone would even *believe* they understood it! [^2]: Electrons and photons specifically, but it turns out to be the right model for all types of subatomic particles. [^3]: The precise description is: Sum the probability amplitudes and phases over all paths the particles could have taken, including an infinite hierarchy of virtual particle interactions. I ask the pedantic reader to forgive my evocative simplification. This, of course, makes no sense. This sounds like Mayan cycles that miraculously spit out the correct answer, not how the universe could really work. Albert Einstein thought as much, famously trolling "God does not play dice with the universe." To prove his point he, along with [Boris Podolsky](https://en.wikipedia.org/wiki/Boris_Podolsky) and [Nathan Rosen](https://en.wikipedia.org/wiki/Nathan_Rosen), described the "[EPR Paradox](https://en.wikipedia.org/wiki/EPR_paradox)"--an experiment where the QM model predicts an even more "absurd, impossible" result, therefore (they felt) "proving" that the QM model is a more sophisticated kind of Mayan cycle computation, and might even be downright incorrect. Unfortunately for Einstein, physicists have run the EPR experiment many times in the subsequent decades, and the model-nobody-understands has always been correct in every detail. ![Erwin Schrödinger in 1933](https://public.asmartbear.com/books/anthology/chapters/models/erwin-schro-dinger-narodowe-archiwum-cyfrowe-1-e-939-simple.png) Erwin Schrödinger was also personally entangled with the apparent problem that the QM model was absurd, yet useful. His [Schrödinger Equation](https://en.wikipedia.org/wiki/Schr%C3%B6dinger_equation) is the center-piece of QM: It dictates how the entire universe evolves over time. Nearly every QM calculation runs through this equation. And yet, like Einstein, Schrödinger agreed that although the model is successful, its description of how the world works is ludicrous, quipping "I don't like it, and I'm sorry I ever had anything to do with it."[^4] He invented the famous [Schrödinger's Cat](https://en.wikipedia.org/wiki/Schr%C3%B6dinger%27s_cat) (Figure 2) thought-experiment to prove that QM must not be describing how the universe "actually" works, just as Einstein attempted with EPR. And, like Einstein, his attempt failed; physicists have run this experiment dozens of ways over nearly a hundred years, and the model has always been correct. [^4]: Here "it" refers specifically to the Copenhagen interpretation of the collapse of the wave function when an observer observes it; another facet of QM that obviously makes no sense as soon as you ask "What do you mean by 'observer' and why isn't that just another physical system?" ![The QM model says that Schrödinger's cat is simultaneously alive and dead until you look inside the box; another example of "everything happens, but with probability, resolved when you observe it." Both the "simultaneously" and "when you observe it" are nonsensical concepts (how does the cat-system "know" that some human-system "observed" it?), yet hundreds of experiments have confirmed the predictions of the model.](https://public.asmartbear.com/books/anthology/chapters/models/qm-simple.png) ## Beautiful, but wrong And so we come to models of companies, markets, and people. Economics, modeling how companies operate in isolated, simple paradigms (micro) or in bulk (macro). Management theory, modeling how information and control and human behavior flows across organizations. [Strategy theory](https://longform.asmartbear.com/great-strategy/), modeling a company's most important constraints and levers, strongest [capabilities and assets](https://longform.asmartbear.com/pivot-points/), modeling competitors and the market at large, resulting in the top-level decisions that we hope will bring success. [Product Management](https://longform.asmartbear.com/great-product-manager/), modeling customers' whims, incentives, "pain-points," "delighters," "JTBD," and [willingness-to-pay](https://longform.asmartbear.com/willingness-to-pay/). Startup theory, giving frameworks for methodically [transforming an idea into profit](https://longform.asmartbear.com/product-market-fit-formula/) while [avoiding failure](https://longform.asmartbear.com/avoid-blundering/). All of these models work some of the time. All explain the past retroactively but often fail to predict the future, and thus none are on par with theories of physics, and arguably shouldn't be called "theories" at all. A company is not [an experiment](https://longform.asmartbear.com/p-hacking/) with controlled variables and many trial-runs. Even using the term ["expected value" is a fallacy](https://longform.asmartbear.com/fallacies/). Are these models more like the Mayans or more like QM? Are they occasionally useful but not representative of how the world actually works, or do they indeed model how the world operates, where their outputs are incorrect not because they are intrinsically flawed, but because of noisy environments, faulty inputs, missing inputs, or human operators who are more interested in selling consulting services off their own HBR articles and books than they are in acknowledging the limits of our simplistic models in the presence of an irreducibly complex world? **This is partly how I judge frameworks: Does the framework attempt to model the messy real world, or does it seem like a pretty fantasy that looks nice in presentations, HBR articles, and consultants' sales materials?** For example, I immediately disbelieve any framework about human beings that comes packaged in a nice, symmetric diagram, with identical quantities of bullets and sub-categories: ![The DISC personality assessment](https://public.asmartbear.com/books/anthology/chapters/models/image-simple.png) Human beings are more complex than saying "there are four categories, and each of the four have exactly six subcategories of descriptors, and each of _those_ have an identical number of components." No, that's never how it is with people. If you had said there are five major categories, and some don't subdivide, while others are complex, and some are fairly well-understood, while others are still a mystery, I'd believe that you were trying to model reality instead of ensuring some picture had 90° rotational symmetry. A more accurate model of human activity is seen in Craigslist, where they have "however many" categories containing "however many" subcategories: ![Craigslist listings for Austin, TX](https://public.asmartbear.com/books/anthology/chapters/models/image-6-simple.png) Human emotion had better *not* be modeled as a perfectly-symmetrical list. In 1980 Dr. Robert Plutchik created a better model, designed to help his patients identify their feelings--quite useful! It is suspicious in that every second-level category contains exactly two third-level emotions, but at least the first-level categories vary in quantity: !["Feelings Wheel" Plutchik, Robert. "A General Psychoevolutionary Theory of Emotion." Theories of Emotion, edited by Robert Plutchik and Henry Kellerman, Academic Press, 1980, pp. 3–33.](https://public.asmartbear.com/books/anthology/chapters/models/image-7-simple.png) Another red flag is any [list with exactly 10 items](https://longform.asmartbear.com/top-10/). That's how many fingers we have, not how many things should probably be on that list. Instead, my lists of things like [what goes into a great strategy](https://longform.asmartbear.com/great-strategy/) or [deciding whether an investment is worthwhile](https://longform.asmartbear.com/investment/) contain however many items make sense. Or in this analysis of [why startups fail](https://longform.asmartbear.com/avoid-blundering/), the categories and quantity of bullets under each category are imbalanced. Or my [system for PMF](https://longform.asmartbear.com/product-market-fit-formula/) has steps of varying length and detail, and even gives counter-examples to show how it's an interesting guide but not a law. And so on with other models. If you're modeling human organization, does it reflect the complexity and capriciousness of real humans, the good and bad incentives and emotions, the different personalities and ways those react to the world, or is it modeled as if people are fungible worker-units with predictable responses to stimuli, built by a professor who has never managed a team beyond six sycophantic grad students? ![Obviously "marketing" does not have exactly these quantities of these things, that just so happens to perfectly fit the periodic table of elements.](https://public.asmartbear.com/books/anthology/chapters/models/image-2-simple.png) Does the model of strategy fit into 2x2s and symmetric diagrams, with rubric scoring, with the same questions for all companies of all stages in all industries in all markets? Or does it grapple with the complexity of interacting systems of markets, customers, competitors, alternatives, employees, technology, products, and global trends, each of them dynamic, each affecting the others, each unknowable and unquantifiable in some of their most important dimensions? I trust more in diagrams that are imbalanced, asymmetrical, or even ugly, because perhaps they're primarily interested in modeling the messy truth of the world, like in Figure 7 and Figure 8. ![Wardley diagram, sussing out which activities support growth and differentiation, versus which are necessary but commoditized, so that teams can respond with appropriate investment and action.](https://public.asmartbear.com/books/anthology/chapters/models/d1-hq1rwzqtceittnk16fue5g-simple.png) ![Though Roger L. Martin's slide has "five boxes," the instructions have "however many questions are useful," and the order-of-operations is non-linear.](https://public.asmartbear.com/books/anthology/chapters/models/image-5-simple.png) All models are wrong, but some are useful. The most useful are the ones that genuinely attempt to model the real, complex, ugly, asymmetric world, not the ones made to look pretty on brochures for consulting services. ![Feynmann's blackboard when he died. The boxed statements at top-left are: "What I cannot create, I do not understand" and "Know how to solve every problem that has been solved"](https://public.asmartbear.com/books/anthology/chapters/models/blackboard-simple.png)

"I scratched my own itch" isn't good enough

Jason Cohen — Sun, 02 Mar 2025 00:00:00 +0000

The most common single-line origin story: "I had the problem myself, so I built a product." This is the standard defense mounted by new founders to justify their new ventures. But is it justified? For a company to succeed, many conditions must be true--or true enough that they're not fatal. Those things can be formulated as a [series of risks to be mitigated](https://longform.asmartbear.com/startup-drake-equation/) or as a [list of blunders to avoid](https://longform.asmartbear.com/avoid-blundering/) or as a [list of questions you have to answer](https://longform.asmartbear.com/explore-execute/). Regardless of form, [a market must exist](https://longform.asmartbear.com/problem/), at [an acceptable price](https://longform.asmartbear.com/pricing-determines-your-business-model/) , matching customers' [willingness](https://longform.asmartbear.com/willingness-to-pay/) and [ability](https://longform.asmartbear.com/never-compete-on-price/) to buy. Often left out of these lists is _you_. The company has to be _right for you_, personally. The simplest test is to ask: Why are *you* the right person to start and grow this company? Which brings us back to the original claim. You are the right person, because you had the problem, you were struck with a vision of how to solve it better than the alternatives, and you did. Sounds good, but is it? Here's what you are implying by that statement: 1. I understand the problem. 2. I have an attachment and passion for the problem. 3. I know how to solve it. 4. I have the ability to execute it. 5. I understand my customers (because I am one). 6. Others have this problem, and will choose my solution. 7. I have what it takes. The worst part is, some of those are true! Intermittent reinforcement is the strongest mechanism for conditioning. That makes it problematic: Because some are true, you can't see why some are false. This blindness consistently results in failure. It is possible that most startups begin this way; it is definite that most of those startups fail. Look! Smart people agree: > "Our customers did a lot of stuff that I would never do. We think differently. We solve our problems differently. We have different needs and wants. Repeat after me: You are not your customer." > *--*[*Eric Ries*](https://www.startuplessonslearned.com/2009/10/case-study-using-loi-to-get-customer.html)*, creator of The Lean Startup* > > "Be a user of your own product. Make it better based on your own desires. But don't trick yourself into thinking you are your user." > *--*[*Evan Williams*](https://evhead.com/2005/11/ten-rules-for-web-startups.asp)*, founder of Blogger & Twitter* > > *"If the VP of Engineering thinks the target customer is just like him/her, you're doomed. If the VP of Marketing thinks the target customer is just like him/her, you're doomed." > *--*[*Cranky Product Manager*](https://blog.sueraisty.com/about-crankypm/)* Let's pick apart the seven implications to see how to leverage "I had the problem myself" as a springboard, instead of following the usual glide-slope to demise. ![Tell me about a time when you came up with a brilliant idea that revolutionized your industry and saved your company from almost certain ruin.](https://public.asmartbear.com/books/anthology/chapters/scratched-my-own-itch/d7470print-simple.png) ## 1. I understand the problem. You do. At least, you understand _your version_ of the problem. The world is large, and getting 1000 customers means solving a variety of related problems, even if you're targeting one niche. Sometimes you really do have this broader understanding. Maybe you're a consultant and you've seen 13 variants of this problem. Or you've been engineer in this field for ten years at three different companies and you've seen everything. Or you've researched this for years as a passion. (Although, [being an "expert" is another form of blindness](https://longform.asmartbear.com/expert-harmful/).) Either way, you're off to a great start, but you need to view "the problem" through your customers' eyes. They might see the same problem but use different language.[^1] They might have the same type of problem but it appears in four distinct forms,[^2] so you need to either pick some to solve, or use features and language to show how you address all four. They might need to integrate with systems you haven't head of. Their budget might be `$\frac{1}{10}$`th or 10x larger than yours was. [^1]: For example, Discord used to say "get a free community space," but it turned out that kids were accustomed to "setting up servers," therefore it made more sense for them to say "get a free chat server." Their community spaces are called "servers" to this day. [^2]: For example, you've noticed that product on-boarding is a common pain-point. But Enterprise products require multi-phase, white-glove processes and training sessions, whereas small businesses might need templates and ready-access to tech support, whereas consumer products might need better self-service options and wizards. Treat this initial understanding as a starting point rather than the end-point, and [interview customers purposefully](https://longform.asmartbear.com/customer-development/) to get the full answer. ## 2. I have an attachment and passion for the problem. ![bear doing a puzzle](https://public.asmartbear.com/books/anthology/chapters/scratched-my-own-itch/bear-puzzle-2-simple.png) This one is very likely to be true. You cared enough to investigate the problem and derived satisfaction from solving it. You were drawn to both problem and solution. It ticks several personal boxes, which explains why it's such a common startup origin story. I believe you. Passion is required, because it gets you through the hard times. The pain of constant rejection--potential customers not buying, potential hires not accepting, potential investors not investing, friends and family and Twitter and Hackernews dismissing you, bad product reviews, and cancellations. The setbacks, the engineering problems that took long than you thought, the customer acquisition that isn't happening as fast as you dreamed. The pain of seeing others succeed while you struggle, especially if they're a direct competitor.[^3] [^3]: Although, remember the day before a competitor goes out of business, all their social media and press is positive, talking about hiring, growing, new features, and happy customers. Do not believe what you read. You need the motivation to push through. You must have some personal connection, whether you love the work or the problem--ideally both. This company will be [an extension of who you are](https://longform.asmartbear.com/identity-selling-sadness/). "I solved my own problem" is a good motivator, but you will have to be strong enough to face what is coming. Which means doing the other things in this article. ## 3. I know how to solve it. You know how to solve one facet of it, where exactly one person understands it, with the exact configuration and constraints that you had. I believe you can solve the other cases too. Most things with computers can be solved. So, again this one is likely true. Good! There are many cases where it is not, [especially in AI](https://longform.asmartbear.com/ai-startups/) where demos look great but they immediately fail (to be good enough, often enough) in the real world. ![Stop calling it a 'head scratcher!' I know it's a 'head scratcher!' Stop saying 'head scratcher!!!'](https://public.asmartbear.com/books/anthology/chapters/scratched-my-own-itch/d7891print-simple.png) ## 4. I have the ability to execute it. But "it" isn't the code. "It" is everything else a company must do to succeed. Most of those things isn't "writing code." Do you have the ability to write a headline on the home page that attracts the right person in three seconds, or does it just say "______ doesn't have to suck" because you're a [lazy writer](https://longform.asmartbear.com/authentic-is-dead/) who has never written a high-performing hook? Do you have the ability to get dozens of qualified leads onto the pricing page, or have you never [created a single successful marketing campaign](https://longform.asmartbear.com/first-marketing-channel/)? Do you have the ability to interview twenty potential customers before they buy, or do you not know [how to find them](https://longform.asmartbear.com/find-customers-to-interview/)? There are many other things. (A list appears in the middle of [this article](https://longform.asmartbear.com/explore-execute/).) That's what "it" is that you have to execute. Does this mean you're going to fail? No... unless you ignore all this and [go back to writing code](https://longform.asmartbear.com/code-is-your-enemy/), assuming that just because you have something working, the rest of the company will unfold naturally. ## 5. I understand my customers (because I am one). You are absolutely not like your customers. You were so frustrated with the current solutions, you decided to build your own rather than buy and implement something that existed. So, you're like exactly none of your customers. Your customers have day jobs, and you're lucky if this is one the [top three problems](https://longform.asmartbear.com/purgatory/) they're experiencing right now. And yet, those three problems are what they have the budget and time to solve. So, you have to [find](https://longform.asmartbear.com/find-customers-to-interview/) and [interview](https://longform.asmartbear.com/customer-development/) potential customers, to learn what *they* are actually like, using your experience as the justification of your initial theory (as described in that article), [iterating until](https://longform.asmartbear.com/good-startup-ideas/) you actually do understand your customers. ## 6. Others have this problem, and will choose my solution. This fallacy is the #1 reason why companies fail when they start with "I solved the problem for myself." I believe you identified a real problem and that you have a solution, but so many other things have to go right before there's a market that can sustain even a solo entrepreneur. There have to be enough people who have the problem, _and_ know they have the problem so that they're out searching for a solution, _and_ have a budget for solving it, _and_ would rather buy from you than a competitor, *and* will stick around for longer than a couple of months. The list continues; [this article on finding good markets](https://longform.asmartbear.com/problem/) helps you make sure all the other pieces are in place. ![bear making a purchase](https://public.asmartbear.com/books/anthology/chapters/scratched-my-own-itch/bear-purchase-2-simple.png) While *you* might find existing solutions inadequate, potential customers might consider them "good enough," and therefore unwilling to risk switching to a new product made by a new company, that certainly lacks features, doesn't lack bugs, and might not be around in two years. There are a hundred times more legitimate problems in the world than there are markets to buy solutions to those problems. So when you erroneously think that "where there's a problem, there's a company," you will almost always be disappointed. ## 7. I have what it takes. I have no reason to doubt you. In fact I would encourage you: That you yourself don't know what you're capable of, until you try. Indeed, _today_ you probably *don't* have what it takes, but that was true of every other successful first-time founder. So don't give in to [impostor syndrome](https://longform.asmartbear.com/impostor-syndrome/), but get ready to experience the [gauntlet](https://longform.asmartbear.com/startups-emotionally-draining/) of [Klingon Painstiks](https://memory-alpha.fandom.com/wiki/Painstik) that await you. Rather than thinking it's all in the bag, think the opposite: Almost everything you'll need to succeed, you don't have yet: Knowledge of customer, market, marketing, sales, finance, skills, everything. Yet. Even second-time founders typically fail. I've had four successful startups, two unicorns, both bootstrapped and funded, and *still* most of my initial ideas about most things are wrong. So, everyone needs a [Beginner's Mind](https://en.wikipedia.org/wiki/Shoshin). Everyone needs to leverage their [Pivot Points](https://longform.asmartbear.com/pivot-points/) to win in their own way. **Your current idea is the spark; the right idea is still out there.** You have to go get it. I know you can do it.

How to select your first marketing channel

Jason Cohen — Sun, 16 Feb 2025 00:00:00 +0000

![intersection of where your customers already are and what you are good at](https://public.asmartbear.com/books/anthology/chapters/first-marketing-channel/marketing-simple.png) You've built a product, and it even works! Mostly! Now, how do you select your first marketing channel? There are dozens to pick from, and hundreds of products and services to help you execute each one: ![Scott Brinker's Marketing Technology Landscape from 2018; that was the last year it was "small" enough to be displayed in this format.](https://public.asmartbear.com/books/anthology/chapters/first-marketing-channel/image-2-simple.png) But there *aren't* dozens of marketing channels that make sense *for you*, *right now*. At the beginning you are beset by constraints of time, money, brand, reach, and [specific knowledge about your customers](https://longform.asmartbear.com/customer-development/). These constraints eliminate most channels as non-viable. The bad news is: You have fewer options than you'd like. The good news is: You can leverage those same constraints to answer the question: Which marketing channel is right for me? There are two primary constraints relevant to this question: 1. Where your customers already are. 2. What comes naturally to you. ![I miss the old days when we could just tout things.](https://public.asmartbear.com/books/anthology/chapters/first-marketing-channel/d7220print-simple.png) ## 1. Where your customers already are You don't control where your customers already go for industry news or to discover new software. You have to fish where the fish are. ![bear selling tools](https://public.asmartbear.com/books/anthology/chapters/first-marketing-channel/bear-selling-tools-glow-simple.png) For example, if you're selling accounting software, most of your customers will be on LinkedIn, some on Twitter, occasionally on Threads, maybe Facebook if it's for, let us say, a more mature connoisseur. But not Pinterest, not Snapchat, and not TikTok. Besides social channels, which keywords do they search for when they encounter the problem that you solve? Which podcasts do they listen to? Which YouTube channels do they subscribe to? Which consultants do they already pay to solve problems like these? (The answers to some of these will be "none".) On the other hand, if you're selling all-natural fair-trade miniature-alpaca yarn to weavers, they might be on Facebook, Pinterest, and possibly Threads.[^1] There might be weaving clubs that would be better targets than selling directly to consumers. There might be magazines (even in print!) and certainly some popular influencers. Because you don't control this, it is a constraint. You have to go where customers are already engaged. [^1]: I don't make the rules, I only make the puns. ## 2. What comes naturally to you This sounds self-indulgent, contrary to typical business advice. Aren't you supposed to get out of your comfort zone, learn new skills, dive into new media, and become an ["expert"](https://longform.asmartbear.com/expert-distraction/) in three months through a crucible of a hundred intense hours of coursework and enabling software? ![bear cell phone](https://public.asmartbear.com/books/anthology/chapters/first-marketing-channel/image-simple.png) No. Doing something unnatural, that you despise, will fail. Think about the social network you do like. You instantly notice impostors--people who don't understand the medium, ham-handedly exploiting it for selfish marketing. It's awkward, it gets no engagement, and in fact you might not even see it as the algorithm silently buries their posts. Plus, you're going to hate using a social media platform that you disdain. Taking myself as an example: I don't understand Pinterest. I don't understand collecting pictures on boards, I don't understand what happens with boards, I don't understand how you share things, I'm not even sure it's called a "board" and I don't care what it's actually called. If I tried to do it, not only would I be bad at it, I would hate doing it. My disdain would leak onto the page, making the whole effort even less effective. Ineffective and unhappy--is this why you started a company? So, take stock of your [Pivot Points](https://longform.asmartbear.com/pivot-points/), and consider only those marketing options that you're naturally good at, or genuinely wish to become great at; you will select your first marketing channel from that subset. ## But what if... So we've come to the obvious-in-hindsight conclusion that you should operate at the intersection of where the customers already are, and what comes to you naturally and joyfully. With practice, you'll move from being good to being great, which was unlikely to happen had you selected a channel that wasn't a good fit. This is smart even if your chosen channel is low-volume. Suppose there's a small-but-vocal community on Threads, but you know that most of your customers are on LinkedIn. But you don't understand LinkedIn--it all looks like AI to you, you don't understand why anyone would read it, it all seems fake, you just can't get your head around it. It is better to be one of the top three creators on this topic on Threads than to be the 200th biggest creator on LinkedIn, even though LinkedIn has many more people. Algorithms don't like promoting the 200th-biggest thing; they do like promoting the 3rd-biggest thing. And you can be 10x or 100x more effective in your messaging. So even though you're talking to a smaller audience, you can be successful. And then, once you become [powerful on one medium](https://longform.asmartbear.com/marketing-platform-independence/), you can [expand to others](https://longform.asmartbear.com/adjacency/). With a foothold on Threads, and revenue that can pay for consulting or tools or hiring a part-time marketer, you can repurpose your content from Threads but in the style of LinkedIn. Now you're expanding into a second marketing channel from a position of strength. --- Another possibility is that the Venn diagram intersection is empty--your customers are only on Facebook and you absolutely despise Facebook with every fiber of your being. What do you do? One conclusion is that this is the wrong business for you. You just said that you're [so different from your customers](https://longform.asmartbear.com/scratched-my-own-itch/) that you can't relate to them. Perhaps that means you're not the right person to run this business. The business might be great, just for somebody else, not for you. ![bears shaking hands](https://public.asmartbear.com/books/anthology/chapters/first-marketing-channel/bear-shake-hands-simple.png) Another possibility is you need to partner with someone for whom this particular marketing channel is their superpower. Someone who's an absolute wizard at Facebook, accomplishing things everyone else says are impossible. What takes other people a year, they complete in a month. What takes other people two months, they did last weekend. Since you've stipulated this is the most important channel, and that you're going to be terrible at it, consider how powerful it would be if you focused on the areas of the business that are your superpowers, while this other person hauls in new customers every day. This could be exactly the team that maximizes the chance that this company is really going to work. In any case, you want to [pick one marketing channel and go deep](https://longform.asmartbear.com/one-channel/). Not spread yourself over several channels, none of which work well. Notice that in all these cases, the answer isn't for you to force yourself to figure out Facebook. Of course you *could*. This is your life, this is your company, this is your idea; you can do whatever you want. You're just adding even more risk to [a venture that's already very risky](https://longform.asmartbear.com/startup-drake-equation/), and that's not the right way to solve the equation.

The Startup Drake Equation

Jason Cohen — Sun, 02 Feb 2025 00:00:00 +0000

![Things went from bad to worse, but we're hopeful now that we're doing badly again.](https://public.asmartbear.com/books/anthology/chapters/startup-drake-equation/d3709-simple.png) Most startups fail, even when the founders are smart, driven, passionate, capable, and are solving a problem that people really would pay to have solved. Why? We already explored the [primary causes of startup failure](https://longform.asmartbear.com/avoid-blundering/) and how to avoid them. If you know where you're going to die, don't go there. However, all that notwithstanding, failure remains the most common outcome. Perhaps "why do startups fail" is the wrong question--startups fail by default; we don't need fancy explanations. The question is: Why do they ever survive? And: Why *specifically* are they default-dead? Is there something we can learn from that? ## The Drake explanation Frank Drake created his eponymous [Equation](https://en.wikipedia.org/wiki/Drake_equation#) in 1961 to guide discussions at the first meeting of SETI (the Search for Extra-Terrestrial Life). It became a famous a way of estimating how many alien civilizations we should expect to see in the night sky: `$$N = R_* \cdot f_\mathrm{p} \cdot n_\mathrm{e} \cdot f_\mathrm{l} \cdot f_\mathrm{i} \cdot f_\mathrm{c} \cdot L$$` ![Frank Drake, Cornell, 2017](https://public.asmartbear.com/books/anthology/chapters/startup-drake-equation/image-simple.png) In English: There are billions of stars `$(R_*)$`, some fraction of which have planets `$(f_\mathrm{p})$`, some fraction of which are habitable `$(n_\mathrm{e})$`, on which life sometimes forms `$(f_\mathrm{l})$`, and sometimes becomes intelligent `$(f_\mathrm{i})$`, and transmits detectable signals into space `$(f_\mathrm{c})$`, and have been doing so for long enough for us to see it `$(L)$`. So that's how many aliens civilizations we should see `$(N)$`. Or in modern [Marketing language](https://longform.asmartbear.com/the-leading-provider/): > The galaxy produces many warm leads, but every step of the conversion funnel is brutally leaky, so it's hard to convert to a sale. Of course the most salient fact about detecting alien civilizations is: We haven't detected any alien civilizations. Even though our telescopes provide that the first few values of the Drake Equation are astronomically large. So that means one or perhaps all of those conversion steps are vanishingly improbable. Startups feel like this too. Countless side projects are started each day, some fraction of which are intended to become money-making endeavors. While the success rate isn't as low as alien civilizations apparently are, [perhaps 999 out of 1000](https://twitter.com/asmartbear/status/1820192235981332735) drop off the chain of probabilities--failing to create a venture where the owner quits their day job and brags to outwardly-supportive-but-inwardly-jealous Twitter "followers" about [achieving Product/Market Fit](https://longform.asmartbear.com/product-market-fit/). Startups face a chain of risks, or as I like to say, a chain of "[ands](https://longform.asmartbear.com/capturing-luck/)"--many things all have to go right. Of course "all things" rarely go right simultaneously; this is why startups typically fail. ![take us to your leader](https://public.asmartbear.com/books/anthology/chapters/startup-drake-equation/d8362print-simple.png) ## The Startup Drake Equation Here are just some of the factors in the Startup Drake Equation, the failure of any one of which is terminal: * Product that people (really!) want to pay for * Able to grab those people's attention amidst the noisy internet * Pricing that those people will accept (and substantially greater than costs) * Competitive and distinctive enough to be chosen * Able to build the product as promised by the home page * Sustained value-delivery months and years later, so customers stay and keep paying * Able to fund the venture, either through early profits or fundraising * Able to work well with co-founders (or able execute everything alone) * A repeatable and profitable customer acquisition process * Able to attract and retain talent * Able to psychologically handle many years of deep effort, stress, and pain * Get lucky (capture good luck, dodge bad luck) It's easy to find examples of failures due to each factor. The non-technical founder who unsuccessfully outsources the product to a consultant (fails "can build it"). The technical founder who builds forever without validating with customers (fails "product that people want to pay for" and "able to get attention"). The tried-but-not-always-true ["I had the problem myself, so I built it"](https://longform.asmartbear.com/scratched-my-own-itch/) origin story, where not enough other people have the problem and the budget and desire to have it solved. Or it was too hard to push through the pain of iteration and pivoting, and anyway the day job pays well, and there's a two-year-old at home, so after six months the founder gives up. The insights of this model are: 1. The failure of just one element is fatal, so we should spend more time identifying and then addressing the biggest areas of risk. 2. We can analyze and decide strategy in terms of "reducing risk" or "increasing chances" rather than "best ideas" or "unique strategy" or "changing the world". What follows is how to use these insights to increase your chance of success. ## Completely crush a few areas to overcome risks and weaknesses No organization is low-risk in all areas. But perhaps some areas can be 100%, or effectively "greater than 100%," which then makes up for other deficiencies. For example, a top-1% engineer might satisfy the question of whether we can build it, but "greater" is wrapping a strategy around the founder of a successful open source project backed by a burgeoning community, which then becomes a unique competitive and marketing advantage, which overcomes deficiencies like not having unique features in the product or not having special skills in advertising. Or, teaming up with a successful influencer who already has distribution, is worth giving up 50% of your equity, because it far more than doubles the likelihood of success. Or, being a renowned expert in some market decreases market risk, both because it's a marketing advantage and because you have insights that others lack.[^1] Whereas if you're entering a market you know nothing about, your education might prove fatal. [^1]: Although be wary; your [experience could be blinding you](https://longform.asmartbear.com/expert-harmful/). I give several examples of this in [The Important Thing](https://longform.asmartbear.com/the-important-thing/). You gain both focus and a higher probability of success when you have a singular winning attribute, [even when you're terrible at everything else](https://longform.asmartbear.com/worse-but-unique/). Singular focus and differentiation can overcome deficiencies elsewhere in the Startup Drake Equation: > Even better than "different," is to be extreme in that difference. Not just a minimal UI, but so minimal it works on the command-line. Not just great design, but so remarkable people buy it only for that, and it's written up on designer's blogs. Not just a new algorithm that solves an old problem, but one that uncovers new things that no one else does, even at the expense of missing things that others catch. You can't do this for all aspects of your product and business--indeed, even a single one is already powerful--but extremity is how you maximize the power of the few things that make you special. ![The rejection rate for this kind of organ transplant is quite high, so we're going to cram three or four in there to better our odds.](https://public.asmartbear.com/books/anthology/chapters/startup-drake-equation/d7349print-simple.png) ## Select the easiest product/market/customer We typically think about "target market" with a success-oriented question like: "Who would be delighted by this product?" But a risk-oriented version of this question is sometimes easier to answer: > What would be the *easiest* customer segment for us to target? Here "easy" doesn't only mean that you *can* do it, or that it's fun, but also that it is profitable. As a negative example, indie hackers[^2] enjoy selling to their peer indie hackers, but indie hackers have no money and usually go out of business in less than a year, so that's a terrible segment. Instead, stable small businesses like dentist offices have large budgets, rarely change software, and last for decades; this is a better market.[^3] Large, growing markets are better still, because "large" means are many [niches](https://longform.asmartbear.com/icp-ideal-customer-persona/) in which to get started, many [adjacencies](https://longform.asmartbear.com/adjacency/) to expand into later, and [incumbents](https://longform.asmartbear.com/startup-beats-incumbent/) are fighting over new customers, not focussed on new entrants who aren't big enough yet to be worrisome. Big, growing markets are "easy" in this sense. [^2]: Found mainly on Twitter but increasingly on Bluesky, the quintessential "indie hacker" is a solo founder, who never wants to hire even one employee, who can build software without assistance, who values freedom, flexibility, and autonomy over maximizing money or traditional prestige. They therefore build simple (but hopefully delightful) products, at low prices, designed to be profitable without scaling. [^3]: Better for sustainable revenue, but difficult to sell, as dentists are hard to get on the phone, and aren't usually in the market for new software. Nothing is easy! --- **Koan #44**
_The bootstrappers_

As an army of 10,000
battles another army of 10,000...

...three bandits sneak into camp,
stealing enough to feed themselves for years.

They are ignored; retaliation is low-ROI. --- ![bear with telescope](https://public.asmartbear.com/books/anthology/chapters/startup-drake-equation/bear-telescope-simple.png) The other key factor is in the phrase "for us". Enterprise customers might be willing to pay gobs of money for a decade, but a new company run by a single person will not be able to deliver the complex software, integrations, governance protocols, and professional services that even one Enterprise customer demands, therefore "Enterprise" would be a risky segment choice _for them_. You must select low-risk markets that are easy for you to address. You could pick a different word than "easy"--lucrative, growing, profitable--but I like "easy" because it keeps things personal. Do you think it will be easy? If so, you're wrong--it will be harder than you think, but still possible. Whereas if you already think it will be difficult, it's also worse than you fear: impossible. ## Founding team When a co-founder is a top-1% engineer, execution risk goes close to zero.[^4] When a co-founder is a top-1% growth marketer, and if the market exists, getting-attention risk goes to zero. [^4]: [Except in AI](https://longform.asmartbear.com/ai-startups/), where the normal rules are different, and the world's best engineers often in fact aren't making it work. This is one reason why investors like two founders--"one to build it, one to get rid of it" as we used to say at ITWatchDogs. It's not just "getting twice the work done," and not just "someone to commiserate with," but rather it's because you might dramatically improve two different variables in the Startup Drake equation. (Two technical co-founders is far less interesting.) ## Design out weaknesses You have [weaknesses](https://longform.asmartbear.com/pivot-points/), which creates risk across the Startup Drake equation. But, you can make choices that slalom around most of your weaknesses, making them irrelevant, rather than forcing you to do the impossible: Become great (or even good-enough) at seven different things. If you're creating a startup on the side, while you hold a day job and a two-year-old, then you should serve an audience who doesn't want tech support, or at least is fine with a 48-hour response-time. That might have implications on how complex the product is, how intuitively it is designed, what customers expect of it, and [its price](https://longform.asmartbear.com/pricing-determines-your-business-model/). Rather than seeing those as negative constraints, instead realize that this is a superior strategy and product, because it avoids a weakness (amount and consistency of time-available). Indeed, besides solving for the weakness, there are benefits: Your profit margin is higher (because you don't have support costs) and you can everywhere in the world (because neither language nor timezones are a barrier) and it will be delightful to use (so the only communication is asynchronous praise on Reddit). This can even be done at scale (GMail, Facebook, Twitter, most hardware products). Suddenly a negative "constraint" looks like an insightful advantage. Or if you're a [terrible designer like me](https://longform.asmartbear.com/design/), it would be high-risk to make a product that must appeal to designers or marketing agencies, i.e. people who value and appreciate great design. You'll do just fine selling to infrastructure engineers or backend Enterprise systems managers. ![if engineers were treated like designers](https://public.asmartbear.com/books/anthology/chapters/startup-drake-equation/designers-2-simple.png) Or if you're terrible at marketing, you could create a collaborative product where people have to invite other people in order to use it. While that mechanism is difficult to get started, it means even poor marketing can result in a growing, healthy company. (It's funny that a "[viral](https://longform.asmartbear.com/going-viral/)" company is also "healthy.") Or if you cannot write code, but you are good at selling yourself and solving a class of valuable problems, you could avoid the world of software (whether as-a-service or not), instead creating a "productized service," in which you sell services, but fulfill that service at low internal cost (and therefore high profit) thanks to your "secret sauce" internal workflows, spreadsheets, and no-code software, which only has to be good enough for your own employees to use. By acknowledging your weaknesses as fervently as you're proud about your strengths, you can increase the chance of success by avoiding them, and embracing the knock-on implications. I cover this in detail in my article on [Pivot Points](https://longform.asmartbear.com/pivot-points/). ## Play asymmetric games As the old joke goes: "The probability of anything is 50%: Either it happens, or it doesn't." In truth the probability of success is typically unknown--the variables in the Startup Drake Equation don't have [clear values](https://longform.asmartbear.com/probability-words/). Although that means there will be failures, it also suggests there will be some successes. If the positive magnitude of the few successes exceed the negative magnitude of the failures, we are a success overall. After all, in most startups, most things are mostly a disaster most of the time. New people joining the company often say, "Wow, I can't believe you're doing ______ and yet you're still in business!" They aren't wrong; they're unwittingly proving that some things can be so powerfully positive, that it overwhelms deficiencies. Many examples of this appear in the ["Asymmetric" section of What makes a strategy great](https://longform.asmartbear.com/great-strategy/#asymmetric). It's summarized best by Jeff Bezos, who led a company that never stopped taking asymmetric bets: > "I've made billions of dollars of **failures** at Amazon.com. Literally. > > But a few big successes compensate for dozens and dozens of things that didn't work." > --Jeff Bezos, [The Guardian](https://www.theguardian.com/technology/2014/dec/03/jeff-bezos-billions-dollars-failures-amazon?utm_source=longform.asmartbear.com&utm_campaign=longform.asmartbear.com&utm_medium=post), 2014 On a smaller scale, there are marketing efforts with a fixed cost, but also a fixed maximum upside, like advertisement where you pay whether or not someone signs up. Or there are marketing efforts with a fixed cost, that could generate new customers for years to come, as with reputation (social media, community forums) or media (successful SEO). Or there are markets where there are many different ways to win, many niches to try, many routes to customers, many ways to expand from a successful foothold into a wider space, and markets which are stagnant, perhaps shrinking, where every sale is a hard-scrabble against desperate yet monied incumbents. Pick games where the downside is 1x but the upside potential is 10x or 100x. Then if the realized benefit is "only" 3x, the game is still a success, and hopefully makes up for other weaknesses. ## Attack the high-risk areas first With complex projects, the [Risk-First Heuristic](https://riskfirst.org/) tells us to tackle the high-risk things first. If you start with low-risk tasks you'll surely succeed, but the high-risk tasks remain unresolved, and remain high-risk. Nine months later, when you finally tackle the high-risk areas, you might discover the entire venture is unworkable. Even if not fatal, new insights means rework, which indicates you've wasted time. It's smarter to address the risky things first, learn from them, change because of them, and complete the rest of the project informed by them. The Startup Drake Equation lists potential risk areas. Even after designing our product, market, and strategy to circumvent or remove risks, some high-risk areas will remain. Circle those, and address them first. So if we're [incorporating AI](https://longform.asmartbear.com/ai-startups/) into the product, make sure that actually works first, in real life, because usually it doesn't[^5] (at least not as of this writing, as thousands of self-styled "AI Startups" have discovered). Or if we're unsure what potential customers would actually pay for (as often they _have_ a problem but don't want to pay to solve it), we should [find that out](https://longform.asmartbear.com/customer-development/) before writing code. Or if we've never marketed a new product before, we should make advertisements and landing pages that lead to a waiting list, to make sure we can garner attention before [building something that no one will ever see](https://longform.asmartbear.com/put-down-the-compiler/). Or if we've never developed a reputation that enables us to talk to customers, we should [do things to create those conversations](https://longform.asmartbear.com/find-customers-to-interview/). [^5]: As an executive once quipped to me twenty years ago, explaining why a new product line had failed: It was a platform portability problem--we couldn't port it from PowerPoint to Java. All startups are risky, and even with this model in hand, most startups will still fail. But, by clearly articulating the risks, reducing some risks through the founding team and selecting the market and product that is right _for us_, solving one or two so well that they overwhelm other risks, constructing a strategy that designs around things that would otherwise be weaknesses, and attacking the remaining high-risk areas first, you can dramatically improve the chance of success. Those aliens are out there! Maybe you're one of them. ![We'll probe you in a bit, but first I'd like to get to know the real you. Your hopes, your goals, your dreams... Who is Fred Wilson? What makes him tick? And then the probing. Lots and lots of probing.](https://public.asmartbear.com/books/anthology/chapters/startup-drake-equation/d7878print-simple.png)

"Stealth mode" and other f'ing brilliant strategies

Jason Cohen — Sun, 19 Jan 2025 00:00:00 +0000

![bad news something that happened in vegas has been spotted in des moines](https://public.asmartbear.com/books/anthology/chapters/stealth-mode/d6040print-simple.png) Here are some of my super-secret winning strategies from 26 years of building startups. ## Stay in stealth mode until the last minute The last thing any startup needs is people finding out about it. You can get attention later--that's easy. You don't need the distraction of all those customers clamoring outside your office, begging to give you money, while you're still refactoring your NoSQL database schema. It's instant death if a competitor were to launch at the same time as you, so you're right to keep all your ideas completely secret. Once you launch, *then* millions of people will know about you, including competitors, but by then you'll be a full 4 months ahead of the whole world, making competition impossible, even if they are two Stanford kids with $9,000,000 in funding and the wind of YC at their backs. That means you can't [talk to potential customers](https://longform.asmartbear.com/customer-development/) either, because the word might get out! Worse: some of those potential customers--the ones willing barely willing to part with $20/mo--might rather save that money by quitting their day jobs and start a brand new company to rip you off. Their feedback isn't worth the risk! Oh and *absolutely* don't talk to other entrepreneurs or advisors who can and surely will copy your idea. Just read the history of any startup or [interviews of famous founders](https://www.amazon.com/gp/product/1430210788?ie=UTF8&%3Btag=asmbe-20&%3BlinkCode=as2&%3Bcamp=1789&%3Bcreative=390957&%3BcreativeASIN=1430210788) and you'll find one thing in common--no one had mentors or found value vetting ideas and brainstorming with people who have trod this path before. ## Get it right the first time With those millions of customers anxiously awaiting your launch like the next book in Games of Thrones, you absolutely *must not* disappoint them with [a shaky v1.0](https://longform.asmartbear.com/slc/). Look around--other software companies wait until there's no bugs before they release. That's why they have no usability problems nor lack important features. With all those fully-completed products, it's a mystery why "feedback gathering" tools or bug trackers even exist. I guess some people just enjoy project management. There's no accounting for taste! An ounce of prevention is worth a pound of cure. That makes prevention, like, 16x more valuable than cure. So take your time before you release, even if that means years. Perfection is worth the wait. ## Don't ask anyone if they'd pay for your product Of course they'll pay; the math is simple: > They'll save 45 minutes a day by using your product. Even if they value their time at only $20/hr, and work only 20 days per month, that's a savings of $300/mo. Your tool is $20/mo, so this *is a money-printing machine for customers!* Customers can't afford not to buy it. Maybe they should buy it twice. That's how buying decisions are made--cold, rational, and based on micro-economics 101--so why waste your time and theirs, just to verify the obvious? ## Don't work too hard ![bear relaxing on a yacht](https://public.asmartbear.com/books/anthology/chapters/stealth-mode/image-simple.png) Building a startup is hard enough--don't make it worse by [working too much](https://longform.asmartbear.com/sacrifice-your-health-for-your-startup/). All the great startup founders are known for 30-hour work-weeks. It's one thing to be passionate--that's great of course--but that doesn't mean you should be waking up at 2am in a cold sweat. You need your sleep! Steve Jobs didn't work constantly, Bill Gates had lots of hobbies, Mark Zuckerberg wasn't tethered to his laptop, and Tim Ferris really did become a best-seller by writing and then promoting his book while working just 4 hours each week. Startups don't require obsession--that's an unhealthy rumor perpetrated by all 2,000 startup founders ever [interviewed on Mixergy](https://mixergy.com/interviews/). They're all lying--they actually lead healthy, balanced lives. They don't want you to know their secret, because this keeps potential competition at bay. Don't fall for it. A startup is a job just like any other--you can leave work at work and make sure to use up all your vacation days! ## Write the code first Writing software is tricky. You've been doing it for 8 years, so this is the part of the business you know best--and you know the difficulties that await you! The easy part of this startup will be getting attention and making sales. Getting people to a website is easy--it's not like there's 1,317,681,984 other websites clamoring for attention. Getting them to buy once they hit the website is even easier--why did they come to the website if they didn't want to buy? Getting consistent attention from the media is easy too--why *wouldn't* popular influencers want to talk about you all the time? [Getting reseller deals is simple](https://longform.asmartbear.com/startup-biz-dev/)--why wouldn't they want to make more money, in the grand tradition of the [win-win](https://longform.asmartbear.com/authentic-is-dead/)? No need to work on that end of the business, because a great product sells itself. The world isn't [littered with failed startups](https://longform.asmartbear.com/avoid-blundering/) with decent products but only four customers. Rather, you need to [focus on coding](https://longform.asmartbear.com/code-is-your-enemy/)--the one part of the business you understand and have the most confidence in. Double-down on what you know! Don't face your fears now. If you shut your eyes and learn a few extra keyboard shortcuts in Visual Studio, everything else will work itself out by the time you get there. ## Raise money before you have customers ![bear venture capitalist](https://public.asmartbear.com/books/anthology/chapters/stealth-mode/bear-venture-capitalist-detailed-simple.png) Every successful startup raised a big pre-pre-pre-pre-seed round before they had customers or revenue. They had to--how else can anything get done? Who cares if you already gave away control? You need ten engineers right now to build dark mode. It's easy to raise money anyway--just take your a Figma mockup and $48B TAM justification to a few pitch competitions and three cocktail events on Sandhill Road. They won't ask questions about those meddlesome customers or revenue. ## Copy the competition Why reinvent the wheel when successful competitors have already figured everything out? The smart move is to just copy their feature list. Or just 70% of it--whichever 70% seems easier. Customers probably don't care about the rest of those silly things, especially if you're 20% cheaper. Speaking of pricing, copy their pricing. Because the competition is always smart and researched with optimal pricing, and is targeting the same segments in the same way with the same brand, so you can copy their pricing instead of talking to customers and figuring it out for yourself. Speaking of talking to customers, don't waste time talking to customers! The competitors obviously did all that research already. Their product decisions must be right--that's why they have market share! And forget about differentiation. Just match their features but 20% better and 20% cheaper. Customers will flock to you immediately since you're basically the same but slightly improved. Whatever "better" means. ## Sell to everyone There's no reason why everyone on Earth wouldn't like your product--it's so great! Of course it is--otherwise you wouldn't have bothered to build it. Besides, if you [target one segment of the market](https://longform.asmartbear.com/icp-ideal-customer-persona/), all you're doing is alienating other people who would have bought it. If you don't say "everyone" on the home page, you'll lose sales. Sure, other products make different trade-offs in features, usability, integrations, design, and price, but nothing is as right-headed as yours! That's why you made it all by yourself, without a team, without talking to anybody, so you wouldn't be distracted. No reason to wonder why people buy from all those competitors. You know the answer: they bought from competitors because they had no choice. Now that they can choose you, they definitely will. --- Now that you have all my secrets, go out there and kill it!

Explore vs Execute

Jason Cohen — Sun, 05 Jan 2025 00:00:00 +0000

![Things are looking good, but we need to be careful to avoid any feelings of pride. Remember what happened last time?](https://public.asmartbear.com/books/anthology/chapters/explore-execute/d6927print-simple.png) ## The arrogance of "what got us here will get us there" Founders are arrogant by necessity. Most startups fail, and yet these cocky founders are absolutely sure they are the exception. "Rules don't apply to outliers like me," they scoff. (Actually most _aren't_ so certain--the honest ones are scared shitless, hoping they can [figure it out](https://longform.asmartbear.com/chaos-at-start/) before everyone finds out [they're a fraud](https://longform.asmartbear.com/impostor-syndrome/) and [they crack](https://longform.asmartbear.com/startups-emotionally-draining/). I find that the ones who are genuinely, deeply confident that their success is assured, are most likely to fail; they are disconnected with reality. Reality wins.) Founder arrogance peaks once the company achieves some success, having entered the vaunted [Product/Market Fit](https://longform.asmartbear.com/product-market-fit/). It was such a hard scrabble to get here, against all odds, and now it's actually *working!* Sure [there was luck](https://longform.asmartbear.com/lucky/), but mostly there were many good decisions, and a great product that [solves a real problem](https://longform.asmartbear.com/problem/), and decent marketing to a decent market, as evidenced by new customers showing up every day and paying for it. Incontrovertible evidence that _we were right!_ What Arrogant Founder doesn't realize is that the needs of the company dramatically change after Product/Market Fit. Everyone and everything at the company will change. Including the founder. This doesn't make sense to Arrogant Founder, because surely the evidence points towards the opposite conclusion: > We achieved our success *because* of the way we've run the company, the decisions we made, the trade-offs we took, the style of work we developed, the team we built, our choice in features versus design versus fixing bugs versus security versus risk. We did it by looking at what was right in front of us instead of pretending that we could build an annual plan. We did it without layers of management and without finance nor HR. > > Therefore, we can avoid doing the dumb things that big companies do. We can avoid becoming the kind of company we quit in order to start this one. We got successful because of certain things, and we will continue to be successful because of those things. And thus the company descends into tumult, frustration, chaos, occasionally even irreparable disaster. Here--after Product/Market Fit but before maturity--is the most difficult part of the journey. The founder insists that "starting the company" was the hardest part, because (a) it *was* hard and (b) it was the hardest thing *so far*. But being hard doesn't make it the hardest. What's hardest, comes next. ![bear overwhelmed with tickets](https://public.asmartbear.com/books/anthology/chapters/explore-execute/bear-orders-simple.png) The immediate reason things get difficult is the company is suddenly growing faster than it can manage. As just one example, there are more support requests than there is capacity to answer,[^1] so queues grow without bound while service quality plummets.[^2] Hiring both quickly and with quality is difficult and unlikely. Even when you succeed, it takes six months before a person you reached out to today makes it through the interview process, quits their current job with notice, takes a week off, gets trained (without training materials, while everyone else is still overloaded), and learns enough on the job to finally be productive on their own. Cross-apply that to engineering, design, product, marketing, sales, and finance. [^1]: At least not with the same care, quality, and personal attention that "got us here," often one of the differentiating factors that a small company leverages to [beat larger competitors](https://longform.asmartbear.com/startup-beats-incumbent/). [^2]: In January 2012 at WP Engine, when we hit Product/Market Fit [as documented in a separate article](https://longform.asmartbear.com/product-market-fit-formula/), our Twitter feedback went from "Best support team in the world!" to "Wow, what happened? They're crap now." It was 2013 before we had enough people and process, both the science of metrics and the art of culture and empathy. Then over the next decade we were known for the best service in the industry, winning many awards, with a CSAT of 98% and an NPS of 64. You're overwhelmed. Digging out makes it worse, and because it happened suddenly, there's quite literally no time in which to fix it. Beyond this immediate challenge, a dozen **facets of the business start breaking** because of scale. Not just software and infrastructure, but people and processes and workflows. Many of these facets are [detailed in this article](https://longform.asmartbear.com/scale/). So, what needs to be done? A radical shift in working style, from "Explore" to "Execute": #### Explore Figuring out what works, as quickly and flexibly as possible. #### Execute Becoming excellent at doing what works, while scaling. You might have heard this dichotomy as "Explore vs Exploit", because that is the normal term of art used by great strategic thinkers like [Roger L. Martin](https://rogermartin.medium.com/balancing-exploration-and-exploitation-adb82146837?ref=anand-panicker.com), [Clayton Christensen](https://www.hypeinnovation.com/blog/8-principles-of-the-innovators-solution), and [James G. March](https://www.jstor.org/stable/2634940). You can call it "exploit" if you wish, especially when searching Google or chatting with AI. But I don't like that term, because while I'm a red-blooded capitalist [like the best of them](https://www.linkedin.com/pulse/deep-dark-thoughts-being-different-dharmesh-shah/), I don't like the aspect of capitalism that exploits employees, exploits customers, exploits resources, or exploits communities. "Exploitation" is when you extract value _at the expense_ of another, rather than maximizing one the best things about capitalism: Creating more value than you consume, such that both parties are better off after transacting. Anyway, "Execute" is more accurate. To meet the challenges suggested above, you don't take advantage of people or goodwill, but rather you move from a modality where you're "figuring out who we are and how we will win in our own way" into a modality where you've figured that out; now you're operationalizing those discovered things: making repeatable, making teachable, making higher quality, automating, and building teams that themselves improve and grow. In short, becoming *excellent at execution*. Applying the wrong modality doesn't work, in either direction. "Scaling too soon" is [a common cause of startup failure](https://longform.asmartbear.com/avoid-blundering/)--switching from "Explore" to "Execute" before the exploration is complete. Prior to Product/Market Fit, you don't yet know what ought to be repeated, what should be excellent, and you have to discover and jettison whatever is wrong as quickly as possible. You don't know which customer segment should be targeted with advertisements and new features. You don't know what price to charge or why someone should pick you over a competitor. Having identified the perils of applying the wrong modality in either direction, we now need a clear, detailed definition of each mode, so the entire organization can align. ## Explore vs Execute | Explore | | Execute | |:---:|:---:|:---:| | experiment | → | standardize | | fail fast | → | fail never | | speed | → | reliability | | creativity | → | stability | | learning | → | leveraging | | gathering evidence | → | data extrapolation | | manual labor | → | automation | | discontinuous jumps | → | continuous optimization | | question assumptions | → | leverage assumptions | | short-term tactical planning | → | long-term strategic planning | | low quality buys speed | → | high quality buys safety | | new | → | valuable | Before Product/Market Fit, you're figuring out what works. That means answering questions like: * What is [the problem we solve?](https://longform.asmartbear.com/problem/) * What is [our target market?](https://longform.asmartbear.com/target-market/) * Who is the [perfect customer?](https://longform.asmartbear.com/icp-ideal-customer-persona/) * Why aren't [all of them buying?](https://longform.asmartbear.com/put-down-the-compiler/) * What [makes them _want_ to buy?](https://longform.asmartbear.com/willingness-to-pay/) * Which [few features make them buy?](https://longform.asmartbear.com/the-important-thing/) * What is [the customer trying to accomplish?](https://longform.asmartbear.com/needs-stack/) * What [does the customer value?](https://longform.asmartbear.com/more-value-not-save-money/) * What is [delightful?](https://longform.asmartbear.com/creativity-over-optimization/) * What [distribution channel will work?](https://longform.asmartbear.com/first-marketing-channel/) * What [makes us different?](https://longform.asmartbear.com/one-benefit/) * What is the [right price?](https://longform.asmartbear.com/pricing-determines-your-business-model/) * What are our [biggest risks?](https://longform.asmartbear.com/startup-drake-equation/) * How do we avoid the [obvious fatal mistakes?](https://longform.asmartbear.com/avoid-blundering/) ![At first I was trying to solve a crime. Now I think I'm making an afghan.](https://public.asmartbear.com/books/anthology/chapters/explore-execute/d8509print-simple.png) The "Explore" modality is appropriate for answering these questions. Trying, testing, [failing quickly](https://longform.asmartbear.com/fail/) when it's wrong, which it often is. Moving quickly, because we're running out of time and money. [Undoing errors](https://longform.asmartbear.com/good-problems-to-have/). Looking for big shifts rather than subtle changes that are [too small to be noticed](https://longform.asmartbear.com/p-hacking/), especially since we don't have enough data to actually measure those changes. Being [confident about nothing](https://longform.asmartbear.com/strong-opinions-weakly-held/); questioning everything; [seeking the difficult truths](https://longform.asmartbear.com/failure-to-face-the-truth/). Exploration does not follow a timeline nor takes a singular path, although leveraging [well-researched patterns](https://longform.asmartbear.com/product-market-fit-formula/) might increase the chance of success. --- After Product/Market Fit, that list of questions have been answered. Let that sink in. All that work, all that worry, all the uncertainty, all the probing, all the interviewing, all the experiments, all the pivots, all the doubt from yourself and others... none of that is relevant now. In fact, it's getting in the way of what needs to be done, which is to execute. The "Execute" modality is required to answer the new challenges: #### Hiring & managing, not building things only Executive roles transition from building things themselves to [building teams](https://longform.asmartbear.com/delegation/) that build those things. Everything important enough to be done, must be done by a team, not by a single person. One reason is the quantity of work; another is for stability, so that someone can get sick or leave the company without that part of the company grinding to a halt. Building teams is a skill set of designing organizational structures, hiring managers (not individual contributors), new methods of communication, being [in command instead of in control](https://longform.asmartbear.com/in-command/), [prioritization](https://longform.asmartbear.com/rocks-pebbles-sand/), [coordination](https://longform.asmartbear.com/strategic-planning/), and [balancing the needs of the organization](https://longform.asmartbear.com/tension-autonomy-admin/) against the needs of individuals. Just because you know how to write code or design a website does not mean you know how to interview a manager or set priorities or make a decision in a meeting or effectively communicate to a large group of people. #### Fixing the shortcuts, not new features only You built an [SLC](https://longform.asmartbear.com/slc/), and incurred [tech debt](https://longform.asmartbear.com/software-quality-mortgage/) in exchange for shipping quickly. That was the correct trade-off in Explore mode, but now customers are hitting those bugs and UX issues, and hitting tech support or even hitting the little red "Cancel Subscription" link that you so desperately tried to deemphasize in a dimly-lit recess of an administrative area. Now you must pay down that debt and turn your band-aid-laden-proof-of-concept into a great, maintainable, delightful, usable product. You also need automation, test suites, CI/CD, auto-scale infrastructure, separate support prioritization and channels, and more, not only because of an ever-increasing project, but an ever-growing team of people who are coordinating their work inside it. None of that is "new features." ![Sometimes I put all the eggs in the same basket just to feel alive.](https://public.asmartbear.com/books/anthology/chapters/explore-execute/d7238print-simple.png) #### De-risking, not new innovation only Now you have something to lose. Before, the company was [likely to fail](https://longform.asmartbear.com/startup-drake-equation/) because it hadn't figured out a business model that works, but now the thing that will cause failure is operations. [Tech support](https://longform.asmartbear.com/tech-support-is-sales/) becomes bad, the product is too buggy, [the product falters under load](https://longform.asmartbear.com/scale-rare/), you can't keep up with [marketing](https://longform.asmartbear.com/first-marketing-channel/), tracking finance becomes difficult, an emergency security issue appears, taxes and legal questions arise and it is no longer cute that you're avoiding it using a self-erected vail of ignorance. You still could lose all this tomorrow; de-risking execution is now the priority, and that's a completely different mentality and skill-set from adding new features. #### Solving for rare things becoming common Before, a bug that affected 1% of customers didn't matter. Now it causes 5 support tickets every day, and mad customers who complain on Twitter. [Rare things are difficult to detect and difficult to solve](https://longform.asmartbear.com/scale-rare/). Growth means we're rolling the dice more often every day, causing new problems to emerge. Quality becomes a priority. Requirements and processes can address it, but those concepts are new to the company, and likely counter-cultural. You hate process and requirements, remember? #### Predictable processes If you're growing fast, you're hiring a lot. The time between deciding to open a position, and having a trained, productive person in that position, is at least six months. Therefore, to know how many people you need to start hiring _today_, you have to know what the company will require in six to twelve months, and _that_ means the business has to become more predictable. This is true in tech support, sales, engineering, and in standing up new departments like finance or HR or account management. Predictability means we cannot permit disruptions, which in turn requires playbooks, documentation, measurement, processes, and other objects that maintain predictable outputs even under conditions of variable inputs. These guardrails also help new people get up to speed faster and do things your way. Your _new_ way, that is. #### Addressing stalls in growth You're going to be underwater trying to satisfy customers while also hiring people, while also not having the documentation and training that those new people need, while also adding new processes, while also changing the culture to match the new "Execute" modality. Addressing all this dunks you further underwater. Meanwhile, [growth can begin to stall](https://longform.asmartbear.com/growth-asymptote/) as you're lagging in new features, competitive activity, and new marketing. See that article for how to diagnose and address those stalls. You have no time to waste. #### Measuring "success" It's easy to measure success in the forms of sales leads, closed-won rate, or sprint velocity;[^3] often the new processes and requirements of "Execute" lend themselves to measurement. This is one of the few advantages of creating systems of processes and workflows. But measuring success during "Explore" can be difficult; maybe impossible. How do you measure progress when we *expect* fail at least half the time? We can protest "but we learned something," but did we?[^4] And how do you measure *that*? Not all important things can be measured; "learning" is probably one of those things. But an explorer who wanders for years and never finds anything, is not succeeding. As a result, teams who have only known "Explore" often resist the transition to [measuring the specifics](https://longform.asmartbear.com/product-metrics/) of "Execute". [^3]: Personally I don't believe sprint velocity should be used as a measure of productivity or "success." It is a classic example of a metric that becomes meaningless if management is watching it; we subconsciously start over-stating point-values and it incrementally creeps up. Rather, it should be a private metric used by the team, so they can honestly measure and management themselves. [^4]: It can be hard to know what we learned, like Edison who apparently didn't learn anything from failures, famously quipping "all I've learned is one more thing that hasn't worked." It wouldn't have taken 1000 trials if he learned anything systematic. But even luminaries who talk about the Explore/Execute dichotomy have admitted that learning is slower in exploration than in execution, and more sporadic, and that the lack of consistent learning is one of the challenges of that modality. ![bear on assembly line](https://public.asmartbear.com/books/anthology/chapters/explore-execute/bear-assembly-line-simple.png) #### Different people, different skillsets Consider the [personality of the person](https://longform.asmartbear.com/pivot-points/) who doesn't want a map, is happy to work hard only to throw it all away because "that assumption turned out to be wrong," and to ship everything at 50% quality and 50% completion. This all perfect for "Explore". Compare with the person who loves having a clear goal, clear constraints, then designs a way to complete the mission, and then executes that mission. This is perfect for "Execute". But these aren't the same personalities. It's probably not the same person. Sometimes people can make the transition; sometimes they can't. This is difficult to manage, especially when those people were critical to your early success. It's most difficult when that person is you. And it always is. ## Transitioning attitude and culture It's not just changing what you do every day, it's a fundamental change in your attitude, skillsets, and in the culture of the company. It is realizing that **things that you're proud of** were perfect for discovering what to make, but those things are now holding you back from scaling something that's already made. It's time to be proud of something else. This is the worst part. To let go of those old ways, the "things that got us here," and sometimes, even the people who got us here. Sometimes, you yourself don't fit anymore. This is going to be hard.

When you have nothing: How to find potential customers to interview

Jason Cohen — Sun, 22 Dec 2024 00:00:00 +0000

![super dark bachelor number three have to admit im intrigued](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/d8176print-simple.png) No matter how many features, no matter how great the UX, no matter how cool the AI, if people don't care, or don't want to pay for it, or want it to work differently, then your company will fail. You know; you also know you should interview potential customers *before* you start building. But **how do you find these potential customers?** How do you get them to talk to you, when you have no brand and no product? ## Ground rules *General advice applying across all techniques:* 1. **Only interview your [ICP](https://longform.asmartbear.com/icp-ideal-customer-persona/).** Early on, you're not entirely sure who that is--fair enough--but you have a general sense. Family and friends will tell you "that sounds cool" and cannot tell you whether *actual* potential customers agree, and want to pay. Go where the ICPs are. 2. **"Good enough" is good enough at first.** Best is an hour-long interview as described in the guide referenced above. But beggars can't be choosers, so it's OK if it's partial or otherwise not ideal. If you run a partial interview, and the other person seems interested, **always ask for a follow-up** so you can go deeper; that will be 10x more valuable. 3. **Frame it as asking for advice.** People hate being surveyed but they love getting asked for their opinion. Prostrate yourself ("it would mean so much to me to have your advice") and compliment them ("because you're an expert" / "... told me you're the go-to person for this" / "I've really loved your article on ..."). 4. **Make it trivially easy for the other person.** Proactively send a calendar-scheduling-link so they can find a time that works for them. Have lots of time slots open so it's not a hassle. Minimize back-and-forth communication to set up the meeting. Respect their time; don't create barriers. 5. **Expect a low success rate.** Cold-calling is the worst. It's soul-crushing for you and annoying for others. Reaching out to strangers is a form of cold-calling (though many of the techniques below warm things up a bit first). A 10% response rate would be fantastic. Don't take rejection or apathy personally. [You deserve this.](https://longform.asmartbear.com/impostor-syndrome/) ## Techniques for talking to potential customers *Techniques that I and other entrepreneurs have employed successfully:* ![bear interview](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/bear-interview-simple.png) ### Ask for referrals Ask your entire network to introduce you to ICPs. "Your entire network" contains almost no ICPs, but _they_ probably know some. Have a tight description of the ICP so they immediately think of the right people. Use family, friends, social media groups, LinkedIn, professors and students, social and religious groups--anyone and everyone might know an ICP to refer you to. Then, **at the end of every interview, ask who else you should talk to.** Try to get two or more. People will agree but then not follow up; here's how to recruit them to be part of the journey: > "I'm trying to have 20 conversations like this, to give this product a real shot. It's my dream to create something people genuinely love. It would mean _the world_ to me if you could help me by intro'ing me to a few other people who are [my ICP]". If you average one per interview, you have an infinite supply of interviews. ### LinkedIn You can search by title, and sometimes get introduced through connections. See, you thought LinkedIn wasn't a useful a social network, but it's ideal for this quest. The way I got interviews for WP Engine is by using LinkedIn to find people who had the title and industry I was targeting (web developers using WordPress), and asked them for an hour of their time to chat about a new startup concept for whom they are the ideal customer. Out of 50 requests, 40 agreed, and 30 eventually became customers. How I got such a high hit rate: 1. In my initial outreach, I said: "I will pay any fee you feel is fair for an hour of your time. I'm not asking for a donation; I'm genuinely interested in your expertise and opinion." This showed respect, both for their knowledge and time. I was shocked at the response: Only one person out of 40 asked to be paid! Reciprocity works. 2. I researched each person quickly--who does their business target, where are they located, was there anything in recent blog posts I could personally connect with or comment on--so that the outreach was personal. Yes, this takes time, but the conversion rate speaks for itself. 3. I tried to get an intro from an existing connection. People will often help, even if they don't know the target person too well; write the InMail for them, where you're polite and don't over-sell yourself, and talk like a [normal person](https://longform.asmartbear.com/little-company/), not [like a robotic marketer](https://longform.asmartbear.com/authentic-is-dead/). ![bear presentation stage](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/bear-presentation-stage-simple.png) ### Networking Events & Conferences People go to network events to talk, and the subject they enjoy talking about the most is themselves. Good! You want to talk about themselves too. Of course the event needs to attract your ICP so you're not learning from the wrong crowd. Some of your questions will be more natural in that sort of environment; have 3-5 of them in mind and pick up some lightweight information. *Bonus:* Have enough useful things to say to earn a speaking slot. Speakers have instant credibility during the entire conference; it will be many times easier both to start conversations and earn longer meetings later. Local meetups and larger conferences alike have a constant appetite for fresh speakers. *Bonus:* Do something creative and noteworthy at the conference. At Fincon, [Noah Kagan](https://noahkagan.com/) [gave his speaker fee](https://www.themarketingstudent.com/noah-kagan-launch/) to the audience ($50 per person), telling them to use it to kick off their entrepreneurial journeys (the subject of the course he was selling). ### Forums: Reddit, Discord, Slack, Facebook Groups Selling your product in a forum is a no-no, but fortunately "customer development" is not selling; it's learning about the lives of others. There will be multiple Subreddits / Slacks / Discord servers / Facebook Groups where your ICPs already congregate. Use posts to ask your questions. You can probably ask all your questions over time. One post per question, and don't flood the system. Responses will be discombobulated--responses from a single person aren't connected like they are in a real conversation--but this is still a great way to survey and start honing your hypotheses. Reddit, Slack, and Discord tend to work better in real-time, so lurk to discover when they are more active, both for posting new questions and to establish your reputation by responding to others. Take the time to provide high-quality answers to others' posts and comments; communities are give-and-take, not just take. The better communities will have moderators enforcing this, and rightly so. This is a long term relationship that will carry you through the coming years, not a hit-and-run. [Here's a mini-guide](https://docs.google.com/document/d/e/2PACX-1vQM5EufRTuKeJ_q11vMSTBDyXzRkf0sYSyVVmCmkSEFfjEK93E0DP8BsQZAxQ4i5U2K_etvSH6kseri/pub) from [Jordan Malone](https://x.com/DevMolone) on how to do this. You might be able to do something similar with [Hacker News](https://news.ycombinator.com/), [Quora](https://www.quora.com/), or even [Craigslist](https://www.craigslist.org/about/sites), but those are general forums where it's difficult to target ICPs, and almost impossible to create long-term relationships. Conversely, sometimes there are small communities with independent websites, who would welcome an additional member, or even entertain inexpensive, tasteful advertising for you to reach their members. Don't be too quick to ignore the old-school forums like Facebook Groups, even for modern software: ![Tweet about Facebook groups working](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/fb-groups-simple.png) ### Ask people who are asking for help, or complaining about the problem / competitor People rant online all the time, especially in social media (don't forget YouTube comments), forums (e.g. Reddit, Slack, Discord, Facebook Groups), and review sites (e.g. G2, TrustRadius, Capterra). Set up searches for your area, the problem, and competitors. When someone complains, reach out, probably by finding them on LinkedIn and referencing their review. [Jordan Malone](https://x.com/DevMolone) [has a mini-guide](https://docs.google.com/document/d/e/2PACX-1vSe8-jZ4wNPSF9jdRQAgaMUmM-Os4kCpkhpoltpFLIM_CE-gsAmZIyZTVS7fFN2Y68GZ-DYTImS7jg4/pub) for this too. Many products have a feedback site where customers can vote on pain points or features. Careful though: Your product will be different, your customer segment could be different, your goals and trade-offs will be different, and your product isn't already mature. Still, trends can be informative, and individual people who are especially verbose are more likely to want to talk to you about it also. Even better: Look for people who have taken action, i.e. evaluating other products, switched products, built their own workarounds, etc.. It's free to complain, but expensive to act, so they must really mean it. Or the positive version: People simply asking for help. "How do I ______?" Use keyword-monitoring tools, and then actually help them. Having delivered something of value, you can now ask whether they'd hop on a call to talk about it more. ![bear web designer](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/bear-web-designer-outlined-simple.png) ### Buy traffic to a landing page Set up a one-page landing page asking for people who are interested, then buy advertising[^1] to send traffic there. Bid on keywords with low competition, even if volume is small; you're looking for a few targeted visitors, not scaling a marketing channel. You will need to [A/B test](https://longform.asmartbear.com/p-hacking/) your ads and your landing page. Tim Ferris famously used AdWords to test book titles and market-positioning for _The 4-Hour Work Week_, before he was well-known and had a following; [Katt](https://x.com/kattrisen) has built multiple products using this method. [^1]: AdWords is the most obvious place to start, as it's the most sophisticated with targeting, A/B testing, and budgeting. However, don't forget about Twitter, LinkedIn, or even Facebook; [go where your audience already is](https://longform.asmartbear.com/first-marketing-channel/). Getting clicks gives you a positive signal on positioning and language--useful, but it isn't a customer interview. Your landing page will need to collect emails from people who are willing to talk. You could offer something in exchange--a gift card to Amazon, a donation to some charity, etc.. You might offer "first year is free once the product comes out." *Bonus:* Add web-chat, and pop up when someone comes to the site. When they see you're not a bot, you've suddenly become a real human being instead of a cold automated form. Explain why you really value their advice, offer a fee if they're considering but unconvinced, and appeal to the idea of helping a struggling founder with a new venture. *Bonus:* Create some content that the ICP really cares about, hidden behind an email address. Then you're giving them something valuable, sometimes earning the right to hop on a call. *Bonus:* Put your self-scheduler link right on the page, or like [John-Paul Anderson did for his startup](https://x.com/JohnPaulAndersn/status/1868578197992005923), minimize conversion steps by making the scheduler _be_ the landing page, including the fee you are paying the participant for their time. ![just run ads](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/image-simple.png) ### Ask someone who knowledgeably talks about it Anyone who is already blogging / newslettering / social-posting about this problem has demonstrated that they like talking about it; of course best is if they are the ICP. Ideally engage them first by thoughtfully responding to things so they have some connection to you. Then a chat might be fun for both of you, especially if you offer to share the results of your total findings with them. It doesn't matter how many followers they have! Indeed, people with fewer followers are more likely to engage. They might also be willing to ask their audience on your behalf. Maybe later they would promote your software, especially with an affiliate link. The smaller the audience, the more likely they are to work with you; so long as the niche is targeted, this is not a bad use of your time when you're just starting out. (Ironically this is partly how Reddit got started... and now they're a primary source in their own right!) Offer to write guest-posts for newsletters and blogs. These have to be fantastic though; significantly better than their median post. It works better with sites that typically feature multiple authors; worse with single-author blogs who never feature guest-posts; you're not going to be the first exception. ![bear-influencer](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/bear-streamer-simple.png) ### Work with an influencer Influencers have an audience, and are always looking for excuses to do something interesting with them. It doesn't make sense for them to spam their audience for you, so get creative with how you could help the influencer: Maybe you will gather your results, share the summary with them, and they can make an interesting post. Maybe they'd be interested in promoting the product when it comes out, with a revenue-split. Maybe you can use your prototype or expertise to give them a report that is genuinely useful to them, or you can offer up a truly great guest article / newsletter / social-post. ### Pay for meetings Websites like [User Interviews](https://www.userinterviews.com/) (and maybe [Fivvr](https://www.fiverr.com/) or [UpWork](https://www.upwork.com/)) are full of people who are willing to talk for money. Beware: You need a strong ICP filter. It could be that your ICP isn't there at all. Expert Networks like [GLG](https://glginsights.com/) and [Third Bridge](https://thirdbridge.com/) connect you with verified industry experts and operators. While it costs more money than most of the options here, you can often interview Directors, VPs, or even C-level officers who you may not be able to access any other way. You can have candid conversations about budget, decision-making processes, and competitive dynamics that would be awkward or impossible in other contexts. ![bear-venture-capitalist](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/bear-venture-capitalist-detailed-simple.png) ### Angels & VCs Whether you want to raise money or not, angel investors and VCs talk to founders for a living. They often have a broader perspective on an industry, and anyway they enjoy this sort of thing. They might be able to give you insights despite not being the target customer, but take everything they say as "ideation" and not "validation" nor "insights," because ultimately they don't know any more than you do. If they did, they wouldn't have such a large failure rate in their investments. However, they probably know many people you _should_ talk to. Having a network is also part of their job. Therefore, even if the initial meeting is mostly ideas and little validation, if they like you they are very likely to introduce you to actual ICPs. ### Ask people who are professionally engaged Your ICP probably hires people. Their job postings reveal their exact pain points - look for patterns in the "requirements" and "responsibilities" sections. Reach out to the hiring managers; they're thinking deeply about these challenges right now and are often willing to chat about potential solutions. Look for people who backed similar or complementary products in your industry; people who prepaid on Kickstarter or voted on ProductHunt might be more willing to talk to you. Newly-funded startups are "on to something," have customers of their own, and yet might be excited to talk to another founder; check Crunchbase and AngelList. ### Build in public (with others) There is a strong online `#buildinpublic` community. Twitter used to be the main place; now Bluesky has a strong game and is getting stronger. There's some on Threads too. (LinkedIn isn't very good for this). Even if you have very little following, you can join the conversations that are already happening, often under that hashtag. By commenting (usefully!) on others' posts, and making some of your own, you build enough rapport that you can start asking them for referrals (see above). Some might directly be your ICP, but beware: other people who are just starting out are almost certainly _not_ your ICP. ### Create a free micro-tool A great technique both for initial conversations as well as a permanent inbound marketing funnel, create a free tool that is [SLC](https://longform.asmartbear.com/slc/)--something useful, simple, well-designed, and complete. Indeed, one of the first companies to pioneer this method was HubSpot, who also pioneered the modern conception of Inbound Marketing on [their blog](https://blog.hubspot.com/) and their book, appropriately-named [_Inbound Marketing_](https://www.amazon.com/Inbound-Marketing-Revised-Updated-Customers/dp/1118896653). Even at $2B in revenue, the technique is still working, with more than 30 tools now available. ![bear-interview-mic](https://public.asmartbear.com/books/anthology/chapters/find-customers-to-interview/bear-interview-mic-2-simple.png) ### Go on a podcast There are 4 million podcasts; most are defunct, and most have no listeners. But you don't want listeners, you want to interview someone. Propose to the podcaster that you want to do a special episode where _you_ interview _them_ on this topic. It should be interesting for others to hear this level of detail; you might need to tune your questions to make it interesting in this context. Of course the podcaster must also be an ICP. Or, create your own. It's more work, but it can become a long-term marketing channel, as it is for [Steve McLeod](https://featureupvote.com/): > I have a podcast for the purpose of contacting my ICP and inviting them to talk about themselves for 30 minutes. > > The podcast description states that it is for my ICP. Once I had done a few episodes, people with my ICP started hearing about it and would then contact me and asking to be interviewed. > What about just one line? > > I now have a nice waiting list of people wanting to be interviewed. ### Cold outreach (email, DMs, social) The conversion rate is low, but the outreach might be inexpensive. Like the landing page, you will need to A/B test a tight, compelling message, and the call-to-action would probably benefit from an offered reward. ### Go where people are (waiting) It's hard to interrupt someone while they are working, or while they are going somewhere. But people standing in a line have nothing to do. This could be at a checkout line, waiting to get into an event, sitting in an airport terminal, standing in line for coffee, or between sessions at a conference. Even virtual "waiting rooms" like the comment sections of live-streamed events can work; people are just waiting for something to happen. Sometimes you can interrupt people if you come bearing gifts. [Nick Basile used free donuts](https://bsky.app/profile/nick-basile.com/post/3ldggbwrkrc2i) to talk to university students. [Karsten Duus Wetteland](https://www.linkedin.com/feed/update/urn:li:activity:7274339476414312448?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7274339476414312448%2C7274342624478580736%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287274342624478580736%2Curn%3Ali%3Aactivity%3A7274339476414312448%29) found people in the building where he worked; the fact that they would probably bump into each other in future made them more friendly and amenable. If you already work in the industry that your product idea is in, you can interview people at your company. ### PR For most companies, "PR"--getting articles and interviews from traditional media--is reserved for late in the maturation of the marketing department. At first you're just trying to get those first few customers, then [getting one marketing channel to work](https://longform.asmartbear.com/one-channel/) well enough to establish the company. However, if you are an expert in a topic, and have insights that others would find interesting, and are good at being interviewed, then early PR is a way not only to start establishing your brand, but to gain the credibility and inbound interest to have those initial customer conversations. You have "given first," and now you can "take." In the extreme, this can take the form of "stunts," as when Brian Chesky (AirBnB) distributed branded cereal boxes called "Obama O's" and "Cap'n McCain's" during the 2008 Democratic National Convention in Denver to generate buzz and media coverage, or when Alexis Ohanian (Reddit) sent personalized luggage tags with a Reddit alien logo and handwritten notes to the first few hundred people who mentioned Reddit on Twitter. ### Make your own events _This is a direct quote from [Giff Constable](https://giffconstable.com/)'s excellent book [Talking to Humans](https://www.talkingtohumans.com/). It's only 80 pages; I highly recommend it._ > One aspiring entrepreneur wanted to target mothers of young children. She had heard stories about talking to people in a coffee shop, but felt like it was too unfocused. So she tried hanging around school pickup zones, but the moms were too busy and refused to speak to her. Next, she tried the playground, where she figured moms would be bored watching their kids play. This worked reasonably well, but she was only able to get a few minutes of anyone's time. So instead, she started organizing evening events for moms at a local spa where she bought them pedicures and wine. The time of day worked because the moms could leave the kids at home with their partner. The attendees had a great time and were happy to talk while they were getting their nails done. In another example, [Jitpal](https://www.superloops.xyz/) created an industry event with a panel of experts and C-suites; the audience was also serious people. It was a lot of cost and work, but establishing themselves this way allowed them to have many conversations with ICPs, and also paved the road to initial sales. You can also create a local meet-up on the topic that your ICPs care about. This works better in a larger city, takes a lot of time, and can last for years. But, it also can become a permanent fixture of your brand for customers and recruiting, and a lot of fun if you're an extrovert. You also don't need to have an existing "following" or reputation to do it; [Meetup.com](https://meetup.com/) helps spread the word, and multiple people have had success using that site alone. --- If all this sounds like a lot of work, that's because it is. Good news: **You only need one or two methods to work**, especially if you're good at generating new referrals from each interview. And this isn't a process you have to do forever, just enough to either [invalidate your idea](https://longform.asmartbear.com/good-startup-ideas/), or to change your idea such that it is validated. What definitely _won't_ help is creating software, adding features, and yapping about nothing on social media. So, put that stuff away, and get in front of some potential customers. ## More resources * [How to create and execute customer interviews](https://longform.asmartbear.com/customer-development/). * [Destroying your excuses for not interviewing customers](https://longform.asmartbear.com/customer-validation/). * [_The Mom Test_](https://www.momtestbook.com/) by Rob Fitzpatrick (2013)--probably the single best book on this topic, and compatible with all the articles on this site. * [_Deploy Empathy_](https://deployempathy.com/) by Michele Hanson (2021)--how to find and talk with customers with a mindset that unlocks what you actually need from them. * [_Traction_](https://www.amazon.com/s?k=traction+gabriel+weinberg&hvadid=580712098231&hvdev=c&hvlocphy=9207466&hvnetw=g&hvqmt=e&hvrand=13366269211832681115&hvtargid=kwd-91263434250&hydadcr=21905_13324233&tag=googhydr-20&ref=pd_sl_dvd6clqld_e) by Gabriel Weinberg (founder of DuckDuckGo and others) and Justin Mares (2015)--an under-appreciated book on how to get initial sales (or conversations). (Sometimes confused by the other book by the same title, which is also excellent but a different topic.) --- *Many thanks to [Carter Bryden](https://bsky.app/profile/carterbryden.com), [Counterplot Consulting](https://counterplot.co/), [Giff Constable](https://giffconstable.com/), [Karsten Duus Wetteland](https://www.linkedin.com/feed/update/urn:li:activity:7274339476414312448?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7274339476414312448%2C7274342624478580736%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287274342624478580736%2Curn%3Ali%3Aactivity%3A7274339476414312448%29), [Katt](https://x.com/kattrisen), [Jason Evanish](https://jasonevanish.com/2013/08/11/95-ways-to-find-your-first-customers-for-customer-development-or-your-first-sale/), [Jitpal](https://www.superloops.xyz/), [John-Paul Anderson](https://x.com/JohnPaulAndersn), [Jordan Malone](https://x.com/DevMolone), [Justin Jackson](https://transistor.fm/), [Marty Markenson](https://bidtreat.com/), [Marie Strauchman](https://bsky.app/profile/mariestrauchman.bsky.social), [Nick Basile](https://bsky.app/profile/nick-basile.com), [Omar](https://bsky.app/profile/omarbytes.bsky.social), [Patrick Gallagher](https://x.com/gridpane), [Steve McLeod](https://featureupvote.com/), [Tony Meijer](https://twitter.com/Tony_Meijer), [Tino Go](https://www.hellobaru.com/), and [Wim Cools](https://thymer.com/wim/) for contributing their experiences.*

Brittle Points: How to make companies robust

Jason Cohen — Sun, 08 Dec 2024 00:00:00 +0000

![not interested putting together concerned about giant chicken](https://public.asmartbear.com/books/anthology/chapters/brittle-points/d6898print-simple.png) [Companies fail for all sorts of reasons](https://longform.asmartbear.com/avoid-blundering/). Sometimes we're not surprised, like if the product never worked or not enough people wanted to pay to solve the problem in question. But sometimes the product did work, people were paying to solve the problem, sales were starting to pick up, and then something broke, and it was all over. How does that happen? * The product was [built on a platform](https://longform.asmartbear.com/marketing-platform-independence/), and the platform changed. A popular app drops to zero downloads after Apple builds it into iOS; a Google Workspace add-on drops to zero sales after Google builds that feature into Docs; a Twitter management tool breaks when Twitter removes functionality from their public API. * The initial marketing channel [quickly saturated](https://longform.asmartbear.com/low-budget-marketing/), so [growth stalled](https://longform.asmartbear.com/growth-asymptote/) at a non-zero but unsustainably-low rate. * The initial marketing channel [was sustainable for a while](https://longform.asmartbear.com/one-channel/), but got wiped by external forces: large bidders tripled the [cost per click](https://longform.asmartbear.com/bootstrapped-cpc/), Google's SEO algorithm changed, the big industry event stopped happening, the link-sharing site became irrelevant, the hot blog lost its traffic, the magazine running the ads finally closed. * [One big customer](https://blog.asmartbear.com/find-first-big-customer.html), representing 80% of total revenue, left. It wasn't a mistake to sign that customer--it funded the company. * A key employee left the company. Early on, [a 10x person](https://longform.asmartbear.com/imbalanced-people/) can mint the company but also could be irreplaceable. A suitable replacement is too rare; it takes too long to find someone, [convince them to join](https://longform.asmartbear.com/startup-hiring-advice/) for almost no salary, and get them up-to-speed and productive. **I call these "brittle points"**--places where sudden changes mean the company catastrophically fails, regardless how wonderful everything else is. All young companies--and some mature ones--have dependencies like these. You can't help it; you have to rely on other technology to build the product, services from vendors to deliver the product, and human beings to do work. Engineers have Brittle Points in their infrastructure, so they've developed common patterns to address it. Let's briefly look in how they do that, because it will give us clues about how to solve the problems in that list above. ## A brittle server Suppose we have a single server that runs our website. Any number of things can cause this server to break--a power failure, a network failure, a bad configuration change, too much traffic arriving at once, bugs in the code, all sorts of things. How do we make this system less brittle? Consider power failure. Power can fail if the power supply[^1] inside the server burns up, or the power strip fails, or the power cord fails (maybe through a [wetware](https://en.wikipedia.org/wiki/Wetware_(brain)) failure like accidentally unplugging it). [^1]: This is the component inside the computer that receives the power cord; it converts city-power into the type of power needed by the other components. We can address this Brittle Point with a second copy of the power components--a second power strip with a second cord plugged into a second power supply. This is, in fact, exactly what data centers do! **In short: redundancy**--having two things that do the same job. **It's twice as expensive, but it buys robustness.** This is also what [airplanes do](https://longform.asmartbear.com/whos-lying/). But what happens if the city power fails? Data centers have their own gas-powered generators. Which means they stockpile gasoline. Rarely-used gas-powered engines tend to fail, so they also test and maintain those units weekly. Data centers often have multiple generators. More robustness, purchased at significant, on-going expense. In modern clouds we go yet another step further, because the entire data center is itself another Brittle Point. So we have additional, identical servers in a physically-separate data center, that draws power and network connectivity from different outside vendors. But now we also need a smart networking system that knows how to direct internet traffic to only the servers that are currently available. Yet another system, which itself becomes--you guessed it--a Brittle Point. The pattern continues--fix more Brittle Points, at more cost and complexity, sometimes creating new Brittle Points. Reliability is expensive. This pattern is applicable to all of the causes of failure above. ![what happens to the supply chain if you and debbie break up](https://public.asmartbear.com/books/anthology/chapters/brittle-points/d6341print-simple.png) ## Neutralizing Brittle Points "One platform" is brittle, because if the platform-owner forward-integrates (i.e. copies you), or removes APIs that you depend on, or themselves fail, that's the end of your company. One solution is to be multi-platform.[^2] Another solution is to only build on platforms where you have a high degree of confidence that the platform owners are committed to supporting their ecosystem by never directly competing with them. Ideally, the platform even promotes their ecosystem, so that it becomes a growth vector instead of a Brittle Point. (Salesforce is currently the best in the world at this.) [^2]: Example: At WP Engine, we run on all three major clouds--AWS, Google, Azure. Example: A marketing tool for listening and responding on Twitter could add support for LinkedIn, Threads, and Bluesky. In this way, additional features can also be risk-mitigation. "One marketing channel" is brittle, because if anything happens to the channel, that could be the end of an otherwise-healthy company. The solution is to [find additional marketing channels](https://longform.asmartbear.com/exponential-growth/), so that variation in any one of them is not fatal. Of course this also creates growth; again this "double-win" of "growth + risk-mitigation" shows why it's especially valuable to invest in. "One big customer" is brittle. One solution is a long-term contract with a serious breakup clause--insurance to bridge the time it will take to win replacement customers. Another solution is to prioritize sales until that customer represents a smaller percentage of revenue. Another is [up-front payments](https://longform.asmartbear.com/annual-prepay/), so you have the cash-flow to invest in that growth. Another is to charge even more than you originally calculated, again for mitigation cash-flow. The typical attitude is, "We now have a large customer, so pour extra money into development to make sure we don't lose it," but the right attitude is to use that money to win other customers. "One key employee" is brittle. Not only might they leave, but they will inevitably get sick or take vacation. The usual refrain in the startup world is that none of these are options--everyone has to [work 70+ hours/week](https://longform.asmartbear.com/sacrifice-your-health-for-your-startup/) to [the exclusion of other things](https://longform.asmartbear.com/two-big-things/). Talk about brittle! **Solving these things takes time and money.** Like the server example, they're not free, and not quick-fixes. You can't just hire three more fantastic developers to create a robust engineering team, and you can't just snap your fingers and find three new efficient, productive marketing channels. Therefore, the right attitude is to **maintain a list of these risks** and then [periodically ask](https://longform.asmartbear.com/strategic-planning/): **Which single one is best to attack right now?** For example, it's cheaper and easier to experiment with new marketing channels than it is to find, interview, convince, and manage a second software developer, and plus if you can get a second marketing channel online, that will generate revenue, which in turn means you can *afford* a second software developer. In this mini-scenario, the best thing is to focus all your energy on getting a second marketing channel working. [As you scale up](https://longform.asmartbear.com/scale/) **the size of the "chunks" that create brittleness also scale up**, which creates new brittle things, and thus new risks and new investments. For example, with $260M in revenue in 2016, still growing at a blistering 60%/year, with a thousand employees, HubSpot was not brittle in any of the ways outlined above. But they recognized that they were a single-product company. At that scale, that's a Brittle Point: if there were a sea-change in the market for inbound marketing software, that could be fatal to HubSpot. It also [limits long-term growth](https://longform.asmartbear.com/growth-asymptote/) as the market matures and saturates. The way out--the redundancy--was to become a multi-product company. Furthermore, the second product had to scale at least as well as the first one; it's still a Brittle Point if one product is 95% of revenue. They attacked that problem, and in 2024 it's clear that they succeeded.[^3] [^3]: See [this article](https://longform.asmartbear.com/product-market-fit/) for data and discussion. ## The biggest brittle point Finally, on a personal note, there's another "chunk-level" that's even larger than all of the preceding, and **it's a brittleness that almost all founders suffer from, including myself**. The chunk of "the entire company." This is a one reason why founders are almost always sad and sometimes permanently depressed after [a successful sale of a company](https://longform.asmartbear.com/identity-selling-sadness/). This was your identity, your life, for years. You don't remember what it was like to "be you" without it, and anyway "you" aren't the same "you" anymore. You don't have hobbies or even good friends anymore. You might have [sacrificed family or health](https://longform.asmartbear.com/sacrifice-your-health-for-your-startup/). Talk about a Brittle Point. Your entire life is a Brittle Point. The solution here is not to have two companies or two jobs. That's burnout; [a lack of singular focus creates worse outcomes](https://longform.asmartbear.com/two-big-things/). Rather, the solution is to realize that there were things you did and loved before and there will be things you will do and love after. They might not all be the same things. Sometimes it's best if they're not; you're a different person now. You are inside a chapter in the book of your life. Even if one chapter is sad or has an unexpected twist, there's the next chapter which you can look forward to, even if you don't yet know how that story will unfold. You can [rediscover who you are with this process](https://longform.asmartbear.com/pivot-points/). But it's rarely easy, or simple, or fast. Hopefully the successful sale has literally bought you time. Robustness, not in many things simultaneously, but in things serially. That's what you do with limited time, and how you navigate the arc of your life's story. Back to today and the here-and-now. Go list all the Brittle Things you have today. Then tackle one or two of those things at a time--you have to [manage risk](https://longform.asmartbear.com/startup-drake-equation/), not try to eliminate all of it at once. Be thoughtful, and build steadily away from brittleness.

p-Hacking your A/B tests

Jason Cohen — Sun, 17 Nov 2024 00:00:00 +0000

![Fine, just stand there being ominously silent. It doesn't change the fact that I have the lab until 9:00.](https://public.asmartbear.com/books/anthology/chapters/p-hacking/d8676print-simple.png) ## Faking results using real experiments You are a pharmaceutical company who has blown through a billion dollars of research and development, only to produce a drug that doesn't work. But you want to get it approved by the FDA[^1] anyway so you can recoup that cost, hapless consumers be damned.[^2] [^1]: The United States government agency that approves drugs for sale. [^2]: There are many such examples in the US, such as the decongestant Phenylephrine, with nearly $2B in annual sales, which an FDA advisory panel [unanimously concluded](https://www.nbcnews.com/health/health-news/fda-panel-says-common-counter-decongestant-phneylephrine-doesnt-work-rcna104424) is [ineffective](https://www.theatlantic.com/health/archive/2023/09/cold-medicine-decongestant-phenylephrine-ineffective/675303/). The FDA requires experimental evidence of the drug's efficacy--a controlled study showing positive results at the 90% statistical confidence level. You need to publish a believable report, even though there isn't a statistically-significant effect. How can you achieve your nefarious goal? ![bear race cheater](https://public.asmartbear.com/books/anthology/chapters/p-hacking/image-2-simple.png) Easy: Run studies repeatedly, until you get a false-positive result. Then publish only that result. All experiments sometimes give false-positives. An experiment that gives the correct result 95% of the time, still gives the wrong answer 5% of the time. If you run it 15-25 times, you will almost surely hit one of those false results. This is in fact what pharmaceutical companies used to do to get drugs approved by the FDA. To prevent this behavior, the FDA now requires companies to pre-register their studies and publish all results.[^3] [^3]: Although [there are plenty of recent cases](https://www.biospace.com/6-drugs-approved-despite-failed-trials-or-minimal-data) where companies were allowed to submit only a subset of trials, were approved, and--big surprise!--were later found to be ineffective. This is also what happens in the social sciences, resulting in [The Replication Crisis](https://en.wikipedia.org/wiki/Replication_crisis). An experiment is run once, often with a small number of college students. Occasionally something "interesting" happened, and a journal publishes it. Journals don't wait for other teams to reproduce the result. [Nobel Laureates have admitted](https://www.nature.com/articles/nature.2012.11535) that this fallacy has debunked their own work, and call for "replication rings" to solve the problem. But scientists are people too, and often prefer the fame generated by an astonishing result to the pain of discovering that they are infamous for perpetuating a false-positive. Before you shake your finger at them, shake that finger at yourself. Because you're doing this too. ## Faking your A/B tests (unintentionally) To see how this unfolds, consider this simple example: You're testing whether a coin is fair, i.e. that it comes up heads equally often as tails. Your experiment is to flip it 270 times, measuring how often it comes up heads. Of course we don't expect _exactly_ 50% heads, because there's randomness. What is a reasonable range to expect from 270 flips, assuming the coin really is fair? According to the binomial distribution, 90% of the time the result will be between 45% and 55% heads, if the coin is fair. So, you run the experiment, and you get 57% heads. You conclude the coin is biased, and you say "I'm 90% sure of that." Is this the right conclusion? Probably? Maybe? ![bear flipping a coin](https://public.asmartbear.com/books/anthology/chapters/p-hacking/bear-coin-flip-2-simple.png) Now imagine you have 10 coins, and you want to test all of them for fairness. So you run the above experiment, once per coin. 9 of the tests result in "fair coin", but one test shows "biased coin". Should we conclude that the one coin is biased? Almost surely not. Because even if all coins were fair, we know that 10% of the time the test will incorrectly conclude "biased". So this result of `9 / 1` is exactly the result we'd expect if all coins were fair. But wait a second... what if in fact 9 coins were fair but 1 were biased? Then this is *also* the most likely result! It could have also come up `8 / 2`, but the `9 / 1` result is the most likely. So: `9 / 1` is the most likely result _both_ if all 10 coins are fair _and_ if only 9 coins are fair. So... what exactly can you conclude from the `9 / 1` result? Nothing, yet, not with confidence. What you conclude is that this procedure is insufficient, and that we need to augment the procedure to correct the issue. The insight is: **You are making exactly this mistake with your A/B tests.** You are running a bunch of A/B tests. You're looking for (something like) "90% confidence". Mostly the tests have a negative result. Occasionally one works; maybe one out of ten. And you conclude that was a successful test. But this is exactly what we just did with coin-flipping. In the real world it's often even worse, like using confidence of 85% or 80% and therefore false-positives are much more common. Or you don't even pick a confidence level. You don't decide how much N you need to make a conclusion. Instead you "run the test until we get a result that looks conclusive." This particular error of "stopping whenever it looks conclusive" is called "p-hacking" by statisticians, and it's been a well-documented fallacy since the 1950s. The reason it's a fallacy, is that when N is small, random fluctuations will often cause a result that looks like "90% confidence of a positive result," whereas if you continue the experiment, the data shifts back into the territory of "negative result." I show some fun real-world examples of p-hacking, and counter-examples when the experiment is done properly, in this video: ![Video by Jason Cohen - Mystery of the broken A/B test](https://public.asmartbear.com/books/anthology/chapters/p-hacking/youtube-fauo2-aqmr0-simple.png) ## Marketers: The accidental p-hackers Marketers have been making these p-hacking errors in A/B testing for many years. You are too. We have data. A study[^4] of more than 2100 real-world A/B tests across Optimizely's customer base found a 40% false positive rate. Marketers never knew it; indeed, the Optimizely software declared the tests "significant"! The Marketers never had a chance. [^4]: [Here is the study](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3204791), and [here is a blog post](https://www.ron-berman.com/2018/12/17/when-a-paper-becomes-a-sensation/) from the author, addressing concerns and caveats. So, roughly half the time you think "this A/B test was successful," it wasn't. This explains another phenomenon that you're probably familiar with if you've done a lot of A/B testing: 1. You run tests. Sometimes one is significant. You keep that result and continue testing new variants. 2. You keep repeating this process, keeping the designs that are "better." 3. Over time... one is 10% better. Another is 20% better. Another is 10% better. 4. So, that should be 45% better overall. 5. You look back between now and months ago when you first started all this... and you don't see a 45% improvement! Often, there's no improvement at all. Why didn't all those improvements add up? Because they were false-positives. Shopify confirms this theory with years of data.[^5] Their 600-person Growth Team constantly runs funnel-optimizing A/B tests, but because they understand the p-hacking phenomenon, they not only look at short term results (2-8 weeks) but whether the results hold over the long run (12 months). Their results: Of the tests which show statistically-significant improvement in the short-term, 35% of the time the effect disappears over the long run. [^5]: From [this 2025 interview](https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams) of Archie Abrams, Shopify's VP of Product and Head of Growth, starting around the 18-minute mark. And other people experience this anecdotally: ![Figure 2](https://public.asmartbear.com/books/anthology/chapters/p-hacking/tweet9-simple.png) ## How to stop fooling yourself The easiest thing is to run the test again. If false positives are 1 in 10 at 90% confidence, then you should be able to run a second test, and get the same result. And don't stop tests early. I know you're excited. Just wait. No p-hacking. And seek large effects, like double-digit changes in conversion rates. Large effects are unlikely to be caused by randomness; small fluctuations are far more likely to be false-positives. And anyway, large effects actually have an impact on the business, whereas small effects don't. This might mean testing drastic changes instead of incremental ones. That's it? Almost--**there's a much smarter way to go about this**. And if you want to keep your job even with the rise of AI, you need to be smarter than just running a bunch of variants. ![You won't believe it! Anderson's experiment... the results... they're conclusive! Conclusive I tell you!](https://public.asmartbear.com/books/anthology/chapters/p-hacking/d6798print-simple.png) ## Form a theory, test the theory, extend the theory Too often A/B tests are just "throwing shit at the wall." We excuse this behavior by saying "No one knows which headline will work; it's impossible to predict, so we just try things." Not only is this thoughtless and lazy, it also means **you haven't learned anything, regardless of the result of the test.** You don't want to be a mindless slinger of random phrases. AI can do that too, and AI isn't a good marketer. Instead, you want to **create validated learning.** ![bear lab coat](https://public.asmartbear.com/books/anthology/chapters/p-hacking/image-3-simple.png) To do this, form a theory, then design experiments to test the theory. Example theories: 1. At this point on the website, visitors are ready to buy, so send them down a purchase funnel with a restricted UX. 2. Here people want to learn more, so talk about options and let them explore features rather than being crammed down a funnel. 3. People are on the fence, so we should be more forceful and confident in our language. 4. People can't see well, especially on mobile devices, so we should have higher contrast colors and less text. 5. Pictures work better than paragraphs, especially since people hate reading and half of them don't speak English natively. 6. People are more likely to click buttons than to click links. 7. People from marketing channel X are more likely to be in a Y state of mind, and to be excited by Z. Perhaps some theories already popped into your mind. Good! Write those down. Then make designs that would perform better if that theory were true. It's not "shit on the wall" because this time you have a specific Theory of Customer that your wall-shit is designed to test. And that makes all the difference: #### The negative result Let's say you pick a theory, run a test, and it fails. Is your theory disproved? Not quite yet. Perhaps your implementation wasn't the best manifestation of the theory. Not extreme enough, or had other issues that covered up the good effects. If you feel this might be the case, run a new experiment. But if you're still not getting positive results after a few iterations, you have accumulated evidence that the theory is incorrect. That is called "learning." Which wasn't happening when you "threw shit at the wall." Now you know that you need to invent a new, different theory and test *that*. How useful, and directed. #### The positive result Suppose you had a positive result. Hooray! Is the theory proven? No, because you read the first half of the article, so you know that positive A/B tests are often false. So, what do you do? You lean even further into the theory. Run another test that's even more extreme, or a different form of the same concept. If the theory is truly correct, that will work again, perhaps even better! If it reverts to nothing, you know it wasn't a real result. Now you're not fooling yourself. You're finding theories that actually correct. That's what "validated learning" looks like. Since you've actually learned something, you can extend the theory. What else is probably true? What new designs and text and pictures would leverage those insights even more? Now you might make multiple leaps of improvement, rather than spraying random things on your website. And you're a smart marketer that AI cannot replace. --- Most theories won't be right (or at least not impactful enough to matter). Most tests will come up negative. That's frustrating but it's the truth. Even for world-class testers like Shopify's Growth Team. You *do* [want the truth](https://longform.asmartbear.com/failure-to-face-the-truth/)... Don't you? --- *Many thanks to [Einar Vollset](https://x.com/einarvollset) for reviewing early drafts.*

No wait, of course THAT is the single most important SaaS metric

Jason Cohen — Sun, 03 Nov 2024 00:00:00 +0000

![Reaction to the news was mixed, largely because we asked more than one person.](https://public.asmartbear.com/books/anthology/chapters/saas-metric/d4033print-simple.png) **The single most important SaaS metric is retention**, because [cancellations](https://blog.asmartbear.com/cancellation-rate-in-saas-business-models.html) indicate [lack of Product/Market Fit](https://longform.asmartbear.com/product-market-fit/), no matter the cause (price, features, severity of need, duration of need). If it cannot be fixed, it means the business is a failure even if other metrics are stellar, because it means customers are rejecting the product. Once you're scaling (i.e. past Product/Market Fit and into tens of millions in revenue), you realize that cancellation scales exponentially with total customers--whereas new-customer growth scales linearly with sales and marketing costs--which means cancellation necessarily and always outgrows inbound activity, thus growth ceases. So, retention is most-important, regardless of stage. **The single most important SaaS metric is top-line growth**, because high revenue growth implies many other (even un-measurable) things are going well, such as Product/Market Fit, ability to get to customers, ability to retain customers, and proving the large size of the addressable--[no really, we're actually addressing it!](https://longform.asmartbear.com/problem/)--market. High growth is the only way to build a large tech company, because tech market dynamics follow a "winner take all" [power law](https://longform.asmartbear.com/10x/), and the only way to be the biggest is to grow the fastest. Fast growth proves every other piece of the company is healthy, or at least healthy enough not to be fatal. High-growth companies have all the options--staying independent, raising money, selling. It's also by far the greatest determinant of equity value, because it maximizes both revenue and the revenue-multiple. **The single most important SaaS metric is net-profit growth**, because the days where equity value is determined almost completely by top-line growth [died in 2015](https://longform.asmartbear.com/unsustainable-companies/), and now both public and private markets are rewarding not just growth, but profitable growth. Which is better anyway, because it means we're building sustainable, value-creating companies, not just companies that [grow like a virus](https://longform.asmartbear.com/darwinian-company-growth/), without purpose and possibly killing the host. The unicorns are dying not for of lack of revenue, but for [lack of profit](https://blog.asmartbear.com/unprofitable-saas-business-model.html). **The single most important SaaS metric is sales efficiency**, because if it's expensive to acquire new revenue, it means the product is being stuffed into the market rather than [embraced by it](https://longform.asmartbear.com/customer-validation/), which means you're artificially growing rather than having found real Product/Market Fit and longevity. It will be too hard to grow at scale because [merely replacing natural cancellation is expensive](https://longform.asmartbear.com/coc/), thus your maximum size is capped. The expense means it's too hard to be profitable in the long run, because too much of a customer's lifetime revenue is spent in costs before they even write the first check. It means you're "buying customers" rather than [building a product people organically want, enjoy, and talk about](https://longform.asmartbear.com/willingness-to-pay/), which in turn calls into question the fundamental value of the product. **The single most important SaaS metric is LTV**, because if you look at the component metrics individually you never get a complete picture. High growth is great, but not if margins are so low there won't be a profitable business at scale. High retention is great, but not if [MRR is so low](https://longform.asmartbear.com/pricing-determines-your-business-model/) that sizable revenue requires too many customers. When you optimize one number in LTV, often the others change in contrary directions. For example, you can increase price which increases MRR but also increases cancellations as people can't afford it. Only by combining these metrics can you get a clear and true picture of the health of the business model. Some people say [LTV isn't valid](https://longform.asmartbear.com/ltv/) because [the future is unpredictable](https://longform.asmartbear.com/predict-the-future/), but that's exactly why it's so important to try. **The single most important SaaS metric is the [Rule of 40](https://www.scalevp.com/insights/a-quick-primer-on-the-rule-of-40/)**, because either your company needs to be growing fast (in which case being unprofitable now creates long-term value), or your company needs to be very profitable (where generated "value" is literally cash coming out of the business, over many years). The "Rule of 40" achieves this--the sum of YoY revenue growth rate and bottom-line profit margin--the "super-metric" that covers both the "growth" and "profit" phases of a successful company's lifespan. **The single most important SaaS metric is NPS** (customer satisfaction), because this is the ultimate measure of value-creation, which in turn impacts every other important metric. It impacts new growth because only high-NPS customers will propagate word-of-mouth marketing--the least expensive form of marketing and one of the few that continues to scale with the business. It impacts retention because high-NPS implies they'll stay. It even impacts objectively important things which don't have numeric metrics, such as brand quality and Product relevancy and customers who [love you even when you screw up](https://longform.asmartbear.com/customer-love/). It covers not only [Product Management](https://longform.asmartbear.com/great-product-manager/) but [Tech Support](https://longform.asmartbear.com/tech-support-is-sales/) and every other function. The customer is the fundamental unit of the company, and NPS measures customer health. --- All of these statements are true, for some companies, at some times, in some stages, in some industries, in some markets. But the larger point is that companies are more complex than "a single most important metric." [The purpose of a metric is to be a tool in service of](https://longform.asmartbear.com/product-metrics/) your goals, timeline, size, circumstance, even philosophy, not a master you that you thoughtlessly obey. Focussing on just one thing is valuable because it creates focus, especially when a company is young and there isn't enough time to maximize multiple goals. Larger companies like WP Engine can have three or four, but still not many. Your job is to figure out what's most important *right now*, what's on fire, what's most important for getting the company to its next milestone, lean into what's working well, and then *simplify* and *clarify* the few goals and metrics that your entire company should align on. [Like this](https://longform.asmartbear.com/strategic-planning/).

The Serengeti Plain: Fallacies that aren't fallacies

Jason Cohen — Sun, 20 Oct 2024 00:00:00 +0000

![What do you have that's sick or injured?](https://public.asmartbear.com/books/anthology/chapters/fallacies/d8997print-simple.png) I never want to hear this narrative again: #### Back when humans were living in the... Serengeti Plain _or_ Saharan Desert _or_ coming down from the trees... #### it made sense for us to... do something the self-satisfied speaker scoffs at but graciously forgives a primitive species for doing. #### This worked because we were just trying not to get eaten by a lion _or_ tiger; by the way there are no tigers in Africa, but don't let facts get in your way! #### But, in our modern society, this is a fallacy! That's why you have to... embrace [buzzwords](https://longform.asmartbear.com/authentic-is-dead/) like "first principles" and "maximize expected value" and "Bayesian priors"... #### to transcend your stupid "lizard brain" and be... a frontal lobe user, like the oh-so-enlightened speaker. You don't know what it was like in [the only African region you can recall from David Attenborough shows], you don't know how [genetic pressures](https://longform.asmartbear.com/going-viral/) actually work, you can't ignore the hundreds of generations since the rise of civilization, and **not all heuristics are "fallacies."** "Fallacy" implies it's dumb because it runs against cold, hard, scintillating, pure, perfect logic. But I don't agree with some of that perfect logic. ## What if "Expected Value" is the fallacy? One of my pet peeves is the idea that we should always "maximize expected value," which I believe is [rarely the correct way to make a decision](https://longform.asmartbear.com/investment/), but always generates a tsunami of arguments on Twitter. Economists claim that "maximizing expected value" is what logical people do. It doesn't bother those same economists that they [cannot predict any major metrics of the economy](https://www.theatlantic.com/ideas/archive/2024/06/interest-rates-inflation/678802/) or [markets](https://longform.asmartbear.com/predict-the-future/), while constantly issuing memos excusing their models for getting it wrong for the 74th time in a row. All while calling everyone _else_ irrational. Let's see why "expected value" might be the fallacy: I invite you to play a game. The game is, we flip a fair coin. If it comes up heads, I will double your life savings. If it comes up tails, you lose all of your life savings. In other words, it's like going all-in in poker with a 50/50 chance, except this is your actual life, not however many chips you brought into the casino. This is for everything. ![bear flipping a coin](https://public.asmartbear.com/books/anthology/chapters/fallacies/bear-coin-flip-2-simple.png) How many people want to play that game? If you have very little in the bank, you might want to play, because it makes little difference either way. But among people who have spent years socking away a nest egg, few would take that chance. From an "expected value" perspective, it doesn't matter whether you play the game or not. The expected value is the same--zero.[^1] But of course, it _does_ matter whether you play the game, and different types of people will want to or not want to play the game, which *proves* that it matters. And I don't think there's anybody on Earth who thinks that characterizing this game as being a "zero" is an accurate or useful characterization. [^1]: The definition of "expected value" is the sum of all the outcomes weighted by the probability of each outcome. In this case, if your savings is `s`, the expected value is `-s*0.5 + s*0.5 = 0` Now let's really mess with the expected value acolytes. The game is the same, except the probability is 55% that you double your life savings and 45% that you lose everything. Does that change whether you want to play the game? Very few people would change their mind based on this minor alteration in the game's rules. Losing all of your life savings at 45% is essentially the same as at 50%, and if it was bad at 50%, it's still bad. But now the expected value of this game is positive.[^2] So the "maximize expected value" people would say you're being illogical if you opt out of the game. [^2]: `EV = -s*0.45 + s*0.55 = s*0.1`, so for example if our life savings is $100,000, the expect value of the game is $10,000. In particular, you would say you are succumbing to the fallacy of "Loss Aversion." This is the notion that we hate losses more than we love gains, and we're allowing this fallacy to drive the wrong decision of not maximizing our expected value. But I beg to differ, and you probably do too. I don't think loss aversion is illogical, and I don't think making someone play this game because of some religious adherence to expected value is wise. In fact, I think expected value isn't the right way to think about the game at all. Indeed, I would say those people are succumbing to the "Expected Value Fallacy," which is you are using statistics that apply only if you were to play the game a million of times, and where you're allowed to keep the average result, and yet applying that to one instance of playing the game, which is just wrong. --- So, what follows is a set of so-called logical fallacies that I don't agree are necessarily fallacies, starting with Loss Aversion that we just demonstrated. And none of it is because we used to run away from lions. ## Anti-Fallacies ### [Loss Aversion](https://en.wikipedia.org/wiki/Loss_aversion) _Valuing the pain of loss greater than the joy of gain._ It took a lifetime of [pain](https://longform.asmartbear.com/startups-emotionally-draining/) and [sacrifice](https://longform.asmartbear.com/sacrifice-your-health-for-your-startup/) and [luck](https://longform.asmartbear.com/lucky/) to accumulate what you have. It is not irrational to be much more protective of losing it than you are greedy about getting more. Especially if the loss is catastrophic, as opposed to an experiment with 1/1000th of your money where the maximum loss is immaterial. Of course, in that case you're happy to play, proving again that the so-called "fallacy" is only invoked when the loss is important. --- ### [Endowment effect](https://en.wikipedia.org/wiki/Endowment_effect) _Valuing an object more, only because you possess it._ ![baby bear hugging teddy bear](https://public.asmartbear.com/books/anthology/chapters/fallacies/bear-baby-with-teddy-simple.png) We are emotionally attached to things that we own. It makes sense: There's an emotional investment in having made the decision, in shifting or cementing your identify as "a person who would have this object," and how you believe that ownership will be perceived by others. All of this is real, tangible value. You also presumably value the object more than its face-value. If you buy a ticket to a concert for $200, [studies show](https://academic.oup.com/jcr/article-abstract/27/3/360/1796841) you wouldn't sell it for less than $300-$400. Is this because you erroneously value it higher because you "possess it," or is this because the reason you bought it in the first place is that you value _going to the show_, so selling the ticket also means _selling the experience_, which is definitionally worth more than the face value of the ticket. Over time, we can also grow sentimental attachments; economic theory is incorrect if it asserts that sentiment has no value. --- ### [Sunk Cost Fallacy](https://en.wikipedia.org/wiki/Sunk_cost) _Continuing an endeavor because of previously invested resources._ In long-term projects, commitment can lead to eventual success whereas "fail fast" actually ensures failure. The emotional and financial investments [drive perseverance even when there's little apparent hope](https://longform.asmartbear.com/chaos-at-start/), which can be the key to overcoming obstacles and achieving long-term goals. Sometimes we pretend we're so smart in avoiding so-called "sunk cost fallacy," when really we're justifying bailing out when times got tough. ![If two negatives make a positive how come two wrongs don't make a right?](https://public.asmartbear.com/books/anthology/chapters/fallacies/d3618print-simple.png) --- ### [Availability Bias](https://en.wikipedia.org/wiki/Availability_heuristic) _Overestimating the importance of information that is readily available._ In startups--and even in scaled-up companies--we often lack comprehensive or statistically-significant data. Extensive research is impossible or impractical. Instead of getting mired in analysis paralysis, scared to act on the data we have at hand, we must act with what's in front of us, and under conditions of uncertainty. This is not a fallacy but a practical necessity, and even allows us to move quickly and adapt. There are [specific strategies](https://longform.asmartbear.com/predict-the-future/) for operating like this. It's not a bias, it's life. --- ### [Confirmation Bias](https://en.wikipedia.org/wiki/Confirmation_bias) _Favoring information that confirms existing beliefs._ Constant self-doubt leads to inaction; frequent changes in plans leads to confusion. A leadership team who is always shifting priorities will confound the whole organization. A strategy that constantly changes, cannot be executed. It's also true that a strategy that never changes is wrong. [There is a time to reevaluate plans and strategies](https://longform.asmartbear.com/strategic-planning/), but that time is not "always." --- ### [Overconfidence Bias](https://en.wikipedia.org/wiki/Overconfidence_effect) _Having excessive confidence in one's abilities or judgments._ ![bear superhero](https://public.asmartbear.com/books/anthology/chapters/fallacies/bear-superhero-2-simple.png) [93% of drivers believe](https://www.sciencedirect.com/science/article/abs/pii/0001691881900056) they are above-average drivers. While mathematically they are incorrect, this also gives them the confidence to act. Constantly second-guessing each driving decision would make them even worse. Founders must be over-confident. The [most likely outcome is failure](https://longform.asmartbear.com/criticize-create/), so you have to be overconfident to remain optimistic. At the beginning, [both successes and failures look the same](https://longform.asmartbear.com/perseverance/), so you have to be overconfident to push through. Before Product/Market Fit, you can't know whether or when you're [going to hit Product/Market Fit](https://longform.asmartbear.com/product-market-fit/), so you have to be overconfident to keep trying things, treating everything as [an experiment, not a failure](https://longform.asmartbear.com/fail/). Overconfidence is not the same as being blind. Being confident in the vision, but skeptical about every detail, is how you find the truth. --- ### [Recency Bias](https://en.wikipedia.org/wiki/Serial_position_effect) _Giving disproportionate weight to recent events._ Early in a startup's life, almost everything is being done incorrectly. Quickly reacting to what's in front of you is one of the ways to iterate to doing things right. "This is how we did it at my last company" doesn't matter, when the last company was 400x larger and 50x older and in a different industry. One of the [few advantages a startup has](https://longform.asmartbear.com/startup-beats-incumbent/) is agility, and reacting to the latest information is the definition of agile. --- ### [Survivorship Bias](https://en.wikipedia.org/wiki/Survivorship_bias) _Focusing on successes even if they were due to luck, or share characteristics with failures._ ![locations of bullet holes in the place, hypothetical example](https://public.asmartbear.com/books/anthology/chapters/fallacies/d7475b770-f4b2-4e5f-a29f-d69b275e9da6-simple.png) I've been pointing out [the problem of Survivor Bias in business advice](https://longform.asmartbear.com/survivor-bias/) for more than 15 years. That said, it's not true that "you learn more from failure than from success." From failure you see what didn't work, but that doesn't point the way to what does work. Yes, successes are [lucky](https://longform.asmartbear.com/lucky/), not just good. Yes, companies that failed often do similar things to companies that succeed, which suggests that those things didn't "cause" success. Yes, often there's just [one or two most important things](https://longform.asmartbear.com/the-important-thing/) that caused the success, _despite_ everything else they did, not _because_ of it. But which is more likely to work: Copying everything about a success, or trying to avoid everything about a failure? Of course the success, because [not all of it was luck](https://longform.asmartbear.com/butterfly-effect/). --- ### [Herd Behavior](https://en.wikipedia.org/wiki/Herd_behavior) _People tend to follow the actions of a larger group._ Your product and company should be different in some way that your [target customers](https://longform.asmartbear.com/icp-ideal-customer-persona/) believe makes you the best in the world (for them). But in every _other_ way, surprises are bad. I don't want to have to calculate the bottom-line price in a unique pricing model, or decode the bizarre controls you created because you didn't want to use menubars, or decipher non-standard icons for things like "copy" and "paste" because you wanted to be creative. ![Kai's Power Goo. What does "UnGoo" do?](https://public.asmartbear.com/books/anthology/chapters/fallacies/image-simple.png) Adhering to norms provides safety, understanding, control, and ease. Those are all desirable qualities in products and companies, except in the very few places where you make a genuine improvement. --- ### [Status Quo Bias](https://en.wikipedia.org/wiki/Status_quo_bias) _Preferring things to stay the same rather than change._ Stability and consistency foster reliability and trust. A startup thrusts constant change upon its denizens; if you can keep some things constant and reliable, it gives everyone something to hold on to. If you have put thought and effort into your decisions, then it should take even more thought and effort to change that decision; it's not a bias to honor our former selves, so long as new information hasn't come to light that would have changed the those decisions. --- You know, back when humans were living in Statistics for Economists class in college, it made sense for us to pretend that the real world was an idealized environment with only two variables, no complex dynamics, and perfectly rational actors. This worked because we were just trying not to get eaten by a pedantic professor. But, in our modern society, this is a fallacy! That's why you have to use ideas and tools that make sense for each individual in their circumstances, thinking for yourself instead of parroting phrases off the internet you don't really understand, running your own experiments instead of believing models and theories with more exceptions than examples, to transcend your lizard brain and be enlightened and strategic. ![It's the gazelles. They got a restraining order.](https://public.asmartbear.com/books/anthology/chapters/fallacies/d4904print-simple.png)

Pivot Points

Jason Cohen — Sun, 29 Sep 2024 00:00:00 +0000

![this oh please its not even mine](https://public.asmartbear.com/books/anthology/chapters/pivot-points/d5340print-simple.png) Gallup® [StrengthsFinder](https://www.gallup.com/cliftonstrengths/en/254033/strengthsfinder.aspx)® is one of those registered® trademark® bedazzled® personality tests claiming to be *uniquely* insightful, honed over *decades* of scientific validation, in contrast to all those *other* personality tests who only _claim_ to be data-backed, but are _actually_ horoscopes recast in [corporate lingo](https://longform.asmartbear.com/idioms/), that are thinly veiled excuses for consultants to charge you for assessments. Oh, by the way, your Official® Gallup® StrengthsFinder® Assessment® [costs](https://store.gallup.com/p/en-us/10003/cliftonstrengths-34?c=1) $59.99. But OK, for years at WP Engine every new employee takes the StrengthsFinder® assessment as they on-board, and they're treated to their "Top 5 Strengths". We've done it thousands of times, and people like it. I like it too, to be fair. It's fun to feel important enough to warrant analysis. My third-strongest strength is "Competitive", which means what you think it means: I always want to win.[^1] Specifically, I want to beat other people in order to win.[^2] [^1]: The part of Capitalism that is good. [^2]: The part of Capitalism that is bad. Is "Competitive" a *strength* though? **Yes**, if I'm in a sales call. Yes, if I'm the last interview for a candidate who has other offers. Yes, if the person I'm trying to beat is "my past self," generating motivation to become a [better version of myself](https://longform.asmartbear.com/be-yourself/) without overflowing into [feeling that I'm unworthy](https://longform.asmartbear.com/impostor-syndrome/). **No**, if I'm in a team meeting where the [goal is to help each other](https://longform.asmartbear.com/productive-meeting-activities/) rather than to win the argument. No, if it sucks the joy out of games and hobbies. No, if I [base my self-worth](https://longform.asmartbear.com/feelings-of-inadequacy/) on (my false perception of) other people's accomplishments. ![My greatest weakness? I'd have to say depth perception.](https://public.asmartbear.com/books/anthology/chapters/pivot-points/d6668print-simple.png) So if it's unclear whether it's a "strength," what _is_ it, exactly? ## The right analysis: Pivot Points "Competitive" is a fact-of-the-matter of my personality. It's a thing that exists, outside of judgment. I call this non-judgmental fact a **Pivot Point** because of how you can use them to win at life and business. The rest of this article explains how to do that. Pivot Points are strengths in some situations, hindrances in others, and irrelevant in still others. This is why I don't like the ideas of "strengths" and "weaknesses" generally--whether in personality tests or [SWOTs](https://longform.asmartbear.com/swot/) or other tools of strategy and planning. Those frameworks imply that we already know the context for evaluation, but often we don't. Pivot Points are neither intrinsically good nor bad, they just *are*. But why the word "Pivot"? Because you're "stuck there" like your "pivot foot" in games like basketball and lacrosse and Ultimate Frisbee. Your other foot--and the rest of your body--is free to move anywhere, subject to that constraint. So Pivot Points are the constraints you build around. For example if you hate managing people (Pivot Point), you shouldn't become a manager, nor should you make a company that will eventually require a team; alternatively, you shouldn't be the CEO of that company. The other reason is that Pivot Points can change, but not often, and not capriciously. Sometimes you pick up your foot, run somewhere else, and establish a new pivot; this is investing in a new skill, or learning a new industry, or overcoming something that's a weakness relative to your goals. Short-term planning should assume Pivot Points are fixed, because in the short term, they are. Long-term planning, especially when deciding where to [investing your time and money](https://longform.asmartbear.com/investment/), can ask: What new Pivot Points do we want? This also neatly matches the "pivot" concept from [Lean Startup](https://longform.asmartbear.com/lean-startup-ballad/): When a company [realizes that one aspect of the business is fatally broken](https://longform.asmartbear.com/predict-the-future/), yet other aspects are insightfully correct. You don't throw everything away; you're realizing which things are correct or immutable--a.k.a. Pivot Points--and then pivoting the rest of the company around those things. So let's see how to find your personal Pivot Points (for your life), as well as those of your company (for strategy), and then how to exploit them to win. ## Finding your personal Pivot Points "Know thyself!"[^3] We _ought_ to know ourselves, but we're too close to the problem. You're biased, you can't get outside your own head, [you have baggage](https://longform.asmartbear.com/scars/), and few of us are naturally talented at honest introspection. [^3]: Inscribed at the Temple of Apollo at Delphi, independently opined by many people throughout history, Thales gave this as the answer to the question: What is the most difficult thing? When asked what is the easiest thing, he answered: To give advice. Which is what I do in these articles. The truth hurts! ### What drives you These prompts will help you uncover who you are. * Even when I was a kid, I would ______, and I still find myself drawn to it. * Lately, I love it whenever ______ (which would surprise my ten-years-ago self). * If I could go back in time, I would tell myself to worry less about ______ and more about ______. * When I'm on an extended vacation, I get itchy to ______; I just can't help it. * My parents/friends always laugh when I start talking about ______ because I get so excited I can't stop talking about it. * My parents always said I would be a ______ because even when I was just six years old... * Whenever I ______, I get lost in the work, and feel energized (not exhausted!) when the work ends. * If I could go (back) to college, I would get a degree in ______. * The last project I *really* enjoyed, and wish I could keep working on, was ______. * I was surprised how much my peers praised my work when I ______; maybe I'm better at that than I thought. * Recently I was totally immersed, engaged, excited, and happy while doing ______. * I asked a few people who know me, and who I trust to be thoughtful and observant, and they said my special strengths are ______. ### What hinders you Sometimes it's easier to avoid something you hate, then to construct a situation where you're constantly doing something you love ([always invert](https://longform.asmartbear.com/avoid-blundering/)): * When I'm faced with ______ I physically feel the "pit of my stomach" falling. * The last time I had to ______, I did an embarrassingly minimal job, because I couldn't bring myself to do better. * I have an intense dread of any meeting where we ______. * If my job starts requiring me to ______ with even 10% of my time, I would at least think about changing jobs. * When I'm faced with ______ I procrastinate so much, I do chores that I dislike and normally avoid. * If I'm being honest, although I would really love to be great at ______, the fact is I'm just never going to be good at it. * Whenever I ______ all day long, I know I do great work, but I'm absolutely exhausted; the rest of the night will be vegging out of the sofa with mindless entertainment. * I know I'm supposed to like / do ______, but the truth is I never get excited about it. You should be honest, even if you think "society" doesn't like your answers. There's nothing wrong with the drive to make money, or to become famous, or to prove a point. > Desire to seem clever, to be talked about, to be remembered after death, to get your own back on the grown-ups who snubbed you in childhood, etc., etc. It is humbug to pretend this is not a motive, and a strong one. Writers share this characteristic with scientists, artists, politicians, lawyers, soldiers, successful businessmen--in short, with the whole top crust of humanity. > --George Orwell, [Why I Write](https://orwell.ru/library/essays/wiw/english/e_wiw) ![funeral stinging random guy](https://public.asmartbear.com/books/anthology/chapters/pivot-points/d8803print-simple.png) ### Outside-in Ask other people about your strengths and weaknesses--yes, that language, because it helps others generate answers, even though you're going to untwist their observations into non-judgmental Pivot Points. Unfortunately they're rarely fully honest, even when anonymous. My favorite reference-check interview question can help. Candidates refer you to friendlies who are primed to say nice things, so there's no value in their sanitized recommendations. Instead, I ask the following: > What is the ideal scenario, under which this person thrives? > > That is: Construct the absolutely perfect set of circumstances--subject matter, goal, team (or lack thereof), direction (or lack thereof), incentives, etc.--where this person absolutely kills it, is efficient, is productive, is extremely happy, and makes everyone around them happy. > ![bear interviewing](https://public.asmartbear.com/books/anthology/chapters/pivot-points/bear-interview-simple.png) The answer generates Pivot Points. If they say "Oh yeah, you need to give them a goal because they want to know what 'success' looks like, but then get out of their way because they're really creative and they explore quickly and you don't want to squash that," then you extract Pivot Points like "goal-oriented" and "craves exploration". That means working in an R&D group might be a bad fit (not goal-oriented), as would working on a mature product (needs execution, not exploration), but an early-stage startup would be great (clear goal, and exploration is mandatory and valued). Also ask the opposite: > Construct the scenario where they die inside, mess everything up, and piss everyone off. It's funny to ask it and easy for them to answer, but doesn't feel like you're being negative about the person; after all, we all have a personal hell. More Pivot Points. Another great question to ask others: > What are my strengths that I'm unaware of because they come so naturally to me? Where I don't realize other people aren't like that too? These are our most natural, powerful Pivot Points, because we can't help but be great at it, and yet they are special. These constitute your "edge." Definitionally you can't see them yourself, because you don't realize how special they are. Ask others. It's not easy to know yourself, and "yourself" [changes over time](https://longform.asmartbear.com/be-yourself/). But it's worthwhile to identify major Pivot Points, because pivoting around them (or deciding when and where to invest in altering them) is how you construct a [fulfilling life](https://longform.asmartbear.com/fulfillment/). Now we'll do this for a product or company. ## Finding your Company's Pivot Points "Knowing thyself" is hard enough for an individual; collectively we're even more complex, and each person holds different knowledge about different areas. We can't ask our team to just "write down what we are like." Here's what to do. Suppose you knew nothing about the inner workings of your business. Pretend you are an outside consultant, hired to reverse-engineer every aspect--not what the people inside the business _think_ it is like, _think_ are its strengths and weaknesses, but what's *actually* going on. You'll be sussing out the decisions that led to their strategy, even if they never consciously made those decisions or wrote down that strategy. You job is to write down "what is." You're allowed to observe behaviors and results, but not allowed to interrogate people to understand *why* they took those actions. Indeed, it's important that you *don't* interview people, because they will unwittingly rationalize, or even mount a defense. We're discovering, not judging. The generative procedure starts with write-storming[^4] observations about **what is special or peculiar about the product and company.** [^4]: [Write-storming](https://www.fastcompany.com/3033567/brainstorming-doesnt-work-try-this-technique-instead) is brainstorming in which participants write ideas in isolation, then bring them together for deduplication and enhancement. This eliminates the bias towards the fastest, loudest voice, supports people whose brains need more time to ponder, takes advantage of background-processing (i.e. "I thought of something in the shower"), and more fully includes non-native speakers who will be much more precise and compelling when given time to translate. "I was shocked to find there's not a single published study in which a face-to-face brainstorming group outperforms a [write-storming] group"--Leigh Thompson, _[Creative Conspiracy](https://www.amazon.com/Creative-Conspiracy-Rules-Breakthrough-Collaboration/dp/1422173348?tag=wwwfccom-20)_. Also, [recent research](https://www.nature.com/articles/s41586-022-04643-y) suggests that people are less creative in generating ideas over video-conferencing versus in-person, but there is no such difference in the quality of *selecting* good ideas from a list. Therefore, the optimal process appears to be write-storming to maximize creativity, then meeting up for synthesizing. ### Precipitation You can't ask people to list "things about us that are special and important"--it's too vague, and won't be comprehensive. Instead use specific, evocative prompts: #### Undeniable comparative strength What specific things are customers buying, when they choose us [despite our many foibles](https://longform.asmartbear.com/worse-but-unique/)? What does [our company do so well](https://longform.asmartbear.com/one-benefit/), even the competition admits it during their own sales calls, because otherwise they'd lose credibility? What is our team [capable of](https://longform.asmartbear.com/the-important-thing/), that most other teams are not? *(Whether it is difficult for others to copy or not, whether customers or competitors seem to care about it or not.)* #### Consistent complaint What complaints do our customers have, that we've heard so many times in so many channels that we don't even need data to know it is true? On what point does a competitor instantly win, because we have no defense against that claim? What is so problematic that people leave us for this reason alone, even when they're otherwise happy, even when they rate us 10/10 on NPS and apologize as they cancel? *(Whether it's smart for us to react or not, whether it's intrinsic to our product or relative to a competitor.)* #### Customers advocate for When our customers brag about us on social media or in private meetings, when it's genuine advocacy, what do they highlight? What positives do even disgruntled employees begrudgingly admit on Glassdoor? What concessions do one-star reviewers make? *(Whether we think they're exaggerating or not, whether it's what the majority of customers would agree with or not, even if the NPS-0 customers disagree, even if customers in their exit interviews disagree.)* #### Best customers What are the characteristics of our best customers: The ones who pay the most, never call tech support, score 10/10 on NPS, never leave us, upgrade consistently, advocate for us externally. More specifically: What characteristics are prevalent that aren't shared by other customers? _(Whether that's a large market or not, whether it's shared by only 30% of them, so long as it's shared by <10% of the rest of the population.)_ #### Proud of What about our product or team are we especially [proud of](https://longform.asmartbear.com/proud-of-founding/), whether because we consider it to be great workmanship, or because it reflects "who we are," or because it was difficult to do, or because it's just fun? Or the opposite: What do you absolutely hate that some other company does? *(Whether customers agree or not, whether there's data to support that feeling or not, whether it's a competitive advantage or not.)* #### Talking & Walking We say we're great at certain things on our website and sales material. How much of that is true? What else are we great at, that we don't brag about? We say certain things because we know that our customers desire that quality, and we at aspire to be great at it. Use the [Talk/Walk Framework](https://longform.asmartbear.com/talk-vs-walk/). *(Whether or not this directly harms sales or retention.)* #### Clear and present existential threats What is either happening now, or has at least a [70% chance](https://longform.asmartbear.com/probability-words/) of happening in the next few years, which would seriously disrupt our business? Something that would dramatically tank sales, where customers might start cancelling in droves, maybe even end the entire business. *(Whether there's anything we can do about it or not, whether it's our own fault or not. But hold to the "70%-100% chance" requirement so that there's few if any; otherwise, every company has twenty theoretical disaster scenarios.)* #### Structural attributes What does our technical architecture or human organizational structure specifically make possible, easy, or natural? Or the opposite: unstable, manual, too difficult, [unscalable](https://longform.asmartbear.com/scale/)? *(Whether we think that's a "good thing" or not, whether competitors are similar in that respect or not.)* #### Philosophy & Values Things you believe in so much, you'll honor them even if it loses you customers, even if it loses you money, even if it slows you down, even if you have to fire people because of it. [Because this is who you are](https://longform.asmartbear.com/emptiness/). What do you believe in your gut, about what it means to do great work, what kind of organization you want to build, what impact you want to make in the world. Example: Is "great design" absolutely critical for success, at least the kind of success you want to be a part of, or [is "design" overrated](https://longform.asmartbear.com/design/), nice-to-have but a functional product and superior sales will beat it every time? Example: Is "[ship fast with bugs](https://longform.asmartbear.com/software-quality-mortgage/)" the best policy, because you learn the fastest and therefore get to the truth and product/market fit the fastest, or is "ship fewer things with high quality" the best policy, because you win lifelong loyal customers with craftsmanship and care, not by exploiting them as [unwitting lab rats](https://longform.asmartbear.com/slc/)? *(Whether lots of other people agree or not, whether there are lots of successful examples to point to or not, whether this is the "in vogue" thing to believe or not.)* #### Great ideas What ideas keep recurring, typically because of an ingrained sense of conviction stemming from one of the areas above, i.e. we *would* be proud of it, we think it *would* become a comparative strength, we think customers *would* become advocates because of it. *(Whether customers are actually asking for it or not, whether or not we have objective evidence that it really is a great idea.)* ![Is this about the wheel again, Og? Because if it is, I swear to God...](https://public.asmartbear.com/books/anthology/chapters/pivot-points/d7856print-simple.png) ### Condensation You'll find this exercise unearths all sorts of things you can act on. For our current purpose, we distill them into fewer, clearer Pivot Points. You do that with the following workshop: 1. **Rotate through observations**
Go around the room, asking each person for _just one_ of their better observations. We'll get to all of their ideas, so don't stress about which one is "best," just pick a good one.[^5] Together the group processes one observation at a time. 2. **Clarify**
Everyone asks clarifying questions--not arguments--to ensure we understand "what it is." Boil it down to a few words so it can go on the board (which is a whiteboard, or Miro, or whatever you like). Use one color for the observations. 3. **Convert to Pivot Points**
The group asks: What Pivot Point does this translate into? For example, "Customers post screenshots on Twitter of long wait-times in tech support" becomes "Slow tech support." Or "Customers brag about us by posting side-by-side screenshots of our product next to a competitor" might become "Delightful, remarkable design." Write the Pivot Points in their own color, and place the observation next to it. Themes will emerge as some Pivot Points will accrete many validating observations. And the other direction: if there are multiple target Pivot Points for a single observation, duplicate the observation card. 4. **Don't evaluate; don't act**
It's tempting to jump to a conclusion like "Slow support means we should hire more people," but fast support might not be of strategic importance, so maybe you should deemphasize support from marketing and sales materials instead of "fixing it." And even if you decide you _do_ need to fix it, should you really hire more people, or should you instead write better documentation, start using AI support chatbots, switch from tickets to chat, improve the UX of the product to prevent common questions, change what segment of the market you're marketing to, or what? "Now" is not the time to answer that question. 5. **Merge Pivot Points as you go**
It's better to have fewer, stronger Pivot Points. A smaller set is easier to hold in mind, easier to act on, easier to set in stone. But beware the trap where you make them so general that they cease to be useful. For example, "We love our customers" is too generic. Use the [Opposite Test](https://longform.asmartbear.com/opposite-test/) to make sure you haven't gone over the line. When in doubt, don't merge; being specific and actionable is more important than quantity. And definitely [do not have a target quantity](https://longform.asmartbear.com/top-10/) like 3 or 10. There are as many as there are. 6. **Loop until exhaustion**
As you repeat the loop around the room, you start to skip people who are out of items, until everyone's list is exhausted (and so are they).[^6] [^5]: This is a facilitation trick I learned from [Jonathan Slain](https://autobahnconsultants.com/). It ensures everyone gets time and attention, not dominated by the person who jumped at the chance to go first and unload dozens of items. [^6]: The process moves slowly at first, as people get used to the system and talk too much about each item. People will look at how many more items remain on their own list, and will worry this will take eight hours to complete. But it never does; you accelerate naturally (or the facilitator can start pressing for brevity), and people realize some of their items aren't that important relative to what's already up there, or are duplicative, and suddenly it's all finished. The process will throw off lots of ideas, follow-ups, and potential actions. Collect those during the process, and visibly write them down elsewhere so people know their ideas aren't lost. But stay on-task. ## Applying Pivot Points [Great strategy](https://longform.asmartbear.com/great-strategy/) and a [fulfilling life](https://longform.asmartbear.com/fulfillment/) starts with honoring Pivot Points as facts-of-the-matter that you construct strategies and careers around. There are articles on this site about how to do that, and there will be more in future. Here's some direct ways you can use Pivot Points create better product strategies and a better life: 1. How does our current strategy, or a proposed strategy, map to our Pivot Points? Where is it reinforced (therefore we should go deeper) or contradictory (therefore we should pivot)? 2. How do we do even more things that leverage multiple Pivot Points? When we do that, not only are we perfectly suited to it, they are automatically differentiating in the competitive market, because others would have trouble copying us, because their Pivot Points are different from ours. 3. What changes to our strategy would design around the negative consequences of our Pivot Points, so we're not in our own way, or playing a game that others are better suited to win? 4. Given the Pivot Points we identified, what additional ones must also be true, either as a downstream consequence, or because any self-consistent strategy would have to include them? Add those to the list. Yes this is recursive and it's turtles all the way down; that's what a "self-consistent, mutually-reinforcing" strategy looks like. 5. Is there a [singular concept](https://longform.asmartbear.com/one-benefit/) leveraging/avoiding multiple Pivot Points, around which we could build our entire positioning and strategy? 6. What type of customer--demographic, firmographic, job-to-be-done, budget-size, location--would find our Pivot Points refreshing and attractive? Is that our current [ICP](https://longform.asmartbear.com/icp-ideal-customer-persona/), or how should we adjust it? 7. Are we making promises we can't keep? Are there promises we should be making because we'll naturally win there? In short: [Does our marketing match our behavior](https://longform.asmartbear.com/talk-vs-walk/), and what level of the [Needs Stack](https://longform.asmartbear.com/needs-stack/) should we be operating at? 8. Are there contradictions in our Pivot Points? For example, do we want to deliver "Instant 24/7 support" but also "Can work only 4 hours/day" because of [a day job and a family](https://longform.asmartbear.com/two-big-things/)? Some contradictions must be resolved by changing the Pivot Points, but sometimes we can synthesize new ideas that make everything work after all; these are often our most valuable and differentiating ideas. Here's how to navigate [balance vs choice vs synthesis](https://longform.asmartbear.com/conflicting-choices/). 9. What if one of our Pivot Points are wrong, i.e. it's not the truth? Can we challenge each one, making sure we _know_ it is true? If it weren't true, what else would change? Should we actually make that change? [Here are specific examples](https://longform.asmartbear.com/startup-beats-incumbent/) of how younger startups can defeat large incumbents, when the startup leverages their own Pivot Points while designing around the Pivot Points of the incumbent. And how those same Pivot Points are [different with AI](https://longform.asmartbear.com/ai-startups/). In my case, being Competitive lends itself to entering a large, existing market with stagnant competitors, with a strategy of "build a [10x better product](https://longform.asmartbear.com/10x/) and then win in marketing and sales." On the other hand, someone who hates the idea of having to "win" at "sales" could build a niche product, simple enough to explain and advertise that it doesn't need a sales team, with a brand voice of a one-person boutique that makes a perfect, specialized product and IYKYK. Or someone who loves people (Pivot Point) but hates sales (Pivot Point) could start a consulting company where they forge long-lasting relationships with clients through on-going work like taking over an entire department of the company (e.g. marketing, support, engineering, or finance). All of these are wonderful strategies, so long as they match the founder's Pivot Points, and [the needs of the chosen market](https://longform.asmartbear.com/problem/). Some people call Pivot Points "[enabling constraints](https://cynefin.io/wiki/Constraints)". The theory is that constraints helpfully tame complexity, and then [increase](https://www.wired.com/2009/02/dp-intro/) [creativity](https://buffer.com/resources/7-examples-of-how-creative-constraints-can-lead-to-amazing-work/). Which they do; one might say the definition of "design" is "art + constraint". ![It's a transformative piece touching on themes of joy, desire, loss, and fourth quarter earnings.](https://public.asmartbear.com/books/anthology/chapters/pivot-points/d7178print-simple.png) I don't like the "enabling constraint" phrase, however, because people get hung up on constraint. "Constraint" sounds limiting, and indeed it is, even if it also triggers new and better ideas. It's not obvious how being "limited" is also "enabling". Whereas, "Pivot Points" are facts with all manner of consequences, that we should leverage or design around. Finally, although you can invest to change Pivot Points, beware. Some things might be too difficult to change. Some things might be too expensive to be worth changing. There's a risk that the change isn't successful. [Change disrupts the organization](https://longform.asmartbear.com/change/). Sometimes employees get mad and leave. Sometimes customers get mad and leave. Sometimes people can't change. Sometimes they shouldn't. Change Pivot Points only when the upside is so enormous, that even if you achieve only 30% of the transformation you were hoping for, it was still worthwhile. And don't do too many at once. Maybe just one, whether for yourself, or the company. As Tim Ferris describes it, when anyone asks whether they ought to write a book: > If the rewards are 1/4 of what you think they will be, and it takes 2x longer than you think it will take, is it still a *no-brainer* to do it? Not just "good" but a "no-brainer," like you almost have to do it? --- When you design a life or a strategy around Pivot Points, it's magical. It means you're "following your calling." It means you'll be more efficient, more effective, produce better quality, compete better against those who don't share your Pivot Points, and have a lot more fun. It's fun to do what you're meant to be doing. Life is short and companies are difficult. Don't fight Pivot Points. Embrace them.

Legacy

Jason Cohen — Sun, 08 Sep 2024 00:00:00 +0000

The first time you die is when your heart stops beating. The second time you die is the last time someone speaks your name. Or is it? --- ## Memory as legacy The concept of "second death" has always been deeply human. ![bear in Egypt](https://public.asmartbear.com/books/anthology/chapters/legacy/bear-egypt-simple.png) The ancient Egyptians believed that the afterlife persisted only as long as living people remembered one's name, and therefore built edifices that still stand with a permanence of 4000 years and counting. Jews recite the Kaddish on death's anniversary, intentionally recalling the person. Many Native Americans "keep the spirit alive" through storytelling, passed down through the generations. The ancient Greeks called it "glory" (κλέος); Achilles is given the choice between a long, uneventful life or a short life with enduring fame, and chooses the latter. The Japanese Obon Festival and the Mexican/Aztec Día de los Muertos reaffirm continued second lives through remembrance. The Korean tradition of Jesa invites the departed with food and bowing, and the Chinese Qingming Festival (清明节) brings honors and remembrance to the tombs of loved ones. In modern times, the rich perpetuate their names on charitable foundations and carved into university building cornerstones. Discoverers' names endure in theorems (Pythagorean) and particles (Higgs) and frameworks (Myers-Briggs) and conquered lands (Sandwich Islands). But is this the best way to live beyond death--affixing our name to a thing? Recently I've become enamored with a different concept of legacy: The on-going impact of ideas. ## Contribution as legacy The authors of religious texts are largely unnamed, and yet their stories, lessons, laws, and ethics have determined the world view of tens of billions of humans, and will continue to do so. If Charles Darwin had written his book anonymously, his ideas would still have echoed through science. Newton "stood on the shoulders of giants," even if we don't bother to enumerate those giants ourselves. If we erased all names from all discoveries, they would not cease to be discoveries, and their effect on us would not diminish. As Juliet observed, a rose by any other name would smell as sweet, and her world would have been happier had it not been poisoned by a name. The plot of "Romeo and Juliet" predates Shakespeare by thousands of years in the forms of Pyramus and Thisbe, Layla and Majnun, Tristan and Isolde, 梁山伯与祝英台 and surely many others. Though the ultimate origin is lost in time--and might not even be singular--ideas can echo wide (in geography) and deep (in time). ![bear in boat throwing a rock](https://public.asmartbear.com/books/anthology/chapters/legacy/bear-fishing-rock-1-simple.png) Like water waves, ideas propagate without reference to the originator. The creator lobs an idea-stone into the lake of humanity. Waves of influence move in all directions, potentially infinitely far across time and space, the original stone forgotten, or never known. Waves fizzle out without support, but they grow if whipped up by winds of repetition, and they grow when they inspire other creators, remixing additional ideas into new stones. Most waves become indiscernible on the tumultuous surface, but they were undeniably part of the force eventually crashing on a distant shore of a distant mind of a distant human. It's happening to me, now. People are repeating my [SLC](https://longform.asmartbear.com/slc/), sometimes [outright plagiarizing the original](https://www.linkedin.com/pulse/alternative-mvp-minimum-viable-product-simple-lovable-anant-kajale/). Some [consultants](https://web.archive.org/web/20240909063022/https://www.one80training.com/slc-vs-mvp-elevating-your-product-strategy/) and [agencies](https://basecode.ca/blog/slc-vs-mvp-whats-the-difference-and-which-is-best-for-your-software-project) present SLC as their own idea. And [another](https://www.hubtree.ventures/post/simple-lovable-complete-slc-products-a-comprehensive-overview) and [another](https://medium.com/@nimay/shifting-prototyping-mindsets-from-mvp-to-slc-eab5bc42b6b2) and [another](https://wecreate.digital/blog/mvp-vs-slc/) and [another](https://10clouds.com/blog/design/minimum-loveable-product-what-is-it/) and [another](https://x.com/s4nxbt/status/1918946145780568501) and [another](https://www.bluent.net/blog/mvp-vs-slc/) and [another](https://helio.app/product-discovery/minimum-viable-product-alternatives/slc/) and [another](https://impalaintech.com/blog/alternative-to-mvp-minimum-viable-product/) and [another](https://x.com/Kindfiree/status/1936039417912938755). Even [video](https://youtu.be/2F0uSW8j72I?si=klb0BWBnriOBkZZe). When I first discovered these, I was indignant, maybe even bitter, maybe even angry. Who wouldn't be? But these are the ripples on the lake. These are the winds of repetition with no reference to the originator. If the ripples are the legacy, then they can be met instead with gratitude and pride. ## Releasing legacy What does it mean for all of us, today? It means we should worry less about "content" for the sake of "eyeballs" and more about creating ideas that are worth repeating--ideas that are so important and true and compelling and repeatable that people *wish* to repeat them, even if our names aren't attached to the broadcast and our tweets aren't the ones being retweeted. We should worry less about the attention we bring to ourselves, and more about how we can positively influence others, how we can [help others become](https://longform.asmartbear.com/bad-advice/) better versions of who they already are. ![Smart Bear](https://public.asmartbear.com/books/anthology/chapters/legacy/bear-smart-simple.png) This is all easy for *me* to say. I have a modicum of fame from writing about 26 years of building companies. This article was broadcast to 65,000 newsletter [subscribers](https://longform.asmartbear.com/subscribe/). It's also easy for me to say, having made enough money through building those companies that I was never compelled to monetize content. So sure, *I* can say "it's not about the fame nor money." A rare luxury. Of course attribution is not without value. Artists should be paid for their work. Fame and money matters while we're alive, and these are not trivial things. Proper attribution is ethical. Plagiarism is theft. Still, for all of us, in addition to accumulating fame and money it's useful to ask: And what else? ## What stones will you drop? Which of your ideas deserve repetition? Which of others' ideas deserve repetition, with your own take, your own style, your own exposition? What will you do with your influence? What are the behaviors you wish to encourage in others? What are the lessons you wish humanity would heed? I've been unwittingly answering those questions on this site for 19 years, without labelling the ideas with my name. Hence not only [SLC](https://longform.asmartbear.com/slc/), but [Binstack](https://longform.asmartbear.com/maximized-decision/) and [Needs Stack](https://longform.asmartbear.com/needs-stack/) and [Talk/Walk](https://longform.asmartbear.com/talk-vs-walk/) and [Fairytale Strategic Planning](https://longform.asmartbear.com/strategic-planning/) and [Brittle Points](https://longform.asmartbear.com/brittle-points/) and [Pivot Points](https://longform.asmartbear.com/pivot-points/) bearing self-descriptive names, or crediting another in [Fermi Estimation](https://longform.asmartbear.com/roi-rubric/), or in cases of my [Roadmap for PMF](https://longform.asmartbear.com/product-market-fit-formula/) and [Love/Skill/Need](https://longform.asmartbear.com/fulfillment/) and [Love/Utility/Coercion](https://longform.asmartbear.com/willingness-to-pay/) and "[Is there a problem](https://longform.asmartbear.com/problem/)," and [Unpredictability](https://longform.asmartbear.com/predict-the-future/), remaining simply untitled. You must drop some stones, because the alternative is a nihilistic descent, realizing your life is a flicker between the 13,770,000,000 years of near-nothingness that came before you and the far longer expanse after you die, most of which will be a featureless soup of maximum-entropy minimum-energy photons. And even during this impossibly improbable blip, you're just a speck on a soggy marble that is a speck in a star system that is one of billions in a galaxy that is one of billions in a universe 880,000,000,000,000,000,000,000,000 meters wide. ![Hubble deep field](https://public.asmartbear.com/books/anthology/chapters/legacy/image-simple.png) And therefore we should live only for today, and only for ourselves, because to an extremely high degree of precision, we are nothing, and nothing matters. I can't argue with those numbers, but I don't have to make that conclusion. Because it _does_ matter. To be human is to defy the uncaring universe by caring deeply anyway--caring for our loved ones, caring about our creations, and caring about our legacy. This is perhaps the meaning of life: To discover who we are, to be driven by what we were meant to do, to create beloved and useful things, and to love and help others. --- My dad died 20 years ago while I was holding his hand. I had a vivid dream a few weeks after his death. He's looking directly into my eyes. His face looks like it did when I was growing up, not the way it did in those final moments. He's not speaking. Somehow I hear the message regardless. I don't mean a ghost is communicating, I mean my brain is processing something--something deeply important--just as thoughts intrude upon meditation. The dream intruded upon me, carrying wisdom that some part of my brain already understood. The wisdom was that although someone is dead, as long as you remember them, as long as their words or ideals are reflected in what you say, what you do, what you value, then that part of them is literally and physically living on, in you. And you to the next.

Profitable on day one!

Jason Cohen — Sun, 25 Aug 2024 00:00:00 +0000

![If by profits you mean an excess of revenues over outlays and expenses in a business enterprise over a given period of time, then yes, it doesn't look good.](https://public.asmartbear.com/books/anthology/chapters/ramen-profitable/d7790print-simple.png) --- > "My company is profitable, and has been from day one." > --every high-tech bootstrapped founder I know what you *really* mean. What you *really* mean is that the only charges on your Business Select PayPal MasterCard this month are: * AWS "medium" instance for the SaaS with one paying customer: $40 * Stripe fees for the one paying customer: $1.72 * AWS S3 storage to back up your loose "Best of Pat Benatar" mp3s: $0.043 And since your one paying customer is at the $49/mo tier, you're **profitable**! I know that's what you *mean*, but when you say "I'm profitable" it's a turnoff, because it's actually bullshit, and anyone with a modicum of experience knows it. Which means either you're coming off as a full-blown bullshit artiste or--more likely, since we're giving you the benefit of the doubt--you're coming off as ignorant. Neither is a good look. Let's be serious about what "profitable" means: * If your savings is going down every month, you're not profitable yet. * If you're making less than minimum wage, you're not profitable yet. * If you couldn't afford to pay someone else to do the job that you are doing, you're not profitable yet. * If someone bought your company, and hired people to do the work (even at below-market rates), and each month they'd have less money in the bank than the previous month, you're not profitable yet. * If you have to work another job to pay the bills, you're not profitable yet. ![OK, let's not get into profit shaming here.](https://public.asmartbear.com/books/anthology/chapters/ramen-profitable/d7444print-simple.png) ## Yet! The key word is "yet." You're not a [failure](https://longform.asmartbear.com/fail/). You're not doing anything wrong. It's just that businesses aren't profitable on day one. They're profitable later (if ever). The main error is ignoring the cost of yourself. Although arguably [your time is worth $1000/hr](https://longform.asmartbear.com/value-time/), let's just say you need to pay yourself "enough." Which means what? An easy definition is: You are _taking out of the business_ the same amount of money you were making at the job you quit. (Oh, you haven't quit yet, because you need the money?) The market has already decided your time and skills are worth at least that; if the startup replaces it, it's fair (in my book) to call it profitable. Indeed, the majority of small business owners make about the same money as they were making when they were an employee, without all the stress and extra hours: ![entrepreneurs make about the same money as employees](https://public.asmartbear.com/books/anthology/chapters/ramen-profitable/image-2-simple.png) And most small business owners make less than the average CEO salary: ![How much small business owners pay themselves](https://public.asmartbear.com/books/anthology/chapters/ramen-profitable/image-simple.png) At minimum, you need to be [ramen-profitable](https://www.paulgraham.com/ramenprofitable.html), i.e. enough for you to eat as cheaply as possible and not be malnourished, and also not be burning savings. You can dramatically change that income requirement through lifestyle and location; this is why it can be easier to start a company when you're young and unattached and childless, when you have few material needs and might like living in another country for a while. **The strongest definition of profitability** is to imagine that you've sold your business to a high-quality operator who is hiring people to do the work. You likely "wear a lot of hats," so some of those roles are part-time. You might be excellent at some--the new owner will have to hire great talent at market rates--and poor at others--the new owner can skimp on those. The company is profitable when it would also be profitable in that scenario. In short, when _it_ is profitable on its own power, not "it + a founder pouring their heart and soul into every waking hour to the limit of their endurance and ability, for free." ## Don't despair So now that I've perhaps unfairly ridiculed you, let's take a step back and recognize what's *really* going on, because it's wonderful and amazing and fantastic and exciting: You're building a business! Sure it's just begun, sure it might need a kick in the ass, sure it might be struggling, sure sure sure, so what? You and every other little new business. You and everyone else who doesn't explode out of the blocks. Almost no company explodes out of the blocks, including [all the successful ones in all industries](https://longform.asmartbear.com/product-market-fit/). This is exactly what you'd *expect* it would do, even if you're actually the next 37signals or Smart Bear or WP Engine. Indeed, that's exactly what my company WP Engine looked like for the first 9 months. And then [it was chaos for two years, like it always is](https://longform.asmartbear.com/chaos-at-start/). Same with my previous company Smart Bear--it took 2.5 years before I could even hire one employee, and even then it was 1/4 of the salary he deserved (and later ended up making). Eventually we, too, made millions of dollars a year--in profit!--but not for years. In other words, there's nothing strange or bad here. It's just that it's not "profitable from day one." Stop saying that. Dispense with the feather-fluffing and get to what *is*--the strengths you have, the challenges you want to overcome, the resources at your disposal. And then set your mind and goals on making that sucker profitable *for real!* ![And we all lived profitably after.](https://public.asmartbear.com/books/anthology/chapters/ramen-profitable/d6129print-simple.png)

Individual efficiency vs administrative efficiency

Jason Cohen — Sun, 11 Aug 2024 00:00:00 +0000

![Don't get me wrong, I'm no fan of the heat or the backbreaking toil. It's the corresponding paperwork that gets me.](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/d8967print-simple.png) Everyone has their own favorite note-taking app: Notion versus Google Docs versus Apple Notes versus OneNote versus Obsidian. Dropbox Paper shrieking "what about me?" into the void. I wrote this article in [Bear](https://bear.app/). (No relation.) ![Smart Bear](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/bear-smart-simple.png) From the individual's point of view, the optimal company policy is to allow everyone to use whatever app they like. It's both [autonomy and mastery](https://longform.asmartbear.com/fulfillment/). Everyone's comfortable and efficient. From the team's point of view, however, this is a bad policy. I don't want to learn how pseudo-database-table-thingies work in Notion, but Bear doesn't support sharing notes between people. Efficiency for the individual reduces efficiency for the team. Beyond the team, there are company-wide administrative concerns. Once we're sharing notes, who has access to those notes? And who decides who has access to which notes? What if company secrets are in those notes? What if customer [PII](https://en.wikipedia.org/wiki/Personal_data) are in those notes? Is note-authentication linked to the corporate identity system, so that when someone leaves the company they automatically no longer have access to the notes? Can the central IT team back up the notes? Can the central security team audit the notes? Since the notes are shared, they probably bounce through a server somewhere; is that secure? Administrative requirements have a negative effect on the efficiency of the team, and certainly on the individual. While you might be laughing because "this is why big companies are dumb and slow," and indeed [startups often win exactly because](https://longform.asmartbear.com/startup-beats-incumbent/) they don't have to be "dumb" and "slow" like this, it's also why the big company will add more ARR today than the small company will add in the next few months. (A result that is neither dumb nor slow; both the [benefits and drawbacks are due to scale](https://longform.asmartbear.com/scale/).) So, when should a policy optimize for the efficiency and happiness of the individual, and when should it optimize for the team, or the company? ## In search of efficiency Imagine a scenario in which 10 people each receive a meal. Each meal is unique, and each person has unique food preferences. ![bear eating a sandwich](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/bear-cooking-sandwich-2-simple.png) A maximally efficient way to *administer* the meals is to dole them out randomly. Nothing to manage or track. This is also *fair*--a desirable quality in policies--in that everyone is (mis)treated identically. Even so, this is obviously suboptimal for the recipients. Next, everyone looks around the room. Some person `P` sees another person `Q` holding a plate that `P` would prefer to have. Perhaps `Q` is thinking the same thing about `P`! In that case, it's wise for them to trade plates; both are happier. Or the scenario where person `P` would be happier if they traded with `Q`, and `Q` doesn't care either way; they also should trade. Trades won't happen if one person would be less happy; that person would simply refuse the transaction. If we allow these sorts of trades to proceed until no further trades are possible, the system reaches a state called "[Pareto-Optimal](https://en.wikipedia.org/wiki/Pareto_efficiency)." Formally, this is a state where any transaction would result in at least one party being worse off than they currently are. "Pareto-Efficiency" is not the only kind of efficiency, nor does it necessarily reach a "maximally efficient" state by some measures. Consider the goal of "maximizing total happiness," a kind of utilitarianism, and apply it to the following scenario, where persons `P` and `Q` rate their plates on a 1-10 scale, where 10 is the best: | Person | `P`'s Plate | `Q`'s Plate | |:----|:----|:----| | `P` | `1` | `10` | | `Q` | `5` | `9` | `P` would very much like to trade, but `Q` would not; this is Pareto-Optimal. But if they did trade, total happiness would increase, because currently total happiness is `1 + 9 = 10` whereas trading would result in `10 + 5 = 15`. So, should we *force* them to trade, pissing off `Q` for the greater good? Clearly there won't be a single answer to defining "efficiency" or "fair policy." Nevertheless, with this backdrop as our guide, and taking up practical considerations arising in real companies, there are many actionable things we can do to make policies more fair, and people more efficient, and even more happy. ## When to standardize Under what conditions do the benefits of standardization outweigh the penalties on individual efficiency? ![a business is a living organism that can have needs that contradict the needs of any individuals within it](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/image-simple.png) ### Maximize the individual; satisfy administration Individual autonomy leads to everything good: The proverbial ["win-win"](https://longform.asmartbear.com/authentic-is-dead/) of both happiness and efficiency. Therefore, we should maximize individual autonomy. Administrative requirements should take precedence only with reason, such as a current problem with harms we can easily identify, or because the law requires it, or because our values dictate it, or because important jobs (like those of the executive team or the security and IT teams) cannot be done without it. Use the [Satisficing vs Maximizing Framework](https://longform.asmartbear.com/maximizing/) to navigate this dynamic. ![I'm sorry, Jim, I'm afraid this is only a win win situation.](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/d4018print-simple.png) ### Minimize the scope of standardization Most developers agree that code-formatting should be standardized, at least within a single team. One implementation is to require everyone to use the same IDE, which in turn enforces the formatting rules. But that's over-scoping the solution, because many IDEs are capable of enforcing the same formatting rules. The solution is to mandate the *rules*, and allow developers to use any system that enforces those rules. Sure, some IDEs might make that easier than others, but if a developer really wants to use a different editor, and will abide by the rules (perhaps with an external tool and custom automation that the developer maintains), then individual efficiency has been preserved, while the team enjoys the benefits of standardization. ### Use standardization in one area to create individual autonomy in other areas It would easier for the IT department if everyone used the same laptop, with the same operation system, and also used the same smart phone and same tablet. But people have their own smart phones, and would prefer not to carry two. So, as we create the "note-taking policy" mentioned earlier, we could constraint ourselves to consider only those note-taking apps that work well across *all* of Windows, Mac, iOS, and Android. Perhaps we also add the requirement that the note-taking app must have a fantastic web-UI (so that any laptop with any operating system has a good experience), and that it have an at-least-4-star app for both iOS and Android devices. That will narrow the field of possible note-taking systems, but in doing so, we preserve the individual's choice of device. While we're at it, perhaps we have requirements for supporting the visually-impaired and for a wide variety of languages; this additional constraint again increases happiness and effectiveness for individuals. ### Standardize on outcomes, not on implementations Goal-setting and metrics-reporting are common examples of tension between the needs of administration and the needs of operators. The only way for the CEO to keep track of the business is to have a consistent summary of activities, metrics, and how departments and major initiatives are pacing to expectation. But Sales operates very differently from Product; Marketing operates differently from Support. Not just in the obvious ways like which metrics are tracked, but in how work is scheduled, how impact is quantified, and planning cycles. Product might plan tactics every two weeks and strategies annually, yet metrics and goals are reported monthly. Sales typically runs on a monthly cadence and therefore has no problem reporting and reacting monthly. Product doesn't want to ship new things in December; Sales doesn't want any meetings in the final few days of the month. Everyone uses different systems-of-record--Jira vs Zendesk vs HubSpot vs Segment vs Salesforce; there is no natural place for goals and metrics to live. It would definitely be incorrect to force everyone to use a single tool to manage all their work. Therefore, the right solution is to standardize on how goals and metrics are *reported upward*, but explicitly *not* standardize on how each department *operates*. (And to implement a [KPI philosophy](https://longform.asmartbear.com/product-metrics/) accordingly.) ![I am multi-tasking! I've done this report three or four times already!](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/d3501print-simple.png) ### Policies should describe explicit benefits for individuals, not just for administrators No one creates a policy with an explicit intent of causing pain; it happens by accident. If the policy dictates only "what must be done," and not "how it benefits everyone, in different ways," it is likely that the latter wasn't sufficiently considered while the policy was being made. Require policies to have sections that detail how this is beneficial for various parties; if any piece of that section is found wanting, that means our policy isn't good enough yet. ### Look for cases where local optimization creates problems that global optimization can solve Sometimes, optimizing of one component of a system harms overall system performance. This often happens in marketing funnels. One team is responsible for bringing more traffic to the site, so they optimize and succeed. But this new traffic turns out to be low-quality--perhaps that's why it was so easy to generate--so the "home page → purchase page" conversion rate plummets, harming another team's numbers, and making it harder for them to improve, because now they're wading through garbage traffic rather than improving engagement with quality traffic. This is a moment when global optimization should take precedence over local "efficiency." Another example is in assigning tasks, where [people tasked at 90% utilization leads to catastrophic failures](https://longform.asmartbear.com/utilization/), to say nothing of unhappiness and burn-out. ### Accept sub-optimization in areas that are not the primary bottleneck In the [Theory of Constraints](https://fortelabs.com/blog/theory-of-constraints-101-table-of-contents/), a system's throughput is limited by a single component (the "bottleneck", Figure 1), and therefore optimizing other components does not increase total throughput (and in fact [can ironically decrease total throughput](https://fortelabs.com/theory-of-constraints-107-identifying-the-constraint-29c7922a69d4)). Among the [many techniques](https://fortelabs.com/theory-of-constraints-106-the-five-focusing-steps-741f1b770bf1) for solving the bottleneck is that we should use people who are not part of the bottleneck to [unblock or delegate tasks](https://fortelabs.com/blog/theory-of-constraints-109-the-psychology-of-subordination/) from within the bottleneck, even if these new recruits are far less efficient at executing those tasks, or if the tasks are menial. This is definitionally sub-optimal from the point of view of all teams who aren't the bottleneck, but the result is higher throughput for the entire organization, and therefore it is the right choice. Encourage everyone to participate in diagnosing the problem as well as inventing the solution, so they realize they're working for the greater good, not working for an ignorant organization. ![In the Theory of Constraints, yellow activities should be subordinated to help with flow in the red activity.](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/flow-simple.png) ### Consider whether local inefficiency is temporary or permanent All change creates temporary inefficiency, as people and systems acclimate to the change. [People often dislike change](https://longform.asmartbear.com/change/). It's a [well-documented rule of design](https://uxdesign.cc/help-your-customer-navigate-change-1bb674f6ce23) that any time you change the UX of software, many existing customers will complain simply because it's different. When imposing standardization, we nearly always create temporary inefficiency; assuming the standardization is valuable, we should just accept this cost. But when the inefficiency is permanent, we need correspondingly higher conviction that the standardization is worthwhile. ### Document the cases where it's clearly better to (not) standardize Product-line strategy is shared across teams, so it should be centralized. Communication across the entire company is better as a single report than as five disjointed reports. Conversely, a team's inside jokes only work when they stay inside the team, and no one is harmed when someone wants to use a family photo as their laptop's desktop background. Writing these categories down keeps us honest--not allowing standardization to encroach where it has negative value, and also agreeing that administrative needs should be paramount in certain areas. ### Proactively encourage Pareto-Efficiency ![bears trading plates](https://public.asmartbear.com/books/anthology/chapters/tension-autonomy-admin/bears-plates-simple.png) In the "meals" example, we claimed that "random assignment" was efficient for management, but also that we could arrive at a better result if people are allowed to trade. Furthermore, trading does not create any problems for management. Therefore, not only should trading be *allowed*, it should be *encouraged*; everyone is better off, even management. Get creative about how people can self-organize within the constraints of the policy. For example: Trade time; some people help another team accomplish one of their goals faster now, and later the reverse happens, all without "management" getting involved. (Also "trading" isn't the only technique.) A fun real-world example was a motorcycle courier service. Bikers start at Central Dispatch, where they are given packets for delivery and addresses for pickup. They would do a round and return, a few times per day. It's a difficult optimization problem to minimize "total time on the road," e.g. not only to minimize the length of one person's route, but to distribute the packets and addresses intelligently. Frequently the bikers would discover that, for example, one was doing a drop-off just blocks away from where another was doing a pickup. So they instituted a new system: Every packet for delivery or address for pickup also came with two physical tokens. The way you get paid is by the number of tokens you returned at the end of the day. Every token is worth the same amount. A biker could just perform the assigned route, and make the same amount of money they usually make. But, they could also trade. In particular, a biker could say to another: "If you deliver this package for me, I'll give you one of the two tokens." The giver is happy because they still earn one token for doing nothing, and the taker is happy because--assuming it's only a small detour--they make more money per hour. The bikers self-organized far more efficient routes, all made more money per day than they were previously, and the company could handle more clients without hiring more bikers. Everyone wins through self-organized Pareto-Efficiency. ### Use global-optimization explicitly, and sparingly In the "meals" example, we saw that global-optimization can be worse for individuals. It's unclear whether that's "more fair" or "better." And yet, global-optimization *sounds* like the smartest thing to do, and indeed it often is. When it really is smartest, we should be explicit about why that is, what global effect we are maximizing, and why it's so worthwhile for the collective good. If you're going to give me a meal I don't want, at least tell me why the [higher purpose](https://longform.asmartbear.com/darwinian-company-growth/) makes it a worthy sacrifice. --- All these are variations on the key idea: Individual autonomy should be our paramount goal, and thus our default. But often local optimization does not lead to global optimization, and the latter is what we should all want for our organization. Since there are many legitimate times when administrative needs *should* supersede the individual, we should always be open to them, but be explicit, be thoughtful, justify the decision, and keep the individual in mind.

"Authentic" is dead. And so is "is dead."

Jason Cohen — Sun, 28 Jul 2024 00:00:00 +0000

**It's time to retire the following phrases.** They should no longer be used in any context except derisive mocking: ![higher calling](https://public.asmartbear.com/books/anthology/chapters/authentic-is-dead/gapingvoid-yak-yak-yak1-simple.png) * The Holy Grail of * Designed with you in mind * Putting customers first * All new * Win-win * Proven track record * [Fast and easy](https://longform.asmartbear.com/opposite-test/) * [The leading provider of](https://longform.asmartbear.com/the-leading-provider/) * [And more](https://longform.asmartbear.com/and-more/) * [For everyone](https://longform.asmartbear.com/icp-ideal-customer-persona/) * The go-to choice for ______ * Transforming the way you ______ * Designed with ❤️ in ______ * The future of ______ is here * Everything you think you know about ______ is wrong * ______ considered harmful * ______ is broken * ______ is dead * ______ [sucks](https://longform.asmartbear.com/is-it-ok-to-sucks/) * Saves you time, so you can get back to doing what you do best. Also eschew these words, as devoid of meaning as a yogi's mantra and as useless as a simile that doesn't contribute new information: ![welcome to NOBODY CARES, population 6 billion](https://public.asmartbear.com/books/anthology/chapters/authentic-is-dead/gapingvoid-welcome-nobody-cares-simple.png) * **Authentic** * Solution * Turnkey solution * Genuine * Powerful * Secure * Simple * Innovative * Insight * Revolutionary * Unsurpassed * Unparalleled * Cutting-edge * World-class * State-of-the-art * Game-changing * Ground-breaking * Value-add * [Disruptive](https://longform.asmartbear.com/disruptive/) **These words have been corrupted by those who claim to honor their meaning but do not act accordingly.** When a company claims to "put customers first" but then [uses "Level 1 support" as a shield](https://longform.asmartbear.com/hello-support/) to prevent customers from intruding on profits, we realize talk is cheap. When a company claims to have "secure" payments but then 100,000 credit card numbers are stolen, we realize you don't need a permit to claim that you're secure. When a company claims to be "innovative and [disruptive](https://longform.asmartbear.com/disruptive/)" but then pitches an idea you've heard ten times in the past month, it reminds us that [if you have to say it, it's probably not true](https://longform.asmartbear.com/outside-in/). When 78% of "About Us" web pages claim "the leading provider" of something, we are no longer impressed. Like a song over-played on the radio, like a restaurant over-hyped in the magazines, **repetition of even powerful, wonderful phrases will kill them.** Oh I know 21% of you stopped reading as soon as you saw that "authentic" made the list, and shot over to Twitter to unleash a scathing missive explaining how "authenticity" is the [prime mover](https://en.wikipedia.org/wiki/Cosmological_argument) of modern marketing, [honorable salesmanship](https://longform.asmartbear.com/bad-advice/), and meaningful relationships. I agree! In fact all these words and phrases *theoretically* carry meaning, but theory is for people who don't need to sell $2,600 more software by next Friday so they can make rent. And sure, it actually _is_ good to be "authentic." I respect the work of all those bloggers and Twitter-ers and lecturers and consultants who drove this word deep into our psyches. Indeed, it is a gift: bringing genuine authenticity and the give-first-sell-later behavior, in contrast with to traditionally sterile, aggressive, non-engaging, selfish world of sales and marketing. The more people honor this code of conduct, the better for us all. ![buzzword jargon hyperbole trite rhyming platitude looks good](https://public.asmartbear.com/books/anthology/chapters/authentic-is-dead/d7291print-simple.png) Nevertheless, it's time to retire words like "authentic." The misuse is to too widespread, the abuse too deep. ## What should you do instead? ### Be specific. Many of the dead words weren't especially illustrative to begin with. As far as I know, a "solution" just means "product and/or service," so the word isn't adding information. Instead, inspire me by being specific. * Instead of "easy" say "so straightforward, you won't need a manual." * Instead of "inexpensive" say "just a dollar a day." * Instead of "powerful" say "processes 6,253,427 requests daily." * Instead of "secure" say "blocks 96 million attacks daily." * Instead of "disruptive" say "72% of our customers say they'll never go back to a normal email client." * Instead of "beloved" show your NPS or CSAT or G2 rating or a massive wall of real, unsolicited testimonials, like tweets. [Here's more about being specific](https://longform.asmartbear.com/specificity/). ### Show, don't tell Some dead words are descriptive, but they don't paint a picture. "Powerful" sounds nice I suppose, but how does that change my life? *Showing* something in action is more evocative than *describing* it. * Instead of saying it's fast, show a speed test (especially against competitors). * Instead of saying it's easy, have a video demonstrating your tool solving someone's problem in 60 seconds flat. * Instead of saying you have eager, responsive, intelligent tech support, put a "chat now" bar on every page of your website. * Instead of saying that customers love you, show testimonials from 100 customers. (No one will read them all, but you can impress with sheer quantity.) * Instead of saying "we're innovative," show your change-log, impressing the viewer with your product velocity. * Instead of a bullet-list of benefits, quote actual customers describing your impact on their lives. * Instead of generic-sounding testimonials, reproduce unsolicited tweets that show genuine love and gratitude. * Instead of saying you [value your customers](https://longform.asmartbear.com/hello-support/), tell them to call you to test it out, and then answer the phone on the second ring. ### Name & Embrace My favorite way to start a sales pitch is to make fun of typical sales pitches. What I always said at Smart Bear: > I know you were hoping for a 22-slide PowerPoint deck with our mission statement and company history. I'm really sorry to disappoint! 'Cause I'm just going to start the demo and let you interrupt me with questions. And: > People claim that peer code review tools will do magic things like make your developers smarter or fix existing social problems with the team. Actually, if anything, code review can magnify latent social issues! However, I do believe our tool will save you time and aggravation in these 4 specific ways [...], so as we go through the demo, see if you agree. Because you're willing to say the quiet bit out loud, to say what others hide, especially when we all know the truth, you earn credibility. (And often some laughs.) Now folks are more open to your claims--even those that are well-worn. ### Own it completely You *can* still use an abused word if you totally, 100% own the concept. You can claim "legendary customer service" if you back that with first-ring, human phone service, online chat from your home page, quick-response Twitter monitoring, and 15-minute turn-around time on tech support emails even at 3am on a Sunday. Be sure to communicate all that too, because if you *lead* with the dead phrase I'll leave before you get the chance to prove it. > "Be the change you wish to see in the world." > --Gandhi When old ideas become cliché, that's an implicit call for new ideas. This time around, can you lead instead of follow? Of course this is unfairly difficult. Quick: Come up with a compelling new philosophy for human interaction and global communication, marketing, sales, and relationships! Yeah it's unreasonable, and not certainly required, but remember the best ideas often aren't reasonable (excuse the clichés) ground-breaking, innovative, out-of-the-box, Earth-shattering epiphanies. **Often great ideas are a synthesis of other ideas** with just a smidge of novel insight, or just **putting into words what others sense but cannot articulate**. This is the hardest and most time-consuming way to break out of the mundane, but also the most rewarding. And if you do come up with something, then your [who will love you for it](https://longform.asmartbear.com/customer-love/), and then [help you grow and thrive](https://longform.asmartbear.com/needs-stack/). [You're a little company, so act like one.](https://longform.asmartbear.com/little-company/) And enjoy the love.

How annual pre-pay creates an infinite marketing budget

Jason Cohen — Sun, 14 Jul 2024 00:00:00 +0000

![Something's clicking. I want you to find out what, and click the holy hell out of it.](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/d6026print-simple.png) Multiple founders have told me the ideas in this article were responsible for the financial success of their startup. They might be exaggerating out of kindness, but if it's even 10% as useful for you, it will have been worth your time. We'll explore how growth affects cash-flow, and conclude with several techniques that can transform the cash-flow of your business. ## The cost of a dollar of MRR What does it cost a SaaS company to add $1 of new monthly recurring revenue? Using the typical acronyms: `CAC` (**C**ost to **A**cquire a **C**ustomer) is the total cost to get one new paying customer--Marketing and Sales costs, including fully-loaded salaries.[^1] The simplest way to compute it is "total spend in a month" divided by "total new customers added during that month." `ARPC` (**A**verage **R**evenue **p**er **Customer**) is the average monthly-recurring revenue you get from a customer. The simplest way to compute it in aggregate is "total recurring-revenue in a month (`MRR`)" divided by "total number of paying customers during that month (`N`)". [^1]: In smaller companies, typically the founder and others are spending time on marketing and sales activities as well; include the fraction of their time, or the salary that you would have to pay someone else to do those same tasks. Also include commissions. Since it costs `CAC` dollars to get `ARPC` new dollars of recurring-revenue: The cost to create one more dollar of `MRR` = `CAC / ARPC` If you haven't done it before, computing this metric will be eye-opening. Let's posit a hypothetical company with a $10/mo consumer-targeted SaaS product, where they pay $1.60/click for Google Ads; that traffic converts at 5% to a free trial, and those trials convert at 40% to a paid customer. Their `CAC` is $80,[^2] and their `ARPC` is $10, therefore we would say "it costs them $8 to create $1 of MRR." [^2]: `$1.60 / 0.05 / 0.4 = $80` It's tempting to conclude that "It takes 8 months of customer revenue to 'pay us back' for the marketing and sales costs of getting that customer. That customer is unprofitable before then, and becomes more and more profitable after." (Figure 1) ![diagram of 8-month payback period](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/simple-simple.png) But, unfortunately, it's worse than that. ## Revenue we can spend ![bear accountant](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/bear-accountant-glow-simple.png) All companies have mandatory expenses associated with delivering the entire product to the customer. Not just the product itself, but everything else the customer expects, like being able to pay with a credit card or call tech support: * credit card processing fees * tech support[^3] * infrastructure (if SaaS) * professional services (if consulting) * bill of materials (if physical goods) * _Anything else which, if missing, the customer would say "You're not delivering the product I paid for."_ [^3]: Even if you're a solopreneur, doing support yourself, wanting to claim that therefore "support costs me nothing," the opposite is true: Your time is valuable; you could have used that time for anything else, such as marketing or building a new feature. How should you account for this? Use whatever it would have cost to hire someone else to do the service for you, and remember that low-wage people who aren't fluent in your language, can't provide the level of service you're currently providing! In finance we measure these costs relative to revenue: `GPM` (**G**ross **P**rofit **M**argin) is the percentage of revenue remaining, after subtracting these "expenses required to deliver the whole product and experience." Continuing our example, suppose credit card fees are 3% of revenue, infrastructure costs are 5%, and tech support is 25%. Then `GPM = 67%`, i.e. after fulfilling our promises to our customers, we're left with 67% of the money they gave us. ## Payback period (p) Now we're prepared to compute the _actual_ pay-back period: The amount of time needed to recover `CAC` expenses, which is the number of months of revenue *we can spend*: `p` = "payback period" = `CAC / (MRR ✕ GPM)` In our example, `p = 80 / (10 ✕ 0.67) =` 12 months. (Figure 2) ![diagram of 12-month payback period with GPM](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/gpm-simple.png) The cash-flow implications are harsh. Consider: You're bootstrapped. You scrape together $1000 from life savings or credit card debt. You spend it on ads. Happily, it works! With the numbers in our example, you now have $125 in MRR, but expenses are $42/mo, so it's going to be a whole year before you get that $1000 back. Meanwhile, you're just _out_ that cash. So... how do you get more customers? Another $1000... but how many times can you do that before you run out of savings? Now you can see why people raise money... And **the faster you grow the more cash it takes**, because no matter how much you spend, it still takes a year to get it back. Now you see why people raise money for companies that are designed to grow quickly, and why it [always takes more money than you think](https://longform.asmartbear.com/more-money/). ## A few optimizations Your mind immediately jumps to the question: How can I improve the metrics in this equation, so that my pay-back period is reduced, so I can grow more quickly with less cash? ### Reduce CAC Of course you'll [optimize advertising](https://longform.asmartbear.com/local-minimum/). However, everyone does this, and most advertising is an auction model, which means it's difficult to find spaces that simultaneously (1) get enough traffic to matter and (2) are low-cost. It's still worthwhile to optimize, or to try to find less-expensive channels, but this is incremental; it won't transform your business. In fact, `CAC` _increases_ as a company scales (because low-hanging fruit is already plucked) and _increases_ as a market matures (because of auctions from more and more competitors, who have more and more budget), as [I've documented](https://longform.asmartbear.com/exponential-growth/). Some people scream "Social media is free! SEO is free! Content marketing is free!" It's not free--you have to write, manage, jockey for attention, follow Google's whims, Twitter's whims, YouTube's whims, Instagram's whims, TikTok's whims, even though they don't tell you what the whims are, and the whims change, optimize site layout as well as content, post often "for the algorithm," and so on. These might indeed be great channels for you, but let's not pretend that they are "free," any more than you are "[profitable on day one](https://longform.asmartbear.com/ramen-profitable/)." ![I miss the old days when we could just tout things.](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/d7220print-simple.png) ### Increase MRR This is one of the main reasons why so many startup pundits (including myself) are always telling you to "raise your price." The difference between $39/mo and $19/mo might not matter to your customers,[^4] but can cut your payback period to 1/3 of what it currently is.[^5] [^4]: I know it sounds like doubling your price would dramatically reduce how many people sign up. However: (a) If sign-ups halve, you still have the same-sized business, except far more profit, and higher-quality customers, and (b) my experience and many others' is that signups don't halve; they might even go up. Examples below. [^5]: Cut into a third, not a half, because `MRR` doubles but the costs behind `GPM` do not, which means `GPM` also increases, so you get double-benefit. You're also probably not positioning the product properly. [Here's how you can 8x the price](https://longform.asmartbear.com/more-value-not-save-money/) through better positioning, or [how you can increase willingness-to-pay](https://longform.asmartbear.com/willingness-to-pay/), which then allows you to increase price and growth and get word-of-mouth (which means lower CAC!). You probably need more encouragement to summon the courage to try higher prices, so here's a bunch. ![raised prices and people were happy](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/image-2-simple.png) ![raised prices every month](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/image-3-simple.png) ![customer not caring that prices were raised](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/image-4-simple.png) ![unlocked growth](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/image-5-simple.png) ![raised prices and MRR skyrocketed](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/priceadjust-simple.png) ![One of the lessons from data from 4200 founders is that you should raise your price](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/image-simple.png) It doesn't always work, but typically, startups are under-charging. ### Increase GPM (by cutting costs) This is tempting, because it's easier to cut costs than to raise prices, and easier to automate some tech support tickets or infrastructure rather than finding a new advertising channel. Especially for Engineers, who constantly fall into the trap of [doing engineering stuff](https://longform.asmartbear.com/code-is-your-enemy/) rather than what [the business actually needs done](https://longform.asmartbear.com/put-down-the-compiler/). Of course this _is_ valuable work, and if it's easy to cut support-time in half with a few days of keyboard macros or better documentation or an AI first-line-of-defense, then do it. Just be warned that your natural tendency will be to file the rough edges off of costs--which isn't impactful--rather than work on pricing and marketing--which have the power to transform the trajectory of the company. Remember, you can only cut costs so much, but the potential upside of better marketing or pricing is uncapped. This is one of several reasons why better [marketing](https://longform.asmartbear.com/needs-stack/) and [pricing](https://longform.asmartbear.com/pricing-determines-your-business-model/) is [strategic](https://longform.asmartbear.com/great-strategy/), while cutting costs isn't. ## The transformation: Annual billing There's another thing we can try: Billing plans where the customer pays annually instead of monthly. ![bear with a calendar](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/bear-calendar-2-simple.png) How much should they pay? Typically you reward the customer for their up-front payment with a discount. In our example, the product is $10/mo; you might offer a plan at $100/year, exclaiming "get two months free!" on the pricing page. This is a great excuse to raise prices slightly: Offer the annual plan at 12 times the _current_ monthly price, then increase the month-to-month price by something like 20% (i.e. the "2 months free" amount). Existing customers can be grandfathered in, or ask them to switch to annual to preserve their monthly rate. What does this do to our payback period? In terms of cash: * Before: We paid $80 to get $10 of MRR, which is $120/year. * Now: We paid $80 and also got $120/year, **but we got the $120 today.** **This gives us an infinite marketing budget.** To see why: * We spend $80 on ads. But *not* in cash; it's on our credit card. * We get $120 in our bank account, _in cash_. We mentally set aside the `GPM` costs that we will incur, that this income will need to cover. Those costs are $40, which leaves us with $80 that we're free to spend. * _Later_, our credit card bill comes due, and finally the $80 ad cost leaves our bank account. * **Which means we never actually lost the $80, in terms of cash!**
We received the $80 from the customer _before_ we sent away the $80 for the ad, even after setting aside `GPM` costs. ![diagram of annual-plan payback period](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/annual-billing-simple.png) This completely transforms the growth potential of the business, without raising money. In real life, not all customers select annual plans. However, this is still transformative because: 1. Even if only a third of customers select an annual plan, it's still a huge impact to cash-flow. 2. You could raise prices on the monthly plans even further, which both (a) increases selection of the annual plan and (b) decreases the pay-back period of the monthly plan. 3. You could offer an annual plan only. (However an "even more expensive monthly plan" is typically a better way of [saying yes instead of no](https://longform.asmartbear.com/say-yes/), as it retains optionality for the customer and is very profitable for you.) ## Commonly-asked questions After espousing this policy for more than a decade, here are the common questions and conversations: #### I hear that annual-plan customers cancel less often. So, will encouraging annual plans also decrease my cancellation rate? No, but it is useful in cancellation analysis. The reason people on annual plans have a higher retention rate, is that they self-select into those plans. What type of person does that? A person who is already intending to stay. So, offering annual doesn't change your overall cancellation rate, but it does segregate customers in a useful way: Those more likely to cancel are now identifying themselves. Which means, you can contact them and learn more about why they feel that way, and possibly then do something to improve cancellations, which is likely one of the [most valuable things](https://longform.asmartbear.com/growth-asymptote/) you can do. You can also compare their behavior and responses with those on annual plans, to tease out what's actually different between the cohorts. ![It's not just a raise in my allowance, it's also a boost for consumer confidence!](https://public.asmartbear.com/books/anthology/chapters/annual-prepay/d3130print-simple.png) #### When someone on an annual plan cancels after, say, seven months, should I refund them the remainder of the term? You can pick anything along this scale of generosity: *Refund pro-rata.* If they cancel after seven months, then refund `$\frac{5}{12}$` of their original payment. This has the benefit of simplicity and promoting your reputation as being generous. *Refund as if monthly.* In retrospect, the customer is acting like they are on a monthly plan, so compute what they _would_ have paid in that case, and refund them the difference. For example, if the annual plan is $100/year, and the monthly plan is $10/mo, and they cancelled after 7 months, then they would have paid `$7 \times \$10 = \$70$`, therefore you refund them `$\$100 - \$70 = \$30$`, which is less than if you refunded 5 months of their annual price, i.e. `$\frac{5}{12} \times \$100 = \$42$`. This also means if they cancel very late in the year you don't refund them at all, as a monthly plan would have been even more expensive. *Credits.* Rather than refunding cash, provide credit on the customer's account. Perhaps they'll buy something else in future, or they want to switch to a less expensive plan, or switch to monthly billing. *No refunds.* We have all experienced this with contracted services like our phone bill or corporate services. This is of course the least-generous approach, so you have to decide whether this policy adheres to your [cultural values](https://longform.asmartbear.com/emptiness/), your sense of business ethics, and whether you want to employ this tool as a [type of coercive relationship](https://longform.asmartbear.com/willingness-to-pay/). #### What if the customer cancels before the payback period elapses? Astute readers will notice that the "payback period" model didn't include cancellations. Clearly, not all customers who sign up on day 1 will still be here on day 30, much less day 300 or day 3000. In particular, if they cancel before the payback period, that's a net-loss for the business. Don't we need to account for that somehow? Yes! Cancellations were omitted here for simplicity, but in practice you cannot ignore them. A simple way to account for them, is to first calculate your retention rate across the nominal payback period. For example, if payback is 12 months, what is your 12-month retention (`r`)? Excellent for Enterprise software is `r = 95%`; for SMB is `75%`; Shopify is just `50%`. Then, we declare that the customers who *stay*, must make up the costs for the customers who *leave*. Therefore if the nominal payback period is `p` (as defined earlier), we actually need to wait `p / r` to get paid back. This formula is not exactly right either, but it's a simple method that gets you to the right vicinity. #### What about all the other expenses at the company? CAC isn't the only cost. That's right, saying "infinite budget" is artistic license to make the point, which is that it's transformative for cash flow. --- I hope you can use annual plans to dramatically change your cash-flow! --- *Many thanks to [Fabio Caravita](https://twitter.com/fabcaravita) for the idea of providing credits instead of cash refunds.*

"It's a Balance" isn't always the answer

Jason Cohen — Sun, 23 Jun 2024 00:00:00 +0000

![schrodingers shop is both open and closed](https://public.asmartbear.com/books/anthology/chapters/conflicting-choices/d7513print-simple.png) Faced with two conflicting choices, what should you do? Ironically there are several answers, which are themselves conflicting: #### "It's a balance" After his enlightenment, the first teaching of the Buddha was to solve contradictions through "The Middle Way" (Majjhimāpaṭipadā). Aristotle instructs the same in _Nicomachean Ethics_, observing that one should be neither cowardly nor foolhardy; rather one should be courageous--facing and understanding risks, while also overcoming fears. Therefore, avoid extremes, and find the balance. #### "It's a choice" Only by making a clear and extreme choice can you enjoy the full benefits of that choice. Otherwise your energy and output are diluted into failure. If a product could either be inexpensive and target small businesses, or expensive and target large enterprises, the correct answer is not a middle-price targeting the mid-market. [Strategy requires making decisions](https://longform.asmartbear.com/great-strategy/), not compromise. #### "Why not both?" When faced with solving the global energy crisis, picking between affordable dirty energy and clean expensive energy, surely the answer is to invent energy sources that are both clean and affordable. Though it's an over-used example, Tesla's cars were unique because they were both all-electric and high-performance (and maximally safe)--factors which others assumed or even insisted were impossible to achieve simultaneously. Since there are multiple approaches, we need a framework for analyzing the decision, and deciding which type of resolution is appropriate. ## "It's a balance" **This is the correct approach when both extremes are bad.** In the Aristotelian example, the poles are both poor choices--we should neither be too scared to act, nor oblivious to risks and reality. That means we want to distance ourselves from both, not "take the best of both." Presumably there is something in-between that reduces the negatives of each, thereby achieving a solution that is superior to either extreme. You could visualize this as a curve you're trying to maximize: ![the middle way maximizes a compromise](https://public.asmartbear.com/books/anthology/chapters/conflicting-choices/middle-way-simple.png) "Work vs Rest" is common example. Athletes who overtrain don't achieve peak performance. Athletes with natural talent who don't train will never become great. Athletes therefore need a system that intelligently alternates training and rest. We're tempted to call this a "balance," but I dislike that word because it can imply "equal parts of each," which is rarely optimal. It is not only the _right_ amount of each, but also combined with a system that forms an integrated solution. So for an Olympic athlete it's not "equal training and rest," but a plan that results in peak performance on the critical day when they will contest the gold medal. The "right system" will vary by person, by goals, and by circumstance. "[Zero-sum games](https://longform.asmartbear.com/startup-beats-incumbent/)" are another class of puzzle that falls into this domain--where more of one thing necessarily means less of the other. Time and money are often like this; when you spend time or money on something, it means you're not spending it on something else. So if the question is whether a solo founder should be spending time on product or marketing--making stuff or selling stuff--the answer [has to be something like](https://longform.asmartbear.com/product-market-fit-formula/) "a balance," but that doesn't mean splitting time equally. It depends on what the company needs more right now: more new customers, or retaining existing customers. Or stated in the negative: Which is the biggest problem right now: that customers are rejecting the product, or that not enough potential customers are trying it? ## "It's a choice" **This is the correct approach when both extremes are rational, yet contradictory.** A example facing all B2B companies at their inception: Should you target the low end or the high end of the market?[^1] The world contains a thousand times more small companies than large, but small company budgets are also a thousand times smaller. When you're a company like WP Engine[^2]--a platform for "websites," needed by every company of every size--it is tempting to say "we'll serve everyone," as this maximizes the size of the potential market. We'll be simple enough for small companies, but with mature compliance and features for large companies. Tempting, but wrong. A new startup must [find its Carol--it's ICP--then focus](https://longform.asmartbear.com/icp-ideal-customer-persona/) positioning, marketing, brand, and most of all, product, on that one segment. The linked article explains why.[^3] [^1]: Of course it's typically more segmented than this, but let's be reductive for the sake of example. [^2]: I founded in 2010, now a unicorn with 1200 employees, profitable and growing, serving many market segments. [^3]: WP Engine started with SMBs served by small agencies. Only after we reached $10M ARR, and had anchor enterprise customers, and enough funding to invest, and invested simultaneously in marketing, sales, support, and infrastructure for larger websites, did we [decide to expand](https://longform.asmartbear.com/adjacency/) into larger customers. Hopefully it's obvious that you shouldn't pull an Aristotle, saying "We'll make a product [for the mid-market](https://longform.asmartbear.com/mid-market/)." While that may indeed be a good segment, you would make this decision by analyzing the best segment for your company, not because mid-sized companies are a "balance" between small and large. When the extremes are bad you seek a balance, but when the extremes are both rational choices, it is not a *balance* but rather a _choice_, and choose you must. Trying to find a "balance" means "failing to make a decision," which is at least suboptimal, and probably downright bad. [Great strategy requires making clear, firm decisions.](https://longform.asmartbear.com/great-strategy/) The diagram collapses to a choice: ![making a choice](https://public.asmartbear.com/books/anthology/chapters/conflicting-choices/choice-simple.png) This is often the case with strategic decisions. Consider the choice between building a business that is profitable regardless of its size (required for self-funded businesses) versus a business designed to grow as fast as possible regardless of profitability (required for "winning the market," or for significant investor returns, or for making an impact on a large number of people). Either is a rational goal, but each requires different processes, mindsets, activities, and choices. Advice from the internet often doesn't specify which goal it's designed for; [perhaps even the advice-giver doesn't know](https://longform.asmartbear.com/bad-advice/). Applying a mixture, or a "balance" of these ideas, reduces the chance of either outcome. Another example is targeting niche versus broad markets. Niche companies need to speak only to that niche; broad must appeal to a wide variety of people. Niche needs to charge more, because there's fewer potential customers and because more value can be delivered per customer; broad needs to be inexpensive, as most people and businesses have little money, especially outside of the West. Niche is delivered in one language; broad thrives with forty languages. This is also how to resolve the apparent paradox in all startup advice: For anyone who tells you "Successful companies do X," you can always find someone else saying "Success comes from avoiding X." Indeed, it's easy to find examples of both successes and failures, for each opposing admonishment! This is the "startup advice" equivalent of the old observation that "proverbs come in pairs." Example: "They who hesitate are lost" but also "Good things come to those who wait." Or "Many hands make light work" but also "Too many cooks spoil the broth." All are true--that's why we repeat the bromides--the solution is to pick the advice that best matches the context *you* are in, and the goals that *you* have. ![devil angel live a little](https://public.asmartbear.com/books/anthology/chapters/conflicting-choices/d8342print-simple.png) **Often people claim "it's a balance" to justify their refusal to make a decision.** Whenever you're tempted to claim The Middle Way, pause to make sure that is the correct approach to solving the contradiction. ## "It's a choice... to a limit." **This modification to "It's a choice" is the right approach when one choice is the answer, but the other choice cannot be completely ignored.** The traditional VC-backed company is trying to grow as fast as possible, and thus traditionally it spends more money than it makes. We would say: It resolves the choice between maximizing growth and maximizing profit, by maximizing growth. However, nowadays (2024) both private and public markets are rewarding companies only[^4] if they have at least some profit. Valuations might be several times higher for otherwise-equivalent companies with −5% versus +5% profit margins. Therefore, the current choice among these companies is to maximize growth, *but only if* profit doesn't dip below some threshold. [^4]: [Except for AI startups.](https://longform.asmartbear.com/ai-startups/) This does not mean it's a balance; it is not a middle way, not a compromise. The goal is still to maximize growth. It's that costs are a _constraint_. There are many examples where we have made a clear choice, but we're constrained by some limit. In this mode, the answer is "[Maximize A, while satisficing B](https://longform.asmartbear.com/maximizing/)." One is the goal, the other is the governor. We turn our attention to B when it's in trouble, otherwise we focus on A: ![The dark line follows our attention over time: Mostly on revenue, but jumps over to costs when they grow beyond our pre-agreed threshold.](https://public.asmartbear.com/books/anthology/chapters/conflicting-choices/note-may-3-2024-simple.png) This solves the puzzle of whether we should [sacrifice our health](https://longform.asmartbear.com/sacrifice-your-health-for-your-startup/), our social relationships, and our physical or mental health, for the success of our startups. The fact is it takes an inordinate amount of time and effort to get a company started.[^5] It does [involve sacrifice](https://longform.asmartbear.com/two-big-things/). However, there are cases where people sacrificed their physical health so much that they becoming seriously ill, or sacrificed their family so much that they earned a divorce, or sacrificed their mental health so much that they suffered a breakdown. In a few famous cases, even suicide. [^5]: The people who tell you otherwise have either not created the type of company you are creating, or they're far past that point, now with the luxury of "work/life balance," telling you that it was possible to live like that all along, despite they themselves not succeeding in that manner. The answer is not a "balance," i.e. not working too much. The answer is that you have to work as hard as possible without falling off the cliff. The edge can be hard to see, but the answer isn't to back off so far as to never approach it; the answer is to toe the line, and back off slightly when you feel your feet slipping. The best way to execute this, is to establish an objective measure of the thing that should be satisfied. That way, it's always clear to everyone whether it's currently satisfied (in which case we should spend no time or energy on it), or whether it's not (in which case we need to attack and fix it as quickly as possible). This is one of the critical [use-cases for KPIs](https://longform.asmartbear.com/product-metrics/) and is built into my method for [quarterly planning](https://longform.asmartbear.com/strategic-planning/). ## "Why not both?" **This is the correct approach to create novel, insightful, unique products and strategies.** Before Tesla, there were electric cars with low performance, and gas-guzzlers who could go from a full stop to exceeding the highway speed limit in three seconds. There were car models which optimized for one extreme ("it's a choice") and hybrids with improved gas mileage but still low performance ("it's a balance"). Telsa created a third option: "Why not both." Before you get too excited, note this required several things: 1. **Invention.** Something new had to be created. In this case, new technology (batteries and car design) and new distribution channels (selling directly instead of through dealers). 2. **Risk.** There was a high likelihood that it would fail. The world is littered with failed "why not both" startups, [whose post-mortems bemoan](https://longform.asmartbear.com/avoid-blundering/) the "lack of focus" that came from "trying to do too many things at once" instead of "doing one thing and doing it well." 3. **A set of additional decisions.** It wasn't enough to just invent a new battery; a series of other decisions had to be made: placing batteries in the floor to maximize handling and safety. Creating new manufacturing facilities. Spending hundreds of millions of dollars on recharging stations to address range anxiety. Targeting a market segment that valued novelty over reliability, valued performance over family transportation, and could afford an extra luxury car. Multi-national lobbying for federal donations both to Tesla and in consumer incentives. Cutting out the usual sales channel of dealerships, replacing it with cult-of-founder marketing and PR, so that prices weren't astronomical, but which meant potential customers couldn't even test-drive the car. Synthesizing a new solution, which allows us to select "both," but only if many other things are also true, is the most difficult thing to do, but also by far the most valuable. ![inventing both](https://public.asmartbear.com/books/anthology/chapters/conflicting-choices/both-simple.png) Because it requires invention, others aren't doing it. (Competing car manufacturers didn't copy the batteries, even after Tesla open-sourced their patents.) Because it requires risk, others are unlikely to follow even after you start. (It took more than a decade for other car manufacturers to even attempt to follow Tesla, and even now Tesla is unrivaled in performance and safety.) Because it requires a network of other self-reinforcing decisions, it creates a unique offering, which remains unique even if competitors copy a subset of your strategy. In short, it's a [moat](https://longform.asmartbear.com/moats/)--you are unique and unassailable, even by well-funded competitors. Often the press will focus on the "invention" aspect, and indeed that's the most interesting and press-worthy thing to talk about. But the real key is the set of "other things which also have to be true."[^6] These additional decisions are what convert pure invention and wishful thinking into a complete strategy. [^6]: A phrase made famous by [Roger L. Martin](https://www.rogerlmartin.com/), who suggests that great strategies come from taking the several most critical things that we need to achieve in order to win, and then adding to that: "What else would have to be true?" In particular, this means adding more constraints, more decisions, _even things which are undesirable for customers or for the company_, which together create an interlocking, self-reinforcing web. The undesirable outcomes are acceptable because there are always [customers who value the positive more than they despise the negative](https://longform.asmartbear.com/icp-ideal-customer-persona/), and thus you are uniquely the best product for them. So Zappos was able to offer free shipping and free returns in apparent conflict with also having low prices, by creating scale through fiercely loyal customers, which in turn was generated by having remarkably amazing customer service, which in turn was a result of a unique culture giving unprecedented autonomy to customer-facing people. One negative side effect was low profit margins (so the resulting sale to Amazon at $1B was at only 1x revenue). Or Southwest Airlines was able to offer frequent departures on planes that weren't completely full, yet with the lowest fares in the industry, due to an exceptionally low cost structure. They had low costs because they used only one type of plane, ran only a few, short routes, and earned extreme loyalty from employees through a nurturing culture. The negatives included only having a few routes, and short ones, no international, no food, and more. This is [more fully documented here](https://longform.asmartbear.com/leverage/); for decades they've remained true to their network of decisions (Figure 4), and have the highest profits of any airline in American history, and the only one to never have gone bankrupt. ![Michael Porter's "Activity Systems Map" for Southwest Airlines, from his 1996 article "What Is Strategy?" Harvard Business Review 74 (6): 61–78.](https://public.asmartbear.com/books/anthology/chapters/conflicting-choices/southwest-transparent-simple.png) This is a common refrain in strategic frameworks. For example, the central idea of [_Blue Ocean Strategy_](https://www.amazon.com/Blue-Ocean-Strategy-Expanded-Uncontested/dp/1625274491/ref=asc_df_1625274491/?tag=hyprod-20&linkCode=df0&hvadid=693465227026&hvpos=&hvnetw=g&hvrand=15655197287935843831&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9028297&hvtargid=pla-318700082110&psc=1&mcid=56178674db62348fb13db63425b6f6f4&gad_source=1&gclid=Cj0KCQjwltKxBhDMARIsAG8KnqUlPAscTCb9JhSJSICR6vc-0YuAIfc7NFMetccl9l0lX9XFgrjjx0YaAmNuEALw_wcB) is to create a new category of product through a combination of choices that are "contradictory" according to existing categories, while also solving for profitability. Seemingly impossible, certainly difficult, but nevertheless a powerful result when it is achieved. The courage to make all of these decisions together, to accept the unique advantages as well as the undesirable consequences, is what creates the best business strategies in the world, and is the complete way of saying "why not both." --- Don't be so quick to say "it's a balance." You might be avoiding a hard decision. Or better yet, turn an apparent conflict into a unique strategy that others cannot copy.

Disentangling the three languages: Customers, Product, Business

Jason Cohen — Sun, 26 May 2024 00:00:00 +0000

![today were going to start with league building exercises](https://public.asmartbear.com/books/anthology/chapters/three-languages/d7776print-simple.png) ## Using the right language for each context Punchline first: | | CUSTOMER | PRODUCT | BUSINESS | |----:|:----|:----|:----| | **Language** | *Industry-specific jargon in the first person*

"I can only use this if..."
"I'd get a raise if..."
"It would be cool if..."
"Everyone needs..." | *Gathering input, deciding, building, deploying*

Mission, ICP, positioning, features, releases, workflows, adoption, usage, deprecation, integration | *Financial success*

Growth, profit, negative net churn, scale, competition, intellectual property, margins, multiples, enterprise value | | **Who** | Named Customer Personas | PM, PgM, UX, Engineering | Executive team, Finance team, Board | | **Work** | creating, building, designing, troubleshooting, calculating, reporting, organizing | epics, stories, sprints, designs, research, planning, strategy, releases, launches, stakeholder alignment, customer feedback | financial forecasting, budget, hiring plans, project ROI, capital allocation, public company comps | | **Metrics** | activation, activity, abandonment, support tickets, time-to-first-success, WAU/MAU, value-creation, reviews | conversion rate, volume, feature-use, API calls/day, deploys/week, sprint velocity, bug backlog, latency, SLOs | MRR (new, cancelled, upgrade, downgrade), R40, NRR, ARPU, GPM, LTV, CAC, EV, IRR, Magic Number, variable costs, fixed costs | | **Translation** | **CUSTOMER → PRODUCT**
JTBDs and user-stories connect the work of the Customer to the work of Product | | **PRODUCT → BUSINESS**
Correct strategy + excellent execution → business success | ## One team, yet cross-purposes The CEO asks "Why isn't 'grow revenue' your main goal for the quarter?" She's not wrong; revenue is the reason we're all here. The Product Manager says "My goal is delivering value to the customer, as quickly as possible. Revenue reflects more than that--sales effectiveness, marketing effectiveness, and market swings from things like COVID or inflation or economic pull-back." He's not wrong: "Revenue" is a shared goal and might not be correlated with product development or even customer satisfaction.[^1] [^1]: My solution to this "who is responsible for what number" problem is in [this article about Product KPIs](https://longform.asmartbear.com/product-metrics/). The customer says "of course *everyone* needs [feature], so it's *crazy* that you don't have it." The Product Manager [tries to understand](https://longform.asmartbear.com/customer-development/) the underlying pain, the core "job that is being done." The customer feels that they've already explained what they want. Six months later, when the feature still isn't added, the customer thinks no one listened. The Product Manager knows that the feature wouldn't make sense to most other customers, but a different update later in the year will solve the use-case in question. The marketing department asks what features will be delivered by September 17th, because that's when the big global event is happening. The finance team wants projections for the next twelve months, not because they're filling in spreadsheets (although that too), but because projected revenue informs hiring plans in sales and support, and investment plans in marketing, and with a nine-month lag-time for both hiring and training, we [have to start that process today](https://longform.asmartbear.com/scale/). The product team has only planned out the next few sprints, because who knows what will happen later in the year; we're _agile!_ ## Three Languages The key to resolving these ([apparent, not real](https://longform.asmartbear.com/conflicting-choices/)) conflicts is to recognize that there are three distinct "languages" being spoken simultaneously. I mean "languages" literally--people using different words, even when they're talking about similar things, like the marketing calendar, the fiscal quarters, the sprint planning, the feature-prioritization, and the jobs-to-be-done-that-the-customers-are-doing. And beyond words: the concepts, the goals, the requirements, the specific challenges they face each day. Conflicts happen when one party applies their language to a party that is using a different language. It doesn't translate automatically. So it's up to us to create systems that not only translate, but that help achieve each others' goals. #### Language of the Customer This is what I'm trying to accomplish. This is what's preventing me from doing my job. This would [make me a hero in the eyes of my boss](https://longform.asmartbear.com/startups-emotionally-draining/) or client. This would [make me more money](https://longform.asmartbear.com/more-value-not-save-money/). This would increase my reputation. This fulfills a [higher-level need](https://longform.asmartbear.com/needs-stack/). This de-risks my project. This is what I will say in a case study. This is what I'm going to say on Twitter. This is what I have written in an online review. #### Language of the Product [Strategy: How we will win](https://longform.asmartbear.com/great-strategy/). Competitive analysis and [market analysis](https://longform.asmartbear.com/problem/). Long-term versus short-term competitive advantages. [Leveraging our strengths](https://longform.asmartbear.com/leverage/). Defining [the ideal customer](https://longform.asmartbear.com/icp-ideal-customer-persona/). Scrum and kanban. Sprints and frequent delivery. Epics and stories and cards and tickets. Tasks and bugs and features. Delivering "customer value" (whatever that means). Retros. Sequencing. Reducing costs. Feature-usage and abandonment. #### Language of the Business Growth. Profitable / sustainable / maximized growth. Profitability. Sales and marketing efficiency. Product ROI. Predictable results. Annual and [quarterly planning](https://longform.asmartbear.com/strategic-planning/). Hiring plan. Budget-to-actual. Ratios that increase the stock price relative to current revenue. ## Translation Of course, everyone is "correct," in the sense that all of this is critical for the success of the business, and they are using appropriate language for their corner of the universe. Therefore, we need translations to bridge the languages. And often in a high-tech company, it's [Product's job](https://longform.asmartbear.com/great-product-manager/) to do that. ![jargon buzzword lingo](https://public.asmartbear.com/books/anthology/chapters/three-languages/d9207print-simple.png) ### Translating from "Customer" to "Product" The bridge between customer language and product language, in modern terms, is the JTBD (Job To Be Done), further broken down into the "story." Customers can completely describe their own pain or requests. They often have ideas for features but lack the context and expertise to [integrate their desires](https://longform.asmartbear.com/jit-backlogs/) with the constraints of development, in capacity and against architectural evolution and other customers' desires. Product work requires definition--requirements, requests, and enough of a description that everyone can decide what work might result in the desired outcome. The high-level translation of the customer's life into product language is the JTBD, in which you project their world onto internal language of your company. For example, in WP Engine's case,[^2] customers who are worried about "hackers," or scared that getting hacked will hit the news or will cause expensive damage (their language), would be translated into JTBD concepts like: [^2]: Top ten public website platform, as measured by the percent of the highest-traffic ten million domains. * **Context:** Marketers are scared of security breaches that (a) hurt their brand, (b) cost them time and money. * **Inciting Event:** After getting hacked once, marketers become highly motivated to move their website to a secure platform, even at increased cost. * **Job-to-be-done**: My website is fast, scalable, and secure. While that characterizes the customer's life in company- and product-centric terms, it's not yet a list of things to actually _do_, which is another role of product. Here we use the language of work. A common tool is the "user story," which often comes in forms such as: * **As a** [who]**, I want** [feature] **so that I** [benefit] **because** [why]**.** Applying to the security sample, we might have: * **As a** [non-technical marketer]**, I want** [a report about how many nefarious attacks were thwarted in the past week] **so that I** [can show my boss] **because** [it proves I'm proactive and intelligent about security; if we get still hacked, my job is still secure]**.** Now we can do the work. (And we can write the sales and marketing copy; this is exactly the bones needed for a sales person to explain both what the product does, and why it matters, from the point of view of the customer.) ### Translating from "Product" to "Business" The bridge between product language and business language, shows how the activities of product advance the strategy and goals of the business. Often the most critical thing the business wants to know is "how much will we grow?" Of course much of this comes from Product: * Are we [positioned intelligently](https://longform.asmartbear.com/more-value-not-save-money/) and [competitively](https://longform.asmartbear.com/needs-stack/)? * Are we [targeting the right market segment](https://longform.asmartbear.com/icp-ideal-customer-persona/)? * Is our product [paying off the promise](https://longform.asmartbear.com/talk-vs-walk/) we made on the website? * Are we [building the right features](https://longform.asmartbear.com/customer-development/)? * Do we have the [right pricing](https://longform.asmartbear.com/pricing-determines-your-business-model/)? * Are customers [delighted by our product](https://longform.asmartbear.com/willingness-to-pay/), so much that they stay forever, growth with us, and advocate for us with word-of-mouth and great reviews? * Are we [expanding into adjacent segments and products](https://longform.asmartbear.com/adjacency/), at the right time? But much doesn't: * Are our marketing campaigns effective? * Is our sales pitch and sales organization effective? * Is our white-glove on-boarding team being effective? * Is our support team increasing retention? * Is our account management team building high quality relationships? * Is our corporate development team building high quality partnerships? * Are we expanding into new geographies? * Are we budgeted properly across the company? Therefore, it's Product's job to clearly articulate how they are contributing, without having full responsibility for the total impact on the business's goals. A good way to do this is with [this metrics framework](https://longform.asmartbear.com/product-metrics/), in which everyone's KPIs are represented as being important, but with context and areas of responsibility and accountability. ![Product KPI Framework](https://public.asmartbear.com/books/anthology/chapters/three-languages/product-kpi-framework-simple.png) --- The first step in resolving these apparent conflicts is to recognize that everyone is doing what they think is best, operating from their own area of excellence. The second step is to translate. Using the right language for the right things creates clarity and insight and helps us work better together.

Adjacency Matrix: How to expand after PMF

Jason Cohen — Sun, 05 May 2024 00:00:00 +0000

![room for new device between mini tablet and mega smartphone](https://public.asmartbear.com/books/anthology/chapters/adjacency/d6598print-simple.png) ## Expansion Our everyday craft of incremental product evolution is obvious and natural. We hear from customers in emotionally overcharged outbursts on Twitter, or public reviews encoding their ire or gratitude, or filtered through the problem-solving of tech support, or teasing us with an if-you-build-it-we-will-come sale that hasn't yet (and may never) close. We then convert what we thought we heard into features and bug-fixes, prioritized by impact and growth ideally, but more likely by pride and intuition; sometimes we get lucky and these are the same thing. This works, after [Product/Market Fit](https://longform.asmartbear.com/product-market-fit/). That is, once everything is fundamentally working, and the job is "don't break what's working." Then we should incrementally add [utility and delight](https://longform.asmartbear.com/willingness-to-pay/) while not disrupting the money-making flywheel. Before PMF, it's not working yet, so [the job](https://longform.asmartbear.com/product-market-fit-formula/) is to identify [what can work](https://longform.asmartbear.com/problem/), and [for whom](https://longform.asmartbear.com/icp-ideal-customer-persona/), not to make slight adjustments to something that's not working. But then we come to another post-PMF decision: **When and how to expand the scope of the business.** Not a *pivot*--that's for companies that are staring death in the face, that must strike out in a new direction for a chance at survival. Rather, an *expansion*--to keep what's working, but sprout new shoots in new directions. Adhering to the [rules of great strategy](https://longform.asmartbear.com/great-strategy/) by [leveraging existing assets](https://longform.asmartbear.com/leverage/) to attack a valuable new opportunity with asymmetric upside. (Add your favorite buzzwords; those were mine.) When is the right moment to venture into new territory, rather than building obvious, incremental things with little risk? #### Bottlenecked growth If existing marketing and sales channels are at their limit--when spending more time or money doesn't yield more output, or at least, not cost-efficiently. In that case, an expansion to a new channel, or new geography, or new market segment, accelerates growth. #### Target market saturation If you've already won 5% or more of your perfect market. Some people use acronyms[^1] to name this concept; I mean of the total number of people who are your [ICP (Ideal Customer Profile)](https://longform.asmartbear.com/icp-ideal-customer-persona/)--oh goodie, another acronym--the absolutely-perfect-in-every-way customer whom you are targeting with all your marketing and product features. 5% is not saturation, but if you got to 5%, [you can presumably get to 10% with similar methods](https://longform.asmartbear.com/lindy-effect/). Since you're well on your way to that already, you can afford to turn your attention to segments that are similar enough to be relevant, but different enough that they require effort to serve well. #### Diversification Adding new marketing channels, new target markets, new geographies, new pricing options should not only add growth, but make the company more robust to market and economic disruptions. At WP Engine we experienced this after expanding upmarket. It wasn't just "growth"--addressing a new segment meant our value proposition became different. Small businesses saw us as "**expensive**, but the best," whereas large companies saw us as "**low cost**, yet still enterprise-grade." This diversification of positioning resulted in robust growth during the COVID crisis, where smaller companies were going out of business (i.e. "expensive" is now bad), but larger companies were looking for ways to save money (i.e. "low cost" is now good). When one segment has difficulty, the other grows; the net effect is stability. At scale, this is nirvana. #### When you can afford to invest A company throwing off more than a million dollars a year in profit can afford to try something new; failure in a new investment is not fatal. Expansions are both costly and risky; extra money covers both liabilities. Extra money could come in the form of outside investment or profit draw-downs, but either way it's a bet, it's an investment, and the [typical investment rules](https://longform.asmartbear.com/investment/) apply. [^1]: TAM (Total Addressable Market), or SAM (Serviceable Addressable Market); there's more. If one of these conditions is met, we come to the crux of this article: **How do you decide in which direction to expand?** ## Adjacency The key idea is "adjacency," meaning "close by." The difference between incremental change and expansion is that expansion is "somewhere else," but the difference between expansion and something too far afield is "adjacency." You don't want to go so far afield that you're not leveraging existing assets, and thus it is too risky, and too costly, such that even if the opportunity is large and tangible, it's still a bad idea _for you_. A great strategy--that doesn't align with your strengths--is a bad strategy _for you_. It's not hard to think of ways to expand the business. You could enter a new geography. You could target a different ICP, different niche, different vertical, different use-cases. You could go up- or down-market in the size of business you sell to or in the customer's [Needs Stack](https://longform.asmartbear.com/needs-stack/). You could add a freemium tier, add a higher pricing tier, add a lifetime plan, add a new product line. You could expand from iOS-only to add Android, from web-only to mobile-app, from integrating with one third-party to ten. You could venture away from the reputational investment of SEO and social channels into the analytical world of paid marketing, or venture from the pay-for-today world of advertising to the invest-in-the-future world of SEO and social. If you want to be more systematic, you can use something like the Lean Canvas to ideate: ![Ash Maurya's version of the popular business model "canvas."](https://public.asmartbear.com/books/anthology/chapters/adjacency/image-simple.png) Fill each box according to the existing business, then brainstorm[^2] ways that you could expand or improve on each box. [^2]: Here are [some fresh and fun ways to brainstorm](https://longform.asmartbear.com/extreme-questions/) these sorts of ideas. LLMs can be helpful--not to select ideas, but to generate possibilities. Calculating the potential upside of these ideas will of course be specific to the idea and the company. In this article, I give my general framework for evaluating the cost and risks of executing those ideas, answering the question: **How adjacent is this idea** to the current business? ## The Adjacency Matrix Start with a table of the major functional areas of your business. It will be similar to the following, but tune it for yourself. For example, a company leveraging product-led growth may have no Sales department, but might have a heavy design culture and thus would add a row for Design. | Functional Area | Types of Activities & Responsibilities | |:----|:----| | Marketing | getting attention; brand personality & positioning; acquisition channels | | Sales | processing leads; managing the sales process; pitch & competitive materials; domain expertise; sales training | | Service | expertise; technical knowledge; domain expertise; product training | | Product | understanding the customer; mix of data and intuition; roadmaps; [everything else](https://longform.asmartbear.com/great-product-manager/) | | Engineering | platforms; architecture; major libraries & frameworks; tech debt; infrastructure; specialized skills and knowledge | | Business Model | Pricing and packaging; unit economics; profitability; budgeting | Now evaluate the "adjacency" of your proposal by determining **how much each of these activities must change** to support the new initiative. Your only choices are: * ✅ **Trivial**--Almost no changes required. "Training" is an email or one Zoom session. Adjusting sales and marketing material means adding a few bullets. * ⚠️ **Adjustment**--Change-management required, but manageable within current processes, norms, and organizational structure, similar to a "large new feature release." Requires training. New sales slides. A new web page for the website. A non-trivial change to pricing and packaging. New [SMEs](https://en.wikipedia.org/wiki/Subject-matter_expert) in Support. * ❌ **Overhaul**--Major change needed: hiring for new skills or for capacity, significant retraining that might require new specializations, structural process or management changes, especially if the org chart is changing. Often you're not entirely sure of the full extent of the changes, i.e. it is sufficiently complex that we can't identify all the challenges and risks that await us. Longtime readers will recognize this as another instance of my Fermi Estimation[^3] hobbyhorse, in which we intentionally limit choices to avoid arguing over details and predictions that we [aren't qualified to make anyway](https://longform.asmartbear.com/predict-the-future/). Sorting into one of these three buckets should be relatively easy, without extensive analysis. [^3]: Described completely in how to use [Fermi estimation for ROI-type decisions](https://longform.asmartbear.com/roi-rubric/), including both objective dimensions like "time" and subjective dimensions like "what makes for a compelling product," and echoed in pieces on [probabilities](https://longform.asmartbear.com/probability-words/), [evaluating markets](https://longform.asmartbear.com/problem/), and [making big decisions](https://longform.asmartbear.com/maximized-decision/), among others. As an example, I'll take our company WP Engine in 2013 when our [ICP](https://longform.asmartbear.com/icp-ideal-customer-persona/) was "small to mid-size company's home page and small to mid-size blogs/media, often built by freelancers and small agencies." We'll evaluate two ideas: (a) expand to support marketing campaigns run by Enterprise-sized companies, and (b) expand to host the main websites of Enterprise-sized companies: | Functional Area | Enterprise
Campaigns | Enterprise
Full Site | |:----|:----|:----| | Marketing | ⚠️ **Adjustment**
(new target audience, and message of "campaign" instead of "home page") | ❌ **Overhaul**
(completely new competitors, new marketing channels, establish brand from scratch) | | Sales | ❌ **Overhaul**
(drastically new sales processes and cycle times) | ❌ **Overhaul** | | Service | ✅ **Trivial** | ❌ **Overhaul**
(e.g. new people and specializations like account management and white-glove onboarding) | | Product | ✅ **Trivial**
(campaigns don't require new features) | ⚠️ **Adjustment**
(new compliance requirements, but few new features) | | Engineering | ✅ **Trivial** | ✅ **Trivial**
(enterprise websites get similar amounts and types of traffic as popular websites belonging to small companies) | | Business Model | ✅ **Trivial**
(existing plans are sufficient) | ⚠️ **Adjustment**
(new plans, but same business model) | In this case, the conclusion might seem obvious--of course it's easier to serve a small use-case in a new segment ("Enterprise") than it is to compete in a major use-case in a new segment. What might not have been obvious is how dramatically different it is to "sell to the Enterprise." Often startups claim this as their growth path, even when they're at only $500k in ARR. This is definitely the wrong strategy at that moment; this exercise makes it clear, yet companies often conclude the opposite. They should instead be considering simple use-cases at larger companies, or they should be ignoring the complexity of large companies so they can continue winning where they are already strong. Furthermore, it shows how much investment is needed if you insist on investing in Enterprise. Sometimes that _is_ the right strategy. After all, calculating "how adjacent" is about *evaluating* the decision, not *dictating* it. If, for example, a company is at $60M ARR with steady growth in absolute dollars but slowing as a percentage of revenue, spending $10M to add an Enterprise-focused business model could be a great growth strategy. In particular, when more than one area requires a "full overhaul," that's a deal-breaker if this is supposed to be an incremental, sustaining innovation. If multiple areas require a full overhaul, this is only acceptable if (a) you are willing to make a huge investment and (b) you're willing to take a large risk that it will not pay off, and this only makes sense if (c) the potential upside is enormous. This reiterates the [first rule of investments](https://longform.asmartbear.com/investment/). ![wonder what mother a simple dairy cow would have made this](https://public.asmartbear.com/books/anthology/chapters/adjacency/d7852print-simple.png) At some point, the idea is so non-adjacent that it's definitionally a bad strategy to attempt it, regardless of upside. Strategy is supposed to leverage existing assets; don't select something that doesn't do that. ## Selecting the best option It is tempting to use a value-divided-by-cost analysis to decide which idea to select, where "value" is some estimate of upside, and "cost" is some formulaic summary of the Adjacency Matrix, or possibly an actual dollar or time estimate. If you are so tempted, I recommend [this ROI system](https://longform.asmartbear.com/roi-rubric/), which will force you to be rough in both variables, ideally bringing the best ideas to the top of the list with a minimum of debate. However, whenever you are [making an investment](https://longform.asmartbear.com/investment/)--doing something with substantial cost, time, risk, and only hopeful upside--I recommend solving first for maximal impact, and only secondarily for cost. The reasons are given in that article specifically in the context of investments, and also in my [work-prioritization system that extends the Rocks, Pebbles, Sand analogy](https://longform.asmartbear.com/rocks-pebbles-sand/). You might want to use [Binstack](https://longform.asmartbear.com/maximized-decision/) to identify the items of highest value, only then looking to cost and risk to break ties. In this context, the Adjacency Matrix is useful for ruling out ideas that are clearly too far afield, or identifying those which are particularly low-risk. **The Adjacency Matrix outlines your cost and risk analysis**, identifying the areas of the business that must be considered. For changes that you already understand, you can describe what needs to be done, what the budget is, and what risks remain. For complex changes, especially the dreaded "unknown unknowns" likely to arise from Overhauls, you'll need to include more: the mitigations you will put in place, objective measures or milestones to catch problems as early as possible, how you will attack the high-risk, high-uncertainty areas first, and specialized hires who have seen this movie before. Finally, **the Adjacency Matrix is useful in communicating the decision** to the whole company--something leaders perennially fail to adequately appreciate and value. It's vital that the decision is simple to explain and justify, so everyone feels that it's natural, intelligent, and clear. The matrix can help form the narrative: > We've all seen the organic pull from larger, enterprise-sized customers. There's obviously opportunity there, but what is the right way for us to approach it, starting from where we are today? > > We considered several options, such as ______ and ______. We decided upon ______ because while it will require [department] to [do something complex and risky], we realized it would be easy for everyone else because [why it's trivial in other areas]. So, this was the least-risky, highest-chance-for-success way for us to approach this new market, add a new growth area, and learn, and see where it goes from there. > > Exciting! And let's remember to give [department] our support and grace as they transform themselves to support this new strategic effort. --- Congratulations on [hitting Product/Market Fit](https://longform.asmartbear.com/product-market-fit/), starting to scale, and now having the "good problem to have" of where to expand next. Hopefully this will help you make the right decision.

It's a torturous chaos until it isn't

Jason Cohen — Sun, 21 Apr 2024 00:00:00 +0000

When you hear "explosive startup growth," a few darlings come to mind. Facebook, Slack, now OpenAI (Figure 1). ![Years from $1M to $100M for various companies](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/openai-simple.png) I wonder... what did they all look like prior to reaching $1M ARR, i.e. prior to "year 1" on that chart? HubSpot is a good example, because all their data is public. They are a proper juggernaut of growth; as of the beginning of 2024, at a whopping $2B ARR, they're still growing quickly: ![Hubspot revenue 2018-2023](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/image-3-simple.png) Shifting eight years earlier, the curve looks similar, just with smaller absolute numbers: ![HubSpot revenue 2010-2020](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/image-7-simple.png) Shifting earlier still, it still looks similar, but now we've arrived at the crux of our investigation: The beginning: ![HubSpot revenue 2007-2011](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/image-simple.png) Wait, how long did it take them to get their first 500 customers? Two years? Yes. In fact, after ending 2006 with just 3 (three!) customers, they ended the next year [with only 48](https://thegrowthplaybook.substack.com/p/if-youre-a-saas-entrepreneur-you). That's less than one per week. For a year. See, here's HubSpot co-founder Dharmesh Shah standing in front of that data: ![HubSpot's founder Dharmesh Shah showing their slow linear growth rate for three years, then fast for five.](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/image-8-simple.png) That does not sound like a juggernaut of growth. In fact, that sounds downright bad. Like, maybe you should shut the company down because it's "obviously not working." Or at least, there should be an [existential struggle over whether it is wise to continue](https://longform.asmartbear.com/perseverance/). What was going on during those first years? What it was _not_: an obviously bright future with a clear vision validated by a torrent of customers with open wallets. So, what _was_ it? A barely-controlled chaos, awake at 2:43am worrying how to make payroll or whether the numbers will look good enough by next spring to raise another round? Unsure which product features to double-down on and which to kill, worried the wrong choice would tank the entire company? Staying bright and cheery on the outside for the press, customers, investors, and employees while that one horsehair tenuously suspending Damocles' Sword is looking awfully thin and worn? Of course it was. It's always like that. Then again, the story is the same when the company "runs off a cliff without leaving skid-marks," never [reaching Product/Market Fit](https://longform.asmartbear.com/product-market-fit/); possibly never having extracted a single dime from a single person. The one that really *should* have shut down. The same struggle, same uncertainty, same near-impossibility, same mess. So how do you tell the difference between the tortuous chaos that leads to unimaginable success and that which leads nowhere at all? I'm not sure you can. I mean, [there are some questions you can ask yourself](https://longform.asmartbear.com/perseverance/). But: Objectively, what's the difference between Basecamp v1.0 and any number of knock-offs? All of them "the tool we wish we had, [so we built it ourselves](https://longform.asmartbear.com/scratched-my-own-itch/)," all of them simple, all of them usable by relatively non-technical people, all of them web-based, all of them missing half the features that any one customer wished it had. Objectively, what's the difference between StackOverflow and the twenty copycat Q&A websites and thousands of forums? Why was Quora still able to stand out? Could you measure this difference? Could you plot it on a chart? One thing you know for sure--what *doesn't* explain it is a "feature comparison chart." Or a [SWOT](https://longform.asmartbear.com/swot/). Or financial metrics. Or social media. Or whether they raised money. Or whether they went to Stanford. Or whether the market was already there. Or whether the competition was already there. Or anything else. Even with my [roadmap for Product/Market Fit](https://longform.asmartbear.com/product-market-fit-formula/) in hand, there are exceptions to everything. I say in that article that there needs to be founder-fit, but the founders of AirBnB weren't experienced in hotels or hospitality and the founders of Uber weren't experienced in car services. And that's just one assertion among dozens from that one article; there are exceptions to every "rule." Take [Peldi](https://balsamiq.com/company/news/personal-whats-your-story-why-start-balsamiq/), founder of [Balsamiq Mockups](https://www.balsamiq.com/). He blogged a bit, but unlike StackOverflow he didn't have a head-start with an online presence, and English is his second language. He lives in Italy, not surrounded by an American startup culture. Prior to entrepreneurship he was a lifer at Adobe, so that didn't prepare him for bootstrapping a company from scratch. His growth chart in year #1: ![Balsamiq Markups revenue, 2008](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/balsamiq-cashflow-simple.png) How do you think Peldi felt during those first six months? Do you think the anguish subsided quickly? Or ever? Do you think Peldi was still sure his ideas and execution were good in June? Or even afterward--this was one-time revenue, not recurring, so it could stop at any time. Or more recently, [Nathan Barry](https://twitter.com/nathanbarry) with [Convertkit](https://convertkit.com/). How did it feel in January 2015, more than two years in and almost nothing to show for it? (Figure 7) ![ConvertKit's early MRR growth](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/image-6-simple.png) Or more recently still, [Tim Bennetto](https://twitter.com/Timb03) with [Pallyy](https://pallyy.com/), with two years of making less than $3k/mo in MRR, and then it changed: ![Pallyy's four-year MRR](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/image-4-simple.png) #### And it's not just startups. ![Zen and the Art of Motorcycle Maintenance book cover](https://public.asmartbear.com/books/anthology/chapters/chaos-at-start/image-2-simple.png) Robert Pirsig's book _Zen and the Art of Motorcycle Maintenance_ was rejected by 121 publishers. How do you keep going after "just" 100 rejections? After 20, even? Then it was published by William Morrow & Company in 1974 and stayed on best-seller lists for decades, with total sales of over 5 million copies. For comparison, Dr. Seuss's _Green Eggs and Ham_ has sold 8 million copies. Speaking of Dr. Seuss, he was rejected by 27 publishers before _Green Eggs_ was published. Stephen King's _Carrie_ was rejected 30 times before Doubleday took a chance on it. J.K. Rowling was rejected 12 times. "I was the biggest failure I knew" she said of herself during that time, before she went on to sell 450 million books and be worth over $1B (and became famous for things other than the world of wizards). > "I've missed more than 9000 shots in my career. I've lost almost 300 games. 26 times, I've been trusted to take the game-winning shot and missed. I've failed over and over and over again in my life. And that is why I succeed." > --Michael Jordan Of course, your struggles might indeed be in vain. The vast majority of authors never succeed. Nor startups. Nor [new product lines](https://longform.asmartbear.com/adjacency/) inside established companies. Most of the time, it doesn't work; that's the brutal truth. But this I do know: The fact that you're in over your head, that you [almost cannot will yourself to continue](https://longform.asmartbear.com/startups-emotionally-draining/), that you're completely in the dark, that you're [working yourself to an early grave](https://longform.asmartbear.com/sacrifice-your-health-for-your-startup/), that you seem to [slide two steps back for every one forward](https://longform.asmartbear.com/perseverance/), that [nothing's ever good enough](https://longform.asmartbear.com/be-yourself/), that your friends and family can't understand why you're turning yourself inside out with no apparent progress, that you're supposed to be [enjoying the journey](https://longform.asmartbear.com/success-guaranteed/) but you're not, that you yourself [doubt whether you're even capable of this](https://longform.asmartbear.com/impostor-syndrome/)... These things mean you haven't yet succeeded, but neither do they prove that you're failing. It's always like this, until it isn't.

Avoid blundering: 80% of a winning strategy

Jason Cohen — Sun, 31 Mar 2024 00:00:00 +0000

![pooh way pas oh bother](https://public.asmartbear.com/books/anthology/chapters/avoid-blundering/d8781print-simple.png) You have been told: 1. Establish a vision. 2. Set goals against the vision. 3. Create actions that achieve the goals. 4. Measure progress against the goals. I've told you that myself. It's [how you should plan work](https://longform.asmartbear.com/strategic-planning/). But... what if success is just as much about what _not_ to do, as what *to* do? What if intentional goals aren't the only way to succeed? What if 80% of winning comes from: Not blundering. ## Not blundering your way to victory Chess Grandmaster and chess-world-famous teacher / coach Ben Finegold has a simple explanation for who wins amateur chess games: > What most people say is: "That guy is better than that guy, so that guy won." > > But that's *not* why that guy won. Normally the game is very close, and then someone blunders and now it's over. > > If you look at it with a computer, it will say "no one's winning" and then "white's winning" and then "black's winning" and then "no one's winning again" and then someone blunders and they lose. > > --Ben Finegold, [YouTube](https://youtu.be/EDgRR7SGf0M?si=aVoR9dcmmvd6zgbZ&t=282) _(with light editing)_ Not strategy, not memorizing opening lines, not practicing your end-game technique, not studying the Great Games of History, not drilling with puzzles to get better at tactics, ... just *blundering*. Yes, _good_ chess players need to do all that stuff, but we scrubs just need to "not blunder." Is this true for me? I'm a decidedly mediocre chess player, despite countless hours of videos, puzzles, courses, and playing. Are the outcomes of my games dictated by blunders, like Ben says? Fortunately, I have perfect data. I have hundreds of games with people who are of similar strength.[^1] Chess.com creates an analysis of every game, including the number of blunders, mistakes, and inaccuracies[^2] made by each player (Figure 1). [^1]: As evidenced by an even win/loss record and a similar rating. [^2]: The definition of these terms is not important here; suffice to say they're in decreasing order of "how terrible that move was," and the analysis is consistent and sensible across all games. ![I won, thanks to four blunders by my opponent. We both suck at chess. As Ben says: The truth hurts.](https://public.asmartbear.com/books/anthology/chapters/avoid-blundering/image-2-simple.png) I checked my last twenty decisive games. I won exactly 50%, which is further evidence that the players were of equal skill, and that the analysis includes both wins and losses. Theoretically, the best estimator of "who will win" is whichever player has a higher Elo rating.[^3] In these games, the player with the higher Elo won 55% of the time--barely more predictive than a coin flip. This makes sense, because the players were close in Elo,[^4] which means the players are of equivalent strength, so each ought to win half the time. [^3]: [Elo rating](https://en.wikipedia.org/wiki/Elo_rating_system) is an objective rating system used by all chess players for the past fifty years. It shifts after every game, calculated by which player was higher rated, and by how much, and the result of the game: win, loss, or draw. So, a higher-rated player who beats a much lower-rated player results in a minuscule change in Elo, because that was the expected result, whereas a much lower-rated player who just draws a higher-rated player will gain significant Elo. [^4]: Games had an average difference of 74 Elo points out of around 1100. In my games, the player who committed more blunders lost 86% of the time. Ben is right! However, in 40% of the games both players had an equal number of blunders. So I also included "mistakes"--the next-worst kind of error. I computed a simple "error score" that includes mistakes while giving blunders more weight: `[number of mistakes] +`
`2 ✕ [number of blunders]` Now almost all the games received a decisive prediction, and the player with the higher error score lost 81% of the time. To win at chess, blunder less. This is true across many sports. In [a wonderful article](https://fs.blog/avoiding-stupidity/), Shane Parrish recounts Simon Ramo's analysis of amateur[^5] tennis games, in which he found that 80% of the points were *lost* rather than *won*. Meaning, 80% of the points were awarded to player P because player Q made an error such as hitting the ball out of bounds, or into the net, or double-faulting. Only 20% of the points were "won," such as a powerful return that landed out of reach of the other player. [^5]: This observation applies only to amateur tennis. In professional tennis it's just the opposite: 80% of the rallies are _won_ rather than _lost_, as unforced errors are infrequent. This is true in chess as well, as high-level players don't blunder, and thus it really is that litany of other skills that results in high standings. Ramo concludes the same thing that I concluded about chess, even invoking the word "blunder": > ... if you choose to win at tennis--as opposed to having a good time--the strategy for winning is to avoid mistakes. The way to avoid mistakes is to be conservative and keep the ball in play, letting the other player have plenty of room in which to blunder his way to defeat, because, being an amateur, they will play a losing game and not know it. Could the same rule be true of other complex systems, like startups? My amateur chess games were 80% determined by blundering, and Ramo's amateur tennis points were 80% determined by blundering. Could "not blundering" be 80% of winning? ## Blundering in startups It's common to say "startups die because they ran out of money" or "startups die because the founders gave up." But that's like saying someone died because their heart stopped, without mentioning that they were battling lung cancer. Doctors call "heart stopped" the "proximate cause"--the last thing that happened--as distinguished from the underlying cause. We need to identify the cancer, not just say "the founder stopped." Otherwise, the advice for how-not-to-blunder would be "never stop." But ["never stop" is not helpful, and indeed not correct](https://longform.asmartbear.com/perseverance/). But it's not [turtles all the way down](https://en.wikipedia.org/wiki/Turtles_all_the_way_down). We mustn't dig too deep, as tempting as "[5 Why's](https://en.wikipedia.org/wiki/Five_whys)" might be. Maybe they got cancer because they smoked a pack of cigarettes every day, so "smoking" is the cause. Maybe they started smoking to fit in with a crowd, but "wanting to fit in with the crowd" was not the cause of death, and it would be incorrect to conclude that "fitting in with the crowd is unhealthy." (Though sloughing off that activity might help you [avoid unhappiness](https://longform.asmartbear.com/fulfillment/).) So we need to identify the blunders which aren't merely proximate, but aren't so distant that they are irrelevant. There are many sources that claim to finally explain why startups fail. I've provided a raw list at the end of this article. I've deduplicated and rearranged those into the lists below, and added a heavy dose of my own opinions. ### Proximate causes First, let's dispense with the proximate causes, as these are not real reasons, and we should neither repeat nor analyze them: 1. **Ran out of cash.**
_(A consequence of failure, not a cause. Unless you over-spent with no plausible way to recover it with revenue.)_ 2. **Pivot gone bad.**
_(the reason you pivoted is because it was already failing)_ 3. **Founder stopped.**
_(As opposed to what?)_ 4. **Didn't find [Product Market Fit (PMF)](https://longform.asmartbear.com/product-market-fit/).**
*(a restatement of "it didn't work")* ### Bad luck Perhaps there was nothing you could have done to predict or prevent the failure. That makes it no less real, but perhaps there's nothing to learn from it, or to do differently next time: 1. **Sudden, dramatic shift in the economy.**
_(If COVID bankrupted the movie theater, there's no "strategy" they should have already had in place to prevent it.)_ 2. **Surprise co-founder break-up.**
_(Some of these are preventable--see below--but if someone falls ill, or breaks a promise, or bails, or commits fraud, it's unclear what should have been done differently.)_ 3. **Known Brittle Point**
[Brittle Points](https://longform.asmartbear.com/brittle-points/) are single-points-of-failure in the business. All new businesses have them; it takes time and investment to address them. This is not a blunder, but a known risk. Sometimes, "known risks" get triggered. ### What _doesn't_ kill startups Equally interesting are things that appear on these lists, but I don't agree that it's a blunder. 1. **Bad idea.**
_(We're repeatedly told that all [great ideas start out looking like bad ideas](https://longform.asmartbear.com/chaos-at-start/),[^6] so "having an apparently-bad idea" cannot be a blunder by itself. It's not the initial idea, it's in [finding the market and in the execution](https://longform.asmartbear.com/product-market-fit-formula/), iterating the idea into a working business.)_ 2. **Can't make the product.**
_(While this can happen, typically the founders know how to build things; indeed, building the product while ignoring all other aspects of building the business, is often the problem. [AI is an exception](https://longform.asmartbear.com/ai-startups/); most AI products don't work well enough to succeed.)_ 3. **The number of co-founders.**
_(It's easy to find examples of successful startups with one or multiple founders, and equally easy to find failures. It's easy to find failures specifically due to having just one founder, or due to or having multiple. Even the great Paul Graham says [in a single essay](https://www.paulgraham.com/startupmistakes.html) that a major cause of startup failure is "Single Founder" and later "Fights Between Founders." I personally believe that 3 founders is a warning flag and ≥4 founders is a bright red flag, but it's common for some of those founders to bleed off anyway (which is also why it's a red flag). It's more important that the quantity of founders matches the style and personality of the founders.)_ 4. **Location.**
_(This is still a valid reason for startups that intend to scale and become Unicorns, because those startups will need to hire many people, in all departments, with experience in those environments, quickly, and that's much harder to do in a location (including "distributed") where the talent doesn't already exist. This is also true of raising money. However this is becoming less true every year, and it's irrelevant if the startup doesn't have that goal.)_ [^6]: Originally attributed to Steven Spielberg. [Attributed to Paul Graham](https://paulgraham.com/swan.html) in the context of startups. I've also [given many examples](https://longform.asmartbear.com/predict-the-future/) along with how to build a strategy around it. ## Preventable blunders Self-inflicted blunders are preventable, or at least one can dramatically reduce the risk of occurrence. So, if you don't, that's on you. #### Didn't talk to customers (and listen) If you didn't talk to customers before you started, and especially after you started, that's a blunder. If you talked to them and weren't honest with yourself about what they were saying, that's your faulty analysis. You should have used the [Iterative Hypothesis Method](https://longform.asmartbear.com/customer-development/) or some other reputable framework. #### No market need / Bad market If the market was small and stagnant or shrinking, you already knew that. If there weren't enough people who needed the product, you could have known that. If they don't agree they have the problem, or that it's worth money to solve, you could have known that. If they don't have the budget or don't accept the price, you could have known that. If they're happy with what they have, you could have known that. The checklist of [things that make for a good market](https://longform.asmartbear.com/problem/) is well-known; you don't have an excuse for not finding out before spending six months writing code. > "There's just one mistake that kills startups: not making something users want. If you make something users want, you'll probably be fine, whatever else you do or don't do." > --[Paul Graham](https://www.paulgraham.com/startupmistakes.html) #### Did not select a target audience, and clearly communicate the value proposition to them Speaking to everyone means speaking to no one. Speaking to everyone is a blunder; it's laziness or fear of deciding who this product is for. This determines the language on your home page, in your advertisements, in your sales calls, inside your product. Especially now, with AI, there's no excuse for not distilling what is special and vital about you, into language that your target audience can understand. You must [sell to Carol, your ICP](https://longform.asmartbear.com/icp-ideal-customer-persona/), which also means knowing exactly who Carol is. #### Too many things had to go right All startups have risks, and have potentially fatally large gaps with a high risk of not being solved. But some have too many gaps--too many things to go right simultaneously. If you have nearly all the requisite skills, if nearly all the objections customers have are addressed, if the market is healthy, if there are many possible distribution channels, if there are many possible niches, if more and more money is being spent in the market, then you should take a risk on the remaining gaps. But if all of those things are uncertain or are gaps that you know you'll need some luck to overcome, then the probability that _all_ of those things will break your way is near-zero, and you already knew that when you started. Your path [should consist of "or's," not "and's."](https://longform.asmartbear.com/capturing-luck/), so you can satisfy the [Startup Drake Equation](https://longform.asmartbear.com/startup-drake-equation/). ![serendipity is up fluke doing well concerned about dumb luck](https://public.asmartbear.com/books/anthology/chapters/avoid-blundering/d7231print-simple.png) #### Founders / investors broke up This can be bad luck, but you do pick your co-founders, and either you've known them for a while (in which case you made a poor selection) or you didn't (in which case you made a rash decision). One reason why "break up" kills startups is that the departing founder left holding a huge amount of stock; the reason for _that_ is there wasn't a vesting schedule for all founders. That is a blunder. In any case, the team did it to itself--it wasn't a competitor, or the economy, or the customers. #### Had no differentiation in the market This blunder is obvious from day one, when you had no idea why you were special, why the product is special, why someone should buy from you instead of someone else, why you're the right person to build this company, why you will succeed in a space where many others have failed before. There are so many [kinds of leverage](https://longform.asmartbear.com/leverage/) you could have used, and you chose none of them. > "Tolstoy opens Anna Karenina by observing: 'All happy families are alike; each unhappy family is unhappy in its own way.' Business is the opposite. All happy companies are different: each one earns a monopoly by solving a unique problem. All failed companies are the same: they failed to escape competition." > Peter Thiel, [Zero to One](https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296) #### Refused to seek the truth / refused to see the truth / refused to learn We all have to force ourselves to see the truth, because the truth hurts. The truth is that our ideas weren't right, our insight isn't shared by customers, our awesome design is confusing, our potential customers love that competitor we've been calling "dumb," that thing we say is "broken" is not in fact broken, the pain we insist our customers are experiencing, they're not experiencing. [Did you even ask?](https://longform.asmartbear.com/customer-development/) If you asked, [were you listening](https://longform.asmartbear.com/good-startup-ideas/)? If you haven't made major changes to your strategy and product and positioning, then you're blundering. Seek the truth, then [face the truth](https://longform.asmartbear.com/failure-to-face-the-truth/). #### Launching too early / Launching too late It's funny that both of these are causes of failure. It's a preventable blunder to launch with a [crappy MVP instead of a simple but lovable v1](https://longform.asmartbear.com/slc/). And because builders love only the building, they build for six months (or two years) without customers. Shipping crap, and never shipping, are both blunders. (P.S. Also, don't "launch." No one cares except your friends, and you're going to have to reliably and consistently get customers for the next three years anyway, so just work on that.) ![no end of november bad for me](https://public.asmartbear.com/books/anthology/chapters/avoid-blundering/d4206print-simple.png) #### Premature scaling Before Product/Market Fit, the job is to discover what works. What customers [will actually pay for](https://longform.asmartbear.com/willingness-to-pay/) (and keep paying for a year later), what is the [perfect, target customer](https://longform.asmartbear.com/icp-ideal-customer-persona/) and [what do they want](https://longform.asmartbear.com/needs-stack/), how do you reach them through marketing and close them through sales (whether self-sold or human-sold), what you should charge and how. Many founders either don't know [what Product/Market Fit looks like](https://longform.asmartbear.com/product-market-fit/), or don't want to know, because they want to declare "I have it!" because it feels good to say and garners hearty congratulations and maybe a touch of envy on Twitter. But scaling is about doubling-down on what works--doing it more, faster, better, higher quality, more efficiently. Spending that time and money before you know what works, means wasting all that time and money. Then you run out of both. A blunder possibly of ignorance, but typically of putting ego before the truth. #### Switch to sell to the Enterprise before $20M ARR If you're targeting the Enterprise segment from day one, and you're already built for the sales, enablement, onboarding, account management, legal, security, and product requirements, then this doesn't apply to you. However, few startups begin there, exactly because of those heavy requirements. The common pattern is to have some success with the small or mid-market, then observe that "Salesforce bought some seats!" and decide to "go upmarket" because "we're seeing signal that they need it." There are always individual teams inside large companies who will buy your stuff--that's good! But that's different from targeting "the enterprise." You probably don't even know what you don't know--a position of weakness that screams "bad strategy." This is an easy blunder to avoid; take the orders from the teams, stick with [the leverage you have](https://longform.asmartbear.com/productive-meeting-activities/), and don't confuse a team using a tool on the side with what it takes to make multi-million-dollar top-down sales. #### Unworkable business model / cannot be profitable Unit economics can be measured right out of the gate. You already have a rough idea of the costs of production, and you know you'll have to spend money and time on marketing and sales. [Mismatching price to the business model](https://longform.asmartbear.com/pricing-determines-your-business-model/) by an order of magnitude is preventable. Many companies have no sensible business model even at scale. They say "we'll figure that out later," but that's just admitting you're blundering and hoping your future self will figure out how to stop blundering. Sometimes you will; if you don't, whose fault was that? ![tell us what you know want big one russian nesting doll](https://public.asmartbear.com/books/anthology/chapters/avoid-blundering/d8673print-simple.png) #### Writing code instead of winning customers After you have a working product in market, why aren't you getting more customers? The builder says: Because I need more features. Or: Because the product has too many bugs. Both are incorrect; there are customers who need only the current features, but you're not working on getting their attention with marketing, piquing their interest with the website, and winning their business with sales. And *potential* customers certainly aren't ignoring you because your product has a bug--they don't even know that yet. Coders like to code; everything else is hard, and not fun, and unclear how to execute, and costs money, and it's so comfy to just sink back into the comfort of Visual Studio and marvel at the prescience of Copilot and tweak a CSS class. So people don't find you, and don't buy, and the company fails, because you blundered marketing and sales. You have to [put down the compiler and talk to customers](https://longform.asmartbear.com/put-down-the-compiler/). #### Expanding the target market before winning the target market / moving too quickly to the second product I'm not here to call people out, so I won't actually quote the many times I've seen someone on Twitter declaring that now that they've reached $2,700 in MRR, they are expanding into a new market segment. This simple blunder belies a lack of focus, a lack of conviction in the strategy, and lack of understanding that it is ten times easier to win another customer in [your existing target market](https://longform.asmartbear.com/icp-ideal-customer-persona/) than it is to expand. This blunder dilutes your attention, marketing dollars, positioning, messaging, and product sharpness, and is easy to avoid. [There is a time for expansion](https://longform.asmartbear.com/adjacency/): Later. #### Lack of passion / endurance It is possible that there was passion at the start, but it evaporated, or there was an honest intention of spending the next ten years [working on this mission](https://longform.asmartbear.com/mission-vision/), but you broke down. But that excuse is only valid if, on day one, you honestly took stock of what your passions were, what strengths you were going to [leverage](https://longform.asmartbear.com/leverage/), understanding [who you are](https://longform.asmartbear.com/fulfillment/), had a mission you deeply cared about (besides "make money" or "be able to say you're a founder"), read about the [emotional whiplash](https://longform.asmartbear.com/startups-emotionally-draining/) that startups always entail, and made the [trade-offs against other important things](https://longform.asmartbear.com/two-big-things/) in your life. If you didn't do those things, it's a blunder, not self-discovery. (If it really was self-discovery, you are forgiven, and more importantly, you should forgive yourself.) ![heart arrows not for everyone side effects may include love](https://public.asmartbear.com/books/anthology/chapters/avoid-blundering/d7163print-simple.png) In most cases, I've linked to specific ways to avoid each blunder. Which leads to the final point: **There is a way to build startups with lower risk**, in large part by avoiding those blunders, which I've summarized in my [Roadmap for Product/Market Fit](https://longform.asmartbear.com/product-market-fit-formula/). Is this a fail-safe path? Of course not. Even in chess. The rest of the game does matter. Still, when you can reduce so much risk by avoiding avoidable things, especially with frameworks that detail what "not blundering" looks like, why wouldn't you do that? 80% winning is not blundering. ## Addendum: External sources of "why startups fail" #### [CB Insights](https://www.cbinsights.com/research/report/startup-failure-reasons-top/): From "111 interviews" (Figure 2) ![Top reasons startups fail from CB Insights](https://public.asmartbear.com/books/anthology/chapters/avoid-blundering/image-simple.png) #### [Paul Graham](https://www.paulgraham.com/startupmistakes.html): The 18 mistakes that kill startups 1. Single founder (as opposed to having a co-founder) 2. Fights Between Founders 3. Bad location 4. Marginal niche 5. Derivative idea 6. Obstinacy 7. Hiring Bad Programmers 8. Choosing the Wrong Platform 9. Slowness in Launching 10. Launching Too Early 11. Having No Specific User in Mind 12. Raising Too Little Money 13. Raising Too Much Money 14. Poor Investor Management 15. Spending Too Much 16. Sacrificing Users to (Supposed) Profit 17. Not Wanting to Get Your Hands Dirty 18. A Half-Hearted Effort #### [Tom Eisenmann](https://hbr.org/2021/05/why-start-ups-fail): Why startups fail, from hundreds of interviews and surveys 1. Good Idea, Bad Bedfellows (not just founders, but investors, key executives and employees) 2. False Starts (not researching enough before starting or MVP--customers, competitors, market) 3. Maintaining Balance (managing psychology and maintaining a healthy determination) #### [Steve Blank](https://steveblank.com/2012/05/14/9-deadliest-start-up-sins/): 9 Deadliest Startup Sins _(You can see Lean Startup screaming out of these: Answers are outside the building, not in your head, now seek it and iterate.)_ 1. Assuming you know what the customer wants 2. The "I know what features to build" flaw 3. Focusing on the launch date 4. Emphasizing execution instead of testing, learning, and iteration 5. Writing a business plan that doesn't allow for trial and error 6. Confusing traditional job titles with a startup's needs 7. Executing on a sales and marketing plan 8. Prematurely scaling your company based on a presumption of success 9. Management by crisis, which leads to a death spiral #### [John Osher](https://www.entrepreneur.com/business-news/business-top-17-startup-mistakes-you-can39t-afford-to/78474): 17 common mistakes from consumer products _(Many are variants of "scaled too quickly" or "spent money ahead of need.")_ 1. Failing to spend enough time researching the business idea to see if it's viable. 2. Miscalculating market size, timing, ease of entry and potential market share. 3. Underestimating financial requirements and timing. 4. Over-projecting sales volume and timing. 5. Making cost projections that are too low. 6. Hiring too many people and spending too much on offices and facilities 7. Lacking a contingency plan for a shortfall in expectations. 8. Bringing in unnecessary partners. 9. Hiring for convenience rather than skill requirements. 10. Neglecting to manage the entire company as a whole. 11. Accepting that it's "not possible" too easily rather than finding a way. 12. Focusing too much on sales volume and company size rather than profit. 13. Seeking confirmation of your actions rather than seeking the truth. 14. Lacking simplicity in your vision. 15. Lacking clarity of your long-term aim and business purpose. 16. Lacking focus and identity. 17. Lacking an exit strategy. #### [Andrew Montalenti](https://thenextweb.com/news/why-startups-die): (founder of Parse.ly) Common startup mistakes 1. Marriage trouble 2. No bootstrapping plan 3. Startup as a career move 4. Refusal to change the original idea 5. Preemptive scaling 6. Growing too fast 7. Scared of code #### [Forbes](https://www.forbes.com/sites/ericwagner/2013/09/12/five-reasons-8-out-of-10-businesses-fail/?sh=5d9302cd6978) 1. Not in touch with customers 2. No differentiation in the market 3. Failure to clearly communicate value propositions 4. Leadership dysfunction, especially founder strife 5. Can't find profitable business model

Stubborn Visionaries & Pigheaded Fools

Jason Cohen — Sun, 17 Mar 2024 00:00:00 +0000

![son comes time in life when need to decide news to believe](https://public.asmartbear.com/books/anthology/chapters/perseverance/d6605print-simple.png) ## The puzzle #### Scenario 1 (S1) At time (A) you start an AdWords campaign. At time (B) it's obviously not working; a waste of time and money. ...But you keep trying, and by time (C), it's working! **You did it!** #### Scenario 2 (S2) At time (A) you start an AdWords campaign. At time (B) it's obviously not working; a waste of time and money. ...But you keep trying, and by time (C), it's still not working, and you've wasted even more time and money. **What a waste!** We've all experienced both scenarios, not just in AdWords but in life in general. But we misunderstand it. S1 we call "**success through perseverance**," and you've heard this echoed in many platitudes. [Winners never quit](https://longform.asmartbear.com/startup-life/), and quitters never win. [Failure is a step](https://longform.asmartbear.com/fail/) on the path to success. Failure is [a pivot away from success](https://longform.asmartbear.com/predict-the-future/). Learn from your mistakes and next time you will succeed. Fake it 'till you make it. The thing [all failed startups have in common](https://longform.asmartbear.com/avoid-blundering/) is that the founders stopped trying. S2 we call "**failure through obstinance**," and you've heard this echoed in many platitudes. Doing the same thing expecting different results is the definition of insanity. Those who ignore history are doomed to repeat it. Those who cannot be [introspective](https://longform.asmartbear.com/good-startup-ideas/) and [honestly see things as they are](https://longform.asmartbear.com/failure-to-face-the-truth/), will fail. The thing all failed startups have in common is that the founders didn't [pay attention](https://longform.asmartbear.com/criticize-create/) to what was happening outside their own egotistical worldview. Are these the correct conclusions? No, they are convenient rationalizations. Consider S1 and S2 at time (B). Up to this point they are identical. So how do you know, *at time (B)*, which scenario you're in? Because if you're in S1 you'd be a fool to stop, but if you're in S2 you'd be a fool to continue. How do you know whether you'll end up as a cautionary tale of someone who couldn't let go when they were clearly wrong, or as a hero who bravely fought through doubt to prove everyone wrong? Maybe you shouldn't find out! Just stop at (B). No again, because if you're on the path of S1 you've lost your win. If you were on S2, you were "smart" to stop, but *either way you've failed* to achieve something useful. Stopping is *sure failure* while persisting is at least *possible success*. Stopping means you'll never create something great. So you cannot know. Not for AdWords, not for [product design](https://longform.asmartbear.com/design/), not for the vision of your company and the [market you hope to create around it](https://longform.asmartbear.com/problem/), not for almost anything, big or small. [It all looks the same at point B](https://longform.asmartbear.com/chaos-at-start/). Venture capitalists don't know either, though it's their job to know. They're smart and do this for a living, but it usually doesn't work; [most VC portfolios lose money](https://www.sethlevine.com/archives/2020/10/vc-fund-returns-are-more-skewed-than-you-think.html). Not even the experts know which path you're on. The typical, backward-looking interpretation of these two scenarios is not the best way for us to understand the choices in front of us today, nor to [evaluate our decisions in hindsight](https://longform.asmartbear.com/unmeasurable-metrics/). It's not even clear that we've "learned anything," whether the outcome was good or bad. Perhaps all we've done is made some choices and observed some results, and that's the end of it. You could read this as depressing, because nothing is predictable and even the wisdom we believed we accumulated along the way is false wisdom. But clearly this point of view isn't exactly true. So, read this as a positive, and realize that it liberates you to make decisions more easily--with less second-guessing in the moment, and less guilt afterwards. (And [taking less credit when things go well](https://longform.asmartbear.com/survivor-bias/).) When you realize you cannot know which scenario you're in, you realize that the job is to **find out which one it is as quickly as possible**, which means to cease your dithering, make a strong decision, keep your eyes open, try to [measure what's happening](https://longform.asmartbear.com/product-metrics/) as objectively as possible, hope for S1, but allow for S2, to not feel guilty if you guessed wrong, and not feel cocky if you guessed right. Still, there are some guiding questions that can help you suss out which path you're on, and thus what to do. ## Solving the puzzle These are my [specific strategies that thrive under uncertain conditions](https://longform.asmartbear.com/predict-the-future/). Because many things in life are uncertain, these are often useful. Besides those strategies, the following tactical questions help you determine which path you are on. Readers have contributed wisdom that I've lightly edited: * **Timebox.** Set a hard deadline for how much longer you'll work on it without seeing improvement. ([@farezv](https://twitter.com/anyuser/status/1742484259213029599)) * Are you still **enjoying** the project and **learning** something from it? ([@colemank83](https://twitter.com/anyuser/status/1742518005836271628)) * **Rate of progress**: Is the rate of progress slowing or accelerating? ([@Daanlo](https://noc.social/@Daanlo@mastodon.social/111727866065565357)) * Use an **external sounding board** to keep you honest. Often the numbers won't tell you to go left or right. And even if the numbers show clear signs, we often ignore them because "they are outliers" and "this time is different." ([@igriff](https://twitter.com/anyuser/status/1742477070662328629)) ([Ton Dobbe](https://www.linkedin.com/feed/update/urn:li:activity:7148238290591125505?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7148238290591125505%2C7148314532505972736%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287148314532505972736%2Curn%3Ali%3Aactivity%3A7148238290591125505%29)) * Don't allow **sunk cost** to decide. ([@igriff](https://twitter.com/anyuser/status/1742477070662328629)) * If the team has **run out of ideas and conviction**, consider pivoting. If the team has run out of ideas and conviction for pivoting, it's time for a full reset or quit. Do you **see yourself doing this in 12 months?** If not, you might as well stop now. ([@sachinrekhi](https://x.com/sachinrekhi/status/1742594429884694858?s=20)) ([@awoodsnet](https://noc.social/@awoodsnet@phpc.social/111695017407127924)) * Is the product **noticeably better** than alternatives in the market or will be there soon? If the answer is no to both, consider stopping. ([@yassin_baum](https://twitter.com/anyuser/status/1742501512688070677)) * **Existence proof**: If others somehow achieved a similar outcome, then you shouldn't stop. Especially if their solutions are bad and yet they're making money. ([@mibenz95](https://x.com/mibenz95/status/1742660395960025508?s=20)) ([@nickresreal](https://x.com/nick_res_real/status/1742629476079444371?s=20)) * Is it (a) **worthwhile**, (b) **fixable**, and (c) you **still have energy** for it? ([@nurijanian](https://twitter.com/nurijanian/status/1742693171833000107)) Am I truly enjoying what I'm doing? Do I have room for improvement? Am I willing to put in the work to improve? Am I marketing this venture/skillset to the best of my ability Would my time be spent better trying a new project? ([Alex Finn](https://www.1percentbetter.io/p/quit-a2b1)) * **Opportunity Cost**: Are there opportunities you're missing because you're fixated on this? ([@Liscoomi](https://twitter.com/Liscoomi/status/1742719163766886544)) * **Consequences**: What's the downside if you don't finish? What do you get if you're successful? Are there smaller wins you can achieve along the way? ([@awoodsnet](https://noc.social/@awoodsnet@phpc.social/111695017407127924)) * **Don't decide out of fear**. If you're scared of the outcome a little bit, stick with it. ([@pascallaliberte](https://twitter.com/anyuser/status/1742523382158016600)) * **Penny in the air**: Go for a long walk and listen deep inside. Often I already know deep inside, but just [don't want to admit it to myself](https://longform.asmartbear.com/failure-to-face-the-truth/). ([@awoodsnet](https://noc.social/@awoodsnet@phpc.social/111695017407127924)) * **Go back to "The Why"** that set you off on your journey to begin with. If you now have more information to assess the credibility of that why, re-assess. If you still have the same information available and still believe in the why, press on. ([@temlabs](https://twitter.com/anyuser/status/1742536876156748116)) In any case, just shut up and get going. And when you're done with that, don't look back too much, just shut up and get going again.

AI startups require new strategies: This time it's actually different

Jason Cohen — Sun, 03 Mar 2024 00:00:00 +0000

![uprising calendar friday](https://public.asmartbear.com/books/anthology/chapters/ai-startups/d8802print-simple.png) Startups must leverage their [specific advantages they have over incumbents](https://longform.asmartbear.com/startup-beats-incumbent/) to overcome the massive barriers erected by those incumbents: Mature products, mature distribution channels, brand, trust, optimized organizations, and cash in the bank. However, in the AI revolution many of those advantages are unavailable. Some even become a disadvantage. This is substantively different from the other two major technological revolutions of the past twenty-five years.[^1] [^1]: The internet and mobile devices. Not blockchain, at least not yet--that has had almost no effect on businesses despite the application of billions of dollars over more than a decade. Specifically: #### Disruption Theory & risk-aversion don't apply Incumbents typically cede market space to startups wherever there's new, unproven technology or a new, unproven market, especially in spaces where they can't use past data to predict the future. But in AI, they're rushing to embrace new technology and uncertain markets, spending historic amounts of money and time. #### Incumbents aren't failing to innovate Typically the race is "whether the startup gets distribution before the incumbent gets innovation ([Alex Rampell](https://a16z.com/distribution-vs-innovation/))." But in AI, the incumbents already have the innovation (whether through closed APIs or open source), while startups struggle as mightily as ever to find distribution. Perhaps an even greater struggle, as every market is over-saturated with new startup competitors, some with massive funding. #### Incumbents have the data "There's no AI strategy without a data strategy," the (now often-repeated) saying goes. For training, testing, benchmarking, and features, you need data. Incumbents have it or can afford it; startups are at another disadvantage. #### Great talent are happy at incumbents While there will always be fantastic people who only want to be at a small company, there are even more people, including the top AI and software engineering talent in the world, who are getting paid above-market rates to work on the most exciting projects, with healthy budgets, with data to leverage, and with the ability to impact huge numbers of customers quickly without having to do marketing, sales, support, or accounting. #### The so-called "AI market" is not what you think it is People say "the AI market will be trillions of dollars so there's enough for everyone," but there's no such thing as "the AI market" unless you're competing directly with OpenAI. The market for chatbots and SEO tools is the same market as before, now with stiffer competition. **The typical startup strategies are wrong** because typical strategies rely on the typical advantages of David against Goliath, which are largely untrue in AI. If [strategy is "how we will win,"](https://longform.asmartbear.com/great-strategy/) then the "how" must be different with AI. Few AI startups have embraced this reality, and most will fail as a result. It is easy to create an AI startup, gain attention, and generate excitement, but it is much more challenging to compete and win. The remainder of the article details and defends this case. --- ## Disruption Theory does not apply to AI The Innovator's Dilemma is not a dilemma this time around. ![Book cover for Innovator's Dilemma](https://public.asmartbear.com/books/anthology/chapters/ai-startups/image-2-simple.png) Disruption Theory explains how [little upstarts leverage](https://longform.asmartbear.com/leverage/) new technology to topple incumbents, even though the incumbents see it coming and act in their rational self-interest. The theory goes: New technology is often (a) better in some ways, (b) worse in some ways, and (c) cheaper than existing technology. The combination of (a) and (c) can overcome (b). The classic example given in the book is the original transistor radio, whose the sound quality was far worse than the incumbent, beautiful-to-look-at, rich-sounding, furniture-sized-and-weight RCA radios. However, the advantage of the transistor radio is that it is portable, a feature made possible because transistors use so little energy. A radio at the beach that's staticky and a tinny is a lot better than not having a radio at all. So, upstarts were able to sell radios that were [worse in a very important way](https://longform.asmartbear.com/worse-but-unique/), but also cheaper and with the advantage of mobility. Of course, over time, the transistor radio improved its sound quality, and this is the other factor in Disruption Theory: The technology that starts out worse becomes better over time, and yet is still cheaper and retains its positive qualities. Then the new technology fully disrupts the incumbents, who had been looking on, thinking "we're the ones who have great sound, so we're safe. We're not even really competing with those toy radios." ![Disruptions start "worse," but then become even better than incumbents](https://public.asmartbear.com/books/anthology/chapters/ai-startups/image-4-simple.png) In AI, the conditions of Disruption Theory are not present. The incumbents do not look at AI and say "this technology is cheaper but worse, so we will ignore it." They're doing just the opposite. They say "this technology is groundbreaking and if we don't embrace it, we will become irrelevant" and "our shareholders are demanding that we invest huge percentages of our annual revenue to do it." Therefore, startups who normally depend on incumbents ignoring new technology and new markets, cannot depend on that in AI, and therefore are competing directly with well-funded incumbents who are selling to their existing customer bases. ## Differentiation when everyone has the same technology The "hard tech" in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents. ![an even match of tug-of-war](https://public.asmartbear.com/books/anthology/chapters/ai-startups/image-5-simple.png) There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn't something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage. In a market where everyone has access to the same core technologies, simply matching the capabilities of established players is not a winning strategy. This doesn't mean it's impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required. ## There is no AI Strategy without a Data Strategy Training AI models requires data. Whether you're training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers' data to train models and tune algorithms, so long as they maintain secrecy and privacy. For instance, Intercom's AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes). Incumbents can also afford to pay for data: ![Axel Springer licenses news to OpenAI for tens of millions of dollars](https://public.asmartbear.com/books/anthology/chapters/ai-startups/image-3-simple.png) ![Reddit strikes deal with unnamed AI provider to train AI for $60M](https://public.asmartbear.com/books/anthology/chapters/ai-startups/image-simple.png) AI will continue to become cheaper, better, and faster in the next few years. Getting access to huge amounts of training data will not; in fact, just the opposite as the companies who possess that data have already learned how valuable it is. ## Great talent are happy working at incumbents Traditionally, startups have attracted dynamic and innovative talent with promises of autonomy and wealth potential, outshining rigid incumbents. However, the landscape shifts when it comes to AI. While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientists who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn't the pure joy of writing interesting code. This is heaven for many. ![two robots at cocktail party have nametags showing bar codes instead of names](https://public.asmartbear.com/books/anthology/chapters/ai-startups/d5518print-simple.png) Many people want to create or join a startup of course. But startups don't have an inherent advantage for attracting talent; at best this is neutral again. ## The "market" is not what you think: AI is not a large, growing market "AI will be a multi-trillion-dollar market" people say. AI is surely the definition of a large, growing market, with many orders of magnitude of growth in its future. Which means it's perfect for startups. Except these are nonsense statements. A "market" is a set of buyers, with sufficiently similar needs, constraints, and goals, that the same product can be sold to all of them. The "AI" market consist of companies like OpenAI and Cohere, and indeed that is a large and growing market, but unless you're competing directly with them, you're not "in the AI market." A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that "AI" is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies). **AI is in the solution-space, not the problem-space**, as we say in product management. The customer problem you're solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that's what's so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different. That said, AI can reignite growth in existing markets. Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market. Still, the right way to analyze this is not to say "the AI market is big and growing" but rather: "Here is how AI will transform this existing market." And then: "Here's how we fit into that growth." Even so, it sounds a lot easier for an incumbent with a trusted brand and existing customers and all the data, than for a startup. Not impossible for a startup, again the startup is at a special disadvantage that it doesn't normally have, especially when the startup is entering a new niche, a new market, or is in a market which is organically large and growing. --- AI is different from previous technological disruptions. The dynamics between startups and incumbents are fundamentally different, and **that means you need a different strategy**. If an AI startup has a strategy that looks like a strategy a non-AI startup had five years ago, that may very well be the wrong strategy. This doesn't mean you shouldn't make an AI-based startup. In fact, all new startups probably do need to include AI. It means the usual startup strategies might not work, so you need to think deeply and have a different point of view about how you're going to win.

How startups beat incumbents

Jason Cohen — Sun, 25 Feb 2024 00:00:00 +0000

![Reaction to the news was mixed, largely because we asked more than one person.](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/d7904print-simple.png) It doesn't seem possible for a startup to beat an incumbent. An incumbent has everything: money, brand, customers, a sales team, marketing that generates thousands of leads every month, product and engineering teams that constantly ship. They mine their big existing customer base for ideas, and then build exactly the right features, and then charge for it. Their 24/7 support team provides faster and better service than someone working in their pajamas at home. They don't have to build the basics or ask Twitter how to manage international sales tax. They can just focus on innovating. Of course if you've ever worked at a big company, you know that **while most of those things are true, it doesn't feel like it.** Big companies are rarely well-oiled innovation machines, and it certainly doesn't feel like you're constantly outpacing the competition. When we analyze how incumbents are vulnerable, we uncover opportunities that startups can exploit to win, where there's often nothing the incumbent can do about it, despite their advantages: * Taking risks that cannot be quantified * Addressing a profitable niche * Doing delightful, valuable things that don't scale * Unsurpassed customer service * Leveraging new technology * Make drastic changes * Having an opinionated personality * Doing things that aren't zero-sum * Being worse-but-acceptable in most dimensions * Being low-cost against a profit center ## The pattern: Every big-company advantage creates exploitable weakness The reason big companies don't function as well as described above is that [things at scale are super-linearly more difficult](https://longform.asmartbear.com/scale/). It's an advantage to have 100,000 customers when you're figuring out what the next feature should be, or when you're launching a second product, or when you get free growth from word-of-mouth. But it's a disadvantage to have a lot of customers when you want to innovate with your product, because no customer wakes up in the morning and says: *Gee, I hope the software I'm accustomed to dramatically changes today*. Customers don't want to learn new UIs. Customers have workflows that you have to accommodate. Old technology that powers those 100,000 customers doesn't support the latest technology. You have to update documentation and videos and the people in support and sales who need to be retrained. Even a simple change can be [difficult and expensive](https://twitter.com/johncutlefish/status/1335822976957247489?s=21), and certainly low-ROI. Besides "scale," a big company must accommodate things startups can ignore. There's the legal department, for example. A startup does all kinds of illegal things. Most startups do not pay taxes properly, sometimes not at all, especially in other countries. Startups don't adhere to all the Acceptable Use Policies of all the products they use. Startups don't have a security team who vets vendors before sending them sensitive data, or vets libraries before they're integrated into the code base, causing all of their supposed "secret intellectual property" to become open-source. ![this just says you wont reveal anything about our nondisclosure agreement](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/d1509print-simple.png) As a result, the startup not only moves more quickly--which is how most people characterize it--but they can completely skip things that a larger company cannot. So Uber decided to just do illegal things in order to grow. An incumbent taxi company obeys the law, so they lose. You could say that that's not fair. You could say that's what regulation ought to prevent. But the reality is that startups often ignore the law, and that can be an edge. **The way a startup wins, is to do things that incumbents cannot or will not do.** So, let's see how to attack where they cannot defend. ## Take risks that cannot be quantified The way a larger company decides to take a risk, such as launching a new product line or entering a new market, is by creating a detailed analysis of the opportunity, and a cost estimate. Then the decision is: 1. Is this is a good ROI? _(potential-revenue divided by costs)_ 2. Do we have conviction that the risk of failure is low? How can a startup exploit this decision process? Starting with decision (1), the analysis is typically wrong. There are studies everywhere--and your own experience, if you've worked at a large company--showing that most development projects are significantly late and over-budget, and also that the outcome is typically worse than expected. Both sides of the ROI fraction are worse.[^1] [^1]: I cover exactly what to do about this in [an article about how to do ROI analyses correctly](https://longform.asmartbear.com/roi-rubric/). So, whichever projects _appear through traditional cost-benefit analysis_ to be low-ROI, are unlikely for an incumbent to do, even though there's a good chance that (a) they're rejecting genuinely good ideas and (b) they're accepting weaker, more straightforward ideas, only because those more readily lend themselves to ROI analysis. A startup who selects "apparently" low-ROI projects, will probably have no competition from incumbents. With decision (2), big companies don't like to take big risks _even when the outcome might be large_. The fear of failure is an order of magnitude more motivating than the desire to innovate or even the greed of success. One reason is that the core business is probably going well, and you "don't want to mess that up." Another reason is that no one wants to be the one who proposed, fought for, and then presided over a multi-million-dollar failure. Another reason is that none of us really knows what a "big risk" is anyway, because [even experts can't predict what will happen](https://longform.asmartbear.com/predict-the-future/), and we don't know how to [talk about risks](https://longform.asmartbear.com/probability-words/) or [measure risks](https://longform.asmartbear.com/forecast/), even [in retrospect](https://longform.asmartbear.com/unmeasurable-metrics/). At a big company, it's safer to say "let's gather more data" and "let's wait for consensus" than it is to take a risk. ![we all agree lets go back to desks discuss why this wont work](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/d5327print-simple.png) But innovative things are often high risk or unknown risk.[^2] Therefore, a startup can pick things which are risky, or where the risk is impossible to ascertain, but where the potential upside is high, and incumbents are unlikely to follow. [^2]: "High risk" means we know number that is the probability of success, and it is low. "Uncertainty" is unknown risk, meaning we don't know what the number is at all. Though different, they have the same result in this context: It's too scary to take the chance. Indeed, this is also what I recommend for work-planning in general in [selecting and prioritizing Rocks versus Pebbles](https://longform.asmartbear.com/rocks-pebbles-sand/), where the "big Rocks" [that move the needle](https://longform.asmartbear.com/strategic-planning/) should be selected on the basis of potential outcome rather than ROI, whereas the Pebbles should indeed be based on ROI. Startups can do it, while incumbents are almost always too fearful. That's OK, the incumbent can buy you later at 10x revenue. ## Address a niche An incumbent wanting to expand into a new market or launch a new product line, must apply a significant amount of money and people--a large investment, even for them. What kind of return do they need on that investment? The answer is: It has to materially affect their growth rate. The rule of thumb is it should increase their overall revenue by at least 10%; some people call this "the materiality threshold." However, that number goes up as the (perceived) risk or (actual) investment increases. For an incumbent with hundreds of millions in revenue, that means the product line must have a good chance of making $50-$100 million, or they won't even try (nor should they). > "Given our size, we only see a few good things [to invest in]. If we were smaller, then we'd see lots of good things." > --Warren Buffett. Another financial metric that creates a materiality threshold is a certain ratio on the Profit & Loss statement. A software company at scale [spends](https://www.scalexp.com/blog/saas-benchmark/rd-spend/) around 20% of their revenue on R&D, which includes Product, Engineering and Design. A fully loaded team, including salaries, taxes, software, hardware, training, management, travel, office space, and so on, can cost upwards of $2 million a year.[^3] If that cost is supposed to be 20% of revenue, the team needs to generate at least $10 million in annual revenue, which means the product they're working on must do that, even if the product is a small add-on or the team owns a subset of a larger product. [^3]: Obviously this varies by geography, employment laws, tax laws, and the size and composition of the team; this estimate assumes a team of eight engineers, a product manager, a designer, and a manager, in the US, and rounding-off for rhetorical simplicity. Of course, a small startup doesn't see it that way. A team of two founders and one employee isn't thinking "We have to make $10 million a year, otherwise it's a failure!" This means the startup can focus on a niche that doesn't need to generate $10 million; it could generate $1 million. The startup can [focus on a niche and ICP](https://longform.asmartbear.com/icp-ideal-customer-persona/) that a big company cannot afford to target, either because that niche wouldn't hit the overall revenue materiality threshold, or wouldn't hit the one-team P&L threshold. With that focus, the startup has no direct competition from an incumbent. In fact, the larger the incumbent, the less the startup has to worry about competition. Feels good. ## Do things that don't scale WP Engine was the first in our market to support [LetsEncrypt](https://letsencrypt.org/).[^4] We knew nearly all of our customers would want it. We wanted to promote it heavily, but we were already deep into scale, with 100,000 customers who could potentially use it on day one. [^4]: If you're unfamiliar with this web technology, don't worry--the details aren't important to the story. Suffice to say: It was a desirable capability, and is now ubiquitous. ![bears working](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/bears-working-simple.png) Therefore, it had to be scalable before we released it. That means breaking down the components into queues, in case one step in the process was faster than another, or in case one failed and had to be repaired before the system could make progress. And we had to monitor those queues, and send alerts to humans if it stayed broken for too long. And we had to run at-scale tests to make sure it worked when there were 1000 simultaneous requests with random failures. And we had to train hundreds of folks in tech support on the questions we anticipated, and train hundreds of folks in sales on how to leverage this to make sales, and work with marketing on how to message it. And we had to make sure it had close to zero bugs, because if thousands of people start using it, and 10% ran into a bug, we'd crush our support team, and hundreds of people would take to Twitter to complain. We were correct to invest many months of time in all these areas; on the first day, thousands of people *did* start using it, tens of thousands in the first month, and indeed some components did break, and lots of people asked questions in tech support. And people praised us on Twitter as a result. The good news about a large customer base, is that you can have 1000 users on day one and 10,000 on day 30. In this case we gave it away for free, but in general an incumbent can be generating $1M ARR or even $10M in short order. A startup cannot do that. But a startup can launch something in a few weeks and iterate. We had to be heads-down for most of a year. And so another startup opportunity emerges: A startup doesn't have to operate at scale. You might say: A larger company can still make an alpha version of a product, show it to a few dozen customers, and iterate from there. Indeed, we also do that; it's a wise process. However, what a larger company is *not* willing to do is take the next couple of years just to get to $1M ARR and then take a couple more years to get to $5M and then take a couple more years to get to $20M. That's just way too long to get the "material" amount of revenue and does not leverage their at-scale assets. But the ramp I just mentioned is ideal for a startup; in fact, that is a highly successful growth rate. It allows time for the product to settle in and slowly get to the point where it is scaling. This is a reasonable, fun, and plausible path. No one knows you exist; that's bad for sales, but good for iterating without harming your reputation. Therefore, if a certain product idea is naturally very easy to scale from day one, that's easy for an incumbent to copy. But if the product is naturally difficult to scale from day one, that's ideal for a startup. ## Unsurpassed customer service from founders & engineers I personally handled support tickets every day at WP Engine until we had around 35 people; this is typical for a customer-oriented founder. There's no better way to understand how people interact with the product than to talk to them about when it's going wrong or not meeting their expectations. You get feature ideas, you understand how to streamline the product and how to increase retention. Customers are impressed by the quality of support and the range of problems you can solve. They won't get that from a company with a thousand tech support reps. Some startups aren't interested in providing great support, but those that do are naturally and even effortlessly orders of magnitude better than a large incumbent. It's a competitive advantage available to everyone. ![so as you can see customer satisfaction up considerably since phasing out complaint forms](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/d0512print-simple.png) As if those benefits to both customers and product development aren't enough, it also fosters real love and loyalty from customers. That love translates to forgiveness when you do have problems; see the mountain of supportive tweets when a small-but-lovable company has a big outage or security issue. It also translates into word-of-mouth advocacy, as customers naturally reciprocate, and thus great support results in inexpensive growth. [Love is the best form of "willingness to pay."](https://longform.asmartbear.com/willingness-to-pay/) Which incumbents cannot compete with. ![Tweet of a founder confirming this advantage](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/image-simple.png) As a startup scales, it loses this advantage. I distinctly remember each time in the past 16 years at WP Engine that a new competitor would brag about their amazing tech support. While WP Engine continues to objectively[^5] have world-class support, it's not the same as personal attention from the founder of the company! [^5]: Won a dozen Stevie Awards, and maintains 98% CSAT. You could decide to never grow that large, optimizing for profit and efficiency rather than revenue and scale, and make Support a permanent competitive advantage. No matter which future you pick, this is still a great way to get started. ## Leverage new technology When a technology is new, the risk of using it is high. Maybe it won't be supported in five years. Maybe you won't be able to hire dozens of engineers who are familiar with it. Maybe[^6] it will have a big security problem. Maybe it works well for the "Hello, World!" case, but doesn't work at scale. Maybe it's efficient for one developer but too difficult to coordinate with thirty. These are all reasons why incumbents are absolutely correct in avoiding new technology. [^6]: In fact, *certainly* it will have *many* security issues... the question is whether they will be identified and addressed. But a startup doesn't have these concerns. Not because the big company is wrong, but because the constraints are different. The thing that will kill the startup is not going to be the tech stack; it's going to be that it's too hard to find customers, or they don't have a budget for this problem, or it's too hard to compete, or you run out of money, or any of the other [things needed to get to Product/Market Fit](https://longform.asmartbear.com/product-market-fit-formula/). And new technology often creates a competitive advantage. New technology makes certain things efficient, or enables things which previously were impossible, as in the current case of [AI](https://longform.asmartbear.com/ai-startups/). The startup *is* taking a risk on that technology. If you're banking on a new open source project, and it doesn't take off, you might have a product built on a platform that is no longer supported, and that's bad even if you're a small startup. However, what is the worst case, assuming the startup isn't already dead by then? It's that, five years from now, you've built a sustainable company, and now you have to redo your platform using different technology. That does really suck for you. You might have to pause new features for a year to make the transition, and engineers, product managers, and sales reps alike will hate that. But if this penalty "buys" you a successful company, then it was worth it. ## Make drastic changes Of course it's nice to have 100,000 paying customers, but it also means all change is difficult, expensive, and risky. You can't change the user interface even slightly without a torrent of angry tweets, confused people clogging tech support, and updating all documentation and screenshots. You have a solidified brand with expectations on positioning and pricing; changing any of those is not only disruptive, but risky. On the back end, you've optimized and built for scale, which means you're locked into architectural choices, technological integrations, and business partners. New startups can change any of those things, even drastically, even if they have 100 customers. Obviously you'd prefer to have more customers, but so long as you don't, take advantage of the fact that you pivot into the market reality and your own strengths. Especially when the market shifts--like today [with AI](https://longform.asmartbear.com/ai-startups/), and yesterday with mobile devices--the incumbents are the ones who cannot quickly adapt to the new world, whereas the little startup can adapt immediately. Part of why "big companies are dumb" is because they can't change even when they know they're doing something dumb. New companies can. ## Have an opinionated personality It is rare for a large company to express a personality. There are many reasons for this. They want to address a large and therefore diverse market to sustain their revenue and growth; by speaking to everyone, they speak to no one in particular. They have an established brand, which creates trust, which is one of the reasons they win sales, embodied in the now-outdated phrase "No one ever got fired for hiring IBM." So the language on the homepage and inside the product needs to reinforce this trust, which means being impersonal but solid. Another reason is that customer communication is spread over hundreds or thousands of people, from Support to Sales to Marketing to Product to Design. There isn't such a thing as a genuine, human personality and style that a thousand people share and express identically. Whereas everyone can conform to generic but professional language. ![bear streamer](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/image-3-simple.png) But a startup doesn't have these constraints. Indeed, the founder often has a strong personality, with specific ideas of what's good and bad and how things should be done and how to express it. That's at least partially why they started a company in the first place. Some potential customers will be attracted to that personality and some will be repulsed. But that's true of anything that is wonderful and different and powerful in this world. [You're a little company, now act like one.](https://longform.asmartbear.com/little-company/) In fact, you will [directly win customers](https://longform.asmartbear.com/authentic/) because of it. When a large company tries inject personality, it often comes off as contrived, not genuine. Whereas with a startup, it feels genuine because it *is* genuine. Some customers only want to buy from the market leader. That's rational, and if that's a primary deciding factor, there's little a startup can do about it, no matter what their home page says. Therefore, the startup should ignore that segment and focus on [customers who want to buy](https://longform.asmartbear.com/icp-ideal-customer-persona/) from a plucky upstart that has something to say. ## Do things that aren't zero-sum Some things in business are a zero-sum game. In a zero-sum game, when one player wins, another player necessarily loses. Poker is an example: When one player wins chips, other players lose exactly that number of chips. Blackjack is a counter-example: Players at a table individually win or lose, unaffected by other players; no player loses chips when another player wins. Marketing gives us examples of each. Zero-sum marketing channels arise when there's a [power law](https://en.wikipedia.org/wiki/Power_law) or where there is exclusivity. Examples: * SEO _(The top positions generate more traffic than all other positions combined)_ * AdWords _(The top positions generate more traffic than all other positions combined)_ * Affiliates _(The top few affiliates generate more leads than all others combined)_ * Retail shelf space _(Limited surface area)_ * Exclusive distribution deals _(The zero-sum game is created by agreement)_ * Government fiat _(A vendor can be written into law)_ Conversely, there are channels that are non-zero-sum, and more to the point, where even a well-funded, strongly-entrenched incumbent cannot prevent others from winning: * Social media _(anyone can create a great social presence)_ * Newsletters _(anyone can create great content marketing)_ * Collaborative promotions _(both players more money than they would have)_ * Ecosystems like Salesforce and the Apple Store _(all players make more money)_ * Consumers who buy multiple products _(e.g. 3D animators often use multiple tools)_ Market dynamics can also create either type of game. Stagnant or shrinking markets are zero-sum; without new customers entering the arena, winning one customer means a competitor cannot have that revenue. In growing markets there's a steady stream of new dollars from new customers, so many competitors can grow. (This is yet another reason why startups should target growing markets, whether the goal is to build a small, profitable company or a unicorn.) Incumbents are stronger in zero-sum games, because they can apply money and specialized expertise. They can even over-spend, because their scaled business model can absorb a low ROI activity, intentionally losing money in order to stop competitors from using that channel. This doesn't mean startups should _never_ play zero-sum games, but they are more difficult, and sometimes impossible. But incumbents cannot stop startups from winning non-zero-sum games, so that's where a startup [should invest](https://longform.asmartbear.com/investment/). ## Be worse but acceptable in most dimensions No one wants their website to go down. It's surprising how hard it is to keep a website up for an entire year. For example, "99.9% uptime" might sound excellent, but that equates to 44 minutes of downtime every month! If our company WP Engine had even close to that much downtime, customers would revolt, and rightfully so. Yet, most hosting companies[^7] promise only 99.9%−99.95% uptime. [^7]: Including the major cloud providers, managed WordPress platforms like WP Engine, and specialized providers like Toasttab for restaurants The reason is that it's very hard to push the number higher. It takes an order of magnitude more direct spend, software development, and processes to get to 99.99%, to say nothing of [the proverbial "5-9's"](https://en.wikipedia.org/wiki/High_availability) that industrial operations sometimes target. Every little rare, strange, unpredictable thing will knock you out of compliance; it's expensive and difficult to solve all those cases. And yet, 99.99% just doesn't look that different from 99.95% on the pricing page. Incumbents, however, often do have to invest in optimizing these expensive dimensions like. One reason is that [at scale, rare things become common](https://longform.asmartbear.com/scale-rare/); at scale you have no choice. Another reason is that it can win sales in some segments of the market; at WP Engine we have enterprise customers who have internal policies demanding 99.99% uptime, so we win against competitors with lesser guarantees. Startups don't have the rare-at-scale problem, and they can [choose their target market](https://longform.asmartbear.com/icp-ideal-customer-persona/) such that customers don't have extreme demands,[^8] and therefore startups can win while incumbents labor. [^8]: Indeed, startups should actively avoid those customers. A startup with a new product definitionally won't satisfy customers with myriad, difficult demands. ![kathy if you agree to these terms of service click i do](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/d6314print-simple.png) More examples where incumbents have to care, but startups don't: #### Security WP Engine spends tens of millions of dollars a year on security, ranging from internal teams to corporate policies to annual employee training to SOC and ISO compliance to software reviews to vendor security reviews. For us, security is one of our main selling points, so this is important both for scale, brand, derisking, and because it's what customers pay us for. But that's not true of most products, and most customers don't demand it. #### Quality If the entire product is low quality in every dimension, that's just a bad product. I doubt anyone is excited to build that, not even as an [SLC (MVP)](https://longform.asmartbear.com/slc/). For example, uptime is important for WP Engine, but for a SaaS product that is used only during normal working hours, targeted at a certain geography where "working hours" is a well-defined time frame, having even an hour of downtime in the middle of the night doesn't affect customers at all. A large, global company doesn't have that luxury. #### Scalability If a product will never need to handle "big data," then the product can be built with all sorts of simplifying assumptions[^9] that make development faster, safer, even more enjoyable. The UX can be simpler if the users have basic needs, as opposed to nested security groups driven by an external LDAP service. #### Compliance Large companies accumulate internal policies. These are for good reasons, like ensuring that not everyone has access to all data (especially customer data), ensuring that IT teams are capable of managing and upgrading thousands of devices, safeguarding ownership of their intellectual property, and enabling sales to large enterprises and governments in various verticals who impose policies on their vendors. This is one of the reasons they can get multi-million-dollar three-year contracts and the start can't. But it also severely hampers what they can do, or at what speed they can do it, or at what cost, and therefore at what customer-facing price. #### Legal We covered this earlier. While I would never advocate for startups to do illegal things on purpose, it's a simple fact that startups often (unknowingly) don't comply with all laws. It doesn't affect their sales; in fact, it might increase it. [^9]: PostgreSQL instead of BigQuery, SQLite instead of PostgreSQL, Python instead of Erlang, reading into memory instead of streaming, batch jobs instead of assemblages of queues and auto-scale groups, standard algorithms rather than distributed computation, normal debuggers instead of distributed logs, off-the-shelf libraries and vendors instead of bespoke solutions Most customers don't care about most things. This is great news for startups, who can select one or two dimensions to care about, and the [ideal customer segment who also cares mostly about those specific things](https://longform.asmartbear.com/icp-ideal-customer-persona/), and win that segment while incumbents chase complexity in all quarters. While incumbents have to charge more to cover the costs of multi-dimensional excellence at scale, a startup can charge less for a product that's objectively "worse" along many dimensions, and thus the startup can win on price and still be profitable. Startups can be [worse, but unique](https://longform.asmartbear.com/worse-but-unique/), and better where it counts. ## Be low-cost against the profit-center An incumbent cannot change its business model. The assets that give the incumbent its advantage are also static constraints. The brand is entrenched in consumers' minds. The software relies on platforms and languages and libraries that cannot be changed without a massive rewrite which infamously almost always fails. The business model of marketing, sales, service, and profits is set; a company with the costs of a global sales team, the white-glove on-boarding team, the expensive infrastructure, the vendor costs, and shareholder expectations that profits will only increase, cannot drop prices. Therefore, an incumbent cannot compete with a startup which is "a 'lite' version for 1/10th the price." This is a softer way of restating Disruption Theory,[^10] in which incumbents see the startup coming but, rather than compete directly, reposition themselves to focus on their best, most profitable customer segments, thereby allowing the startup to thrive. [^10]: Famously explained in [_Innovator's Dilemma_](https://en.wikipedia.org/wiki/The_Innovator%27s_Dilemma), the theory is more specific than what I'm saying here, involving new technology that is "worse, but cheaper, but in some ways better," where the incumbent seems to act rationally but ends up being completely disrupted. More specifically: Whatever generates the most profits for the incumbent, is the thing they are least able to change. You have to be careful, because incumbents will spend a lot of money and attention defending their profit centers from attack. But price won't be how they defend. That defense means you should leverage other topics from this article; for example an incumbent can decide to spend "too much" money on AdWords to make that a worse channel for you, but they cannot stop you from having a great content marketing strategy. --- ![tweet about how to compete against stale incumbents](https://public.asmartbear.com/books/anthology/chapters/startup-beats-incumbent/image-2-simple.png) Incumbents are strong in most ways, but they are vulnerable. The only mistake is for a startup to go head-to-head with an incumbent where the incumbent is strong. Attack [where you are strong](https://longform.asmartbear.com/leverage/), and they are weak. This is how to build a [winning strategy](https://longform.asmartbear.com/great-strategy/).

Deciding whether an investment is worthwhile

Jason Cohen — Sun, 04 Feb 2024 00:00:00 +0000

![surprised investors expecting worst performing miserably](https://public.asmartbear.com/books/anthology/chapters/investment/d4713print-simple.png) Whether it's your career path, which job to take for the next four years, investing in your personal brand, creating a startup, or investing in a startup, the question is:
_How do I decide which investment is worthwhile?_
I'm defining an "investment" as distinct from other bets by having these characteristics: 1. Spending time and money and reputation for months or years, hoping for a large return in the far-future. 2. Can do [only 1-2 of these simultaneously](https://longform.asmartbear.com/two-big-things/), because you are "all in." 3. Because [the future is uncertain](https://longform.asmartbear.com/predict-the-future/), there is significant risk: Whether it will pay off at all, and if so, by how much. This is in contrast to short-lived experiments, like a bet in poker, an A/B test, or making a brochure for a product concept to see whether people will buy it. Bets, experiments, and trials are excellent techniques, but by "investment" I mean a big commitment (time, money, reputation, opportunity cost), over a long time-frame, with a large potential payoff, under conditions of uncertainty. Tackling this combination of constraints leads to a framework for evaluating which investments are worthwhile. ## Don't use "Expected Value" Economics instructs us to use "expected value" to evaluate investments. This is almost always the wrong tool for the job. The theory appears sound: "Expected value" means multiplying the value of the outcome by the probability of attaining that outcome, resulting in a single number that encompasses both risk and value; a "risk-adjusted value," as they say in finance. Let's take an example to see why this number isn't useful for evaluating investments. Investment "A" requires you to spend $50,000, and takes an entire year of your life. The payoff is $1,000,000. Furthermore, the probability of payoff is 100%--it's a signed contract with cash in an escrow account. Therefore, the "expected value" is **$1M**,[^1] and the return on our investment is **20x**.[^2] Great investment! Now consider investment "B"--you also spend $50,000 and a year of your life, and this time the payoff is a whopping $10M, but the probability of that payoff is only 10%. The other 90% of the time, you receive no money at all. The "expected value" is also **$1M**,[^3] the same as investment A. And therefore investment B is also a **20x** payoff. [^1]: Because `100% ✕ $1M = $1M` [^2]: Because the $1M expected value is 20 times the cost of $50k. [^3]: Because `(10% ✕ $10M) + (90% ✕ $0) = $1M` Perhaps you already see the flaw in the logic. With investment B we will almost certainly lose money and lose a year of our life, yet we're still claiming it represents a "20x payoff." We're also claiming that B is economically indistinguishable from A. But unless you already have many millions of dollars in the bank, you probably wouldn't pick investment B, because investment A is a guaranteed [life-changing amount of money](https://longform.asmartbear.com/rich-vs-king-sold-company/), while B is almost certainly a waste of a year and $50k. They are not even remotely equivalent. It's even worse in real life because you rarely know either number--neither the risk nor the payoff. Whether it's the stock market, a startup, or whether a certain career path will still be lucrative in ten years, [even experts can't predict](https://longform.asmartbear.com/predict-the-future/) probability or outcome. So, what garbage numbers are you multiplying together to get "expected value," and isn't the result even worse garbage? There is one case where expected value is the right tool: A portfolio of bets. When a VC invests in twenty companies, when you write 100 newsletters and 1000 tweets, when you play 1000 hands of poker, when you have a balanced, uncorrelated financial portfolio, then you'll have some winners and some losers, and they roughly balance out, and therefore an "average" like expected value is indeed what you should expect. This relative stability in outcome is why [a portfolio of asymmetric bets is a good strategy](https://longform.asmartbear.com/great-strategy/). The types of investments covered in this article aren't like that--e.g. what job to take next, what startup you're going to spend your savings on--but if you can place a number of uncorrelated[^4] bets, then expected value becomes useful. [^4]: Correlation is a key factor which often brings down portfolios, including angel, VC, and financial. "Correlation" means "as goes one, so very likely goes the other." In that case, you're back to the original scenario. Only if they are independent--like successive coin flips--can you employ "the average" to determine the likely total result. ## Mandate an outsized payoff In the success case, the investment must have an enormous payoff. If the payoff isn't huge, then it's not worth the (unknown!) risk. Not just the risk that it pays off at all, but the magnitude of the payoff. When it can pay off 10x or 100x, or when it can be life-altering, then even if you fall short of the goal--only 3x or only life-improving--then it was still worthwhile. > "If riskier investments could be counted on to produce higher returns, they wouldn't be riskier." > --Howard Marks How do you define "enormous?" How can you calculate the payoff, especially with things like career paths? Use Fermi estimation. As I also suggest for [ROI calculations](https://longform.asmartbear.com/roi-rubric/), [probabilities](https://longform.asmartbear.com/probability-words/), and [evaluating markets](https://longform.asmartbear.com/problem/), estimate to the nearest power of ten. Estimation in detail is difficult and often incorrect; estimating to the nearest power of ten is easy, often requiring little research. For example, if one career path means earning 50% more per year for ten years (after you get established), that's 10x more earning power over your career. Or a startup targeting a small, stagnant niche might someday be worth $1M, whereas a small software company in a larger, growing space could be worth $10M. You'll be tempted to round up; in fact I just did in the "50% more earning power" example. Don't do that, because you're exaggerating how good the investment is; typically if there's any error, it's in the direction of being less valuable. If, for example, your main career goal is to maximize your earning power, then "50% more" is not good enough; you need to find a different route that potentially doubles your current trajectory.[^5] [^5]: 2x more for 10 years is actually 20x of course, but now rounding down to 10x makes sense, especially since it might not be 2x exactly and it might take a few years to ramp up. This is the same logic as selecting Rocks in the [Rocks, Pebbles, Sand prioritization framework](https://longform.asmartbear.com/rocks-pebbles-sand/). Your large personal investments are Rocks, and must be evaluated accordingly. ## Accumulate value over time For the payoff to be large, value needs to accumulate, which means it needs to be possible to add value reliably, and for value-already-added to not erode quickly. At a minimum, value must accrete, but ideally you construct a positive feedback loop, where growth or size itself begets more growth. Social media and SEO are like this--the more you write, the more others interact with you, the more others link to you, the more attention you get, the more traffic you get, which makes it easier to get more attention and interactions. Writing consistently increases your ability to write well and often, and thus there is a positive feedback loop of growth and value. Product companies are like this, especially with the recurring-revenue business model, where the revenue grows so long as each month there are more customers starting to use the product then there are stopping, which will happen so long as you're making a product that people truly [want and love](https://longform.asmartbear.com/willingness-to-pay/). (Here's my [roadmap to Product/Market Fit](https://longform.asmartbear.com/product-market-fit-formula/) for how to build such a company.) Consulting and professional services companies are often *not* like this. You'll have regular customers, but the reason consulting companies and accounting and law firms are [often valued](https://www.olmsteadassoc.com/resource-center/law-firm-succession-exit-strategies-valuing-the-firm) around 1x revenue or less, while SaaS companies with similar growth-rate are valued at 5x−10x revenue, is because the work you do for a client isn't accumulating and applying automatically to all other clients, and [consulting companies are hard to scale](https://longform.asmartbear.com/consulting-company-accounting/). > "The most powerful force in the universe is compound interest." > --Albert Einstein. Some people insist that investments must be *exponential*, but often they don't understand what that means, believing for example that ["viral products" are exponential, when in fact they are not](https://longform.asmartbear.com/exponential-growth/). Exponential is wonderful of course, but not required, and in fact is probably a red herring for most ventures. ## Stable success conditions over ten years Just because the world values something today, doesn't mean it will be valued ten years from now. If you spend 3-5 years investing, expecting a payoff in the subsequent 5 years, the world had better still value that thing during years 5-10. This summarizes Jeff Bezos's famous explanation of why Amazon's strategy has always been based on "what will not change." He articulates it so well, I'll leave it here without further commentary (except my emphasis, in bold): > I very frequently get the question: "What's going to change in the next 10 years?" And that is a very interesting question; it's a very common one. I almost never get the question: "What's *not* going to change in the next 10 years?" And I submit to you that that second question is actually the more important of the two--because **you can build a business strategy around the things that are stable in time**. ... [I]n our retail business, we know that customers want low prices, and I know that's going to be true ten years from now. They want fast delivery; they want vast selection. > > It's impossible to imagine a future ten years from now where a customer comes up and says, "Jeff, I love Amazon; I just wish the prices were a little higher." "I love Amazon; I just wish you'd deliver a little more slowly." Impossible. > > And so the effort we put into those things, spinning those things up, we know the energy we put into it today will still be paying off dividends for our customers 10 years from now. **When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it.** > > --[Jeff Bezos](https://www.youtube.com/embed/O4MtQGRIIuA?start=267), 2012 (it's been 10 years since this quote, so we can confirm in retrospect that this was indeed the correct analysis.) ![second quarter is up no fourth quarter now second again](https://public.asmartbear.com/books/anthology/chapters/investment/d7899print-simple.png) ## Align with global trends If the best investment is in something that doesn't change, the next best is aligned with a global trend with sufficient momentum that it is unlikely to change soon, if ever. For a startup, you want to be in a large, growing market. This maximizes the number of niches you might find traction in, without triggering a reaction from a large incumbent. It maximizes the number of marketing channels you might be able to leverage. It means people are already spending money, so you're answering "why buy from me" rather than "why should I allocate budget for this at all" or arguing against "that's a problem but not a priority" or evangelizing against "I never heard of that problem." It means the pie is growing, even if your percentage of the pie is small. Other current trends are the increase in virtual and distributed workforces, the increasing average age of humans on Earth, the increase in physical delivery services and online shopping, the increasing global demand for health care, education, and energy, and of course the rise of AI in unlocking novel features and capabilities for products and services that are changing the nature of work and life. > "The stock market is a device for transferring money from the impatient to the patient." > --Warren Buffett A trend you can depend on is either (a) so large today that it would take a decade to change significantly, or (b) is growing so fast (e.g. at least 5x in next 5 years) that it will be a strong trend even if your growth estimate is significantly wrong. ## Have an "edge" Why should _you_ make this investment, not someone else? A good investment, that _you_ cannot execute with excellence, isn't a good investment for _you_. What's your edge? In financial or startup investing, the edge is often your ability to evaluate the risk. You might understand that a certain execution in a certain product in a certain market carries lower risk than others know. This is an edge, though it is a rare one, in part because even experts are bad at evaluating risk (and [the majority of VC funds lose money](https://www.sethlevine.com/archives/2020/10/vc-fund-returns-are-more-skewed-than-you-think.html), and Angel investors are worse). More likely it's something special about your execution. I'm avoiding the word "unique," because although it's great if you're truly unique in some way, this is rare, and in fact unnecessary. You should, however, [leverage your strengths](https://longform.asmartbear.com/leverage/) so that you have an edge, despite also [being worse](https://longform.asmartbear.com/worse-but-unique/) in most other areas. One way to be special is to be the top 0.1% in the world at something (e.g. Olympic athlete), but that's probably not what you are. The other way, and the way available to all of us, is to be in the top 25% at a few uncorrelated[^6] things, which together creates a special edge. The quintessential exponent of this idea is Scott Adams, writer of the Dilbert cartoon and now political activist, who [points out](https://dilbertblog.typepad.com/the_dilbert_blog/2007/07/career-advice.html) that he is a decent illustrator but not great, a decent humorist but worse than any comedian on Netflix, and has held jobs in the tech sector, which almost no artist or comedian has done, and therefore it is at the intersection of those three "circles of competence" such as that he gives something unique to the world. [^6]: For example, being great at both math and computer programming doesn't make you any more special, because those often occur together, whereas being artistic and great at programming places you in a much more elite group. "Passion" can be part of an edge, but it is not enough. Passion creates motivation, which is especially important when you're [persevering](https://longform.asmartbear.com/chaos-at-start/) through difficult times. However, having passion doesn't mean the risk is low, the payoff is high, the market is good, the customers are there, the product is desirable, or the career is lucrative. Having an edge reduces risk while increasing not only the potential size of the payoff, but your own [personal fulfillment](https://longform.asmartbear.com/fulfillment/), because it puts you at the center of what you love, what you're good at, and what the investment needs to be successful. If you're not sure what your edge is, [here are some questions to ask yourself](https://longform.asmartbear.com/pivot-points/). ## Align with your long-term vision If you run quickly in the wrong direction, that's not progress. If you make investments that pay off, but the pay-off isn't what will make you [proud](https://longform.asmartbear.com/emptiness/), or [happy](https://longform.asmartbear.com/identity-selling-sadness/), or [fulfilled](https://longform.asmartbear.com/fulfillment/), or [rich](https://longform.asmartbear.com/rich-vs-king-sold-company/), or whatever your goals are, then it's not a good investment. If you don't have a long-term vision for yourself, consider making one. [Taylor Pearson](https://x.com/TaylorPearsonMe) has some great advice on [how to make a vision](https://taylorpearson.me/how-to-get-focused/) and [how to set short-term goals](https://taylorpearson.me/three-focus-failures/) against it. See also the last section of my [article on fulfillment](https://longform.asmartbear.com/fulfillment/), with a series of questions that help you discover what you love and hate. In much of my life, I haven't had a vision. Fortunately, my path has largely been in the right direction anyway, but I wonder [how much of that is due to luck](https://longform.asmartbear.com/survivor-bias/). Best not to leave it to luck. ## Good investments are rare With all these restrictions and rules, it's hard to find a good investment! > "Given our size, we see few good things [to invest in]. If we were smaller, then we'd see lots of good things." > --Warren Buffett Yup. Most startups fail. [Most VC portfolios lose money](https://www.sethlevine.com/archives/2020/10/vc-fund-returns-are-more-skewed-than-you-think.html). [80% of day-traders lose money](https://www.energyandcapital.com/why-95-of-traders-lose-money-and-thats-in-a-bull-market/). That's why it's so important to adhere to these guidelines--because most investments aren't right for you, and you don't want to spend the next five years doing the wrong thing. Of course [you'll never know](https://longform.asmartbear.com/perseverance/) if you made the best choice, [even with hindsight](https://longform.asmartbear.com/unmeasurable-metrics/). So the goal isn't to make the "best choice," but rather to make an excellent choice. To invest wisely, with the information you have. Good luck!

The "errors" that mean you're doing it right

Jason Cohen — Sun, 21 Jan 2024 00:00:00 +0000

> "If you don't make mistakes, you're not working on hard enough problems." > --Frank Wilczek, 2004 winner of Nobel for Physics Intellectually we know that [failures](https://longform.asmartbear.com/fail/) are inevitable when we're striving, growing, and learning. In practice we're not always so understanding when it comes to our teams, our revenue targets, and especially when it comes to [flogging ourselves](https://longform.asmartbear.com/impostor-syndrome/). Indeed, not all errors signify progress. Some are negligence, or just bad luck. We don't always learn from failure; in fact, sometimes there's nothing in particular to learn. ![lesson learned no one listen to don](https://public.asmartbear.com/books/anthology/chapters/good-problems-to-have/d8669print-simple.png) The following "errors" are the natural by-product of good decisions, or the result of a fundamentally positive circumstance that is attended by the proverbial "good problem to have." Most demand a response, but they should be regarded as a necessary side-effect of success, and celebrated as such. ### Re-adding features/bugs you removed from the backlog If you're not adding back feature-requests or bugs you cleaned out of the backlog, you're not cleaning out enough. Backlogs grow without bound unless they are culled. 1000 tickets is the same as 100 tickets, except that you haven't identified which 10% are most important. Which means you're definitely not working on the most important ones. But if you delete things, it will sometimes turn out we needed to do it after all. That's a sign that you're handling your backlog well. (In part for this reason, you [should have multiple backlogs](https://longform.asmartbear.com/jit-backlogs/), except for the work you're doing right now.) ### Pivoting a strategy just after creating it A strategy that never changes is wrong, and the most likely time to discover that it's wrong is just after you wrote it down, because you have the least practical experience with how it intersects with real life. If you're not pivoting a strategy that turned out to be wrong, you're penalizing the company with months or even years of useless work, followed by rework (if you haven't run the company into the ground), putting the entire future of the company at risk. ![whats that boy paradigm shift](https://public.asmartbear.com/books/anthology/chapters/good-problems-to-have/d5038print-simple.png) [Great strategies](https://longform.asmartbear.com/great-strategy/) are hard to create, and released with great fanfare: Sparkling documents, inspiring presentations, pulpit-thumping speeches, reorganized teams and strategic-pillarized work. So the last thing you want, is come back in a month and say, "just kidding, we were wrong about something important." Will you lose credibility? Will anyone believe the new strategy? Will people think "management doesn't know what it's doing?" These are risks you have to take, because executing the wrong strategy is far worse. Indeed, this is the expected result of a new strategy; it's highly unlikely you got everything right the first time. The best way to communicate, is to say everything in this paragraph out loud, so everyone knows that you know that they know, and that you're putting the company first, and ensuring that no one is doing work that we secretly know is the wrong work. ### Refactoring infrastructure after growing 10x If you're not refactoring your infrastructure after a tenfold increase in growth, you over-engineered your original infrastructure. Having scaled WP Engine to millions of websites serving tens of billions of requests daily, I can tell you that [scale is hard](https://longform.asmartbear.com/scale/), and that you don't know what will break under scale until you're already scaling (because you have to handle [things that you can't even measure yet](https://longform.asmartbear.com/scale-rare/)). If you over-engineer your original product, you're simply not [shipping your SLC fast enough](https://longform.asmartbear.com/slc/), and your naïve attempt at engineering massive scale from the start is just another form of [premature optimization](https://news.ycombinator.com/item?id=23096321). ### Adding words because messaging was too terse If you're not adding back words because the messaging was so terse that it became confusing, your marketing is too verbose, too fluffy, and probably doesn't know what it's trying to say. ![editing advertisement](https://public.asmartbear.com/books/anthology/chapters/good-problems-to-have/image-2-simple.png) People don't read. Tweets are short. Google Ads are shorter. Email titles are shorter. People bounce off home pages in three seconds. No one reads the paragraph of text in the dialog box. You're not even reading this paragraph. It's 100x more likely that your messages aren't punchy enough, aren't [specific enough](https://longform.asmartbear.com/icp-ideal-customer-persona/), than that they're so brief as to be unintelligible. Nowadays you can use ChatGPT prompts to get you 80% of the way there, so you have no excuse. #### Adding back features you removed If you're not adding back features you removed, you're not removing enough. Many products never remove features. This indicates we're not being critical enough, not weeding our garden, not [learning what customers really want](https://longform.asmartbear.com/customer-development/), not understanding what's useful, not admitting when we got it wrong, not shifting when the market shifts. When we do remove a feature, sometimes it will turn out the feature really was important after all. While of course in a perfect world we wouldn't have made that mistake, it's a natural consequence of weeding. ### Fixing lots of bugs just after a major release If you're not fixing bugs due to releasing quickly, you released too late. ![chrysalis late butterfly](https://public.asmartbear.com/books/anthology/chapters/good-problems-to-have/d8816print-simple.png) While [releasing garbage is a bad policy](https://longform.asmartbear.com/slc/), it's also bad to wait until "everything's perfect." Windows 95 shipped with tens of thousands of known bugs,[^1] and was heralded as one of the greatest software innovations and most successful product releases of its time. Contact with customers lets you know which bugs are more important to fix next, and always reveal new bugs that you weren't going to find on your own anyway. [^1]: There were so many, [there's an entire book](https://www.amazon.com/Windows-95-Bug-Collection-Work-Arounds/dp/0201489953) explaining how to work around 1000 of them. ### Waiting too long to scale support or sales If you held onto support and sales for too long, rather than hiring a team, you learned a lot about your customers, and a lot about how to do Support and Sales. It's a classic funded-startup mistake to scale out either of these organizations too soon. Without a system in place, with materials, knowledge bases, and scripts, new hires don't know how to do the job. A distributed-work environment makes this 10x more challenging. The second you put someone else between you and a customer, your pace of learning and understanding falls off a cliff. Wait until it's breaking for lack of scale,[^2] then scale. [^2]: Once [at scale](https://longform.asmartbear.com/scale/), this rule no longer applies; at that point, you're mismanaging the company ### Letting someone go soon after hiring If you held onto someone even though you knew isn't was never going to work, you're doing a great disservice to that person, and your team, and your company, and yourself. Of course this shouldn't be done capriciously, but no one benefits from dragging it out. One likely outcome is that you lose good people, because they see you building a team they don't want to be a part of. Another is a deluge of meetings, complaints, side-conversations, and general worsening of morale. And lower productivity, as competent people cover for the incompetent. You have to [face the truth](https://longform.asmartbear.com/failure-to-face-the-truth/) and act quickly. If this is happening a lot, it's also urgent that you fix your hiring process; in the meantime, the rule still applies. ### Ignoring a competitor's move that turned out to be important If you're not ignoring most of your competitor's moves, then you're playing their game, not yours. ![as you can see weve not only kept up with jones but surpassed them next up nelsons](https://public.asmartbear.com/books/anthology/chapters/good-problems-to-have/d0311print-simple.png) It's essential to stay focused on your unique value proposition and not get sidetracked by every move your competitors make. For instance, when a major competitor of Dropbox launched a similar service at a lower price, Dropbox chose to stay the course, focusing on their superior user experience and brand loyalty, rather than engaging in a price war. Sometimes it will turn out that you really do need to react, but that signal will come from customers, in the form of them asking for features "because so-and-so has it" or cancelling and going to a specific competitor. That indeed demands a reaction, but only because you're seeking what's genuinely best for your customers, not because you're reacting to everything that competitors do. ### Rejecting a lucrative, distracting deal If you're not rejecting lucrative deals that don't align with your strategy, then you [don't have a strategy](https://longform.asmartbear.com/great-strategy/). If you're not rejecting relationships that don't align with your core values, [you don't have core values](https://longform.asmartbear.com/emptiness/). Money is too tempting to reject. Money is one of the main reasons you're building a company in the first place. Money is what keeps the doors from closing and enables the next set of things you want to do. It's even [wise to say "yes" instead of "no,"](https://longform.asmartbear.com/say-yes/) so long as there's enough money in it. But money is not more important than strategy, and it cannot be more important than your values, otherwise you're saying that you don't actually have either one. There's always a way to make more money--a different product, different industry, or breaking the laws or being unethical. There's a reason why you're taking the path you're currently on. --- Not all problems are indicative of poor decisions. It's easy to be hard on ourselves, but sometimes we should do just the opposite: Celebrate our devotion to good decisions and good strategy, even when they have negative consequences. That means you _have_ a strategy, and have the ability to make the tough, wise decisions. --- *HT [Hassy Veldstra](https://twitter.com/hveldstra) for finding the Staedtler advertisement.*

Selling to Carol: Why targeting an ICP brings 10x more customers than you expected

Jason Cohen — Sun, 07 Jan 2024 00:00:00 +0000

![its not that i dont like you ted you just dont fit my target demographic](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/d0106print-2-simple.png) ## Selling to everyone means selling to no one Everyone goes to your home page, therefore your home page has to speak to everyone, right? Buyers, users, existing customers, curious explorers, all working at companies large and small. Most companies approach this the wrong way, which is to speak to "everyone." The worry is, if you were to speak only to a narrow customer segment, everyone else feels excluded or confused, and will bounce off the home page and buy something else. So you end up with generic positioning statements like "The Power To Know." (Figure 1) ![SAS is The Power To Know®, but know what exactly?](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-simple.png) SAS has trademarked "The Power To Know®" but I'm not sure why. Can you tell me what the product does? Who it's for? Did you read that entire paragraph? Surely not. Could anyone in the world--even their perfect, ideal, best customer (ICP: Ideal Customer Profile)--be intrigued and excited to learn more? If that ICP happened across the website, would they even _know_ they are the ideal customer? The first step in disabusing yourself of this fluffy language is to see that by *not* speaking to a specific customer, you're saying _nothing_ to _everyone_ and thus _everyone_ will bounce off the home page, and never think of you again. (You didn't give them anything to think _about_.) If your ideal customer doesn't know you're selling to them, who will? By the way, The Penny Group also delivers "The Power To Know®," also trademarked (Figure 2). So much for defensible differentiation through trademarks. ![So much power! So much knowing!](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-2-simple.png) Generalized messaging has no power, no emotional connection, no interest, no information. Exactly the opposite of what you need from your advertising or home page. The next step in disabusing yourself of this idea of "speaking to everyone" is to acknowledge that it is impossible to speak to everybody at once. You want software developers to know you have an extensible API, but you want the corporate buyers to know that your product [makes them more money](https://longform.asmartbear.com/more-value-not-save-money/). You want to look professional so that managers at large companies are comfortable trusting your company, but you don't want to so aloof that small company buyers see you as "too corporate" and can't relate. You want to highlight configurable workflows and reports that allow large customers to apply your tool to all their teams, but you want small customers to realize that you can turn all that off so things are simple and easy. Once you agree with these two points--you must be evocative and specific, and you can't speak to everyone--you must conclude that you have to speak to just one type of customer. But how do you pick, and how you avoid excluding everyone else, reducing your target market to a tiny niche? We'll start with the first question. ## Selling to Carol Describe a perfect customer. We'll call her Carol. Pick a concrete company that she works for, a company similar to one of your existing, thrilled customers. What's her official title and what does she do? If your potential market includes a wide variety of company types and positions, *just pick one in particular*. Whatever problems your product solves, Carol has *all* those problems. She has all the problems, she knows she has the problems, she already has the budget to spend on those problems, and she's already looking for how to spend it, just like it says in [your market analysis](https://longform.asmartbear.com/problem/). Write those problems down from her point of view, the way she would describe them if complaining to a friend over lunch. Whatever [advantages you have over your competitors](https://longform.asmartbear.com/one-benefit/),[^1] Carol needs exactly those things. Whatever makes people love you,[^2] Carol has those attributes. Whatever makes people continue paying for the product forever,[^3] Carol has that. List all these things.[^4] Carol is literally custom-built to be blown away by your product. [^1]: Not sure? Ask your Sales team--they'll tell you, "oh yeah, any time someone says ______, I know we're going to close the sale." [^2]: Not sure? Ask your Support team--they'll tell you, "oh yeah, the best people are ______ because they ask great questions and they're always telling me how much they love us, even when they have a problem." [^3]: Not sure? Look at your retention data--which customers stay forever, even if you think they shouldn't? Not sure about that either? Use the [Iterative-Hypothesis method to interview them](https://longform.asmartbear.com/customer-development/)--you need to do that anyway so you can build great products. [^4]: Some pundits recommend inventing a personal back-story like "Carol has two cats and loves Mahler." Don't do that. It's irrelevant and inaccurate. Stick with professional attributes, what Carol is interested in, why she is interested in it, why she is looking for a new solution right now, how she makes those decisions, what her budget is, and so on, all topics listed in the aforementioned [Iterative-Hypothesis method](https://longform.asmartbear.com/customer-development/). Now the question is: What would a web page / Google ad / print ad / trade-show booth / postcard be like, such that Carol would immediately understand that you are her savior? Remember, you get only 3 seconds to grab her attention and another 5-10 to convince her that your product is the second coming. Can you make it clear in a picture? Maybe a before/after she can relate to? Will describing three features make it plain? Will pointing out your best competitive advantage make her weep for joy in finally finding a company who "gets it?" Can you ask a provocative question, something she identifies with? Is there a phrase where she'd laugh out loud because "that's so true?" You only get a few seconds, so a paragraph won't do. You have to communicate in a picture and a few words. The good news is you have to please *only Carol*, and you know Carol. You even know she'll honestly be thrilled to find you. **If your ad can't grab Carol's attention--your perfect customer--why do you think it will grab anyone else's attention?** If you still say it's impossible to communicate your message in 5-10 seconds, *no one in the world will get your message*. Simplicity is as important in positioning as it is in [strategy generally](https://longform.asmartbear.com/great-strategy/). When you're willing to speak only to your target audience, it is incredibly powerful (Figure 3) (Figure 4) (Figure 5) (Figure 6) (Figure 7). ![Twilio targets developers, even though the buyer isn't a developer. This campaign was so effective, it turned into an entire book.](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-3-simple.png) ![Dollar Shave Club was unabashedly "cheap but good," and didn't care about sleek advertising like Gillette. It got everyone's attention because it was such a clear message, and sold razors to everyone, not cost-conscious consumers only.](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/dollar-shave-simple.png) ![Subaru knows it isn't cool; it highlights its positive attributes.](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-5-simple.png) ![Subaru is about accessible, safe family life.](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-4-simple.png) ![Porsche's message is... differentiated from Subaru's.](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/porsche-simple.png) ## Surprise: You'll automatically sell far beyond Carol Now let's tackle the fear: That targeting Carol means you're excluding the rest of the market, and therefore limiting your growth and reach, even if you perform well in your niche. That's not what happens. Here's what actually happens: You have a target market--the bullseye--and the center of that bullseye is Carol: ![Carol in the bullseye](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/selling-to-carol-justcarol-simple.png) Every product has strengths and weaknesses. For Subaru: | Strengths | Weaknesses | |:----|:----| | • Low-cost
• Reliable
• Safe *(top-rated by government)*
• Rugged *(take it off-road)* | • Low performance
• Not sporty
• Not cool
• Ugly | Carol is constructed such that she values everything on the left, and actively does not want anything on the right. To her, those aren't weaknesses, those are actually strengths, because she wouldn't be caught dead in a Porsche, her lifestyle isn't sporty, and she disagrees with your definition of "cool" and "ugly." (Figure 9) ![Carol's choices in the bullseye](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/selling-to-carol-full1-simple.png) But Carol isn't the only person attracted to this set of trade-offs. There are other people who either like the things on the left or are at least indifferent to them, and are indifferent to the things on the right. For example: I drive my daughter to school, drive to work, and spend a lot of time on highways and residential roads, so I want something safe and reliable, and I don't care about high performance or being sporty. That's the next ring in the bullseye--people who generally value your positives and aren't dissuaded by the weaknesses. **There are 10x more of those people than there are of Carol.** Let's call that person Diana: ![Diana's choices in the bullseye](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/selling-to-carol-full2-simple.png) But that's not the end of it. Buying a car--like buying anything--involves a set of trade-offs. You rarely find a product that fits your preferences exactly; you're rarely a Carol (or even a Diana). So, you weigh the pros and cons. Maybe I don't care about the car being rugged because I don't go off-road, but I do care about safety and reliability, and I'm neutral on cost. And on the weaknesses, I'm disappointed that it's low-performance, and I do care what it looks like. But, I don't care about "sporty" or "cool" specifically. So where does that leave me? Who knows--everyone weighs these things differently. Clearly some of those people will still decide that Subaru is the best-possible set of trade-offs, and **there are 100x more of those kinds of people than there are of Diana** because this describes most people in the market. Let's suppose 1/10th of those eventually decide your trade-offs are superior to your competitors, so they pick you. Call them Eddie: ![Eddie's choices in the bullseye](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/selling-to-carol-full3-2-simple.png) This is why, contrary to your fear that targeting Carol dramatically limits your target market, your effective target is actually at least 10x larger (because of Diana) and more like 20x-100x larger (because of Eddie). Targeting Carol makes your message clear, compelling, evocative, even emotional. But trade-offs are how people buy, and exactly because you were so clear about what those are, you've paved the road for many people to make that choice. ## Does this theory work in practice? Continuing the Subaru example,[^5] the advertisements above are about being low-cost but practical. These are attributes valued by many people. But in the 1990s, sales were in decline, so they researched which consumer segments were most likely to buy a Subaru. They found five categories: Teachers, healthcare professionals, IT professionals, "outdoorsy types," and lesbians. Lesbians were the strongest category--four times more likely to buy. [^5]: This story is expanded and cross-referenced in [Alex Mayyasi's 2016 article in Pricenomics](https://priceonomics.com/how-an-ad-campaign-made-lesbians-fall-in-love-with/). Targeting lesbians was a risky move in the '90s. There were still no mainstream TV shows or movies with gay characters, and celebrities were still in the closet. When Ellen DeGeneres (both a celebrity and star of a popular TV show) came out as gay in 1997, advertisers pulled out, including Chrysler. When Ikea ran a commercial featuring a gay couple, their stores received bomb threats. But as Warren Buffett says, "Be greedy when others are fearful," and Subaru invested where others feared to tread (Figure 12) (Figure 13). ![Entirely comfortable with its orientation](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-7-simple.png) ![It's not a choice. It's the way we're built](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-8-simple.png) The campaigns were a hit, but not only with lesbians. Subaru grew faster in the subsequent ten years than any other car company,[^6] and while the gay and lesbian demographic remained their strongest niche, the vast majority of people who bought a Subaru were straight. And they were buying the car because they value its attributes--affordable, safe, rugged--and because on balance they cared more about those than the attributes it lacks--sleek, sporty, high performance. When you target Carol, your positioning is strongest, and you win where you deserve to win. But far more people will buy because they have similar purchasing preferences. [^6]: Measured as a percentage of [total sales volume](https://knoema.com/infographics/floslle/top-vehicle-manufacturers-in-the-us-market-1961-2016) from 1995 to 2005, of car companies that were founded before 1990. ## Finding Carol in the noise When a customer cancels, we need find out why--what did they think would happen, and how did we fail them? Unless the customer stopped their project or went out of business, [cancellation indicates a gap](https://longform.asmartbear.com/product-market-fit-formula/) between what we promised them (marketing) and what we delivered (product). This investigation is most urgent when the customer is Carol. You stipulated she is the perfect customer, but she disagrees. Perhaps the product needs to change, but perhaps your definition of Carol needs to change to better match what the product has turned out to be or what [you are uniquely excellent at delivering](https://longform.asmartbear.com/leverage/), or some other facet of [your strategy](https://longform.asmartbear.com/great-strategy/). The same could be said Diana, but when it comes to Eddie it's less clear whether this is signal or noise. Eddie is a mismatch for some of our attributes, and might cancel as a result, but that doesn't mean we should change those attributes, if they align with Carol and Diana. If we treat all customers as if they are Carol, and therefore all cancellation data as a vital signal, we lose track of building for Carol and we end up building for nobody. Some customers aren't even Eddie--they're not on the bullseye, yet they signed up. You might think this would be rare, since it is irrational, but in fact it will happen all the time. You need to identify these customers and completely ignore their data--the noise will push you off course. ![Daniel Zarick explaining on Twitter that he rejected a sale for $48,000 because it wasn't their ICP](https://public.asmartbear.com/books/anthology/chapters/icp-ideal-customer-persona/image-9-simple.png) Therefore, you need a way of identifying who is a Carol. Ideally, right from the start, during on-boarding, maybe with a few questions, maybe based on activity indicating they're in that perfect zone. Then, you can measure cancellation rates of Carol (the only rate you care about), and spend extra time finding out why they cancel. The "extra time" comes from the time you saved by not pursuing and interviewing other customers. ## Prioritizing Carol over revenue Of course it's not just in cancellations that you want to segment the ICPs from the rest of the customers. It's in your activation funnel. It's in your trial or freemium conversion rate. It's in individual feature usage. It's in conversion to monthly active users (meaning a single user actually using it consistently month over month). It's in tech-support requests both for bug fixes and features. It's in every aspect of understanding how customers are intersecting with our product and company. It's always about the Carols; everything else is noise, and if you don't separate the two, you can't build the best product, for any definition of "best." The typical failure mode for this is that your biggest customer, who you desperately want to keep because you really want that revenue, occupies you constantly in tech support and in feature requests, some of which you've implemented just for them, with the internal excuse that "if they want it, other larger customers probably will want it, so this is a path of growth," yet they're still perennially unhappy, unsatisfied, obviously a bad fit, an anti-Carol, but dangit you _need_ that money and if we can _just_ make this work there _must_ be more customers like that coming. And after all that, of course they leave in a huff, having been incredibly unprofitable despite their high nominal revenue, and you *should* turn around and say, "Wait a minute, that customer was not in our bullseye all along." Rather than noticing that from the start, and helping them to leave your company, you clung to them because you so desperately wanted it to work, and for good reason--it was a lot of money--but ultimately that wasn't the right reason, and the whole thing was an enormous waste of time. You could have been finding and on-boarding a few more Carols instead, and they would still be here, happily paying you money, profitably. You'll know that you're doing this right when you turn down a sale, even though it would have been your largest customer, simply because they're not your ICP, as in these miniature stories from [Daniel Zarick at ArrowsHQ](https://twitter.com/DanielZarick/status/1743010670763782204) and [John Doherty at EditorNinja](https://twitter.com/dohertyjf/status/1742739614031323572). **This separates first-time founders from seasoned founders**. It's not a coincidence that both Daniel and John have seen this movie before, made this mistake before, and are avoiding it this time. Can you? After all this, if you reread that ad from SAS with The Power To Know®, you'll die a little inside. So much nothing. Nothing to say, said to no one, caring about nothing, changing nothing, being nothing. You can do better. And make more money doing it. --- *Many thanks to [Florian Caeser](https://twitter.com/FlorianCaesar) for contributing insights to early drafts.*

The roadmap to Product/Market Fit (PMF)… maybe

Jason Cohen — Sun, 24 Dec 2023 00:00:00 +0000

![easy follow rules more difficult implement than youd think](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/d7670print-simple.png) What is the formula for going from an initial product idea to Product/Market Fit (PMF)--a company that is growing and sustainable, with customers that want to pay and want to stay. (Assuming you even agree with [that definition of Product/Market Fit](https://longform.asmartbear.com/product-market-fit/).) There isn't a fail-safe roadmap of course, but there is a progression that describes how WP Engine became a unicorn, supported by 19 years of articles in this book. Here's what that progression looks like. Then we'll explore the other truth--that it often doesn't work like this after all. 1. **Personal Fit**: "Passion" is useful, but winning requires a personal edge. 2. **Market Fit**: Most good ideas aren't good businesses. 3. **Customer Fit**: Talk to customers before you waste months building the wrong thing. 4. **Build and ship the SLC** quickly: It's where the real learning happens. 5. **Marketing and Sales** more than writing code and tweaking design. 6. **Retention-driven Product** development; attend to your existing customers first. 7. **Prioritize** systematically, ruthlessly, strategically. 8. **Manage your psychology** on a journey of self-discovery and constant rejection. --- ## 1. Personal Fit: The inner fire, leveraging a personal edge to do what you were meant to do A great idea or a great strategy that _you_ can't execute well, isn't a good strategy _for you_. You start with nothing--no product, no customers, no brand, no distribution, and compared to competitors, no money and no time. To succeed, you need _something_. Something special. Something that gives you an edge, despite being woefully inadequate in every dimension. Is "passion" that something? ### Passion > "Do what you love, and the money will follow." > --Motivational advisors That is false, as evidenced by most artists, philosophy majors, and the 80% of startups that fail despite founders' genuine love and obsession. Passion _is_ required; it's just not enough. Passion is the motivator, especially in the dark times when your savings is depleted, competitors are beating you, as you suffer a constant bombardment of rejection from potential customers and potential employees and potential investors, as you question your [self-worth](https://longform.asmartbear.com/startups-emotionally-draining/) and wonder whether the cause of your [Impostor Syndrome](https://longform.asmartbear.com/impostor-syndrome/) is that you're [actually an impostor](https://longform.asmartbear.com/survivor-bias/). > "You have to be burning with an idea, or a problem, or a wrong that you want to right. If you're not passionate enough from the start, you'll never stick it out." > --Steve Jobs You need to articulate exactly what your passion is, so that you can use it as a filter for business concept. This is difficult; some people spend their whole life trying to figure it out. [Jerry Colonna says](https://www.reboot.io/reboot-leadership-and-the-art-of-growing-up/) "The purpose of life is to discover who you are, and then live fully into that." It is not an exaggeration to say that a [startup is who you are](https://longform.asmartbear.com/identity-selling-sadness/); that's why they call it "your baby." See [this article on Pivot Points](https://longform.asmartbear.com/pivot-points/) for a specific list of questions that help you suss what your purpose and passions are. Your goal is to **find the work you were *meant* to do**. Your calling. The work that you would do for free, but more--what you are *compelled* to do. ![robe sorcery potions use coaster](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/d8849print-simple.png) Still, the existence of passion doesn't imply the existence of a business model. (We'll solve that in the next step.) Passion doesn't give you a competitive edge, because all the other founders have passion, and larger companies have mountains of advantages that make "passion" look like a sling-shot attacking an aircraft carrier. You need an edge. ### Leverage "Leverage" means yielding a huge output from a given input--accomplishing an inordinate amount of quantity or quality, with relatively little time and money. You generate leverage from the mixture of talents, taste, and experiences that you possess. To figure out how this works for you, [see this article for many types and examples of leverage](https://longform.asmartbear.com/leverage/). Leverage is good, but *unique* leverage is far better, because that's your source of differentiation[^1] from competitors and alternatives. Often this appears at the intersection of your peculiar above-average talents and experiences, which taken together are unique.[^2] This uniqueness must then be coupled with a particular way that you are approaching this problem and solution, such that the combination of your leverage against your path is uniquely excellent, even for a [small number of potential customers](https://longform.asmartbear.com/icp-ideal-customer-persona/). This leverage + path combination also forms the kernel of your strategy[^3] as your competitive positioning. You need something that competitors lack, because your customers are going to compare you to competitors whether you like it or not, and you have to have an answer besides "we have this one minor feature that they don't have" or "we're $10 cheaper." [^1]: "Different," not "better." Yes, you will be "better" according to some set of people, but definitionally that means you're "worse" for another set. The latter might even be orders of magnitude more numerous than the former. That's OK, and that's why I don't like using the word "better." You should be distinctive, aligned with your calling and your strengths, so that the set of people who do find your brand of different to be "better" will flock to you, buy from you, and even [love paying you](https://longform.asmartbear.com/willingness-to-pay/) for it. [^2]: The quintessential exponent of this idea is Scott Adams, writer of the Dilbert cartoon, who [points out](https://dilbertblog.typepad.com/the_dilbert_blog/2007/07/career-advice.html) that he is a decent illustrator but not a great one, a decent humorist but worse than any comedian on Netflix, and has held jobs in the tech sector, unlike almost any artist or comedian. It is in the intersection of being in the top 25% in each of these three "circles of competence" that he is unique, not because he is top 1% or "best in the world" at any one thing. [^3]: Leveraging strengths into durable competitive advantage is one of the [six characteristics of great strategy](https://longform.asmartbear.com/great-strategy/). The key question that summarizes this intersection of "you" and "path" is: **Why are *you* the perfect person to build *this* company?** > "Be yourself. Everyone else is taken." > --Oscar Wilde ## 2. Market Fit: A working theory of why they will buy. Once you inventory the inner world--the shapes of spaces where you were meant to play--you turn your gaze externally to figure out whether your idea works out in the market, in the world of customers, competitors, trends, problems-to-solve, jobs-to-be-done, and products. These things you do not control, and thus must understand, conform to, but also exploit. It's right there in the name: "Product/Market Fit" means fitting into the market, not just building something that would be fun/interesting/edifying/curious/exciting/ego-enhancing. Those are all good reasons to have a side-project, but none is a reason why that side-project will become a profitable business, even at a scale that feeds a single person. That's why most "indie hacker" startups and "AI is cool" startups fail--the genesis was "fun project," not "plausible business." "I had the problem myself, so I built a product to solve it." This might be the most common origin story, tacitly concluding that "this must be a business because I would have been a customer." Indeed, my startups all started this way. But your understanding of "the problem" as it pertains to you alone is much less likely to be a real business than you think. You are, in fact, not like your customer.[^4] [^4]: For a start, your customer isn't quitting their day job to create a business. Sometimes a passion project turns into a business anyway. That's what happened to me at Smart Bear. More on that later. But that's luck. The point of a framework like this one is to reduce your reliance on luck. > "The way to get startup ideas is not to try to think of startup ideas. It's to look for problems, preferably problems you have yourself. > > The very best startup ideas tend to have three things in common: they're something the founders themselves want, that they themselves can build, and that few others realize are worth doing. Microsoft, Apple, Yahoo, Google, and Facebook all began this way." > *--Paul Graham, [How to Get Startup Ideas](https://paulgraham.com/startupideas.html)* ### A theory of why they will buy You need a plausible theory of the customer, market, and business model. "Plausible," because many ideas fall apart under honest scrutiny. "Theory" because of course it won't be completely correct, but it will be your working understanding of the world, which you modify as you learn. **Here's exactly how to [develop a specific theory of the market, customer, and your positioning](https://longform.asmartbear.com/problem/)**. You'll analyze these market characteristics: 1. **Plausible:** Do 10M people or 100k companies have the problem? 2. **Self-Aware:** Do they know & care they have the problem? 3. **Lucrative:** Do they have substantial budget to solve this problem? 4. **Liquid:** Are they willing and able to buy right now? 5. **Eager:** Do they want to buy from you, specifically? 6. **Enduring:** Will they still be paying(-it-forward) a year from now? A lot of people read that article, then [said](https://twitter.com/randfish/status/1647666512290013184?s=20) [they](https://www.linkedin.com/feed/update/urn:li:activity:7053396708713459712/?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7053396708713459712%2C7053504417685274624%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287053504417685274624%2Curn%3Ali%3Aactivity%3A7053396708713459712%29) [wish](https://twitter.com/nathanjpowellux/status/1656536138901778432?s=12&t=mnlAhEmLkMKMeX_fkP_pzw) [they](https://www.linkedin.com/feed/update/urn:li:activity:7053766029830754304/) [had](https://www.linkedin.com/posts/aaronmichaelroy_excuse-me-is-there-a-problem-activity-7096617697798434816-1U22/?utm_source=share&utm_medium=member_desktop) [read](https://x.com/TheWenJie/status/1735476053135737109?s=20) [it before](https://twitter.com/advany/status/1745429907788689516) [they](https://twitter.com/advany/status/1746873616354926881) wasted two years of their life (and savings and heartache) building [insert name of failed startup]. Don't be one of those people. You might think it infeasible to answer these questions, because the research is impossible or the idea is so new that existing data and trends are irrelevant. However, as you'll see in the article, you _can_ do it using Fermi Estimation--a technique useful not only in market analysis but for [ROI](https://longform.asmartbear.com/roi-rubric/), [probabilities](https://longform.asmartbear.com/probability-words/), and [decisions](https://longform.asmartbear.com/maximized-decision/). ![well its not the worst ive seen](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/d7605print-simple.png) ### Early strategy Even better than having a great business model is to have a [great strategy](https://longform.asmartbear.com/great-strategy/). Not a twenty-page document, but a one-pager that conforms to the guidelines in that article, explaining "how we will win." It's never too early to be asking yourself how to [leverage your strengths](https://longform.asmartbear.com/leverage/) to build products that are great and differentiated [despite your weaknesses](https://longform.asmartbear.com/worse-but-unique/). It's never too early to write down your assumptions and decisions, to ensure they are at least self-consistent if not self-reinforcing, so that when contrary evidence appears, you can notice that, and react methodically. To generate concrete strategic ideas for your business model or strategy--explaining "how we will win"--consider creating a theory of where your product will fit in the customer's [Needs Stack](https://longform.asmartbear.com/needs-stack/), pick a few of these tactics for [navigating the fact that the future is unpredictable](https://longform.asmartbear.com/predict-the-future/), and decide how you will generate "Love" and "Utility" types of [Willingness-to-Pay](https://longform.asmartbear.com/willingness-to-pay/). All of these are fun, accessible, strategic, and they work in practice. "But often the first idea is wrong, so why do I need a specific idea with a specific business model?" Because walking in a random direction is not progress, and because the main way to discover the _right_ direction, is to have a clear theory of the world, and notice when the world contradicts it, so you can pivot into a better theory, and thus iterate into a genuinely great strategy and market-understanding. That's what *actually* happened with Smart Bear, and Slack and WhatsApp and Flickr, [as described and analyzed here](https://longform.asmartbear.com/predict-the-future/). Only by having a specific, strong, clear idea, was it possible to notice what customers _really_ wanted *instead*, which led each of those companies to pivot into the idea that became successful. In all those cases, the idea was very personal to the founders--the personal-fit of passion for the solution and leveraging an edge to create something that resonated with early users. But it was only with a specific theory of business model and strategy, which was then negated by reality, and then an intentional pivot, to achieve Product/Market Fit. ## 3. Customer Fit: Find the ideal customers first. There's only one source of truth for what customers will buy: Customers. Not advisors, not experts, not analysts, not Twitter polls, not research, not [past data](https://rogermartin.medium.com/overcoming-the-pervasive-analytical-blunder-of-strategists-a5a4c52289b4), not even competitors' behavior. **You have to talk to customers.** ![so tell me a little bit of what you think i want to hear about yourself](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/d7030print-simple.png) Customers are fickle. You ask whether they'll pay $100 if your product does ______ and they say "yes;" then you build it and they don't buy. So why talk to them at all? Don't you need to put the actual product in front of them, and see what they actually do? Customers can tell you what their lives are like, which is how you validate your business model and strategy from the previous section. Customers can tell you what they _won't_ buy, which has happened to me repeatedly. You can discover that the average customer doesn't know the problem exists, or doesn't have a budget for it, or isn't prioritizing it. When they say "yes," it's a "maybe," but when they say "no," it's a "no," and you just saved yourself months or years of wasted time. You can find out where they go to find products like this, so that you can advertise in the right place. You can find out what language they use to talk about the problem or solution, so you can copy that language in your advertising and social media home page and capture their attention. You can find out how their budgets work, so you can price and package and position accordingly. You can find out how they're addressing the problem today, so you know what you're selling against, whether that's a competitor, an alternative, or something they're doing by hand. You can find out what causes them to break out of their daily life and say [as Bob Moesta puts it](https://www.intercom.com/blog/podcasts/bob-moesta-on-unpacking-customer-motivations-with-jobs-to-be-done/), "Today is the day I'm going to buy ______," so that you can try to be there when that event happens, or possibly even cause it. It is easy to find examples of successful companies who never asked customers what they wanted. That happened to me at Smart Bear. But again that relies on luck, and most of the time, you don't get lucky. This is not a good way to gamble the next few years of your life. **But you don't want to do this work.** You want to build the product because that's the fun part. Potential customers are hard to find and they don't want to talk to you. It's going to be like that when you have a product too, so if you can't do it now, you won't be able to do it later. Building the product first won't make it easier to find or talk to customers. Building the product first will, however, ensure that you haven't actually built what people want, because you never found out what people want. ![tweet that you need to talk to customers](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-7-simple.png) ### How to interview customers Once you get someone on the phone, how do you interview them in a way that maximizes learning and leads to a specific theory that you can execute on, from your [unique winning advantage](https://longform.asmartbear.com/one-benefit/) to [pricing](https://longform.asmartbear.com/more-value-not-save-money/)? **The customer validation system I've developed is [the Iterative-Hypothesis customer development method](https://longform.asmartbear.com/customer-development/).** This provides you with goals that you achieve using interviews, how to create the hypotheses that will drive your business model, and how to write questions that will maximize learning. 17 years ago it invalidated a startup idea that I *thought* was good, and then validated the startup idea that became WP Engine--now a unicorn. ### How to get customers to talk to you The way I got interviews for WP Engine is by using LinkedIn to find people who had the title and industry I was targeting (web developers in WordPress), and asked them for an hour of their time to chat about a new startup concept for whom they are the ideal customer. Furthermore I offered to pay any amount they wished for that hour, because I value their expertise and their time; I'm not asking for a donation, I'm genuinely interested in their expertise. Out of 50 requests, 40 agreed, and only one asked to be paid. (30 eventually became customers.) This might not work for you; that was a long time ago. **[This article details many more techniques for finding potential customers](https://longform.asmartbear.com/find-customers-to-interview/)**, by going where they already are, where they're already talking, and where they might be willing to talk to you. ### Find your ICP Your goal is not only to validate your theory of the market, but to discover your [ICP (Ideal Customer Profile)](https://longform.asmartbear.com/icp-ideal-customer-persona/). This is your "perfect customer"--a segment almost comically over-specified to be so perfect for your product that you are truly the best choice in the market, and they would be crazy not to buy. You will then aim all of your marketing messaging at this person: Website, advertisement, terminology, writing style, pricing. When the ICP lands on the home page, it should be obvious in three seconds that this is perfect for them, that you "get" them, and they will be pulled through the process until they're a customer. ![tweet that building for the ICP is better](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-3-simple.png) You are scared that targeting only the ICP limits your potential market, but this is not what happens. In "[Selling to Carol](https://longform.asmartbear.com/icp-ideal-customer-persona/)," I explain the mechanism and provide examples showing that for every ICP there are 10x more people who make similar buying decisions, and 100x more who take more convincing but ultimately also agree, and therefore you end up selling to a far wider market than you feared, yet earning attention and loyalty from having a clear, unique message. Your company, brand, and product will actually mean something. ![im looking for something in small pond](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/d5228print-simple.png) Find and talk to the customers; they're the only ones with the answers. ## 4. Build _and ship_ the SLC. Interviews give you a better model of the world, but the real learning doesn't start until people are using--or more often, _not_ using--a product. [SLC is my alternative to MVP](https://longform.asmartbear.com/slc/): **Simple**, because complex things can't be built quickly, and you must ship quickly so you can learn quickly so you can create the right product before you run out of money and willpower. **Lovable**, because crappy products are insulting, and you didn't start this company to make crappy products. The love overpowers the fact that the product is buggy and feature-poor. There are many [wonderful, powerful, competitively-defensible forms of "Love."](https://longform.asmartbear.com/willingness-to-pay/) Pick some. **Complete**, because products are supposed to accomplish a job. Customers want to use a v1 of something simple, not v0.1 of something broken. Build something small (but also lovable and complete), and ship it to customers, because that's when real "progress" begins. That's how you learn [the truth](https://longform.asmartbear.com/failure-to-face-the-truth/). If you're building for six months and a customer hasn't used it, you're not on the road to Product/Market Fit; indeed you're not making progress on the business at all. What you've done is proved what you already knew--you can build software. The learning starts only when customers are (trying to) use it. ![Tweet saying how important it is to watch people using the product](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-2-simple.png) ## 5. Focus more on marketing and sales, less on product. Your natural inclination is to do what you enjoy and understand, so you need to press yourself to do so much marketing and sales that it feels like "too much." Ask yourself: What can you do *today*, that will (a) get more people from the target market to come to the website or (b) convert more of those people to try the product or (c) convert more of those to buy the product. The answer _might_ be found in writing more code. Improving the new-user experience, for example, could increase the conversion rate from "try" to "buy." But more often the answer isn't inside the product; indeed several of those steps happen before the person has used the product at all. [The code is actually your enemy.](https://longform.asmartbear.com/code-is-your-enemy/) You'll advance the product anyway--I know that. And you should, but you don't need to be told to do that. After all, it's all you ever wanted to do, even at step one. That's why you need to set an intention every day to make progress on one of those questions. That's how you're going to generate growth. ![Growth at ProjectionLab went from "not enough to quit my day job" to "now I have a team and $1M ARR" thanks to hiring a marketer. From the founder: "I stay focused on building, while he handles growth, marketing, partnerships, and some ops stuff."](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-9-simple.png) ![Pallyy didn't grow until marketing started](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/do-marketing-simple.png) ![Focus on growth instead of coding](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-5-simple.png) ## 6. Prioritize retention-based product work. What does it mean, that one person out of billions cared enough about your problem-space to notice your advertisement or social media post, then felt compelled to follow the link, then was so intrigued by the website that they joined the 1-out-of-100 that tried the product, then liked what they saw enough to take out their wallet and pay, then started to use it... and then *cancelled?!?* After all that--clear signals of desire, need, [willingness to pay](https://longform.asmartbear.com/willingness-to-pay/), and apparent "fit"--they decided "Nope, this is not what I want." What it means is: You made the right promise, but didn't keep it. Marketing is where you discover the promise the customer wants you to make, and retention[^5] is where you discover whether you're fulfilling that promise. [^5]: Actually, retention is a lagging, multi-factor indicator, which makes it useful as a high-level check, but not useful as an operational day-to-day metric. Here's a [complete system for practical metrics](https://longform.asmartbear.com/product-metrics/) that embraces this fact and points to the right set of metrics. ![no youre thinking of cupid im one night stand fairy](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/d1906print-simple.png) Some people believe that low retention is fine because you can make up for it with growth. That's not true for two reasons: 1. Growth will start out good, but then will slow and halt sooner than you realize.[^6] 2. If customers don't want to use the product, your "growth" is fake news. It's not Product/Market "Fit" if the "market" decides "not a fit" after actually using the product. [^6]: See these articles with [data from Product/Market fit companies](https://longform.asmartbear.com/product-market-fit/) and [the math behind growth grinding to a halt](https://longform.asmartbear.com/problem/) at companies with low retention. Therefore, when you do work on product features, build things that increase retention. This starts by understanding why people cancel. Gaining this understanding is difficult, because once they leave they don't want to talk to you anymore. You might coerce them by paying for interviews with Amazon gift cards or charitable donations. Some people will respond to an email (no harm in asking). Better is to look for signs that the customer isn't being successful (e.g. activity within the product) and reach out before it's too late, while they're still in the middle of their struggle, and therefore might be willing and able to articulate it. Remember that your goal is not to discover what unsuccessful customers have in common, because successful customers often have those things in common as well. Rather, it is to find patterns in unsuccessful customers _that are not shared by successful customers_. Those are the attributes that lead to action. For example, if "using feature X" means people are successful, maybe unsuccessful customers don't know about it (so you promote its usage), or maybe they can't use it because it's incompatible with their workflow (so add integrations or options). Sometimes, however, it's about the customers themselves, where tuning the ICP means you find more customers who are already a fit, perhaps targeting different industries or geographies, different stages or sizes of company, the title or attitude of the buyer, or the specific use-cases they are trying to solve for. The [Talk/Walk workshop](https://longform.asmartbear.com/talk-vs-walk/) might help, because it helps elucidate the difference between the promise and the execution. But probably, as usual, you just need to talk to customers. ## 7. Ruthlessly, methodically, systematically prioritize. More than just "[focus](https://longform.asmartbear.com/focus/)," more than a generic admonition to "prioritize," use a system that ensures you're leveraging your most valuable, most limited asset (time) to maximize progress. ![bear making a strategy](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-8-simple.png) Do this by combining [Fairytale Planning](https://longform.asmartbear.com/strategic-planning/) with the [Rocks, Pebbles, Sand](https://longform.asmartbear.com/rocks-pebbles-sand/) work-prioritization system: #### Identify the next milestone. At all times, be crystal clear about the single most important thing to achieve. _(Ex: Finding a good business model. Public launch. 20 paying customers. Getting trial conversion rates from 1% to 5%. Getting cancellation from 7%/mo to 4%/mo. Getting to $10k MRR so you can quit your day job.)_ You have to be executing against the next milestone every day. Write it down and look at it every morning. Everything else is a distraction to be ignored, or a necessary evil that you should dispatch as quickly as possible, including [delegation](https://longform.asmartbear.com/delegation/) or accepting that it's fine if it's executed poorly or late. #### Identify the current obstacle to achieving the milestone. At all times, be crystal clear why it is difficult or slow or expensive to achieve that milestone. _(Ex: Can't figure out how to leverage your strengths for something differentiated and desirable. Easy to get someone to use it once, but only 15% use it again. Got 17 customers from Product Hunt but now there's no repeatable way to get more. Can't get people to agree to be interviewed. SLC isn't accepted by beta testers. Freemium users don't have enough incentive to convert to paid.)_ You must [face the difficult truth](https://longform.asmartbear.com/failure-to-face-the-truth/). Do not pick the obstacle that feels comfortable; pick the existential crisis that is uncomfortable exactly because it is critical and scary and true. You have to be attacking or side-stepping the obstacle every day. Write it down and look at it every morning. #### Split work into Rocks, Pebbles, Sand. Use this specific [Rocks, Pebbles, Sand framework](https://longform.asmartbear.com/rocks-pebbles-sand/) to prioritize and schedule different types of work with the correct philosophy and process appropriate for each type. #### Select the Rock that attacks the obstacle and milestone together. Use [Binstack](https://longform.asmartbear.com/maximized-decision/) to prioritize one Rock that addresses that most important milestone and attacks the obstacle. (You don't have time for more than one.) If you can't think of one that's good enough, don't just proceed with a mediocre plan that will occupy the next three months; grab some friends--or better yet, customers!--and [brainstorm](https://longform.asmartbear.com/extreme-questions/) a better one. #### Schedule Pebbles sparingly. Because you have to focus on the Rock, and you have little time for anything else, use this [ROI framework](https://longform.asmartbear.com/roi-selling/) to prioritize just a few other activities that are valuable enough to justify spending your remaining time. #### Almost everything should be only "good enough" You have taste, you have craft, and part of the reason you're doing all this is to express yourself. But almost everything you do will not determine whether the company is successful. (Repeat that last sentence until you fully internalize it.) Which means most things should only be "good enough." Let it go. Most metrics should be [satisfied, not maximized](https://longform.asmartbear.com/maximizing/). Pick [KPIs accordingly](https://longform.asmartbear.com/product-metrics/). [Design matters less than you think](https://longform.asmartbear.com/design/). Having said all that, sometimes the very best aspects of a design are the little things, the obsessive things, the things you are compelled to make just so. Whether web design or UX design or a "perfect" feature which is only perfect because of the myriad corner cases that took a surprising amount of code and care to cover, but which means a new customer has an amazing experience. Pick your battles. Don't stray from the system. You don't have the time. ## 8. Manage your psychology. Throughout this a step-by-step roadmap, you face demanding psychological challenges. * **[Facing the truth](https://longform.asmartbear.com/failure-to-face-the-truth/)**--not allowing "what you wish were true" to get in the way of finding out what is true, about your abilities, customer's wishes, the shape of the market, why people leave. Recognizing that there's always someone who is better than you at any given thing, and it becomes your job to [find and hire them](https://longform.asmartbear.com/delegation/). * **Finding yourself**--coming face-to-face with who you are, and who you are not, figuring out what is [actually important](https://longform.asmartbear.com/emptiness/) versus what is necessary or temporary, [enjoying the journey](https://longform.asmartbear.com/success-guaranteed/) especially when even a [successful end result](https://longform.asmartbear.com/rich-vs-king-sold-company/) is often [not what you hoped it would be](https://longform.asmartbear.com/identity-selling-sadness/). * **Making clear decisions**--deciding what you're not good at, and not trying to do it anyway; deciding your place in the market, and not trying to be all things to all people; deciding your [ICP](https://longform.asmartbear.com/icp-ideal-customer-persona/) and having the fortitude to go all-in; deciding the current milestone, the main challenge, the one big thing you have to do, and sticking to it without getting distracted. * **Constant rejection**--your ideas won't be good enough, your customer interviews will negate key assumptions, 99% of website visitors won't take an action, long sales calls will result in nothing, customers will buy but then leave, employees won't join you, investors won't invest. * **Fluid change**--despite clear decisions, rejections often demand a change, which is difficult to accept, [difficult to enact](https://longform.asmartbear.com/change/), difficult to admit when things are wrong, and difficult to decide whether some rejection should be ignored as an anomaly, or whether it's indicative of a "learning" that must result in a change, difficult to tell the difference between the [chaos that results in PMF and the one that results in failure](https://longform.asmartbear.com/chaos-at-start/). * **Acting while uncertain**--being "all-in" even while knowing you must be wrong about some things, even while getting rejected and making changes, even while doing things you've never done while [experts tell you what to do differently](https://longform.asmartbear.com/bad-advice/), even while feeling like an [impostor who has no right to be here](https://longform.asmartbear.com/impostor-syndrome/), even while knowing [you have no idea what you are doing](https://longform.asmartbear.com/startups-emotionally-draining/) while it appears everyone else knows exactly what they're doing. (Don't worry, they really don't.) ![youll never be part of a royal flush me neither thats ok](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/d7782print-simple.png) This might be the hardest thing you've ever done. It's a [gauntlet of pain](https://longform.asmartbear.com/startup-life/), even if it's also the most exciting thing you'll do in your professional life. Many people stop because it's so hard. You can't blame them. --- ## But you're not going to do this, are you... You have a fun feature idea so you're just building it. You always wanted to learn Rust so you're just learning it. AI is astounding and it's the future so you're building things with it, even though it's a technology and not a customer's problem that needs solving. You've started your own company in part because you don't want to be shackled by "processes" and "frameworks," so you're just doing whatever you want. Visual Studio is comfortable and Google Ad Manager is foreign and like Las Vegas is designed to separate you from your money, to say nothing of the discomfort of [asking for the sale](https://longform.asmartbear.com/startups-emotionally-draining/) or getting someone on a call to talk about their workflows or get an earful about how your [precious software](https://longform.asmartbear.com/startup-life/) actually sucks. Uncomfortable. Scary. And you're lost, unskilled at those things, not making progress, not even _wanting_ to. So you slip back to Visual Studio where you know what to do, and enjoy doing it, and you're good at it. Then you post on Twitter about how you have $1600 in MRR after 18 months but it's okay because perseverance is a virtue. When in fact you've fallen into the [well-known trap of doing what you're good at, what you love, but not what the business needs done](https://longform.asmartbear.com/fulfillment/). Perseverance *is* a virtue, if you're doing the right work, with the right goals. It's a vice if it means you're moving diligently in the wrong direction. ## Smart Bear only halfway followed the roadmap But who am I to talk? The company I founded in 2002, sold in 2007, and that gave me the online handle that persists to this day on this site, Smart Bear's first "product" Code Historian was a personal project, built because I want to learn .NET (we would later rewrite everything in Java), built because I thought there was value in the historical record encoded in version control (all products that operated under that assumption failed). ![Screenshot of viewing how old each line of code is, in Code Historian](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/recentchanges-simple.png) I didn't talk to customers ahead of time. I did talk to them a lot after they started paying, and learned that many didn't really want Code Historian at all, but rather they were abusing it to accomplish something else (in a story [told here](https://longform.asmartbear.com/predict-the-future/)). As a result, I created Code Reviewer, which sort of hit the need in question, but the architecture was all wrong and the features were all hacks. But all this customer feedback gave me the confidence to rewrite everything from scratch (which everyone says you should never do), resulting in a product called Code Collaborator, which made millions of dollars. Would I have found the idea for Code Collaborator by interviewing customers using the "Iterative-Hypothesis" method above? No, because I wouldn't have known what to ask them, and they wouldn't have known to tell me. I would have discovered that Code Historian wasn't a good business idea... and that's it. Maybe I would have had a completely different and better idea. Maybe. But the fact is, the actual "path to success" was to create something I liked, just interesting enough to attract initial customers even though it's not a great business, and then pivot to Code Reviewer, and then pivot to Code Collaborator. The interviews would have stopped everything cold. Does that make "interviewing" the wrong road to Product/Market Fit? I do think building and getting product into customers hands is far more valuable than conversations. Smart Bear was an example of (1) having a specific idea matching my passions, which then because of (4) building and shipping and (5) marketing and selling and (6) building for retention by talking to customers, lead to the right idea. So, it was part of the path, but not the full path. So, while the path to success didn't honor every step in the roadmap, it still would have failed if I had violated those other steps--coding in isolation, coding instead of selling, coding instead of honestly figuring out what customers really wanted. ## WP Engine followed the roadmap and became a unicorn That said, with WP Engine I traveled this roadmap exactly. WP Engine wasn't the initial idea. The first idea was for a marketing analytics tool with features that Google Analytics lacked: A real-time event dashboard, a clever goal-setting-and-measuring system that worked retroactively, and the ability to capture web form data even when it was only partially filled-in and never submitted, all with just one line of Javascript.[^7] But I ran that Iterative-Hypothesis method and after 20 interviews I learned that the idea wasn't strong enough.[^8] I was on the road; I (1) had a specific idea that (2) was clearly a great market and (3) vetted it with customers. Thanks to (3), I did not waste years on that project. [^7]: These features are typical of analytics tools today, but in 2009 it was innovative. [^8]: [This article gives the details of this invalidation](https://longform.asmartbear.com/good-startup-ideas/), and how it was different from WP Engine, which the same method validated. I then had the idea of a managed WordPress platform because I needed it myself--my blog would crash when I got to the top of Hackernews (a link-sharing site). I asked other bloggers what they use to keep WordPress fast and scalable; many said, "I don't know, but if you find it, tell me, because I need that too." I ran the [Iterative-Hypothesis method](https://longform.asmartbear.com/customer-development/) again, and this time it was very clear what the need was, what people would pay, and how to find them. And the market was already huge (WordPress powered 11% of the largest 10 million websites) yet growing fast (in 2023 it's 43%). I knew WordPress because I was a user, and for nearly twenty years I've loved code optimization problems, so this was a problem I understood and am ideal to solve, where customers agree the problem exists, are already spending money on it, and the market is large and growing. That's steps (1), (2), and (3), but this time the concept was validated. I built just enough software to fulfill the promises of speed and scale, building the first version in 30 hours. Good enough to not only stay alive and fast on a Hackernews day, but with almost no load on the server, which meant I was still far away from a [capacity limit](https://longform.asmartbear.com/utilization/): ![Hundreds of simultaneous connections on a Hackernews day in 2010](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/nginx-graph-simple.png) The first version didn't even have a customer dashboard. I took credit cards with the default website supplied by the online credit card service, and the rest was just WordPress. The first version of the website was... "spare," shall we say? (Figure 2) ![Very plain initial website](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/wpengine-com-20100330-simple.png) The second version of the website was so bad, there were database errors shooting out the bottom. Database errors, on the home page, of the company that you're supposed to trust with your precious website (Figure 3). ![The market need must be overwhelming, if customers walk through this wall of fire and say "yeah just take my money."](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/footer-87f376b55fc470e7-simple.png) So that's step (4), the SLC product that fulfills the job completely with the absolute minimum of "Product." We reached $1M ARR after 18 months. The reason--besides having the right product with the right promise in the right market, which was elements (2) and (3)--is step (5) focus on marketing and sales. Because I did marketing and sales before even having a product login screen, I was able to launch with 30 paying customers. Sure I worked on the product and worked support tickets, but what I focused on was how to get customers: ![From an "advisory group" email update from 2010; my asks were for marketing and sales, not product and engineering.](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-simple.png) When you do marketing and sales, with a product that people actually need, in a large and growing market (all covered by the exercise under (2)), then you might have a nice growth curve: ![Hit $1M ARR in first 18 months at WP Engine. But wait, the Product/Market Fit is still to come...](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/h2aiagzwtkvgh-ziuvhaoyqextusligoalcocyvkrrib17cf0r6pwq-p7nptl-nx5vsbjwvheitxjnsjykpvnqnrnse88h2ltvixjwrqegty5alj2udrrwoh7oqlwrfouqq03l6-e-zetqvx9abbdshh-nw-simple.png) We've always spent a lot of time on (6) retention. Sometimes years would pass where we wouldn't add a new feature, because all our engineering time was spent on the [challenges of scale](https://longform.asmartbear.com/scale/) and continuing to improve on the promise of speed and uptime. As a result, we've had best-in-industry customer retention for 16 years. High retention due to happy customers folds back into growth through word-of-mouth referrals and a thousand top-of-class reviews: ![87% of 1007 Trustpilot reviews are 5-star](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/trustpilot1007-simple.png) All that, combined with the hard work, the craftsmanship, the large and growing market--did I mention the large and growing market?--and yes also the luck, meant that in early 2012 we hit [Product/Market Fit, as further detailed here](https://longform.asmartbear.com/product-market-fit/): ![The previous chart's "up and to the right" growth from 2010-2012 looks almost flat compared to the sudden moment of Product/Market Fit, when it became hyper-growth, and never slowed.](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/wpengine-growth-simple.png) --- This roadmap worked for WP Engine, and it worked for others. Consider this thread from [Simon Hoiberg](https://www.simonhoiberg.com/), explaining why his second bootstrapped company succeeded ([FeedHive](https://www.feedhive.com/)) where his first failed. This roadmap echos within it--playing in a large, growing market, finding and focusing on an ICP, building only an SLC to start, and treating marketing and sales as more important than features and innovation: ![The PMF roadmap in action](https://public.asmartbear.com/books/anthology/chapters/product-market-fit-formula/image-4-simple.png) Do you *have* to walk this road like Simon discovered with FeedHive and I discovered at WP Engine, or can you stumble onto it halfway through like Smart Bear, or can you take a completely different route and just emerge at the finish line? Or can you just cherry-pick the things you like from this framework, and ignore the rest? Yes to all the above. Everything is possible. But not all roads are equally likely to result in success. We enjoy stories about outliers because exceptions are interesting. But exceptions are not repeatable, and therefore they may not be teaching you anything useful. I believe the roadmap above _is_ repeatable. Not a guarantee of success, but a process that makes sense. It's your life; you can take any road you please. What are you going to do? --- *Many thanks to [Gordon Daugherty](https://shockwaveinnovations.com/), [Rowan Udell](https://rowanudell.com/), and [Sathyanand S](https://www.linkedin.com/in/sathyanands/) for contributing insights to early drafts.*

How many things should there be? (Hint: Not 10)

Jason Cohen — Sun, 10 Dec 2023 00:00:00 +0000

How many things should there be on the list? ![lots of acronyms acrostic help remember](https://public.asmartbear.com/books/anthology/chapters/top-10/d8527print-simple.png) #### 1: Focus It's reductive to say [only one thing is important](https://longform.asmartbear.com/maximized-decision/), but it also ensures that it gets done, as quickly as possible. #### 3: Magic Number Humans across the globe have always liked when things come in threes. Also you can remember them all. #### 7: Because 7 ± 2 From one of the most-cited [papers in psychology](https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus_or_Minus_Two), seven items is what the average person can keep in their head simultaneously. If you can't keep it in your head, even when you're trying very hard, it's too many items. #### 5: Because with post-COVID brains, now it's 5 ± 2 I have no data to support this claim, but I claim it nonetheless. #### 17: One per team If you have 17 teams, perhaps you have 17 items. Normally this is too many items, but this is actually a repeat of "1: Focus" at the team level. It's still arguably too many if projects span multiple teams. #### 132: 100% completion If there are 132 flags in the security audit, you could set a goal to address all of them. Getting through 100% of a specific set of things is a powerful statement. One could call this the same as "1: Focus" but with sub-items, where it's nice to have a checklist, and nice to feel progress as the team gets through the checklist, and clarity on why there are 132 items specifically, and a clear state-change when the very last item is finished (e.g. "We pass this audit, and then [large customer] renews for three years.") #### 402,302: Bankruptcy An inbox with this many unread emails is the same as an inbox with no unread emails, except a number looms over your head. Declare "bankruptcy"--whether it's the email inbox or the Jira backlog. Archive[^1] it all. The important things will come back. [^1]: "Archive," rather than "delete," because disk space is close to free, and if you _really_ needed something after all, you still have it. Not quite Marie Condo. --- Always Ask: **What is the purpose of this list?** Given its purpose, should there be more things or fewer things? Is there a _good reason_ why this the right number of things? Usually, fewer is better, and the right answer is almost never "10."

Quarterly strategic planning using the fairytale structure

Jason Cohen — Sun, 26 Nov 2023 00:00:00 +0000

![case two kids chow down on witchs house then burn her alive](https://public.asmartbear.com/books/anthology/chapters/strategic-planning/d8067print-simple.png) Using the narrative structure of the fairytale, we can execute the classic "Double-Diamond" design pattern, driving our analysis, decision-making, actions, and our final exposition, in an intelligent, systematic way that everyone can easily understand. It's going to look like this: 1. **Objectives:** What are we trying to get / what happens if we don't? 2. **Obstacles:** What stands in our way? 3. **Actions:** How do we overcome the obstacles while advancing our objectives? 4. **Measures:** How will we know we've been successful? 5. **Not Doing:** What important things have we chosen not to do, so that we can achieve the above? Now let's tap into humanity's collective unconscious, and see exactly how to run this framework in practice. ## The Fairytale cheat code The fairytale structure is thousands of years old, crossing all peoples of the globe, a reflection of a deep, shared humanity. You've heard it your entire life, from centuries-old childhood stories to [every Pixar film](https://www.aerogrammestudio.com/2013/06/05/back-to-the-story-spine/) to [advertising](https://twitter.com/theandreboso/status/1713910012051865602) to the news, so you need only a reminder, not an explanation: 1. Once upon a time there was *an unsuspecting hero*. 2. Every day, *the way things are*. 3. But then, one day, *change/event that motivates action*. 4. And so *adventure begins*. 5. It was almost impossible because *challenges/obstacles*. 6. Until finally *victory*. 7. And forever after, *the world is different*. **Fairytale structure is a cheat code for effective communication**, as journalists have exploited for years. For example, this simple "case study" that I've used in sales calls at WP Engine takes less than a minute to tell: > One of our customers is ______. Like you, they are a popular food blogger, making money through advertising and occasional affiliate product sales. One Thanksgiving, on their busiest day of the year, their site crashed because of all the traffic; they missed out on thousands of dollars of revenue and hundreds of newsletter sign-ups. They moved their site to us, and that never happened again; they just focus on their work, build their brand and business, and never worry about technology. It's equally effective for presentations, fundraising, and product proposals--a ready-made outline, resulting in a straightforward narrative structure. **Our brains are pre-wired for fairy-tales**, so we naturally follow the presentation as it flows into those primordial ruts in our gray matter. A less obvious but extremely powerful application of this principle is in creating a **strategic plan**, i.e. **analyzing the current situation and deciding what needs to be done**. For a single team, this means being [in command](https://longform.asmartbear.com/in-command/) of the most critical things that need to be done right now. For a department at a larger company like WP Engine, this is how I build our quarterly strategic plans.[^1] [^1]: It looks like I'm just slapping the word "strategic" on everything, to make it sound important. Indeed, people often do that. As you'll see, the purpose of this is to connect work to the strategy, so it really *is* strategic planning! Over the past two years, together with a few product teams at WP Engine, we've created and honed a simple but effective framework for the **Fairytale Plan**--strategic work-planning that has worked well both to drive the process of figuring out what the plan should even be, as well as communicating the result throughout the company. The process is akin to the widely-admired [Double-Diamond design process](https://en.wikipedia.org/wiki/Double_Diamond_%28design_process_model%29), and the final result matches the fairytale structure. No wonder it works, and resonates with everyone. Here's how you can do this too. ![understand burden exposition just see numbers](https://public.asmartbear.com/books/anthology/chapters/strategic-planning/d8412print-simple.png) ## The narrative structure of the strategic Fairytale Plan Here's how our strategic plan will look, and the guiding questions that help us create each section. | | Strategic Plan Narrative | Guiding Questions | |:----|:----|:----| | **Objectives** | **We must advance our strategy by achieving these objectives.**

_Fairytale_
1. Once upon a time...
2. Every day...

| • What is happening now?
• What do we want?
• What happens if we don't get it?
• If we weren't encumbered by roadblocks, with no distractions and no interruptions, how would we advance our strategy right now?

| | **Obstacles** | **But obstacles stand in our way.**

_Fairytale_
3. But then, one day...
5. Obstacles[^2]

| • What is standing in the way[^3] of "what we want?"
• What makes it difficult to achieve the objectives?

| | **Actions** | **So here's what we're doing.**

_Fairytale_
4. Adventure begins...
5. Tackling obstacles...

| • How do we overcome the obstacles...
• ...while advancing the objectives?
• ...with the people and capabilities and strengths we currently have?
• What changes need to be made, so that the answer becomes obvious?

| | **Measures** | Here's how we'll know **whether we're making progress**, and **when we're finished.**

_Fairytale_
6. Until victory...
7. The world anew. | • How will our company be different?
• How will we know, objectively? | | **Not Doing** | **Important topics and ideas that we're not doing** (yet). | • What do we really _want_ to do, but have to wait so that even more important or urgent things can be done?
• What did we leave out that is controversial? | [^2]: In a fairytale it's fun to surprise the hero with obstacles. Our plan must do the opposite: Clarify the obstacles up front. [^3]: There are many kinds of obstacles: internal execution challenges, interruptions, dependencies, competitive pressure, market dynamics, just to name a few. There are techniques and pitfalls for each of these sections. Here's how to navigate it: ## Strategic Objectives #### Narrative Plot Point We must advance our strategy by achieving these objectives. #### Guiding question If we weren't encumbered by roadblocks, with no distractions and no interruptions, how would we advance our strategy right now? #### Why can't we skip this section? If you run very fast in the wrong direction, you fail. This names the right direction. --- ![i think i see why were not getting anywhere](https://public.asmartbear.com/books/anthology/chapters/strategic-planning/d8072print-simple.png) Objectives shouldn't summarize the entire strategy; your [strategy document](https://longform.asmartbear.com/great-strategy/) already does that. Instead, link to that document for reference, and highlight 1-3 specific objectives as the subset of the strategy that make sense to advance right now. "Right now" is the difference between this _plan_ and your _strategy_. The strategy is the long-term vision and the main ways that you will achieve it. The plan is where you select the specific next steps that materially advance the strategy while achieving results that you need today. "Results today" is typically revenue growth, cost reduction, or some internal transformation. Later in the process of creating the plan, when you're determining _how_ to achieve these objectives while also dealing with the obstacles, you might get stuck, unable to invent anything that works. In that case, you might need to choose different objectives. Or you might decide that "eliminating obstacles" is all you can accomplish right now--unfortunate, but useful to declare if true. During the planning process you should expect and even encourage fluidity between exploring the problem-space and solution-space. The specific objectives will evolve over time, but it's OK if they're the same for a few quarters in a row. Indeed, that gives the team stability and focus. How do you know if you're sticking with an objective for too long? Later you'll be asking the question "how will the world be different." If there are good answers, then we'll know whether we're making progress, despite the objectives remaining the same. ## Obstacles #### Narrative Plot Point But obstacles stand in our way. #### Guiding question What is preventing us from making rapid progress on the objectives? #### Why can't we skip this section? Often the most important business challenges appear here, not in the objectives. This is where you face difficult truths about your current situation, which will ensure the work you select will actually overcome them. --- > "If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." > --Albert Einstein. I agree with Einstein (but then, who among us is in a position to argue against Einstein?). This is the most important section, and often where you'll spend the most time. **You'll be tempted to ignore Einstein, but it is crucial that you do not.** You'll want to jump right to the solution. You'll say you've already talked about this stuff, and you know what to do. You'll say you don't want to rehash all our challenges; let's cut to the action. I understand, I feel the same way. I want to activate on any reasonably-good idea right away. But without fully exploring the problem-space, you're not in a position to even know the goal of the solution-space. You'll just keep repeating mistakes, keep [avoiding the difficult truths](https://longform.asmartbear.com/failure-to-face-the-truth/), and you won't improve. It's important to generate many options first; only by exploring the whole space will you uncover the things which are the _most_ important to solve now. Your team will thank you for the clarity, not only about what they need to tackle, but what they're allowed to leave alone, even though it's on fire. Here are some types of obstacles you should explore, in no particular order: #### Difficulty executing the strategy: "Why is this hard?" Executing the strategy might be intrinsically difficult, like building something we've never built before (thus maximally risky and uncertain), or having to create novel algorithms, or having to deploy infrastructure [at scale](https://longform.asmartbear.com/scale-rare/), or pivots, rewriting, rebranding, or repositioning that will take intense effort and coordination. Whatever makes our strategic goals difficult or risky, is an obstacle that is definitionally strategic to remove. #### Team challenges: Humans are hard. Do we have the right skillsets on the team? The right experience levels? The right motivation? Are they burned-out? Is there enough people to do all the work? #### Competitive pressure Is a competitor winning business in a way that demands a response, even if it's not directly related to our long-term strategy? #### Customer retention Are customers leaving at high rates,[^4] and therefore we have to address that immediately even if it's unrelated to our long-term strategy? #### Interruptions Are we dealing with interrupt-driven work instead of planned work, such that we're unable to make enough progress on strategic work? Do we need to do work to reduce the frequency or magnitude of the interruptions, perhaps through automation or rejecting certain types of work? #### Too many bugs Are we failing on our basic promises to our customers of quality and performance, due to mounting bugs or other issues? Do we need to attack those now, even if they are unrelated to our long-term vision? #### Dependencies / coordination Is it impossible for us to execute or deploy or iterate, due to some other team or process or platform or architecture? Do we need special coordination of work in order to achieve our own goals? #### Process bottlenecks Are our tools getting in our own way? Is our process preventing throughput? Not just nice-to-have improvements, but step-changes in our productivity, and likely also our happiness? #### Market evolution Are there trends that are making our current product or positioning less effective or less relevant? Does this even mean our strategy needs to change, to keep up with the changing external landscape? [^4]: My rule of thumb for "what is a 'good' cancellation rate" is: 3%/mo for consumer, 2%/mo for small business, 1%/mo for enterprise. [More on that here](https://longform.asmartbear.com/product-market-fit/). Narrow these down to 1-3 most critical issues. The guiding question is: Which are the few issues where, if we solved them or took a huge bite out of them, but allowed all others to fester without any progress whatsoever, we would have substantially improved our velocity, or competitiveness, or team dynamic, or retention rate, or something similarly critical. Whereas if we allowed those few issues to go untreated, then even if we completely solved five others, we would still be unable to achieve our strategic objectives? If that question is still hard to answer, then as a guideline I like the following order of precedence. That is, if you have a _serious_ problem in some of these categories, I generally pick whichever comes first: 1. Retention (if customers are leaving, the business model and product isn't working) 2. People (must have the right team) 3. Throughput (can't do enough strategic work per month) 4. Growth ([Details on how to tackle growth challenges](https://longform.asmartbear.com/growth-asymptote/)) 5. Competition 6. Health of the code base 7. Execution risk > "A perfect formulation of a problem is already half its solution." > --David Hilbert ## Main Activities #### Narrative Plot Point Here's what we're doing. #### Guiding question How do we overcome or side-step the obstacles, while advancing the strategic objectives, with our current people and capacity? #### Why can't we skip this section? If you don't select a few, large, impactful things to do ("Rocks"), you will wile away your time chipping away at incremental things, believing that you're making progress because you are "agile," but in fact not tearing down the obstacles nor achieving the objectives quickly enough. --- ![witch old lady children deal](https://public.asmartbear.com/books/anthology/chapters/strategic-planning/d8705print-simple.png) While putting your finger on the crux of the challenges is often the most emotionally-difficult part, deciding what to do is the most intellectually-difficult. It's hard to think of great ideas, and no framework or process magically causes you to be creative and insightful. As with obstacles, you should start in "generation mode," coming up with as many ideas as possible, before switching into "selection" mode, where you pick which ones to do. You should already have [queues of ideas from around the company](https://longform.asmartbear.com/jit-backlogs/); if not, now is a good time to create some. If you're having trouble coming up with creative ideas, use [Extreme Brainstorming](https://longform.asmartbear.com/extreme-questions/) to shake things up. If you're having trouble figuring out how to deal with the obstacles, consider the parable of Herbie (from [_The Goal_](https://www.amazon.com/Goal-Process-Ongoing-Improvement/dp/0884271951) by Eliyahu M. Goldratt). A group of hikers are climbing a mountain. One hiker--Herbie--is constantly falling behind, preventing the group from achieving their goal of reaching the summit. There are three ways to deal with Herbie: (1) Leave Herbie behind; (2) Help Herbie get better; (3) Decide that you're not going to achieve your goal. These options help you think of solutions; respectively: (1) Let that problem burn or ignore certain work that you've been assuming "must" be done; (2) Do extra work that mitigates the problem; (3) Change your objectives to something you can actually achieve. Often the best solutions come from [synthesizing several ideas](https://longform.asmartbear.com/conflicting-choices/). For example, often at WP Engine an idea that increases website performance for our customers also decreases our costs, because it takes less CPU time or fewer bytes transferred over the internet to accomplish the same end result. This is another reason to use things like [Extreme Brainstorming](https://longform.asmartbear.com/extreme-questions/); even if an individual idea is bonkers, it might be synthesized with a practical idea into a great solution. ![ok but if we work together whammodepth perception](https://public.asmartbear.com/books/anthology/chapters/strategic-planning/d8070print-simple.png) For the final selection process I recommend the [Binstack method](https://longform.asmartbear.com/maximized-decision/), because you're picking one or just a few critical big things to do. Your objectives and obstacles act as natural filters, immediately eliminating anything that doesn't directly address those, but allowing yourself a few secondary prioritization dimensions. You might be tempted to use an [ROI analysis](https://longform.asmartbear.com/roi-rubric/), but this is the wrong framework. ROI is good for smaller activities, where the goal is to find the "best use of time." For your most critical pieces of work, the goal is not to optimize your time, but rather to maximize your impact on the most critical things. See this [Rocks, Pebbles, and Sand work-planning framework](https://longform.asmartbear.com/rocks-pebbles-sand/) for a complete system that covers all kinds of work, including these most-critical activities and medium-sized work where ROI analysis is appropriate. > "A successful solution to a problem makes the problem appear to have been nonexistent in the first place." > --[James Surowiecki](https://twitter.com/JamesSurowiecki) ## Metrics & Indicators #### Narrative Plot Point Here's how we'll know whether we're making progress, and when we're finished. #### Guiding question How will we know whether it's working? What is changing? What did we learn? #### Why can't we skip this section? It's easier to avoid accountability, believing that hard work and high throughput is the same as having an impact. Saying "focus on inputs, not outputs," which is true when executing work but false if it means we're not actually getting the outputs we need. We must not let ourselves off the hook, but neither should be measure ourselves against metrics we don't believe in, or insist that all important things can be boiled down to a number. --- ![weve got three sides now i think were making good progress](https://public.asmartbear.com/books/anthology/chapters/strategic-planning/d7887print-simple.png) The "Activities" are supposed to make progress on the objectives, but how will we know if that's actually happening? They're supposed to deflating the challenges, but are they really getting eliminated? In the ideal case, we should measure progress with numbers that are objective, well-defined, measured daily, and have a clear "stopping-point" where we can declare victory. But we all know that most things we can measure aren't so ideal, and sometimes the most important things aren't a number. To the extent that important things _are_ numbers, use this [KPI framework](https://longform.asmartbear.com/product-metrics/) to decide what things should be measured, and as a checklist of what a "good" metric is. For concepts that are not a number, capture their essence in writing. Make the language crisp and obvious not only inside the team but to outside stakeholders. Nowadays you can use AI to take a rambling audio description and turn it into a pithy statement. An example might be: "Be revered as the most innovative product in the category" or "Have the best social media content." These statements can be surprisingly powerful despite not being connected to a number. Even when people disagree later whether we're making progress against them, those are exactly the right sorts of debates to have, because you're sorting out how things are going, and whether you should make a change. That's the point of metrics. ## Not Doing #### Narrative Plot Point Here's what we're _not_ doing (yet). #### Guiding question What do we really _want_ to do, but we just have to wait so that even more important or urgent things can be done? #### Why can't we skip this section? Decisions are about [saying "no"](https://longform.asmartbear.com/say-yes/) even more than saying "yes." Without articulating the "no's," the team is less focused, the decisions are less clear, and stakeholders wonder whether you're even aware of the problems that you chose not to tackle. --- > "The difference between successful people and really successful people is that really successful people say no to almost everything." > --Warren Buffett Deciding to do a few things always means deciding not to do dozens of other things. "Well, not doing them _for now_" we like to say, to put the listener at ease. But actually, most of them are "not ever," because we [always have 10x more ideas](https://longform.asmartbear.com/prioritization/) than we have time to execute. That's the lesson of every Jira backlog, feature-collection system, or prioritization spreadsheet. When you don't articulate what we're _not_ doing, it can appear that you don't even know those things exist. Perception is reality. Stakeholders or even people on the team believe you're ignoring important things, when in fact you've _selected_ certain important things. You need to say that out loud. It's not just about proving yourself to an executive, it's about reminding yourself and your own team, especially when a big customer complains about one of the things you're intentionally ignoring, and the pressure is on, the emotions are high, and you need the resolve to stand firm on "no." > "You can do anything, but not everything." > --Derek Sivers Next quarter, when you're going through this process again, this is a great list to revisit. You'll be surprised how many items seem less important now than before. And some might be more important; perhaps now is the time. At WP Engine this a simple bullet-point list, just one line per bullet. It says what the topic is, and doesn't even explain why it wasn't selected. You could go further, explaining why each didn't make the cut. You could go still further, creating a "Now / Next / Later" roadmap.[^5] [^5]: Or, my personal preference in that three-column genre: "Now / Likely / Maybe." This better indicates not only timeline but our confidence that those are even the right ideas. After all, how many of those "Later" items do you ever get to? If the answer isn't at least 50%, then they're _not_ "Later," they are merely a possibility. Remember, this isn't a random bucket of 100 feature-requests. It's a list of things that _are_ important, possibly even seem _urgent_, that you really do want to do, that maybe only barely didn't make the cut. In short, these were the difficult decisions. > "I'm actually as proud of the things we haven't done as the things I have done. Innovation is saying no to 1000 things." > --Steve Jobs ## Full Example: Tesla The final result is clear and simple, hiding the complexity of the process that created it. ![once upon time lazy writing go ahead](https://public.asmartbear.com/books/anthology/chapters/strategic-planning/lazy-writing-simple.png) Let's use Tesla as the example, because although it's over-used, that means you're probably familiar with it, and thus you can see how it all comes together: #### Strategic Objectives Tesla's long-term vision is to accelerate the world's transition to sustainable energy through the widespread adoption of electric vehicles (EVs). To do that, we need to: 1. Invent vehicles that are not only battery-powered but also desirable, because of their performance and safety. 2. Create production lines that are scalable but also cost-effective, so we can be profitable. 3. Generate excitement and loyalty by bucking consumer expectations that EVs are impractical and have low performance. #### Obstacles *These obstacles stand in our way of achieving our vision:* * **Intrinsic difficulty:** Inventing new types of battery and production lines is near-impossible. * **Market Skepticism:** Overcoming consumer and industry skepticism about the viability of EVs. * **Manufacturing challenges:** Scaling up production to meet demand. * **Infrastructure:** Lack of widespread EV charging infrastructure creates range-anxiety. #### Main Activities *We will overcome these obstacles while achieving our strategic objectives by:* * **Innovate in Battery Technology:** The insight is that the battery is the key. Develop more efficient, longer-lasting, fast-discharge batteries. * **Direct-to-Consumer Sales:** Bypass traditional dealership models to control pricing and customer experience. * **Scale Production:** Invest in Gigafactories to massively increase production capacity, hiring specialists and investing with the insight that factories are not just a necessary step but strategically vital. * **Infrastructure Development:** Invest in a network of Supercharger stations for convenient charging. #### Metrics & Indicators *We know whether these activities are working through:* * **Battery Cost and Efficiency:** Measure improvements in battery cost per kWh and immediate-discharge rate. * **Vehicle Sales and Market Share:** Track the number of units sold and market share in the EV sector. * **Production Capacity:** Measure fully-completed cars/day per Gigafactory. * **Infrastructure Expansion:** Number of new Supercharger stations operational. * **Consumer Delight:** People absolutely love their Teslas, calling it a magical experience and never wanting a gas-powered car again. *(not a number)* #### Not doing *We are intentionally not doing these valuable, important things, so that we can focus on the above:* * **Safety and Practicality**: Although we believe eventually we will make the safest sedans in the world, first we have to solve the challenges of batteries and performance, because those are the obstacles. We know that safety is a matter of good design, but we already know from existing EVs that if the car is safe, but not performant and low-range, then it will not be popular. So we have to solve those first. * **Affordable models**: The company will truly scale and make the impact we want in the world, when it is accessible to most people. But at first, before factories are at scale, before the bugs are worked out, before all the costs are minimized, our cars have to be expensive. So we have to start out as a luxury brand. * **Expand to China:** The second-largest market in the world, eventually will be the largest, and the first manufacturer there might have a decisive advantage. But it is its own special challenge, and we need a popular car that works before we jump into that pool. --- Tesla is often lauded for having a clear strategy, and you've heard them report the metrics above as indicators of progress or success. You need a plan that is just as clear. Each team needs one, and your company needs one. Otherwise, you're probably not working on the right things, and certainly not communicating what must be done, in a way that everyone can understand and implement. Tell your story.

Product/Market Fit (PMF): Experience & Data

Jason Cohen — Sun, 05 Nov 2023 00:00:00 +0000

![had winner winner dream again](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/d8796print-simple.png) Every day people debate how to define "Product/Market Fit." It's best to start with the subjective experience, because **there is no doubt when you have it**. (If you're not sure whether you have Product/Market Fit, you don't.) _If every day it's a struggle to get customers, it's not working yet.
If every day it's a struggle to keep up with demand, it's working._ It is a momentum change from "push" to "pull"--a change from fighting for each customer, one at a time, to a flood of signups that you can't even explain. It's a change from "how do we get more customers" to "how do we handle the influx of demand?" It's _working_. Something clicked, like a final puzzle piece snapping into place, a "fit," and suddenly the floodgates are open. It's not all good. The avalanche of customer complaints outstrip your ability to deliver bug fixes and simple enhancements. You feel behind in every department simultaneously; you can't keep up with the work no matter how "productive" you are. You obviously should hire someone--and this new revenue means you can afford it--but who? Someone just like you, because you can manage them, or someone who complements you, so you can deal with the burgeoning scale? Does it even matter--you don't have time to interview. It's the proverbial "good problem to have," but it doesn't feel good in the moment, with a crushing workload and unhappy new customers, and your brand slipping away ("I heard they were good but it took them a day to answer my support question" says the public review). Although of course it _is_ the _best_ problem to have. You might think all this doesn't matter--you're just running your company; who cares how some article defines a term like "Product/Market Fit?" But it _does_ matter, because it determines whether the company is sustainable, and fundamentally changes how you operate each day, how you plan for the future, whether and who you need to hire, and what you need to build, and whether you're going to start [tackling scale](https://longform.asmartbear.com/scale/). *(P.S. How do you go from idea to this state? [This is my PMF system](https://longform.asmartbear.com/product-market-fit-formula/) that I used to build a unicorn.)* --- What does Product/Market Fit look like numerically? In my experience building several companies myself and angel-investing in dozens of others (with a wide range of outcomes), **I define Product/Market Fit as _all_ of the following:** 1. **Easy growth (pull, not push):** Growth rate suddenly spikes up, and sustains at the new rate. You often don't know why. 2. **High retention:** Cancellation under 3%/mo (for B2B) or 5%/mo (for B2C), because if customers are leaving in droves, you are not a "fit." 3. **Critical mass:** At least $20,000/mo in revenue or increasing WAU's by at least 200/mo. Everyone can grow 1000% month-of-month when the baseline is 7 customers; the fact that you have only 7 customers means there's not yet evidence of "fit."[^1] [^1]: What if you have a single huge customer? Congratulations, lots of people would love to have a huge customer; great start. That demonstrates you have fit for a single customer, but not for a market; that's consulting, not product. What follows are the data to back up this definition, that also matches the lived experience that is the essence of hitting Product/Market Fit: in which life will never be the same again. ## The characteristic growth curve of "Product/Market Fit" ### Bootstrapped products When you see growth charts like [Pallyy](https://pallyy.com/)'s, you can see the exact moment when Product/Market Fit was reached: ![Pallyy's four-year MRR](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-simple.png) Successful startups often have a growth curve like Pallyy's: Revenue growing linearly, slowly, often for about two years, then in a moment--Product/Market Fit--when it suddenly turns upward, and continues growing at that new trajectory. The new growth is still linear, but dramatically faster. In particular, it is _not_ exponential, though that's the word people frequently use ([this universal fallacy is explained here](https://longform.asmartbear.com/exponential-growth/)). [ConvertKit](https://convertkit.com/) was similar, bumping along for two years before growing quickly but linearly, just like Pallyy: ![ConvertKit's early MRR growth](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-5-simple.png) And then that growth continued linearly, not "exponentially" as many pundits like to say: ![ConvertKit's scale-up MRR growth](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/convertkit-simple.png) You can see the sudden change between "every sale is a struggle" and "the orders just won't stop coming in." ConvertKit founder Nathan Barry famously [tells the story](https://www.groovehq.com/blog/nathan-barry-interview) of how he personally reached out one by one to his ideal target customers, manually onboarding them to not only get the sale but to get the testimonial.[^2] It's a brilliant playbook that others would be wise to copy. It's also what it means to hard-scrabble for customers, and it's really obvious when the forces flipping from having to scratch and claw for every dollar of revenue, to the company running away from him. [^2]: I was one of them; perhaps you're reading this article because of an email from [my newsletter](https://longform.asmartbear.com/subscribe/), which is still on ConvertKit. ### VC-funded products The preceding companies were bootstrapped, but the pattern is the same in the world of venture, because Product/Market Fit is about products and markets and customers and fits, not about funding models. Beehiiv is a competitor to ConvertKit, which raised a small round from angels and VCs. With [a strategy of](https://x.com/denk_tweets/status/2013618810016690461?s=20) simplicity, agile reactions, and viral components, the curve nevertheless looks almost identical, because the forces that shape PMF transcend those activities: ![Beehiiv's MRR](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/beehiiv-simple.png) My company [WP Engine](https://wpengine.com/) started 16 years ago self-funded but two years later became VC-funded, eventually raising more than $300M, experiencing hyper-growth[^3] for a decade, yet it exhibits the same two-year slow-growth preamble to high-growth, and both linear: [^3]: Often defined as 2T3D, i.e. in successive years after attaining $1M in annual revenue, the company's revenue triples, triples, doubles, doubles, and doubles, thereby getting close to $100M five years later; WP Engine did that and more, starting with 5x. ![WP Engine's early growth curve](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-2-simple.png) You can't tell from the chart when we first raised money (it wasn't January of 2012), nor subsequent rounds, nor the size of the rounds. The shape of growth transcends all of it. I distinctly remember when we flipped to "Product/Market Fit." Only five of us, completely sustainable, and suddenly everything changed. We fell behind on our stellar tech support; one of the dangers of bragging about how "the founder does Zendesk tickets" is that process doesn't scale. We couldn't keep up with our free white-glove site-migration service for new customers. And then, completely behind on all work, trying to stand up new servers and answer phone calls, knowing we needed to hire, we got even _more_ behind because hiring takes time--finding candidates, interviewing, not wanting to hire the wrong person just because we're moving fast, and even after we hired, there's no documentation or training, so we're just working side by side, hoping folks learn by osmosis, while the team gets even further behind. And customers were complaining on Twitter that our service is going downhill--and worst of all, they were right. They blamed it on us raising money; what they didn't know is we hadn't spent a dollar of it yet; in fact our error was _not_ spending the money on hiring ahead of need, to blunt the impact of a growth increase. We did get back on top of it later in the year, and we've won all sorts of awards for our service, maintaining 98% CSAT to this day, because that's who we are. But that's not what it felt like in 2012. The same thing happens to the "cool kids" companies in Y-Combinator: ![Anonymous YC company growth chart from YC co-founder Paul Graham](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/fsya-rwaaykldb-simple.png) This isn't a new phenomenon, nor a "SaaS" phenomenon. [Peldi Guilizzoni](https://twitter.com/peldi) famously shared his (one-time!) revenue for Balsamiq Mockups back in 2008: ![Balsamiq Markups one-time (not SaaS!) revenue, 2008](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/balsamiq-cashflow-simple.png) ### Second products HubSpot is a hyper-growth VC-funded company, now public. Early on, you can see the linear behavior, fairly consistent even through the 2008 recession, as co-founder [Dharmesh Shah](https://twitter.com/dharmesh) indicates on a chart from his [personal blog](https://www.onstartups.com/tabid/3339/bid/10799/holy-crap-hubspot-has-now-raised-a-total-of-33-million.aspx): ![HubSpot's early growth curve](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/hubspot-growth-simple.png) And for five years after that point: ![HubSpot's founder Dharmesh Shah showing their slow linear growth rate for three years, then fast for five.](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-8-simple.png) Sometimes there's another shift in the slope of the line, when the company adds a second successful product, expands to a lucrative new market, or changes its business model. HubSpot, after twelve years of linear growth, changes to a new (but still linear) growth rate around 2021, around the time their second product-line reached its own fit: ![HubSpot's at-scale growth curve, exhibiting a bend, and linear on both sides of the change](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-3-simple.png) ### Viral products All of the previous examples were of B2B companies without "viral" or "network" effects, which are often associated with so-called "exponential growth." However, even classic "viral growth" examples like Slack (at the time the fastest-ever-growing B2B company in history) and Facebook (at the time the fastest-ever-growing B2C company in history) did not grow exponentially. Instead, they grew in the same way again: linear, then a sudden change, then still linear but faster. Here's Slack, which looks exactly like HubSpot: ![Slack's early user growth curve](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/slack-dau-novet-062415-png-simple.png) And Facebook with a material bend in early 2009 (coinciding with the introduction of the "like" button that you could embed on your own website): ![Facebook's early user growth curve](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/icrossing-facebook-user-growth-january-2012-simple.png) Elsewhere I've gone into great detail about why even viral products like Slack and Facebook specifically [don't grow exponentially](https://longform.asmartbear.com/exponential-growth/). You can also see it in AirBnB with a bend in late 2010, coinciding with the [realization that professional photography was the key](https://medium.com/@etch.ai/how-airbnb-got-their-early-traction-cb059e902ea4) to "fitting" with customer expectations: ![Airbnb's final piece of the puzzle](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/d1-2jgwufz59xbt8g8eqaifww-simple.png) ### Media products In many ways, media and content is unlike building and selling products. But in growth curves, and this characteristic PMF curve in particular, they are similar. Here's one for a successful YouTube channel: ![YouTube channel viewer growth](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-6-simple.png) Or email newsletters like [Lenny Rachitsky](https://www.lennyrachitsky.com/newsletter)'s: ![Lenny's Newsletter traffic](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/f-bjov-a8aa5apl-simple.png) Oh, and what did Lenny do to cause that sudden growth? You guessed it: ![Q: What did you change in Jul 2022? / A: Not sure TBH](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-11-simple.png) _Update: Sept 2024:_ A year later, Lenny published his subscriber chart, and sure enough the pattern continues: ![The first small bend in 2020 resulting in a line with a new slope, then the dramatic bend in May 2022 with nearly perfect linear growth for the next two years.](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/image-7-simple.png) The bottom line is, these growth curves matching "fit" are common with all business models--B2B and B2C, recurring-revenue or one-time revenue, products or media, self-funded or VC-funded. ## High cancellation means there's no fit There is one killer metric that will stop growth in its tracks, even with the curves above: Cancellation. Founders often think cancellation of 5-7% per month is OK so long as they're getting lots of new customers. But they are mistaken. High cancellation puts a ceiling on growth. This is on public display with [Buffer](https://buffer.com/), who started with the same sort of linear-change-linear growth curve (2012-2014, then 2014-2020), but then growth leveled off (2020) and never returned (and even shrunk): ![Buffer's ARR](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/buffer-arr-simple.png) The reason is churn. Note how churn was higher during the slow-growth period of 2012-2014, then reduced to 5%/month, permitting faster growth: ![Buffer's monthly cancellation rate](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/buffer-cancel-simple.png) But 5% is still too high; there's no way to keep increasing new-customer acquisition to outpace churn, for the simple reason that "5%/mo" definitionally grows in lock-step with your total size, but marketing and sales does not continue to automatically grow just because your company is bigger. So, cancellations always win that race, and at 5%/mo, you top out pretty quickly: ![Buffer's unit economics; new-MRR naturally tops out, whereas cancellation never stops growing in absolute dollars.](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/buffer-combined-simple.png) You can see a similar effect at [SparkToro](https://sparktoro.com/), an impressive company that is a testament to founder Rand Fishkin's vision to build a great company instead of a company that attempts to accelerate growth forever; [he explains](https://sparktoro.com/blog/sparktoro-year-3-retrospective-investor-payback-systemic-challenges-and-v2-on-the-way/) how SparkToro generates enviable profit, with happy employees, happy customers, and happy investors. So this isn't an indictment of the company, but it is a demonstration of the growth-destroying power of high cancellation rates: ![SparkToro is consistent in adding the same dollars-per-month of new-MRR, but their consistent cancellation rate continues to grow in dollars per month, which means at their current size, cancellation "wins" that race, and growth becomes flat.](https://public.asmartbear.com/books/anthology/chapters/product-market-fit/sparktoro-2023-growth-graph2-gif-simple.png) **It's not actually about finances; it's the fact that customers are choosing to leave.** The numbers are the objective measure that this is happening, but they're not the point. The point is that customers don't want to stay. That means it's not a fit. Therefore, both because "lots of customers leaving means it's not a fit" and because high cancellations means the growth curve will quickly flatten, **cancellation higher than 3% for B2B or 5% for B2C indicates a lack of Product/Market Fit**. Easy inbound growth is indeed a sign of fit, but if customers leave in droves, it means the promise was right, the price was right, but the product didn't deliver, or the customer's need was too brief to sustain a recurring-revenue business. Some people call this "a fit, but not sustainable" or "a fit, just with limited upside." I call it a lack of fit, because if customers are leaving, it's not a fit.[^4] Don't get distracted by dollars. Pay attention to customer behavior. [^4]: See [this amazing five-minute segment](https://twitter.com/MacBudkowski/status/1723724725392707706) from Twitch founder Michael Seibel, describing how his Socialcam app got 60M downloads in 4 months and ranked among the top 5 on the App Store, yet retention was so horrible (almost zero retained users after 10 days) that despite that inconceivable growth, it was not Product/Market Fit. If your company is growing slowly, that [doesn't mean you failed](https://longform.asmartbear.com/fail/). It just means you haven't hit that success curve yet, and haven't found Product/Market Fit. Maybe you never will, but you'll grind out a profitable company anyway. Maybe you never will, and growth is so slow, it's best to [face facts](https://longform.asmartbear.com/failure-to-face-the-truth/), stop, [figure out why it was too hard](https://longform.asmartbear.com/problem/), and try something else. Or maybe your growth spurt will start today. [It's impossible to know](https://longform.asmartbear.com/chaos-at-start/). **Maybe if you [follow my roadmap to Product/Market Fit](https://longform.asmartbear.com/product-market-fit-formula/), which I used to build a unicorn, you will succeed.** Then again, in that same article I explain that my previous startup used only half of those techniques. No one knows; that's life. But now you know what it looks like when it is truly a fit. Or that you still have work to do, if you're not there yet.

Metrics that cannot even be measured in retrospect

Jason Cohen — Sun, 22 Oct 2023 00:00:00 +0000

![where do see myself five years](https://public.asmartbear.com/books/anthology/chapters/unmeasurable-metrics/d9215print-simple.png) Some things cannot be measured, even after months pass and we have all the facts and data and analysis at our disposal. If they cannot be measured in retrospect, they cannot be measured now, and you certainly cannot predict them or make a goal from them. This sounds obvious, but many of these are so tempting, seemingly so important, we end up trying anyway. But it's a frustrating waste of time, and we ultimately fail. Here's some common examples so you can train your pattern-matching engine, and see how to navigate the conversations. ## Impact of a single feature on the revenue of a product There are features at WP Engine which sales team pitch because people respond with genuine excitement. But then, after the sale, customers rarely use them.[^1] Does that lack of usage mean the feature isn't contributing to revenue? No, it might have helped the sale.[^2] [^1]: Don't worry, we don't charge extra for those features, so it's not unethical. [^2]: P.S. Do you think it's wise for our competitors to copy those features? This is why you need to listen to customers instead of copying your competitors; who knows what's going on over there. A feature that is used frequently is almost surely valuable; it's reasonable to claim that this feature is one of the main reasons why people pay for the product. But how much influence is it really? What if ten other features are also used frequently; do each "earn" 10% of the revenue? What if the feature is also used frequently by people who cancel--that would indicate the feature wasn't keeping them here after all. But maybe it was, and something totally unrelated caused the cancellation. What about all those other reasons customers are [willing to pay](https://longform.asmartbear.com/willingness-to-pay/) that are unrelated to that oft-used feature? This is too difficult to unravel. This is why [I like using a variety of KPIs](https://longform.asmartbear.com/product-metrics/), only one of which is "usage." Of course product teams should celebrate when they create features that are widely used. It's just not the same thing as revenue. Even after the fact, with 100s of 1000s of customers (in our case at WP Engine), it's still not possible to attribute revenue to individual features. ## Impact of incremental activities on customer churn As covered in [another article](https://longform.asmartbear.com/problem/), customer churn severely limits growth and, more importantly, is a lagging indicator that customers don't want what you're selling. Therefore, it's almost always a good use of time to uncover the main drivers of churn, and address them with product, process, people, price, and other words that start with "p." So the common and very reasonable question is: If we do X, how much will churn go down? And while we're actually doing X, how much is churn going down? And this leads to the crux: A year from now, will you _then_ know the impact X had on churn? Unless X has an enormous and immediate impact, the answer is no. The reasons often apply in cases besides churn, so let's list them separately to train your pattern-matching engine, while continuing to use churn as the example: #### Cannot measure rare things, because signal is swamped by noise If churn is 3%/mo, an initiative that reduces churn by 10%--a big impact!--will result in 2.7%/mo. How hard is it to measure a difference of 0.3%, month over month? How much does churn vary through pure randomness? Probably more than that. Some months have 15% more weekdays than subsequent months; if most customers churns on weekdays, that could make churn vary by 15% for that reason alone--again, pure noise. You probably just can't measure something that is so rare. #### Lag between action and reaction A customer who churns today has probably been unsuccessful for a while. They've been researching alternatives for weeks or months, weighing whether to pull the trigger. Larger customers might have tried to make it work for a year or more, then ran a multi-month process to investigate the options. So, an activity you start today is unlikely to change the trajectory of customers who have already decided to leave, and only today happened to push the red button. #### Many causes of a result, means it's hard to measure a change in any one cause There are many reasons why people leave. Some were never that interested, and didn't get hooked. Some got mad because of support interactions, even though they like the product. Some finally gave up waiting for you to make a feature they've needed, even though they love the support interactions. Some were never the right fit, but tried to make it work, and so did you. Some forgot they even had it unless their spouse asked what this credit card charge is. So any one action you take is likely incremental, difficult to measure in the fog of almost every customer _not_ caring about that particular thing. #### External forces that are larger than forces under your control The market, competition, and the economy at large has a large yet unmeasurable impact. You can just go blaming the global economy--you have a business to run, and you are in control of more than you're currently controlling. Still, it's easy to map things like growth rates and churn rates onto the US stock market or inflation. Sometimes it can be the largest factor. How, in the face of these major factors, will you measure the tiny blips that your incremental work might accomplish? Does this mean we shouldn't work on improving churn? Of course not--again it's one of the most valuable things any company can do, because retention compounds, and all the profit generated by a customer of a SaaS business happens in the later years, after they've paid back the sales and marketing expenses, gotten past the higher-than-average initial support load, and after your gross margins expand with scale. But, trying to measure the impact of a single activity is folly;[^3] even a year later, you'll never know how much that factored in relative to all the other things. [^3]: The exception is when your churn is especially bad; anything over 3%/mo is scary. Then, sometimes it's possible to make large improvements. ## Probabilities of "risks" The "Risk Slide" lists things that could derail the project or even tank the company. Executives quite rightly want to know: What is the chance that this will happen, and what is the magnitude of the impact if it does? We can't invest in mitigating low-probability risks, although we might monitor the situation if the impact would be high. Conversely, high-probability risks might need proactive mitigation efforts. Thus the "probability of the event" is critical information, because it determines our actions. But these probabilities aren't possible to measure, even after the fact. Each risk is independent. It will happen, or it won't. You won't find out for months, maybe years. Then one or two happen and six don't. Maybe the one that happened was listed as a 30% probability,[^4] and one that didn't was listed at 70%. Does that mean the probabilities were wrong? No, because 30% doesn't mean 0% and 70% doesn't mean 100%. [^4]: If you're lucky; I often see probabilities listed as "medium" or "low." Here's [how to talk about probabilities](https://longform.asmartbear.com/probability-words/) properly. This is similar to weather forecasting. If there's a 30% chance of rain, and it rains, was the forecast correct? While you cannot answer that for a single day, there is an answer across hundreds of days: You take all of the days for which the forecast was 30%, and see how often it did rain. If the real result is, say, 34%, the forecaster is doing a great job; if the real number is 70%, the forecaster needs improvement.[^5] [^5]: Actually that's not the full story; [here is the fascinating math behind evaluating forecasts](https://longform.asmartbear.com/forecast/). ![but to be fair theres fifty percent chance of just about anything](https://public.asmartbear.com/books/anthology/chapters/unmeasurable-metrics/d5221print-simple.png) That's great, but not for our Risk Slide. We don't have hundreds of measurements from forecasts of well-defined events. We have a slide of a handful of independent, complex risks, none of which have ever happened before. So we can't know whether our probabilities were correct, even years later when we know with complete certainty what actually happened. So don't put probabilities on the slide at all. Only list the risks that you feel are so important that they either merit action or awareness. ## More examples Now that the general pattern is clear, here are more examples to avoid: #### Measuring the effect of small design choices on user experience Things like color, typography, layout, and word-choice, definitely matter, but typically noise overwhelms signal in attempting to measure it, especially in small iterations, and it's not effective to survey users about exactly which font they like better. Also, any UI change initially results in lower satisfaction scores, because people don't like change. #### Measuring the effect or ROI of company culture or values Having a [strong culture, with a specific direction](https://longform.asmartbear.com/emptiness/), helps you create a healthy organization of people who are alike in the ways that matter, while creating diversity in all other ways. But, measuring the impact of a specific value or a specific culture-related initiative, doesn't make sense. #### Measuring the value of brand While there are some techniques that could make sense for the largest companies in the world (e.g. ["unaided awareness" or "brand recall"](https://en.wikipedia.org/wiki/Brand_awareness)), you generally cannot look at winning specific deals, or competitiveness, or growth, and answer "How much of that was due to brand?" This includes marketing campaigns that are primarily about brand, such as company-sponsored events. #### Measuring the value of proactively fixing bugs or good engineering practices You cannot measure the impact of a bug that never shipped, or the impact of an engineering practice that is simply how you work. There is no counter-factual. #### Measuring "willingness-to-pay" or "value delivered" I've detailed a [theory of willingness-to-pay](https://longform.asmartbear.com/willingness-to-pay/) as a path both to profits and genuinely happy customers, yet directly measuring "value" or "willingness" is elusive. Even if you raise prices, and measure the change in the number of new customers or the ire and cancellation of existing customers, that still doesn't measure "value" or "willingness." Anyway, that's a test you cannot run often, and a test that's often destructive for brand and customer relationships. #### Measuring the efficacy of employee compensation or retention programs Overall retention is easy and important to measure, but it is always multi-factored, and the rules can change over time. Thus this is like measuring the impact of a feature on product retention. So stick with [KPIs that measure specific aspects of your systems](https://longform.asmartbear.com/product-metrics/), and stay clear of things you cannot measure even after the fact.

The fundamental forces of scale

Jason Cohen — Sun, 24 Sep 2023 00:00:00 +0000

![im not letting myself go im scaling](https://public.asmartbear.com/books/anthology/chapters/scale/d7574print-simple.png) Idealistic founders believe they will break the mold when they start scaling. They will *not* turn into a "typical big company." By which they mean: No stupid rules that assume employees are dumb or evil, with everything taking ten times longer than it should, with wall-to-wall meetings, and hiring "average" or "normal" people. That is, preserving the positive characteristics of tiny organizations while avoiding the common problems of large ones, by adapting the startup's existing processes, that have served them so well thus far. We're merely doing it with more people now, and still figuring it out as we go along, exactly as we always have. **Why do they never succeed?** Why is it impossible for all of these intelligent, well-meaning founders to run a 500-person organization like a 50-person one? What are these unavoidable forces? Are they really unavoidable? ## The two fundamental challenges of scale After understanding these two primary drivers, we'll see how they explain the challenges that arise in every corner of the business. ### Rare things become common [At scale, rare things become common](https://longform.asmartbear.com/scale-rare/). Rare things are difficult to predict, and typically difficult to prevent. Before scale, they are, well, _rare_, so this doesn't matter; they can be handled manually. But when they start happening daily, new processes become necessary. For instance, with 1,000 servers, a server failure every three years translates to one failure per day. This frequency impacts not just DevOps but also customer support and social media teams. Customers are impacted and complaining every day, even if each individual event is rare, impossible to predict, and impossible to prevent. Automation helps, but doesn't solve the problem. This is inescapable math crops up all over the business. ### Complexity beyond human comprehension Human minds have limited computational abilities. A sufficiently complex system cannot be fully comprehended at all. "Complicated" systems can be comprehended. These types of systems are intricate, but they exhibit properties which allow people to tame the beast. Components of complicated systems can be separated, built and tested on their own, then assembled; thus, divide-and-conquer is a useful technique. Challenges are difficult but are solvable, especially if you have seen them before; thus, "experts" and "specialists" can solve the puzzles. Repeatable processes can monitor the system. "Complex" systems, in contrast, have components which affect each other, often cyclically. Therefore, while it's still useful to build and test components separately, there's just as much complexity in their interaction. Indeed, most of the difficulty lies in those interstitial spaces. The human brain is composed of neuron "components" which are relatively well-understood; it's in the complex, multi-directional, cyclical interactions that everything interesting--and inscrutable--unfolds. Large-scale effects like "consciousness" completely evade our understanding, as it emerges from complexity we have yet to comprehend. ![This "Complicated" vs "Complex" terminology comes from the Cynefin Framework.](https://public.asmartbear.com/books/anthology/chapters/scale/cynefin-framework-2022-simple.png) As a company scales, there are both kinds of systems, but the complex ones are fundamentally difficult, and never stop being difficult. Their difficulty grows non-linearly as the company grows. And even our unthinkably complex human brains, cannot comprehend the system. --- With the two fundamental principles in hand, we can detail specific challenges of scale. ## The human cost of robust teams A "team of one" is the fastest, most efficient team, if you measure by "output per person." There's no communication, no meetings, and decisions can be made instantly. Small companies operate this way by necessity, and it works! It's a big reason why they move quickly, and "punch above their weight" as is often said. But, an illness takes the velocity of the product or the quality of customer service from heroic to zero. With a small team, if one person leaves, you've just lost six months to hiring and getting-up-to-speed on that project. Or twelve months or a complete rewrite because there wasn't any processes and documentation in place... because it was just one person, who didn't need that stuff, because after all we're moving so quickly! And not communicating! Or it's fatal because that was a co-founder. "Founder trouble" [is a leading cause](https://fortune.com/2014/09/25/why-startups-fail-according-to-their-founders/) of [startup death](https://www.paulgraham.com/die.html).[^1] [^1]: Although data also show that [companies with only one founder are more likely to fail](https://www.entrepreneur.com/business-news/why-startups-fail-according-to-their-founders/238088). So which is better? To me, it comes down to temperament--some people really want to be the only one at the top, and some want to share the burdens with others. ![i dunno first mate has been done to death how about this co pirate](https://public.asmartbear.com/books/anthology/chapters/scale/d5924print-simple.png) So **a team of one is fast, but brittle**. When you're small, this is a good trade-off, because speed is critical for combating the things that are constantly about to kill the company--lack of customers, lack of market attention, lack of core features, [all the things](https://longform.asmartbear.com/problem/). When you're large, with 15-25% annual employee turnover, not to mention illness, vacation, and family, people will be leaving (temporarily or permanently) every day--rare things becoming common. Maintaining the same attitude as when you were tiny is disorganized and irresponsible, and the company would fail to function. So, at scale, no project can have fewer than, say, three people dedicated to it, plus management and possibly some form of Product or Project Management. But that team of 4-5 will not be 4x-5x more productive than the one-person team; **per-person productivity goes down in exchange for robustness and continuity**. On the other hand, while the small company loses 9 months to the loss of a key employee, or even implodes, the big company is the steady turtle that adds thousands of customer per month like clockwork and wins the race. ## The high cost of robust systems You might think that software systems wouldn't suffer the same brittleness as human systems, because we have things like automation and DevOps. Sadly, robust software is similarly much more costly and complicated. Consider the case of serving a website (don't worry, you don't need to be an software engineer to follow this example!). Suppose there's a network connection to the internet, a server that runs the web site's software, and a database that holds the web site's content. We have a single server, and it works most of the time. Let's say it works 99.9% of the time; that's sounds pretty good! Well, it's not that good, actually. Failing for 0.1% of a year means it has failed for more than 500 minutes--surely an unacceptable amount of down-time! This happens because a rare thing (0.1% failure) became common (at the scale of about half a million minutes in a year). What if we had two servers instead? That way, when one fails, the other is still available. They'd *both* have to fail *simultaneously* for the website to be offline, which would happen `0.1% ✕ 0.1% = 0.0001%` of the time, which would be only 30 seconds per year, so that really *is* great.[^2] OK, that's twice as expensive, because we now have two servers, but it's worth it for being more robust.[^3] But wait, that's not enough, because the traffic that comes in from the internet needs to be distributed across the two servers, and it needs to have some intelligence to route traffic to the healthy one, in case one is failing. That's called a "Load Balancer," and all cloud infrastructure providers have them, and they cost more money. So that's another component to manage, and more expense. [^2]: Sadly this is not really how the world works; typically failures are correlated or cascade. We'll keep things simple for sake of the example, but this complication proves the point yet again. [^3]: You might think we can save money by buying cheaper servers, because each needs only one half the capacity of the original. But no, because in the failure mode, a single server still needs to serve all the traffic, so each needs to be powerful enough to work on its own. And wait, there's also that database. The two servers need to share a common database of content, so the database needs to be moved off to its own server. And what if *that* server fails? Then we have downtime again. So we also need two of *those* for continuity. Now we're up to four servers plus a Load Balancer, and more than 4x the original cost. You can buy robustness, but it's far more expensive than you might think. We haven't even gotten to the fact that managing multiple servers is more difficult, or that there can be failures in the communications between servers, or failures in the Load Balancer which takes down the whole system[^4]--all decreasing our robustness, requiring even more effort to deliver the end result. [^4]: Yes, there's solutions to this too--more redundancy at the network layer, often with a different vendor, but again, all more cost and complication! **Robustness always carries a cost.** Whether it's running four servers instead of one, or a team of four instead of one, you trade local inefficiency for whole-company continuity. ## Predictability (that you actually need) When you're small there's no need to predict when a feature will ship. Marketing isn't scheduling a launch and Recruiting isn't timing the start-dates of the next 50 hires in customer service and sales. This means you can--and should!--optimize myopically for speed-to-market. Small companies correctly brag about their speed as an advantage, but it's easy to see how a larger company has a different--and massive--advantage of execution. Sure, when our company WP Engine launches a new product, the marketing department needs predictability for the launch date, but that's because it's a highly-skilled, well-funded, coordinate group of teams that explode with press, events, campaigns, social media, and newsletters, grabbing more attention in a single week than a smaller company might scrape together in a year. There are also global Sales and Support teams, so we're immediately selling to 200,000 existing customers as well as thousands of new customers per month, which means we'll add more additional revenue each month than a small company will earn over a whole year. But all this requires predictability. We didn't line up that press and have those sales materials and train hundreds of support reps and ensure that code-quality is high enough to scale on day one, without predictability. Predictability requires going slower. Predictability requires estimation (takes time), coordination (takes time), planning (takes time), documentation (takes time), and adjusting the plan across all teams when life [inevitably unfolds differently from the prediction](https://longform.asmartbear.com/predict-the-future/) (takes time). ![surprised investors expecting worst performing miserably](https://public.asmartbear.com/books/anthology/chapters/scale/d4713print-simple.png) When you're hiring lots of people, you need predictability. Consider the timeline of adding a technical support team member. First, Recruiting is casting about for potential candidates. Then scheduling and performing interviews. Then waiting for them to quit their job and take a week off. Then new-employee-orientation. Then classroom training. Then paired up with senior folks on the floor as they ramp up their skills and comfort. Therefore the time between deciding to grow the support team, and having new people up-to-speed, is four to six months. Therefore, we have to predict the demand for technical support four to six months in advance, because we need to be hiring for that *right now*. If we under-estimate, our support folks get overwhelmed with too much work; their quality of life suffers, and service to each customer suffers. If we over-estimate, we have too many people, which is a [cost penalty](https://longform.asmartbear.com/more-money/). Of course the latter is a better failure mode than the former, but both are sub-optimal, and the solution is predictability. "The future is [inherently unpredictable](https://longform.asmartbear.com/predict-the-future/)," insists the small company, spurred on by Lean and Agile mindsets, and the truth. Indeed, blue-sky invention and execution are unpredictable. But this is also a self-fulfilling prophecy; to insist the future is unpredictable is to ignore the work that could make it more predictable, which makes it in fact unpredictable *to that person.* ![but to be fair theres fifty percent chance of just about anything](https://public.asmartbear.com/books/anthology/chapters/scale/d5221print-simple.png) Small companies don't have the data, customers, institutional knowledge, expertise, and often the personal experience and skill set to predict the future, so they are usually correct in saying it's impossible. But is it impossible in principle, or is it impossible for *them*? At scale, it becomes required. Not because Wall Street demands it, nor because investors demand it, nor any other causal derogatory excuse made by unpredictable organizations, but because it's critical for healthy scaling. ## The Materiality Threshold If Google launches a new product that generates $10,000,000/year in revenue, is that good? No, it's a [failure](https://longform.asmartbear.com/fail/). They could have taken the tens of millions of dollars that the product cost to develop, used that to make their existing operation just 0.01% more effective, and made far more money. At more than $100B/year in revenue,[^5] Google can only consider products which have the potential to generate $1B/year in revenue as an absolute floor, with the potential to grow to $10B/year if things go better than expected. Things like YouTube, Cloud, and self-driving cars. [^5]: *Editor's note:* This was originally written in 2017; in 2023 Google's revenue is nearly $300B, which reinforces the original point; they could not have achieved that growth if they worked on projects that generated revenues of "merely" $100M. This principle is called the "Materiality Threshold," i.e. the minimum contribution a project must deliver, for the project to materially affect the business. With a small business, the materiality threshold is near $0. A new feature that helps you land just a few new customers this month is worth doing. A marketing campaign that adds two sign-ups/week is a success. Almost anything you do, counts. That's nice--it feels good to be moving forward. The financial success of the larger company dictates a non-trivial materiality threshold. This is difficult. Even a modest-sized company will need millions in revenue from new products, maybe tens of millions in the optimistic case. Very few products can generate that sort of revenue, whether invented by nimble, innovative startups or stately mature companies. As proof, consider that the vast majority of startups never reach a $10M/year run-rate, even with decent products and extraordinarily dedicated and capable teams. Yet, it's the job of a Product Manager at that mid-sized company to invent, discover, design, implement, and nurture exactly those products--something that most entrepreneurs will never succeed at. [Tough job](https://longform.asmartbear.com/great-product-manager/)! ## Recruiting Employee #2 will [join a startup for the experience](https://longform.asmartbear.com/startup-hiring-advice/). Even with a significant salary cut, and even if the company fails--the most likely outcome--it's worth it for the stories, the influence, the potential, the thrill, the control, the camaraderie, the cocktail-party-talk. Employee #200 won't join for those reasons. Employee #200 will have a different risk-profile regarding their life and career. Employee #200 will be interested in different sorts of problems to solve, like the ones listed in this article instead of the ones where you're trying to understand why 7 people bought the software but the next 3 didn't. Employee #200 will not work for a pay-cut. ![so where do you see yourself in ten minutes](https://public.asmartbear.com/books/anthology/chapters/scale/d4408print-simple.png) Small companies view this as an advantage, and certainly it's advantageous to recruit amazing people at sub-market rates. But there hundreds of employees at WP Engine today who are much more skilled in their area of expertise than I've ever met at a small startup, including my own. Why? One reason is that people with a lot of experience are often in a different phase of life, where family and other demands means they want a predictable, larger paycheck for a predictable, well-defined job. When you combine their advanced skills with the raw materials of a scaled company (i.e. customers, brand, funding, teams that can execute larger ideas), they can generate huge value while still tucking their kids into bed at night. Another reason is that, after developing that expertise, they find it's enjoyable to apply their skills within a larger environment. For example, there are advanced marketing techniques that would never make sense with a smaller company, that are fascinating, challenging, and impactful to the top line at a larger company. There are talented people who love that challenge and would hate going "back to Kindergarten" as Jeff Bezos famously quipped, [scratching out an AdWords campaign with a $2000/mo budget](https://longform.asmartbear.com/low-budget-marketing/) or assembling the rudiments of SEO or just [trying to get a single marketing channel to work](https://longform.asmartbear.com/one-channel/) or being called a "growth hacker" because they finagled a one-time bump in traffic. This has implications on compensation, how you find that talent, and why that person wants to work at your company instead of the one down the block who can pay a little bit more. Therefore, it's critical to have a [mission that is genuinely important](https://longform.asmartbear.com/mission-vision/), have [meaningful](https://longform.asmartbear.com/fulfillment/) and interesting work to do, [connect everyone's work to something bigger than any of us](https://longform.asmartbear.com/emptiness/). These matter even more at scale, because it's the reason why talent will join and stay. ![i dont get it weve got mission statement credo and mantra](https://public.asmartbear.com/books/anthology/chapters/scale/d5966print-simple.png) ## Communication With four people in a company, any information that everyone needs to know can be told to just three other people. Everyone *can* know everything. If there's a 5% chance of significant misunderstanding, that doesn't happen often. With four *hundred* people, it's never true that a piece of information can be reliably communicated, in a short period of time. A 5% chance of misunderstanding means twenty people are confused. That's assuming they even read the communication, or listened during the entire presentation over Zoom. What's the chance of *that*? [Don't ask](https://longform.asmartbear.com/probability-words/). "Slack" is [not the answer](https://medium.com/the-flux/why-slack-hurts-your-teams-productivity-af9804d06606). "Email" is not the answer. The answer is: **Repeating simple messages.** "Repetition" is the answer to "I didn't see it." Repetition in different formats, at different times, by many leaders. Asymptotically achieving 100%, although also creating collateral damage for the people who really do read and listen to everything, who are tired of hearing the same thing, and wondering why they're being punished for actually paying attention. "Simple" is the answer to "I didn't understand / remember it." Just as with [simplicity in strategy](https://longform.asmartbear.com/great-strategy/), you have to accept that people don't read, people don't remember, people have other things on their minds, people don't understand language as well as you wish, and you didn't write as clearly as you hoped. But doesn't this mean you cannot communicate anything complex to 1000 people? Yes, that's what it means. Scale is hard. ## Technology & Infrastructure Managing 10,000 virtual servers in the Cloud Era sounds easy. Automate everything, then any process that works for 100 servers, will work for 10,000 servers just by doing the same thing repeatedly--exactly the thing computers are excellent at. It never works like that. [Reddit took 18 months](https://redditblog.com/2017/05/24/view-counting-at-reddit/) to get "number of likes" to work at scale. [StackOverflow took 4 years](https://nickcraver.com/blog/2017/05/22/https-on-stack-overflow/) to get everything converted to HTTPS. [Wired did that conversion](https://www.wired.com/2016/09/wired-completely-encrypted/) in a "mere" 18 months. Everything is hard at scale. What are the patterns in those stories? One is another application of "rare things are common." Rare things are hard to predict and can be hard to prevent. Often they're hard to even identify and sometimes impossible to reproduce. This is fundamentally difficult. Another is continuity or compatibility with existing technology. New companies get to start from scratch, but at-scale companies must transform. New companies like to make fun of large companies for how hard it is to transform, neglecting that the cause of the difficulty might also be generating $100,000,000 in revenue this year.[^6] [^6]: Instead of "legacy code" we call it the "revenue code." Another is bottlenecking. All hardware and software systems have bottlenecks. At small scale, you don't run into any bottlenecks, or at least the ones you do can be solved with simple techniques like [increasing capacity](https://longform.asmartbear.com/utilization/). Eventually something difficult breaks and you have to rearchitect the stack to solve it. Even something simple like converting HTTP links to HTTPS or updating "number of likes" in real-time, becomes a monumental architectural challenge. Another is [Hickam's Dictum](https://en.wikipedia.org/wiki/Hickam%27s_dictum): Problems in complex systems generally have more than one root cause. Consider the case of a software upgrade causing problems for seven customers, in a way that wasn't detected. What is the "root cause" of this problem? * The problem is we didn't detect the failure; had we done so, we could have reversed the upgrade immediately. * The problem is we didn't test the thing that failed; had we had more tests, the problem would never have reached customers. * The problem is we didn't document the code that broke; had the code been clearer, the human wouldn't have coded the error. * The problem is we didn't review the code properly; had the code been reviewed with a proper checklist, we would have discovered the bad code before it was deployed. As with [Five Whys](https://en.wikipedia.org/wiki/Five_whys), there are always deeper reasons, as well as different reasons. Different from Five Whys, there isn't just one that is the "root" cause; instead there are a lot of things that interact with a lot of things. This doesn't mean it's hopeless, it just means it's complex to analyze, complex to decide what to do, and that Occam's Razor doesn't apply. All this slows down development and adds investment. There will be entire teams who focus on infrastructure, scaling, deploys, cost-management, development processes, and so forth, none of which are directly visible to or driven by the customer, but which are necessary to manage the complexities of scale. ## Risk-mitigation For a small company, the most likely cause of death is [suicide](https://techcrunch.com/2011/06/27/startups-don%E2%80%99t-die-they-commit-suicide/). Usually it's starvation--can't get enough customers (distribution) to pay enough money for long enough (product), in the [cascade of things-that-must-be-true for product/market fit](https://longform.asmartbear.com/problem/). But also things like founders splitting up, not getting enough traction to self-fund or to secure the next round of financing, having to go back to a day job, and so on. At scale, the risks are different. There is very low risk that WP Engine will not sign up thousands of new customers this month. Other risks, however, are not only possible, but likely. Addressing those risks head-on, is required for a healthy and sustainable business that can last for many years. Take the risk of business continuity during a disaster scenario. What if an entire Amazon data center became disabled for a week? How quickly could we get all those customers back up and running? Would that be true even though thousands of other businesses are also trying to spin up servers in other Amazon data centers at the same time? Could we communicate all this with our customers quickly and simply, so that our support team isn't overwhelmed by repeating the same message to tens of thousands of justifiably-angry customers? ![correlation does not imply causation we know it was brian](https://public.asmartbear.com/books/anthology/chapters/scale/d7252print-simple.png) Risk-mitigation can even result in growth. Serious customers want to work with vendors who understand and mitigate risk; this maturity becomes a selling point. That's why enterprise suppliers (like WP Engine) are constantly flouting their compliance with SOC 2 and ISO 27001 and all the others. Small companies make fun of those things as being unnecessary at best or a false sense of security at worst, but while they're busy making that point, the larger companies are busy signing three-year multi-million dollar deals. Early on, you do not need a disaster-recovery plan. That won't be the thing that will kill the business, and your customers will understand if a young business is subject to that sort of risk. Later on, this becomes critical, and worth investing in. ## Large companies are not small companies × 100 These forces cause larger companies to be fundamentally different than small ones. It isn't a bad thing or a good thing. It's a different thing. Some idealistic founders believe the root cause of scaling issues is the "command-and-control" organizational structure. But none of the examples above make reference to any organizational structure. It's universal. This is why [Holacracy](https://en.wikipedia.org/wiki/Holacracy) and [Teal Organizations](https://en.wikipedia.org/wiki/Teal_organisation) do not solve these problems. It could be a fantastic idea to experiment with organizational structure, but the fundamental forces above will not be eliminated with a recombination of roles and power structures. Scaling is hard, the road is foggy and bendy, it lasts for years, the set of people you need might be different, and no one emerges unscathed. So, it is not a sign of disaster if you have difficulty wrestling with these forces. Everyone does. Disaster is when a company is scaling, but the leaders don't appreciate these forces, don't work constantly to morph the organization accordingly, don't bring in experienced talent, decide they can figure it all out as they go along without help. Rather, it should mean new people, new roles, new values, new processes, new recruiting, new stories, new constraints, new opportunities. Too many founders and leaders want to believe: > What got us here is what's important and unique about us, and thus we should preserve *all* of it. Other companies fail because they "act like big companies," but we'll avoid all that because we're smarter than they were. As evidence of our acuity, just look at our success thus far. We will continue to succeed in the future as we have in the past. But they're wrong. There should be a few values that are kept constant, that's true. Otherwise [none of it means anything](https://longform.asmartbear.com/emptiness/). But the details [must change](https://longform.asmartbear.com/change/). Many founders and leaders can't make the shift. This always hurts the company, and sometimes kills the company. The world is full of those horror stories. It's sad, because it's an avoidable waste of opportunity and sometimes hundreds of person-years of effort. Don't become one of those cautionary tales.

Stop saying "fail"

Jason Cohen — Sun, 10 Sep 2023 00:00:00 +0000

![instead failure rebrand dark gritty reboot previous profits](https://public.asmartbear.com/books/anthology/chapters/fail/d7618print-simple.png) It's not fail fast. It's not fail forward. It's not "fail" at all. "Fail" is almost never the right word for what is happening. It's so final, so negative, and it's a dead end: What do you do after a failure? Mope? Words have meaning. They can explain what's really going on, and suggest what should be done next. The word "fail" accomplishes neither. ## Exploring new territory If you're solving a maze, and a path doesn't work out, you haven't "failed," you've only backtracked. This is a necessary and natural aspect of exploration and creating businesses and products; in fact it's even called the [idea maze](https://cdixon.org/2013/08/04/the-idea-maze). It often comes in the form of an experiment. The result of an experiment is evidence supporting or rejecting the hypothesis. Neither result is a "failure." A/B testing is a good practice, even though most of the time you will not have stumbled upon an improvement. Words you could use instead of "fail" include: *experiment, test, bet, learning, investigate, trial, retry, probe*. > "There are two possible outcomes: if the result confirms the hypothesis, then you've made a measurement. If the result is contrary to the hypothesis, then you've made a discovery" > --Enrico Fermi ## Revolutionizing If you're creating something new, never accomplished by anyone before, and it doesn't immediately revolutionize the industry, you haven't "failed," you've just not yet found the breakthrough. Often innovations require many trials before the right solution is uncovered. If the right answer were obvious, it wouldn't be an innovation. Words you could use instead of "fail" include: *backtrack, iterate, trial, attempt, step, miss*. > "I have not failed. I've just found 10,000 ways that won't work." > --Thomas A. Edison ## Learning If you're learning a new skill, and you stumble, get confused, get lost, or think you've mastered it only to find there's still a long road ahead, you haven't "failed," you're growing. No good teacher would get mad at a student for not learning a skill instantly, or even sliding backward. The learning curve is well-understood, and it is not a straight line. Words you could use instead of "fail" include: *practice, cultivate, acquire, develop, learning curve, learning moment, trial, progression, evolve*. > "I am always doing that which I can not do, in order that I may learn how to do it." > --Vincent van Gogh ## Iterating Are you iterating on something that is fundamentally working, just making it better? This is optimization. Not all attempts at improvement will work, or have as much effect as hoped, but surely we shouldn't call ourselves "failures" when things are basically fine as they are. Words you could use instead of "fail" include: *refine, calibrate, optimize, polish, hone, correct, fine-tune, dial-in*. > "The only real mistake is the one from which we learn nothing." > --Henry Ford ## Solving problems Are you solving a problem, but a minor one, particular to your current circumstance, where your high-level strategy and plan is still correct? Problem-solving in a complex space--whether a one-person startup or a thousand-person organization--cannot possibly proceed with innovation and speed and yet also no setbacks, no complications, no false-starts. Words you could use instead of "fail" include: *iterate, adapt, adjust, course-correct, tweak, revise, morph, refactor, amend, fix*. > "Success is not final, failure is not fatal: It is the courage to continue that counts." > --Winston Churchill ## Major errors Have you made a major error, perhaps a complete misjudgment, incorrect strategic decision, or a project where months of effort by dozens of people has proved useless? That is indeed a big deal, and perhaps you're even justified in saying "we failed." Still, that doesn't explain its nature, and doesn't point towards a solution, which would be the constructive thing to do. Words you could use instead of "fail" include: *Pivot, evolve, migrate, overhaul, revamp, transform, reconstruct, repurpose, reimagine, redesign, recreate, reinvent, reengineer, remodel, rebrand, realign*. > "Success is not built on success. It's built on failure. It's built on frustration. Sometimes it's built on catastrophe." > --Sumner Redstone --- There are moments when we've truly, irrevocably, fatally failed, and we should say so. If a company has to shut down, especially before it achieved some meaningful success, then okay, we failed. Otherwise, find a better way to describe what's happening, so we understand the situation better, and are pointed in the direction of what we should do next. > "Success is stumbling from failure to failure with no loss of enthusiasm." > --Winston Churchill

Worse, but unique

Jason Cohen — Sun, 27 Aug 2023 00:00:00 +0000

![chess bishop qbert dating moves](https://public.asmartbear.com/books/anthology/chapters/worse-but-unique/d8851print-simple.png) A perfect storm created a renaissance in chess: COVID driving people to occupy their minds in physical isolation, the hit Netflix show "The Queen's Gambit," and a surge of chess Twitch streamers and YouTubers fueling that nascent interest in chess with equal parts education and entertainment. And so beginners learned openings like the titular Queen's Gambit--one of the most popular ways for white to open the game. It's easy to find courses, books, and online content about the Queen's Gambit because it's been popular for more than a hundred years. The top chess players in the world today still play the Queen's Gambit; if they play it, it must be good! And it is. Except, it's smarter for beginners to play worse openings. "Worse" is an objective evaluation. There are openings, like the *King's* Gambit for example, which were popular in the 1800s but have since been "refuted," meaning that for each move by white, black always has a counter-move which results in black ultimately achieving a superior position. "Superior" as consistently demonstrated in games between top-level humans, as well as computers that are orders of magnitude stronger than those humans. Thus, the King's Gambit is objectively worse than the Queen's Gambit. So why should beginners use the refuted King's Gambit opening? First of all, beginners aren't playing top chess players; they are playing someone near their own strength. At their level, the King's Gambit isn't refuted, because neither player has memorized the sprawling branching sequences of all possible moves and counter-moves and counter-counter-moves, *ad nauseam*. Nor can they figure out the best move in the moment. Anyway, [the game will be decided by stupid blunders](https://longform.asmartbear.com/avoid-blundering/), not brilliant tactics. Second, and more importantly, exactly because the Queen's Gambit is popular, it's likely that your opponent has also studied it. So it's quite possible that your opponent knows a defense to the Queen's Gambit better than you know the attack of the Queen's Gambit. As you progress in chess, this becomes more and more likely. So now the question becomes: Is your preparation--memorization of the branching forest of moves--better than your opponent's preparation? That's not the sort of "luck" you want to depend on. That's not a good strategy. The solution is to learn a refuted-but-not-completely-atrocious opening like the King's Gambit. It isn't refuted at your level, and also there's almost no chance your opponent has memorized any specific defense for it, because "no one plays that." So they're reacting to it for the first time, surely not finding all the best counter-moves in the moment. But you *have* memorized a few of the best moves. So you have a clear advantage, even though you're a beginner. This is why [leverage, as defined here](https://longform.asmartbear.com/leverage/), results in a superior strategy for products and companies, even if many things about you, your team, your product, and your company, are "worse" than the competition. Playing on your own terms changes the game. "Worse, but unique," is in fact "better." A startup is a "beginner" in the market, facing a variety of "opponents" who have varying degrees of preparation and skill (age, capabilities, customers, brand, funding). A startup needs to fight the competitive battle on its own terms, not attack where the opponent is already strong. Even on a personal level, you must use your leverage to do something special, be something special, at [the intersection of what is special, enjoyable, and what the world needs](https://longform.asmartbear.com/fulfillment/). > "Be yourself. Everyone else is taken." > --Oscar Wilde Many potential customers won't want to play your game, and you'll feel bad when you lose them to a competitor, and when they say mean things about you and your product and your decisions. Especially when it's in public, on review sites and social media. But don't worry, you weren't going to win them anyway--they're just not into you. The customers who *do* want to play your game, will see that you're the best in that game. Even if you're two people and there's a dozen competitors ranging from well-funded, well-covered-by-the-media startups to old-but-reliable incumbents. If you pick a different game, you're the best at that game, and you can become extremely successful.[^1] [^1]: In my case, WP Engine powers as many substantial websites as Google Cloud or Digital Ocean, and we still have only 2.3% of that market. That means 98% of potential customers are in fact not customers, and yet WP Engine is a unicorn. By the way, this is another reason why it's wise to [seek large markets](https://longform.asmartbear.com/problem/). Even better than "different," is to be extreme in that difference. Not just a minimal UI, but so minimal it works on the command-line. Not just great design, but so remarkable people buy it only for that, and it's written up on designer's blogs. Not just a new algorithm that solves an old problem, but one that uncovers new things that no one else does, even at the expense of missing things that others catch. You can't do this for all aspects of your product and business--indeed, even a single one is already powerful--but extremity is how you maximize the power of the few things that make you special. All those competitors *cannot* play the game as well as you, because you picked something that was not only different, but extreme. They can't make every choice you made, and since they didn't, they're definitionally not the best at your game. Play your King's Gambit. Don't worry if others claim your strategy is refuted. It's not refuted for you. Indeed, the fact that they *think* it's refuted, is why you will win.

What makes a strategy great

Jason Cohen — Sun, 13 Aug 2023 00:00:00 +0000

![strategy big data synergized paradigms something that rhymes](https://public.asmartbear.com/books/anthology/chapters/great-strategy/d6515print-simple.png) **Strategy is:** *How we will win.* You can debate the form a strategy should take, whether a [four-sentence "master plan"](https://www.tesla.com/blog/secret-tesla-motors-master-plan-just-between-you-and-me) or a [few dozen bullets](https://twitter.com/chamath/status/1369328384812093441) or a [six-pager](https://writingcooperative.com/the-anatomy-of-an-amazon-6-pager-fc79f31a41c9) or an eighty-pager or a template like [Salesforce's V2MOM](https://www.salesforce.com/blog/how-to-create-alignment-within-your-company/) or a Notion template you found on the internet or answering [three questions from a Twitter pundit](https://twitter.com/shreyas/status/1384008853826592776) or some sort of "[Canvas](https://leanstack.com/lean-canvas)." Regardless, its job is to communicate "how we will win." There are a lot of documents titled "Strategy," but very little *great* strategy. The formula for great strategy isn't decided by the format of the output document. > "Greatness needs luck, but it's never by accident." > --Unknown For strategy to correctly determine and communicate "how we will win," it must tackle the reality of complex systems, it must parry the follies while exploiting the strengths of human nature, and it must justify and spell out the decisions which lead to the desired outcome. Great strategies accomplish this with the following characteristics: * **Simple:** Reshapes complexity to be manageable and actionable. * **Candid:** Dares to spotlight the most difficult truths. * **Decisive:** Asserts clear decisions *and* accepts their consequences. * **Leveraged:** Magnifies strengths into durable competitive advantage. * **Asymmetric:** Defeats uncertainty with higher upside than downside. * **Futuristic:** Solves for the long-term. Without these qualities, the so-called "strategy" is at best a plan; at worst, it's wishful thinking masquerading as "vision." --- ## Simple > "Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it's worth it in the end because once you get there, you can move mountains." > --Steve Jobs The world is complex, and therefore difficult to reason about. Strategy intentionally omits detail in exchange for clarity. Armed with a simple narrative, mere mortals can achieve understanding, and make every-day decisions that remain aligned to that narrative. No one reads anything. You're in the Top 1% just for reading this sentence. No one remembers anything. Certainly not an 86-slide PowerPoint.[^1] So the strategy has to be simple--even *simplistic*--to have a chance at being read or remembered. [^1]: Long *supporting* documents are useful, because they explain and justify complex topics, inspiring conviction even in people who don't read it but are impressed by its size. Even the famously pithy [Tesla "Master Plan" strategy](https://www.tesla.com/secret-master-plan) consisting of just 39 words was preceded by 1200 words of justification. People are mired in their day to day work; only when the strategy is simple, does it have a chance of being incorporated. Only when the context is over-simplified, having made scrutable the complexity of the real world, can we easily explain "why," and retell those stories in our weekly meetings and prioritization sessions. Those are the places where strategy lives and breathes, where teams can move quickly, independently, fulfilling their individual missions with a minimum of coordination, yet all supporting a common theory for how we will win, together. > "The bigger the project, the more we need a reminder of the goal, and simple things to execute today." > --Warren Buffett, shareholder letter, 1982 If the strategy is simple, it might appear obvious. If it is obvious, it might appear uninsightful. Humans assume that complex puzzles require complex answers, when in fact often the simplest answer is the best answer.[^2] "Obviousness" is a sign of a strategy that not only is easy to communicate and execute, but also believed. [^2]: See, for example, the [Midwit Fallacy](https://philo.substack.com/p/the-midwit-trap), or how small sets of equations explain a wide range of physical phenomena. Its job isn't to be non-obvious, but rather to cleanly specify what is most important. It might be obvious to do X, but Y and Z are also "obvious," so by selecting X and not Y nor Z, you have created focus, and specified "how to win." Simplicity at its worst becomes *reductive*--overlooking complexity rather than tackling it, resulting in conclusions that, while admittedly simple and clear, are wrong. We cannot pretend that complex problems can always be waved away by simple statements. The process of strategy *creation* must indeed tackle the complexity of the world, but the *output* of that process is a document that frees everyone else from having to re-solve every puzzle. Like a street map, we omit detail in exchange for clarity of the most important context and routes. Simplicity that ignores reality is reductive, but simplicity that arises from an exceptional summarization of having already processed the messy, complex world, is elegant. Characterizing the world in a few, simple, clear assertions, and solving our challenges with a few, clear directives of what we must do, is required for a strategy to be "how we will win." _Example: The most famous sports coaches seem to credit their success to a few [simple principles](https://medium.com/the-ascent/greatness-starts-with-tying-your-shoes-59fbeff027dc). They must be simple for a team to remember them, especially when they're tired and battered near the end of a game, as in this description of the mentor of one of America's most famous football coaches:_ > "Blaik's signature talent was using all this data to create something clean and simple. He had what Lombardi called "the great knack" for knowing what offensive plan to use against what defense, and the "discarding the immaterial and going with the strength." All the detailed preparations resulted not in a mass of confusing statistics and plans, but in the opposite, paring away the extraneous, reducing and refining until all that was left was what was needed for that game against that team. It's a lesson Lombardi never forgot. > --David Maraniss, *When Pride Still Mattered: A Life of Vince Lombardi*, 1999 --- ## Candid > "See, and don't be afraid to see what you see." > --Ronald Reagan Strategy must lay bare the most frightening, embarrassing realities. It must [face the truth](https://longform.asmartbear.com/failure-to-face-the-truth/) that we all avoid facing during the struggle of daily work. If the strategy doesn't expose--and then solve--the most brutal facts, the strategy is wrong. In particular, the strategy must diagnose the primary challenges facing the business, even if the facts are so scary that it seems hopeless. Existential threats are the most important to articulate. Too often a strategy claims "threats" that are lazy, generic, non-actionable pseudo-concerns that nearly every company could claim, like "Google could copy us" or "A new startup could invent a great product and get huge funding" or "A massive security breach could cause half our customers to leave." Real threats are either happening now or are at least [70% likely to happen](https://longform.asmartbear.com/probability-words/). Real threats are specific, ideally backed by data that proves they are happening, e.g. your market is shrinking; a competitor has accelerating market share; cancellation rates prove that even paying customers don't value the product. Real threats are written in the present tense because they are happening, not hypothetically with "could" or "might." When the puzzles are clear, everyone can help solve them. When the puzzles remain hidden, what's the chance that they'll be solved by accident? > "Average players want to be left alone. Good players want to be coached. Great players want the coach to tell them the truth." > --[Nick Saban](https://twitter.com/TheCoachJournal/status/1171955834462646272) (won more US national college football titles than any other coach) If you're worried someone might leave the company when they hear how scary it is, maybe they *should* leave the company. These *are* the challenges we're facing together; if they're not willing to solve them, they need to make space for someone who will relish the challenge. Not just for the company's sake but for their own happiness and [fulfillment](https://longform.asmartbear.com/fulfillment/). ![lets say honesty isnt going to work whats second best policy](https://public.asmartbear.com/books/anthology/chapters/great-strategy/d4501print-simple.png) Facing the truth, being specific about current reality and about what needs to be done, is required for a strategy to be "how we will win." _Example: In a story retold [here](https://longform.asmartbear.com/failure-to-face-the-truth/), originally from Jim Collins's book *Good to Great*, A&P and Kroger were successful grocery stores in the 1960s who both possessed the data showing that their business models were becoming obsolete. They each discovered the new, correct business model through real-world experimentation, but only Kroger was willing to face that truth and change their strategy. Despite being half the size of A&P, Kroger increased its value 100-fold, while A&P shrank, then went bankrupt._ --- ## Decisive A strategy asserts a set of justified, self-consistent decisions, such as: * Which subsets of the market to target
*...and conversely which we'll ignore, even if some of those sign up as customers anyway, and ask for things we're not going to do, and then cancel in anger* * Which customer personas are most important to delight
*...and conversely which will dislike our product, causing us no dismay, whose feature-requests we will quickly close as "won't do" rather than wring our hands at all the features we still need to build* * How to position against the competition
*...and conversely where the competition will be stronger, unlike those fake-news marketing charts comparing our product with the competition, where only our product scores 100% along every dimension* * What we value (e.g. quality, service, speed, design, compatibility, lock-in)
*...and conversely what we will give up, e.g. releasing features faster but of lower quality, or being infinitely extensible versus top-to-bottom thoughtful design* * What we must *(and must not)* build, to pay off that positioning and win those customers in that market. > "You can do anything, but not everything." > --David Allen There are both positive and negative second-order consequences of any complex decision. If these aren't identified--in particular, if the problematic consequences aren't embraced within the strategy--then it's not a clear decision. When those consequences inevitably arise, the team must be able to say "we expected that" rather than "we have to address that" or even "this is a signal that the strategy is wrong." One decision in every strategy is what market and customer-segment the company will target. Customers outside that target segment will inevitably buy anyway. Then they'll complain about missing features, awkward UX workflows, missing integrations, high prices, and more. Then you'll log those complaints into the bug-tracker and product-management Miro boards and start prioritizing, whereas in fact these ideas should be ignored so that the team can focus on winning the target customers. This is difficult to remember when the non-target customers complain on Twitter, supply low NPS scores, drag down your average star-rating on review sites, and cancel at high rates. Only with a strategy that has clearly articulated not only the decisions, but also the negative consequences of those decisions, can everyone stay true to those decisions even when emotions are running high and paying customers are canceling. A common tactic for avoiding making a decision is to use non-specific language. "We will leverage synergies to create unique solutions" is, in fact, a good thing to do, but it doesn't specify which synergies to leverage, [what is unique](https://longform.asmartbear.com/worse-but-unique/) about it, or what the unique solution is. [Fluffy language](https://longform.asmartbear.com/authentic-is-dead/) is a hallmark of indecisiveness, and therefore of bad strategy. [Being specific is good marketing](https://longform.asmartbear.com/specificity/), anyway. > "Strategy is a set of interrelated and powerful choices that positions the organization to win." > --[Roger Martin](https://rogerlmartin.com/thought-pillars/strategy) The opposite of the decision must also be a rational choice, made by other successful organizations. For example, deciding to be open source is strategic, because plenty of companies are successful with a closed-source strategy. However, deciding to be "customer-first" is not a serious decision, because successful companies don't use a "customer-last" strategy. This "[Opposite Test](https://longform.asmartbear.com/opposite-test/)" is useful both to form proper strategic decisions, and to form great positioning statements for marketing. Decisions and consequences must at minimum be self-consistent. If you've decided to have a low price, you can't also have white-glove service. If you've decided that everything requires high-quality design, you can't also release features faster than the competition. Or, perhaps you can creatively build a solution that does say "yes to both" of those things, but only by also [accepting additional constraints that resolve the conflict](https://longform.asmartbear.com/conflicting-choices/). Better than "self-consistent" is "mutually-reinforcing." This means that one decision makes another more powerful, or easier, or less expensive, and vice versa, so that adhering to both makes you far stronger than having only one. For example, deciding to have only a few features, and also amazing design. Normally customers might not put up with less functionality, but if the design experience is exceptional, they might be happy with something that "does only a few things, but so delightfully!" And vice versa: It's easier to execute on great design when you don't have to tackle a complex product with tons of use-cases and personas and functionality. At the end of [this article](https://longform.asmartbear.com/leverage/), you'll find a complete, powerful example of mutually-reinforcing decisions creating a durable (60-years!) competitive advantage. > "The difference between successful people and really successful people is that really successful people say no to almost everything." > --Warren Buffett You know the decisions are strong when they--and their consequences--cause you to [say "no" to most things](https://longform.asmartbear.com/say-yes/), including things which otherwise sound reasonable. An example of a strategy that makes a decision but also accepts negative consequences can be found at the end of [this article about Moats](https://longform.asmartbear.com/moats/); an example of how to make sales pitches while accepting negative consequences can be found in [this article](https://longform.asmartbear.com/one-benefit/); several examples of how YouTube used a single, strong decision with multiple major downstream consequences are detailed in [this article](https://coda.io/@shishir/eigenquestions-the-art-of-framing-problems). ![rest assured however i vote it will be staunchly](https://public.asmartbear.com/books/anthology/chapters/great-strategy/d8066print-simple.png) When a strategy has articulated clear decisions, including the major consequences, especially accepting the negative consequences, causing us to say "no" to pretty-good ideas in order to make room for the [very-best ideas](https://longform.asmartbear.com/maximized-decision/), and to ignore the wishes of paying customers if they're not the target paying customers, it is truly prescribing "how we will win." _Example: [Craig's List](https://craigslist.org/) is classified ads on the internet. Their strategy includes crisp decisions like valuing consistent user experience more than great design (the website looks like it's from 1995, because it is), and user-accessibility over monetization ("Craigslist president Jim Buckmaster has stated that creating a superior user experience is more important to the company than making money [[source](https://smallbusiness.chron.com/craigslist-money-27287.html)]"). The strategy is clear and differentiated, which should be seen as a strength, but it has bothered pundits for twenty-five years, who have therefore predicted its demise: "The main obstacle to sustainability and growth at Craigslist is likely the company's and its founder's strong principles valuing customer-offering over monetization, trusting consistency over innovation. Although admirable in many ways, the issue is that without innovation, the company's customer-offering will soon no longer be strong enough to stay relevant, undermining its very well-intended purpose."--[HBS](https://digital.hbs.edu/platform-digit/submission/craigslist-the-remarkable-but-potentially-obsolete-success-story-of-the-ultimate-platform/). But crisp decisions make for a great strategy, even if controversial. Despite that well-reasoned death-sentence from HBS, despite others pointing out that [dozens of successful companies have](https://www.businessinsider.com/craigslist-versus-startups-chart-2014-8) made sections of Craig's List theoretically obsolete, despite data that [predicts Craig's List "demise"](https://techcrunch.com/2015/04/10/as-vertical-marketplaces-rise-craigslist-faces-its-demise/), despite [protestations](https://money.cnn.com/magazines/fortune/fortune_archive/2005/12/12/8363113/index.htm) that Craig's List could be a "gold mine of revenue, if only it would abandon its communist manifesto," (i.e. if only it would abandon its strategy), it continues to be one of the [most-visited sites in America](https://www.semrush.com/blog/most-visited-websites/), with revenues of $1,000,000,000 in 2020 with a staff of just fifty people, even after twenty-five years of internet evolution._ --- ## Leveraged > "Be yourself. Everyone else is taken." > --Oscar Wilde If Sandra is a great singer, and Peter is a great pianist, then a good idea for a duet would be for Sandra to sing and Peter to play piano. A bad idea would be the other way around. Great strategy leverages the strengths and assets of the organization; a bad strategy asks the organization to win even while acting unnaturally, often under the cover of "overcoming weaknesses." "Leverage" means generating a large effect from a relatively small effort, where time and dollars are far more effective than one might expect, because we are riding tailwinds of natural abilities or hard-won assets, rather than fighting a battle against so-called "weaknesses." You know you're leveraging strengths when you see other people shake their heads in amazement at how much you accomplished in so little time. It's good to leverage strengths. Much literature on strategy dwells on how to create [moats](https://longform.asmartbear.com/moats/)--permanent competitive advantages--but so many organizations still aren't leveraging the straightforward, undifferentiated strengths that they possess. They expend most of their energy shoring up "weaknesses," which despite their efforts will at best become "less weak," but never become a strength. Whereas applying that same energy in leveraging their strengths will have a large positive effect. It's even just more *fun* to play to your strengths instead of wallowing in weaknesses. It is of course better when the strength is *differentiated* from the competition. This is especially obvious in a mature market where everyone is saying the same things on their home page, pricing the same way, and different only in tertiary characteristics. Winning in your own way [can defeat "better" competitors](https://longform.asmartbear.com/worse-but-unique/). It's even better when that differentiation is *durable* over time. It has never been more difficult to establish a permanent advantage, when all software can be reproduced, all business models can be replicated, and the entire world is both your market and your competitors, which makes it all the more important to decide what one or two moats you will build. The strategy is the place to name those moats. **[This companion article on "leverage"](https://longform.asmartbear.com/leverage/)** provides expanded examples and ideas for all of the above. Tailoring the decisions for the strengths of this organization, avoiding (rather than reversing) weaknesses, even better when the strength is differentiated, identifying and investing in durable differentiation, so that moats are constructed in the long run, is required for a strategy to be "how we will win." *Example: Zappos started in 1999--early internet, no social media, few people with broadband, few people comfortable buying anything online, much less buying shoes, which for 100 years was bought only after first trying them on in person. Zappos started with a differentiated insight (the companion article explains how "insights" can be leverage): That people would actually buy shoes online, under the right circumstances. They built a moat out of a combination of surprising decisions (also explained in the companion article): (a) postage-paid returns even 364 days after purchase, (b) legendary support who did things like buying pizzas for customers, (c) a corporate culture of employee empowerment; it takes that combination of strong, expensive decisions to create the conditions by which people would dare to buy online, thus unlocking an entirely new retail channel. Only by having all three of those components did the moat work; if you don't empower employees, they can't order pizza on the spur of the moment; if your return policy isn't ridiculously generous, people will be too scared to order shoes sight-unseen. Because their vision and decisions were so incredible (literally "not credible"), competitors laughed at them while they enjoyed a constant stream of positive press. The result was the most successful online shoe store for a decade, reaching annual revenues of $1B before its tenth birthday.* --- ## Asymmetric ![Wind flowing over a sailboat's sail](https://public.asmartbear.com/books/anthology/chapters/great-strategy/air-flow-2-simple.png) A sailboat always moves forward. This remarkable fact is due to its asymmetrical shape: it is pointy at the front and flat in the back. This creates resistance to moving backwards, but a natural ease in moving forward. Even when the water is randomly undulating, "backward" forces are muted, while "forward" forces are allowed, so the boat glides forward. Asymmetries can amplify positive effects while muting negative ones, resulting in a net-positive force even under conditions of random inputs. Great strategies prescribe activities that always move the company forward, despite the inevitable bad luck and setbacks. To do that, the activities must exhibit asymmetry, where the upside vastly exceeds the cost, so that even if you [took 2x longer to achieve 50% of what you expected](https://longform.asmartbear.com/roi-rubric/), you still win. > "If riskier investments could be counted on to produce higher returns, they wouldn't be riskier." > --Howard Marks This is the mechanism behind Venture Capital portfolios, which are investments in a slate of early-stage startups. The worst-case outcome for each bet is that they lose 100% of their investment, but in the best case they can gain 10,000%. A few large successes more than make up for the many [failures](https://longform.asmartbear.com/fail/), so the portfolio in total comes out positive. Investors call these "asymmetric bets." Economists call this "convexity." Strategy must create a portfolio of bets having this "VC-like" asymmetric quality, whether for a small startup trying to find product/market fit or a mature company entering new markets. A sign of a bad strategy is when success requires everything to go right. With a set of asymmetric bets, the successes render the failures moot, and so the unpredictable waves crashing into the boat still result in forward motion. **[This companion article details strategies that work well in an unpredictable world](https://longform.asmartbear.com/predict-the-future/).** One of the few strengths of a new startup is that it can [learn faster, react faster, deliver faster](https://longform.asmartbear.com/startup-beats-incumbent/); startups are favored when the waters are uncertain. One form of asymmetric bet is entering a large and growing market. Besides the obvious benefits[^3] there is the asymmetry of optionality: There are many niches to exploit, many possible ways for a product to deliver value, many marketing and sales channels, and there's more of all of it every year. Because there are many options to try, there are many ways to succeed; if your first few ideas don't work, the next one might. By having [lots of ways to succeed](https://longform.asmartbear.com/capturing-luck/), you are more likely to find one. Waves on the boat. [^3]: Potential customers are already spending money, which means budgets are pre-allocated and pricing structures are well-understood, the press is already talking about it, marketing channels already exist, and the "pie" is growing, so even a small slice of the pie automatically grows. > "The most powerful force in the universe is compound interest." > --Albert Einstein Another kind of asymmetry is a process that compounds, meaning that the more of it there is, the faster it grows. Things with this characteristic naturally grow larger than anything that grows in a more linear fashion, even if they start out small. Examples are customer retention, customer upgrades, and employee retention. Another example is a growth-vector that is proportional to the size of the current customer base, such as word of mouth referrals and viral products (e.g. once you join a social platform or collaborative online tool, you tend to get other people you know to join as well). Most things that grow non-linearly [don't grow exponentially](https://longform.asmartbear.com/exponential-growth/)--that's normal, and still a great strategy. A common example is a reseller channel, because each reseller could bring you a number of clients over time, so when the number of resellers grows by `N`, the number of potential customers they bring grows by `N ✕ M`. For example, Intuit grew Quickbooks by providing software to CPAs that made their life easy, but only if their clients used Quickbooks. The CPAs started requiring their clients to buy Quickbooks, and so a smaller number of CPAs created a much larger number of Quickbooks sales. In the negative, companies also face asymmetric threats. With ten competitors, only a few need to become break-out winners to pose a significant challenge. Like the famous statement by the IRA after [a bombing](https://en.wikipedia.org/wiki/Brighton_hotel_bombing) failed to kill British prime minister Margaret Thatcher: "Today we were unlucky, but remember we only have to be lucky once. You will have to be lucky always." A strategy that places a number of asymmetric bets doesn't need to be lucky always. Every plan will face challenges, both foreseeable and bad luck. If everything has to go right for the plan to succeed, it won't succeed. Whether a single investment has asymmetric upside, or a portfolio of bets collectively has large upside, exploiting asymmetries maximizes the chance that the strategy will succeed despite the [inevitable travails](https://longform.asmartbear.com/chaos-at-start/) and [uncontrollable luck](https://longform.asmartbear.com/lucky/), and thus is vital to "how we will win." _Example: Amazon is the cliché example, but only because it's apropos, even ignoring AWS. Originally a book-seller only, they have never stopped taking bets, whether on adjacent markets (e.g. selling electronics), reselling internal systems (e.g. Amazon warehouses, logistics, robotics, fulfillment, were all broken apart and sold as separate products), or entirely new kinds of product (smart phones, cloud computing). More impressive is when the new bets purposefully disrupted previous businesses; according to early employee Andy Johns ([article](https://medium.com/sand-hill-road/unpacking-growth-with-andy-johns-partner-at-unusual-ventures-79778fc7bdd8), [podcast](https://thetwentyminutevc.libsyn.com/20growth-top-growth-lessons-from-the-early-facebook-twitter-and-quora-days-the-most-important-question-to-ask-when-building-your-growth-team-how-to-test-for-true-candidate-depth-and-quality-when-hiring-for-growth-with-andy-johns-venture-partner-unusual-ve)), Bezos was so adamant that the Kindle be successful, despite the fact that it would hurt their physical book business, that he assigned the Kindle project to the executive who was currently over the book business, saying "Effective tomorrow, your job is to kill your old business with a Kindle." The key thing is: **many of these bets failed, and failure was expected.** Failure isn't desirable, but each bet had out-sized potential upside and budget-able downside. Even Kindle was [unprofitable for years](https://www.forbes.com/sites/kellyclay/2012/10/12/amazon-confirms-it-makes-no-profit-on-kindles/?sh=3f08d58d6b43) and the Amazon Fire Phone was an abject [failure](https://time.com/3536969/amazon-fire-phone-bust/). Bezos explains in his own words that their strategy is exactly the theory of a portfolio of asymmetric bets with expected failures:_ > "If you're going to take bold bets, they're going to be experiments. And if they're experiments you don't know ahead of time if they're going to work. Experiments are by their very nature prone to failure. But a few big successes compensate for dozens and dozens of things that didn't work." > > "I've made billions of dollars of failures at Amazon.com. Literally. You have to be super clear about what kind of company you're trying to build. ... We said we were going to take big bets. We said we were going to fail." > > --Jeff Bezos, [The Guardian](https://www.theguardian.com/technology/2014/dec/03/jeff-bezos-billions-dollars-failures-amazon), 2014 --- ## Futuristic > "The best time to plant a tree is one hundred years ago. The second-best time to plant a tree is today." > --A proverb claimed by many peoples, across thousands of years "Being agile"[^4] is a great way to climb the proverbial mountain-shrouded-in-fog. Some paths are the right ones, but backtracking is inevitable; it's a sign of puzzling-out, not a sign of failure. The job of strategy is to identify which mountain we're trying to climb in the first place--the puzzle we're solving for, the opportunity we're exploiting. If an "agile, self-managed" team climbs the wrong mountain, it was all a waste. [^4]: Meaning: Don't pretend you can predict the future, assume the quantity of "things we don't know" is larger than the quantity "things we do know," iterate quickly on [hypotheses](https://longform.asmartbear.com/customer-development/) that you proactively attempt to disprove, and adjust in the presence of new information. The team that learns the fastest, wins. The team that spends three months trying predict the future, is now three months behind, and the future still won't unfold as they predicted. Strategy looks further into the future than anything else at the company. Therefore, it has the responsibility to take the long view. Which is especially difficult, as [the future is unpredictable](https://longform.asmartbear.com/predict-the-future/), and [data tells you about the past, but rarely about the future](https://rogermartin.medium.com/singing-the-data-analytics-blues-58fdf93b27bf). If you can solve a problem in a month, you probably should, but that also means it's not a *strategic* problem. Anything that can be built in three months, isn't the way you will have constructed a moat that will take competitors years to overcome. Anything that specifies features or timelines is a roadmap, not a strategy; a strategy specifies market and business outcomes and the primary decisions and secondary consequences. Anything that specifies teams or roles or hiring or processes is an operational plan, not a strategy; a strategy details the outcomes and activities that require the entire company to accomplish together, not what one team needs to accomplish alone. People naturally get distracted by the immediate, the urgent, the tactical, the "low-hanging fruit." Strategy is the place where we select the high-hanging fruit, the "important, but not urgent" quadrant of the [Eisenhower Matrix](https://en.wikipedia.org/wiki/Time_management). Then executed as the Rocks in your ["Rocks, Pebbles, Sand" prioritization framework](https://longform.asmartbear.com/rocks-pebbles-sand/). Part of explaining "how to win" is defining what the "winning" state looks like. Often called the "vision statement," it describes what the world will look like when we're successful: | | | |:----|:----| | Oxfam | A just world without poverty | | Habitat for Humanity | A world where everyone has a decent place to live | | Stripe | Increase the GDP of the internet | | Microsoft
(years ago) | A computer on every desk and in every home | | Tesla
(original) | Create the most compelling car company of the 21st century by driving the world's transition to electric vehicles | | WP Engine | Power the freedom to create online | | YouTube | Give everyone a voice and show them the world | | GoDaddy | Radically shift the global economy toward independent entrepreneurial ventures | ![youre right its not much of mission statement](https://public.asmartbear.com/books/anthology/chapters/great-strategy/d2714print-simple.png) The Vision statement is often the first sentence the strategy document, but was the last thing crafted by the authors of that document. Only once you fully understand the challenges you face, the main, coherent courses of actions to undertake, and the results you want, can you summarize a clear vision of what the future will be. Because words like ["mission" and "vision"](https://longform.asmartbear.com/mission-vision/)have indefinite meaning and are often misused, I prefer alternate words and intentions, as described in the article just linked. Strategy is where we specify the most critical long-term challenges facing the company, and the [rocks](https://longform.asmartbear.com/rocks-pebbles-sand/) that are [the most important things](https://longform.asmartbear.com/maximized-decision/), not to win the battles today, but to determine how we will have won the war three years from now, how we will achieve our vision, how we will win. *Example: After Google purchased YouTube, they set a far-future goal of attaining a billion daily views. This filtered down to everything from demands on technical architecture, to strategic debates such as whether that enormous quantity of views could be achieved by user-generated content alone or whether they needed to license libraries of existing TV shows and movies. At one point they realized that the global internet infrastructure did not have enough capacity to stream a billion views per day, so Google invested billions of dollars in fiber and data centers to prevent that from becoming a bottleneck--billion of dollars they would not have spent without a vision of the future that mandated that investment.* --- ## Bad Strategy Tell-tale signs of a strategy that lacks these qualities: #### Not simple Pages of detail.[^5] Slides with more than 20 words. A litany of numbers without a narrative explaining what insights they create. No diagrams "painting the picture," or diagrams with 20 boxes. Too many points for someone to [recall from memory](https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus_or_Minus_Two). Important concepts that aren't summarized by a short phrase that people can use as a daily short-hand. #### Not candid Nothing where the future of the company hangs in the balance. Nothing that makes the reader say, "Oh wow, dang, what are we going to do about *that!?*" Nothing scary that demands action. No serious consequence if the directives aren't followed. A reader who finishes the document and thinks, "We're still ignoring the elephant in the room." #### Not decisive No clear decisions that would cause us to "easily say 'no'" to many otherwise excellent, reasonable ideas. Not obvious what we're *not* doing. Non-specific target market (e.g. "for everyone"), target customer (e.g. "any [title]"), target jobs-to-be-done. Directives and headings using the word "and" to expand scope rather than limiting it. No negative-but-accepted consequences of the decisions. Decisions that conflict with each other. Decisions that don't reinforce each other. #### Not leveraged Strategy would apply equally well to a competitor, or even to a business in another industry. Strategy doesn't call out the special strengths and durable assets of the organization, or doesn't explain how to apply them to win, especially how it will position against the competition. No obvious moats being constructed. No network of interlocking decisions that together makes the company special. Demanding that the organization overcome more than one or two major deficiencies. #### Not asymmetric Potential upsides aren't at least 10x larger than costs. Linear cost/reward, or risk/reward. Not creating optionality in how each aspect can go right, leading to one thing that absolutely must work. A course of action that requires multiple different, difficult things to simultaneously go right, otherwise the whole strategy fails. #### Not futuristic Doesn't describe a specific future destination of the company or product. A "vision" that describes what the company already does and already is, rather than how things will be different once we successfully execute our strategy. Doesn't specify which moats are being created, and how. Specifies teams, products, timelines, or features. Deals with temporary challenges that can be solved in a quarter rather than long-term challenges that will take years to fully overcome. Describes how to win this year instead of in three years. Relies primarily on data to predict the future. #### Not strategy (bonus) Generic statements that would apply equally to nearly any company, even in a different field (e.g. grow faster, lower attrition, hire the best talent, delight customers, beat the competition). Aspirations about what we wish would happen, without specifying how it will happen. Financial goals rather than how to win competitive markets. Plans that are in someone's head instead of written down and shared. Written documents that aren't referenced when creating plans. [^5]: Detail is great, if it is attached as optional reading, expanding on a summary. --- A sailboat always moves forward, but if you don't decide on a specific destination, it will end up *somewhere*, but probably not where you wanted to be. Time to decide how you will win. ## Postscript: How do I construct a strategy? Stay tuned for future articles that lay out a process for constructing a great strategy. Partial answers appear in [The roadmap for Product/Market Fit](https://longform.asmartbear.com/product-market-fit-formula/) and [Excuse me, is there a problem?](https://longform.asmartbear.com/problem/). But don't wait. A mediocre strategy is still better than no strategy. ## Further reading * Article: [WTF is Strategy?](https://hackernoon.com/wtf-is-a-strategy-bcaa3fda9a31) by Vince Law: Defining the nested concepts of vision, mission, strategy, roadmap, and execution, thereby contextualizing the role of "strategy." * Book: [*Good Strategy, Bad Strategy*](https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239) by Richard Rumelt: Doesn't explain how to construct a strategy, but terrific observations about what good and bad strategy looks like. ([Summary](https://jlzych.com/2018/06/27/notes-from-good-strategy-bad-strategy/) by Jeff Zych) * Book: [*Blue Ocean Strategy*](https://www.blueoceanstrategy.com/what-is-blue-ocean-strategy/) by W. Chan Kim & Renée Mauborgne: Explains how to construct a strategy that is not only "different from the competition" (i.e. competes better in so-called "red-ocean" force competitive markets) but competes in a wholly different way, creating a new value-proposition, often at higher profit, in so-called "blue oceans" where you've defined yourself such that there isn't any direct competition. * Article: [Gibson Biddle's multi-part series](https://gibsonbiddle.medium.com/intro-to-product-strategy-60bdf72b17e3) on how to build a product strategy that converts "how to win" into "what to do" * Article: [Taylor Pearson on optionality](https://taylorpearson.me/optionality/), and how to apply the Nassim Taleb quote in practice, even personally. * Article: [Excuse me, is there a problem?](https://longform.asmartbear.com/problem/) on selecting the right "problem to solve" and "for whom" to increase the probability of success. * Article: [Using the Needs Stack for competitive strategy](https://longform.asmartbear.com/needs-stack/) on thinking about customer value, markets, competitors, and disruption. * Article: [When customers are "willing" to pay](https://longform.asmartbear.com/willingness-to-pay/) on how to create strategies that capture and keep customers as your allies, who help you grow and find new customers, rather than forcing them to stay through coercion. * Journal article: [The Design School: Reconsideration of the Basic Premises of Strategic Management](https://onlinelibrary.wiley.com/doi/pdf/10.1002/smj.4250110302) by Henry Mintzberg, *Strategic Management Journal* (1990), with a summary of traditional strategy construction, a critique of its drawbacks, and conditions when it is appropriate.

In command

Jason Cohen — Sun, 30 Jul 2023 00:00:00 +0000

![stocks rose slightly in early trading then plummeted on news that stocks rose slightly in early trading](https://public.asmartbear.com/books/anthology/chapters/in-command/d0710print-simple.png) When a founder or CEO or team leader is "in command" of her business, I don't worry about that business, regardless of its challenges. Being "in command" doesn't mean you always meet your own expectations for yourself or your team. It means you *have* expectations of yourself and your team, and when you don't meet them, it bothers you, and you react. Being "in command" doesn't mean the metrics are always healthy, nor even that they're moving in the right direction. It means you've already [thought about what to measure](https://longform.asmartbear.com/product-metrics/) and you're watching the things that really matter. When metrics go awry, you're the first one to identify that, and are already taking action in proportion to their importance and how far off the mark they are. Being "in command" doesn't mean the product has no bugs. It means you have a list of 100-1000 bugs, and the reason you're not working on them is that you're working on the top three that matter most. Being "in command" doesn't mean you're always the smartest person in the room. It means you've hired a team of great people who can challenge you and bring diverse perspectives to the table. Being "in command" doesn't mean you never make mistakes. It means you're proactively on the look-out for mistakes, and call them out when you find them, so that now you can rally many people in finding solutions, and make a change. Being "in command" doesn't mean you never feel overwhelmed. It means you recognize when you're overwhelmed, take steps to manage your stress, and seek support when needed. Being "in command" doesn't mean there are no problems with any team member. It means you've already talked to that person about it, and you're working with them to change. Whether that means changing and staying, or changing by leaving. It means you're proactively and intentionally solving for [fulfillment](https://longform.asmartbear.com/fulfillment/) for the whole team, which includes yourself. Being "in command" doesn't mean there are no existential threats looming over the business. It means you've articulated what those are because you [sought them out and faced them head-on](https://longform.asmartbear.com/failure-to-face-the-truth/), and you're mitigating the [more likely](https://longform.asmartbear.com/probability-words/) ones with a few, logical actions. Being "in command" doesn't mean customers aren't leaving due to lack of features. It means you've identified what those features are, which ones are consistent with your strategy, which ones lead to the [ultimate benefits the customer is seeking](https://longform.asmartbear.com/needs-stack/), and that you're working on one or two of them right now. Being "in command" doesn't mean you're always popular. It means you're willing to make tough decisions, even if they're not always well-received, because they're in the best interest of the customer, the team, and the business. Being "in command" doesn't mean you're constantly telling people what to do. It means you're acting as an editor, not a writer, unless being a writer is necessary to make progress. And if *that* is happening constantly, that you make a change on the team so that it's *not* happening constantly. That might mean changing someone else, or changing yourself.[^1] [^1]: If you're constantly having to do someone else's work, that's your fault. Either you've hired incorrectly, which is your fault, or you have hired correctly and you're not allowing them to do their job, which is your fault. Being "in command" doesn't mean your strategy is completely correct. It means you are adhering to [the characteristics of great strategy](https://longform.asmartbear.com/great-strategy/). You have one, that is written down, that a normal person can understand, that people genuinely try to follow. When strategic flaws become apparent, you write those down too, and work on one or two at a time, updating your strategy calmly but purposefully. Being "in command" doesn't mean surprises never occur. It means when they do occur, it's not due to negligence. It means something actually changed, or an important new fact was uncovered. Even better, if you were [intentionally seeking new information](https://longform.asmartbear.com/customer-development/), and thus seeking surprise. Being "in command" doesn't mean you win every sale. It means you seek to understand the patterns in the wins and losses, and when those intersect with something consistent with your strengths and strategy and thus where you *should* win, you make changes so you can win the next such deal. Being "in command" doesn't mean every A/B test is a success. It means you're running A/B tests and honestly assessing the results, so that you improve over time. Being "in command" doesn't mean you don't have a long backlog, most of which you'll never get to. It means you've [identified the few Rocks](https://longform.asmartbear.com/rocks-pebbles-sand/) that are your bets on how to win strategically, and you have a [system for prioritizing the rest](https://longform.asmartbear.com/maximized-decision/), which also means doing almost none of the rest. Being "in command" doesn't mean you know everything about your target customer. It means you are [actively seeking out](https://longform.asmartbear.com/customer-development/) what they say and do, so you can understand them better, so you can make better decisions. > "Well when events change, I change my mind. What do you do?" > -- Paul Samuelson, winner of 1970 Nobel Prize in Economics, about how his models of inflation during WWII kept changing over time, and he was criticized for "not being able to make up his mind." Being "in command" doesn't mean you never change your mind. Indeed, a mind that never changes, is very likely wrong. It means you wrote down what you think, so that it's more obvious when your mind needs to change, and then you communicate that change--both what and why. Even if just to yourself. Being "in command" doesn't mean you do everything that stakeholders or even executives ask you to do. It means you listened with curiosity and empathy to what has been asked, [having a system](https://longform.asmartbear.com/jit-backlogs/) to reject bad requests and prioritize good requests, communicating that result and the rationale behind it. Being "in command" doesn't mean you're "in control" of everyone and everything. It means you're proactively seeking the truth, working rationally, and communicating. This is how you create autonomy, coupled with accountability, with realistic expectations. Strive to be "in command."

Selecting the right product metrics (KPIs)

Jason Cohen — Sun, 16 Jul 2023 00:00:00 +0000

![you redefine key metrics dynamic optimization alignment you figure what that means](https://public.asmartbear.com/books/anthology/chapters/product-metrics/d6720print-simple.png) Executives want financial outcomes, strategists want systemic impact, managers want team accountability, teams want credit for executing work, planners want to track progress, ops wants to know that systems are stable and secure. How do we select metrics that satisfy everyone? Much blood has been spilled on this topic. Frameworks range from poster-sized interconnected [networks of boxes and arrows](https://tinyurl.com/ybylrxq9), to reductively selecting a single [North Star metric](https://amplitude.com/blog/product-north-star-metric) to [rule them all](https://future.com/north-star-metrics/), to cascading goals like [OKRs](https://en.wikipedia.org/wiki/OKR). An organization should pick whichever framework is most likely to be adopted and honored. I used to believe that "one true metric" with a smattering of operational indicators was the best way to focus a team on "what matters most." But I've come to believe in a more comprehensive system, that addresses all of the needs outlined in the opening paragraph. Here is that system. Because all departments can "see themselves" in the result, I believe it makes stakeholders more comfortable, while giving the product team at the center of the maelstrom a practical view, not only of everything they do, but of everything they affect. It allows the team to measure things in the short-run while also maintaining the long view. ## Metrics as a value-chain A product sits in the middle of a chain of events, executed by the team, customers, and peers across all departments. The first step in understanding metrics, is to plot these events in time, by actor, and by the type of so-called "value" we might measure: ![Product KPI Framework](https://public.asmartbear.com/books/anthology/chapters/product-metrics/product-kpi-framework-simple.png) Use coloring to specify which metrics you are [maximizing vs satisficing](https://longform.asmartbear.com/maximizing/). Often "maximizers" are critical for the success of the product, whereas "satisficers" are important operational indicators that can't be ignored, and require attention if they become a problem,[^1] but under normal circumstances shouldn't distract us from what is most important. Once you have this map, the metrics almost write themselves. Each box has one or more metrics that explains whether "it is happening" or "we're making progress." You could go a step further, adding arrows[^2] to indicate influences or funnels or conversions, adding metrics to each arrow. [^1]: At [WP Engine](https://wpengine.com/) we're very happy with [Google's system for managing SLOs](https://sre.google/workbook/implementing-slos/), in which "satisficing" KPIs are tracked continuously, but the team acts only when a KPI slips into "violation" territory, as opposed to fielding requests to invest in improving those KPIs. This threshold is pre-agreed with stakeholders, in periodic meetings outside of high-emotion catastrophes, when everyone can soberly decide under what circumstances we will interrupt high-value work to address a critical problem. [^2]: Also called a [systems diagram](https://www.focusedchaos.co/p/your-startup-is-a-system-you-can-map), it could make the chart too busy, especially when "many things affect many things," resulting in a dizzying bird's nest of arrows. Perhaps the simplicity of "boxes only" outweighs the benefit of specifying all the value-flows. Or maybe include only the few, most-important arrows. ## The axis of "time, immediacy, and control" The horizontal axis emphasizes that some events happen prior to activity controlled by the product team, and some happen after. Most metrics frameworks have this concept of "leading" and "lagging" indicators, though some confusingly mix them all together.[^3] [^3]: For example, often top-level OKRs are multi-input, lagging indicators, whereas team KPIs might be immediate, and it's unclear where leading indicators go. This leads to unhappy conversations when teams meet their immediate KPIs, but the company's overall KPIs appear unaffected. Between those temporal bookends, we highlight that some of a product team's activity can (1) be measured immediately and (2) the team is [in full control](https://longform.asmartbear.com/in-command/) of those effects, and therefore the team should be held directly accountable to those things. This resolves the typical conflict that arises when executives ask "why isn't the team more focused on increasing revenue in the next 60 days" while the team insists "other people don't understand that we're doing a lot of important work." Work can--and should--be measured sprint-by-sprint, whereas revenue is a multi-input, lagging indicator of success. The product team *is* responsible for generating revenue, but it is not the *only* team or actor contributing to that final result, and a change in the product can take a while to show up in revenue; [individual features often cannot be directly linked to revenue](https://longform.asmartbear.com/unmeasurable-metrics/) at all. This doesn't make "revenue" less important--indeed, it might be *the* most important metric! Rather, we have placed the metric in context, and understood that it can lag by months or even years,[^4] and therefore isn't a good measure of what's happening right now. If we're successful at our "work" but not our "financial impact," our conversation is naturally directed towards diagnosing that disconnect. If we're not even successfully completing our "work," or if the features that we made aren't being used often, those are immediate facts and within the team's control, and metrics should reveal it, regardless of downstream consequences on revenue. You can't argue against solving for the customer! [^4]: Blackberry's revenue continued to grow for two years after the iPhone launched; other KPIs were changing far more rapidly, and therefore were even more important to track. Notice that often the "definition of success" also resides in those lagging indicators. This is another traditional cause of confusion, often articulated as "we should celebrate outcomes, not work." It is true that if our work doesn't result in the desired outcomes, we're not finished yet. But, if we believe that [the world is inherently unpredictable](https://longform.asmartbear.com/predict-the-future/), that [not all work will yield a large outcome](https://longform.asmartbear.com/rocks-pebbles-sand/), that outcomes require a [combination of execution and luck](https://longform.asmartbear.com/survivor-bias/), then we should agree that the job of an agile team is to continuously tackle that complex challenge, as opposed to expecting every idea to consistently produce the desired outcome. Seeing how *all* the metrics are performing gives the team and stakeholders the information needed to completely understand what's going on. Which is the whole point of KPIs. The agile team must honestly and clearly measure *both* direct results *and* lagging outcomes. They are accountable for all of it, and measuring is required for accountability. But "not yet achieving the outcome" is [not a complete failure](https://longform.asmartbear.com/fail/), but rather a learning that will shape the work that will be executed in the next two weeks. > "If the result confirms the hypothesis, then you've made a measurement. If the result is contrary to the hypothesis, then you've made a discovery." > --Enrico Fermi ## The axis of "hidden, internal, external, and strategic" We control what we do in our sprint; we don't control what customers do. The company controls what other teams are doing; the product team doesn't directly control that (much to the chagrin of [product managers](https://longform.asmartbear.com/great-product-manager/)). It's useful to draw a bright line between what is external and what is internal. Often completely different people work in these two domains, e.g. product, design, support, and sales working directly with customers, whereas engineering, infrastructure, and security work inside the company. The diagram helps us appreciate everyone's role, and use the right metrics for the right things. Furthermore, some internal activities are close to the customer (e.g. releasing new features), while others are far away (e.g. applying a security patch). If all our work is invisible, we have a problem: Customers perceive a stagnant product, competitors appear to be moving faster, sales doesn't have new things to say. On the other hand, if we value only the visible things, we end up with a bad product, with tech debt and unhappy engineers with slow delivery due to an under-invested foundation. The diagram makes this clear, honoring all of these important types of work. The most valuable, strategic outcomes are often even more distant from the product team, whether because they are down-stream, or because they are second-order effects for the customer. We control "satisfaction" more than [self-motivated external "advocacy,"](https://longform.asmartbear.com/willingness-to-pay/) yet the latter is clearly not only the ultimate measure of the success of the product, but also drives efficient growth. Product teams should take ownership of creating those outcomes, while not allowing those lagging, multi-factor metrics to be the *only* way we measure progress. The most valuable thing is for the customer to achieve their own ultimate goal, as defined by the [Needs Stack](https://longform.asmartbear.com/needs-stack/). If the customer's business doesn't thrive, they'll stop paying for your software, no matter how good the software is. While of course the customer's business is again a multi-factor, lagging metric, where nearly all the factors are outside of your control, it's still ultimately the greatest form of value. Even if you can't control it, you can notice the attributes of [customers who tend to thrive](https://longform.asmartbear.com/icp-ideal-customer-persona/), and direct your marketing, sales, and features towards that subset of the market, yielding higher growth and retention, and likely higher profitability. ## Checklist for great metrics From [SMART goals](https://asana.com/resources/smart-goals) to [FAST goals](https://sloanreview.mit.edu/article/with-goals-fast-beats-smart/) to [North Star Metrics](https://amplitude.com/books/north-star), there's plenty of prior art on how to pick good metrics. This is my own list. #### Defined in normal language "Customers are using feature X" makes sense. "Total unique IPs which caused at least one event from P, Q, or R to fire in our analytics system over a rolling 14 day period, divided by total unique IPs from the same system in the same period" is a precise way of measuring "using feature X," but it's too hard for normal people to scan and understand. #### Defined precisely In the previous example, you need that technical definition also. Frequently that definition is where we realize either (a) we can't get exactly the metric we wanted, or (b) we have to do engineering work before we get the metric we want. This is especially important with concepts like "cancellation rate" or "cost to acquire a customer" which can be defined in myriad ways; it's often useful to use different precise definitions for different metrics within the same company. #### Matches the intent Often the technical metric doesn't actually measure what we stated in plain language. For example, we intend to measure "User Portal usage" but instead we measure "User Portal logins," which only triggers when someone's session expires. It is common to want to measure P, but P is too difficult or maybe even impossible, so we pick a proxy metric Q. That *can* be OK, but make sure the proxy really does measure the intended concept. #### Causes action If the metric does something differently from what we expect, would we act? Would we re-plan the next sprint, or even interrupt the current one? If the answer is "no," it doesn't belong on our main metrics board. It might belong on an operational board, if it's explanatory, helping us to understand how things are functioning. #### Obvious what "good" look like Not all metrics should be attached to explicit goals, because some are for monitoring and understanding the situation, as opposed to something we're actively trying to change or maintain. However, it should be obvious "what good looks like." That could be as simple as "not changing," or directional rather than specific (e.g. "usage increasing" as opposed to "usage going up 10% month-over-month"). #### Measures "what is happening" (not "work") You already track work; metrics shouldn't duplicate or summarize that. Metrics are about "what is happening" around us--[the dials on the airplane dashboard](https://longform.asmartbear.com/whos-lying/), not the actions the pilots are taking. (Exception: Metrics that explicitly measure whether we are completing a volume or quality of work.) If the metric can be moved by, or applies to only one possible course of action, it's measuring work and even presupposing solutions, instead of measuring "what is." #### Measured easily Many metrics are useful, but remain un-updated in spreadsheets because it's too hard to get them. Best is automated; second-best is manual-but-trivial. #### Measured frequently Daily is best. One of the advantages of "rolling N days" is that you can update it daily, yet still think in units like "week" or "month" if that's sensible. Caveat: if the number doesn't naturally change frequently, then it's not important to measure it frequently. Still, in that case you won't check it often, which diminishes its value. #### Stable definition and applicability Measure things whose definition is stable over time, ensuring that any observed changes are the result of deliberate actions or environmental shifts, rather than random fluctuations or alterations in the nature of measurement itself. This stability allows for meaningful month-to-month comparisons and more accurate assessments of strategies and outcomes. #### Uses common definitions when possible It's tempting to invent your own metric, even when there are so-called "industry standards" or "best-practices." Standard definitions might not be appropriate for your business, or violate one of the other rules above. Inventing your own language is also a form of team-cohesion. However, reinventing the wheel makes it harder for others to understand what you're doing and precludes using benchmarks to see whether your metric is "good" objectively. There are even metrics where special definitions hide the signal; sometimes these are even used for deception.[^5] Don't do that. #### Signal at least 2x stronger than noise Metrics often vary for reasons unrelated to the underlying signal. Monthly revenue is like this--new, upgrade, and cancellation alike. Calendar months vary in days by ±5%. Furthermore, daily numbers can vary by 2x between a week-day and week-end, and calendar months vary in the number of weekdays by ±10%. So, if you're tracking something like new revenue per month, even a real change as large as 10% is the same size as the noise, so you can't actually tell if there was a real change. Sometimes a clever alteration to the definition can remove noise. For example, "new revenue over rolling 28 days" eliminates the two factors just mentioned, and is fairly close to a calendar-month worth of revenue.[^6] Or if noise is infrequent, something like "median" or "95%tile" can ignore outliers. But often noise is less predictable. In that case, it's useful to ask how much is noise, and whether it's so much that the metric isn't useful. If you don't know how much is noise, it's probably a bad metric. #### The team actually cares about it Whenever management requires a KPI that the team doesn't believe in, doesn't respect, doesn't care about, the team always wins. You're lucky if six months later the number is even being tracked; certainly they won't be taking action on it. The team has to *want* to depend on it, look at it weekly at least, and change behavior if it hits some threshold. If you just assign it, or if there's a sense that the number is meaningless or unfair, they simply will not react to it at best, and decrease morale at worst. [^5]: Common examples are revenue-recognition, classifying costs in or out of COGS/GPM, and conveniently leaving certain costs out of EBITDA. [^6]: To make it an "average calendar month," you can multiply your trailing 28-day figure by `52/48`, which scales it to the average number of weeks in a month. I hope this system will be as useful to you as it has been for us. --- *Many thanks to [Jaakko Piipponen](https://jaakkopiipponen.com/), [Jonathan Drake](https://twitter.com/JonathanDrake), and [Vinod Valloppillil](https://twitter.com/vinodv) for contributing insights to early drafts.*

For probabilities, use Fermi numbers, not words

Jason Cohen — Sun, 02 Jul 2023 00:00:00 +0000

![Study: Distribution of numeric probabilities implied by probability-words](https://public.asmartbear.com/books/anthology/chapters/probability-words/fwwsqodxgaax-ff-simple.png) ## Probability words Words have fuzzy meanings (Figure 1), but that alone doesn't mean words are useless. However, we find serious problems when we examine these words in more detail. Another study gives us even more insight: ![whisker plot of numeric probabilities implied by probability-words](https://public.asmartbear.com/books/anthology/chapters/probability-words/d0ab80bb4-3129-49a1-b7f7-4228feebb4b5-simple.png) Note that the median intended probability of the word "likely" is 70%, whereas the intention of "unlikely" is 20%. Since that is the same word, just negated, we should expect the numbers to be symmetric like 70/30 or 80/20, but instead we're biased.[^1] Even worse with "probable / improbable" at 70/10, and strangely while "probable" and "probably" are the same at 70%, "probably not" is 25% while "improbable" is 10%. Perhaps that makes sense to a grammatician, but surely this causes confusion in normal people, especially when we're applying these words in analytical contexts like risk-analysis or debating a strategy. [^1]: There is extensive literature on our innate bias regarding both rare and common events; people are surprised how often 5% things happen, and how often 95% don't. Nassim Taleb's *Black Swan* expounds on this, and it's obvious in our everyday experience, like how we get upset when a "10% chance of rain" rains out our picnic. The communication problem is even worse because *individual people* disagree to an even larger extent. For example, these dots are a single person's evaluation for these words: ![single person's evaluation of probability words](https://public.asmartbear.com/books/anthology/chapters/probability-words/d258a51b6-f9f6-45c0-875d-fd80e8519d74-simple.png) Note how pessimistic they are on the positive words, scoring "probable," "probably," and "likely" as only 50%. Then matching the median of 20% with their negatives ("probably not," "we doubt," "unlikely"), but then they claim "improbable" is a straight-up 0% while "highly unlikely" is still 30%. I feel for this person. Although I admit the formal definition of "improbable" cannot be exactly 0%, what do I think in real life? If the consensus is that some project is "improbable" to complete on-time, I would certainly *act* as though that probability were 0%. So that's the point: Words don't work, because *the differences between individual interpretations are larger than the differences between the words themselves*! Furthermore, even if you assign an official numeric probability to words, and train competent people to adhere to those definitions, it still doesn't work. The CIA [attempted exactly this](https://waf.cs.illinois.edu/visualizations/Perception-of-Probability-Words/), publishing what they call the "Sherman Kent Scale." It explains, for example, that "probable" is defined as 75% and "almost certainly not" is defined as 7%. However, when officers were assessed against the scale, fully half the time they failed to stay in-range (the grey bars): !["Clearly, the readers in this experiment were not using the Sherman Kent scale even though they were familiar with it." --Scott Barclay, author of this study](https://public.asmartbear.com/books/anthology/chapters/probability-words/d63a16bed-8dff-45ee-9b55-cd01794b0c52-simple.png) We should give up on using words to describe probabilities. ## The solution: Specific probabilities, Fermi-style An obvious solution is to force people to use numeric probabilities, never[^2] using vague words. Indeed, this one rule will already improve communication. [^2]: Oops, I mean 0.2% of the time. But writing down precise probabilities is hard. It's hard because usually[^3] we cannot specify the probability with precision. The world is often[^4] [unpredictable](https://longform.asmartbear.com/predict-the-future/) even with expertise and data, so we *need* fuzzy ranges of probability to gesture towards our intent. [^3]: Oops, I mean 83.6% of the time. [^4]: Oops, I mean 62.9% of the time. In this sense, it could appear more accurate to say "the project is unlikely to succeed" exactly because it's unknown whether the true probability is 10% or 40%. Still, given individual interpretations of the word "unlikely," we're not accurately communicating that range. Furthermore, as we know from [Fermi Estimation](https://longform.asmartbear.com/roi-rubric/) in domains like "impact" and "time estimation," it's unproductive to haggle over details like "is it a 20% or 30% probability." None of us likely[^5] knows the true number, and anyway we need crisp signals to make smart decisions. [^5]: Oops, I mean 9.1% of the time. Therefore, the solution is a Fermi-style probability: 1. Use numbers, not words. 2. Select from a small set of options. This is the same conclusion the CIA came to in the above-referenced 1964 study. Their mistake was to continue using words, but their categorical probabilities were Fermi: | Probability Word | Probability Value | Expected Range | |:----|:---:|:---:| | Certain | 100% | 100% | | Almost Certain | 93% | 87% ... 99% | | Probable | 75% | 63% ... 87% | | Chances About Even | 50% | 40% ... 60% | | Probably Not | 30% | 20% ... 40% | | Almost Certainly Not | 7% | 2% ... 12% | | Impossible | 0% | 0% | **My recommendation is to use just a few raw numbers, without words**, with instructions for "rounding off" that depend on the context. For example, in estimating the likelihood that a project completes on time, we know that in general things are more likely[^6] to be late than early, therefore we should "round off" towards the lower probability. [^6]: Oops, I mean more than 51% of the time. ![a is more likely but i gotta say feel like b wants it more](https://public.asmartbear.com/books/anthology/chapters/probability-words/d7935print-simple.png) So go to your "risk" slides, and use Fermi probabilities to force yourself to decide what you think the risk *really* is, so everyone can decide whether or not to act. Go to your strategy, and put Fermi probabilities on each assertion, so people know what is more or less likely to change as we learn and grow. Go to your work-prioritization system, and put Fermi probabilities on the "value" or "estimate" metrics that you're using for decision-support. Ask everyone in the team to supply their own numbers independently; where there's disagreement, that's worth a discussion; where there's agreement, you can save time by just moving along. Or maybe don't, because you realize that [probability isn't even applicable to such things](https://longform.asmartbear.com/confidence/). Perhaps the real reason we use wishy-washy probability words is because we really don't know the probability, and rather than admitting that, we just glide past the challenge. That's the worst reason of all. Now you have no excuse. Be brave, and put a number on it.

The three kinds of leverage that anchor effective strategies

Jason Cohen — Sun, 18 Jun 2023 00:00:00 +0000

![dunno what if we leveraged something](https://public.asmartbear.com/books/anthology/chapters/leverage/d8465print-simple.png) "Leverage" means generating a large effect from a relatively small effort, created by riding tailwinds of natural abilities or hard-won assets, rather than fighting a battle for which you are ill-equipped. Whatever you can do, or your company can do, that causes others to shake their heads in amazement, wondering how you accomplished so much in so little time and so few resources, is more than just a "strength." It is leverage. Some say that [great strategy](https://longform.asmartbear.com/great-strategy/) requires unassailable, hyper-differentiated, completely unique [moats](https://longform.asmartbear.com/moats/). While this is obviously *preferable*, it is unwise to ignore the more immediate and accessible forms of leverage. --- Leveraging strengths is the only way to do great work. (Not "fixing weaknesses.") Better yet, leveraging *differentiated* strengths means you beat the competition. Best is when that differentiation is *durable* over time. --- ## Leveraging strengths > "How do you beat Bobby Fischer? Play him at anything but chess" > --Warren Buffett Why is it so easy to explain why a company succeeded, in just a few words? Netflix pivoted to streaming. Snap had disappearing messages in a uniquely simple interface. The iPhone had unparalleled design. Tesla solved both performance and safety for EVs. Google's PageRank algorithm was an order of magnitude better than anything else, so it beat the other thirteen major search companies that had a head-start. Twitter's limit of 140 characters, apparently uselessly trivial, created a uniquely addictive communications pattern, and meant it could be used even on cheap devices with cheap cellular plans. The story is always about how the company was amazing at one or two specific things. The story isn't about the 47 complex details that all added up, and isn't about a 28-page strategy document. The story is also never about how the company "shored up weaknesses." Twitter had massive weaknesses, like constantly going off-line: ![Twitter Fail Whale circa 2010](https://public.asmartbear.com/books/anthology/chapters/leverage/d5151041232-676410f62d-b-simple.png) Fundamental issues like downtime and security flaws do need to be solved, but that's not a *strategy*, it's an operational necessity. Facebook famously made this point when they pioneered the catchy motto "Move fast and break things," explaining that rapid innovation is more important than anything else: ![Move fast and break things](https://public.asmartbear.com/books/anthology/chapters/leverage/d1280px-mark-zuckerberg-move-fast-and-break-things-simple.png) Books were written extolling the benefits and wisdom of this admonition: ![Book: Move Fast and Break Things](https://public.asmartbear.com/books/anthology/chapters/leverage/d51xc7slj5pl-sl500-simple.png) Except, once you're no longer a scrappy startup, it's not smart at all. The Fail Whale is not OK. Not even for Facebook, who officially changed their motto more than a decade ago: ![Move fast with stable infrastructure](https://public.asmartbear.com/books/anthology/chapters/leverage/mark-zuckerberg-move-fast-with-stable-infra-simple.png) Facebook didn't defeat MySpace because of stable infrastructure or breaking things; they won because of their strengths in hypergrowth. Twitter continued to grow during the many years of Fail Whale because of its unique communications experience. **Reversing weakness is hard, painful, likely to result in something merely neutral, not great, and is at high risk of failing completely.** Leveraging our innate greatness is "how we will win," and ["how we will win" is the entire purpose of a strategy](https://longform.asmartbear.com/great-strategy/). This is why typically the best answer to "what tool/language/framework/process should I use to do ______?" is "the one you already know."[^1] That's the one that best leverages your time--your most limited resource. [^1]: Slack was famously built in PHP, a language derided at the time as passé. But PHP is what the founders knew, and customers don't care either way. Facebook used PHP too; when they scaled and needed more performance, they invented their own version of PHP. Expensive, yes, but companies that never get off the ground never earn the "good problems to have" of scaling. Occasionally you do need to correct a strategic weakness in order to win. Not an operational weakness like "unstable infrastructure," but one where you're constantly losing deals to a competitor, due to a major deficiency in product or positioning. Something where we need to change "who we are" in order to win. A good strategy will explain why this risky, difficult, unnatural, expensive journey must nevertheless be undertaken, and why it's the best way to win. There must not be many; probably not more than one. You can't afford the time, people, or energy. Better is to avoid weaknesses through [decisions on target market, target personas, and product](https://longform.asmartbear.com/problem/). So if your weakness is that you cannot supply 24/7 customer service like some competitors, call yourself a "boutique," meaning a business where customers have incredible interactions with experts, but of course those experts are available only at limited times. If your weakness is a small team who can't build millions of features, make a product targeting users who have simple needs and appreciate simple, well-built software that "doesn't have all that crap I don't want." ![superman psychiatry upset cant see through lead](https://public.asmartbear.com/books/anthology/chapters/leverage/d8360print-simple.png) Some weaknesses can be debilitating if they cross some threshold, but otherwise they should be ignored; uptime is one of them. For these types, at WP Engine we've had great success with the [system of SLO management from Google's SRE Book](https://sre.google/workbook/implementing-slos/);[^2] while written about infrastructure, it's a general-purpose system for managing things that should be [satisfied rather than maximized](https://longform.asmartbear.com/maximizing/). [^2]: You define a metric that measures the effect in question (e.g. "uptime in past 28 days"), and you define the acceptable threshold (e.g. "99.5%"). The rule is: while the metric exceeds the threshold, we do not attempt to improve it--that's like over-investing in a weakness that will never become a strength. But if the metric drops below the threshold, the team addresses it--that solves for the "minimum acceptable weakness." By being objective and explicit about when we invest, we invest just the right amount, at the right times. Imagine a downtime incident, with emotions running and and demands that "we must invest so this never happens again." That's when you need a previously-agreed-upon decision-making rule. A cross-functional team meets quarterly to agree on the threshold; this way, when some downtime happens, and everyone is emotional and demanding that "something must be done, so this never happens again," you can go back to your rule, diffusing the discussion. Leverage isn't just about strengths; it can also come from the business model. A common business model amongst self-funded entrepreneurs is a product with near-zero marginal cost.[^3] Software works this way, and also content: blogs, newsletters, podcasts, videos, books, courseware. A newsletter written for 100 people takes the same effort as for 100,000, yet the latter can generate 1000x more revenue. This is why a self-funded a content company is difficult at first (all effort, no income, no compounding effects) but can be extremely profitable if it takes off (far more revenue for the same effort and almost the same costs). [^3]: Meaning: Creating the first copy of the thing might be expensive, but selling the *next* copy of the thing costs almost nothing. Leverage can also come from supporting tools. As of this writing, AI can generate a simple blog post, albeit with factual errors, no personal examples, and a plodding style. But, AI can *draft* ten articles, and a human can edit those many times faster than generating them from scratch, resulting in a large increase in output. Similarly, a great strategy might mandate the creation of a specific supporting system that doesn't just "make us more efficient," but makes us 10x more efficient, and therefore becomes a new form of leverage. ![AI writes content quickly, but it's garbage](https://public.asmartbear.com/books/anthology/chapters/leverage/fnfonsbayaaqbda-simple.png) Passion is leverage. Your strengths are magnified through sheer will, and that same energy helps you plow through the emotional pain and drudgery of working through challenges. This is why "follow your passion" is correct but incomplete. > "Magic is just someone spending more time on something than anyone else might reasonably expect." > --Raymond Joseph Teller (of Penn & Teller) ## Leveraging differentiated strengths The kinds of strengths we've just identified are good, but your competitors have those kinds of strengths too. (You're not the only organization with "super-talented, passionate engineers," and all content companies have a leveraged business model.) The more important strengths separate your organization from others. These are more strategic, because they help you win against the competition, not just by leveraging your time. It's simple conceptually; these are the clichéd "differentiated competitive advantages" or "unique selling propositions." The simplicity is deceptive: it is difficult to invent differences which aren't trivial, and that also apply to most of your target audience. This difficulty is why these need to be identified in the strategy; we won't just happen across these by accident during day-to-day work. > "Attack where you are strong and the enemy is weak; do not attack where the enemy is strong and you are weak." > --Sun Tzu, *Art of War* Technology and design choices can create competitive differentiation. A technical architecture will make some features and capabilities easy, and others difficult or impossible.[^4] A design can make certain use-cases obvious and delightful, while others are annoying or impossible. There is no such thing as technology or design that makes no trade-offs. Great strategy names those trade-offs, shows how they align with the target market and target persona, and therefore how you'll exploit those trade-offs to win,[^5] while competitors who made different trade-offs won't be able to follow, at least not at extreme cost and taking far more time just to catch up to where you already are today. [^4]: Heroku invented the now-ubiquitous idea of the 12-factor application, but only after mandating a combination of trade-offs which many Rails developers initially hated, like requiring Bundler for package management (back when Bundler was bad), mandating PostgreSQL instead of MySQL (back with the latter was [10x more common](https://db-engines.com/en/ranking_trend/system/MySQL%3BPostgreSQL)), and disallowing writing data to disk. Developers ultimately accepted these restrictions because taken together they enabled a new way to develop, test, stage, deploy, and scale applications. Today, nearly all SaaS applications follow the 12-factor principles, and Heroku cleanly won the Rails market for a decade. [^5]: But also, how those trade-offs create limitations or problems for you, that you will need to accept. You must not call it a "bug" when those limitations appear. They are the consequence of leaning into your strengths, and trying to "fix" them will take inordinate effort, they will not result in a fantastic product, they blunt the power of your strengths, and they remove time and energy that could have been spent magnifying those strengths. A specific type of technical differentiation is solving a particularly difficult engineering problem.[^6] This has the advantage that, if you succeed, you might have created a [moat](https://longform.asmartbear.com/moats/) (e.g. Google search, Tesla cars, OpenAI). The disadvantage is that it is likely to fail: take too long, take too much money, not actually solve the difficult puzzle, not do enough of what the customer needs. Therefore, you should be surgical about which subset of the software should be difficult: Something that creates a competitive moat *and* that leverages some strengths or assets. [^6]: Google Wave was supposed to change everything: Replacing chat, email, word-processing, spreadsheets, file-storage, note-taking, wikis, and websites. Built as the conceptual precursor to Google Docs, it was the first product with remarkably fast multi-user simultaneous editing; you could watch people type character-by-character. The technology was fantastically difficult; no one else had it, and it would be years before anyone else did. Unfortunately it was so difficult that it failed, but this proved the rule that, had they succeeded, it would have created long-term differentiating leverage; project co-founder Lars Rasmussen said it was so difficult, that even if they were allowed to it all over again, with the same team, with all the knowledge they now possessed, it would again take years to execute, and still probably wouldn't have worked. That means the competition would have failed too. Besides "big awesome features," another kind of differentiated advantage is an *insight*--when you understand an important truth while the rest of the world does not, or especially if the rest of the world confidently believes the opposite to be true.[^7] Their disbelief prevents competition. Once proved true, others will follow,[^8] but it's too late; the strategy has already been a success. Made famous as [Peter Thiel's "Secret,"](https://genius.com/Peter-thiel-zero-to-one-chapter-8-secrets-annotated) these are rare but valuable insights that underpin some of the most famous and successful corporate strategies. [^7]: "People will buy shoes over the internet." [Zappos] // "People will take rides from strangers." [Uber, Lyft] // "People want to sleep in stranger's houses." [AirBnB] // "Rockets can be reusable." [SpaceX] // "The highest-performing sedan can be battery-powered." [Tesla] // "People communicating in 140-character chunks can topple governments." [Twitter] // "Large enterprises will acquiesce to putting their customer data 'in the cloud' (a term we just invented)." [Salesforce] // "Open source copycats will defeat multi-billion-dollar incumbents." [RedHat] [^8]: Only after Salesforce convinced the world that SaaS is viable, nearly all software companies copied the model. Only after Linux defeated IBM, Sun, HP, and everyone else, there's now an open-source copycat of everything ever invented. Only after RedHat successfully monetized "free" open source software, reaching tens of billions of market-cap, did investors "open source companies" now resulting in hundreds of billions dollars of market capitalization. Unfortunately, differentiation tends to erode over time, sometimes rapidly. "Secret insights" make themselves known over time, and almost anything in software can be copied. Patents are supposed to provide protection, but what software company prevents copycats through patents? Therefore, most valuable, strategic differentiation is of a kind which takes decades to erode, if ever. ## Leveraging durable differentiated strengths > "The most important thing to me is figuring out how big a moat there is around the business. What I love, of course, is a big castle and a big moat with piranhas and crocodiles." > --Warren Buffett The word for a durable differentiated advantage is a "[moat](https://longform.asmartbear.com/moats/)." Moats often take the form: "I have something that you don't, and furthermore, you either don't have the ability to acquire it, won't invest enough to achieve it, or in the case that you try, you would have to make an incredible investment of time and energy and possibly brand adjustment to do it, which is unlikely to succeed, and even if it does succeed it will take years, by which time we've already won." Visit [this companion article about moats](https://longform.asmartbear.com/moats/) for examples, including a worked case-study. Besides those classic moats, a special and often-overlooked form of durable differentiation is to exploit an ossified structure in a competitor, where they cannot react even if they wanted to. * A competitor with a high cost structure *must* charge high prices, therefore a strategy of have "40% of the features at 10% of the price" leverages a point of differentiation that the competitor cannot match. This is simplistic form of [Innovator's Dilemma disruption](https://en.wikipedia.org/wiki/The_Innovator%27s_Dilemma). * A competitor with a simple product and no tech support won't be able to win complex clients with complex needs, therefore a strategy of "Enterprise offering at Enterprise prices" leverages differentiation that competitor cannot match. * A competitor who loudly, publicly espouses a set of values cannot take actions contrary to those values, therefore a strategy that espouses a contrary set of values will remain differentiated.[^9] * A competitor who invests in a core platform with specific trade-offs will not be able to change those trade-offs for years, if ever; therefore selecting different trade-offs, resulting in different product features (and liabilities), will remain differentiated. * A competitor whose profit is driven by a specific product cannot lower prices when another company decides to build that same product for free as a loss-leader that powers a different line of business: * Netscape (revenue from selling a browser) was killed by Microsoft with IE (loss-leader for Windows) * For-profit smartphone operating systems were killed by Google with Android[^10] (loss-leader for the Google ecosystem of services) * Twenty proprietary Unix operating systems were killed by RedHat with Linux (loss-leader for RedHat's Enterprise Services, later sold for $34B) * [Quoting](https://www.ft.com/content/2d48d982-80b2-49f3-8a83-f5afef98e8eb) Microsoft CEO Satya Nadella on his strategic approach with Bing, "There is such margin in search, which for us is incremental. For Google it's not, they have to defend it all." [^9]: A company who values sustainable sourcing cannot price-compete with those who lack that value; conversely a sustainable-sourcing-minded consumer [will buy from the former despite the expense](https://longform.asmartbear.com/willingness-to-pay/). [^10]: Except Apple of course; still, 87% of global handsets are Android, and Apple arguably remained because of the hardware more than the operating system. Finally, in a crowded competitive space, it's possible that no *single* thing is completely unique, much less durable. A combination of multiple things *taken together* can be both differentiated and durable, so long as the constituents form a mutually-reinforcing, cohesive structure, in which a competitor who successfully copied a fraction of the structure will still not have created a similar product.[^11] [^11]: This idea is also explored in [*Blue Ocean Strategy*](https://www.blueoceanstrategy.com/what-is-blue-ocean-strategy/), though it originated years earlier as in the example below. Consider the example of Southwest Airlines, a low-cost US airline with an incredible business model, remaining profitable even during the US terrorists attacks of September 2001 and throughout the Great Recession of 2008, across a period where every other major US airline filed for bankruptcy at least once. Southwest doesn't have any one thing that is completely unique in the industry, and certainly nothing that a competitor couldn't copy if they wanted to, but it does have a combination of decisions, features, strengths, and even weaknesses, that together are unique, have been durable for sixty years, and explains their success. Michael Porter diagrammed this combination in 1996; his analysis is remains accurate more than twenty years later, underscoring its durability: ![Michael Porter's "Activity Systems Map" for Southwest Airlines, from his 1996 article "What Is Strategy?" Harvard Business Review 74 (6): 61–78.](https://public.asmartbear.com/books/anthology/chapters/leverage/southwest-simple.png) Notice how some attributes would be considered strengths by a customer (e.g. low ticket prices and frequent, reliable departures), while others are weaknesses (e.g. no meals, no baggage transfers, no connections to other airlines, no long-haul routes). The key is that all decisions support a common structure, e.g. the lack of amenities is part of what allows them to have lower prices, and short-hauls on standardized aircraft (which helps with cost-reduction) also leads to frequent, reliable departures (that customers love). > "Strategy is a set of interrelated and powerful choices that positions the organization to win." > --[Roger L. Martin](https://rogerlmartin.com/thought-pillars/strategy) It's easy to claim that "our peculiar set of features makes us unique," but that's not strategic unless it forms a mutually-reinforcing network, including weaknesses. It's not durable leverage unless a written strategy explains this network, so that the company can continue to reinforce the entire network with its actions and investments, including accepting the weaknesses, not treating them as "bugs to be fixed." Otherwise, it's just a pile of features. --- > "Give me a long enough lever and I shall move the world." > --Archimedes Without leveraging strengths (rather than spending far more energy shoring up a weakness that still won't be great), the company will not succeed in creating something great. Without leveraging *differentiated* strengths, the company will not surpass competitors, will have a hard time winning and keeping customers, and will have an even harder time justifying profit-generating prices. Without leveraging *durable*, differentiated strengths, the company's success will be short-lived, differentiation will be temporary, and once again it will be reduced to out-spending on marketing or lowering prices until it is unprofitable. A [winning strategy](https://longform.asmartbear.com/great-strategy/) explains which strengths the company will leverage, how it will side-step rather than "attack" its weaknesses, which strengths can be leveraged for differentiated sales today, and which long-term moats the company is constructing.

Using the Needs Stack for competitive strategy

Jason Cohen — Sun, 04 Jun 2023 00:00:00 +0000

![knew it was bad stopped wanting cracker started needing one](https://public.asmartbear.com/books/anthology/chapters/needs-stack/d7353print-simple.png) ## The Needs Stack How would you describe what is Charlie doing? Charlie creates an account with AWS--Amazon Web Services: pay by-the-hour and by-the-gigabyte for internet servers, storage, and network connectivity--happy to see a "free tier" for the infrastructure they need, just like the "how-to" article explained. All the acronyms are new and unfamiliar; this is tricky, but exciting! Someday, Charlie's website might grow large enough to exceed the free tier--let's hope! Apparently, Charlie's to-do list was: ★ Buy infrastructure: web server, disk space, networking, database. Nominally this is true, but Charlie's life ambition is not "to buy infrastructure." In fact, it's a means to an end. What Charlie *really* wants is to have a functioning WordPress-based website, because WordPress is by an order of magnitude the most popular[^1] way for people to easily write and publish content, and integrate with online marketing software needed to build a successful website. So, a better way to explain what's happening, is that Charlie has a higher-level "need," which is to set up a WordPress site. Getting the infrastructure is a means to an end, rather than the end itself; in this sense, it's a "need" but it's lower in the "stack." [^1]: 43% of the largest 10,000,000 websites use WordPress; Shopify is the next largest with 4%. [(Data from W3Techs)](https://w3techs.com/technologies/history_overview/content_management/all/q) What Charlie is doing *really* is: ★ Set up a WordPress site.
→ Buy infrastructure: web server, disk space, networking, database. This might sound like a verbose restatement of [Jobs to be Done](https://hbswk.hbs.edu/item/clay-christensens-milkshake-marketing) or [Five Why's](https://en.wikipedia.org/wiki/Five_whys), but **there's a strategic competitive insight lurking here**, which companies often fail to appreciate at their peril: *Charlie never wanted to set up infrastructure, and still doesn't.* Charlie wants a WordPress site. Therefore, a company who provides the higher-level need of "WordPress site" makes the lower-level need of "infrastructure" *obsolete*. Specifically, when a company like [WP Engine](https://wpengine.com/) makes it possible to set up a WordPress site in one minute, with no infrastructure to think about, handling all the technology and 24/7 monitoring, and customer support that helps with WordPress, then that company is going to win Charlie's business, and AWS will never see Charlie. **Charlie disappears from the cloud infrastructure market.** This is the insight. If you ask AWS who their competitors are, the answers are other cloud infrastructure suppliers like GCP (Google), Azure (Microsoft), and Digital Ocean. And they are correct, because the Charlies of the world who *do* buy infrastructure, will buy from one of those. But WP Engine is also a competitor of AWS when it comes to Charlie, not because WP Engine competes on infrastructure (which it doesn't, and in fact WP Engine itself buys infrastructure from AWS, GCP, and Azure!), but because WP Engine targets a higher-level "need" in the stack. But you can't stop there. That's the trouble--you always want to stop when you've reached the level you operate on, but there's always another level. > "Instagram's real product isn't photos; it's likes." > --[Alex Danco](https://alexdanco.com/2019/04/28/secrets-about-people-a-short-and-dangerous-introduction-to-rene-girard/) It's not Charlie's life ambition to make a WordPress site; this is just a means to an end. What Charlie *really* wants is to have a personal website for content and self-promotion. Charlie wants to carve out a digital *pied à terre*. In the virtual universe there's infinite *terre*, so why not slap your *pied* on some of it! Now the Needs Stack becomes: ★ Have a personal website for content and self-promotion.
→ Set up a WordPress site.
→ Buy infrastructure: web server, disk space, networking, database. Charlie can still achieve this higher-level need with WordPress running on WP Engine--indeed, over one million websites run this way today. But there are alternatives that target this higher-level need. Wix and Squarespace both let you create websites without WordPress, cutting out the step of "get WordPress" just as WP Engine cut out the step of "get infrastructure." Does this mean WP Engine no longer has a business? No, that's not the conclusion, any more than you should conclude that AWS no longer has a business. WP Engine is 16 years old and still healthy, growing, and profitable; AWS just posted $5.2B in *profit* just last *quarter*. So what's going on? These alternatives to WordPress certainly have advantages, but they are worse than WordPress for long-form content and for customizability. For this reason, they might not be right for Charlie. But they *are* competitors. Similarly, WP Engine doesn't let you customize your infrastructure, so those who need to do so, still need to buy directly from AWS. The pattern here is the next insight: **Up the stack, customers achieve their end-goal faster; down the stack, customers have more flexibility and customization.** But wait... once again "having a personal website" is not Charlie's life ambition, but rather just a means to an end. Charlie *really* wants a book deal. Being a properly-published author with a hard-back volume on the shelf that you can show to your [kids](https://longform.asmartbear.com/startup-life/) (who will just shrug today but maybe someday they'll appreciate it), and that you can gaze upon now and then in your dotage as a great accomplishment. This is Charlie's personal idea of "successful and popular." The way to get a book deal is to already have an online following; you'll have honed your writing craft, you'll have a natural audience to kick-start sales, and the fact that you've won followers in the competitive, noisy internet proves your content is worth reading. Publishers want authors whose content has been de-risked and who come with a built-in marketing channel. So the Needs Stack deepens: ★ Become a popular content-producer, leading to a book deal.
→ Have a personal website for content and self-promotion.
→ Set up a WordPress site.
→ Buy infrastructure: web server, disk space, networking, database. The new layer brings new alternative solutions. There are companies like Substack, purpose-built to help you gain a following, with the tools for long-form content, for building a mailing list, and for promoting your content on social media. Substack doesn't automatically win, because WordPress is better than Substack in many ways. For example, WordPress is far more configurable, so you can make it look unique, unlike a million newsletters at Substack that all look identical (with character-kerning that's too wide for my taste, but I'm biased). Also, the tools for long-form writing in WordPress are far superior, with drag-and-drop elements supporting complex layouts--including different layouts for phones versus tablets versus laptops--that an email newsletter cannot support. If Charlie values those things, then WordPress is still the right choice. This again exemplifies the pattern that software at higher levels of the Needs Stack have fewer features and less customization than software lower on the Stack. In exchange for these limitations, their focus on the higher-level Need means it's easier for the customer to achieve that Need. If the ease of achieving the Need is more important to a customer than the ability to customize and extend, the higher-level product makes all lower levels obsolete. But wait a minute Substack because--you guessed it--there's another level to Charlie's Needs Stack. Because while Charlie wants a book deal as a signifier of popularity and success, it's not Charlie's life ambition. Charlie *really* wants to become a famous speaker. It started at a [Tony Robbins](https://www.tonyrobbins.com/) show; Charlie instantly knew that the stage was their calling. It's the prestige of being "that person" at parties, and the idea of being a jet-setter in first-class and a black car to pick you up with your name in the window, the ego basking in the glow of the lights and the elevation of the stage, gazed upon by an adoring audience who rushes up afterwards to grab a selfie and nervously blurt out a fraction of a story about how the speaker changed their life. And the money. ★ Get lucrative speaking gigs around the world.
→ Become a popular content-producer, leading to a book deal.
→ Have a personal website for content and self-promotion.
→ Set up a WordPress site.
→ Buy infrastructure: web server, disk space, networking, database. Charlie's plan isn't bad, but what if it were possible to skip the website and years of writing and self-promotion and the book deal and the two years of labor creating the book and directly become the next Tony Robbins? It's not impossible; there are speaker bureaus and online services like [SpeakerMatch](https://speakermatch.com/) that help you find gigs and facilitate the transaction. Why spend years building an online following (if you're lucky) when you could spend that time creating inspirational presentations, delivering them to acclaim, earning testimonials for your next gig, building a reputation with an agency, and getting bigger and better gigs over time. In this scenario, Substack, Squarespace, WP Engine, and AWS are all irrelevant for Charlie; with the higher need met, the rest might as well not exist. **The moral of the story is: You need to understand the Needs Stack of your target customers.** Not just your "market" or your "product" or "[solving the problem](https://longform.asmartbear.com/problem/)" or even the immediate Jobs-To-Be-Done of your customer--those are all means to ends. Already you can see how this will help you think about positioning and features; let's see what else it helps you to do. ## How to leverage the Needs Stack ### The solution to "selling benefits" vs "selling features" "Sell benefits, not features" marketers have told us since the universe cooled enough for galaxies to form. We're supposed to say "saves time" rather than showing a screenshot of the feature. (Or better yet, "[grows revenue](https://longform.asmartbear.com/more-value-not-save-money/).") For Charlie, say "Become famous" rather than "Get a website." And yet, does an Engineer looking for a database infrastructure solution *really* want to be told "saves time" or do they want to see specs and features? (A: Specs and features.) If a construction worker is looking for a wrench, do they search Google for "wrench that saves me time" or "socket wrench for a 5/16" lock nut?" And when that person clicks a link to potentially buy the wrench, do they want pictures of happy people "saving time," or do they want to see the specifications and features like "has a mounted light." Do they really need to be told "the light is useful to see in dark corners?" Oh! Thanks for spelling out the benefits of photons! So which is it? ![this is tom he creates awareness](https://public.asmartbear.com/books/anthology/chapters/needs-stack/d7214print-simple.png) The Needs Stack suggests an answer. Charlie is not constantly and consciously thinking "I gotta become Tony Robbins," even though that's the top of their Needs Stack. Charlie is thinking at some other level of the stack at any given moment, for example "I gotta get some content online." Google searches are a good proxy for "what level of the stack are potential customers operating at right now?" When Charlie embarked on this project, the search might have been something like "best way to publish online," not "how to become a famous globe-trotting speaker." After reading some articles about how WordPress is inexpensive, popular and has all the tools you need, maybe then Charlie moved down the Stack to search "how to get a WordPress site." If someone has in their head "get a WordPress site," you want to meet them where they are, and talk about how your product is the best way to "get a WordPress site." **That's the part where you talk about features.** However, benefits still have a role to play, because it's true that these features are a "means to an end," and the more you convince the customer that you're the best means to that end, you win even over alternatives. So, you can **sell the benefits that are one level higher on the stack.** In this case, you could imagine a home page message like: "Become famous online with our turn-key, customizable WordPress website." That's the benefit from one level above, promised as a consequence of the current level. Conversely, if you're Substack, you can't say "customizable" because that's not what the product is, and it's barely even a "website" (it's just back-issues of your newsletter), but you *can* speak to the product "getting eyeballs." So, that message could be "Grow your own audience with our turn-key newsletter platform."[^2] [^2]: Sure enough, as of this writing the Substack homepage says: "Substack lets independent writers and podcasters publish directly to their audience and get paid through subscriptions," which are benefits from one level higher (i.e. "be independent" and "get paid for writing"). Lower on the page it lists features from the level they are on: "A Substack combines a blog, newsletter, payment system, and customer support team--all integrated seamlessly with a simple interface. We handle the admin, billing, and tech." ### Position lower levels as irrelevant If Substack tried to fight the battle at the "get a website" level of the stack, they would lose every time--it has almost no features of a modern website. But, it can fight the battle at the "build an audience" level; for some subset of the market, that's a more relevant level. Therefore, the idea is to make all levels below that irrelevant, by focusing only on your benefits: "While others just give you a website, we help you build a following." Right, because "popularity" is what I wanted, not "a website!" This message won't work on businesses, for example, because they actually do need a website. But for the aspirational speaker, this might be perfect. Conversely, WP Engine does the same thing with cloud services. The major cloud services say: "Oh it's easy to have a WordPress site. You just click 17 times on all this stuff to make a server, and then download and install a bunch of stuff to create server software, then set up and configure WordPress, then use this 8-page tutorial to integrate with a fast caching network (which costs extra), then download and configure WordPress plugins to talk to the caching network, then stay on top of all the security patches and software upgrades, and it wouldn't hurt you to learn how to SSH into your Ubuntu server so that...." Whereas, the Managed WordPress Platform says: "You don't want infrastructure, you want a WordPress site, and in less than one minute, you'll have exactly that. And it's faster and more secure than doing it yourself. And when you have trouble, you can call our support line, which cloud services don't have." In short, make "infrastructure" irrelevant by focusing on the next level up. The customer never wanted to think about infrastructure in the first place. ### Add more value by moving up the hierarchy If products targeted at one level disrupt the products below them, there's an obvious strategic conclusion: You could **disrupt yourself and your competitors** by moving up the hierarchy. ![sounds like a lot of work could we make a big mess instead](https://public.asmartbear.com/books/anthology/chapters/needs-stack/d7965print-simple.png) There are huge barriers to accomplishing this. The barriers are so large, companies almost never overcome them: #### The next level is a different product, and a different business. If you're known for selling WordPress sites, you'd have to re-brand to be known for building an audience, whether that's actually a new brand or expanding an existing brand. You also need to become expert in what that product is, and build it.[^3] You might need to change your business model, e.g. selling infrastructure by the gigabyte, versus selling whole websites by the month, versus selling new subscribers on a pay-for-performance basis, versus taking a percentage of speaking fees. #### The next level isn't possible It's not clear that one *can* build a product that fulfills the promise to get someone a substantial following. Twitter and Facebook sort of can, in that you can pay them to advertise your content. But the internet is a large and noisy place; marketing software doesn't automatically generate attention. Once you get the attention, if your content isn't compelling, software can't force people to subscribe. In short, if you go high enough in the Needs Stack, you might run into something that a product cannot produce. (Or at least *you* can't.) #### The next level targets a smaller market Few people share Charlie's Needs Stack exactly. A business with a broad customer base like WP Engine sells an all-purpose platform for all kinds of sites. There are many different personas whose Needs Stack includes "set up a WordPress site."[^4] In each case, the "next level up" is different. Therefore, targeting a higher level also means narrowing the market. This might be wise--it is a tried-and-true strategy to be the best product in a smaller niche, rather than a small fish in a large, crowded ocean. But, a company who has an established brand in the wider market might not want to narrow into a sub-market. There might not be enough money in it. [^3]: Or buy it, which mature companies often do, trading cash for speed-to-market. [^4]: e.g. individuals wanting attention, companies broadcasting their brand, media companies attracting eyeballs for advertising revenue, eCommerce stores using content-marketing to drive sales, communities using content to inform and engage, non-profits raising money, governments interacting with citizens Since a full pivot is unlikely to succeed, there are other ways to build a strategy that partially climbs the hierarchy: #### Create sub-brands / products Keep the original business going, and launch "vertical" or "niche" products. Leverage the scale and operational excellence of the parent, but treat each product as its own "startup." #### Add features without fully committing to the next level Perhaps you can't completely pivot to a product that promises subscriber-growth, but you could create an add-on product that helps someone do that. This becomes an extension of an existing product, aimed at personas who share that next level of their Needs Stack. #### Better marketing Address the need with words instead of features. Without "promising" results, you can help, educate, and lead in that area. You can have a field guide about how to attract and retain subscribers to a blog. You can give customers ideas for new kinds of content. You could partner with other companies who help with promotion. You could highlight customers who have been successful, wherein they share their experience to help others. Still, while this advice is good for most organizations who cannot completely move up the hierarchy, there are examples of fantastic businesses that succeeded exactly because they successfully made the transition. For example, all of the case studies given in the "Emotional vs Functional" chapter of _[Blue Ocean Strategy](https://www.amazon.com/Blue-Ocean-Strategy-Uncontested-Competition/dp/1591396190/ref=asc_df_1591396190/?tag=hyprod-20&linkCode=df0&hvadid=312175933381&hvpos=&hvnetw=g&hvrand=11171918790451270235&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9028297&hvtargid=pla-522339108124&psc=1&tag=&ref=&adgrpid=60258871817&hvpone=&hvptwo=&hvadid=312175933381&hvpos=&hvnetw=g&hvrand=11171918790451270235&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9028297&hvtargid=pla-522339108124)_, such as Cemex selling "build an addition to your house, for your family" instead of just selling cement or Starbucks selling a lifestyle experience rather than B-grade coffee. ### Measure the next level up The perennial question: Which [few key metrics](https://longform.asmartbear.com/product-metrics/) measure whether the customer is deriving value from our product, and thus predict whether they'll stay a customer? And beyond that, become a vocal advocate? The Needs Stack helps us uncover these more strategic metrics. Metrics from the bottom of the Stack measure whether we're operating well, and certainly this is necessary even if not sufficient. Metrics at the level the customer is actively thinking about are useful to measure whether our Product is delivering on its direct promise, which again is vital. It's strategic to measure further up the stack. Not too far, otherwise it's not only difficult to measure, but too many external factors drive the number. But the very next level up could be a critical way to measure value and to understand whether customers will be happy in the long run. If a customer of Shopify successfully creates a store, the product of "have a store online" is succeeding, and that will be reflected in metrics like "number of products currently for sale" and in user surveys like "how easy was it to set up products for sale." That's important, but what if no one buys their wares? That customer will probably churn, even though the software delivered on its promise. Indeed, Shopify has enormous cancellation rates that would sink most companies; they survive because the ones that stay, grow a lot. Because they have so many inbound new customers, they can manage around the fact that [only 34% stick around for even one year](https://www.theglobeandmail.com/business/article-shopify-customer-retention-problem/). Can Shopify ensure that all customers who create a store end up doing thousands of dollars a month in business? No, this is an example where the next level up is vital, but Shopify cannot control it. However, they can measure it, and help. They could have articles about how to do online marketing, or features inside the store that help potential customers complete a checkout rather than abandon.[^5] [^5]: Features like ShopPay, in which previous customers of other Shopify stores are fast-tracked through the purchase process, increasing the percentage of shopping carts that result in completed transactions. This is why Shopify's primary success metric is GMV;[^6] they [report it to Wall Street](https://news.shopify.com/shopify-announces-fourth-quarter-and-full-year-2022-financial-results) directly after revenue, and in the same large font. GMV measures whether their customers are succeeding in the next level up on the stack. GMV not only reflects the truth about where the customer value is, it also encourages their product mangers to think about how to stretch up from the level of the stack they fully control. [^6]: Gross Merchandise Value: the total amount of money their customers are selling through their stores. ### A source of higher purpose Our direct goals are often simple: Sign up more customers, get them to activate on the product, reduce churn, reduce support tickets, reduce costs. All of that is important, but none of it [creates a higher purpose](https://longform.asmartbear.com/mission-vision/). [What's it all for](https://longform.asmartbear.com/emptiness/)? Why should we even bother? Many companies can't answer the question. Indeed, maybe they have no purpose other than "growth." Will folks be motivated to do their best work at a company that values only conversion rates? Purpose can be found in the higher levels of the Needs Stack. The immediate work is "getting a website," but the higher purpose is to enable someone to build a career for themselves, to engage their creativity and speciality, to help them win the respect of others. Or help them [become a hero](https://longform.asmartbear.com/more-value-not-save-money/) within a bigger organization through some new marketing project, which will eventually lead to their promotion. Or help them raise awareness and money for an important cause. Or enable them to speak truth to power in a region of the world where such speech is forbidden. Tell those stories. On the website and internally. Celebrate with your team when this actually happens for a customer, because that *is* what it's all about. Those stories are why we should care. Identify those upper reaches of the Needs Stack, not only because it helps you build more valuable features, not only because it helps you build a better strategy, but because it is the ultimate reason why any of this is worth doing in the first place.

Willingness-to-pay: Creating permanent competitive advantage for the right reasons

Jason Cohen — Sun, 21 May 2023 00:00:00 +0000

![ok lets not get into profit shaming here](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/d7444print-simple.png) ## Traditional economics: WTP and Consumer Surplus The best businesses deliver $4 of value, charge $2, and costs them $1 to do it. It's an obvious formula for both profit and happy customers, but what does "$4 of value" even mean? Economists have labels for this formula: ![Diagram explaining that the excess WTP over price is called "Customer Surplus," and excess price over cost is "Profit"](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/wtp-simple.png) Willingness-to-pay (WTP) is the maximum price the customer *would have* paid for the product, which the economist claims is how much the customer *values* the product. "Value" could come from anything--utility, pleasure, status, even irrational confusion. The economist claims that any transaction is evidence that WTP > Price, and the difference between those numbers is "Consumer Surplus." It looks trivial at a first glance, but I've come to believe that analyzing "WTP" is not only non-trivial, but also leads to very different strategies, business models, and outcomes. ## "Willingness" to pay I'm irked by this word "willingness." In 2015, Martin Shkreli, then-CEO of Turing Pharmaceuticals, bought the rights to the drug Daraprim, which for 62 years had been used to treat a deadly parasitic disease. He raised the price of a pill from $13 to $750, [skyrocketing](https://www.cbsnews.com/news/turing-pharmaceuticals-ceo-martin-shkreli-defends-5000-percent-price-hike-on-daraprim-drug/) the typical cost of treatment from $1,000 to $63,000. ![Martin Shkreli testifying before congress on a hearing on drug prices, before calling lawmakers "imbeciles"](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/martin-shkreli-simple.png) "Profit" was his only justification for this abuse, in his own words: > "I think it will be huge.... So 5,000 paying bottles at the new price is $375,000,000--almost all of it is profit, and I think we will get 3 years of that or more. Should be a very handsome investment for all of us." _--Martin Shkreli, [in communication](https://www.latimes.com/business/la-fi-senate-drug-price-study-20161221-story.html) with investors_ Patients have no choice: It's pay or die. The economist would say, patients objectively have a high "willingness" to pay. But is this how we should define "willing?" And when patients cannot afford a $63,000 treatment, and therefore don't purchase the drug, and die, should we say "well, I suppose they weren't 'willing' to pay?" This phrase captures neither the intent nor the ability to pay, both of which are critical factors in questions of price, profit, and consumer surplus. While there are many[^1] such examples, it's more instructive to point out mundane, non-life-threatening examples of why "willing" is not the right word. [^1]: There were at least [four egregious cases](https://www.latimes.com/business/la-fi-senate-drug-price-study-20161221-story.html) in 2015 alone. More recently, Moderna quadrupled the price of their COVID vaccines, its CEO Stephane Bancel [saying](https://pluralistic.net/2023/01/25/nationalize-moderna/) that the new price is "consistent with the value" of mRNA vaccines at 45 times the manufacturing cost, after the US government [paid them billions](https://pluralistic.net/2023/01/25/nationalize-moderna/) to cover the cost of developing the drug. Are we "willing" to pay even more? "Yes" in the sense that human life is valuable, but "no" in the usual sense of the word "willing." It happens with commodities, which [economists say are a "perfect market."](https://www.investopedia.com/terms/p/perfectcompetition.asp) When crude oil prices go up, prices at the pump go up immediately, even though costs haven't yet risen. When crude oil prices go down, prices at the pump go down slowly, even after costs have in fact fallen. The same thing is [happening now with eggs](https://pluralistic.net/2023/05/05/wmds-two-point-oh/). Is that because we're all "willing" to over-pay for gas and eggs? It happens with bundling--[often touted as a wonderful strategy](https://stratechery.com/outline/bundling-and-unbundling/). I never liked paying for cable TV, because it seemed expensive considering I still had to watch ads all the time. Most of the channels I paid for, I didn't watch. Cable companies know that of course; they bundle channels specifically because they know consumers are not "willing" to pay for all of them. Because the content-owners have a near-monopoly, consumers have no choice. Even with modern streaming services the problem persists, because whether it's Hulu Live or YouTube TV, it's still bundled, and still the same price. ![i prefer to call it profit sharing](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/d0207print-simple.png) There's also "willing" versus "able." Perhaps many more consumers would be "willing" to pay $1000 for a fully tricked-out smartphone, but most are not "able." This is a vital fact when determining strategy, [business models](https://longform.asmartbear.com/pricing-determines-your-business-model/), and [company viability](https://longform.asmartbear.com/problem/), but an economist would just say "few consumers are 'willing' to pay $1000 for a high-end phone." But it's not all bogus. There *is* a genuine concept of being "willing" to pay more, and thus genuine "Customer Surplus." I am willing to pay more for [Anker](https://www.anker.com/) products (power strips and chargers) because they're extremely high-quality; I don't even notice if there's a competing product that is 20% cheaper. I'm loyal even though there is neither lock-in nor recurring revenue. People pay more for TOMS[^2] and Patagonia[^3] products because of their [authentic missions](https://longform.asmartbear.com/mission-vision/). People routinely pay more for coffee that has a fair and sustainable supply-chain, because they're willing to pay more to have a positive impact on the world, not just to consume the product. [^2]: Blake Mycoskie [was vacationing](https://www.businessinsider.com/blake-mycoskie-argentina-toms-shoes-2011-09) in Argentina, when a knowledgeable American opened his eyes to the outsized impact that a lack of shoes has on poor children. Unprotected feet are susceptible to punctures and infection, and prohibit walking long distances, which in turn means one cannot go to school. He founded TOMS shoes, selling an Argentinian-style shoe, with the logo of the Argentinian flag, with a marketing strategy he dubbed One for One: Every time you buy a pair of shoes, TOMS would give a pair to a needy child. After TOMS's financial success, Sketchers copied the strategy exactly, even down to the style of the shoe, the name ("BOBS"), and the altruism. Consumers were so outraged by this inauthentic strategy, Sketchers was forced to cancel the product line after just 24 hours (although they revived the brand later with a different mission). That strategy was individual to TOMS; it was irrelevant that the strategy was publicly visible and copy-able. TOMS has weaknesses--[people complain](https://www.sitejabber.com/reviews/toms.com) about poor customer service and shoes quickly developing holes--but they win anyway on the strength of the individualized story. [^3]: Besides their publicly-lauded sustainable practices and an outdoor-worshipping culture, they even have a [formal company policy](https://twitter.com/patagonia/status/776223080779833344) to bail employees out of jail if arrested while protesting peacefully. Indeed, genuine "willingness" creates the best, most durable, most profitable businesses. Consumers not only pay more, they're *happy* to pay more, creating profit margin. They become evangelists, driving efficient growth. The company is resilient to competition, because consumers are buying for reasons other than "features" and "price." The world becomes a better place, transcending a zero-sum game of winners and losers. Analyzing the differences between these kinds of WTP yields insights that all products and companies can [leverage](https://longform.asmartbear.com/leverage/) to build the best strategies. ## Three kinds of WTP I divide WTP into three categories, each having different drivers, and much different strategic value: ## Love ![bear loving its baby bear](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/bear-baby-with-teddy-simple.png) * Mission: the joy of supporting a change that's [bigger than all of us](https://longform.asmartbear.com/fulfillment/), or a community or movement you want to see flourish * Reciprocity: when the company gives before taking, or gives more than it takes, or provides exceptional customer service, or is deeply human * Exceptional design: a joy to use, a product that seems to genuinely care about your experience * Exceptional quality: the pleasure and relief generated by reliability * Personal identification: leveraging the company's brand as visible component of your own personal brand * Culture: supporting an organization that treats employees and vendors well [^4] * Transparency: openness from companies sharing their ups and downs[^5] * Social or environmental impact: supporting sustainable, fair practices * Community: a welcoming space where members learn and teach, support each other, create personal connections, grow their career or business, and be part of a tribe * Ecosystem: wherein all members make more money or gain more prestige than had they not been part of the group *Result: Allyship. Consumers are genuinely happy to do business with you, and root for your success; when you make a profit, they cheer, because they want you to thrive; they advocate for you publicly,[^6] tying their personal brand with yours; they don't even consider the competition; the old saying that "people buy from the person they like;" they would be OK with a small price-increase.* [^4]: Counter-example: Walmart and Amazon, known for exploiting workers and suppliers [^5]: Often used by solopreneurs or bootstrapped companies in their early days, with [Buffer](https://buffer.com/) being the most extreme (and well-loved!) example, sharing everything from salaries to strategy to monthly revenue. [^6]: [This single tweet](https://twitter.com/sweatystartup/status/1621548157187284994?s=20) demonstrates this with thousands of responses. ## Utility ![bear selling tools](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/bear-selling-tools-simple.png) * Cheap: even if quality and functionality is low, it's better than not having the product * Integrations: providing functionality while also more difficult to switch vendors * System-of-record: being the official place for important data, making it risky and expensive to switch vendors * Training: invested in having trained an organization, making it expensive and disruptive to switch vendors * Market-share leader: the social-proof of selecting the market-leader is a reason to buy * Location: coffee inside the airport is more expensive than on the street corner * Convenience: groceries delivered to your doorstep are substantially more expensive than getting them yourself * Simplicity: surprising ease is as delightful as it is useful * Quality: a seamless experience with no defects is often worth paying for * Risk-reduction: mitigating potential problems is difficult to measure, but valuable * Unique functionality: a capability that no competitor can match is a sensible criterium for a purchase decision * On-boarding experience: [data shows](https://www.profitwell.com/recur/all/positive-onboarding-boosts-retention-wtp) that ease and reciprocity results in higher WTP * Familiarity: having used a product or a workflow paradigm for years, it is the comfortable way to work *Result: Fair exchange of value. Your product is useful and not excessively painful; the "devil we know;" getting your money's worth; easier to stay than to leave, and no particular desire to leave.* ## Coercion ![bear giving money](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/bear-giving-money-simple.png) * Contract lock-in: retaining your business through paperwork rather than by choice * Data lock-in: retaining your business by holding your data hostage rather than by choice * Effective monopoly: being the only feasible option[^7] * Effective price-fixing: breaking the so-called "free market" * Middle-man: placing yourself in the middle of a transaction, increasing consumer price while decreasing supplier's profit[^8] * Bundle-stuffing: combining many things the customer doesn't want with the few they do want, to charge more in total[^9] * Scale Anti-Pricing: raising prices once an installation is at-scale, knowing that although an alternative might be more effective, more desirable, and cheaper, the one-time cost of switching is incredibly high * Predatory Pricing: using lower-than-cost pricing to destroy competitors and ward off investors (funded by another business unit like Amazon does or [by VCs as companies like Uber did](https://pluralistic.net/2023/05/19/fake-it-till-you-make-it/)), then increase prices once the competitive market has been decimated and customers have no choice. * Patents: abusing a system meant to temporarily protect inventions to block normal competition. * Corporate policy: once a product is written into a company's formal policy (site-wide license or the only approved vendor for some application), that product "wins" even if every user hates it * Government fiat or regulation: required by law[^10] *Result: Adversarial. Customers want to leave; they idly comment that they wish some new competitor would arrive and disrupt you; they hate seeing your charges on their bill; they do business with you only begrudgingly; they lobby their boss to switch vendors.* [^7]: This can be constructed purposefully, e.g. Uber spending tens of billions of dollars subsidizing rides to drive rival taxi and ride-share services out of business, so that they are the only option, and can raise prices, as [they now have done](https://pluralistic.net/2023/04/21/bondage-fees/). [^8]: A classic example is the person who buys a bunch of tickets to a concert, then resells them at 10x the price after the concert is "sold out." Here's an even more [appalling example](https://twitter.com/doctorow/status/1628948906657878016?s=20). [^9]: There is also a positive version of bundling, in which the items are mostly things the customer *does* want, purchased at a discount over buying each item individually, possibly with some useful interoperability. [^10]: Here Uber is an example of "love," breaking the "coercive" stranglehold of taxi industry regulations. ![hit her with lower expectations first](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/d9174print-simple.png) All of these things contribute equally to the economist's definition of WTP: The customer is in fact paying, and might pay more if you raise prices. But strategically they are completely different. ## Effect on growth and competitive pressure #### **Love** creates *inexpensive, non-linear growth*, because your customers are your allies. You get repeat purchases, whether it's a one-time revenue product or a loyal recurring-revenue customer. This creates growth with no additional marketing and sales costs. You get word-of-mouth advocacy. When someone asks what to buy on Twitter, your rabid fans answer the question. When there's a review site, your product ranks number one. When Customer Surplus is enormous, consumers reciprocate by selling new customers on your behalf.[^11] Once again, this is growth without additional marketing and sales costs. Furthermore, the effect grows as your customer base grows: A non-linear effect. When a new competitor arrives, *even when it is superior* in features or price, your customers will stay, because they're here for more than just the features and the price. This yields retention, which is another form of growth.[^12] There is a limit to this effect of course--at some point the product simply isn't good enough--but it carries you through the vacillations of typical competitive one-upmanship. [^11]: Hollow Knight is a high-quality indie game, made primarily by just three people. Released in 2017, people still make YouTube videos about it in 2023. The soundtrack has millions of listens on Spotify. Everyone says it's far too cheap at $15. Plus you get 4 expansion packs for free--something games normally charge for. Everyone repeats the story about how it's just two guys plus one other guy who did the amazing music. Fans even begged them to charge more but they don't--they'd rather be accessible, and people love them all more for it. The economist would say they should raise prices because they can. Yes they *can*, but it's obvious that rabid fans generate millions of purchases, and that financial impact is so much larger than closing the WTP/Price gap to "demonstrate you have market power." [^12]: Don't believe me? [Look at the growth curve](https://longform.asmartbear.com/product-market-fit/) of any startup that went from 7%/mo cancellation to 2%/mo. #### **Utility** *grows existing customers*, and is neutral-to-positive on attracting new ones. As an organization grows, it will naturally buy more seats of software for teams in customer support, sales, engineering, and so on. It will naturally buy more infrastructure and incur more credit card transaction fees. This isn't a negative, and does creates internal growth, which is a powerful growth-driver for any business, especially at scale.[^13] But, a customer's willingness to buy another ten seats of JIRA doesn't imply the customer is going on forums, spending personal credibility to advocate on your behalf. And it doesn't mean they won't take a look at an interesting new competitor.[^14] [^13]: At scale, new customers can be added only so quickly, whereas you have an enormous existing customer base, so growth inside the base is a larger factor than growth from new customers. [^14]: Indeed, new JIRA competitor Linear has quickly amassed a rabid fan base on the basis of exceptional UX. It's easy to imagine JIRA users trying Linear and even advocating to switch, whereas it's laughable to imagine a Linear user trying to convince their team to switch to JIRA. #### **Coercion** causes your customers to be *allied with competitors*; they're internally motivated to leave, so they will. "Just give me an excuse." Your customers, locked in against their will, cannot *wait* for a viable competitor to appear. They will go out of their way to switch, coming up with reasons why investing in the switch will pay for itself ten-fold, despite the cost. Exactly the case you *don't* want your customers building. When your contract is up for renewal, you should be very afraid. When someone asks on Twitter what tool they should use, your customers say: "Well we use X, but don't make the same mistake!" You are constantly vulnerable to disruption, even by mediocre competition. This is the weakest position you could be in, because you're coercing customers instead of delighting them. ![higher next time hankering](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/d9173print-simple.png) ## Profit done right: Create more WTP, then split it with the customer > "[When you increase WTP], you're adding value for the consumer, and then figuring out how to split that with the consumer." > _--Michael Mauboussin, [interviewed on](https://open.spotify.com/episode/30smJfSR30QI1Nwk7YO130?go=1&sp_cid=210062e5df3dc0948de5cef2743a25d6) the Invest like the Best podcast._ Creating value for the customer comes first. Then--and only then--you can decide how to "split it with the customer," either leveraging Consumer Surplus for advocacy, high-retention, and growth, or indeed by raising prices. When you create that value through Love or Utility, this is both sustainable and profitable. When it's through Coercion, it is temporary at best. The strongest organizations have all three. For example, Apple generates Love through appealing design, being a statement of personal brand, and maintaining the highest standard of privacy even if it means the product is less functional or interoperable. Apple also leverages utility, becoming familiar and convenient (and thus a mental effort to switch away), and trying to become the center of everything from family photos to shared files to 10,000 notes to the common way to purchase things, creating a form of "lock-in" that feels useful rather than evil. But it leverages Coercion as well, as users are locked-in even when they'd prefer not to be, unable to export data from apps like Notes, and being forced to buy new devices as older ones suspiciously stop working well after applying new (mandatory) operating system upgrades, and changing the connectors on charging cables every 5-10 years. In any case, Apple has increased WTP in all three ways over the past ten years, and they've split that with their customers, as evidenced by a consistent profit margin: ![Apple's net profit margin has ranged from 21-26% from 2010 to 2022.](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/d4b63fc22-e70c-4c73-b979-2ba286e6fe6a-simple.png) ## Even the cold-blooded capitalist should eschew Coercion Here's why Love and Utility results in more valuable companies, even though it prioritizes Consumer Surplus over profits: Imagine there are two companies, alike in every way: Same product, same industry, same market, same number of customers at the same price, at the same costs, and thus the same revenue, same profit, and same WTP. The only difference is: 1. Company's WTP is generated only by Love. 2. Company's WTP is generated only by Coercion. Which one is most likely to grow in volume and profit over the next five years? Which is more likely to capture more market share? In other words, which is the better investment for a Venture Capitalist? I'd pick (1). I know their customer base will help them grow efficiently, while competitors look on helplessly, unable to convert customers even with the lure of unique features and lower prices. Whereas I know (2)'s customer base will be trying to leave, praying another competitor comes to save them, publicly warding away potential customers from repeating their mistake. It is also possible for (1) to add Utility or even Coercive WTP to their strategy, further strengthening their position, whereas it is much more difficult for (2) to generate Love starting from their current position. It's not that Coercion is never an appropriate ingredient, but rather that the other two are better.[^15] [^15]: It's like the [Agile Manifesto](https://agilemanifesto.org/): When it says "Working software over comprehensive documentation," it isn't saying "Documentation is bad." Rather, it's saying "Working software" is *more* valuable, so that's where we should spend most of our energy. Love beats Coercion, even as cold-blooded, money-grubbing capitalist investor, indifferent to ethics or the betterment of the world. And yet Love makes money while in fact bettering the world, and making everyone happier. **So choose Love** by building it into your strategy, investing in it, and then reveling in what you've created. --- ## Appendix: Relationship to other frameworks You can apply this concept directly to your strategy, and merge it with other techniques. #### Kano (Figure 3) "Love" feels a lot like [Kano](https://foldingburritos.com/blog/kano-model)'s "Delight"--a joyous, perhaps even unexpected upside. "Utility" maps to "Performance"--where the more of it there is, the more value it is to the customer. "Coercive" maps to "Inverse"--something that customers actively dislike, even though you gain the selfish corporate benefit of retention. ![Kano model](https://public.asmartbear.com/books/anthology/chapters/willingness-to-pay/kanomodel-simple.png) #### Moats Many of these things sound like [moats](https://longform.asmartbear.com/moats/), and for good reason: Increasing WTP of any type increases your ability to capture and retain customers. The more forceful (whether positive or negative), the more that becomes a permanent advantage that others cannot dislodge. No one can take away a fantastic brand, and government fiat can last for decades. An interesting example is "network effect," because it shows up in all three types: * "Love" network effects include community and ecosystem, where participants help one another personally and professionally. * One "Utility" network effect is a functioning marketplace, so e.g. eBay was for decades the destination having the greater number of buyers and sellers of collectable objects, and thus genuinely the most useful place to transact. You might not "love" eBay, but certainly people went there because it was useful, not because they were forced to. * One "Coercive" network effect is when choice is limited to "preferred vendors," creating a cartel rather than creating choice. For example, the United States health care system features insurance companies who each support their own network of doctors. A consequence is that switching insurance can mean you have to switch family doctors--an unnecessary and "value-destroying" activity as an economist would say. #### Start with "Why" "Love" reinforces the Simon Sinek's admonition that [companies must "Start with Why,"](https://longform.asmartbear.com/fulfillment/) i.e. understand and articulate its higher purpose, it's mission, because when that's strong and important, when it permeates everything from its market-positioning to its culture to its employees, it's extremely powerful, and impossible for a competitor to destroy. Example: [Buffer](https://buffer.com/) has a relatively undifferentiated product and pays lower salaries than many people can get elsewhere, but their culture and transparency is second-to-none, and people want to be a part of that. Example: TruthSocial, which can't pay salaries like Twitter, and doesn't have the reach of Twitter, and [has technical issues with downtime and slow innovation](https://www.washingtonpost.com/technology/2022/04/05/trump-truth-social-media-failure/), nevertheless possesses a rabid fanbase because of the mission and community. #### Blue Ocean Strategy: The six kinds of "buyer utility" In [*Blue Ocean Strategy*](https://www.blueoceanstrategy.com/what-is-blue-ocean-strategy/), W. Chan Kim and Renée Mauborgne highlight six ways in which you can deliver "value" to customers. These are a subset of the more general reasons why people are compelled to buy, but it's useful to emphasize the cases where the customer is benefiting directly: | Blue Ocean
Buyer Utility | WTP Category | Commentary | |:----|:----|:----| | Customer
Productivity | Utility | This category is too broad; it is important to distinguish between "more value" and "less cost." Both contribute to "productivity," but it is an order of magnitude [more important to increase value](https://longform.asmartbear.com/more-value-not-save-money/). It's also important to [define value](https://longform.asmartbear.com/needs-stack/). | | Simplicity | Utility | Included above. | | Convenience | Utility | Included above, in several forms; for example "location" is a specific kind of convenience. | | Risk | Utility | Included above. | | Fun & Image | Love | Included above. | | Environmental
Friendliness | Love | Included in a more expansive "social and environmental impact," as nowadays (2023) it is more common for customers to make buying decisions on factors like [Fairtrade](https://www.fairtrade.net/), or purchasing from local or minority-owned business, or supporting businesses with specific values and public commitments, in addition to the idea of being friendly to the environment. | #### Long-term engagement metrics Many products wish to "drive engagement." Some point to Facebook as the pinnacle of "growth hacking," driving up numbers, often slipping away from Utility (to say nothing of Love) and into Coercive tricks. But even at Facebook, solving for Utility over Coercion worked better. In a fascinating [multi-year UX experiment](https://medium.com/@AnalyticsAtMeta/notifications-why-less-is-more-how-facebook-has-been-increasing-both-user-satisfaction-and-app-9463f7325e7d), Facebook found that when they reduced the quantity of notifications (by keeping the quality high), it had the expected negative result on engagement: Customer satisfaction increased, but app usage decreased (because it was leading you back to the app less often). But, after a year, app usage actually increased and remained higher that it was before the change. Increasing genuine satisfaction created more engagement in the long run. They had to be patient to see the results; traditional "growth hacking" did not discover the best solution. --- *Many thanks to [John Doherty](https://twitter.com/dohertyjf) for contributing insights to early drafts.*

Excuse me, is there a problem?

Jason Cohen — Sun, 16 Apr 2023 00:00:00 +0000

![Tweet: Dude the PTSD hit me so hard while reading this. I've heard all those excuses and make all those mistakes!](https://public.asmartbear.com/books/anthology/chapters/problem/tweet-simple.png) How many companies fail: 1. Founder gets a flash of insight: The world has a Problem. 2. Founder talks to three potential customers who are experiencing The Problem, or who are expert in the domain of The Problem. They agree The Problem exists. *(And they're correct!)* 3. Founder builds a product that solves The Problem. *(And it really does!)* 4. Founder fails to make enough sales, and the company shuts down in 6-24 months, when the founder runs out of patience or money. 5. Founder laments into the void (i.e. posts on Twitter): Why were sales so hard when the product clearly solved a real problem? These companies fail because solving a problem is--perhaps surprisingly--not nearly enough to build a successful company. The following model explains an extremely common reason why this happens, and what you can do about it. In the discussion, you'll figure out where your challenges are, and whether you can design a strategy to side-step the issue, or whether your business simply isn't viable. > "Greatness needs luck, but it's never by accident." > --Unknown ## The path from "The Problem" to "Viable Business Model" The main challenge facing a new startup is that so many different things have to go right for it to succeed. A subset of those things is the path "Problem" to "Viable Business Model." ![Flowchart of the steps between a problem and a full solution](https://public.asmartbear.com/books/anthology/chapters/problem/problemsolution-flow-simple.png) Let's dive in. ## Plausible: Do 10M people or 100k companies have the problem? These numbers sound larger than necessary, but here's why it *is* necessary even for an indie startup, using [Fermi Estimation](https://longform.asmartbear.com/roi-rubric/): #### 1% conversion: Impression → Visitor An AdWords campaign with multiple keywords, ads, and bids would be very successful at a 2% click-through rate. Display ads are more like 0.3%-0.5%. *(source: [HubSpot](https://blog.hubspot.com/agency/google-adwords-benchmark-data))* Top SEO position can be 3%-5%, but that's almost impossible to achieve for even a mildly competitive keyword. *(source: [HubSpot](https://blog.hubspot.com/agency/google-adwords-benchmark-data))* #### 1% conversion: Visitor → Paid A typical, successful product website converts 1% of its traffic to paying customers. I don't have firm data, though I did an informal poll on this question years ago. [Some](https://www.contentgrip.com/conversion-rate-business-benchmark/) [data](https://adoric.com/blog/what-is-a-good-conversion-rate-2020/) show 2-5% conversion rate even for just a sign-up form or free trial, of which a fraction will become paying customers. #### Therefore: 10,000 Impressions → 1 paying customer 10,000 × (1% click-through) × (1% convert-to-pay) = 1. #### 10,000,000 Impressions → 1,000 paying customers Not every impression will be a unique person, but you still need closer to 10M potential eyeballs than to 1M, because while some people will see your material more than once, most of the market will never see your material. 1,000 paying customers is the minimum[^1] needed for a sustainable, small company. It will take about two years,[^2] 10M marketing impressions, and [luck](https://longform.asmartbear.com/lucky/). [^1]: This is a rough rule of thumb companies charging $30-$100/mo; if less, you'll need even more customers to become sustainable; if more, you need fewer customers, but they are more difficult to find and convert than the numbers above suggest. [Pricing determines your business model](https://longform.asmartbear.com/pricing-determines-your-business-model/). [^2]: WP Engine, the company I started over a decade ago, was a hyper-growth company and then a Unicorn, reaching $100M in ARR in a similar time frame to other hyper-growth companies (as in the chart mid-way through [this article](https://longform.asmartbear.com/predict-the-future/)) now with over 200,000 customers, yet it took two years to get the first 1,000 customers. Competitors who started after us also took that long, and [there](https://twitter.com/CoryMckane/status/1639664575686619138?s=20) [are](https://twitter.com/r00k/status/1639667146409009153?s=20) [many](https://twitter.com/timb03/status/1637211704093478912?s=12&t=mnlAhEmLkMKMeX_fkP_pzw) [many](https://twitter.com/shl/status/1639669362885021696?s=20) [many](https://twitter.com/jdnoc/status/1639669338587164674?s=20) [many](https://tinyurl.com/yhyyar7y) [many](https://twitter.com/hmps_/status/1639687300777951233?s=20) [many](https://twitter.com/jfriedlaender/status/1639766616270602240?s=46&t=mnlAhEmLkMKMeX_fkP_pzw) [many](https://twitter.com/danielkempe/status/1639894339521327104?s=20) [many](https://twitter.com/AlecEllin/status/1640008710507360257?s=20) [many](https://twitter.com/aprilzero/status/1639810561624899584?s=46&t=mnlAhEmLkMKMeX_fkP_pzw) [many](https://twitter.com/thedannorris/status/1639842020570308609?s=46&t=mnlAhEmLkMKMeX_fkP_pzw) [other](https://medium.com/swlh/how-do-you-get-your-first-1-000-customers-dd3b3968212a) [examples](https://ghost.org/about/). Of course it can also take [forever](https://twitter.com/AngeloRobertsJr/status/1640103585550614529?s=20), [4 years](https://twitter.com/masonarnold/status/1639965501118922752?s=20), [6 months](https://twitter.com/alxberman/status/1639900347136380928?s=20), or [52 hours](https://twitter.com/cemper/status/1639728089440681984?s=46&t=mnlAhEmLkMKMeX_fkP_pzw), because this is a rule of thumb, not a law of physics. If you're selling directly to consumers, there needs to be 10M who have the problem, otherwise it's too small even for an indie company who wants to stay small forever. If you're selling to businesses, the total number of potential customers is an order of magnitude smaller, but they will pay orders-of-magnitude more to solve problems, and conversion rates are higher, thus 100k is sufficient. A lot of great ideas attack problems that just aren't actual problems, at least not for more than a small handful of people, and therefore fail to yield a successful company. **Can you be successful anyway? Yes, there's an exception to every rule.** For example, a high-price-tag product in a small niche can be a fine company. Or, perhaps you're happy staying frugal, never hiring an employee, and making $100,000/year post-expenses post-tax, replacing a salary but on your own terms.[^3] That's wonderful. You can be the exception, but with conditions. [^3]: This is, in fact, what [80% of small businesses](https://reactionwheel.net/2019/01/schumpeter-on-strategy.html) do. It is a vibrant and valuable driver of [fulfillment](https://longform.asmartbear.com/fulfillment/) and the economy; ignore those who claim this is somehow less important than "swinging for the fences." Rather, there are two kinds of companies: Those which endeavor to replace a salary (and then some), and those who are trying to become huge, and they are simply different paths. ## Self-Aware: Do they know & care they have the problem? It seems like the answer should be "obviously yes," but often the answer is "shockingly no." If a person does not already believe they have a problem, they will not be surfing the internet looking for a solution, and even if they happen upon your website somehow, you cannot get them to spend money to solve a problem they don't think they have. ![sign reads 162 days without addressing the real problem](https://public.asmartbear.com/books/anthology/chapters/problem/d6712print-simple.png) Sam Altman is the co-founder and CEO of OpenAI, and before that ran the Y-Combinator accelerator, and is therefore one of the world's most experienced experts on startups. He previously co-founded Loopt--a location-based, mobile social-network app. Oh look, those are *all* the keywords of 2005, when it was founded. It raised $30M and failed. When asked what happened, he said: > The market wasn't there. You can't force a market. You can have an idea, and as a startup part of your job is to be ahead of it, and sometimes you're right about that, and sometimes you're not. > > Sometimes you make Loopt, and sometimes you make OpenAI. You just keep trying. > --Sam Altman, [interviewed by Kara Swisher](https://podcasts.apple.com/us/podcast/on-with-kara-swisher/id1643307527?i=1000605522804) I've [given the example](https://longform.asmartbear.com/customer-development/) of website security, which I know[^4] is a real problem plaguing millions of websites whose owners think "those mean hackers won't attack lil' ol' *me*; I'm nobody!" False. Hackers indeed don't care about lil' ol' *you*, but they do want to gain control of your lil' ol' *server*, so they can do their nefarious things, like spamming, [advertisement-click-fraud](https://www.searchenginejournal.com/the-global-ppc-click-fraud-report-2020-21/391493/), [remote-controlling](https://itnext.io/how-companies-are-hacked-via-malicious-javascript-code-12aa82560bdc) your visitors' browsers, or just bouncing off to yet another server as a way of covering their tracks. Everyone has the problem, but millions of people don't *think* they have the problem, so they're not searching for website security software, and certainly not buying it. [^4]: A million websites run on WP Engine's platform, serving tens of billions of requests daily, as 9% of the global online population visits a WP Engine property every day. We block hundreds of millions of nefarious requests daily, and internally run SOC Type II and ISO/IEC 27001:2013 certified security processes. So we know a lot about what hackers do to websites large and small. "Security" is a case of ignorance, but the other version of this challenge is when the customer knows they have the problem, but genuinely does not care. This could be because this problem is the ninth-most-important priority on their list, and they can only give attention to their top three... and this item will never bubble up to the top three. An example I see a lot at WP Engine is accessibility.[^5] Given lip-service by many marketing departments and product managers, it rarely makes the priority list for the public website or the product. Some companies choose to care, or are mandated by governments or contracts. This author chooses to care.[^6] But the fact is, though everyone agrees they have the "problem" of a non-accessible website or product, most don't have the will to act. [^5]: "Accessibility" means working well for people with various challenges; in the visual sphere, consider cases like red/green color-blindness, needing high-contrast colors, needing larger text, or complete blindness, needing "screen reader" browsers to navigate menus, forms, and content. [^6]: This site uses semantic tags for content and navigation, the entire stylesheet of both fonts and layout supports arbitrary changes in font size, automatically respecting browser-specific settings, supplies keyboard shortcuts for menu actions, has alt-text for all images, uses `aria-title` and related attributes, uses high-contrast foreground/background colors, also supports high-contrast mode, and works well in screen-readers (including those built for everyone, like Safari Reader and Readwise Reader and Pocket). That said, let me know if there's more I could be doing! Sometimes "willing and able" is a matter of market-timing. A famous example is Instacart: Successful after 80% of Americans carried a smart phone, unlike [WebVan](https://en.wikipedia.org/wiki/Webvan) which was exactly the same idea, solving the same problem in much the same way, but the market wasn't ready for it. A lot of great ideas, attacking real problems, fail to become successful companies, because the target market doesn't know they're even in the market. Because they're not. **Can you be successful anyway? Yes, there's an exception to every rule.** Some founders are not only the first-and-best sales-person, but also natural evangelists. More, they're [on a mission](https://longform.asmartbear.com/mission-vision/) to educate the world about their passion. They don't see a lack of interest as a barrier, but as an opportunity to change minds. That is a difficult, expensive, and slow path,[^7] but it is a path, and one that could result in zealous, loyal customers and a fulfilling existence. But you really have to want that path, doing that work with those consequences, if you're going to enter a market that you also have to create. [^7]: "Difficult" because changing someone's mind about anything is almost impossible, especially when they're not seeking to have their mind changed. "Expensive" because of the marketing and attention you have to bring to the cause, on top of the usual work of making a sale, with certainly-worse-than-average conversion rates. "Slow" because you're having to create demand and then fulfill it, rather than meet demand that already exists. ## Lucrative: Do they have substantial budget to solve this problem? * *"You have a great product! I'd have to get [someone else's] approval for this though, and they don't understand all this."* * *"This is nice, I would use it, but they're carefully watching all expenditures and the truth is I can manage without it."* * *"I'd love to, but our budget is closed for the year and I can't start a new project."* * *"This is pretty cool, but our internal team who manages [the problem] says they don't need help. They might be just trying to save their jobs, but it is what it is."* We've all heard these objections. Some are normal; you can't win every sale. But sometimes the target customer agrees they have the problem, yet doesn't have the money to solve it. ![Every year there are naughty children who don't get any toys. That's a vastly underserved niche I think we can capitalize on.](https://public.asmartbear.com/books/anthology/chapters/problem/d7423print-simple.png) At [Capital Factory](https://www.capitalfactory.com/),[^8] there's a constant stream of kids coming out of college with a startup that "sells ______ to college students." It's easy to find "problems"--restaurants and bars want to advertise to students, students don't want to spend much on food, students need books and supplies, and so on. The founders explain they "had the problem themselves, so they *really* understand it." [^8]: Capital Factory is by far Austin's largest and most prolific "Center of gravity for entrepreneurs in Texas," now a tiered system from co-working to mentoring to multi-million-dollar investments with hundreds of companies in orbit. The University of Texas, also in Austin, has a high-quality Computer Science department that is also one of the largest in the country. That's probably true. The deal-breaker is that college kids have no money, and don't spend what little money they have on SaaS products. And businesses that cater to college kids have to charge low prices (because college kids have no money), and therefore don't have budgets for oddball new ideas. In 17 years, none of these startups worked, even though arguably most of the "problems" existed. You might think a large company will definitely allocate budget for a known problem, but here again the answer is often in the negative. Budgets are applied to the top few most-important problems of the year; if this is a problem, but not a top one, it won't get attention. Large companies have to allocate more than money--they have compliance teams who have to approve, they compare multiple vendors, they run pilot programs, and all of this won't be set in motion unless it's a top problem. Large companies have internal teams that are already tasked with the problem, which might means there's no additional budget for outside solutions. Those teams often fight against outside tools that are seen as making their jobs obsolete, or at least converts them into vendor-managers instead of innovators. You want to target companies who outsource this particular problem to outside vendors. Once you get over the hurdle of there being a budget at all, is the budget large enough? I'm always shocked how little people will pay for productivity applications like to-do lists and note-taking. These are applications you might use dozens of times per day, as much as email. The slightest increase in efficiency or even simple delight will have a massive impact on the customer's life, every day. And yet people complain that the Pro version of [Bear App](https://bear.app/)[^9] is a whopping $15/year, or that [Remember the Milk](https://www.rememberthemilk.com/) is $40/year, or how they've been paying €29/year for Evernote for eleven years, but a change to €43 is so devastating that they will completely change to another application that has 1/10th the functionality and no tech support: [^9]: My note-taking application of choice, and not just because it's called "Bear!" ![Comment on Reddit on leaving Evernote over a €14/year price increase](https://public.asmartbear.com/books/anthology/chapters/problem/aa272ce2-d320-4c6d-ba33-995782b4f045-simple.png) In general, consumers don't like paying for stuff, hence the multi-trillion-dollar success of having people "pay" with attention (advertisement) and data (privacy). This is why I think[^10] self-funded companies in particular should target businesses as customers; unlike consumers, they will spend money to solve problems and to [make more money](https://longform.asmartbear.com/more-value-not-save-money/). [^10]: This is clearly a personal bias. I can't wrap my head around the mentality described above, and that's why I don't build in and generally don't invest in B2C--I'm fully aware that I don't understand the customer! A lot of great ideas, attacking real problems that customers acknowledge and seek solutions for, are in areas where budgets don't exist, or not often, or are so small that it requires an enormous number of customers to make money (often also in a crowded competitive space), and therefore the company fails. **Can you be successful anyway? Yes, there's an exception to every rule.** If there are a huge number of potential customers, and if your cost-basis is extremely low,[^11] you can create a strategy targeting a large market at a low price with a simple product.[^12] This works even better if existing products are poor (so you can stand out and make a splash) and expensive (so your low price is itself a differentiator). It's still risky and difficult, but you could accept that and make decisions consistent with that challenge. But you have to really want that path, and increase the "10M" number, since you'll need a lot more customers to make ends meet. [^11]: e.g. no direct customer service, no substantial infrastructure costs, a route to market that costs almost nothing to acquire a customer, the ability to build and maintain the application with very little design or engineering or product outside of the founders, switching costs are low so you don't have to do a lot of work or spend a lot of money to get a customer off another product and on-boarded onto your product [^12]: When a product will be widely used with little-to-no customer service, it must be simple or it won't scale. ## Liquid: Are they willing and able to buy right now? * *"Oh yeah, we spend $100,000/year on this. But our contract isn't up for renewal for another 15 months."* * *"We just implemented a version of this in Workday. I'd rather use your product actually, but it's just part of Workday and the HR team likes that everything is integrated."* * *"This is way better than our current system, but we've invested a lot integrating with seven other systems, plus a few custom things some engineering teams did, so we can't really consider switching away at this point."* * *"That looks like great software for weddings. I'll let you know when I get married again! Haha!"* When the customer is already paying to solve the problem, or actively comparing options to solve the problem, you still run into the barrier of whether they have the organizational will to buy from you. ![we found problem all of your internal organs are in this little bag](https://public.asmartbear.com/books/anthology/chapters/problem/d6158print-simple.png) This can be for legal reasons, like being locked into a long-term contract or government fiat. This can be for convenience, as in the Workday example which at WP Engine caused us to cancel several other SaaS products because "now it's all in one system, which we're paying for anyway, so this is simpler and safer to manage." This can be because of other forms of lock-in, like difficulty in extracting and moving existing data, having to retrain thousands of employees, or having to re-implement a variety of cross-systems integrations that people rely on for their workflows, data, reports, and governance. Notice that **all these forces have nothing to do with your product or its price**. They are so powerful, they overwhelm a product that is solving the problem the best, at the best price. That's why they cannot be ignored, and why founders are surprised when their genuinely-great product in the definitely-extant market where customer are definitely paying for solutions, is still too difficult to sell. On the bright side, this is a prompt for your strategy. How will your strategy create these sorts of "lock-ins" that will prevent your *competitors* from kicking *you* out of *your* customers? A final way that customers might not be able to buy right now, is when the product is needed at a specific moment in time, but not before or after that moment. Websites for events and occasions are an example, as are tools that solve temporary problems like sophisticated code profilers or load-testing tools. Because Smart Bear[^13] was in the developer tools market, I know a number of founders of products in the latter two spaces; all of them struggled to maintain even small companies, exactly because people didn't proactively need the product (i.e. "Don't have the problem"), but then suddenly did (i.e. "Willing to buy, but only *right now*"), and then didn't any more (i.e. "No longer willing to buy"). [^13]: My company before WP Engine and the namesake of this website. Or, that moment in time might be in an ill-defined future, when the problem is of higher priority. If you're not one of the customers' top three priorities, they can't devote the time or budget to it, even if they agree the problem exists and that your software will solve it for a reasonable price. [This article explains this effect in detail](https://longform.asmartbear.com/purgatory/). **Can you be successful anyway? Yes, there's an exception to every rule.** If there's a legal contract, you can offer to pay the penalty for them breaking their contract (so long as that isn't so expensive that you can never be profitable). If it's hard to migrate, you can offer to do the migration as a service, perhaps even for free (this is common in WP Engine's market). If you're fighting the all-in-one enterprise systems like Workday, you can focus instead on a target market that cannot afford Workday and sees Workday as overly complex and cold, so that Workday isn't even a competitor. If timing is important, you might offer a way to buy that is cheap or even free while not using it, so you're "right there as soon as you need us again." But you have to really have answers for these challenges, and be ready to walk that path. ## Eager: Do they want to buy from you, specifically? * *"It seems like it would work for us, but it looks like you've only been in business for a year?"* * *"It definitely worked fine during the trial, but we're expecting to grow 100x and we're not confident that you'll be able to handle it."* * *"For the features we need, [competitor] looks the same to us, and they're cheaper."* * *"I like how you do X, but [competitor] does Y and Z, which we really like, so we're going with them."* * *"Our policy requires that all vendors are SOC 2-compliant and provide a security audit trail API, so you did not meet our basic requirements."* Even in a real market, with customers spending real money, in a purchasing process right now, you haven't yet won. There are other options, ranging from direct competitors to indirect alternatives. They will buy, but will they buy from *you*? ![its not that i dont like you ted you just dont fit my target demographic](https://public.asmartbear.com/books/anthology/chapters/problem/d0106print-simple.png) The first hurdle is **trust**: Do they trust not only that the product works, but that your company will be around for many years to come, that you will maintain a high pace of development, that you won't have security issues, that your customer service will truly help them when inevitable problems arise, and that you can scale as the customer's needs scale? The second hurdle is **differentiation**. This doesn't just mean "you have [something unique](https://longform.asmartbear.com/worse-but-unique/)." You might have a feature that no one else has, but if only 10% of the market cares about that feature, that's not enough. Worse, your competitor will have some feature *you* don't have, and what if 30% of the market cares about *that* one? How to do this? See [this companion article on leverage](https://longform.asmartbear.com/leverage/). A special difficulty comes when the product over-serves a large segment of the market. This means that, for example, you have ten features, but most of the market really cares about three of them. You might have all sorts of differentiation in the latter seven, but that won't sway most people. Picking something simpler or cheaper is the rational choice, even though your "feature comparison matrix" shows that you're a much more complete solution. A sales team can combat both of these challenges. The job isn't just to schedule calls, cajole potential customers into action, and press for the close; the job also to build trust in your organization and talk around competitive points, positioning so that the customer wants to buy from the mixed bag of plusses-and-minuses that every product contains. The best way to overcome these challenges are the "Love" and "Utility" types of [willingness-to-pay](https://longform.asmartbear.com/willingness-to-pay/). **Can you be successful anyway? Yes, there's an exception to every rule.** Trust can be side-stepped by building a type of product that doesn't require much trust.[^14] Or trust can be built by mitigation, for example open-source means the customer can shift to another vendor or take over the code base in a worst-case scenario. Differentiation is harder. Sometimes you're competing on price, which isn't ideal for the bottom-line or for the quality of customer, but can work very well. In a large market, differentiation can come from specializing in a niche. In a small market, you might not have many viable competitors, lessening the importance of differentiation. A company mission that is "bigger than all of us" can also be a distinguishing reason to buy,[^15] although typically more for consumer products where that's an allowable purchase-criterium. Still, it difficult to survive when you're no different from more mature, feature-rich, stable, innovative alternatives. [^14]: "Security" isn't a concern if the data in your app isn't private, such as a social media management tool. "Uptime" isn't a concern if the product is run locally or the service isn't time-critical. "Company maturity" isn't a concern if it's a tool for individual use; in fact it can be an advantage for a new startup to sell to freelancers or other people who want to support other startups. [^15]: People buy windbreakers from Patagonia that are undifferentiated from other outdoor apparel vendors, because Patagonia is well-known for spending hundreds of millions of dollars on conservation and sustainability, and for treating their employees well. ## Enduring: Will they still be paying (or paying-it-forward) a year from now? I cannot count the number of indie developers who grow to $15k MRR and start slowing down because their cancellation rate is 7%. Many don't think this is a problem; many of the rest believe that 5% would be a success. It's not. [Financials and SaaS metrics](https://longform.asmartbear.com/product-metrics/) aren't even the primary reason why this is fatal. The problem is that the customers don't want the product. 5%/mo cancellation means only half the customers will still be customers a year from now, which means you're not building a sustainable business. The reasons can vary. Perhaps they needed the product temporarily; they might have loved it, but "the problem" disappears. More often, the product didn't work well enough--insufficient features, too many bugs, didn't integrate with some other system, too expensive for the end result, turns out the problem is not important enough. The financial reality is illuminating too. The challenge is that top-line growth is linear for many companies, and [quadratic (_not_ exponential)](https://longform.asmartbear.com/exponential-growth/) even with hyper-growth companies. But cancellation *is* exponential--that's why it's measured as a percent of the current customer base. Exponentials grow faster than lines or quadratics, therefore cancellation "catches up" faster than you can add new customers. Growth fades, and finally ceases, as one customer cancels for every new customer who signs up. With $15k MRR, adding $2k/mo of new customers--a healthy 15% *per month* growth rate--a 7% cancellation rate means already half of that growth is negated by customers leaving. The company barely got started and already its growth is being decimated. At that rate, only one year later, having grown to about $27k MRR, the company has stopped growing completely (Figure 2), despite spending time and money on marketing and sales.[^16] Don't forget--those new customers cost money to attract, sell, and on-board with tech support, but all the value of that expense is negated by an equal number of customers walking out the door. [^16]: Growth stops because the $2k of new customers arriving in a month are negated by $27k × 7% ≈ $2k customers cancelling in that same month. ![Charting adding $2k/mo new MRR with 7%/mo using the SaaS Plateau Forecaster from Summit.](https://public.asmartbear.com/books/anthology/chapters/problem/e7de4ba8-c921-494d-99db-c1fdd3f7b787-simple.png) You certainly aren't required to have a goal of "grow forever,"[^17] but capping growth because customers don't really value your product, is not a healthy business no matter what your end goal is. [^17]: But especially if you do have the goal of becoming a scale-up Unicorn, it is impossible to do that without low churn. As just one example, Gainsight CEO Nick Mehta recently pointed out why "This stall will happen to all companies eventually," [explaining](https://cloud.substack.com/i/112444339/mistake-not-starting-act-ii-fast-enough) that launching a second product was their solution. This example was for a recurring-revenue business, but the same principle is true for one-time revenue businesses. **One-time revenue businesses still require repeat revenue**, in two ways: 1. Customers buying again. 2. Happy customers telling other people to become customers. Both of these require that customers are happy with the product. This reinforces the point that the most important problem is that the customer isn't satisfied, regardless of business model. **Can you be successful anyway? Yes, there's an exception to every rule.** Shopify is a fantastically successful business with a 8% cancellation rate--[only 34% of new customers stick around for one year](https://www.theglobeandmail.com/business/article-shopify-customer-retention-problem/). They're successful anyway because (a) the ones that do stay tend to grow forever and super-linearly, (b) the market is enormous and still growing fast, so they won't run out of new customers anytime soon, and (c) customers cancel because their own business didn't succeed, not because Shopify's product is problematic. If the problem really is the product, I don't believe there is an exception. The product has to be work well for some segment of the market. The rest can be mitigated if other factors of the market, strategy, and business model can overcome this massive weakness with even-more-massive advantages. ## Evaluating startup viability This path of "problem" to "business model" is not the only factor that determines success. There are still questions like whether you can reach customers, get their attention in the noisy internet and competitive space, can you do that cost-effectively, are company costs too high, do you have the skills, can you hire for skills, do you have enough time and money to do it, and so on. Still, we can evaluate the viability of this path with the following model. We'll use [Fermi Estimation](https://longform.asmartbear.com/roi-rubric/) to avoid the analysis-paralysis of deep research and arguing over details. **Some people have already started using this to think through their businesses**, like [Sam Bhagwat](https://twitter.com/calcsam), co-founder of [Gatsby](https://www.gatsbyjs.com/), [thinking through](https://twitter.com/calcsam/status/1645045534326145025) his next startup. There's also some [online calculators](https://www.codelantis.com/blog/startup-viability-score) if you like that sort of thing. Answer with regard to a *specific* target market, which means a specific type of buyer solving a specific problem with a product that has made specific trade-offs, at a specific price. | Criteria | Score | |:----|:----| | **Plausible**
Number of potential customers (consumers or businesses) who actually have the problem | *Power-of-ten only*
`1k`, `10k`, `100k`, `1M`, `10M`, `100M`, `1B` | | **Self-Aware**
Willing to solve the problem | `0.01`: Few agree or care
`0.1 `: Thought-leaders care and evangelize
`0.5 `: It's an industry standard-practice
`1.0 `: Almost impossible to find someone who doesn't care | | **Lucrative**
Annual *allocated* budget | *Power-of-ten only, of net-revenue*[^18]
`$1`, `$10`, `$100`, `$1k`, `$10k`, `$100k`, `$1M` | | **Liquid**
Frequency of purchase decision | `0.01`: Every few years
`0.1 `: An annual decision
`1.0 `: Always in the market, easy to switch | | **Eager** *(identity)*
Attitude towards your company | `0 `: They cannot buy from you
`0.1 `: Structural challenges
`0.5 `: Indifferent; no red flags
`1.0 `: Mission-level emotional desire to select you | | **Eager** *(comparative)*
Competitive differentiation | `0.1 `: No material differentiation
`0.5 `: Some features are so good, some people will buy just for that
`1.0 `: One-of-a-kind solution that has no viable alternative | | **Enduring**
Will they still be here a year from now? | `0.01`: One-off purchase without loyalty
`0.1 `: One-off purchase, but happy customers will buy again and tell their friends
`0.5 `: Recurring-revenue from a recurring-problem
`1.0 `: Strong lock-in from fiat, integrations, or being the system-of-record for a business-critical system | | *Normalize*
Normalize the score so that `1` is the threshold for an indie startup, `2` or more for a scale-up.[^19] | *Divide by* `625,000` | Now you multiply. Why multiply? Because this is a series of "ands"--there needs to be customers with the problem *and* they have budget *and* they are buying today *and* so on. The effects compound. [^18]: "Net-revenue" means your revenue after subtracting pass-through costs. For example, an eCommerce platform might process a $100 purchase on behalf of its customer, keeping $10 for itself as payment; that is net-revenue of $10. Pass-through costs do not include cost-of-goods-sold, i.e. you should not subtract out the marketing and sales costs to acquire the customer, nor customer support, nor infrastructure costs for SaaS products. Those are important for profitability, but in this exercise we are focused only on top-line revenue, not on the efficiency of your operations. [^19]: Justification: Using the figures earlier in the article, you could be successful with 10M consumers at $10/mo, or 100k businesses at $1000/mo (e.g. dentist practice-management), so consider the threshold of those numbers combined to be 100M. Taking the middle value of all other questions--neither a deal-breaker nor a strong advantage--you end up with `100M ✕ 0.5` `✕ 0.1 ✕ 0.5` `✕ 0.5 ✕ 0.5` `= 625k`. Arguably you should have some strong advantages, but also some of these values will be on the low side, so this is a reasonable Fermi-style acceptance threshold. **This is dangerously close to a silly quiz**, so we have to be careful to use the final score as guidance rather than precise analysis. Still, different choices of target market, target customer, and product trade-offs can result in dramatically different results. As usual, **having to think through the answers and trade-offs is most of the value of the exercise**, even more than the final score. ### A few examples Still, let's try it, using WP Engine[^20] as the example. Note how our research is simplistic, but because we only have to be accurate to a power of ten, the answers are easy anyway:[^21] [^20]: WP Engine is the largest platform for WordPress-based websites among the top ten million websites in the world. Therefore, we'll take the market as "businesses using WordPress." [^21]: For example, our figures for the first row are just page-one Google search results, but even if the figures are off by 50%, certainly the true answer must be far larger than 10M and far smaller than 1000M, hence 100M is the easy choice. | Criteria | Score | Justification | |:----|:---:|:----| | **Plausible**
Number of businesses who have the problem | `100M` | There are [334M](https://www.businessdit.com/how-many-businesses-are-there-in-the-world/) businesses in the world, [71%](https://www.zippia.com/advice/online-business-statistics/) of which have a website. [43%](https://w3techs.com/technologies/overview/content_management) of websites are WordPress. | | **Self-Aware**
Willing to solve the problem | `0.1` | While everyone using WordPress definitionally hosts it somewhere, and industry practitioners often use a specialist vendor, most target customers don't care enough to do more than the bare minimum. | | **Lucrative**
Annual allocated budget | `$100` | Difficult to say over such a large market; one could argue that `$10` is more accurate because most businesses are small and most don't buy expensive American things. | | **Liquid**
Frequency of purchase decision | `0.01` | People rarely change their website platform. | | **Eager** *(identity)*
Attitude towards your company | `0.5` | Could argue that it is `1.0` today because of our leadership position, but ten years ago we were one option among several, all of which were viable for many customers. | | **Eager** *(comparative)*
Competitive differentiation | `0.5` | We have many capabilities and features that are either unique or we are the best, but competitors have other advantages, whether in features or price or geography. | | **Enduring**
Will they still be here a year from now? | `1.0` | Today we easily justify this with our world-class retention metrics across 16 years of customer data, however even early on we saw high retention, and comparables also have high retention. | Score = `2,500,000 / 625,000 =` **4**, so it qualifies as a scale-up, and indeed that's what happened. Let's try it again with an indie startup: [ConvertKit](https://convertkit.com/), a email marketing product competing with giants like Constant Contact and newcomers like Substack, designed to help you grow and then monetize your subscribers. They target creators who want to monetize their newsletters, not "anyone and everyone," which reduces the target market but increases willingness-to-pay. | Criteria | Score | Justification | |:----|:---:|:----| | **Plausible**
Number of people who have the problem | `10M` | There are tens of millions of newsletters using competing products. | | **Self-Aware**
Willing to solve the problem | `1.0` | This is well-known as a best-practice, with multiple at-scale companies serving a mature market. | | **Lucrative**
Annual allocated budget | `$100` | Customers with complex workflows and many thousands of subscribers will pay more, but most customers aren't in that category, and might pay $9/mo.[^22] | | **Liquid**
Frequency of purchase decision | `0.01` | Customers buy newsletter software and then want to just use it, not be switching | | **Eager** *(identity)*
Attitude towards your company | `0.5` | The founder was well-known among other indie founders, however in the broader market there's no reason not to trust them, but no particular mission-driven reason to pick them either. | | **Eager** *(comparative)*
Competitive differentiation | `0.5` | Most features are similar, but there are interesting things at the margins for routing readers through flows and monetizing content, whereas more generic newsletter products don't have as many tools for direct monetization. | | **Enduring**
Will they still be here a year from now? | `0.5` | For the customers who avail themselves of complex automations, this would be a `1.0`; most people will probably use it as a normal newsletter, with a simple follow-up flow that competitors also provide. | [^22]: Alternately, you could focus on the customers who pay more; you might then reduce the "number of people who have the problem" to `1M`, and the annual budget to `$1000`. While results in same score, it's a different product, serving a different market, so this is an important decision. Score = `1,250,000 / 625,000 =` **2**. This is a good business model, possibly even a scale-up, and indeed ConvertKit grew quickly as a bootstrapped company, and while not a Unicorn, is an order of magnitude larger than most small businesses ever become. Finally, let's take the case of selling security software to consumers. This is a tough market; there are success stories (e.g. Norton, 1Password, Cloudflare), but it's hard to find small indie companies who are successful in this area (whereas it's easy to find successful indie WordPress hosting companies): | Criteria | Score | Justification | |:----|:---:|:----| | **Plausible**
Number of people who have the problem | `1B` | There are [~5B](https://www.statista.com/statistics/617136/digital-population-worldwide/) people online today, who might have online security concerns | | **Self-Aware**
Willing to solve the problem | `0.01` | Have you bought special security software for an online project? Almost no consumer does. | | **Lucrative**
Annual allocated budget | `$10` | Consumers don't pay much for things, and globally consumers spend far less on average than Americans on equivalent online goods; arguably this should even be `$1`. | | **Liquid**
Frequency of purchase decision | `0.01` | Consumers are rarely in the market. | | **Eager** *(identity)*
Attitude towards your company | `0.5` | Unclear without specifics, we'll be generous and assume you can earn trust despite being an unknown brand. | | **Eager** *(comparative)*
Competitive differentiation | `0.1` | Consumer-grade security products are undifferentiated. | | **Enduring**
Will they still be here a year from now? | `0.5` | Consumers cancel at higher rates than businesses, but at a low price this could be something that is easy to maintain; industry data would be helpful. | Score = `25,000 / 625,000 =` **0.04**. This is not a good business model. *But there's hope...* ![why no one likes math branding issue everythings a problem](https://public.asmartbear.com/books/anthology/chapters/problem/d8144print-simple.png) ### What to do with a negative answer We don't have to give up just yet. What if the security company targets high-net-worth individuals instead of "everyone?" The "number of people" would fall to `1,000,000`, but willingness to solve the problem might rise to `1.0`, and budget certainly rises to `$100` if not `$1000`. The orders of magnitude can change dramatically, which might reveal a workable model. What if the security company targets mid-sized businesses? The number of organizations is smaller than the number of consumers (but is still large), willingness to solve is very high (they have security policies and fear of the downside of a security incident), budgets are substantial, and so on. In general, **targeting a niche often results in a better business model**, because although it reduces the number of target customers, it can increase several other numbers. This is very often the right answer whether your goal is to build a small, profitable, sustainable business (in which you stay in that niche forever) or a large multi-billion-dollar enterprise (in which the niche is your way to get started, and you expand the target market over time). > "I'd rather be wrong than do something wrong." > --Larry Ellison **Or, finally, your business idea might simply not be viable.** That is a sad and tough [reality to face](https://longform.asmartbear.com/failure-to-face-the-truth/), but it's better to figure that out early, so you can spend your time finding a new idea. I hope this framework helps you build a winning strategy! ## Further Reading * [Five ways to build a $100M business](https://christophjanz.blogspot.com/2019/04/five-years-later-five-ways-to-build-100.html), the classic by Christoph Janz that uses five animals to show the different orders-of-magnitude of price versus size of company and quantity of those companies (Figure 3). This is equivalent to two of our rubric lines. ![Five ways to build a $100M SaaS business, in the form of analogy to five animals](https://public.asmartbear.com/books/anthology/chapters/problem/new5ways-001-simple.png) * [Pricing determines your business model](https://longform.asmartbear.com/pricing-determines-your-business-model/): How orders-of-magnitude in pricing changes your product and target market. * How to use the [Needs Stack](https://longform.asmartbear.com/needs-stack/) to determine both features and benefits that make sense for your customers. * The factors that cause customers to be [willing to pay more](https://longform.asmartbear.com/willingness-to-pay/) for a product. * Trading off [many customers at low price-points versus few at high price-points](https://longform.asmartbear.com/price-vs-quantity/). * Why [selling to the mid-market](https://longform.asmartbear.com/mid-market/) can be the "worst of both worlds." * [How small companies can win against Enterprise incumbents](https://longform.asmartbear.com/competing-big-companies/). * [Selling to Carol](https://longform.asmartbear.com/icp-ideal-customer-persona/): How targeting your one "perfect" customer is the right way to market in general. --- *Many thanks to [Daniel Zarick](https://twitter.com/DanielZarick), [KimSia Sim](https://twitter.com/KimStacks), [Matt Wensing](https://twitter.com/mattwensing), [Sam Bhagwat](https://twitter.com/calcsam), [Tony Meijer](https://twitter.com/Tony_Meijer), and [Willis F Jackson III](https://twitter.com/wfjackson3) for contributing their insights to early drafts, and to Daniel Veihelmann for the [online calculator](https://www.codelantis.com/blog/startup-viability-score).*

Navigating the unpredictability of everything

Jason Cohen — Sun, 19 Mar 2023 00:00:00 +0000

![Reaction to the news was mixed, largely because we asked more than one person.](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d4033print-simple.png) ## Predicting markets Analysts at Goldman Sachs spend their whole life learning advanced mathematics, building sophisticated models of complex corners of the world, and are financially incentivized to predict the future accurately, because Goldman Sachs makes billions of dollars if they can be 10% more correct than the next firm (who also employs brilliant analysts). So, how accurately did they predict economic metrics within their area of expertise? They got it very wrong, for 25 years, often not even directionally correct, in things like T-Bill rates (Figure 1) and oil receipts (Figure 2) and solar installations (Figure 3). ![Chart of an actual economic metric over 25 years, overlaid with quarterly predictions of where the metric will go](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/interest-rate-predictions-simple.png) ![Chart of industry Oil and Gas industry revenue over 18 years, overlaid with predictions of where the metric will go](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/image-1-simple.png) ![Predictions of the number of solar installations have consistently been 3x too low for two decades.](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/image-simple.png) But maybe the macro economy is too hard to predict, even though these sorts things should be in the domain where we can leverage the "[wisdom of the crowd](https://longform.asmartbear.com/wisdom-of-crowds/)." It is "chaotic," we are told, in the mathematical sense that small changes in inputs result in large, unpredictable changes in outputs. The "[Butterfly Effect](https://en.wikipedia.org/wiki/Butterfly_effect)" and all that. ![flap wings crazy things caused sounds lot responsibility](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d8129print-simple.png) When we create strategies, we're told to "skate where the puck is going." That means predicting the future, such as macro economic trends, and the trends of our industry and immediate competitive markets. But if the smartest, most motivated experts can't do that, why do we think we can? Let's try something closer to what corporate strategists must do: Predict the future for products in well-understood industries. ## Predicting sales > Researchers at McKinsey & Company, a consulting firm, studied the sales forecasts for new drugs made by analysts at brokerage firms. The data included more than 1,700 individual forecasts on 260 drugs over a recent ten-year span. ... The average error was large. **Almost two-thirds of the estimates missed the peak revenue amount by 40 percent or more.** Further, **forecasts for follow-on drugs were no better** than the first launches within a therapeutic class. > --Michael Mauboussin & Dan Callahan in _Total Addressable Market_, Credit Suisse 2015 *(my emphasis)* Once again, highly incentivized, highly trained analysts, within their area of expertise, within a well-understood, highly-regulated industry, mostly get it really wrong, whether predicting sales of a new drug, or predicting sales of a new competitor to an existing drug. Mauboussin and Callahan suggest that one way to increase the accuracy of predictions is to "use base rates as a reality check." That is, use the industry average as the starting point for your prediction. Does this method work? They illustrate: > Here's an example of how the base rate approach can figure into your judgment of TAM. During a conference call in February 2015, Elon Musk, the chairman and chief executive officer (CEO) of Tesla Motors, suggested the company might be able to achieve a **50 percent compound annual growth rate of sales** for the next decade ... > > ... The base rate method ... simply asks: "What happened to other companies when they were in a comparable position?" [Figure 4] shows the distribution of 10-year sales growth rates for more than **1,200 instances of companies of a similar size as Tesla is now**, measured by sales. The average growth rate, adjusted for inflation, is **less than three percent**, with a standard deviation below eight percent. Further, **no companies achieved a compound annual growth rate (CAGR) in excess of 40 percent** ... We have placed a star at the growth rate Musk mentions. > --Michael Mauboussin & Dan Callahan in *Total Addressable Market*, Credit Suisse 2015 *(my emphasis)* As we see from Figure 4, Musk's call is completely outrageous when using the base-rate method. ![10-year growth rates for companies with $6B-$13B of sales. Source: Michael J. Mauboussin and Dan Callahan, "The Base Rate Book – Sales Growth," Credit Suisse Global Financial Strategies, May 4, 2015.](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d8b39e9e8-6b32-4cba-ba69-83c1799968e6-simple.png) It is 2023 as of this writing, so we can now evaluate Musk's call, and see whether the base-rate method was correct: ![Actual Tesla revenue CAGR since 2015 is 55% -- even more than Musk's 2015 claim.](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d761e1e48-749b-4104-bd4a-6719de83f60f-simple.png) So, the base-rate method is suspicious, and the analysts are suspicious. Is anything not suspicious? When we build strategies, we're predicting what sorts of products are going to sell, often inside a fast-changing market, selling to customers with fickle desires in an ever-changing competitive landscape. Drugs that address a well-known disease with well-known rates of incidence and trends, with well-known competition, are easy in comparison. Easy, but already almost impossible even by the experts. And using base-rates doesn't give us much confidence either. So why do we think *we* can do it? ![Daily Mail, from the year 2000. Choice excerpts: "e-mail [is] far from replacing other forms of communication" // "the future of online shopping is limited" // "teenagers' use of the internet has declined ... they've been through all that and then realized there is more to life in the real world and gone back to it." // And who generate these nonsense predictions? "Experts from the Virtual Society Project" comprised of "research from 25 universities across Europe and the US."](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/aiseg2v6pli61-simple.png) ## Predicting winners Chess Grandmasters spend far more than 10,000 hours reaching an elite, unbelievable level of skill. They instantly pattern-match board positions against thousands of games they've memorized. They have an intuitive sense of how the future might unfold, without actually thinking through every possibility. They understand the relative strength of the best players, because they've studied their games for years. So when they guess who is going to win the 85th annual Tata Steel tournament--arguably the most prestigious tournament on the annual circuit--it is more than just "sports betting." It's betting by deeply educated experts. How did their predictions go? We can see in Figure 6. ![Probability of winning Tata Steel Masters by round](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/l6dpe9b4buea1-simple.png) The "base rate" prediction is that Magnus Carlsen--the world #1 in all three major categories of chess, arguably the best player to have ever lived--should win, and indeed he was given the highest probability of winning before the tournament began, even up to round 4. But the world doesn't necessarily unfold around the base-rate. Nordirbek Abdusattorov--an 18-year-old junior player rated more than 100 Elo points lower than Carlsen (an enormous gap)--was given such a small sliver of a chance at the start that it's hard to even read the number. He lead the tournament standings until the very last game. He also beat Carlsen in round 5. And Giri was never given more than a 30% chance; usually 10% or less. He won the entire thing. The same thing happened during the last FIFA World Cup. Argentina--the eventual winner--was never given much of a chance; even in the quarter-finals the bets were on France: ![Probabilities of final victory according to bookmakers during World Cup 2022](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/exi4s1qotc5a1-simple.png) When we build strategies, we're predicting the competitive market and which products will be winners. Chess and soccer games have a higher variance than companies, but analyzing sales funnels sometimes feels a bit like predicting Tata Steel. How do you build a strategy around this level of unpredictability? ## Predicting product Stewart Butterfield always wanted to make a game. So, in 2002, he did: ![Screenshot of "Game Neverending"](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d640px-game-neverending-2008-simple.png) It failed, but it had some interesting features. Alpha testers liked that they could share game objects by dragging them into chat windows. So, the engineers created an online application for real-time chat with image-sharing. Stewart Butterfield is the founder of Slack, so you might guess that this "better chat" idea became Slack, but no. The chat application only worked in real-time--your pictures didn't stick around when you closed the app. And this was fatal because it turns out people were interested in the *sharing* part more than the *real-time* part. So in yet another upheaval they rewrote the Flash application as a regular website, and Flickr was born. By 2008 it had become the largest photo-sharing site in the world with 3 billion photos and 5,000 more uploaded every minute. Yahoo bought Flickr for $25M in 2005; Butterfield left three years later. Now that he had money in his pocket, he was free to go back and do what he always wanted to do, which was to make a game. So, in 2009, he did: ![Header graphic for the game called Glitch](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/banner-64470-4eb84ef-intro-simple.png) There's [a whole story](https://arstechnica.com/gaming/2011/11/glitch-the-battle-to-build-a-massive-multiplayer-game-without-combat/) about their dreams of a massively-multi-player experience, with APIs so others would build even more things on top of the game. But it doesn't matter, because once again it was a failure, and [shut down](https://www.glitchthegame.com/closing/). But once again there was a piece of the game that people really liked, and once again it was the chat system. Butterfield pivoted the company with a new mission, and a new name that belied the mission: SLACK, the Searchable Log of All Conversation and Knowledge. Still not "chat," but rather a "searchable log of knowledge." It's too hard, Butterfield insisted, for corporate denizens to search Google docs, and email, and chat sessions, and intranets, and knowledge bases, and support channels, and sales logs. Slack brings all that content together into a single omni-search, thus solving a knowledge-management problem common to all companies. Butterfield was certain that merely "building yet another chat system" was a bad strategy, whereas "transforming communications" was a good strategy: > We are unlikely to be able to sell "a group chat system" very well: there are just not enough people shopping for group chat. > > That's why what we're selling is organizational transformation. > --Stewart Butterfield, Slack founder, [in 2014](https://medium.com/@stewart/we-dont-sell-saddles-here-4c59524d650d) Once again, great theory, great mission, great strategy... and it didn't work. Slack was, and is, yet another chat system. Fortunately for him, Butterfield was also wrong that "just chat" wouldn't sell very well. Slack was one of the fastest-growing companies in history: ![Graph of how long various SaaS companies took to go from $1M to $100M in ARR](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/bessemer-intercom-1-100-1-simple.png) What it is about people not wanting to build chat? WhatsApp has a parallel story. Initially it was just a way to post a public status message, so friends could see what everyone is up to, hence "WhatsApp" (like "What's Up"). No one cared. Then the iPhone launched push-notifications, so they added a feature to alert you when a friend's status *changed*. People started abusing this as a crude form of group-chat. So they added group-chat as a proper feature. The correct strategy was simple: SMS costs money for most people in the world, so a winning product strategy is "SMS, but free." That strategy worked: ![Graph showing that the rise of WhatsApp directly correlates with the fall of SMS messaging](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d2789696900000578-3037554-image-a-1-1428963375855-simple.png) When we build strategies, we're predicting how customers will receive our product, how it will solve problems or be delightful. And yet so often the idea we start with isn't the idea that wins. Even once Slack became "Slack" and not "Glitch," the strategy was still wrong. How can we ever be right? ## The Solution The Slack and WhatsApp stories of unpredictable success have at least two things in common: 1. They had a strategy--a philosophy of what would be entertaining or useful, a design sense of what would be delightful, brand new ideas for features and user interactions that people loved. They didn't just throw random things at the wall. 2. They went where the customers took them--if not a game, then sharing images; if not another game, then better chat; if not corporate omni-search, then chat with APIs, if not status updates, then free SMS. #### A strategy is required, even when it's wrong. A strategy gives you a direction. A direction creates something interesting. Something interesting might be used by zealous early fans, even if it ends up uncovering the answer rather than being the answer. If Game Neverending wasn't delightful enough to attract alpha testers, there would never have been a reboot into Flickr; if Glitch didn't have 150,000 users at its peak, there wouldn't be the idea for Slack; if Slack didn't have enormous ambitions, it would not have received VC funding (for the same reason Butterfield himself gave for why "yet another chat" isn't valuable). A corollary: Making a decision and moving forward is often more effective than extended deliberation about the decision. Deliberation assumes we know how to reason about the future, but even experts aren't good at that. Making decisions and gaining experience is how to find the right answers, if the organization is introspective enough to also [face the truth](https://longform.asmartbear.com/failure-to-face-the-truth/) when it turns out the original strategy is incorrect. **Dispense with the idea that there is One True Solution to the puzzles**, and that the way to get there is to gather enough information. While "iterate fast, learn fast" is [good advice](https://longform.asmartbear.com/survivor-bias/), it still doesn't mean you're iterating towards the One True Solution. It means you're taking a path towards *something*, which will turn out to be an integration between your own creation and the swirling reality outside of you. > "More than any other time in history, mankind faces a crossroads. One path leads to despair and utter hopelessness. The other, to total extinction. Let us pray we have the wisdom to choose correctly." > --Woody Allen #### The customer (behavior) is always (directionally) right. There is a contradiction in these two points. Point (1) is to have a firm strategy, yet point (2) is to go wherever the customers are, even if that violates (1). If you just do "whatever customers want," how is that a strategy? The answer is: Customers are where you discover how to upgrade your strategy. Since you know your initial strategy is wrong, following customers' lead is how you correct it. When a customer loves one feature especially, or asks for adjacent features, those requests themselves are not your new strategy; the intent behind them might be. So "give me push notifications for my status updates" is a feature request, but the *reason* they want that is to circumvent SMS, and incorporating *that* idea is the strategy upgrade. Perhaps the most interesting signal is when **the customer abuses your product to accomplish something else**. The desire for that "something else" is so great, they're willing to use the wrong tool to get it done. Not only exemplified by WhatsApp users abusing "my status" to be "group chat," but also in my own experience getting Smart Bear to start growing: Our first product (Code Historian) let you visualize the history of your codebase, which seemed cool to me but few people paid for it: ![Screenshot of viewing how old each line of code is, in Code Historian](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/recentchanges-simple.png) But people abused it to enable peer review. We were getting feature requests like: "Let me package up what's on my screen and send it to someone else," and "Let me write directly on a line of code and send it back." (Figure 13) ![Screenshot of writing a note on a line of code in Code Collaborator](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d3258184b-672d-44ad-be89-f01853b0e365-simple.png) A new product was born (Code Collaborator) which within a few years represented 97% of the revenue of the company. "Abuse" is a strong, positive signal for customer demand, and worthy of a change in strategy. So yes, the future is unpredictable. But that doesn't absolve you from creating a strategy, and it certainly doesn't mean you should accept that life is a random walk, with no way to bias results in your favor. In fact, sometimes strategies are correct right from the start, like it was with Google, LinkedIn, Amazon, Apple, and my latest company WP Engine.[^1] [^1]: Although one reason WP Engine was correct at launch was because I used a [system to discover what was important](https://longform.asmartbear.com/customer-development/) before building it. You need a strategy, a fixation like Butterfield has for a certain type of game, a galvanizing reason for everyone to act together with a common purpose. And yet the strategy is always under suspicion, always updating, always reacting to unpredictable realities. How, exactly, do you do this? ## Strategies that defeat unpredictability While it might not be possible to predict the future, it *is* possible for a strategy to side-step unpredictability through these mechanisms: ### Build a moat [Covered in this companion article](https://longform.asmartbear.com/moats/), a "moat" is a long-term, durable competitive advantage. A structural advantage that others cannot disrupt, lasting for years, is resilient to the volatility of your competitive market. ### Have more than one way to succeed If multiple things all have to go right for a plan to succeed, it probably won't succeed. That sort of plan tacitly assumes we can predict the future of many different components; surely a bad bet. This is why startups are difficult in the first place: We have to have an insight *and* build a compelling product *and* be able to reach target customers *and* do that cost-effectively *and* at a price customers will accept *and* be better than the competition for some segment *and* not lose a key team member early on *and* not have global economic failure. Like [this](https://longform.asmartbear.com/problem/). [A resilient plan is built of "or" not "and."](https://longform.asmartbear.com/capturing-luck/) We could reach customers through social media *or* paid advertisement *or* influencer marketing *or* channel sales. A product that at least *could* be sold in all those ways is more likely to succeed than a product which can only be sold a single way. Optionality defeats unpredictability: * A product that is very low cost to create has many [options for pricing](https://longform.asmartbear.com/pricing-determines-your-business-model/), and therefore more likely to find effective pricing. * A product in a large, growing market has many niches and personas and channels to potentially target, and therefore it's more likely you'll find some combination that works. * Some indie developers build multiple small products, then pour their effort into whichever one happens to take off. More examples are given in Nassim Taleb's concept of [Antifragility](https://en.wikipedia.org/wiki/Antifragility). In his words: > If you "have optionality," you don't have much need for what is commonly called intelligence, knowledge, insight, skills, and these complicated things that take place in our brain cells. For you don't have to be right that often. All you need is the wisdom to not do unintelligent things to hurt yourself (some acts of omission) and recognize favorable outcomes when they occur. > --Nassim Taleb, _[Antifragile: Things That Gain from Disorder](https://www.amazon.com/Antifragile-Things-That-Disorder-Incerto/dp/0812979680/)_ (2014) ### Leverage your assets "Strike where you are strong and the enemy is weak" said Sun Tzu 2,500 years ago, echoing into strategy-design from the SWOT of the 1970s to modern agile frameworks like the "Means" in [Effectuation](https://effectuation.org/the-five-principles-of-effectuation). This is obvious but often forgotten by entrepreneurs who believe they have found lucrative opportunities with amazing product ideas, but which fall outside of their sphere of competency, where the enemy is strong and they are weak. Any venture is likely to fail for many--unpredictable--reasons; the very least you can do, is [leverage](https://longform.asmartbear.com/leverage/) your capabilities, knowledge, network, (professional) friends, and accumulated assets. That might mean building a very different kind of company or product or target market; in fact that's exactly the point. When you pick the battle you are best-suited to win, you have a higher chance of winning regardless of the unpredictability of the world around you. ### Intentionally reactive If you cannot predict the future, one option is to spend no time whatsoever trying to plan. Just react to what you see now, and use your judgement and a long-term strategy to solve the immediate opportunities while advancing a long-term agenda. We know, we know... iterate fast, learn fast, get to the right answer fast. We know that, we say that, but then we take four months to deliver one feature, or take 18 months before we're "ready" to launch. Truly embracing and living the idea of constant delivery, constant feedback, constant learning loops, constant adjustment of hypothesis, quick decision-making, quick updating of prior decisions, no ego tied up with who was right or wrong about what, treating all doors as [two-way doors](https://www.inc.com/jeff-haden/why-emotionally-intelligent-people-embrace-2-way-doors-rule-to-make-better-faster-decisions.html), more than lip-service but encoded into the DNA of the organization, can overcome unpredictable barriers. There was no real prediction to begin with, just a constant stream of hypotheses, continuously updated, possibly using frameworks and processes specifically designed for this mindset.[^2] [^2]: e.g. building in public; the [OODA loop](https://en.wikipedia.org/wiki/OODA_loop); [Iterative Hypothesis Testing](https://longform.asmartbear.com/customer-development/); [Continuous Discovery](https://www.producttalk.org/), [Continuous Delivery](https://en.wikipedia.org/wiki/Continuous_delivery) (not to be confused with Continuous Deployment, in which deployment is automated but isn't happening many times per day) ### Hedged bets If you buy 30-year life insurance and live for 60 more years, you will make far more money than the insurance costs, so you come out ahead. If you buy 30-year life insurance and die in 2 years, your family will receive far more money than the insurance costs, so you come out ahead. Thus "insurance" is a way to always come out ahead, without predicting the future. The catch: You've reduced your maximum upside. In the 60-year case, you paid insurance premiums for 30 years while receiving no money in return. On balance, however, this "tax" is worth it, because you're trading slightly less maximum upside for predictable, net-positive outcomes. ![huff puff we move with brother all split insurance money](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/d7723print-simple.png) Strategies can also "buy insurance," i.e. come out ahead either way, albeit with a tax. Examples: * **Multiple vendors for same service**.[^3] Shift workloads based on price and performance. *(How would your price-negotiations go, if you were in this position of power?)* * **Multiple brands** (whether created or acquired). Each finds a different niche, some will [fail](https://longform.asmartbear.com/fail/), some will take off. Common with acquisitions of growing companies, or the "house of brands" business model, or the "holding company" investment vehicle. * **Multiple simultaneous solutions**. To create iOS, [Apple had](https://daringfireball.net/linked/2022/01/05/apples-dueling-iphone-os-projects) the iPod team try to expand their OS to be more capable, while simultaneously having the Macintosh team try to reduce their OS to fit on a mobile device. One result was in-fighting, demonstrating that "cost" can be personal. But the result was that they selected the correct way forward for the iPhone. * **Redundant systems**. City power, and a generator. Multiple servers in different geographic regions. Teams, not solo engineers. [Lying dials in airplanes](https://longform.asmartbear.com/whos-lying/). Redundancy is a cost, but the result is more predictable operation. As the military saying goes, "Two is one, one is none." * **Disrupt thyself**. Create new products that disrupt your existing products. The quintessential example is Amazon launching the Kindle; Bezos [instructed](https://medium.com/sand-hill-road/unpacking-growth-with-andy-johns-partner-at-unusual-ventures-79778fc7bdd8) the newly-anointed Kindle leader--who was the leader over their book-selling business--that "Effective tomorrow, your job is to kill your old business with a Kindle." Whether the future is in physical books, electronic books, or both, Amazon prospers. [^3]: Wrap 3rd-party APIs in a proxy layer. Use multiple public clouds. Process online transactions with more than one party. Keep your cash in more than one bank. ### Extreme novelty Zappos, Airbnb, Uber, SpaceX, Tesla, Bitcoin, OpenAI, all took enormous risk by creating a new category that everyone else thought was impossible, even after they heard the plan. On one hand, this is the definition of unpredictability. On the other hand, they avoided the unpredictability that comes from existing competition in existing markets. It doesn't matter what's happening at Amazon if you can return shoes for free even after 364 days; it doesn't matter how the hotel industry is shifting when you're selling a different experience; it doesn't matter what Ford and Toyota are doing with electric vehicles if you're changing the fundamental technology without the constraints of existing supply lines, dealer distribution rules, and ties to the oil and gas industry. If everything is unpredictable anyway, why not earn strategic advantage: No direct competition, exciting place for top-talent to join, immense upside. That upside does need to be immense for the risk to be rational. If it is, this a strategy for trading into a better set of uncertainties. ### Form coalitions The more massive the object, the more it resists being moved by external forces. Price-collusion[^4] removes the uncertainty of market pricing. Peace treaties prevent some wars. Open source projects attract more talent and advocates than any one organization could afford. Industry standards reduce the risk that core technology or protocols change, allowing members to build systems on top of those standard for decades, not having to "hedge." Good things happen when we all agree to use HTTP, HTML, and SSL. [^4]: This is unethical and illegal in many countries; don't do it. It is illegal because it works, which makes it a good example. > "If you want to go fast, go alone. If you want to go far, go together" > --African proverb ### Expand the scope of prediction Predicting exactly what will happen is folly, but mapping possible futures can be illuminating: !["Futures Cone" from Voros 2003, expanded by [](https://sjef.nu/theory-of-change-and-the-futures-cone/) Sjef van Gaalen in 2016](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/futures-cone-1-simple.png) Plotting possibilities that you believe are improbable, helps you recognize that perhaps they're not quite as improbable, helps you think of solutions that mitigate plausible challenges,[^5] and help you recognize if an "improbable" thing is in fact happening. [^5]: The "pre-mortem" is an increasingly popular workshop for accomplishing this, in which you brainstorm answers to the following question: "It's 12 months from now, and the project is a disaster. What went wrong?" The idea isn't to solve everything you can imagine, but rather to pick a few things to intentionally mitigate, and intentionally leave the door open for mitigating other things if they come to pass. Of course you're still ignorant not only of probabilities, but of eventualities that you never predicted. Gaalen acknowledges this by further expanding the framework: ![Expanding the "Futures Cone" to include "Unknown Unknowns"](https://public.asmartbear.com/books/anthology/chapters/predict-the-future/premeditated-futures-simple.png) While this doesn't mean you suddenly can predict the future, it might mean you're paying more attention to what's actually happening, allowing you to react faster, and thus to *manage* the unpredictability better. ### Stay simple The more tasks have to be broken-down, the more dependency arrows are drawn, the more intricate the analysis, the less believable the project estimate is. Complexity breeds unpredictability. The inverse is that simplicity is predictable. While not strictly true, is it true that simple things can be more predictable. A simple product, with a simple value proposition, in a simple market, has fewer dependencies that require prediction, and thus are more likely to succeed. ### Bet on what will not change Made famous by Jeff Bezos, it is difficult to predict how the future will change, but it can be easy to predict the ways the future will *not* change. In Amazon's case, he cites "low prices" and "fast delivery" as two of those things; in ten years, people will still want that. Therefore, Amazon can (and does) invest billions of dollars to achieve those results. In the case of WhatsApp, consumers want to chat and don't want to pay; that was true ten years ago, it's still true today, and it's a good bet that it will be true ten years from now. In the case of WP Engine, the company I founded 16 years ago, people wanted websites to be fast, stay fast even when they get a lot of traffic, and be secure; they still do, and I'm sure they still will in another 16 years. --- Gukesh Dommaraju entered the 2024 Chess World Championship as the overwhelming favorite, with pundits unified in predicting his victory. The only debate was how quickly and decisively he would defeat Ding Liren, the reigning champion who had been underperforming for years. Then Ding won the first game with the black pieces--a remarkable feat, since black is typically played for a draw. The pundits immediately reversed course, declaring the match 50-50 and Ding a force to be reckoned with. By the 14th and final regulation game, each player had won twice, with the rest drawn. Five hours into that decisive game, commentators could see it was heading for a "dead draw"--meaning that even lower-rated players would have no trouble avoiding a loss, but also unable to force a win. One pundit confidently declared in his livestream: "There's a 1% chance Ding doesn't draw this game." Five minutes later, Ding blundered, Gukesh won, and became the youngest World Champion in history. --- And so the lessons are: * Have a strategy, even though the world is unpredictable. * Decide quickly → get customer reactions quickly → learn quickly → make new decisions quickly. * Upgrade the strategy by following customer behavior. * Product "abuse" is a strong signal for updating the strategy. * The strategy must include [building a moat](https://longform.asmartbear.com/moats/) or two. * The strategy must create optionality in how to succeed (or avoid failure) so that single failures aren't fatal. * The strategy must [leverage your existing strengths](https://longform.asmartbear.com/leverage/), building a product for a market for which you are already well-suited. * Either keep it simple, form coalitions, or do something completely novel. * Bet on things that *won't* change, rather than predicting how things *will* change. * [Do your homework](https://longform.asmartbear.com/customer-development/), but don't stall in analysis paralysis. Map future possibilities, to mitigate possible things and to better notice when your assumptions turn out to be incorrect. That's how you win in an unpredictable world. --- *Many thanks to [Adam Brock](https://au.linkedin.com/in/adamdangerbrock), [Amy Hoy](https://twitter.com/amyhoy), [Daniel Zarick](https://twitter.com/danielzarick), Darla Cohen, [Derrick Wolbert](https://twitter.com/bigdeechi), [John James Jacoby](https://twitter.com/JJJ), [KimSia Sim](https://www.entrepreneurial.engineer/), [Nitin Punjabi](https://twitter.com/mothcamp), [Paul Huggins](https://twitter.com/phuggins), [Prasanna Krishnamoorthy](https://twitter.com/prasanna_says), [Rhys Jeffery](https://twitter.com/RhysJeffery2), [Seth Chasin](https://twitter.com/sethchasin), and [Tony Meijer](https://twitter.com/Tony_Meijer) for contributing their insights to early drafts.*

Finding Fulfillment

Jason Cohen — Sun, 25 Dec 2022 00:00:00 +0000

![twelfth final thing you change right now be your true self](https://public.asmartbear.com/books/anthology/chapters/fulfillment/d7548print-simple.png) Many blogs, resulting in book deals, resulting in speaking careers, have been forged by taking a swing at a basic human question: *What creates a fulfilling existence?* Managers desperately ask, forced by the Great Resignation to acknowledge that people are more mobile than expected when sufficiently (un)motivated.[^1] Enlightened leaders ask proactively, because intrinsically-motivated people do great work that is just as fulfilling for themselves as it is productive for the company--capitalism on its best day. Outwardly-impenetrable leaders ask it of themselves to conquer burn-out or to decide what to do with the balance of life that follows the societal definition of "success." [^1]: Your best talent are volunteers--they can always walk across the (now virtual) street for more money, because they're worth it, and because their resume has been incrementally improved by the experience at your company. You may protest that the grass isn't greener over there, but rather their feelings arise from the modern malaise instigated by COVID-imposed isolation from genuine relationships and the vile so-called online "relationships" fueled by toxic politics. You might even be right, but it's too late. What follows is my original framework I used to answer this question for myself ten years ago when I made the decision to step aside as CEO of the (now unicorn) company I founded. I still use it myself and recommend it to others. First, some of the best prior art on the topic. ## Pink's Motivation: Autonomy, Mastery, Purpose Daniel Pink famously surmised[^2] that "carrots and sticks" are poor motivators for most people. Worse, they're demonstrably counter-productive in common real-world scenarios. Instead of external motivators, blatantly designed for the benefit of the organization rather than the individual, people prefer--people *deserve*--to be driven by internal motivation, aligned with genuine personal fulfillment. [^2]: in [his book](https://www.amazon.com/Drive-Surprising-Truth-About-Motivates/dp/B0032COUMC/ref=sr_1_1?crid=19W217WIQZUM1&keywords=drive+daniel+pink&qid=1643834774&sprefix=boooks+the+ancient+cit%2Caps%2C90&sr=8-1), though you might prefer to watch his [extremely popular TED talk](https://www.ted.com/talks/dan_pink_the_puzzle_of_motivation?language=en) My subjective experience confirms this science-backed insight. The best people don't have to settle for anything less than personal fulfillment at work, as evidenced by the Great Resignation. And anyway it results in an organization that we are all proud to build. Pink suggests that internal motivation arises under three conditions: #### Autonomy People are fulfilled when they decide what to do and how to do it. Counter-examples include micro-management, inflexible working conditions, and one-way command-and-control structures. Positive examples include self-managed scrum teams, work-from-anywhere-and-whenever schedules, and agreeing on the goals of the final product rather than dictating the details of what that product is and how it must be created. #### Mastery Great people want the opportunity to do great work. They want to be around other people who are doing the same. Experts enjoy deploying their expertise; novices with vim and aptitude enjoy learning and growing. #### Purpose As the janitor famously answered in 1962 when president Kennedy asked him what he did for NASA, "I'm helping put a man on the moon." Everyone--not just Gen Z--wants to be a part of something bigger than themselves. That could be a noble cause, or something more incremental but tangible, like genuinely helping another human being in their own endeavors, as one might do in a world-class customer service organization. Other work agrees with and extends these ideas.[^3] However, I believe that to leap from Pink's original question--*What motivates people?*--to my question--*What is fulfilling?*--at least one vital component is missing: **Joy**. [^3]: For example, [Self-Determination Theory](https://www.urmc.rochester.edu/community-health/patient-care/self-determination-theory.aspx) asserts that motivation arises from "autonomy, competence, and relatedness"--essentially the same thing. ## 生き甲斐: A reason to be alive For thousands of years the Japanese have revered those who devote their lives to the mastering of a craft, having "craftsman spirit" 職人気質 (shokunin kishitsu). The samurai and the flower-arranger hold equal value, the chef and the janitor hold equal prestige, when each are whole-hearted in their endeavors. Furthermore, world-class skill is only half of the meaning of being a shokunin; you must also play a mindful and intentional role in bettering the community. It is not only "Mastery" but also "Purpose." The Japanese have venerated 2/3rds of Pink's trifecta for millennia. An equally ancient concept, that received a direct name in the 1960s, is 生き甲斐 (ikigai)--a motivating force that gives someone a reason for living: > ... ikigai ... usually means the feeling of accomplishment and fulfillment that follows when people pursue their passions. Activities that generate the feeling of ikigai are not forced on an individual; they are perceived as being spontaneous and undertaken willingly, and thus are personal and depend on a person's inner self. ([Wikipedia](https://en.wikipedia.org/wiki/Ikigai)) This adds color to the idea of intrinsic motivation as well as that of extrinsic purpose. We need purpose, whether it's the Silicon Valley notion of "changing the world" or to be a great-grand-parent. To be useful, to be needed, even to be wanted, one person to another, is already a higher purpose. One person at a time can be enough, as in this serendipitous Slack exchange I had with [WPGraphQL](https://www.wpgraphql.com/) founder [Jason Bahl](https://twitter.com/jasonbahl): ![Jason Bahl, inventor of WPGraphQL, making a difference for another person](https://public.asmartbear.com/books/anthology/chapters/fulfillment/bahlslack-simple.png) A "higher purpose" might elicit an eye-roll from the skeptic, but it turns out to be useful even if you're a cold-blooded, mercenary, even authoritarian-style leader. ## Sinek's Fanaticism: Start with why Simon Sinek wanted to know why some organizations produce fanatics. Not just customers and employees, not just people who like and buy the products, but people who personally identify with the company, incorporating the company's brand into their personal brand. When competitors build something objectively better, they don't switch, because for them it's not just about the features and the price. They'd work for that company if they could, and many do. As a result, employee turnover is low and productivity is high. Therefore every aspect of the business is stronger. How does this arise? Sinek sums up the answer: These companies **Start with "Why."** Meaning: These organizations have clear, simple, compelling *raison d'être*, a reason for being, something they stand for, something they would never contravene with their actions, even if it hurts sales or profitability. ![Simon Sinek's Golden Circle: Why, How, What](https://public.asmartbear.com/books/anthology/chapters/fulfillment/sinekgoldencirclee1378664887408-simple.png) It could be a "higher purpose," like Patagonia's incontrovertible mission to save the Earth,[^4] or SpaceX's mission to make humanity a multi-planetary species, or the Gates Foundation project to save millions of lives by eradicating malaria. [^4]: Beyond the obvious--sustainable practices, an outdoor-worshiping culture--they have a [formal company policy](https://twitter.com/patagonia/status/776223080779833344) to bail employees out of jail if arrested while protesting peacefully. But it doesn't have to be grandiose or holy to be motivating. Sinek frequently returns to the example of Apple's "Think Different" campaign; this taps the personal brand of those who are (or want to be seen as) independently-minded, creative, creators, iconoclasts, and artists, including artists embedded in fields like software development and engineering. Or Salesforce with its "[1-1-1](https://www.salesforce.org/pledge-1/)" or WP Engine with its "[Engine for Good](https://wpengine.com/engine-for-good/)"--thoughtful, systematic ways of giving back to the communities they are a part of (without pretending to single-handedly "change the world"). Or Rackspace with their "fanatical support," transforming the traditional status of Customer Service from a begrudged cost-center hell-bent on cost-reduction, elevating it instead into the honor of serving others, and a critical product differentiator in a market that is otherwise commoditized. > "When people are financially invested, they want a return. When people are emotionally invested, they want to contribute." > --Simon Sinek All of these companies were created by and are still run by Gen-X-and-older. So, wanting a higher purpose isn't just "a Gen Z thing," though clearly younger generations do, on average, talk about this more, and more conscientiously. In any case, the freshest talent and the future leaders of the world are Gen Z, so even a leader unsympathetic to "causes" should realize that having a higher purpose is more useful than not having one. This confirms and clarifies the "Purpose" component of the Pink Trifecta. Sinek approached it from an analysis of fanaticism and loyalty to organizations, and arrived at the same place. ## Cohen's Circles & Traps: Joy, Skill, Need It is possible to be empowered to work how you want (Autonomy), to be leveraging your skills and expertise (Mastery), and to be proud of your role in a cause (Purpose / Why), and yet *still dislike every day of your existence*. More than contentment (ikigai), you need *Joy*. Not only is this possible, it is common. There's the classic example of the startup founder who wakes up six years into the journey, realizing she's been [surreptitiously brought to a boil](https://en.wikipedia.org/wiki/Boiling_frog), burned out, dreading each day, drinking too much "to turn my brain off so I can sleep" but actually because she's deeply unhappy: > Each morning for the past couple months, my first thought has been "What could today be like if I didn't work here?" I drift off into exploring what it would be like to work at WalMart, or the construction site outside, or as a grocery store bagger. It seems so stress free. This morning, I locked myself in the bathroom with the shower running (don't want wife to know) and cried my eyes out. I haven't cried in many years. It felt great, but only for an hour. > --[Anonymous founder](https://news.ycombinator.com/item?id=14417758) Eight years ago I created my own framework for understanding how I could avoid this burn-out trap at WP Engine,[^5] especially when making decisions that are contrary to ego. The ego says "Being the CEO is the best." Silicon Valley says "The founder being the CEO is the Only Way."[^6] So it's hard to convince the ego to let go of being the CEO, as I did at WP Engine, even if being the CEO creates unhappiness. [^5]: Whereas I did burn out at my previous company Smart Bear. Even [selling the business didn't immediately fix the problem](https://longform.asmartbear.com/identity-selling-sadness/), although it was the beginning of the answer. [^6]: Typically citing Apple, Facebook, and Amazon while ignoring counter-examples like Google, LinkedIn, and Intel, although there do exist [thoughtful arguments that embrace those exceptions](https://a16z.com/2010/04/28/why-we-prefer-founding-ceos/). Here's the insight: Not only do you need *all three* of the following components, but any two alone create a specific trap: ![Fulfillment lives at the intersection of Joy, Skill, and Need](https://public.asmartbear.com/books/anthology/chapters/fulfillment/hww-jasoncohen-v8-30m-saastr-simple.png) #### Joy You love doing it. When you do it all day, you forget to eat and pee. At the end of a long day of doing it, you still want to do it. #### Skill You're great at it. Your work is so good, even *you* are proud of it. It stands out, and others notice. Those who don't understand how much effort you expended say: "You're a natural." #### Need The company needs it done. It's a top-three priority. Doing it well means a critical part of your strategy will succeed. Not doing it is crippling. Having any two without the third creates a well-defined yet common trap. It's instructive to understand the traps, because it can *feel good* to be in the trap: #### Trap: Joy + Skill − Need = Useless Flow At the intersection of Joy and Skill is "being in the zone," a.k.a. Flow.[^7] Wonderful! Unless you're working on something the company doesn't need done. Being in flow is intoxicating, and does "recharge the batteries," but it's unproductive. Continuing the example of the burned-out founder living in this trap, there's no one willing or able to tell you to your face that you're not contributing to what the company needs, so you stay in your happy place. A classic example is the technical founder writing code instead of making sales, fixing the website, handling the accounting, or hiring the next great team member. #### Trap: Joy + Need − Skill = Indulgent Failure The company needs to begin advertising. You've never done AdWords before, but you always wanted to try it, and anyway it'd be fun to learn something new. You might hire someone to do it later, but not now, because how do you hire and manage someone else without understanding the job yourself? So you make the AdWords campaigns. And since you're unskilled at both marketing and AdWords, you waste three months. You might even erroneously conclude "AdWords doesn't work" because that's an easier conclusion than "I don't know what I'm doing." If the company needed advertising so badly, you did a disservice to the company by indulging your desire to "play with it," because now months have passed without accomplishing what needed to be done. You tell yourself that *now* you can hire that person, because three months of flailing somehow makes you an "expert." As founder, no one will contradict you, so you indulge, and the company falls that much further behind. #### Trap: Skill + Need − Joy = Burn-out This is classic burn-out. When you do the work all day, you feel drained and exhausted rather than energized (as you would if it were Flow = Skill + Joy). You do the work, because the company needs it done. You do the work, because you are undeniably great at it. Even though you hate doing it, you'd rather take it on yourself rather than foist it on others, whether because you want to "protect them from the drudgery,"[^8] or because you believe they can't do as good a job as you can, or because you can't afford to hire someone. Because you create great results that the company needs, it doesn't look like a problem--not to you, nor your team. But because you dislike it, you grow to resent it, and eventually you can't face it, and you're finished. [Many startup founders agree](https://twitter.com/asmartbear/status/1772302431994442196?s=46). [^7]: [Flow](https://en.wikipedia.org/wiki/Flow_%28psychology%29) is when a person is fully immersed in an activity for an extended period of time. Time passes unwittingly, performance is at maximum, and it is universally described as pleasurable. [^8]: No one wants the boss doing all the drudgery, especially if the boss visibly hates doing it. Some people actually *like* the thing you hate, and you're preventing them--and you--from being happier. I created and used this framework nearly a decade ago, to recognize the wisdom of changing roles from the CEO of the company I founded ([WP Engine](https://wpengine.com/)) to the CTO, so that both our new CEO and I could operate in the center of our Venn diagrams. The thousands of people who have since worked at our company concur that this was a fantastic decision, both personally for the two of us, and for the success of the business, which is now a unicorn and an iconic landmark in Austin, Texas (Figure 3). ![Jason Cohen and Heather Brunner winning the E&Y Central Texas Entrepreneur of the Year Award 2017](https://public.asmartbear.com/books/anthology/chapters/fulfillment/image-1-simple.png) This framework is not a replacement of Pink's model; it is compatible. It adds the missing "Joy" component, while reinforcing "Mastery" with the label of "Skill." It lacks "Autonomy," however, perhaps because I created it with the founder in mind--a person who definitionally possesses autonomy, even to their detriment. "Need" is more tactical than "Purpose," really about being *useful*. Therefore, my recommendation is to identify that higher purpose, as described by "Start with Why" or ikigai, and fulfill your own part in that purpose at the center of the three circles. When we intentionally create a work environment where others have a chance at fulfillment, we're already improving the world substantially. When we care enough about others to find out what fulfills them, we can help that materialize. When we're closer to our own center of fulfillment, we can better serve others. How can you find out who you are, what drives you, what stops you? [Here's the system I've used for myself and others](https://longform.asmartbear.com/pivot-points/), as well as how I figure out "who we are" as a whole product or company. I wish you luck on your journey! --- *Thank you to [Jason Evanish](https://jasonevanish.com/) for providing feedback on earlier drafts.*

The Iterative-Hypothesis customer development method

Jason Cohen — Sat, 17 Sep 2022 00:00:00 +0000

![plundering small part of job](https://public.asmartbear.com/books/anthology/chapters/customer-development/d8822print-simple.png) This is a simple but effective process for building knowledge through interviewing (potential) customers that I've employed multiple times in the past 17 years. This system led to [rejecting some startup ideas](https://longform.asmartbear.com/good-startup-ideas/), selecting the idea of WP Engine, and selecting the right features during the early years, which then led to hyper-growth, which led to a Unicorn company with three dozen teams who do customer development for themselves. This article explains how to build and execute interviews. If you don't have anyone to interview, [this article explains how to find potential customers to interview](https://longform.asmartbear.com/find-customers-to-interview/). > "Deep in the forest there's an unexpected clearing that can be reached only by someone who has lost his way." > --Tomas Tranströmer ## The goal is to uncover the truth, not to sell I've been the interviewee for many startups doing customer development, and their most common mistake is that they spend most of the time selling me on how great their idea is. If you--a reasonably intelligent, excited, passionate person--sit down with someone who meets the criteria of a potential customer, and make an hour-long sales pitch explaining all the features and benefits, with that person wanting to be kind and supportive of this interviewer who is as passionate as they are desperate, that person will probably say something like, "yeah, that sounds pretty good." So, what have you accomplished? Nothing. **If you don't come away knowing something new and actionable at the end of the interview, you've wasted your time and theirs.** ![investigators](https://public.asmartbear.com/books/anthology/chapters/customer-development/image-simple.png) In the Lean Startup method they call [this](https://en.wikipedia.org/wiki/Customer_development) "validating your ideas," so it's tempting to spend the time convincing the person to provide you with validation. Instead, your mindset should be: "What does this person know, that invalidates something I thought was true?" If you've set out to confirm your ideas, not to disconfirm, then you will easily see the confirmation and conveniently miss the disconfirmation, and you will have done worse than waste your time--you have convinced yourself to believe incorrect assumptions. This process is a specific way to achieve that outcome: Maximizing genuine learning. > "Listening is being able to be changed by the other person." > --Alan Alda ## The Process ### 1. Goals: What you're trying to learn What is it that you're trying to learn? If it's a new product at a new company, in a B2B space, where the fundamental value-proposition is to solve some specific existing customer pain-point, the list should include [everything standing between the customer's problem and your potential product](https://longform.asmartbear.com/problem/); a list might be something like: 1. What does the "[perfect customer](https://longform.asmartbear.com/icp-ideal-customer-persona/)" (PC) look like? 2. What outcomes does PC need to deliver in a typical month?
*(e.g. [JTBD framework](https://hbr.org/2016/09/know-your-customers-jobs-to-be-done))* 3. What does PC actually do in a typical day?
*(e.g. tools, workflows, things they love, things they dread)* 4. What pain points does PC experience today?
*(i.e. what actually happens, and what pain does the customer actually know about?)* 5. How does PC cope with that pain today?
*(i.e. what is your competition, including DIY?)* 6. How much would PC pay to eliminate that pain? How is PC able to budget and execute the payment?
*(i.e. what are viable prices and terms?)* 7. What is the triggering moment? What causes PC to decide: Today's the day I'm going to buy something?
_(because no one randomly switches vendors)_ 8. What causes PC to resist or fear buying?
_(habits of the present, anxiety of change, risk or cost of implementation)_ 9. Where does PC go to discover and buy products like this?
*(i.e. what are the best distribution channels?)* 10. What specific words does PC use to talk about the space; what tacit assumptions does PC have?
*(i.e. how should you talk about the product?)* 11. What ultimate, [higher-level goal](https://longform.asmartbear.com/needs-stack/) does PC have?
*(i.e. what outcomes are they expecting as a byproduct?)* Decide on your list of goals first, as they will drive the content of your interviews. **Notice what is _not_ here: Asking the customer what you should build, or whether they would buy some specific feature.** This has happened to me dozens of times in my 26-year career: I asked a customer "Would you buy if we build ______?" and they say "Yes", and then we build it, and then they don't buy. Every seasoned Product Manager will regale you with the same story. This question doesn't work. Here's where pundits trot out the Henry Ford quote: "If I asked my customers what they wanted, they would have said 'a faster horse.'" Except most of the time "a faster horse" is a wonderful product and a successful startup. And sometimes you should indeed invent a car. And also Ford never said that[^1] and didn't have that attitude towards his customers. In any case, you can't decide what to build by asking customers what you should build. [^1]: The Henry Ford Museum of America doesn't include this quote in its database of around 200 authenticated Ford quotations, and it doesn't appear in Ford's autobiography *My Life and Work*. The apocryphal quote first appears over fifty years after his death: in 1999 in "The Cruise Industry News Quarterly" and in a letter sent to the UK publication Marketing Week in 2001. Instead, what you _can_ learn from talking to customers, is what their current life is like, which answers the many important questions listed above, leading someday to [Product/Market Fit](https://longform.asmartbear.com/product-market-fit-formula/). ### 2. Hypotheses: Your current best-guesses It sounds funny to write down the answers ahead of time; after all, the whole point of interviewing is to empirically discover the answers, not to presume you already have them! "Learning" and all that. The first reason to do this comes from the literature on the science of predictions. People are more objective at seeking the truth when they're forced to record their predictions--for example as formal "bets"--and observe how reality confirms or clashes with those bets. We autonomically [retcon](https://en.wikipedia.org/wiki/Retroactive_continuity) our beliefs in the presence of new information, or just discard the new information (i.e. [Confirmation Bias](https://en.wikipedia.org/wiki/Confirmation_bias)). Writing down our predictions helps us avoid this fallacy. The second reason to write down hypotheses is that, as we're about to see, they will help us generate great interview questions. Create hypotheses for each of your goals. At least one per goal, but more is fine. It's also fine to have hypotheses that aren't attached to a goal, if you're really curious about it. Here's an example list of hypotheses I had about WordPress hosting in 2009 before I started WP Engine; I've added a "mapping" to the goal-list above: 1. [G1,G4] Bloggers with more than 100,000 page-views per month have trouble keeping their blog fast. 2. [G1,G4] Bloggers with more than 10,000 RSS subscribers have traffic bursts that take down their site, even if the site functions just fine under normal conditions. 3. [G4] All WordPress bloggers worry about getting hacked, because it's common knowledge that blogs get hacked constantly. 4. [G3] Serious bloggers spend at least 3 hours per day inside WordPress--whether writing or answering comments. 5. [G3] Serious bloggers spend at least 2 hours per week on IT tasks related to hosting. 6. [G5,G6] Some bloggers spend thousands of dollars on consultants to make blogs fast and scalable. 7. [G5] Some bloggers just live with the problems. 8. [G1,G6] A blogger with 50,000 page-views per month will pay $50/mo if these named problems go away. 9. [G6] Bloggers use personal credit cards to buy supporting software for their blog. 10. [G6] Bloggers need to try software before they're comfortable buying. 11. [G7] When bloggers get hacked, it's a traumatic moment in which they say "I never want that to happen again," and they're ready to switch hosting providers. 12. [G8] Scary, unclear, expensive to move all your data; what if you get there and it doesn't work? 13. [G9] Bloggers read blogs-about-blogging for tips. 14. [G9] Bloggers trust the advice of WordPress consultants. 15. [G2] Bloggers care about driving RSS subscribers more than anything else, because those are repeat viewers. 16. [G2] Serious bloggers publish at least four times per week, to stoke page views for advertisements, reader interest, and Google search results. 17. [G10] Bloggers call themselves "bloggers," not writers, authors, content-marketers, etc.. 18. [G10] Bloggers call their website a "blog," not a "website." Even if you don't know anything about "blogging in 2009," I'll bet this set of theories sounds reasonable to you. So reasonable, that maybe you'd agree it's not worth spending time validating them. But you'd be wrong. (And so was I. We're all wrong, at the beginning.) After dozens of hours of interviews, I found that half of these hypotheses were wrong. The ones that were correct still needed to be tuned in detail. And equally valuably, I discovered additional attitudes and behaviors that weren't in my original list. > "The greatest enemy of knowledge is not ignorance. It's the illusion of knowledge." > --Stephen Hawking For example: It's not true that most people are willing to pay extra for extra security. It turns out that selling security to bloggers is like selling backup software: If you've never had a hard drive failure, it's unlikely you'll pay $30/mo for a backup service. Once you experience that devastating event, the first thing you do with your new laptop is sign up for any service that promises 100% full automatic backup. (Selling security to mid-sized companies is different; by then, they're proactively executing a formal security policy.) Another example: While it is true that bloggers want to "try before you buy" for most software (whether "trial" or "freemium"), this was not the case with hosting their website. It's such a disruption and technical ordeal to move their website to a new vendor, they think of it as permanent, not temporary. Therefore, "free trial" is not the most compelling offer, whereas "free migrations" is. By the way, we tried "free trial" anyway--how can people not love a free trial!--but the conversion rate was over 90%, and when we removed the free trial, signups didn't decrease at all. It's important to list even the most obvious, mundane assumptions, because you'll be surprised how often you're wrong or they need adjustment, [even if](https://longform.asmartbear.com/expert-harmful/) you're an expert in the field. Put these hypotheses in the first column of a spreadsheet, one per row. ### 3. Questions: What you ask during the interview Generating good questions is the hard part for most people. Armed with your hypotheses, however, it becomes easy, if you adhere to the following system. > "Questions are places in your mind where answers fit. If you haven't asked the question, the answer has nowhere to go. It hits your mind and bounces right off. You have to *want* to know." > --Clayton Christensen Questions are designed to test your hypotheses, and to suggest better ones. To achieve this, go to the second column in your spreadsheet, and write one question per hypothesis. Sometimes one question can cover a few hypotheses, if they're closely related. **Questions must be open-ended.** This is where most people go wrong. They'll have a hypothesis like the "security" example above, so they'll ask a question that "leads the witness," because they're still in "selling" mode instead of "discovery" mode: > Blogs get hacked all the time, and when they do it's devastating, right? Would you like it if your hosting company had extra security measures to protect your blog? Of course everyone will say "yes." They would sound dumb if they didn't agree. That's why it's a useless question. You didn't find out what the person actually thinks. And therefore you didn't find out what everyone else will think when they look at your advertisement or arrive on your home page or review your pricing page. The assumption baked into your question could be wrong, and now you'll never know. Instead, write open-ended questions that: 1. Confirm or negate the hypothesis *(the point of the exercise)* 2. Do not hint at any one specific answer *(seek unbiased truth)* 3. Invite the generation a specific answer *(uncover the correct answer)* 4. Invite more information *(seek answers to questions you didn't know to ask)* On "security," for example: > Do you ever think about website security? If so, how do you think about that? Do you do anything about it today? Here's more examples from the hypotheses above: | Hypothesis | Bad Question | Good Question | |:----|:----|:----| | Bloggers call themselves "bloggers." | Do you consider yourself a "blogger?" | When you meet someone new, how do you explain what you do in a few sentences? | | Serious bloggers publish at least four times per week. | Do you publish often, so Google ranks you high in SEO and there's a lot of surface area for people to find you? | How often do you publish new content? Why at that rate--what led you to that decision? | | Some bloggers spend thousands of dollars on consultants to make blogs fast and scalable | Would you spend $2000 on a consultant, if it meant your blog would become much faster and more scalable, so you rank higher on Google search results and get more traffic? | How valuable is the speed of your website? Have you ever spent money to improve it? If so, how much, and what did you do? Did it work? Were you happy with that investment? | ### 4. Iterate the hypotheses During the interview, take notes in the spreadsheet, in a new column, next to each question (which in turn is next to each hypothesis). The conversion might deviate from the original point; that's OK, maybe straying will lead to learning new things. > "The most exciting phrase in science isn't "eureka," but rather, "that's funny." > --Isaac Asimov If you hear anything surprising, ask follow-up questions. **Surprise means you're learning**, and since "learning" is the whole point of the exercise, you should use "surprise" as a signal that you should dig deeper. You can use an old interviewing trick: Just say: "Tell me more about that." After each interview, consider what supported or contradicted your hypotheses. Should you alter some of them? Not necessarily, especially after just a few interviews, but definitely if you're seeing a pattern. Also create new hypotheses (and associated questions) based on new learnings. The more insight you can get about your potential customers, the better. Don't worry about whether every hypothesis maps cleanly onto one of your goals; just accumulate insight. > "There are two possible outcomes: if the result confirms the hypothesis, then you've made a measurement. If the result is contrary to the hypothesis, then you've made a discovery" > --Enrico Fermi ### 5. Stop when it's boring Do you have all the right answers? Who knows. Probably not. But when this method stops producing new information, then you need a new method for making progress. That could be a proof-of-concept, a high-fidelity demo, an MVP (or actually an [SLC](https://longform.asmartbear.com/slc/)), or something else. When the surprises stop, that means learning has stopped, and that means you should stop the process. ![thing youre here to talk about talk about that a little](https://public.asmartbear.com/books/anthology/chapters/customer-development/d7885print-simple.png) A typical mistake is to do three interviews, and then stop because "I'm not learning anything." With so little input, you might not be genuinely seeking to learn. That's like a marketer saying "I tried two variants of my AdWords ad, and none of them are better than my first attempt, so I'm not going to try any more variants." It's also possible there's nothing to learn because you're fishing in the wrong spot. Maybe there's no patterns because you haven't found a real pain-point that more than a few people have and are willing to pay for. That means you need different ideas. [Here's more detail on how to determine](https://longform.asmartbear.com/good-startup-ideas/) whether the result of customer interviews is telling you "don't pursue this idea." ## Maximizing your results So that's the whole process. Here are more tips. ### Emergent segmentation You might notice that customers are segmented. Meaning, a certain type of customer tends towards one set of answers, while another has a different set. In the case of security, for example, when you talk to marketing departments at large companies, they do think about security, whereas when you talk to independent bloggers, they almost never do. In this case, it's useful to make a note of the segments you think exist. First, write hypotheses and questions that you believe are the determining characteristics of the segments. You'll ask these at the top of the call. Then, keep separate spreadsheets of hypotheses, one per segment. If you're lucky, you'll end up with clarity on the types of customers, and what each are like. You might choose to target one, some, or all of these; regardless, understanding the landscape is invaluable. ### Discuss price This one is controversial; many intelligent people insist that you shouldn't discuss price in early customer interviews because it unnecessarily conflates financial considerations with the discovery of customer attitudes, behavior, and pain-points. But in my opinion the price-tag is an essential component of the interview, because I believe the price is inextricably linked to what the product means to that person, therefore how they think about it and how it affects their life. [Price also determines the business model of the company](https://longform.asmartbear.com/pricing-determines-your-business-model/), so it should not be "figured out later." ![bear interview with mic](https://public.asmartbear.com/books/anthology/chapters/customer-development/image-2-simple.png) In early interviews for WP Engine, near the end of the call I would float a price of $50/mo for a service that made their website faster, more scalable, more secure, and came with genuinely good customer service. The responses were immediate, emotional, and vehement. One group was shocked--*shocked*--that the price tag would be so high; they would *never* pay even *close* to that amount. Another group said they would only buy the service if it were much *more* expensive, because otherwise they know it couldn't possibly fulfill its promises. (Those groups turned out to be emergent segments; see above.) Had I not discussed price, I would never have learned about the segmentation, or the expectations behind those segments, and thus how to price correctly, and for whom. I had another startup concept before WP Engine. [Insights stemming from pricing discussions](https://blog.asmartbear.com/vetting-startup-ideas.html) was one of the primary ways I was able to invalidate that idea, which in turn created the space for WP Engine, which is now a unicorn. Pricing questions can be open-ended (i.e. "How much would you expect to pay for ..."), however I think quoting a specific price is an acceptable breach of protocol. Putting a specific price in front of people elicits a strong, visceral response. When someone actually visits your pricing page in future, this is also the experience they will have--reacting to a specific price. It's smart to test what that experience will be like. You can also tie pricing questions into your other questions, to test whether the person really values that topic. For example, another way to test the hypothesis that bloggers care about security would be: > Would you pay extra for a security package that really worked, or do you not really worry about being singled out for getting attacked by a hacker? By asking if they'd pay extra, and by almost suggesting that they shouldn't bother, you're testing whether they *really* ascribe value to the concept. This worked in practice--most bloggers initially claimed security was important to them, but admitted they wouldn't pay extra to have more of it. ### Expect contradictions You're going to get all sorts of contradictory signals. People are different. Sometimes because they have different goals, different values, different past experiences, different roles, different projects, or for no discernible reason whatsoever. So your data is going to be noisy. Some of your hypotheses will end up reflecting the variation. You might conclude that some number varies a lot rather than staying in a small range, or that there is no pattern in people's opinions about some topic. That's still learning: Knowing what patterns *don't* exist prevents you from making false assumptions. Real patterns will stand out from that noise; that's your fundamental truth, that you can build products and strategies around. There might not be much of it. All the more reason to highlight it. ### Ask them to explain, step-by-step, how they will use it. The pattern: You ask a customer if they have the problem; they do. You ask whether they'd buy your product to solve it; they say yes. Then you build it, and they don't buy. Somehow, your interrogation didn't work. Sales conversions are never 100%, but one technique is to ask them to describe, in painstaking detail, exactly how they will use the product in their daily life. When would they open it up, how does it fit into their workflow, which features do they invoke, how do they move the outputs into other systems? This works because while they really do mean "yes, I think that _sounds_ good," thinking it through uncovers barriers that in fact blocks the sale; it turns out they need it to integrate better with something, or they actually need a feature you weren't contemplating, or some other hiccup. ### Create your positioning from customers' exact words Discord uses the oddly cold word "server" to mean "room" or "community." Why? Founder Jason Citron discovered early on that kids were setting up virtual servers to host audio or chat. So Discord's early pitch was: "Get a free server!" While it might sound better in an investor deck to say "we create communities," that wasn't how to sell the product. Use your customers' words, not your own. Discover those words, by recording the meetings and noting exactly what they say. ### Everyone "knows" what everyone else wants, except they don't I don't think I've ever conducted a customer interview where the interviewee didn't switch into "market guru" mode. This is where the customer stops talking about her own life, her own problems, what features or price tag would be acceptable to her, and starts talking *on behalf of other people*. > "I wouldn't pay $50/mo, but a lot of people would." > "I would pay $50/mo, but most people would expect this to be free." > "I care about security, but most people are completely clueless about that." > "Well I use a free tool to do that, but most people don't." Of course it's coming from a good place--they want to help you, they want to explain the "state of the market," they want to leverage their expertise. But they don't know what everyone else wants. Neither do you; that's why you're doing these interviews. And when you see all the crazy, different things people think, you realize that it takes tons of interviews to uncover even a modicum of truth. The person you're interviewing hasn't done that, so they don't know the truth. One of the hallmarks of successful companies is that they found some untapped aspect of the market and owned it. That might be a feature no one had considered, or a technology that before now hadn't been accessible, or a realization that the human touch trumps everything (Zappos) or that the human touch doesn't matter at all (Geico). Even if the person you're interviewing is a market expert, you're specifically looking for interesting holes and niches that the experts haven't noticed! So be polite, calmly tell them that's great insight, reinterpret "everyone else wants X" to either mean "I want X" or just throw out the comment completely, and redirect the conversation back to themselves and their own specific situation. ### Leveraging AI... maybe AI might help. Perhaps it could... * Generate hypothesis from the goals, adhering to the guidelines, specifying which goal attaches to which hypothesis *(although of course you'll treat them as templates and correct them to what you actually believe)*. * Generate questions for the hypothesis, specifying which question attaches to which hypothesis, adhering to the guidelines above. * Clean up transcripts. * Scan conversations for common themes. * Scan conversations for "things that contradict a specific set of hypothesis." * Scan multiple conversations for themes. However, **half the value of this exercise thinking through this stuff for yourself.** The "aha" moments come only when you wrestle with the details. You find the contradictions. You discover maybe you didn't think that after all. You realize [new ideas can solve for conflicting inputs](https://longform.asmartbear.com/conflicting-choices/). You realize which hypothesis are right, wrong, different. So, I advise you *not* to use AI, except to accelerate busywork such as: * Clean up transcripts so they occupy less space and are easier to process. * Double-check your thinking *after* you do the thinking, maybe come up with new ideas. * Summarize your thinking, i.e. take your thinking that you wrote out or said aloud, and make the result pithy and clear. ### STFU If you're talking, you're not learning. To maximize learning, minimize talking. When you do talk, it should be because you're clarifying your understanding about what they just said, digging deeper on the current conversation, or opening up a new vein of conversation. ### Further reading * [40 Tips for B2B Customer Development Interviews](https://www.skmurphy.com/blog/2020/01/30/40-tips-for-b2b-customer-development-interviews/) (by [SK Murphy](https://x.com/skmurphy)), lots of specific tips, plus a list of even more articles on the topic. * [12 tips for customer development interviews](https://giffconstable.com/2012/12/12-tips-for-early-customer-development-interviews-revision-3) (by [Giff Constable](https://twitter.com/giffco), 2012, just as relevant today as then, wholly compatible with this process.) * [11 Customer Development Anti-Patterns](https://giffconstable.com/2013/06/11-customer-development-anti-patterns/) (also by [Giff Constable](https://twitter.com/giffco), 2013; sometimes it's easier to list what *not* to do.) * [Customer Interviews: Get Actionable Insights from Every Interview](https://www.producttalk.org/customer-interviews/) (Comprehensive advice from [Teresa Torres](https://twitter.com/ttorres), also the author of the fantastic book _[Continuous Discovery Habits](https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309)_ that also explains what to do with the information you get from interviews.) * [_The Mom Test_](https://www.momtestbook.com/) by Rob Fitzpatrick (2013)--probably the single best book on this topic, and compatible with all the articles on this site. ![youve given me a lot to forget about after you leave](https://public.asmartbear.com/books/anthology/chapters/customer-development/d7869print-simple.png)

JIT selection from independent streams: An alternative to the "big backlog" of work

Jason Cohen — Tue, 23 Aug 2022 00:00:00 +0000

![moved everything from to do list to it is what it is list](https://public.asmartbear.com/books/anthology/chapters/jit-backlogs/d7045print-simple.png) Scrum--as well as innumerable articles [including one](https://longform.asmartbear.com/maximized-decision/) from this very site--teaches us that a "single, ordered list of work" is the correct way to prioritize work. I find, however, that this is unsatisfying at best, and fails to achieve sensible prioritization at worst. It also doesn't solve the [political challenges of prioritization with stakeholders](https://www.mironov.com/pri-politics/). The following system is simple, and has worked for me. ## Prioritization ≠ Work-Planning Prioritization means: *What is most important?* Critically, it does **not** mean: *What will we work on next?* Product Managers typically conflate these, trying to do both with a single process, and a single backlog. A traditional backlog efficiently encodes the answer to the second question, but isn't the right tool for answering the first question. You might say, "But we should work on whatever is most important, right?" By unpacking the subtle answer, we'll arrive at a better way to prioritize, and a better way to plan work. ## Why "the big backlog of work" isn't good for prioritization ### Prioritization If prioritization answers "What is most important," it raises another question: What do we mean by "important?" * Important to calm our largest customer who is threatening to leave * Important to catch up to our biggest competitor on a specific feature * Important to unleash the sales and marketing teams onto a new market segment * Important to satisfy our security policy * Important to decrease the largest cause of support tickets * Important to mitigate the largest cause of cancellations * Important to refactor code so that engineers are happier and more productive * Important to increase profitability * Important to achieve our long-term strategy, even if there's no next-quarter revenue Sorting a "Big List" of epics doesn't seem like the right way to tackle this complexity. The first problem is that you're sorting things that are incomparable. Each bullet point above describes something undeniably "important" but has different motivations, with different impacts, measured differently. So on what basis, with what metric, would you order the Big List? The second problem is that the resulting decision is unsatisfying to stakeholders. Sales wants a few specific competitive features, and doesn't have the context to understand how those are interleaved with requests from support or why the engineers need an entire month to "clean up a mess, which by the way, they made themselves?" The Big List doesn't yield satisfactory explanations. ### Work-planning The third problem is that the Big List conflates the idea of "prioritization"--what is most important--with "work-planning"--which is the order that specific work will be executed. While there's a correlation, work-planning rarely matches our ideal prioritization because... 1. Hard deadlines--often externally-imposed--requiring work even if something else is nominally "more important." (e.g. zero-day security patch is not on our list of prioritized strategic work, but must be scheduled immediately. Or: Have to get specific functionality shipped for a live customer event). 2. We might not currently have capacity on the teams who are capable of working on a high-priority thing. 3. We might not currently have the skillsets to execute a high-priority thing. 4. Dependencies--i.e. A is very important; B is not. But A cannot be done unless B is done. Therefore, we must work on B first, even though A is more important. 5. Unknowns that have to be investigated before work can commence, or even be planned. (e.g. spikes or proofs-of-concept) 6. The amount of work, or amount of risk, of executing a high-priority thing might mean we shouldn't tackle it yet. 7. Getting success on some smaller things, while working on longer things, is better for throughput, better for morale, and is more agile. 8. Blockers--dependencies on other work, maybe even other teams, before we can make progress on a high-priority thing. 9. Starving--sometimes we've put off low-priority things for so long, they effectively become higher-priority. 10. Holidays and other seasonal things (e.g. kicking off a huge new initiative on December 15th is a bad idea, even if it's strategic). The solution is to prioritize items in separate, actually-comparable streams, and to keep that prioritization effort cleanly separate from work-planning. ## Separate prioritization streams ![Ghostbusters movie](https://public.asmartbear.com/books/anthology/chapters/jit-backlogs/gb1film1999chapter28sc017-simple.png) > "Don't cross the streams!"
"Why?"
"It... would be bad." > --Egon Spengler, Ghostbusters ### The stakeholders Folks in Sales may not know which items are most important for Support, but they sure as heck have an opinion on what would be best for Sales! It's fun to sit down with sales reps and sales leaders and ask them to generate and then prioritize[^1] the things they believe would be most impactful. Tell them they don't have to worry about other departments or other requirements--this time is just for them, and this list is just for them. [^1]: Wait, that means a single, prioritized list! Yes. Because this is a single group, and we're asking them a single question from the bullet-list above (e.g. "what would be best for increasing sales, whether because of increased win-rate or increased average transaction size"), we don't have the problem of comparing unlike things, and so the "single list" is a good forcing-function to generate a clear prioritization. The ideas will pour out. Some ideas come from the last sale someone lost; that might be insightful or might be noise.[^2] Some ideas are critical causes of lost sales--bonus points if you can tie them to specific lost sales. That's motivating for Engineering as much as for Product. Some ideas are just a feeling--"I *know* I could sell the piss out of X." Don't dismiss those out of hand in your quest to be data-driven; multi-billion-dollar startups have been built atop experience-informed gut-instinct. [^2]: One way to separate the short-term emotional reaction from the important trend is to observe what happens in subsequent prioritization sessions. When you see the group dramatically shifting the priority of items quarter-to-quarter, that indicates the group doesn't have a consistent sense of what would be most impactful. Surely some of the ideas are truly great; *this* team just isn't sure which ones those are. Sounds familiar--Product Managers struggle with this challenge all the time! By forcing that group to prioritize their ideas, they'll argue amongst themselves over which are most impactful. This creates empathy for what product managers have to do all the time--struggle with prioritization in the face of multiple goals and imperfect data. It also means they can't make everything "Priority #1," which in turn sets up a healthier future discussion: When, in two months, they complain that "you still haven't made feature X," you can point out that "X" was number seven on their list, so they themselves felt that it wasn't as important; you were just listening to their input. (Of course, everyone is allowed to change their mind; it's useful to run this process multiple times per year.) You're not promising to do everything they want, in the order they want. That's work-planning, and also this is just one list; you're going to do this with other groups too. And this list doesn't include the most important voice of all--the voice of the customer. You *are* promising that you will take this list seriously, because you genuinely value their input. That's more than most Sales teams get out of Product Managers. Now repeat for other groups. Sales Engineering has a view unique from Sales, more technical and more in tune with technical customers, often the users rather than the buyers. Support often knows the product better than anyone, and with the right tools and processes, has better data about which topics are causing the most problems or confusion. Remember that this isn't only about reducing work and costs for the Support team--although that's already a great result. It's about creating a better customer experience, because the customer who never needs to contact Support to get their work done, is a happier, more successful, more loyal customer.[^3] [^3]: There [are fascinating data](https://www.amazon.com/Effortless-Experience-Conquering-Battleground-Customer/dp/1591845815) supporting the claim that "needing Support less" is correlated with "customer loyalty." Engineering also needs their own list. There's always things to refactor, libraries to replace, spikes for new technology to try, replacement of a continuous-integration system, test automation we neglected before but desperately need now, systems that last year were adequate but now we've scaled past them, and so on. Their stuff is about operational excellence and a happy, fewer-bugs, higher-productivity environment, not about growth or the customer. Therefore, it's easy for these things to never be prioritized, which in the long run creates resentment and grinds development velocity to a halt. But also, engineers--like everyone else--have more ideas than time, so they too should be forced to prioritize. So... which of these lists is correct? There is truth in all, but none contain the complete picture, like the 2,500-year-old Buddhist allegory about [The Blind Men and The Elephant](https://www.sloww.co/blind-men-elephant/): ![Source: J. Himmelfarb (artist G. Renee Guzlas)](https://public.asmartbear.com/books/anthology/chapters/jit-backlogs/the-blind-men-and-the-elephant-simple.png) It's the [job of a Product Manager](https://longform.asmartbear.com/great-product-manager/) to consider all of these inputs and deduce the elephant. Keeping the inputs separately ordered helps the PM with this daunting endeavor. ### The customer These are internal groups; what about the customer, the star of the show! This is typically within the purview of Product Managers and Designers and UX Researchers, as opposed to internal stakeholders. For simple products, perhaps there's only one prioritized list representing our best idea of what's important to the customer, but products with non-overlapping areas of functionality or that serve multiple personas might have one list per persona or functional space. For example, you might have separate lists for the needs of small versus large companies, or first-time users versus power-users. Another common technique is to have a list per step in the customer journey, reflecting the fact that it's important for trial users to find success quickly, and also important for paying customers to continuously see value, and also important for the highest-revenue power-users to stay and grow. As with stakeholders, the motivation for the prioritizing items within each category are not comparable. There is plenty of prior art on this topic. This is essentially what is happening with [Story Mapping](https://www.jpattonassociates.com/the-new-backlog/) or [Opportunity Solution Trees](https://www.productplan.com/glossary/opportunity-solution-tree/) (Figure 2), where the customer-journey steps or the main opportunities each contain a list of potential work-items. Indeed, you could argue that Story Mapping is just exactly the idea expounded here, extending the concept of "independent lists" from only customer-focused topics, to topics from Sales, Support, and Engineering. ![Story Mapping diagram](https://public.asmartbear.com/books/anthology/chapters/jit-backlogs/d851cedafd54c4808058928d14d222422-simple.png) ### Alternate types of list If you don't like separating lists by function or by customer journey, here are other systems that some people like to use: #### Kano You could also create lists using the [Kano Model](https://foldingburritos.com/blog/kano-model). Imagine one list each for Delight, Performance, and Must-be. In general you don't want to starve any one of these categories, but it's difficult to prioritize items between categories. For example, it's easy to claim that everything in Must-be has to be implemented first; after all it's called Must-be for a reason! There's no point in bringing on new customers who won't be successful. That sounds reasonable, but it's simply not true that products should supply only basic requirements; indeed it's often the (unexpectedly) Delightful items that cause a customer to [stay _despite_ failings](https://longform.asmartbear.com/the-important-thing/). Having separate lists, none of which should be completely starved, helps to combat this problem. #### The "Four Lists" From Jyoti Bansal, [summarized by Adam Thornhill](https://podup.substack.com/p/reorganize-your-roadmap): 1. **Customer engagement.** Tuning into user feedback and desires. 2. **Sales intelligence.** Learning from the field to stay abreast of the competition. 3. **Technical debt.** The debt engineers must address to prevent performance issues. 4. **Vision for the future.** Where the product should head, expanding the addressable market. --- In all cases, all of the lists are important, in the sense that you cannot ignore any one of them forever. But you don't have capacity to move all the needles at once, and the items within each list are different-in-kind, and should be prioritized separately. ## Crossing the streams for work-planning Eventually, human beings need to do work. It makes sense to select work from these various streams and--yes--place the work in an ordered backlog. When you're actually doing work, you want a clear answer to "what's next?" without additional meetings and debate. ![never know who was first can tell you whos gonna be last](https://public.asmartbear.com/books/anthology/chapters/jit-backlogs/d7546print-simple.png) By waiting until the last second to compare incomparable things, prioritization remains organized and clear. By waiting until the last second to shift your mindset from "most important" to "planning work," you maximize clarity in the process of selecting and scheduling work. In software, when you wait until the last possible moment to do something, it's called JIT ("Just In Time"), hence the titular name of this technique. You still have the unenviable job of selecting which items from which streams are going to happen next (or "soon," in the case of a roadmap). But that was always going to be the case--there's always more work than time, so there's always a selection process. This technique keeps that process more organized than it would have been, and it allows you to grapple with work-planning puzzles separately from prioritization, which at least compartmentalizes the challenge. Divide-and-conquer is a good way to overcome complexity. > "We're obviously in no danger of arriving at consensus." > --Warren Buffett You're selecting from the top ideas at the company, not from the 1000 ideas everyone collectively has. It's a lot easier to pick the best from 12 instead of from 1000, and in some sense you can't go wrong--on average all 12 are probably decent ideas. And stakeholders will be happy. This last point is under-appreciated. This process is more explainable after-the-fact. Suppose it turns out Sales's first item is impossible to tackle right now, but you can knock out Sales's second item because, by the way, it's also Support's number four and fits nicely into a multi-month customer journey improvement project you have. That's a great message to send back. Or, a message like: We knocked out two of your top items recently; we've been starving the engineering list so we're going to hit a few of those in the next few months, then resurface. This process also helps keep yourself honest. You can easily report on: * How much of each kind of work are we doing? * What is the age of the top three items on each list? (i.e. Are we starving something?) * How much of this sprint is addressing someone's "top 3" thing? * How much work is related to internal projects, rather than visible to the customer? It also leads to better ideas: * Are there trends that everyone is seeing across all functions in the business, that therefore should inform our strategy? * Can we think of new product ideas that hit multiple top-three items simultaneously, and thus are especially impactful? Don't forget to [schedule rocks, then pebbles, then sand](https://longform.asmartbear.com/rocks-pebbles-sand/). That's an even more primary principle for work-planning. Separate prioritization streams help identify which rocks and pebbles should even be considered for scheduling. > "When we differ, Charlie usually ends the conversation by saying: "Warren, think it over and you'll agree with me because you're smart and I'm right." > --Warren Buffett on Charlie Munger

Rocks, Pebbles, Sand: How to implement in practice

Jason Cohen — Sun, 17 Jul 2022 00:00:00 +0000

You know the geology-in-a-jar lesson [from Stephen Covey](https://resources.franklincovey.com/the-8th-habit/big-rocks-stephen-r-covey): Schedule big things first, otherwise you run out of time (Figure 1 & Figure 2). ![If you do little things first, there's no time for big things.](https://public.asmartbear.com/books/anthology/chapters/rocks-pebbles-sand/sand-rocks-pebbles-bad-simple.png) ![If you do big things first, you can fit in smaller things.](https://public.asmartbear.com/books/anthology/chapters/rocks-pebbles-sand/rocks-pebbles-sand-good-simple.png) A common mistake is to think this applies only to the *size* of the work. That is, "Rocks" means "stuff that takes a few quarters," "Pebbles" means "a few sprints," and "Sand" means "less than a sprint." This misses the most important point of work-ordering: It's about maximizing *impact* by not allowing the easy or urgent things to crowd out the strategic things that take years to unfold but are more important than everything else combined. A thousand "quick wins" do not create durable advantages or fulfill a long-term vision. Another mistake is to think that the previous paragraph is the end of the story. "Schedule revenue-growth stuff, then maintenance updates, got it." No. Each type of work requires different prioritization frameworks, has different goals, and hide different traps that make you unwittingly ineffective. If you pretend these differences don't exist, your team will be working hard and delivering lots of code-commits--the appearance of "productivity"--but they'll feel like they're not making progress fast enough, competition will start catching up, and they'll (correctly) complain that they can't see how their work is connected to the strategy. The good news is: It does not take additional time to do it right. This is an instance of "smarter, not harder." You need the right frameworks. ## Three mindsets A tabular summary is trite, but it's a handy reference: | | Rocks | Pebbles | Sand | |:----|:---:|:---:|:---:| | Effort | ≥3 Months | 1-4 Sprints | ≤1 Sprint | | Maximize | Impact | ROI | Throughput | | Outlook | Long-term | Short-term | Immediate | | Scope | Strategic | Tactical | _any_ | | How Decide | [Deliberate](https://longform.asmartbear.com/maximized-decision/) | [Analytical](https://longform.asmartbear.com/roi-rubric/) | Intuitive | | Role of Exec | Decider | Observer | _none_ | | Role of PM | Driver | Decider | Decider
*(but engage devs)* | | Beware | insufficient impact | over-estimating
ROI | over-thinking /
over-planning | Now we'll justify and explain how to use this to maximum effect, each stone in turn. ## Rocks maximize Impact ![sisyphus treadmill working from home](https://public.asmartbear.com/books/anthology/chapters/rocks-pebbles-sand/d8717print-simple.png) #### Duration: 3-12 months Rocks take the most time; let's call it 3-6 months. Long projects are not only expensive, they're also most likely to over-run, because they're the most complex, contain the most unknowns, and have the most dependencies. So it's really 3-12 months. > **Hofstadter's Law** > It always takes longer than you expect, even when you take Hofstadter's Law into account. Well-oiled agile teams will point out they can hit earlier deadlines by adjusting scope and pushing less-urgent items past the deadline. That's wonderful for learning and customer-delight. But the leftover work still needs to be done, even if rearranged, so this doesn't change the magnitude of the effort required to achieve the full effect of the idea. And anyway, [there are good reasons](https://twitter.com/housecor/status/1552324251876593664?s=21) why we're consistently incapable of estimating big projects. A team might complete only one Rock in a year--certainly no more than three--especially because other work also needs to get done. If you can do only one big thing this year, that thing had better be extraordinary. #### Maximize impact A Rock must deliver dramatic, measurable impact, not merely "incremental improvement." It must be *strategic*, meaning that it must attack the [most important challenges you face](https://longform.asmartbear.com/strategic-planning/), materially advancing the company down its [unique path](https://longform.asmartbear.com/worse-but-unique/) for winning its corner of the market, leveraging existing advantages to reduce risk and to forge a path that others cannot easily follow, and build new [durable advantages](https://longform.asmartbear.com/leverage/). This is where teams most often fall short: Not delivering enough impact to justify their [investment](https://longform.asmartbear.com/investment/) of time. Sadly, big projects not only over-run on time, but also often under-deliver on impact.[^1] These sorts of [predictions are famously inaccurate](https://longform.asmartbear.com/predict-the-future/). So it's even more imperative that we demand an enormous impact: That way, if we under-achieve, it was still worth the time. Crucially, and perhaps controversially: **Do _not_ maximize ROI**.[^2] Your primary job is to execute your strategy to the fullest, spending the most-possible time on the most-impactful thing. If an idea is less impactful, yet also quicker to achieve, that is *not* the right choice. When things go worse than planned, that "less impact" turns into "incremental impact," and you cannot spend half a year on something so trivial. [^1]: Fortunately, on occasion they can over-deliver by an order of magnitude or two; this is always the *post hoc* story of a successful company, the founders shaking their heads saying they never believed it would be *this* successful. [^2]: ROI is "Return on Investment," computed as a measure of impact divided by a measure of effort, resulting in a measure of efficiency, i.e. "value per sprint." (Whatever "value" means.) #### Beware: "Maximizing impact" is harder than you think The most common problem is executing Rocks that aren't impactful enough. The Rock claims to "make a difference," but not *enough* difference, and after a few years, it feels like "we're not moving fast enough" or "why isn't revenue higher" even though the work from engineering is high-quality and stories are duly delivered every sprint. Hofstadter's Law applies not only to the time-estimate, but to the impact-estimate, and thus to the height of the bar that need to set: It must be higher than you think, even when you take Hofstadter's Law into account. Over-shooting is the antidote to Hofstadter's Law. Even if your ideas aren't good enough, you will be tempted to select the most impactful idea on the list and just do it. This is a mistake. It's such a common mistake, it is a cliché: ["Good" is the enemy of "Great."](https://www.goodreads.com/quotes/701885-good-is-the-enemy-of-great-and-that-is-one) You have to [face the truth](https://longform.asmartbear.com/failure-to-face-the-truth/): Your biggest problem is a lack of a truly great idea, and you must solve *that* rather than embarking on a long, misguided journey. The team can do Pebbles and Sand in the meantime, thus staying productive, while also helping create and validate better ideas. #### Deliberative decision-making process Because you're committing so much of the team's life,[^3] and because you have to be so confident that the Rock is strategic and impactful, you need to spend time on this decision up-front. This is not an "agile" decision, it's a strategic one. Once the direction is set, the big picture is clear, the mountain you want to climb is identified, *then* it is ideal to be "agile" in how you climb it. Execution details are never certain; backtracking is necessary. But if you're climbing up the wrong mountain in the first place, being "agile" doesn't help; the result is a team self-managing themselves into a mediocre, unfulfilling result. Although most decisions should be fast, [sometimes they should be slow](https://longform.asmartbear.com/decisions-fast-slow/); Rocks should be slow, or at least deliberate. [^3]: Agilists argue that if you find yourself part-way through a failing project, you can just abort, because "that's agile." That's true, and that's smarter than plodding forward in a [sunk-cost](https://longform.asmartbear.com/sunk-costs/) refusal to face reality, but on a human level it is demoralizing and ruins trust to abruptly cancel a project a team has been laboring on for months, "because we're agile." Canceling is necessary, but not free, so you should act *as if* a Rock is a [one-way door](https://www.businessinsider.com/jeff-bezos-on-type-1-and-type-2-decisions-2016-4). **Use this framework to select the most impactful idea: [Binstack: Making a maximal multi-dimensional decision](https://longform.asmartbear.com/maximized-decision/).** This process enshrines "impact" as the highest priority, allows other dimensions to participate but neither confuse nor dominate the decision, and produces a pithy, clear explanation of the decision at the end. If you're not coming up with good-enough ideas in the first place, [try these prompts](https://longform.asmartbear.com/extreme-questions/). But also consider whether the real problem is that your strategy is too vague. #### Execs decide, but ideally PMs are in command Rocks materially advance the strategy, and executives[^4] ultimately own the strategy. So, ultimately the final decision of what Rock to execute rests with the executive. In practice, however, the PM should be in command of the strategy and the ideas, driving the discussion and the decision. Ideally[^5] the PM is actually [in command](https://longform.asmartbear.com/in-command/), and the executive is happy to play the role of coach and [Devil's Advocate](https://longform.asmartbear.com/productive-meeting-activities/) during the decision process, and to sign off on a well-thought-out proposal. [^4]: At a smaller company, this means whoever has the executive role, whoever is running all of Product, or R&D, or the CTO, or the CEO. [^5]: We won't cover workplace dynamics in this article, so suffice to say that this is what "healthy" looks like, and the further reality is from ideal, the more one or both parties needs to change. ## Sand maximizes Throughput Because Pebbles are the Goldilocks of work-items, it's instructive to leap over them and solve for the smallest items, establishing bookends around the middle-child. ![scissors rock lineup](https://public.asmartbear.com/books/anthology/chapters/rocks-pebbles-sand/d8798print-simple.png) #### Duration: ≤1 sprint Sand are items that don't need to be broken down. They're short, and typically "just need to be done" without discussion or ado. They can't take longer than a sprint; sometimes they take less than an hour. There are a million little things that are individually unmeasurable but that add up to a significant impact. Great user interfaces require a hundred tweaks to attain greatness. High-quality error-handling requires esoteric corner-case unit-tests. High performance is often the result of innumerable optimizations. High-quality code means fixing myriad little bugs, some of which customers never experienced. Continuous, low-risk tech-debt reduction requires a hundred small refactorings. Security patches and library upgrades are mandatory maintenance. Documentation tweaks are helpful, low-risk, and should be done continuously. Great writing in general requires myriad tweaks, not grand organizational "pivots;" most edits to this document change a single sentence. In bulk, Sand is mandatory for wonderful, high-quality software. When you use a piece of software and say, "Wow, this is really well-done," that's a result of Sand. While each grain is typically impossible to "measure," ignoring them means we will never attain greatness, even with the best strategy. #### Maximize throughput Sand has a material impact only when executed *en masse*, as a sort of opposite to "death by a thousand cuts." Therefore the goal is to maximize how many of them we fit into the interstitial spaces between the Rocks and Pebbles. The measure of success is throughput: How many we complete per sprint. We cannot--and should not--try to measure or prioritize "impact." It's too small to measure.[^6] It's enough to agree that fixing ten bugs this sprint is a great accomplishment, and that our customers and support techs will thank us for it. [^6]: It is conceivable that two days' work has a measurable impact on [some metric](https://longform.asmartbear.com/product-metrics/). When you happen across one of these four-leaf clovers, you obviously should do it, regardless of whether you consider it Sand (because it's fast) or a Pebble (because it has impact). The point is to grant yourself the grace for Sand to *not* have a measurable impact. Also, high-throughput is fulfilling and energizing for teams. It just feels good to cross lots of things off a list. Do not discount the importance of the *feeling* of productivity and usefulness. #### Beware: Administrative overhead destroys throughput Exactly because each grain of Sand takes little time, management processes will dramatically bloat the total time from conception to prioritization to completion. Administration is the biggest impediment to throughput, therefore PM's must ruthlessly fight against the natural urge to debate and arrange and carefully assess and estimate the work, instead of just going about the business of completing the work. If you're using the same processes to prioritize and define Sand as to define Pebbles or Rocks, the process is wrong. Add up the time you debate the merits, craft user stories, fill out the fields in JIRA, vote and prioritize into this sprint then re-prioritize into the next and the next, and assign and balance work across people. Don't forget to multiply meeting time by the number of people in the meeting. This can easily occupy more time than it takes to complete the item in the first place. Sand will often originate from engineering; notice how many of the examples above are internal requirements for great software development rather than customer-driven requests. In these cases, it's often a waste of time to perform the usual formalities like user-story-writing, because the engineers already know what to do, and why. You can observe this waste in senseless force-feeding of uninformative "proper product language" and template-filling, e.g. | | | |----:|:----| | Type: | Task | | Priority: | Moderate | | Source: | Engineering | | Estimate: | 1h | | Must-do by: | 2022-03-15 | | Story: | As a software developer I want to upgrade npm package `minimist` from version `1.2.2` to `1.2.3` so that I don't have a security vulnerability. | | Linked Goal: | Adhere to corporate security policies of applying security patches within thirty days of a known vulnerability of "low" severity and "low" risk. | | Risks: | Might break unit tests, might require refactoring around new APIs or behaviors, might delay other work scheduled in the sprint | | Testing Considerations: | Normal unit tests | | Acceptance Criteria: | Nothing changes after the upgrade | It can get much worse, all for something that could just be a one-liner to "upgrade `minimist` to at least `1.2.3` because of a security patch," and in practice the work will usually be done in a few minutes, changing one line in `package.json` and re-running the unit-tests. #### Prioritize with intuition and desire, not math and metrics Because Sand is largely not measurable, complex prioritization systems won't produce meaningful results. Fortunately, exactly because they don't take long to implement, it's typically not important when they're done, and indeed many never will get done because we always have more ideas than time. Therefore, this is a great opportunity to engage people's emotions, and go with things people *want* to do. Since delivered-value is near-nil, why not choose things people have energy for? This increases happiness, morale, and often quality. Because people naturally work harder and better on things they want to do, you get "productivity for free," which in turn increases throughput, which is the primary goal. #### Self-managed teams schedule their own Sand In keeping with the rule to minimize administrative overhead, teams should schedule Sand themselves, not debate prioritization with executives. If the management is constantly engaged in Sand-scheduling, something is amiss and needs to be corrected, and it's always the manager's fault. Perhaps the team is perfectly capable of scheduling Sand; in that case, the manager is micro-managing, which is the manager's fault. Perhaps the team really doesn't understand the customer, the market, the technology, or the work, and therefore truly is incapable of scheduling Sand; in that case, the manager has hired incorrectly or not created an environment where the team can understand these things, which is the manager's fault. Little requests and suggestions are normal; indeed, these will come from all over the organization. But if you're debating with spreadsheets and virtual-sticky-note-boards, it's gone too far. ## Pebbles maximize ROI Pebbles [are not a "balance"](https://longform.asmartbear.com/conflicting-choices/) between Rocks and Sand; they are their own creatures. Their time frame is definitionally constrained between that of Sand and Rocks; the more important difference is that while Rocks are strategic, with a view towards winning over the next few years, Pebbles are tactical wins that have an impact in the next few months, attacking the challenges you're facing right now, or a great feature idea you can surprise customers with sooner than they expect. ![start with some small pebbles](https://public.asmartbear.com/books/anthology/chapters/rocks-pebbles-sand/d8651print-simple.png) #### Duration: 1-4 sprints Pebbles take multiple stories and possibly sprints. Unlike Sand, they do need to have a measurable impact; you can't spend a month or two of the team's time and have nothing objective to show for it. It is difficult to craft great Pebbles, because impactful things have a tendency to explode in effort. Anything longer than four sprints invokes the Hofstadter problem of time and impact, and therefore must be analyzed and prioritized as a Rock. If a Pebble starts expanding, you have two choices: 1. Reduce the scope of the idea so it can be achieved in a smaller time frame, or 2. Move the item into the "Rocks" category, where it will be prioritized appropriately. In both cases, you often discover that the impact is no longer big enough.[^7] Either this means the team needs to get more creative in how to deliver more impact with less effort, or maybe this idea simply isn't a good-enough use of your time. [^7]: For (1), reducing scope might also reduce impact, in number of customers affected or in the magnitude of the effect. For (2) the impact might have been great for a one-month project, but too small when compared to other large projects. #### Maximize ROI If Rocks maximize strategic impact over the long-term, Pebbles maximize immediate impact in the short-term. Said another way: They are the "most effective use of the team's precious time." Pebbles maximize ROI: A measure of value, divided by a measure of effort, resulting in a metric of efficiency. Modest value won in a short time, or more value over more time, are both great uses of time. What you cannot do is deliver little value but still take a long time. #### Beware the surprisingly high impact of estimation error on ROI The Hofstadter problem is magnified with ROI calculations, so you have to be especially careful, especially with classic frameworks like rubrics. For example, consider a task that ends up producing 20% less impact and ended up taking 50% more time than expected--a common real-world result: | | Estimated | Actual | |:----|:---:|:---:| | Impact | 60 | 48 | | Effort | 4 | 6 | | **ROI:** | **15** | **8** | This item has *half* the ROI than we originally thought. This is an immense magnitude of error, swamping the signal you thought you computed. Many other items' ROI will fall within this range of error, telling us that the noise from the error exceeds the signal from analysis. Which means the rubric is useless. **To avoid this issue, [use this framework for performing an ROI analysis](https://longform.asmartbear.com/roi-rubric/).** #### Product Managers decide Pebbles You could argue that [the Product Manager's most important job](https://longform.asmartbear.com/great-product-manager/) is to make decisions exactly like this one: Which Pebble will we tackle next? Ultimately it's a judgment call: A synthesis of what customers need most, what competitors are doing, what is consistent with the strategy, what is best for company metrics, and what is best for customer delight. Input from many directions is appreciated, but one mind needs to make the call. That should be the mind closest to the customers, to the product, to the competitors, and to the market, not a drive-by decision from a manager, and not a hostage negotiation with engineers who would rather rewrite a whole module from scratch. That said, committing a few months of time to anything is a big decision, which means it should have a sensible justification, and some objective measure of impact so we can see whether this activity is having the desired effect. With "self-managed teams" comes not only the freedom to decide and act, but the responsibility to own the results. ## A simple sprint-planning system How can you put all this together in practice, in real sprint-planning? Schedule things in this order, skipping one if there is insufficient capacity to make significant progress on it given the other items in the list, or if high-quality stories aren't ready-to-work: 1. Time-critical items, regardless of size. *(Examples: security patches, bugs actively impacting customers, critical work for a launch or other event with an externally-imposed, immovable date)* 2. One or more stories from the current Rock. 3. One or more stories from the current Pebble. 4. Sand. Life is never as simple as that, so here's how to manage the common issues: #### "Time-Critical" takes up so much time, we can't make progress on Rocks and Pebbles You have a meta-problem: Your team doesn't have enough time to be effective; solving *this* is now your top priority. This problem is even more important than your Rock, because in this condition you won't actually complete any Rock. There are myriad causes, and maybe multiple simultaneously: Is it a problem of individual productivity, of the team owning too many things, of architectural dependencies, of the problem-domain requiring more people, of lacking specialized skill sets, of greater fortitude of saying "no" to certain requests, or what? You must diagnose and cure the disease. Schedule sprint time to work on the solution. #### Starving the Rock Just one story per sprint will cause too much context-switching, and take too much calendar-time. If you're constantly starving your most strategic item, this is an impediment that the PM needs to address with the team. You're probably falling prey to the [Eisenhower Matrix](https://www.productplan.com/glossary/eisenhower-matrix/) fallacy of working on things that are urgent, rather than things that are important. Maybe you need to pause your Pebble for a while? #### More than one Pebble If you're truly going to execute on all four sections, you don't have time for two Pebbles at once. Plus, the context-switching is worse for morale and for productivity. The exception is when a Pebble is essentially complete, or will be blocked for at least a week, and therefore you truly do have the time to work on something else. #### Always the Rock, never the Pebble Because Sand is definitionally small, a few of those items always fit. But, a significant story for a Pebble may not. What if Pebble stories *never* fit? Consider that this might not actually be a problem, if the Rock is so valuable. Declaring the Rock "the theme of the next four months" might be exactly what the team needs for focus and maximum impact. It could be that once the Rock is "done" (in the sense of "first complete version"), you can then tackle a Pebble and then another, just doing incremental (small) updates to the Rock as you continue to learn and evolve its result, rather than immediately tackling an entirely new Rock. #### Zero Sand Since it's last on the list, and often aren't even full stories, it's easy to just never do Sand, but this will result in a poor product and unhappy engineers (as much of their internal work falls under this category). Consider toning down stories from other areas, maybe pausing the Rock or Pebble for one sprint, or even having a sprint devoted only to Sand, as a fun way to get a ton accomplished in a short amount of time and to break up the monotony of the sprint-planning cycle. #### Starting a new Rock or Pebble too soon, rather than creating more quality and value from the ones that just "completed" With long lists of genuinely terrific Rocks and Pebbles, it's tempting to start a new one as soon as the current one is complete. But software rarely works that way. Between learning how customers actually use (or don't use) things in the field, completing small items that were originally deferred (so that we shipped sooner and started learning sooner and started selling the feature sooner), and both incremental and significant follow-on functionality, often you need to keep the Rock and Pebble around longer than it first seemed, or at least not start a new one quite yet. This is realistic for great software and a healthy, sustainable pace of work, and this is another reason why our "time estimates" on both Rocks and Pebbles are subject to Hofstadter's Law, and further justifies our draconian admonitions about identifying and prioritizing that work. #### Trying to "balance" every sprint It's not important that every sprint is perfectly balanced between all types of work. It *is* important that we're balanced over a period several months, otherwise something important is getting starved. Indeed, it's often wise to build imbalanced sprints intentionally, because that means greater focus, less context-switching, and therefore getting more quality work done. Of course all this is easy to say, but hard to execute. Still, when we write it down as simply as possible, and try to honor it, we'll make better decisions sprint by sprint, which in turn creates the most impact year by year.

Binstack: Making a maximal multi-dimensional decision

Jason Cohen — Sat, 02 Jul 2022 00:00:00 +0000

![do it dont do it we dont care anymore](https://public.asmartbear.com/books/anthology/chapters/maximized-decision/d6618print-simple.png) Many decisions in life and business are instances of "multi-dimensional maximization," in which we wish to pick the "single best" among a set of choices, but we're confounded because each choice is variously better or worse along different dimensions. Examples: * Which major feature should we spend the next six months building?
*(P would generate revenue, but Q would reduce cancellations, but R would save us money)* * Which candidate should we hire?
*(P has the best skills, but Q has more experience in our market, but R seems like the best culture-fit)* * Which new marketing campaign should we spend thousands of dollars to test?
*(P is cheaper to try, but Q has a larger reach, but R is targeted at our industry)* Not only do you need the best decision, you also need to be able to **explain your decision to others**, especially to those who wish the decision had gone a different way. Do not under-value the importance of crisp explanation. The "rubric" is the typical framework for these decisions; a separate paper on this site explains how to use one effectively for ["ROI-style" decisions](https://longform.asmartbear.com/roi-rubric/).[^1] Unfortunately, while it may feel productive to fill many cells with many numbers, and while it may feel analytically rigorous to convolute those numbers into a final score, this fails to clearly identify the best choice, and fails to create a clear explanation for the choice. After demonstrating and analyzing the causes of this failure, I present an alternative framework I've nicknamed "Binstack."[^2] [^1]: The goal of ROI is to maximize efficiency, i.e. deliver the most amount of value per unit of time. This paper asks a different question: How to decide the single most valuable thing, regardless of cost, with incommensurate and [conflicting dimensions](https://longform.asmartbear.com/conflicting-choices/) [^2]: Additional resource: [Adam Waselnuk](https://twitter.com/awazels) created a [Notion Process Template for Binstack](https://adamwaselnuk.notion.site/Binstack-Make-tough-decisions-b3a80f7c06a44d77b180d7bd252bbfb1). ## Why rubrics don't add up Consider two players in a game, with attributes: | Attribute | Player P | Player Q | |:----|:---:|:---:| | Health | 8 | 5 | | Strength | 5 | 7 | | Speed | 2 | 6 | | Endurance | 5 | 2 | Which player is better? If one scored higher than the other in every dimension, the choice would be simple. In this case, each player is better than the other along two dimensions, and worse along two; objectively there's no clear winner. So let's try a rubric. In its simplest form, we add up the scores contributed by each dimension, and the total score decides the winner. Unfortunately, this doesn't tell us which one is better: | Attribute | Player P | Player Q | |:----|:---:|:---:| | Health | 8 | 5 | | Strength | 5 | 7 | | Speed | 2 | 6 | | Endurance | 5 | 2 | | **Total:** | **20** | **20** | Games often engineer this result on purpose, to create players that are different but not over-powered. This creates a balanced game, but to make a confident decision about which one is "best," we need something imbalanced. Real-life rubrics often look like this example: a pile of options that share a similar score, resulting in no clear winner. Even if we make a decision, we can't explain the decision to others, because in actual fact it's a tie, and the tie was broken arbitrarily. That's no way to make a decision. To create separation, people often add "weights" to the raw value to create a new kind of "score." In this case, suppose that with our game-playing style we don't care much about Speed, but we can really leverage Strength. So we assign weights which we multiply against the scores, to compute a customized metric of "value." | Attribute | Orig P | Orig Q | Wt | Wted P | Wted Q | |:----|:---:|:---:|:---:|:---:|:---:| | Health | 8 | 5 | 1.0 | 8 | 5 | | Strength | 5 | 7 | 2.0 | 10 | 14 | | Speed | 2 | 6 | 0.5 | 1 | 3 | | Endurance | 5 | 2 | 1.0 | 5 | 2 | | **Total:** | 20 | 20 | | **24** | **24** | That didn't help. What if we contrive to *force* Q to be better, by intentionally using weights that penalize the two attributes where P is superior? | Attribute | Orig P | Orig Q | Wt | Wted P | Wted Q | |:----|:---:|:---:|:---:|:---:|:---:| | Health | 8 | 5 | 0.5 | 4 | 2.5 | | Strength | 5 | 7 | 2.0 | 10 | 14 | | Speed | 2 | 6 | 0.5 | 1 | 3 | | Endurance | 5 | 2 | 0.5 | 2.5 | 1 | | **Total:** | 20 | 20 | | **17.5** | **20.5** | Even with a conspiracy to throw the election for Q, it wins by a mere 2.5 points out of 20--hardly a resounding victory that would give everyone confidence in the decision. This happens in the real world. Even with weights, a clear winner often does not emerge, and again we're back to a weak, indefensible decision. Worse: In the real world we rarely have precise scores. Attributes like "potential new revenue" and "increased customer satisfaction" [are not predictable](https://longform.asmartbear.com/predict-the-future/) with accuracy. For more qualitative measures we use scales "from 1 to 5" which are even less precise. This imprecision creates inherent error, which is then compounded by multiplying weights. Differences in the final results are more error than signal. Worse again: The attributes aren't comparable. Whatever the unit of measure for "Health," it isn't related to the unit of measure for "Speed." By adding them together, we're implicitly saying "these are comparable," but they are not. Weights are supposed to solve this by converting everything into some sort of "value," but if you say out loud what the weights are doing, it sounds incorrect. For example: "Every $150k of additional ARR is exactly as valuable to us as 10% more customer satisfaction. Indeed, we would be fine with customer satisfaction going down 10% if we added $300k in new ARR." Really? It's largely noise, which is why we're unhappy with the so-called "winner." ## Solution: Binstack: Stack-ranked binary attributes To transcend the noise, stop pretending that the values in the rows are precise or comparable. A general rule of complex decisions, is that often they're difficult because we've avoided making other, smaller decisions. That is the case with the rubric, as we will now fix. Our top-level purpose is to pick the item that maximizes impact, and to be able to explain our decision. First we're going to make smaller decisions about the true impact of each item, and then we're going to make a decision about which impacts are most important. ### Binary materiality No more values, no more weights, no more scores. Either an item *materially* contributes to that attribute, or it doesn't. "Materially" means the effect is so large you can measure it easily: * Not just "more revenue," but at least a 10% bump so that the curve visibly changes. * Not just "more retention," but a 20% decrease in cancellations tied to a specific cause. * Not just "more intuitive," but a 40% decrease in support tickets on a certain topic. * Not just "more competitive," but sales will add it to their standard presentation and marketing will add it to the feature-table on the pricing page. * Not just "more profitable," but overall gross profit margin will improve by 1%. * Not just "will pay for it," but putting it in a higher pricing tier or add-on will cause 5% of customers to upgrade. * Not just "better UX," but a 50% increase the success-rate for people moving through the interface. * Not just "widely used," but 40% of customers surveyed scored at least 4 out of 5 on whether they'd use the feature. * Not just "customer satisfaction," but moving from a 4/10 to a 8/10 on a survey related to this area of the product. * Not just "thought leadership," but marketing commits to getting ten external articles to reference it in the next quarter. Force people to write down exactly what the material change is expected to be. Not because the estimates are accurate, but because it forces the person to think through the answer. Most ideas, we'll eventually admit, are so incremental that we won't be able to measure the effect; that means it is "not material." That's a tough [fact to face](https://longform.asmartbear.com/failure-to-face-the-truth/), but remember the point of the exercise is to force exactly these conclusions, to drastically reduce the field of ideas so that only the actually-best ideas remain. These smaller decisions make the larger decision tractable. Because this "material change" is just a guess, we won't put it in an equation--no computing with noise! But if you can't justify a magnitude greater than "business as usual," the idea is simply not impactful enough. Your standards are higher than that. In our example, if we simplistically considered any score that is "6 or greater" to be "material," we'd already have a winner: | Attribute | Player P | Player Q | |:----|:---:|:---:| | Health | ✓ | | | Strength | | ✓ | | Speed | | ✓ | | Endurance | | | | **Total:** | **1** | **2** | Each "point" in this method is meaningful, so even a difference of 1 point crowns a clear winner. With real-world attributes, and a sufficiently high bar, you will reject nearly all items quickly. People won't like that--their favorite thing will be cut--but it's the only way to stop wasting your time dithering between a pile of things that won't make a difference. It's also extremely easy to explain your decision: Q materially impacts two important things; everything else is less impactful. ![rest assured however i vote it will be staunchly](https://public.asmartbear.com/books/anthology/chapters/maximized-decision/d8066print-simple.png) It is, of course, still common to have ties. Indeed, if in our example we considered anything "5 or greater" to be material, it's back to a tie:[^3] [^3]: Although in this case perhaps the problem is that our standards for "materiality" are too low, as opposed to the options being too good. | Attribute | Player P | Player Q | |:----|:---:|:---:| | Health | ✓ | ✓ | | Strength | ✓ | ✓ | | Speed | | ✓ | | Endurance | ✓ | | | **Total:** | **3** | **3** | To address this, we need one more rule. ### Stack-ranked attributes Back to the top: (1) We're trying to isolate the *one* thing that would be *most* impactful, and (2) complex decisions feel impossible because of a lack of smaller decisions. We've made some decisions already, but we need a few more to isolate the one winner. We have to decide which attributes are most important. Currently we are treating all attributes as equally important--a check mark next to "Endurance" is equal to a check mark next to "Health," but is that really true? With a standard rubric, the fact that all attributes are not equally important drove us to reach for "weights." But that computation confounded us with noise. Instead, we simply order attributes by importance, in a single ranked list. No numbers. "Simply order them" is easy to say but not easy to accomplish, because people get stuck in circular debates: > "Growth is more important than profit, because it's possible to optimize our costs later." > > "Yeah, but if we're unprofitable on a unit basis we'll cause a cash-crunch, so we have to be profitable first." > > "Yeah, but if it's only about profit, the best thing to do is just 10x prices, and whichever customers stay are super profitable, but that would be wrong." > > "Yeah, but if it's only about revenue, the best thing to do is to sell $1 bills for $0.80, and that would be wrong." This conflict highlights the "smaller decisions" that still need to be made. Both sides are correct in saying it's bad to maximize one thing with no regard to any consequences. But surely you've already ensured your list contains nothing outright absurd.[^4] So these *reductio ad absurdum* arguments are moot and can be ignored. Assume (and enforce that) the ideas are sensible, then decide what outcome is most important. [^4]: If you have trouble ensuring that items are meeting basic standards, create criteria for an idea making it onto the list to begin with. Examples: it can't be less unit-profitable than some pre-determined target, it can't take longer than N sprints to execute, and it can't require significant retraining of the support team. A typical ordering for a VC-backed B2B company, optimizing for "growth is paramount, because if growth is there, we'll be able to raise more money," could be: 1. Revenue growth (i.e. "the single biggest driver of equity-value") 2. Number-of-customers growth (i.e. "market share") 3. Product experience (i.e. "customers love of the product") 4. Support cost (i.e. "a cost; more importantly, a measure of usability") 5. Infrastructure cost (i.e. "a cost; should organically improve with scale") 6. Net-profit expansion (i.e. "profitable business model") A bootstrapped company designed to create wealth for its founders and employees, while being a place where employees genuinely love coming to work and customers genuinely love the product, might be: 1. Cash-basis profit expansion (i.e. "wealth creation" + "mandatory to keep the business alive") 2. Product experience (i.e. "the reason we get up in the morning is building a great product people love") 3. Fun (i.e. "I built this business to be the place I *want* to work for") 4. Number-of-customers growth (i.e. "stagnation is the prelude to death") 5. Minimizing number of employees (i.e. "we joined a small company to avoid bureaucracy") These are generic attributes; "more revenue" could include almost anything. It's better if your lists are more specific, based on current circumstances, or focused on a subset of the strategy. For example, suppose a product that targets mid-sized restaurant chains is having trouble with customer retention. A better list might be: 1. Dramatic increase in usability
*The #1 reason customers give us when they cancel in their first year, is that training their employees is too difficult, so the software never gets used.* 2. Reduce costs
*Price is the #1 reason that long-tenured customers give when they switch to a competitor; reducing costs means we can reduce prices while generating the same profit.* 3. Increase market-differentiation
*If there were features that customers couldn't get anywhere else, they would stay despite (1) and (2).* Note how obvious corporate goals like "grow revenue" and "happy customers" are embedded in these goals, but insights or data produce more specific immediate goals. This will cause even better ideas to be selected, and will help the team brainstorm better ideas in the first place. ### Binstack: The final process You've finally made all the "small decisions" that make the big decision clear. With your stack-ranked attributes and binary scores of which items materially affect which attributes, here's what you do: 1. Cross out items that don't materially address the top-ranked attribute. 2. For the second attribute... 1. If no tasks address it, move on to the next attribute. 2. If exactly one remaining task addresses it, that's the winner; you're finished. 3. If multiple remaining tasks affect it, cross out all the others and continue on to the next attribute. 3. Repeat step (2) for the third attribute, fourth, etc. These steps honor our smaller decisions about which results are most important to manifest (ordered attributes), and what these options *really* accomplish for us (binary materiality). It also ensures that we'll materially affect our #1 attribute; even if another idea moves several other needles, we still have to honor our "small decision" about what is of paramount importance. The final decision is trivial to explain. It goes something like this: > We decided the most important things we have to accomplish in the next few months are to grow top-line revenue and create defensible technology. Item P does both of these things; none of our other choices did. Or defending why you didn't pick some other item: > Item Q is a solid idea; indeed it would both increase profitability and increase our differentiation in the market. However, the most important thing right now is to grow revenue, and item P accomplishes that whereas item Q doesn't. However, in future, if we change our priorities, or complete item P, item Q will be wonderful to consider! ![golf pencil no eraser decisive](https://public.asmartbear.com/books/anthology/chapters/maximized-decision/d8950print-simple.png) ## Real-life errata ### "Effort" conspicuously absent Many rubrics are set up as "ROI" calculators, i.e. measuring impact relative to the cost of achieving that impact. This often results in selecting less-impactful items, because they are cost-effective to implement. Sometimes that's the right choice, but Binstack is about selecting only for maximum impact, not cost-efficiency. If you actually want to [maximize ROI, use this method](https://longform.asmartbear.com/roi-rubric/). And [here's how you decide](https://longform.asmartbear.com/rocks-pebbles-sand/) which method is right for your current decision. ### Fun is underrated. Add it in. It's still possible to tie. If you have lots of items left over, perhaps your materiality threshold isn't high enough; raise it to thin the herd. Supposing you have two ideas that are truly indistinguishable, you could flip a coin. I don't recommend that, because you can't explain your choice. A person passionate about the choice you rejected would be upset to hear you ruled against them so flippantly. Instead, pick whichever item the folks doing the work *want* to do. Do what's fun. It still sounds flippant. What business does "fun" have in business? When people work on something fun, they work harder and better while enjoying themselves more--more productivity yet more happiness. Do not dismiss this life-hack. Of course we cannot do what's fun at the expense of what *needs* to be done, but when those two things are not in conflict, why would you not round off in favor of fun? If you like this idea, take it further: Put "fun" in the attribute stack-rank, and rank it high. Even second position is not ridiculous. Knock that #1 priority out of the park while having fun. What's wrong with that? ### Evolving stack-rankings, and different stack-rankings per team You should expect the stack-ranking to change over time, even rapidly. In the early days of a company, just getting any customers is hard, at any price, so that might be much more important than revenue or profit. A mature company who reliably gets customers in the door might be more interested in expanding efficiency or profitability, not because growth is unimportant, but because it's so systematic that it is no longer an existential threat, and other things are more pressing. If you have multiple teams, and therefore the time to execute multiple items, you might want separate lists for different goals. For example, you might say, "We want one initiative that will materially increase profit, one that materially increases our internal effectiveness or efficiency, and all the rest should maximize growth." Each of those would be the number-one item in its own stack-ranked list, and the attributes below that might be copied from the company-wide general list. In all cases, remember to hone the attributes with more specificity, to generate better ideas. > "I've generally found that the best product ideas live at the intersection of "duh" and "holy shit." > --[Aaron Levie](https://twitter.com/levie/status/1313236118117076993?s=12) ### What if nothing is left? Sometimes we're so harsh with our materiality threshold that none of our ideas meet our exacting standards. What does that mean? It means your ideas aren't good enough. It means your problem wasn't one of prioritization after all, but rather of not having ideas worth prioritizing. Focus the team on this new, more pressing problem: To generate better ideas. [Here's some help with generating better ideas](https://longform.asmartbear.com/extreme-questions/). But this will take time! Fine, put it in the sprint. But we need a plan right now! Too bad; it's better to take a month to find a wonderful thing to spend the rest of the year on, than to plod along doing things that aren't valuable enough. Do [high-ROI](https://longform.asmartbear.com/roi-rubric/) small projects in the meantime. ### Why bother scoring everything when most will be rejected immediately? Indeed, you needn't bother. That saves time. However, remember that an important aspect of decision-making is explaining your decision. Often, explaining "why not Q" is just as important as explaining "why P." Other people will want to know that you seriously considered other options. Most importantly, there are people who really wish you had selected Q. Maybe Q was even their idea. The rejection will be easier to accept if Q was seriously and genuinely considered. Perhaps, by being invited into the decision process, the person who invented Q will come to the right conclusion on their own. This is important; do not underestimate the human--and humane--part of the process. ### Reductive Binstack can feel reductive--over-simplified, ignoring the reality of a complex world, therefore resulting in an incorrect conclusion. It is true that for complex problems like foreign policy, national economics, and climate change, a reductive approach is invalid. But for finding the right feature to build, or the right marketing campaign to launch, or the right bug-tracking software to adopt, or the right database to use for a new project, or the best candidate to hire, being (intelligently) reductive is how you transcend the noise, arrive at a clear decision, and explain it to others. This isn't foreign policy, it's a feature list. Make an impact with Binstack, and be happy!

Fermi ROI: Fixing the ROI rubric

Jason Cohen — Mon, 20 Jun 2022 00:00:00 +0000

![traded for some big data two spreadsheets and an algorithm](https://public.asmartbear.com/books/anthology/chapters/roi-rubric/d6857print-simple.png) "Delivering maximum value in minimum time" sounds wise, but it's not exactly a stunning insight. It's not like we're *trying* to take an eternity to deliver trivialities. It is more analytical--but no more insightful--to insist on "Maximizing ROI." Return-On-Investment is some measure of "value," divided by some measure of "time," yielding some measure of "productivity." With good intentions and this reasonable-sounding goal, we reach for the rubric: A spreadsheet of inputs of numeric "value" and "time," a calculation of the metric of productivity, and an output where the best ideas are sorted to the top. ## Invisible failures of the rubric Unfortunately, the rubric fails us for several reasons. The failures are not obvious, so we don't realize they're happening. Explaining the traps will lead to a framework that avoids the traps. ### "ROI" contains more noise than signal Consider a feature that ended up producing 20% less impact and taking 50% more time than originally expected--a common outcome in the real world: | | Impact | Effort | ROI | |:----|:---:|:---:|:---:| | Estimated | 60 | 4 | 15 | | Actual | 48 | 6 | 8 | In this reasonable scenario, it ended up producing *half* the ROI than originally calculated. Therefore, in retrospect, the initial ROI estimate was largely noise. Apply this observation to a typical ROI rubric: | | Impact | Effort | ROI | |:----|:---:|:---:|:---:| | Feature A | 34 | 2 | 17 | | Feature B | 60 | 4 | 15 | | Feature C | 36 | 3 | 12 | | Feature D | 10 | 1 | 10 | The spreadsheet suggests (A) is the winner, and in particular that (A) is clearly better than (D). But neither of those statements are true. If (A) could easily result in half its stated ROI, there's no objective justification to claim its superiority over (D), or over any other item. Shocked at this revelation, we could react by ignoring the lesson. We could justify (A)'s supremacy by telling ourselves "estimation errors cancel themselves out." But the errors do not cancel out. Effort is almost always under-estimated; 4-sprint projects that stretch into 6 sprints are not "cancelled out" by an equal number of 4-sprint projects that are completely finished in 2 sprints. ### "Impact" is ill-defined No matter how you measure impact, your numbers end up far less precise than they appear in the spreadsheet. [It's difficult to predict](https://longform.asmartbear.com/predict-the-future/) numbers like "revenue-increase due to feature X." Teams use various techniques to address the uncertainty, but in every case the number in the spreadsheet contains significant error: * *Revenue we would get from existing customers currently asking for this feature.*
That's precise but often uncorrelated with how much money we *will* make from it in the long run, so it's not an accurate measure of impact.
* *Additional sales we would make in the next year if we had this feature.*
This number is low-confidence, high-error. Even in hindsight it's hard to attribute revenue from a single customer to a single feature; if it's unmeasurable *post hoc*, surely it's unpredictable *ex ante*.
* *Use a relative scale, e.g. "1-5," calibrated by previously-created features.*
Defined as "1 is like feature X, 5 is like feature Y." Not only is this a wild guess, different people will interpret the scale differently, yet answers differing by a single point generates a large variance in computed ROI. It's even worse when want to combine [multiple metrics of impact](https://longform.asmartbear.com/product-metrics/), e.g. revenue, unit-profitability, and retention. Each contains intrinsic error, then a mathematical combination compounds the error, then the ROI calculation expands the error yet again. You can't tell from glancing at the spreadsheet, because input numbers appear typical and output numbers boast many digits after the decimal place. But those digits aren't indicative of precision. They're a random number generator. ## The Fermi Solution These traps dictate requirements for a solution. We need scores containing dramatically more signal than noise. The scores must be well-defined, which means their definition and estimation should be obvious and uncontroversial. ### Only Fermi estimations allowed The first full-scale nuclear bomb was detonated at 5:29am, July 16, 1945, in the New Mexican desert of the United States. The physicists who invented it were huddled in a truck behind a plate of welder's glass to reduce the radiation to non-lethal levels. The physicists were already causing trouble. Future Nobel Prize-winner Richard Feynman inexplicably decided to observe the blast without eye protection, causing frightening but ultimately temporary blindness. Current Nobel Prize-winner Enrico Fermi had taken bets with military guards about how much of the atmosphere would ignite, and whether it would incinerate the entire state or the entire world; some of the guards asked to be excused from the base, angering the project director. !["Enrico Fermi" by Argonne National Laboratory is licensed under CC BY-NC-SA 2.0.](https://public.asmartbear.com/books/anthology/chapters/roi-rubric/d5039459604-6621d3eaac-b-simple.png) Fermi was also interested in the amount of energy released by the blast--one of the main goals of the test. Not wanting to wait for official analysis, he made his own estimate on the spot, using a technique that now bears his name, and that we will use to fix our rubric: > About 40 seconds after the explosion the air blast reached me. I tried to estimate its strength by dropping from about six feet small pieces of paper before, during, and after the passage of the blast wave. Since, at the time, there was no wind I could observe very distinctly and actually measure the displacement of the pieces of paper that were in the process of falling while the blast was passing. The shift was about 2 1/2 meters, which, at the time, I estimated to correspond to the blast that would be produced by 10,000 tons of TNT. > > --Enrico Fermi, [Top Secret interview](https://www.dannen.com/decision/fermi.html) July 16, 1945, declassified in 1965 The official estimate of the energy output of the blast was 21,000 tons of TNT. Fermi's estimate was surprisingly accurate given such inaccurate input data and quick, simple, mental calculations. How did he do it? The trick--useful everywhere in life--is to estimate values using only orders-of-magnitude, a.k.a. powers-of-ten. No "low/high ranges," no precision, not even any digits other than a 1 followed by a quantity of 0s. It sounds far too imprecise to be practical, and yet Fermi's bits of paper demonstrate that it just might work. ![gas pumps in front of prophecy gas read fulfilling and self fulfilling](https://public.asmartbear.com/books/anthology/chapters/roi-rubric/d5206print-simple.png) Joel Spolsky [famously](https://www.cse.unr.edu/~sushil/researchAdvice/GIV1.html) loved an interview question built for Fermi estimation: How many gas stations are there in Los Angeles County? "I don't know," although accurate, fails the interview. Fermi estimation, succeeds. * There are 10 million people in the LA area _(or at least, more than 1 million and less than 100 million, so by the rule of "only powers-of-ten," our number is 10 million. The actual number in LA County happens to be [10 million](https://en.wikipedia.org/wiki/Los_Angeles_County,_California))_. * 1 car per person _(or at least, not 10 and not 0.1. The actual number is [0.54](https://la.streetsblog.org/2010/12/13/density-car-ownership-and-what-it-means-for-the-future-of-los-angeles/))_. * A person refills 1 time per month *(the actual number is closer to 2.5/month, but it's not 10 and not 0.1)*. * A gas station handles 10,000 refills per month *(200 per day yields 6000 per month; it's not 1000 nor 100,000)*. * With 10 million refills per month (10 million cars with 1 refill per month), divided by 10,000 refills per station, 1000 gas stations are required. Despite being wildly inaccurate in detail, the end result of 1000 gas stations is indeed the nearest order-of-magnitude to the actual number of [600](https://lastfiascorun.com/brazil/how-many-gas-stations-in-los-angeles.html). Surprisingly accurate, considering we arrived at an answer in a minute or two, without looking up a single number. What would happen if we used only Fermi estimates in our ROI calculator? An immediate result is that most numbers are trivial to estimate. Note how easy it was to decide the values in the gas station example, because the two adjacent power-of-ten choices were definitely wrong. It's nice to be so confident! Relish that. Another result is that we've satisfied one requirement generated from the failures of the rubric: Scores are either identical or *very* different, causing calculation error to be small in comparison. The values are largely obvious and uncontroversial, if only because our artificial constraint on their values makes the adjacent choices absurd. This is progress. Sometimes the numbers are still controversial, e.g. *Could this product produce $1M in the next few years, or actually $10M because it would dramatically increase both retention and our ability to win new deals?* But this debate is a wonderful use of our time, because it means people have wildly different assumptions, or different levels of optimism, or a different understanding of the customer, the market, or the idea. So another constructive result of Fermi estimation is that we spend our debate-time on strategic-level discussions, and no time on areas where we disagree in small degrees that are in any case overwhelmed by typical estimation-error. In exchange for this progress, we lose precision. And admittedly, sometimes we *do* possess precise input data. For example, maybe we could look up the fact that the average number of cars per person is `0.54`, and use that figure instead of `1`. Surely it's wise to retain precision whenever possible? Yes, but only if you recognize that, after doing math with other Fermi estimates, the end result is still not more accurate than the power-of-ten. That is, even using `0.54` instead of `1`, as soon as you combine it with your extremely inaccurate estimate of 10,000 refills-per-station, you still have to zero-out all the digits in the result except the first one. You must meticulously discard the noise; if you're diligent about that, your estimation is more accurate. Otherwise, maybe you should just enjoy the simplicity of the basic Fermi system. So we've satisfied the first requirement. What about the final remaining requirement, which is to clarify the definition of "impact?" ### Fermi Impact: Quantitive edition Fermi estimation solves the puzzle of how to estimate the revenue impact of a sizable feature. We can pick any definition we want, but limit ourselves to Fermi estimation, and suddenly it becomes easy, or at least a simpler discussion. Any of these definitions could work: * This feature will increase revenue by: $1k/mo, $10k/mo, $100k/mo * This feature will increase new customers/month by: 1, 10, 100, 1000, 10,000 * The take-rate of this feature in our existing customer base would be: 1%, 10%, 50% * The number of customers who would actively use this feature would be: 0.1%, 1%, 10%, 50% We cheated a little in those last two, because "100%" is not a possibility. Still, a 5x separation is pretty good; the danger is when the difference is only 2x or 20%. This is still a much wider spread then, say, [Fibonacci estimation](https://en.wikipedia.org/wiki/Fibonacci_scale_(agile)). It's clear how this works with quantitative measures, but what about important things that are not numbers? ### Fermi Impact: Qualitative edition How you score things which aren't numbers like "brand development," "competitive advantage," or "customer delight?" The following is a real example from a few years ago, when WP Engine was launching a new product line called [Atlas](https://wpengine.com/atlas).[^1] We decided to build a thought-leadership presentation that would engage software developers on topics relevant to the new product line. We wanted it to be genuinely intriguing and useful. We brainstormed twenty-two topic ideas, all pretty good at first glance. How should we select just four or five topics, given that "intriguing" and "useful" are qualitative and subjective? [^1]: Tagline: The complete Headless WordPress Platform for absurdly fast dynamic sites. ![meeting person realizes hes only brought his c game](https://public.asmartbear.com/books/anthology/chapters/roi-rubric/d6039print-simple.png) First, we decided on our goal, even if impossible to measure, or not even a number. The primary goal was for **everyone to come away loving the content and interested in our software**. We broke this down into four sub-goals, where the overall goal would be achieved only to the extent that *all four* of the sub-goals were achieved: 1. Topic is widely applicable 2. Topic matters to real people 3. Topic is insightful (i.e. non-obvious to a typical practitioner) 4. Topic is relevant to specific features or capabilities in Atlas (i.e. self-promotion) The next step was to convert these concepts into something we could "score," i.e. specific questions that a person could contemplate and debate with another person. Otherwise, two people will have different ideas of what each goal means. The answers still won't be *numbers*, but at least the questions are specific: 1. How many customers care about this topic? 2. How *much* do *those* customers care? 3. How insightful is our perspective on this topic? 4. How powerfully or uniquely does Atlas pay off the insight? Because the answers aren't things you can measure with numbers and units, we had to use some sort of "1-5" scoring system or, to take the Fermi lesson, powers-of-ten numbers. The key is to *be specific* as to what each number *means*, otherwise two people will have different interpretations for a phrase like "Insight is a 2 out of 5." #### 1. How many customers care about this topic? | Fermi Value | Definition | |----:|:----| | `100` | Definitionally everyone | | `70` | Most | | `30` | Some | | `0` | I can think of one or two | | `0` | No one | #### 2. How much do those customers care? | Fermi Value | Definition | |----:|:----| | `1000` | Mission-critical to the success of their project | | `100` | "I care; this is a serious concern we've discussed internally" | | `10` | "I'm curious, tell me more" | | `1` | "Meh, whatever" | | `0` | "Don't waste my time with this" | #### 3. How insightful is our perspective on this topic? | Fermi Value | Definition | |----:|:----| | `1000` | "Wow!!! This changes everything." | | `100` | "I took notes, thanks, that's a great point." | | `10` | "Yup, OK, makes sense." | | `1` | "No shit, Sherlock." | | `0` | "No clue what you're even talking about." | #### 4. How powerfully or uniquely does Atlas pay off the insight? | Fermi Value | Definition | |----:|:----| | `1000` | "Whoa, I'll buy Atlas just for that alone!" | | `100` | "Ohhh, nice, OK I'll take a look." | | `10` | "That's fine." | | `1` | "So what?" | | `0` | "I actively do not want this." | The combination of Fermi values and specificity in both the questions (rather than broad concepts) and values (rather than "1-5") made it easy to score. Because "all four" scores were required to deliver on the original goal, multiplying the scores[^2] sorts the best to the top. [^2]: The mathematically-inclined reader might point out that because all values are powers-of-ten, the operation "multiply everything and sort" yields exactly the same result as "Number these 1-5, then add everything and sort," because the latter is just the logarithm of the former. Why, then, bother with Fermi values? (a) As in the first question, specific values (as opposed to strictly only powers-of-ten) might have a valid meaning, which creates different values from "1-5." This "breaks ties" in a meaningful way. (b) By emphasizing that the values are designed to be widely divergent, we generate better specific phrases, and more agreement in scoring each topic, and therefore hopefully a more accurate output. It worked; the presentation was routinely scored "5 out of 5" in post-event surveys,[^3] and the biggest complaint was a desire for even more detail. That is a "complaint" indicative of success: It means "I want to hear more from you," and therefore is further evidence that the choice of topics was spot-on. [^3]: Oh the irony of scoring the presentation 1-5 after everything we just said! ### Fermi time estimates We've explored how to estimate "impact" with Fermi-approved values, but what about time-estimates? Those also exhibit large errors. They, too, need the Fermi treatment. ![so where do you see yourself in ten minutes](https://public.asmartbear.com/books/anthology/chapters/roi-rubric/d4408print-simple.png) Nearly twenty years ago, before Scrum defeated rivals like Extreme Programming to become the *de facto* standard for waterfall-averse software developers, our engineering team at Smart Bear Software used a simplistic but effective method of estimating work. Our software was installed on-premises by customers (this was The Time Before SaaS), so "continuous deployment" was impossible. Instead, we made a few major releases per year, supplemented with minor bug-fix releases. We planned about four months of work at a time. We scrawled each idea on an index card, with only enough space for a title and a few bullets about scope and intent. We placed time estimates in the bottom-right corner, but the only three choices were: `2d`, `2w`, `2m`--two days, two weeks, or two months.[^4] Whatever you think the real estimate is, round up. [^4]: I hear you yelling "No! Story Points!" Hang in there, it's OK, this was long ago in the Third Age when the Elves still abided in Middle-Earth. Elves don't use story-points. The overwhelming majority of cards generated no disagreement over the estimate; this saved us hours of analysis and debate. The controversial ones were always a matter of definition and scope, which is a typical and important conversation to have, regardless of your system for estimating work. To select the items and plan team capacity, we spread the cards across a table, and each person held the set of cards they were going to execute. The project time frame was measured in weeks; a typical window was 16 weeks. We used a rough conversion that two `2d` cards equals one week, and one `2m` card equals eight weeks.[^5] It was easy to see who was over- or under-scheduled, and then load-balance according to capacity and ability. [^5]: You might wonder why we didn't just use a mathematically identical scale like `0.5w, 2w, 8w` or some abstract unit of "effort" that could scale like `1`, `4`, `16`, or a Fibonacci number of weeks. The reason for using real time is that we felt it was easier to imagine than abstract units. The reason for keeping the quantity of values to three is the Fermi reason: This minimizes controversy and forces you to think critically about scope. If a project required multiple cards (nowadays we'd call that an "epic" or "big rock"), we marked that set of cards with a color. The idea is that either *all* the cards of that color should be accepted, or *none*, otherwise we would have spent a lot of time on something without having shipped a usable feature. Here's the punchline: It typically took a few hours to create the entire plan, and four months later we typically hit the plan within a week of the original estimate. In retrospect, it's a form of Fermi Estimation. It's not *exactly* powers-of-ten, but measured in work-days it's close: `2` vs `10` vs `45`. Essentially powers-of-five, it still maintained the key idea of Fermi--to have so few choices that the correct one is easy to identify--while also being chunks of time that a human can relate to. ## Putting it together Let's combine these ideas, do the math, and decide whether it's helping us make better decisions. ### Single-valued impact Suppose we're picking features to implement, and the only metric of value is the revenue we believe that feature will generate in the next twelve months. We'll convert "effort" into work-days as given just above, and round ROI to the nearest 10% to make it easy to read. Here's a typical result: | | NTM Revenue | Effort | ROI | |:----|----:|:---:|----:| | Feature A | $100,000 | 2m | 2200 | | Feature B | $10,000 | 2w | 1000 | | Feature C | $1,000 | 2d | 500 | | Feature D | $10,000 | 2m | 200 | | Feature E | $1,000 | 2w | 100 | This has some nice properties: #### Biased towards more impact Products A and B are examples of "takes more time, but generates more impact." The rubric prefers more impact, even compared to "quick wins." This is a good bias, especially since impact in reality is often less than we had hoped. #### Still correctly ordered if we misjudged Consider what happens if we misjudge impact by 20% and effort by 50%--the original hypothetical from the beginning of this article. If Feature A delivered only $80,000 and took three months, the actual ROI would be about 1200, still ahead of Feature B. Or if Feature B delivered $8,000 in three weeks, actual ROI is 533, still ahead of Feature C. Therefore, typical misjudgments are not changing our decision. We fixed that deficiency. #### Easy to explain to others It's important to be able to explain your decisions crisply. The typical ROI rubric does not; the explanation is that one item slightly edged out another item, which isn't confidence-inspiring, and doesn't sound like a strategic decision. In contrast, Fermi ROI is easy to summarize; for example, why Feature A was selected: *Feature A has by far the greatest potential for impact. The impact is so large, it's worth spending multiple months on it.* Or why Feature B wasn't selected: *Feature B would be faster to implement, and therefore is a great idea, but Feature A's impact is so much larger, it's still the better choice. If we get part-way into Feature A and realize it's much harder than we thought, or much lower impact than we thought, then we'll pivot into Feature B.* ### Multi-valued impact In the "presentation" example, we had four impact scores, which we multiplied together. The reason it made sense to combine them in that way, is that *all four are required* to achieve the goal (as explained previously). When you have a case where multiple factors are needed *together* to achieve a singular goal, then multiplying is the correct way to compute the score for that goal. A common mistake is to use this formula when there are scores for things which are *not related*. For example, suppose you're wanting to maximize revenue, and also customer delight. You could make a Fermi score for each, but how do you combine them to get an ROI? There are two answers, depending on your philosophy: #### Do not combine unlike attributes There is no way to compare "revenue" with "delight." These are different units of measurement, so any combination is nonsensical. Instead, decide which of the factors is *most* important, and compute the ROI of just that. Then, if there is a tie, you can break the tie by comparing "delight." This is also easy to explain after the fact: *Features A, B, and C are equally effective at generating revenue, which is our most important goal. Among those, Feature B also increases customer delight more than the others, so we're implementing B.* #### Add instead of multiply (only when attributes are equally important) Although it is arguably a lack of strategic decision-making to claim that multiple attributes are all equally important, it can make sense if you are scheduling secondary features. That is, suppose you've already scheduled the most important strategic items, and now you're "filling in" with "quick wins" where you truly don't care in what manner each is a "win." Here you add the values instead of multiply. This way, a huge ROI in one area wins (but we don't care which area). Getting *some* value in two areas, is not as good as getting a *lot* of value in *one* area. This final point is satisfied when you use Fermi values, but traditional rubrics get it wrong. ## Breaking ties The Fermi-style rubric has another interesting property: **There are only a few unique values of ROI.** In the "revenue" example above, there are only 3 realistic numbers for revenue, and only 3 values of effort, so only 9 unique combinations of ROI. The good news is, the computation clearly separates the best from the rest. The bad news is, you can easily make ties. In the "presentation" example earlier, in real life we had two topics in clear first and second place, but a three-way tie for third. We didn't have time for all five topics, so we had to break the tie. These ties are not a failing of the system. Just the opposite: It means we are able to identify ties, rather than allowing noise to trick us into believing one is the "winner." ![recommended against upping game while taking to next level](https://public.asmartbear.com/books/anthology/chapters/roi-rubric/d7195print-simple.png) Still, we have to break the tie. There are several ways to do it, while preserving the advantages of this method. ### Adding precision or intentional bias It's tempting to add "precision" to your input values, and indeed that might break the tie. But beware of falling back into the accuracy problem and the explanatory deficiency of typical rubrics. You should add precision only if: 1. You are certain the precision is accurate, or 2. You are creating an intentional bias, e.g. to over-weight one factor relative to another. There's a mathematical trap in option (2). It's tempting to "weight" a factor by multiplying it by a constant, e.g. "we'll double this score, so it counts twice as much as other scores." However, this actually doesn't do anything at all, because it just means all totals for all items are multiplied by 2; this doesn't change the ordering, and doesn't break ties. Instead, what matters are the sizes of the *intervals between* the choices. That is, if scoring for this item was originally `1`, `10`, `100`, you could *reduce* how sensitive the total is to this value by diminishing the differences between values, e.g. by going up by powers of three instead of powers of ten: `1`, `3`, `9`. You also don't need to keep the spacing regular. You might rank more options as a `0` to effectively "filter out" things that don't meet a desired standard. You might decide that the two options at the top of the scale, that originally were `100` and `1000`, should be more like `800` and `1000`, reflecting a bias that "both options have essentially the same value, one just slightly more." ### The run-off Given a set of items that we've agreed are "equal in ROI," pick a new dimension to break the tie. Time-estimates are no longer part of the process. We already agreed these are efficient in terms of impact-versus-time, so now we focus only on some additional dimension of value. This simplifies the process, which is not only nice for decision-making, but also for explaining the decision. If you don't want to evaluate the items along a new dimension, you could eliminate some of the existing dimensions. For example, in the "presentation" case, we decided that, all else being equal, we would rather select topics where we have a really interesting insight, rather than topics that are great at selling the product. Therefore, we performed a run-off using "insight × reach," ignoring the other values. To determine which is the most important dimension to maximize, using this guiding question: > If we maximize this dimension in the next few months, then even if we get nothing else accomplished, it will still be a success. > > Conversely, if we moved other needles, but this dimension remained unchanged, it will have been a failure. ### The human factor A different way to break the tie is to include something else that you value, but that shouldn't be used to make the primary decision. Since the primary decision is a tie, we're "allowed" to indulge ourselves in something else, even something unconnected to "business metrics" in the usual sense. Because we've already maximized a business metric, you could break ties using something completely different: ##### Team Excitement Whether because it's fun, interesting, or the team is just tired of hearing certain customer feedback and really wants to do something about it, "excitement" is an excellent, under-appreciated attribute. Excited teams are happier, feel more fulfilled, feel more listened-to, and work harder and better. It is the elusive ["win-win."](https://longform.asmartbear.com/authentic-is-dead/) Since "excitement" is an ambiguous term, one way to convert to a Fermi value is the following: | Fermi
Excitement | Definition | |----:|:----| | `0` | No one is excited to work on it. | | `1` | One person is excited to work on it. | | `10` | About half the team is excited to work on it. | | `100` | All, or all but one, is excited to work on it. | ##### Confidence Confidence is hard to measure, but you know it when you feel it (or don't feel it). It might not be correlated with risk--just because you are confident, doesn't mean you are correct. Still, faced with two choices, where you're sure you know how to execute the first, but with the second you're full of doubt, it's logical to pick the first. Since "confidence" is an ambiguous term, some people use some sort of risk percentage, e.g. "I'm 60% sure we can do this." But [pinning down a number is suspicious](https://longform.asmartbear.com/probability-words/), and debates don't seem fruitful. One way to convert to a Fermi value is the following: | Fermi
Confidence | Definition | |----:|:----| | `0` | There's no way we can pull this off. | | `1` | I mean we can try, but don't be surprised when it fails. | | `10` | We can probably do this; we'll be surprised if it goes very wrong. | | `100` | This is completely within our domain of expertise, we've successfully executed something similar in the past, and nothing in the task-breakdown is uncertain. | Good luck!

The "Talk vs Walk" workshop

Jason Cohen — Wed, 08 Jun 2022 00:00:00 +0000

![go back and give that part little more oomph really make me believe](https://public.asmartbear.com/books/anthology/chapters/talk-vs-walk/d5328print-simple.png) Despite the insipid rhyming, it's a surprisingly useful question to analyze:
_You talk the talk, but do you walk the walk?_
Whether it's the early-stage company, understandably exaggerating its product's qualities on the home page in a lived experience of "Fake it 'till you make it,"[^1] or the venerable company whose marketing department isn't taking enough credit for legitimately world-class qualities, because they've become so second-nature that it doesn't occur to them to brag about it. [^1]: Again with the rhyming? Plotting items on a "talk" vs "walk" chart is not only useful in analyzing current-state, but also in uncovering ways that both the Marketing and Product groups can sell better and create better products. ## The method ### Write-storming ideas Using a whiteboard,[^2] set up a vertical axis called "**Talk**: How much/loudly do we claim this" and a horizontal axis called "**Walk**: How well do we pay this off in practice." (Figure 1) [^2]: Likely virtual nowadays, e.g. Miro, LucidChart, or even Google Slides; something where folks can easily create and drag around "cards," and see the results immediately. ![The two axes of the Talk/Walk Framework](https://public.asmartbear.com/books/anthology/chapters/talk-vs-walk/talk-walk-simple.png) Then, independently write-storm:[^3] Create "sticky notes" and place them wherever each person believes they should go on the chart. You could even allow several days, giving people time to think and to schedule the thinking during periods when their brains and work-environments are most effective at tackling such projects. [^3]: **Do not brain-storm items one-by-one.** In live brain-storming the conversations follows the fastest and loudest people, rather than leveraging everyone's brains to invent different ideas. Live brainstorming filters out ideas that take longer than a few minutes to think of. Live brainstorming eliminates the possibility of having an "ah-ha" moment in the shower. "There is not a single published study in which a face-to-face brainstorming group outperforms a [write-storming] group" observes Leigh Thompson in _[Creative Conspiracy](https://www.amazon.com/Creative-Conspiracy-Rules-Breakthrough-Collaboration/dp/1422173348?tag=wwwfccom-20)_. [Recent research](https://www.nature.com/articles/s41586-022-04643-y) suggests that people are less creative in generating ideas over video-conferencing versus in-person; write-storming restores the creativity because it's independent, even if the discussion is over Zoom. Where do ideas come from? Because the exercise is about "what our company claims," obvious sources are the public website, advertisements, public presentations and webinars, quotes from your employees in the media, sales materials, and support materials like knowledge-base articles or copy/paste shortcuts. If you're quoting directly, put the phrase in double-quotes to emphasize that "we really do say this!" There are also the things you're doing that you're *not* talking about. Those things are more difficult to generate. Sources are what you're maintaining (i.e. existing systems owned by some team), work from recent sprints (i.e. work you're actually doing), long-term planning documents (i.e. larger initiatives), and prompts like "What are we really good at?" and "What technology assets or human capabilities have we built up over time?" The hardest things to think of, are the things you're not talking about and also not doing. Isn't that an infinite number of things? Yes, but it's useful to list things you reasonably *could* say or do, but are intentionally avoiding. One source is backlogs of features requests from customers, support, sales, marketing, product, and engineers; whether you plan to do them eventually or not, they are "items of interest." Another source is key, differentiating things that competitors are saying or doing (whether you're avoiding those things purposefully or reluctantly). Yet another is your own long-term plans for big initiatives you haven't gotten to yet. ### Debating placement is one-third the value Come together as a group, take each idea in turn, and debate where it should go on the chart. Coalesce duplicates into a single card; if the duplicates were originally strewn all over the chart, that's even more indication that a discussion is useful. If there's immediate widespread agreement on the right location for a card, skip the discussion to save time. Generally you'll find three sources of disagreements. All three are useful, but in different ways: #### Lack of knowledge "We don't talk about this." "Yes we do, it's in all the sales materials!" "In support, we tell people the opposite." "We say this in sales but not on the website." "We *used* to say that, but not since the latest website revamp." This is a great excuse to get everyone up to speed about what we're saying, and where. If the answer varies (e.g. "we say this a lot on the home page, but nowhere else"), you can place the item more in the middle of the chart, but add a "comment" to record that detail; it will be useful later when you decide how to act on this information. #### Disagreement on terminology People often interpret the same phrase differently. If you can't agree on what it means internally, imagine how confused customers are! Or a new employee who isn't part of the echo chamber? An example will illustrate how this is useful: In a recent exercise at [WP Engine](https://wpengine.com/), multiple people put "Enterprise-Grade Security" on the board. We talk about this constantly, so no one disagreed that it was high on the "talk" axis. But there was disagreement on "walk," ranging from the middle to the far-right. But how is this controversial? We're well-known for having the best security in the industry. Our customers include some of the largest security companies and financial institutions in the world, both of which maintain the highest bar for security; we are certified with multiple accreditations; we leverage myriad security products covering every layer of the tech stack; we commission third-party pen-tests for every new product and periodically for all products; we conduct annual company-wide training to guard against social engineering attacks; we have formal written security policies that we actually enforce. With all this, how could you not agree "Security" is 100% on "walking the walk?" Turned out, the person who placed it in the middle was specifically thinking about a few items in the backlog that would make a certain corner the platform even more secure. We'd never experienced an incident because of, and there was no reason to believe we would. Still, this person thought, If there's still a few obvious items in the backlog that we *could* do, then we're not yet fully "walking the walk" on security. A discussion resolved the difference of opinion. We decided that "great security" isn't defined by never having a ticket in the backlog; after all, nearly all software updates contains security patches (check the version history!). That doesn't mean the software wasn't already secure. Indeed, it's the opposite: Being so diligent that we even "fix" things that aren't broken, thinking a few steps ahead, proactively preventing incidents, *is* what *makes* us secure; it's a sign that we are in fact "walking the walk." The group finally agreed that the ticket belongs on the far right side. The realization, however, was helpful both for the engineers who have a better mental model of "what security means," and for the marketers who have more stories to tell about how excellent our security is. #### Disagreement on target persona Sometimes you realize that a subset of your customers would agree that you're "walking the walk," but others would disagree. For example, perhaps the performance of your product is excellent for most customers, so when you say "fast user interface that never gets in your way," most people would agree. But maybe 2% of your customers have huge amount of data that slows your UI to a crawl. Furthermore, that 2% are important to you--they might be your reference customers, or the ones paying you the most money. What do you do in these fractured circumstances? A simple technique is to place the item in a sort of "weighted" location. So e.g. if 80% of your customers (by revenue) would consider this completely "walking the walk," but 20% would say it's pretty low, perhaps you place the item at 80%. However, add a comment to the sticky-note explaining this detail; it will be useful later when you decide what actions you want to take. Another technique is to split the item. So e.g. "Fast UI (for customers <100 properties)" and also "Fast UI (for customers with ≥100 properties)." Then place them independently. If you have different, distinct target personas, you might find a lot of the cards are being split. In this case, consider making two different charts altogether, i.e. one for "Talk vs Walk for Persona A" and the other "Talk vs Walk for Persona B." This is easier to read and to act on. ## Taking action Just having these discussions is valuable, but most of the value is in doing something with the resulting chart. In general the question is: Is it useful to take action to shift cards in various directions? It's useful to consider this by quadrant (Figure 2), because those items have different *status quo* interpretations. ![The four quadrants of the Talk/Walk Framework](https://public.asmartbear.com/books/anthology/chapters/talk-vs-walk/talk-walk-2-simple.png) ### Quadrant A: Exaggerations These are the original source of the line "you talk the talk but you don't walk the walk." When you're "faking it 'till you make it," but still just faking it. What to do? #### Do nothing (ethically) We understand our claims are tenuous. We should ensure that our exaggeration is not unethical, e.g. claiming a medical benefit that does not exist, or charging for a feature that does not work. #### Live up to your own expectations (shift →) This message is resonating, and is a critical to our positioning. But if it's so critical, we have to back it up with reality! We'll build and ship specific things in the next six months that will allow us to shift these sticky-notes rightward to meet our customers' expectations. #### Don't write checks you can't cash (shift ↓) We're attracting the wrong sort of customers--people who expect something we don't deliver. As a result they waste time in customer service and then cancel anyway--a net loss for us, and a terrible experience for them. We're not even getting useful product feedback, because we're interviewing people who aren't our target persona. Let's stop saying these things. ### Quadrant B: The trusted brand Trust is earned by repeatedly keeping your promises; that's what's happening in this quadrant. This is your brand, because it's what you say and also what customers actually experience. This is a strength, because everything is constructively aligned. What to do? #### Do nothing We're doing well here, but doing incrementally better will not win us more customers (i.e. [Kano](https://foldingburritos.com/blog/kano-model) "must-be" or "indifferent"). Therefore, we should track our performance on this item as a KPI, but rather than continue investing to maximize it, we should only promise that it won't fall below some minimum threshold of acceptability. This ensures we won't unintentionally lose this advantage. Instead, focus our attention on accumulating more strengths, or mitigating some issue. #### Leverage strength for sales (shift ↑) Can we make even more fuss about how awesome we are? More confident competitive positioning, leveraged in advertising, more content marketing on the subject, emphasized more in sales processes, highlighted more in the product UI. We might even be able to make sales by [emphasizing one strength above all others](https://longform.asmartbear.com/one-benefit/). #### Double-down on strength (shift →) It's often wise to invest more in what's already working, especially early in a product's life when it's hard to have even one thing that is special, or a [reason to buy despite its many deficiencies](https://longform.asmartbear.com/the-important-thing/). This only works when customers value the additional investment (i.e. [Kano](https://foldingburritos.com/blog/kano-model) "attractive" or "performance"), but if that's the case, it might be the most strategic thing you can do. #### Pivot (shift ↓) While it's unusual to jettison a strength, it makes sense if the company is pivoting: Changing its target, or how its brand and product is positioned against the competition. ### Quadrant C: Speak no evil Often there's not much in this quadrant, because it's easier to think of things you're saying and doing, and these are neither. However, it's valuable to list "things we know are weaknesses" or "things we're saying 'no' to," especially if it's a common topic of conversation at the office, or if a competitor is successfully talking and walking that item. #### Do nothing This is the correct answer for nearly everything here. It's expensive and high-risk to turn a weakness into a strength; your energy is probably better spend executing on your current promises and doubling-down on strengths. #### Turn it around (shift →) If this is truly a key area that is critical to succeed in the market--which is another way of saying "strategic"--then this is a weakness to highlight, and to invest in. Should you shift it up (start talking publicly) before you've shifted it right (pay off the claims)? Probably not: You tip your hand to competitors, allowing them to react before you can defend. You also set incorrect expectations for customers; later when you really *can* pay off your words, you've already trained them to disbelieve you. Conversely, talking first can freeze the market,[^4] but only if your market leadership position is so strong that a press release actually does change market behavior. Unlikely. [^4]: "The U.S. District Court ... suggested barring Microsoft from making vaporware announcements because doing so can allegedly freeze the market and discourage buyers from purchasing competing products. ... 'I feel very sure there have been many times when Microsoft has announced products to freeze the market, [but] lots of companies have done that,' Kertzman said."--[Computer World, 1995](https://books.google.com/books?id=jNkvgvGj198C&pg=PA2&lpg=PA2&dq=meaning+of+%22freeze+the+market%22+press+release&source=bl&ots=SZXMvh35zf&sig=ACfU3U1H306Av97xzkykJFEKXgOzi9Gogg&hl=en&sa=X&ved=2ahUKEwjX_JGf4fv3AhVsBDQIHYWoAN8Q6AF6BAgfEAM) ### Quadrant D: The strong, silent type If you are excellent, why not get credit for it? #### Do nothing There might be a good reason for not making a fuss. It could be a trade secret that you don't want competitors knowing about (but you could brag that a trade secret is part of the magic that makes you different). It could be "boring" internal operational stuff (but customers might be comforted knowing that you're operationally advanced, and a tech blog is a recruiting tool). #### Talk about it (shift ↑) Even if this is a secondary or tertiary message, it's nice to arm sales with more material, or test it in advertising, or make a landing page for the subset of customers who would be impressed. ## Application to competitive strategy Turn the tables on your competition to reveal more insights. ![vendors one sells balloons the other sharp little pins](https://public.asmartbear.com/books/anthology/chapters/talk-vs-walk/d7904print-simple.png) ### Adding the competition to both charts Build the Talk/Walk chart for a competitor. Place a copy of your cards onto their board. There might be a lot in quadrant C ("Speak no Evil"). Then create the new cards that makes sense for them. These prompts help generate ideas: * What do they emphasize (big or bold font) on their home page, "pricing" page, and "about us" page? * What main features do they highlight on their "features" page? * On what points do you lose to them in sales? Which are they legitimately great at, versus which are they just really good at selling? * On what points do you beat them in sales? For which do they deserve it, versus which are you just good at claiming? * What do their customers say about them (e.g. on Twitter or testimonials)? * What do your customers say about them (e.g. when complaining or asking for features, do they reference the competition?) * If you were making an investor pitch on their behalf, what would you say? How much of that would be true, versus aspirational? * What cultural or moral principles do they or their founder or their CEO publicly espouse? Whatever new cards you add to their chart, also add to your own chart, even if it mostly creates a pile in quadrant C. This is where it gets interesting. ### Analyzing the competitor's strengths It's especially instructive to compare your competitor's strengths to everything on your board. Place their strengths onto your board in a unique color, i.e. evaluating whether you also talk about those things or not, or you also do those things or not. Every quadrant is actionable: #### Quadrant A: Exaggerating against their strength You're both making a claim, but only your competitor is fulfilling that promise. Over time this erodes your brand; potential customers start dismissing you, saying "Yeah, everyone claims to have X, but only P really does X." You were already claiming something ahead of having it, and now the competitor is exposing your little fibs. Before you decide to knuckle down and match the competitor strength-for-strength, critically consider whether you want to run a race where you're starting out behind. Do you have what it takes in long-term will-power, and investment in time and skill, not only to catch up to where they are today, but to where they will be in another year? Will the result of that expensive and risky activity pay off enough to be worth it? Occasionally the answer is "yes," but usually the answer should be "no, let's move this card downward--stop making this claim--and instead focus on increasing our differentiating advantages in Quadrant B, where *we* are the ones in the lead." #### Quadrant B: Table-stakes; find the differentiation You're both doing well here. Perhaps these topics are considered "table-stakes" for any competitor in the market. That's not a problem, but neither is it an advantage for you. Table-stakes are important to maintain, but it's a mistake to over-invest in them at the expense of things that differentiate you, or at the expense of the few key weaknesses that you're converting into strengths. Your comparative advantages are the cards in this quadrant that *aren't* shared with the competitor. Do you have a significant set of those cards? If not, inventing some might be the single most strategic thing you could do. #### Quadrant C: It's not your thing, and that's OK This is where your competitor is differentiated against you. It's easy to get discouraged or think this is a problem; it's not. The only problem is if you don't have your own [unique strengths](https://longform.asmartbear.com/worse-but-unique/). If you both have your own strengths, that's just defines your respective niches, which is useful to recognize. As with Quadrant A, it's highly unlikely that you should attempt to "turn it around" and become strong where your competitor is already strong. But, if there's one item that's truly important to win, you could set that intention. Just know that you can't have more than one or two of those at a time, and often you'll only get to mediocre, not a strength. It surely must be [treated as a Rock](https://longform.asmartbear.com/rocks-pebbles-sand/). #### Quadrant D: Don't give them a free pass You're letting your competitor take all the credit, even though you're just as good. Change that! Sure, you'll probably just match them, not create a new differentiated advantage, but since you've already done the work to be excellent in this area, don't give them an uncontested win in sales calls. --- I hope this exercise is as useful to you as it has been for us!

Productive meeting activities: Leverage the team, empower the individual

Jason Cohen — Tue, 24 May 2022 00:00:00 +0000

![we all agree lets go back to desks discuss why this wont work](https://public.asmartbear.com/books/anthology/chapters/productive-meeting-activities/d5327l-83797-1539347627-simple.png) We all hate the meeting where we prove once again that a committee is a beast with twenty legs and no brain, or when we could've just read the materials quietly to ourselves at a more convenient time (and maybe we did, and it's being read to us *again*), but there's also the elation of *esprit de corps* and accomplishment following a productive meeting, where in actual fact "all of us were smarter than any of us." Even when an enlightening discussion ensues, sometimes the person who requested the discussion gets foiled. A person wants to solicit ideas, but ends up getting told which idea is best. A person wants help deciding between two vetted options, but instead receives seven new options. How can we leverage the [wisdom of the crowd](https://longform.asmartbear.com/wisdom-of-crowds/), while ensuring that decisions and other responsibilities continue to reside with an individual? Some answers follow. Enjoy! ### Ranting I need to get something off my chest, in a trusted environment, where I can say things that make me vulnerable (e.g. "I don't know where to start" / "I'm scared to face this"), or might sound frightening to others (e.g. "I don't think what that other team is doing is right" / "I'm doubting my strategy and I'm worried how my team will react if I broach it"). I don't want you fix the problem, I just want you to understand the problem. I wouldn't mind if you supported me while I work through the problem. For example, I have "people problems" in my organization; I don't need everyone to jump in, but I do need them to "be kind" to my teams while we work through this, and understand that we might need extra time to do our work. *This can lift a great emotional weight off the person, while increasing team cohesion, trust, vulnerability, and empathy, therefore it is a fantastic use of meeting time.* ### Ideation I need ideas. Possible causes of a problem, possible solutions of a puzzle, more options for actions I could take. I'm stuck right now, too close to the problem, and I don't have ideas, or I'm tired of looking at my ideas and I need new ones. I sent an email about this ahead of time, so the group has had time to think about it. I don't want you to solve the puzzle or decide which option is best, I just want you to help me see options that I'm not currently seeing. For example, I have a strategic challenge where I need to solve for two apparently-contradictory things, and I need ideas for how that might work. I don't want you to decide my strategy for me, but help me see more possibilities. *Everyone gets stuck; no one can have all the great ideas by themselves. Having a space where ideas can flow but no one is trying to tell you want to do, is fun for everyone and impossible to do alone, therefore it is a fantastic use of meeting time.* ### Decision-Support I need help deciding something. I know what my options are, and I've analyzed them (sent ahead to the group), but it's complex and I'm uncertain which to pick. I might even be uncertain *how* to decide. It could be that the best option isn't yet in the list, so it could be good if we synthesize something new. But ultimately I don't want seven new ideas; I want to make a decision. I don't want you to tell me what to pick, but I do want input, whether on the evaluation of the options, or on what basis I should make a final decision. I might even ask the room to vote; not to dictate my future, but because I trust the room's opinion and I want it as another input in my decision process. *No matter how many [prioritization articles](https://longform.asmartbear.com/rocks-pebbles-sand/) you read and [ROI rubrics](https://longform.asmartbear.com/roi-rubric/) you make and [multi-dimensional maximization charts](https://longform.asmartbear.com/maximized-decision/) you make, complex decisions are difficult. Seeing your choices reflected in others' experiences and points of view can help, and cannot be done alone, therefore it is a fantastic use of meeting time.* ### Sparring Practice I need a Devil's Advocate to sharpen my work. I want find the weaknesses in my strategy, my feature concept, my keynote presentation, my reorg proposal, my new UX design; I need a safe space where people "attack" my work, to surface legitimate flaws, or to reveal ways of making my argument that deflect potential attacks before they begin. I don't want you do my work for me. If you have an idea for improvement I'd love to hear it, but I will decide what to do with that idea. It's OK if you "attack" from a perspective that isn't even accurate, because my future readers might have the same misunderstanding, and I want to be clear even to them. I want to be challenged with the intention of making my work bullet-proof. *Whenever you see a brief, tight, bullet-proof document, it took a lot of work to get there. No one by themselves can think of all the ways that others will (mis)interpret ideas, or think of all the legitimately good counter-arguments that deserve answering, therefore it is a fantastic use of meeting time.* ### Declare the conversation It's counterproductive when someone wants a Ranting, but receives a Sparring Practice. Declare what kind of conversation you want, then everyone can participate in the right way, and facilitators can reroute the conversation when it inevitably strays from the parameters of the discussion. ### There is an "I" in Team ![Joke: Saying there is an "i" in team by filling in space in the "A" to look like a lower-case "i"](https://public.asmartbear.com/books/anthology/chapters/productive-meeting-activities/i-in-team-simple.png) Notice that everything here is selfish--each exercise is about what one person needs. This is contrary to how we often work together. We say things like "We need to decide on our Q2 priorities," instead of "Person X will decide the Q2 priorities by Friday, so let's discuss to ensure everyone is heard, and the best ideas are incorporated." It's healthy for X to seek a consensus, and X often genuinely wants to be influenced by teammates who X trusts and respects, particularly when opinions are unanimous. However, a decision made by "us" is owned by "no one," when in actual fact it *is* owned by X. Acknowledging this reality is useful, even as you also--correctly!--spend 98% of the conversation discussing as equals. This is also a healthy pressure against top-down control, in cases where the decision is supposed to reside in a self-managed and empowered team. That is, suppose person P is responsible for some project, and asks for help in a room containing peers and person Q, who is P's boss. The right way for the entire room to help P are techniques like the above, where P leverages the brainpower of the room, but stays [in command](https://longform.asmartbear.com/in-command/), keeps the responsibility, keeps the autonomy, even with Q present. It is a wonderful experience for a leader to assist without usurping. _Let's put more ideas for constructive meeting activities [in a Twitter thread](https://twitter.com/asmartbear/status/1529105502679998465)!_ ![it seems ive run out of sports analogies so meeting adjourned](https://public.asmartbear.com/books/anthology/chapters/productive-meeting-activities/d6071l-30655-1539347907-simple.png)

Moats: Durable competitive advantage

Jason Cohen — Sat, 07 May 2022 00:00:00 +0000

![location separates from competition](https://public.asmartbear.com/books/anthology/chapters/moats/d8416print-simple.png) [Economics is useless for predicting](https://longform.asmartbear.com/predict-the-future/) what the market will do tomorrow, but it is prescient at predicting long-term forces that shape entire industries. Business strategists ignore this at their peril. One of these forces is that industries commoditize: Companies copy the best ideas from each other, whether in features to attract customers or cost-savings to build profits, so they converge to similar products with similar cost structures. Undifferentiated in most dimensions, the flexible dimension is price, and in a real-life [Prisoner's Dilemma](https://en.wikipedia.org/wiki/Prisoner%27s_dilemma), price reduces until profits are driven to zero. It's great for the consumer--the best products at the lowest prices--but bad for the companies. To avoid homogenization, a company needs differentiation that others cannot copy. Best is product differentiation that customers value, because that should lead not only to higher prices (because customers cannot get the same value elsewhere) but also greater market share. Second-best is differentiation in cost structure, because this allows the company to convert those savings either into profit (keep in the bank, or distribute by dividend or stock buy-back) or growth (spend in sales, marketing, or new product development). Durable differentiation is rare, especially in the software industry where almost anything can be copied. It's not enough to have a "competitive advantage;" the advantage must also be *durable*. Snapchat invented the idea of "Stories," but Facebook and Instagram copied it, so the advantage didn't convert into market share: ![Facebook doesn't invent anything; they copy. But there is no "first mover advantage" unless that advantage is also durable, i.e. not easily copy-able by rivals.](https://public.asmartbear.com/books/anthology/chapters/moats/instagram-stories-whatsapp-status-snapchat-daily-active-users-simple.png) If "competitive advantage" isn't enough for durability, what is? ## A taxonomy of moats > "The most important thing to me is figuring out how big a moat there is around the business. What I love, of course, is a big castle and a big moat with piranhas and crocodiles." > --Warren Buffett Durable advantages are often called "moats," after Buffet. The concept appears everywhere--a characteristic of fundamental truth--for example more recently and famously in Hamilton Helmer's [Seven Powers](https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319). Jerry Neuman collected, organized, and detailed a terrific [taxonomy of moats](https://reactionwheel.net/2019/09/a-taxonomy-of-moats.html) (Figure 2). Such a reference is useful when analyzing other companies (which moats are they building?) or figuring out which moats are best for your business to construct. ![Many kinds of moats - durable competitive advantages](https://public.asmartbear.com/books/anthology/chapters/moats/d02a6be2f-e23b-4112-8835-26586ca2dd77-e1565093714811-jpeg-simple.png) A real-world example will breath life into these trite bullet points. ## Applying moats to a commoditized market: Cloud Computing AWS (Amazon Web Services) was the first and is still the largest "cloud computing" provider. Its industry is an archetypical example of a market that commoditizes. Indeed, all computing infrastructure markets that preceded the cloud also commoditized, from individual components (e.g. RAM and disks) to entire physical data centers (with power, internet connectivity, climate-control, and physical security). Cloud computing--paying only for what you need, by the hour, by the megabyte, scaling up and down at a flick of a switch--is a discrete leap forward in customer value, but commoditization is inescapable, as every by-the-hour-CPU, gigabyte-of-disk, and gigabyte-of-network-transfer from one vendor is the same as from another. If renting infrastructure is the only product, the only remaining dimension to compete on is price, so price falls, and the industry commoditizes. Indeed, that's exactly what has happened, for example in storage: ![Different types of storage have different prices, but each type has identical price across vendors. All prices are low.](https://public.asmartbear.com/books/anthology/chapters/moats/d1-s2-0-s2352551719301052-gr2-lrg-simple.png) But AWS has largely avoided commoditization. The proof is in the market-share and in the profit. Every year since 2014, AWS's profit has been more than half of Amazon's entire profit; in 2022 it's [generating](https://www.cnbc.com/2022/02/03/amazon-web-services-earnings-q4-2021.html) $21B of profit at a 30% operating profit margin, and growing: ![Amazon 6-year financials](https://public.asmartbear.com/books/anthology/chapters/moats/aws-simple.png) If it were possible for a competitor to steal business by undercutting prices, they would. Indeed, they *have*; competitors like Google, Microsoft, and Alibaba charge less for similar service. Google in particular spends billions more dollars per year than they make, plus they have all the requisite "smart people," but even at scale in 2022, on annual revenues of $20B for their cloud computing services, they're still *losing* nearly $4B annually. None of this is hurting AWS's profits, demonstrating that AWS has not commoditized: ![AWS profits growing while GCP is still losing money](https://public.asmartbear.com/books/anthology/chapters/moats/f77675ca-e3b2-48d7-b7d0-981e779ed081-simple.png) Why not? We might expect the answer is "moats," but what are AWS's moats? ### AWS Moats In 2019, then-CEO of AWS (now-CEO of Amazon) Andy Jassy, on stage in a [live interview with the great Kara Swisher](https://castro.fm/episode/uQph8q), was asked directly: Why hasn't AWS commoditized? Jassy immediately fired off the moats from their anti-commoditization strategy, annotated here with the bullet points from the taxonomy diagram: #### Innovates the fastest [Economies of Scale + Willingness to Experiment] Jassy says they use their size to fuel more and faster innovation. Specifically, revenue today plus line-of-sight to revenue growth for tomorrow, means it is easy to justify a massive investment in innovation. The platform with the most innovation, will have the best tools for developers, which they believe is the key to winning. #### Biggest Ecosystem [Complementary Assets] The biggest vendor will attract the most 3rd-party support, which in turn is vital for developers who must integrate with other tools, especially in the modern world where everything is a SaaS. #### Advanced operating maturity [Learning Curve] Jassy says, "There is no compression algorithm for experience." Even Microsoft and Google, throwing billions of dollars and thousands of engineers at the challenges, are not able to overtake AWS. There's another moat that Jassy didn't list, probably because he didn't want to talk about market-share,[^1] and possibly because it could be interpreted as gently insulting to their customers: [^1]: Later in the interview, he refused to answer basic questions about how much market share AWS has, or the relative positions of Microsoft, Google, and Oracle, even under pressure from Kara Swisher, the excellent interviewer. Being the market-leader invites regulation and puts you on the defensive in anti-trust cases, so public companies shy away from such claims; for example Amazon owns 43% of all eCommerce at the time of this writing, but they publicly position themselves as a general "retail" company, so that their share is "only" 6%. #### Brand of "The Leader" [Brand] The old adage was "no one was ever fired for picking IBM." Now that's true of AWS. The idea is that if you pick the market leader, your career isn't in jeopardy if it doesn't go well, because you picked the least-risky choice. Often "the leader" is also "more expensive," so this is not only good for long-term differentiation, but for profitability. There's another moat that is clearly part of every major cloud provider's strategy; perhaps Jassy didn't mention it because it's slightly nefarious, as it involves [lock-in](https://longform.asmartbear.com/willingness-to-pay/): #### Has the biggest menu of technology to select from [Switching Costs] If customers only buy commodity services like CPU, RAM, disk, and network, it's easy for them to leave for a cheaper cloud. So, AWS has created hundreds of "services" covering every imaginable corner in the world of infrastructure: big data storage and retrieval, web analytics engines, AI algorithms, logging, alerting, change-management, source code repositories, network firewalls, and support for dozens of open source software packages, all for rent by the hour or gigabyte. Furthermore, they encourage use of these services by making each one free when each is only lightly used. A software developer who avails herself of this incredible array of services is more productive than a large team of developers would have been ten years ago, however her Faustian bargain is that she gives up her ability to switch to a competitor, which means as her project grows and the pricing tiers kick in, she's locked in. ## Moats are integral to strategy Moats don't appear quickly or by accident; they require consistent investment and prioritization over a course of years. This is exactly what a "strategy" is supposed to organize, and what "just be agile and react to whatever happens" will not. Therefore, a good strategy will have identified one or very few moats that the company will create, and the primary themes of action that will excavate them and fill them with Buffett's piranhas. The example of AWS shows that the taxonomy is only a starting point. The details of exactly how one item applies to a specific company are essential. A strategy that states "We will create switching costs" is not a strategy; it is a statement of what you hope will become true, rather than an explanation of how you will cause that to become true. A strategy to intentionally create switching costs could look more like this: --- #### Objective Create permanent customers through high switching-costs, caused by extensive integration with their technology stacks, development workflows, and IT administration. #### How Maximize the number of cloud services each customer uses. This, in turn, requires: 1. Create the most number of services as quickly as possible, so there's more to integrate with. Must have more than the competition, and a reputation of "innovating faster than anyone else," to attract customers in the first place, and so that customers don't look elsewhere if we don't have their favorite service quite yet. 2. Create compelling "on-ramps" so that services are actually used (e.g. free tiers, great documentation, pre-integration with other APIs, "bundled offerings" that includes many APIs) #### Consequences 1. If we build many services, quickly, some of those services will turn out to be duds, or might be strategic only as a part of the larger offering, not high-growth or even profitable on their own. This is a "cost of doing business" and will not constitute evidence that a team [has failed](https://longform.asmartbear.com/fail/) or that the strategy is failing. 2. Many individual services will be commoditized, especially when we're just reselling existing open-source projects. We still need to create these, because integrating many of them into a single customer project is necessary to fulfill the objective, and customer discovery shows that they specifically want to buy those products from us. Creating genuinely unique services are more valuable and defensible, so we need to do that as well, but they also take 100x more effort. 3. Given that (1) and (2) demand a large quantity of software, built quickly, this is a costly bet; we require $X billion dollars over three years, plus a scaled-up hiring process. --- The moat is named, but also how it will be built, along with second-order consequences, even *undesirable* consequences (high-cost, and frequent failures of usage or of differentiation on a case-by-case basis) that we not only *accept*, but *expect*, in service of the objective of creating that moat. With these details and trade-off decisions in place, it is a strategy, not a hope. Make sure your strategy is explicit about which moats are being constructed, and the major multi-year activities required to construct them. ![i told you to order piranhas earlier](https://public.asmartbear.com/books/anthology/chapters/moats/d5903print-simple.png)

The "Great" Product Manager, a.k.a. the Impossible Product Manager

Jason Cohen — Sat, 23 Apr 2022 00:00:00 +0000

![culmination suggestions employees focus consultants its terrible](https://public.asmartbear.com/books/anthology/chapters/great-product-manager/d6637print-simple.png) What makes a Product Manager *great*? The prolific Shreyas Doshi[^1] gives us the list of requirements [in a tweet-storm](https://twitter.com/shreyas/status/1249039638829793280): [^1]: I highly recommend following [@Shreyas](https://twitter.com/shreyas), despite the following critique; he is a font of wisdom I have bookmarked and swiped many times. * Great PMs consistently and singularly improve the company's trajectory. * Great PMs are masters of the art of blending quantitative and qualitative inputs, as warranted by each individual situation. * Great PMs become the worldwide experts in their domain. When new to a domain, great PMs bootstrap this process by seeking the counsel of existing worldwide experts. * Great PMs are diligent about using a variety of user research methods to inform what product to build in the first place. * Great PMs also listen to what isn't said [by customers] and anticipate where the industry overall is headed when developing their product hypothesis. * Great PMs know that buy-in isn't enough; you need passion & ownership to build great products. Great PMs facilitate discussions that get the entire team to come up with creative product ideas. * Great PMs understand task leverage and spend the majority of their time on the highest-leverage tasks for the company. * Great PMs, in the rare instances of product failure, improve not just their own approach but they also share the lessons learned with the broader company. * Great PMs are adaptive--they have a wide repertoire [of tools and processes and workflows] that they expertly tweak for each specific team's needs. * Great PMs are outstanding problem preventers. Great PMs are discerning about which problems to prevent, which problems to solve, and which problems not to solve. * Great PMs edit the company's product ethos--they identify the unintended flaws in the principles, fix the flawed parts--and only then follow & espouse it. * Great PMs know that career ladders are imperfect proxies: they're more fixated on tangible competence & impact than on checking off boxes on the ladder. * Great PMs also learn through work projects, but they learn a lot more about their craft in their personal time because of their curiosity & passion for self-improvement. * Great PMs ultimately decide what's best for users & the business. * Great PMs ensure the product strategy is optimal. * Great PMs work hard but are rarely overwhelmed. Haha, that last one is funny. Don't be overwhelmed, but also "learn a lot more about your craft in your personal time." These recitals always include a little "out," an opportunity for the writer to wriggle out of responsibility for commanding the impossible. Something like, "The final rule is: You can break any rule, if you know what you're doing." In this case, the 31st tweet: > Naturally, very few PMs are Great. > > And I don't know any Great PM who does all of the above, all of the time. > > That's because Great PMs know that these ideas should be viewed as signposts, not as commandments. So you *could* be super-human at two dozen different things. Now what? I've used the following framework for Product Management at WP Engine to answer this question, so that *as a whole team* we in fact constitute the mythical omnibus "Great PM," but we achieve it with a set of great--but plausible--real human beings. ## The PM decides what to build The role and scope of "Product Manager" varies between companies, but the role centers on **deciding what to build**: ![Diagram of "Strategy" and "Customer Research" as the two inputs to Product Management, and "Scrum Product Owner" and "Coordination & Communication" as the two execution activities, with the central purpose being to decide what to build](https://public.asmartbear.com/books/anthology/chapters/great-product-manager/image-simple.png) Looking backward, there are inputs to the decision--the long-term vision and strategy, and an intimate and evolving understanding of the customer; these are part of Product Management, because without them we cannot make good decisions. Looking forward there's the execution of building and delivering to customers, the complexity of engineering execution together with coordinated efforts by marketing, sales, customer service, and external partners to deliver the entire experience to customers; these consequences of the decision are also part of Product Management. You could argue that this particular break-down is arbitrary, however in my experience this categorization is especially actionable for hiring, careers, and designing the PM organization, and is compatible with other ways[^2] of decomposing the role. [^2]: Product thinkers categorize PM roles in myriad combinations. [Lenny Rachistky](https://www.lennyrachitsky.com/) in a [podcast](https://castro.fm/episode/UzVQdL) says it's three things: (a) Shape the Product, (b) Ship the Product, (c) Synchronize the people. Later in the same podcast he says it's five skill areas: (1) Execution, (2) Collaboration, (3) Leadership, (4) Vision + Strategy, (5) User Research. In another blog post he lists the [ten jobs of Product Management](https://cdn.substack.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F72e7fff6-10dc-429e-bd57-2a2a9d8d9711_2048x1654.png). You'll find the four areas below align with any of these break-downs; while all paths arrive at the same destination, I find this one particularly useful in working with real-world people. ## The four roles of Product Management #### Strategist Analyzing, crafting, communicating, and updating the answer to the question: How do we apply our durable advantages to the opportunities in the market, to win? What do we look like in the success-case, years from now? What are the most important challenges to achieving that? Specifically what must we do in the coming years to overcome those challenges? Competitive analysis, market analysis, customer analysis. Determining the few, key personas to focus on. Distilling noisy data into clear identifications of internal strengths, external challenges, and [market opportunities](https://longform.asmartbear.com/problem/). Positioning the product to maximize value and perceived value, and thus [price](https://longform.asmartbear.com/more-value-not-save-money/) and [willingness-to-pay](https://longform.asmartbear.com/willingness-to-pay/). [Selecting the right high-level metrics](https://longform.asmartbear.com/product-metrics/) to drive the right long-term results, while also acknowledging that not all important things are numbers, and that revenue and retention are outputs of great strategy plus great execution, not themselves a strategy or even an operational goal. Having the fortitude to make decisions that will affect us for years, and cause us to say "no" to 90% of our ideas, so we say "yes" to exactly the right ideas. #### Customer Whisperer Not just listening to customers, *hearing* them, understanding their underlying needs, motivations, even their emotions. Not order-taking the features they know how to name, but [rooting out the things](https://longform.asmartbear.com/customer-development/) they worry about so much they would pay to decrease the worry, or figuring out the result they need to get to earn a promotion or achieve their [ultimate goal](https://longform.asmartbear.com/needs-stack/), as opposed to just "features." More than figuring it out, *wanting* to figure it out. Having the "taste" to sense what features would be especially useful even in the absence of clear quantitative data, or the nuance that separates a serviceable UX from a delightful one that causes an NPS-10 customer to actually "Promote" in real life. Translating these insights into tangible feature ideas or use-cases of things to build that make the customer a hero. #### Scrum Product Owner An unfortunately ambiguous phrase for the uninitiated, the Scrum "Product Owner" owns the work-backlog, and works with engineering to execute it. Owning the work from start to finish, breaking down epics into stories, [prioritizing and scheduling](https://longform.asmartbear.com/rocks-pebbles-sand/) innovations for customers against engineering demands against [requests from other teams](https://longform.asmartbear.com/jit-backlogs/) against the broader needs of the company. Working tightly with engineering not just to shake them upside down until estimates fall out of their pockets, but working together to solve the puzzles of "most value (whatever that means) in the least time (or risk)," together finding the 80% solution that will take 20% of the time, so that we deliver quickly and learn quickly. Using the tools of story points and velocity and retros for team-wide constructive introspection and improvement, fulfilling the responsible aspect of being "self-managed." Stretching to maximize how much we deliver, but practical and celebratory so that constant striving doesn't turn into death-march burn-out. Being [in command](https://longform.asmartbear.com/in-command/) of the product and team. #### Orchestrator Delivering the "[Whole Product](https://280group.com/product-management-blog/whole-product-concept-a-quick-guide-for-the-expert-pm/)," as Geoffrey Moore defines it, means not delivering working code only, but also enabling others: Marketing to grab attention and spark curiosity, sales to convert potential energy (leads) into kinetic energy (sales), customer service to shepherd the customer through good times and bad, enabling the Marcomm teams for PR and events, and external integrators and consultants who operate on behalf of clients. Tracking and communicating status, the good news and the challenges, celebrating the wins and facing the challenges with crisp articulation, calming stakeholders in the knowledge that the team is fully aware of what they need to overcome, and thus in command even in dire situations. Running great meetings--starting with a clear goal and how the discussion will support the strategy, tight agendas so participants come prepared and use synchronous time wisely, with decisions made and recorded. Engaging the whole company on occasion, with inspiring presentations at all-hands meetings so everyone is excited about we're accomplishing together, and so that the teams working directly on the product feel proud, and feel seen. ![fire good except sometimes bucket of water](https://public.asmartbear.com/books/anthology/chapters/great-product-manager/d8678print-simple.png) The conclusion is not that "a Great PM must be the master of all this." In fact, exactly the opposite. ## One excellent, one good In my experience, echoed by a few people I've chatted with, there is a general rule of thumb vis-a-vis the categories of work above:
*A "Great PM" is
**excellent** in one area,
**good** in at least one other,
and doesn't have time for more than two.*
Using "Great PM" language, you should strive to be "Great," as superlative as possible, in one of those four areas, and pretty darn good at a second. I find this particular categorization of four job-areas aligns well to strengths and abilities of real people. Whatever you ascribe to nature or nurture, instinct or experience, for each of those job-areas I can immediately recall specific people who are excellent at nearly all components within a single area, but variously good or lacking in components of a second, and then completely the wrong temperament or lack of desire to excel in others. These four "buckets" seem to me to be natural divisions of ability and proclivity. I don't believe in magical unicorns who are excellent in all four areas.[^3] But suppose they do exist, and you hired them. Would it be physically possible to execute well in all four areas? It's easy to see that it's impossible, if you add up the time it takes to do great work. [^3]: Perhaps there are, but the probability that you'll find one, or hire one, or build a whole organization of them, is near-nil, although you will surely find a lot of candidates who imagine themselves to be one. Strategy is on-going--markets change, competitors change, customers change, new data comes to light (usually only after expending considerable time acquiring it), and whenever you're "pencils down" on one iteration of a strategy, there are always eight more things you still haven't figured out yet. Customer discovery takes a long time--scheduling and running interviews, compiling results, mapping what you heard, prioritizing activities or opportunities or use-cases (depending on your frameworks), trying to (in)validate assumptions with data. Being a Product Owner takes at least twenty hours per week to write great stories and run Scrum ceremonies. Anyone who has program-managed multiple teams across multiple departments knows that scheduling and organizing and cajoling and status-updating and data-gathering and meeting-preparation takes a lot of time. Even with sensational skills, no one has time to do it all (with excellence). > "We are not going to breed a new race of super-humans; we will have to run our organizations with the humans we have." > --Peter Drucker While one person cannot be all things to all people, across the *entire* product team, you *do* need excellence in all four areas. Thus the manager of a Product organization isn't simply a "manager," but rather an "organization designer," choreographing this outcome through intentional hiring. You'll often find these roles in titles other than "Product Manager." Communications and collaboration is often done by a Program Manager--a role where if you think it's not useful, it might be because [you haven't worked with someone who is great](https://techcrunch.com/2012/07/24/you-havent-seen-greatness/). Marketing, sales, and support enablement is often done by Product Marketing (i.e. somewhere under the CMO rather than somewhere under the CTO). With highly technical back-end teams with little or no customer exposure, a sociable engineer or architect can be the perfect Product Owner. Mature organizations might have UX Research teams who are expert in customer analysis--building data-based personas, crafting interviews, compiling results, and pooling insights across multiple product teams who happen to share customers. As the organization designer, your job is to "assemble excellence" in all areas. Even incremental improvements here can be impactful, because so often organizations have excellence *nowhere*. [95%](https://hbr.org/2005/10/the-office-of-strategy-management) of employees complain they don't understand the strategy, or don't know their role in it; founders-in-PM-roles often run on instinct more than systematic customer analysis; engineering teams often complain of underspecified or under-explained stories; engineers are often surprised at the gulf between what they know to be true about the product and what Sales claims on the phone, just as customer service folks are often surprised at the gulf between what they know to be true about the product, and what engineering thinks is "a job well done." Rather than unicorn-hunting, take this more practical route in your career, or in designing your team.

Failure to face the truth

Jason Cohen — Sat, 09 Apr 2022 00:00:00 +0000

![turns out my imposter syndrome was pretty much right on](https://public.asmartbear.com/books/anthology/chapters/failure-to-face-the-truth/d8014print-simple.png) A primary blocker of progress, from our personal lives to our corporate strategies, is a _Failure to face the truth._ "The truth hurts." Yeah, so we avoid it. The truth is hard to find if we're not looking for it, and we're not. Denial (when intentional) and rationalization (when unintentional) is our normal operating mode. Because we don't realize this, it's an immense, invisible barrier. Whether because we don't like to admit we're wrong (even to ourselves, in secret), or because it will be annoying or painful or career-altering for the truth to be said aloud, we avoid the truth. Once you start seeing the pattern of "failure to face the truth," you see it everywhere. In almost every meeting, someone is thinking something and not saying it, even though one of the [best uses of a meeting](https://longform.asmartbear.com/productive-meeting-activities/) is to unveil and discuss insights. In every strategy discussion, there's a monster in the room no one will name, even though the point of strategy is to identify and then construct a battle plan against the monsters. Written plans and strategies are all optimism and confirmatory data, rather than crystalizing the scary challenges so that we can attack them together. Those who break through this barrier are rewarded, as many famous books and frameworks point out. It's fun to see this in action, because the examples are individually interesting, and because a totality of instances illuminates the pattern. ### Radical Candor "When you say 'um' every third word, it makes you sound stupid." Not the feedback Kim Scott was hoping for after what she thought was a successful presentation to executives at Google. And it came from Sheryl Sandberg of all people. (Before she joined Facebook). Now Scott knew, that Sandberg knew, that Scott *wasn't* stupid. Therefore, it was obvious to her that this feedback was meant to help, not "obnoxious aggression" as Scott would later name the same straightforwardness when the motives are to belittle and hurt rather than to coach. And so Scott was grateful for being made to "face the truth." In her words: > Why had nobody told me for 15 years? It was like I'd been walking through my whole career with a hunk of spinach between my teeth and nobody had had the courtesy to tell me it was there. The truth is a courtesy; conversely, failure to face the truth is at least impolite, and at worst "ruinous" in Scott's terminology, as when a manager withholds necessary feedback for fear of hurt feelings or being seen as unkind. Scott is now famous for promulgating this philosophy of direct, honest, empathetic facing-of-the-truth in her book, [_Radical Candor_](https://www.radicalcandor.com/the-book/). It starts with a foundation of personal trust--trust that the feedback-giver genuinely cares for the well-being of the feedback-receiver, and that it is given in the spirit of "facing the truth" and not in the spirit of domination or manipulation or other ill-intent that people in positions of power might visit upon those subject to that power. We're thankful when a coach admonishes us and shows us a better way; a coach that lied to you, even by omission, telling you everything is fine when it isn't, would be a bad coach. ### Five Dysfunctions of a Team Possibly the most famous book on diagnosing problems in team dynamics and building high-functioning teams, [_Five Dysfunctions_](https://www.amazon.com/Five-Dysfunctions-Team-Leadership-Fable/dp/0787960756) by Patrick Lencioni starts with the observation that great teams are built in layers, each requiring the next to be effective: ![Diagram of Patrick Lencioni's "Five Dynfunctions of a Team" framework](https://public.asmartbear.com/books/anthology/chapters/failure-to-face-the-truth/image-simple.png) Exactly like Scott's Radical Candor, it is critical that a team be capable of constructive conflict, "discussing the real issues," facing the truth. That requires the team to first build trust, so that it is safe to have conflict. "Failure to face the truth," together, in the open, prevents teams from being great. We must have enough trust in each other to face the truth, together. If someone can't handle that trust, maybe they're not right for the group after all. ### Good Strategy, Bad Strategy In one of the seminal books defining "strategy," Richard Rumelt lists common hallmarks of bad strategy. Each one is so common, you'll shake your head and laugh (or cry, if you're guilty of these sins): > • *Fluff*. Fluff is a form of gibberish masquerading as strategic concepts or arguments. It uses [non-specific or] esoteric concepts to create the illusion of high-level thinking. > > • *Failure to face the challenge.* Bad strategy fails to recognize or define the challenge. When you cannot define the challenge, you cannot evaluate a strategy or improve it. > > • *Mistaking goals for strategy.* Many bad strategies are just statements of desire rather than plans for overcoming obstacles. > > • *Bad strategic objectives.* A strategic objective is set by a leader as a means to an end. Strategic objectives are "bad" when they fail to address critical issues or when they are impracticable. > > --Richard Rumelt, [Good Strategy, Bad Strategy](https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239), p32 His phrase "Failure to face the challenge" is my inspiration for "Failure to face the truth." Strategy must identify and then address the most important and difficult facts-of-the-matter of the market, competitive space, customers, product, and team. It's scary to say "Our market is shrinking," but if it's true, and you refuse to identify it, if you don't write it down that crisply, if you don't challenge the company to come up with alternatives for how to address it, if you don't build a strategy that expressly attacks it head-on, then it will be fatal. Face the truth. ### Lazy language belies a deeper failure There are traces of "failure to face the truth" even in Rumelt's other bullets. Fluffy language is a personal peeve of mine. In the benign case it is simple laziness--avoiding the work of crafting prose by filling the requisite space on the page with jargon and generic phrases. In the worst case, it belies the lack of having thoughts in the first place. This problem is rampant in marketing and content-marketing, but sticking with the theme of strategy, it's especially common in "vision" or "mission" statements, theoretically summarizing the company's aspirations, but often just a non-specific blob that also applies to any other company in the space, e.g. > A leading provider of website development for businesses of all sizes. Well, that was easy! Easy because it says almost nothing, and as a result, it's wrong. Wrong because it doesn't develop websites both for coffee farmers in Ethiopia and also for Tesla's launch of their next vehicle ("all sizes"). Perhaps it's mere laziness; after all, the only words that communicate anything about what it *is*, is "website development." Or perhaps the company refuses to "face the truth" of what it really does, and who it really serves, where it's really strong but also weak, afraid to say "no" to any potential customer. What if, instead: > We are boutique artisans chosen by discerning businesses who demand the highest-quality, completely unique web experiences. Sounds like a small company ("boutique") who nevertheless charges a lot (also "boutique"), but delivers amazing work ("discerning" and "unique"). By saying "no" to people who don't want that, you get to say "yes" to interesting projects at profitable rates, even beating out larger competitors who you can argue are just "factories that churn out the same website with different colors." But only if you face the truth. ### Confront the brutal facts In [_Good to Great_](https://www.amazon.com/Good-Great-Some-Companies-Others/dp/0066620996), one of the most-cited books on the formula ([if such a thing exists](https://longform.asmartbear.com/survivor-bias/)) for successful businesses, Jim Collins frequently returns to the story of A&P and Kroger--two companies alike in dignity in fair 1960s America, where we lay our scene--yet Kroger catapulted past A&P because, in Collins's words, only Kroger was willing to "confront the brutal facts." ![Kroger and A&P grew about the same for twenty years, both under-performing the broader market. Grocery stores are hard. (Source: Good to Great)](https://public.asmartbear.com/books/anthology/chapters/failure-to-face-the-truth/image021-simple.png) ![Kroger exploded with success after "confronting the brutal facts," while A&P continued to falter. (Source: Good to Great)](https://public.asmartbear.com/books/anthology/chapters/failure-to-face-the-truth/image022-simple.png) Collins tells a fantastic story, abridged here: > A&P stood as the largest retailing organization in the world and one of the largest corporations in the United States, at one point ranking behind only General Motors in sales. Kroger, in contrast, stood as an unspectacular grocery chain, less than half the size of A&P. > ... > A&P had a perfect model for the first half of the twentieth century ... cheap, plentiful groceries sold in utilitarian stores. But in the affluent second half of the twentieth century, Americans changed. They wanted ... bigger stores, ... fresh-baked bread, flowers, cold medicines, forty-five choices of cereal, and ten types of milk. ... and they wanted to do their banking and get their annual flu shots. In short, they no longer wanted grocery stories. They wanted super-stores. > ... > Here's what's interesting: Both Kroger and A&P were old companies heading into the 1970s [Kroger 82, A&P 111]; both companies had nearly all their assets investing in traditional grocery stores; both had strongholds outside of the major growth areas of the United States; and both companies had knowledge of how the world around them was changing. Yet one of these companies confronted the brutal facts of reality head-on and completely changed its entire system in response; the other stuck its head in the sand. Collins goes on to detail this last sentence--how both companies were fully aware of these changes, A&P even opening a test store under a different name, which was a "super-store" that didn't even sell A&P items, which outperformed their standard stores. Not wanting to face those facts, they shuttered the test store and went back to business-as-usual. Meanwhile, Kroger made the same experiments, found the same results, and decided to pivot the entire company to become the modern-day supermarket. A&P saw the truth, but failed to face the truth. Whether it's Jim Collins analyzing dozens of companies or Rumelt with his experience with dozens of strategies, the pattern is the same: Face the truth, or be killed by it. ### When it's hard, we avoid it It seems obvious that avoiding the truth would lead to bad outcomes, so why do we do it? Because it's hard. It's not the only obvious thing in our lives that we avoid, solely because it's hard. Diet? Exercise? Giving feedback? Working on our relationships? It's hard to tell the truth to someone's face. It's hard to realize that your industry has completely shifted, and it's *really* hard to say that out loud in front of the whole company. It's hard to say "no" to a customer when you have bills to pay, and it's hard to make a major strategic choice, because what if you're wrong? That's a good explanation for failure to face the truth, but it's not a good reason. Face the truth. > "Go ahead. Faith will follow." > --Jean-Baptiste le Rond d'Alembert, encouraging early practitioners of calculus, even though rigorous proofs of its legitimacy were still a hundred years away.

Extreme brainstorming questions to trigger new, better ideas

Jason Cohen — Sat, 26 Mar 2022 00:00:00 +0000

![boy what day huh](https://public.asmartbear.com/books/anthology/chapters/extreme-questions/d6064print-simple.png) How do you generate ideas? "Brainstorming" is hard--staring at a blank whiteboard, wondering whether someone could make a real-life "dark mode" whiteboard, then realizing that's what a blackboard is, only dustier. Or the modern version, seventeen pointers flying around a Miro board, zooming to 2000% to read that auto-scaled-down text in each standard-sized virtual sticky-note. We're blinded by our daily work: No forest, all trees. It's too easy to glom onto an idea that's been knocking around for year, its importance undeservedly increased by the familiarity of repetition. It's hard to think past the last seven customer interviews or support tickets or sales calls. Those are great for generating tactical ideas that fuel our roadmaps, but they are tiny increments that cement our tiny thinking. **The following prompts jostle you out of tiny thinking.** Each stretches some dimension of reality to an extreme. So extreme that it is nearly nonsense. But dramatically different perspectives can reveal distinctly new ideas. An idea that would be a 60% solution in an extreme hypothetical case, could be a 2x or even a 10x idea in reality. Sometimes the extreme is surprisingly appropriate. Unique business models emerge when at least one dimension is so extreme that it defies critics and competitors to even conceive of its possibility.[^1] A fantastic idea fulfilling the right extreme can be a company's entire strategy, unlocking a long-term competitive moat. [^1]: All of these were considered impossible barriers or business models, until a company did it anyway and won because of it: Zappos' free shoe returns 364 days after purchase, Robinhood's $0 trading fees, Amazon's free delivery with Prime, Netflix's mail-order DVD rental, open-source software companies charging for something that's 100% free and 0% secret intellectual property (now hundreds of billions of dollars in combined public-company value), AirBnB, Uber, and Lyft trusting that strangers will trust strangers in the intimate spaces of cars and homes. It's worth a try. ## 10x prices If you were forced to increase your prices by 10x, what would you have to do to [justify it](https://longform.asmartbear.com/more-value-not-save-money/)? What sort of brand looks and feels like something that expensive? What positioning would you take? How would the design of both the website and the product need to change? What subset of your market would you have to target? Do they have different problems that need solving, or different needs? Would they consider the high price a positive, because it fulfills a need to be seen as someone who is successful, or because they feel "buying from the best" lowers the risk of their decision? What would you need to do for them to be able to display that badge? What sort of relationship would you want to cultivate with each customer? How would [your business model](https://longform.asmartbear.com/problem/) change? What expensive services might you need to supply? Human support? Infrastructure? Is it possible for those costs to be "only" 2x or 3x more than today, so that the net impact is massive profitability? *Often early startups charge too little, and established companies struggle to charge more to existing customers. Thinking about what could justify a massive hike, you might be able to do some of those things and justify at least 2x.* ## No customers If all our customers vanished, and we had to earn our growth and brand from scratch, what would we do? How would we distinguish ourselves with a unique compelling message, as a fresh competitor in our market? What would we put on our home page? Would we use a different pricing model to compete better? Would our brand need to change to align with the message? Would we throw out some features that take a lot of work but not many people actually use (since there's no pesky customers hanging on to the expectation of the old features)? Would we build some new feature that would make us more competitive? Would we change our infrastructure or architecture or UX dramatically because of what we now know, since we're not ladened with existing customers? *Often we don't make important changes because we've gotten complacent with our marketing, or we never got around to having a truly compelling unique positioning statement, or we don't want to incur the penalty of big changes on our customer base, even if it means we're doing the wrong thing today from a competitive standpoint, and the wrong thing for the future. We don't want to make even 5% of our customers mad, even if it would be better for the other 95%. There will be 10x more customers in the future than there are now, but only if we build for them, today.* ## No tech support If you were never allowed to provide tech support, in any form, what would have to change? ![so as you can see customer satisfaction up considerably since phasing out complaint forms](https://public.asmartbear.com/books/anthology/chapters/extreme-questions/d0512print-simple.png) How would on-boarding need to be improved, to the point where customers would self-serve and be happy doing it? This might be the hardest step, because the customer is least-familiar with your product, and least-motivated to power through barriers to their success. And setup stuff often has to be done only once, so you might not learn how to improve those things from existing customers. Where would the product provide the user with more control, since they can't ask Support to do it for them? What information requires better visibility, since they can't ask Support to look it up for them? What actions would need to be become more intuitive, because they can't ask Support how to use functionality that they know "is in here somewhere?" _Self-service isn't just better for your costs and scaling, it also makes customers happier and [more loyal](https://www.amazon.com/Effortless-Experience-Conquering-Battleground-Customer/dp/1591845815). Therefore, dramatically improving self-service is often profitable for both the top- and bottom-line._ ## Maximize fun What would be the most *fun* thing to build? It has to be something that makes our product better, not a random technology or unrelated market. Aside from that constraint, it can be anything--building a feature you just think would be cool for customers to use, developing a technology that would be fun to work on (that happens to deliver a feature customers want), refactoring infrastructure or architecture using some interesting modern technology that would also improve something like cost-efficiency, scalability, testability, or maintainability. What if you held a one-week hackathon and actually tried to build one of those things? *When we work on things that are fun, we work better and harder, yet are happy to do it. Therefore, biasing towards fun is also biasing towards productivity and long-term fulfillment, which is practical and logical, not merely indulgent as it first appears. Ultimately, work still needs to advance the strategy while improving something substantial for the customer or the team, but typical prioritization under-appreciates the power of fun.* ## Complete rip-off If our biggest competitor copied every feature we have, how would we still win? Is the answer inside our product, in something other than the utility of the features? What is that, and how could we make more of that, so we're differentiated even in the face of a copy-cat? Is it ease of use, ease of sharing, pleasure of great design? Is it building the next unique feature so quickly that no competitor can catch up (since in this hypothetical they're just copying us rather than being insightful on their own), and if so, what is the killer next feature that would leap-frog us from a customer's perspective? If the answer is outside of our product, where is it? A higher purpose or brand-promise that our customers buy into, aside from the product? Is that because we are good at communicating how we make the world better, or because our customers are "rooting for David against Goliath," or how our culture is special, or because our brand is distinctive (even if just "it's the best" or "it's the biggest"), or because our customers trust us, maybe because of our customer service, or because of how we handle situations that go poorly, or because we "give back" in forms like open-source or community-building or significant philanthropy? *All your good ideas will be copied; it's just a question of when. Competing only on features results in bullet-point battles; this is the weakest way to win sales. Creating bigger and more emotional distinction is a powerful way to win, and breaks us of the habit of believing that incremental product updates will dramatically increase differentiation, or growth.* ## No time What if we are forced to ship a full, completed MVP ([or actually, SLC](https://longform.asmartbear.com/slc/)) new feature, in just two weeks, that would delight and surprise some fraction of our customers. > "To achieve great things, two things are needed: a plan, and not quite enough time." > --Leonard Bernstein Every bit of complexity has to be stripped out--we can add it back later. Scope has to be reduced to a minimum, which reduces how many people can use it on day one, but increases how much we'll learn on day one. Can we stub out an API, implementing it manually for now? Can we hook up some 3rd-party system, implementing something more efficient later? Can we skip documentation and internal support training and perfect error handling, not forever, but just long enough to see how people like this new functionality? A 1000-page book can be summarized in a 40-page Cliff's Notes. The key lessons of Cliff's Notes can be summarized in a 1-page blog post. The main theme and one unique perspective of the book can be summarized in one sentence. It can always be smaller, if we trade off detail, or precision, or completeness. *Hackathons prove that we can code really cool things in a short time, when we want to, and if we make certain trade-offs. It's not fully finished, not polished, not ready to scale, but it's something substantial and far more "agile" than teams normally are. If you feel like you've lapsed into a waterfall with two-week report-outs, this might be the shake-up everyone needs.* ## Flipped business model What if you were forced to charge customers in a completely different manner? The product isn't free, but somehow you have to justify value (and costs) differently. If you're usage-based, you're forced to charge a flat monthly rate; if monthly with tiers, you're forced to measure and charge daily with some formula. ![instead of allowance how about subscription model](https://public.asmartbear.com/books/anthology/chapters/extreme-questions/d6535print-simple.png) How would you have to change how you position the value you deliver? Would you have different kinds of customers due to prices effectively being much higher or lower, and how would that affect brand, messaging, marketing, or sales? How would costs have to change, so that the vast majority of customers were still profitable? Would software architectures need to change? Would marketing or sales structures change? Is tech support suddenly much more or much less possible? Are there features which you'd need to build (e.g. so customers could control their costs), or features you'd now be able to supply (e.g. because customers would now see the utility in it, or would be happy to pay more because of it)? Would you create features which caused higher prices, but in concert with additional value, so that customers would be happy to grow with you? *Business models force you into certain frames of thinking about how to monetize value that you create, and how costs need to scale with sales and infrastructure. The general idea for any company is to generate far more value than it charges for, so customers are genuinely better off in the exchange, and are happy to be long-term customers. Shaking up the business model shakes up the value/cost equation; sometimes a different business model is actually better for everyone.* ## No website If you were not allowed to have a website, how would you still grow your business? ![web developer](https://public.asmartbear.com/books/anthology/chapters/extreme-questions/image-2-simple.png) Could you grow by word-of-mouth? Does the product help people do that proactively? Could you create advocacy, as in channel partners or ambassadors? Could your social media be so good, someone would sign up for the product without needing to know more? Could your product *be* marketing? Could you cheat, where your "website" is inside the product, where potential customers are caught up learning more and trying things in the moment, so that they become users before they realize what's happening? *Your advocates are already using your product, yet so often we leave "getting more customers" to the marketing department, rather than realizing the product is already a platform for growth. Truly viral products know this already (e.g. you can't use a chat service unless you invite other people to the chat service), but non-viral products can still create growth machines from inside the product. What if "in-product marketing" is more impactful on growth than any useful feature?* ## No meetings What if you made your most introverted teammates' dreams come true: No more synchronous meetings, ever again? What if you had to on-board new employees without meetings? Or operate the software, deploy the software, find where things are located? What systems would need to exist, and what materials would need to go in there? What format would allow you to update that easily? What if you had to make decisions without meetings--the process as well as the result of that decision? Deciding what to build, prioritizing big or small things, brainstorming new things? Is it possible that brainstormed ideas could be even better without meetings (because people aren't being influenced by other people, ahead of having their own thoughts) or that decisions could be even stronger without meetings (because people have the time and space to think, to research, to try things out, thus more completely exploring the solution space?). How would you create social ties and inside jokes and 1:1 as well as group relationships, if you're never in the same space at the same time? *Meetings are still the best way to accomplish certain things: Social bonding, deep discussions where ideas rapidly ricochet off each other, decisions where it's important to "look in everyone's eyes" to get the final agreement, and more. But one of the most common complaints in any company is "too many meetings," or at least, "too many useless/bad/wasteful/inefficient/boring meetings." Besides the usual admonitions about meeting hygiene, a bigger question is whether your systems and processes can not only prevent them, but increase utility beyond that. For example, how much faster could a new team member get up-to-speed if they could read "the why" behind the last 20 major decisions?* ## No customer contact If we could never talk to our customers again, how would we figure out what to build? ![bear tech support](https://public.asmartbear.com/books/anthology/chapters/extreme-questions/image-simple.png) Could you measure their behavior so well that you could quickly measure whether any change was positive or negative, so at least you could iterate your way to a better product? Are there clear signs of value or happiness that you could use like marketers use conversion with A/B testing? Could you be even faster at testing new ideas? Could a feature start out as just a button that says "coming soon" or asks "why did you want this" or "what did you expect to happen" or "get emailed when we make this feature," so you could directly measure what people are likely to use, before you invest in building it? What could you analyze online, in what customers or competitors or observers are saying, that would inform your strategy or even product roadmap? Could you do better than just copying what others are doing? Could you analyze what people are saying about us or competitors online, understanding that the loudest voices aren't a random sample of the population, but still might have something useful to tell us, even when they're not being constructive about it? *Sometimes you interview customers and come away with the wrong feature ideas anyway. Sometimes you ask them whether they'd use something, and they say yes, and they even meant it, but after spending five months building it, they don't actually use it. Stretching your ability to get empirical signal helps you avoid those issues, and might even create breakthrough ideas, or avoid ideas that seem great, but aren't.* ## Cost is no object What if it didn't matter how unprofitable you were? Maybe prices stay the same but you can spend 10x or 100x more, even if that's wildly unprofitable. Or maybe the product is free, too. How would you spend the money? How much value could you deliver, if you thought of the product as an exercise in philanthropy? It could be infrastructure or software, but what about the personal touch? What would a dedicated, personal concierge do? If software could do just 30% of that job, but at 1/100th of the cost, that could be a good feature. *It can be surprisingly difficult to convert money to real customer or business value, even when money is free. If there is a way, it might lead to a new pricing tier, leveraging that idea but adding back a sustainable business model.* ## Sociopathic CEO What if you could change anything, regardless of what anyone thinks or feels? Terminating an entire product line would shake the organization, and possibly incur layoffs in multiple departments. But what if it's the best thing to do, despite that? What if you allowed yourself to explore what that would be like? What if it turns out you can make that pivot by reorganization rather than layoffs, and now company is executing a much better strategy? Pivoting the entire company could break the organization, destroy trust in leadership, cause people to leave, and piss off investors. But what if that's exactly the transformation required to fix the strategy? Is there anything that might actually be worth such a move? ![restructuring im tom tom anne anne copier copier in charge](https://public.asmartbear.com/books/anthology/chapters/extreme-questions/d7278print-simple.png) *You never want to contemplate catastrophic actions like layoffs, whole-company pivots, terminating an entire product line, and for good reason--the consequences are dire, arguably even inhumane. At the same time, that aversion prevents you from thinking certain thoughts. You should at least be able to explore the thoughts. The very best ideas should not be avoided only because they are hard.* ## Mortal wound What externality has the potential to kill the entire company? Is there a company X, which released product Y, at price Z, that would reduce your new sales to a trickle, and would mean half your customers leave within a year? Can you think of a security breech so significant, most of the customer base leaves within a year? How could the economy change, in which no one would buy your software? Now, is there something you could do to mitigate that effect? Or something that pivots the company such that it is no longer a threat? *I ran this exercise at WP Engine and we came up with 35 existential threats; it's not wise to worry about all of them, and try to negate all of them. Sometimes, however, you find an idea which is wise to do anyway, that also shores up your defenses. Or, one threat sticks out as being so likely that it warrants acting ahead of time.* ## Philanthropist What if our only goal were to create the most good in the world, personally for our customers? Ignore how to make money; assume that works itself out. But still talking about our customers (don't argue about which world-wide causes are "most important"). How could we make their lives better, not because our product has greater utility, but because our product is doing something important. How could we help them achieve [their ultimate goals](https://longform.asmartbear.com/needs-stack/) rather than just "use features?" If our customers are small businesses, could we help them grow and thrive? If our customers are writers, could we help them improve their craft? If our customers are performers, could we get them more attention? If our customers are enormous companies, could we help them use their greater scale and wealth to create more good in the world? If our customers come from all walks of life, could we increase the access and equity in their reach and power? _There's already strong evidence that having a [mission bigger than yourself](https://longform.asmartbear.com/mission-vision/), more than metrics, [creates more--and more loyal--customers and fulfilled employees](https://longform.asmartbear.com/fulfillment/) both. Recent surveys of Gen Z show that they care about whether a company does good in the world just as much as they care about compensation. So, even aside from the ethical reasons, there are "self-interested capitalist" reasons to create more good, not just more MRR._ ## Only one thing this year What if you could only ship one thing this year? What single initiative would make the most difference? What's so impactful that it would actually be OK if we shipped nothing else? You get the entire year, so there's time for a release, and fixes, and enhancements, but it has to be only one topic. What would generate the most revenue? What would be so differentiated in the market, and so desirable, that customers still buy and stay even with your lack of any other substantial product changes? What would be so exciting that customers would stay even if other things are missing or have on-going bugs? *When you look back over years of a business, often the whole trajectory comes down to 1-2 big decisions per year. A critical product launch, a key decision to enter a market for expansion or exit one market to focus on a healthier one, a key hire, a competitive insight. It's difficult to know in the moment which will be seminal, but thinking this way forces you to think of only the absolutely most impactful ideas, which are probably the ones you should be focused on regardless.* > "The dangerous man is the one who has only one idea, because then he'll fight and die for it. The way real science goes is that you come up with lots of ideas, and most of them will be wrong." > --Francis Crick

The Elephant in the room: The myth of exponential hypergrowth

Jason Cohen — Sat, 05 Mar 2022 00:00:00 +0000

A startup is growing fast, the journalists marveling at its "meteoric rise." But don't meteors fall? Inevitably it is breathlessly inducted into the class of "hypergrowth" companies that are "growing exponentially." Especially when the product is "viral." After all, if every person brings three friends, and each of those brings another three, is that not exponential? But "exponential" is an incorrect characterization, as we'll see in real-world data, even for hypergrowth, "viral" companies like Facebook and Slack. This article suggests an alternate model for how fast-growing companies actually grow. Understanding the model is useful not only for predicting growth, but because understanding the foundational drivers of growth allows us to take smarter actions to create growth in our own companies. ## Dispelling "exponential" To evaluate whether hypergrowth is properly described as "exponential," let's recall what that word means. Here's an exponential curve (like `$y=2^x$`), compared to a quadratic one (like `$y=x^2$`): ![Graph comparing exponential and quadratic curves](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/exponential-vs-quadratic-curves-simple.png) In exponential growth, values grow by a *multiple*. For example: In year 1 you grow 10, in year 2 by 100, in year 3 by 1000--each time the amount of growth is *multiplied* by ten. The compounding effect of multiplication causes the numbers to grow slowly initially, then skyrocket. The compounding effect gets journalists and VCs justifiably excited. > "Compound interest is the most powerful force in the universe." > --Albert Einstein In quadratic growth, values grow by a *adding* a constant amount more each time-interval, rather than *multiplying* a constant amount more each time-interval. In the same example, growing in year 1 by 10, then in year 2 by 20, in year 3 by 30: ![Successive values (in blue) are increasing more and more (in green). The green differences are increasing linearly: 10, 20, 30.](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/parabolic-growth-simple.png) Growth is still *accelerating*, so the blue curve slopes upwards, but gently compared to exponential growth. With these patterns in mind, let's examine real-world data, and see whether "exponential" is the right model. --- **Facebook** is the definition of hypergrowth--getting to $50B in revenue [faster](https://www.bnnbloomberg.ca/how-facebook-became-one-of-the-fastest-growing-companies-in-u-s-history-1.1213047) than any company in history. The product is "viral"--friends bring other friends--which theoretically leads to "exponential growth." But Facebook didn't grow exponentially in the number of monthly active users: ![Essentially linear for nearly twenty years, only exponential in the first four years.](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-maus-simple.png) **Slack** was [the fastest-growing enterprise software company ever](https://www.forbes.com/sites/johnkoetsier/2018/11/30/how-slack-became-the-fastest-growing-enterprise-software-ever), going from $0 to $10M ARR in their first 10 months, and 0 to 10,000,000 active users in just five years. It's also a "viral" product--organizations invite their members, who then create their own Slack-groups and invite others. So surely Slack has exponential growth? (Figure 4) ![Slack's own data shows initial quadratic growth, followed by years of linear growth.](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/chart-slack-daily-active-users-5-1-simple.png) **Dropbox** was another "hypergrowth" company, achieving 100,000,000 registered users five years after being founded in 2007, but it wasn't exponential, neither in freemium users nor in revenue, early nor later in life (Figure 5 & 6). ![Early in life, Dropbox registered users grows non-exponentially, nearly exactly 100M per year](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/utpyl96vse14m-gmxarynrsryhh1ji-zy2gu-cwfgdvuxrx8we7vhw2hoarnf7afwwa6ldihh5klety1tfwtahlkkbxozcilt8llsjaiqxlw4hronowhznhekhv-w4a2rnt2gqic-simple.png) ![Later in life, Dropbox revenue grows linearly, and slows down](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/c3e43e95-cdec-4fa5-acf1-fa9c288fb64d-simple.png) **Trello** grew fast too, getting to 10,000,000 registered users in three years. But not exponentially: ![Trello users growth curve](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/image-simple.png) Lyft grew in part due to "network effects" according to [their S1](https://www.sec.gov/Archives/edgar/data/1759509/000119312519059849/d633517ds1.htm), but this chart they presented shows that active rider growth isn't exponential: ![Lyft rides growth](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/g633517g84b30-simple.png) **HubSpot**'s revenue curve is astonishingly consistent, despite hitting multiple inflection points[^1] in their business: [^1]: e.g. launching new business models like selling through agencies instead of only directly, launching new product lines like sales CRM on top of marketing automation tools, and scaling the number of customers and employees by 10x ![HubSpot's revenue growth curve](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/hubspotrevenuegrowth-simple.png) Analyzing this last example, we arrive at a new, non-exponential model. ## Hypergrowth is quadratic The language we use [can determine](https://en.m.wikipedia.org/wiki/Linguistic_relativity) the thoughts we have. The HubSpot slide says "41% CAGR." "CAGR" means annualized growth rate. They're saying that if you start with the first number on the slide, then from there plot growing 41% per each year, compounding each year upon the previous, for seven years, you would arrive at the last number on the slide. This is exactly the definition of "exponential"--multiplying by a number repeatedly. In general when you use "CAGR" or "percentage growth" as a metric, you are implicitly saying "This is an exponential process." But HubSpot didn't grow by 41% every year; in this time frame, it started at 60% and ended around 30%: ![HubSpot's YoY growth rate](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/hubspot-yoy-growth-rate-simple.png) If instead we examine growth in absolute dollars, rather than in percent, a pattern emerges. In the first set of four quarters on this report, they grew $17M. The next set grew $23M. Then $28M. Then $34M. Each year $5-7M more than the previous. This is the definition of a quadratic--*adding* an amount that increases by a constant amount each period, not *multiplying*. Charting these year-over-year revenue differences *in absolute dollars* rather than *in percent*, it's clear that indeed the changes were almost completely linear for years, then suddenly changed in 2020[^2] to a new (but still linear) rate: [^2]: Coinciding with the launch of a new product: HubSpot CMS Hub. ![HubSpot's YoY revenue change](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/hubspot-yoy-revenue-change-in-m-1-simple.png) It is therefore mathematically inevitable that plotting a quadratic curve (rather than exponential) on top of HubSpot's revenue data will be a perfect fit: ![When you said "best-fit," you weren't kidding!](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/hubspot-revenue-best-fit-parabola-simple.png) My thesis is that **High-growth companies grow quadratically, not exponentially.**[^3] [^3]: My guess is low-growth companies are similar, but data are more easily available for the runaway-growth companies who publicly flaunt their success. The consequence of this conclusion is important for operators and analysis and investors. These are all people trying to understand--and possibly change--growth drivers. Getting the right language, and the right model, will lead to right analysis, and right action. ## Why marketing-driven products grow quadratically: A first-principles explanation It's not enough to draw best-fit lines on top of PowerPoint slides. We have to explain *why* this model makes sense, which in turn will create a better understanding of the growth drivers in our own companies. We've been taking a macro view of growth, looking at multi-year trajectories. Now we'll peer into the microscope instead of the telescope, and consider how growth arises from a single marketing campaign. ### The life of a marketing campaign In my experience, marketing campaigns follow this pattern: ![Idealized shape of a marketing campaign](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/idealized-shape-of-a-marketing-campaign-3-simple.png) At the foot of the curve, we've launched a new campaign, but it's ineffective; we haven't figured out the best design and messaging and calls-to-action for this new medium and audience. Sometimes we never figure it out, and abandon the effort.[^4] [^4]: It's hard to distinguish (a) our failure to build effective copy and conversion funnels from (b) channels that are fundamentally a bad fit for our market or product. This uncertainty, together with the rapid evolution of digital marketing, suggests that we should retry campaigns in previously-failed channels every few years. But in the case that we unlock the secret of efficacy, the campaign rapidly reaches a natural level of contribution; in this example, a number of "sales per week." The specific level depends on many things: ad inventory, our budget, audience-receptivity, and the consonance between the audience and our target market. Next we enter the optimization phase. We A/B Test our way to incrementally better results. Also we enjoy the result of multiple exposures--most people need to see the ad more than once before they act. Finally we enter a phase of decline. There are various causes, all instructive: * **The audience saturates.** Everyone in the channel has seen the ad more times than is required to act; it's now falling on deaf ears. Even if the audience is growing, the number of new people is small compared to the number of people that were new-to-us when we began the campaign. * **The channel declines.** A media site that was popular loses readers through over-monetization. An event that was well-attended loses favor. A newsletter that was frequent and insightful becomes less frequent or other writers take over. A podcast moves to a closed platform and loses many listeners. * **The auction becomes uneconomical.** For auction-based systems like Google and Facebook advertisements, or other [zero-sum](https://longform.asmartbear.com/startup-beats-incumbent/) programs like affiliates or limited-inventory spots on newsletters or podcasts, the winner is the one who will pay the most. What is cost-effective for one bidder will be laughably overpriced for another, due to better conversion rates, higher revenue per customer, higher profitability per customer, or due to categorization as a "loss leader" or other way of ascribing value beyond immediate pay-back. This curve leads to actionable ideas for managing marketing (given at the end of this article), but also forms my central thesis about how all sorts of growth works at companies. So I'm giving it a name: ![The elephant curve](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/theelephantcurve-simple.png) ### How the idealized marketing campaign converts to growth The model above shows the number of sales *per week* the campaign contributes. To understand how this looks in terms of revenue growth, let us suppose a simple business model in which all sales are for a recurring revenue product generating $10 per month, with a 1% per month cancellation rate. Revenue grows over time in a certain way: ![shape of growth of typical marketing campaign](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/marketing-campaign-cumulative-contribution-to-revenue-1-simple.png) Growth initially accelerates as the campaign is solved, then grows roughly linearly as the campaign is optimized, and then starts sagging (although still growing!) as the campaign declines, and as the now-sizable customer base produces a non-trivial number of cancellations. ### The layer-cake of quadratic growth Marketing departments don't stop at a single campaign. They add new ones. Some are bigger than others, some can be optimized more than others, some decline sooner than others, some decline more precipitously than others. So, let's model that: A variety of Elephant Curves, with differing parameters, beginning at different times, stacking the revenue-contribution of each to arrive at overall revenue growth for the company: ![Layered campaigns create a "wavy quadratic."](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/layered-marketing-campaigns-simple.png) Scan your eye across the top of this kaleidoscopic cake, and you trace a wavy quadratic. This makes sense mathematically, because each campaign is essentially linear after it gets going, even if it sags during decline. "Adding more linear things over time" is the definition of a quadratic. The reason it's "wavy," is that when we unlock a new campaign we get a burst of growth. Do real-life revenue curves exhibit this waviness? Maybe so; here's another slide from the HubSpot deck: ![HubSpot's growth curve over time, generally quadratic but exhibits that wavy behavior](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/hubspotwavyrevenue-simple.png) HubSpot didn't just add new marketing channels, however, but also layered on new geographies and new products. Do those activities have the same effect as marketing campaigns? ### Multiple product lines at marketing-driven companies: Still quadratic So far we've assumed a single product, driven by marketing campaigns. High-growth companies who want to continue growing quickly after their first product reaches scale, must launch new products into new markets. Is the Elephant Curve also the shape of an entire product line? After all, products often have an initial slow-growth period (because only cutting-edge early adopters are eager to pay to "be first" with bugs and missing features), followed by a faster expansion period, then reach some sort of natural ceiling, and possibly enter a period of decline (as the market evolves or competition overwhelms). Indeed, this is what we see with many products, especially those that are marketing-driven, and without recurring-revenue. iPod sales, for example, are a perfect match: ![Quarterly iPod sales](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/o-ipod-sales-570-simple.png) It should therefore be unsurprising when we look at the overall revenue chart for Apple, and once again see quadratic growth on the top-line, admittedly with a special one-time bump for the unprecedented[^5] success of the iPhone: [^5]: It is rare for a second product to dramatically outpace the first; even juggernauts like Google, Amazon, and Facebook never achieved that. ![Apple's growth in past 20 years](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/large-towra6xiyts-vue2r39dsy1-v-w5tlco9-onxxfnxao-simple.png) Each product is in a different phase of its lifecycle: The iPod declined to zero, the iPad is still declining; Macs are teetering but essentially flat; iPhones and software services are still increasing. ### The quadratic explanation for "growth decay" It's well-known that growth--as a percentage--naturally declines with scale, even when there's nothing wrong with the company. This law of nature has been given a name: Growth Decay (or sometimes Growth Persistence). Because of the traditional insistence of talking about growth as a percentage, the concept is articulated this way: If a company grew X% last year, it's likely to grow a bit less than X% this year. With this formulation, the question becomes: How much less? The data give us the answer of 85%, although with `$R^2 = 0.51$`, this is a tendency but far from a law: ![Recurring revenue companies have an 85% growth decay per year](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/growth-correlation-simple.png) With our new appreciation that growth isn't exponential, and therefore "percentage" might be the wrong way to characterize growth, we could ask what curve would best model the idea of Growth Decay? Specifically, let's plot revenue for an initially-fast-growing company that is subject to the principle of Growth Decay: ![Plot of ARR growth with 85% growth decay](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/arr-growth-with-theoretical-growth-decay-simple.png) The first sixteen years of the curve is quadratic. While mathematically not identical, the best-fit quadratic curve[^6] has a staggering `$R^2 = 0.999$`. [^6]: `$y = 13.6x^2 -43.4x + 34.6$` when fit perfectly; interestingly this is very close to `$y = 13.6(x-1.595)^2$`, showing how simple the curve really is. This is yet another signal that quadratic growth is the correct model. ## Beyond marketing campaigns: "Viral" and other forms of "exponential" growth But some products really do grow exponentially. In theory. In theory, theory and practice are the same. In practice, they're not.[^7] [^7]: This phrase attributed to Benjamin Brewster. Some products don't grow proportionally with marketing and sales, but instead self-propel with a mechanism that theoretically ought to be exponential. There are at least three ways for this to happen: #### Type 1: Virality When each user invites on average another `$a$` users, then each of *those* `$a$` new users bring in *another* `$a$` new users, so we end up with `$a^2$` more. Then each of *those* brings in another `$a$` which yields `$a^3$`. Then `$a^4$` and so on; this is the definition of exponential growth. Biological viruses grow exponentially for a similar reason, justifying the label. *Examples:* social media, chat clients, peer-to-peer payment platforms, massively-multi-player games, fantasy sports leagues. #### Type 2: Word-of-Mouth All products have some word-of-mouth component, but here we're referring to products that are *primarily* driven this way; this creates a growth process that is similar to viral. Typically the mechanism of "telling others" is built into the product, rather than bolted on by marketing or generated by goodwill. The difference between "word-of-mouth" and "viral," is that viral products are *unusable* unless you invite others to become users (thus exponential growth is enforced) whereas word-of-mouth products *encourage* sharing. Thus chat clients are viral because without inviting others you can't chat, whereas Wordle[^8] was word-of-mouth, because you play the game alone, but are encouraged to share results on Twitter, which in turn brings in new users. *Examples:* gamified products that generate significant sharing (self-improvement, game-results), consumer-to-consumer marketplaces where being a buyer plants the idea of becoming a seller (eBay, Airbnb, Uber); organizations with a cause that creates on-going buzz (brazenly unique cultures, a passionate higher purpose, something people feel is linked to their personal identity). #### Type 3: Hot Trend Products that "everyone" (in some well-defined market) is going to buy. For smartphones, that might be half the population of the world. For internet search, that might be 100% of the online world. For backend management systems for large hospital chains, that could be 1000 potential customers. These products hit "tipping points" where "suddenly everyone buys it." Even if, like internet search, the product has no explicitly viral nor word-of-mouth component--when you search on Google, you don't "invite friends" to also search on Google--the ubiquity and inevitability of the trend leads to an explosion of users. *Examples:* word-processing, spreadsheets, broadband internet, the smartphone, the shifts to cloud computing and online shopping, major media delivery platforms of radio, TV, DVD, and video streaming. [^8]: Wordle [exploded](https://www.theguardian.com/games/2022/jan/11/wordle-creator-overwhelmed-by-global-success-of-hit-puzzle) from 90 players in November 2021, to 300,000 in December, to 2,000,000 in January, when it was [bought](https://www.nytimes.com/2022/01/31/business/media/new-york-times-wordle.html) by the New York Times. ### Logistic growth: Nearly the right model for virality Products cannot grow forever, for the obvious reason that markets are finite. The Facebook virus spread to billions of people, but not infinite. Smartphones have been purchased by billions of people, but not infinite. Therefore, even if "exponential" is the correct model for the core growth mechanism of the product, it nevertheless cannot continue growing exponentially because it runs out of market. Furthermore, markets tend to have so-called "low-hanging fruit"--customers who are more eager to buy--so although the virus spreads exponentially through these easy-pickings, it runs into the majority of people who will buy, but maybe later, maybe after more of their friends or competitors are using it, maybe if it's less expensive, maybe once it has more features, maybe once it supports integration with specific other software, and all manner of other excuses. The virus has more trouble infecting these high-strung fruits, so growth slows. This suggests a curve that starts exponentially, but then slows as it runs into the soft back-pressure of more demanding customers, and finally flattens out completely as it runs into the hard limit of the size of the addressable market. Biologists have already done the work for us, because this is the correct model not just for viral products, but biological viruses infecting a population--akin to product types 1 and 2 above. Intriguingly, this is also the correct model[^9] for the diffusion of a gas across a membrane--akin to product type 3. The mathematical model for all of these processes is the logistic curve: [^9]: The similarity is that in both cases you have a sudden demand that enters into a new space, but which slows and eventually stops as the new space becomes saturated. ![The shape of a logistic curve relative to an exponential curve](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/the-shape-of-logistic-simple.png) The logistic curve *is* exponential in the early days when it is far away from its natural limit. As the product (or gas or virus) gets to around 25% market penetration (or infections or saturation), the curve flattens into linear growth, in a tension between the exponential force of growth, countered by fewer and more demanding remaining targets. Finally it levels out at what is called the "carrying capacity"--the fully-saturated market. The logistic curve is evident in the real world, in all three product types (Figure 23 & 24 & 25 & 26 & 27 & 28 & 29). ![Twitter is a type 1 "Viral" product that follows the logistic model](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/twittermau-simple.png) ![Pinterest is a type 1 "Viral" product that follows the logistic model](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/pinterestusers-simple.png) ![eBay is a type 2 "Word-of-mouth" in the number of buyers, following the logistic model (though also sagging towards the end, reminiscent of the Elephant Curve)](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/ebay-buyers-simple.png) ![eBay also follows the logistic model in the number of sellers (with even more pronounced sagging)](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/ebay-number-of-sellers-logistic-then-falling-simple.png) ![Smartphones are a type 3 "Hot Trend" that follows the logistic model all the way to saturation](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/smartphonesales-simple.png) ![Smartphone usage, separate from smartphone sales, is also logistic. Many pundits predicted this percentage would grow nearly without bound; in fact it saturated at 55%.](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/percentage-of-web-traffic-from-mobile-devices-simple.png) ![The internet is a type 3 "Hot Trend" product with a near-exact logistic shape; at 66% global penetration, it hasn't reached carrying-capacity, but it's been in its linear mode for many years, and fell off the exponential path sooner than you might have expected](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/global-internet-users-2-simple.png) ### Stacking logistic growth: The quadratic reappears Marketing-driven products demonstrated quadratic growth, especially once Elephant-shaped campaigns and products were stacked. How does this differ with logistic growth? As already pointed out, logistic growth is similar to the Elephant Curve. The "high growth" portion of a marketing campaign might in fact be logistic; a product might extend that period into years rather than weeks, and the absolute magnitude of the result might be many times larger. If this idea is correct, we ought to see viral-like products exhibit a similar curve to the iPod curve--i.e. a product with initially-exponential growth, then a flattening, perhaps with some small growth, then on a long-enough timeline, a decline. An Elephant with a more stretched-out trunk. The Facebook Messenger product appears to exhibit at least the first half of the logistic curve: ![Facebook Messenger Global Monthly Active Users](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/d5a60cc6028eeccc2008b5971-simple.png) Furthermore, this curve is actually a sum of US growth and outside-US growth. Looking only inside the US, Facebook Messenger is further along the curve, past the linear midsection and already leveling out near some carrying-capacity: ![Facebook Messenger MAUs over time](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-messenger-maus-us-millions-simple.png) The same thing happens with Facebook DAUs and MAUs.[^10] DAUs in the United States and Canada are logistical and have already topped-out at an apparent natural carrying-capacity of 185 million: [^10]: Daily Active Users, Monthly Active Users ![Facebook daily active users](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-lost-daily-users-for-the-first-time-ever-in-the-u-s-and-canada-vox-simple.png) Breaking out MAUs by all geographies reveals that top-line growth of users is an aggregate of some geographies essentially not growing at all (late in the curve), while others are still growing, albeit also linearly (middle of the curve): ![Facebook MAUs](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-monthly-active-users-quarter-4-2019-chart-simple.png) The result of these individual effects of different products, released at different times, in different geographies, each with a "marketing campaign" style growth curve, is that it adds up to linear growth: ![Exponential growth for the first few years crashes down into linear growth for nearly twenty years, from large-scale logistic-shaped products and geographies](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-maus-yoy-change-1-simple.png) Does this conform to the Elephant Curve? Is this really still essentially quadratic? The answer is clear when we plot the same data, this time measuring the year-over-year change in MAUs. Not as a *percentage*, but as numbers: ![Facebook MAU growth is indeed an Elephant Curve: Logistic at first, then flat(ish), then starting to decline.](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-maus-yoy-change-numbers-not-percent-simple.png) Why do we keep seeing this pattern, even at the scale of Facebook, one of the most "viral" products of all time? Because mathematically, things that look like an Elephant Curve, even if the logistic "trunk" is elongated over time, are linear for nearly their entire lifetimes. Everywhere except the very beginning. Adding up linear things definitionally creates a quadratic. As a striking example of this claim--that multiple, various Elephant Curves result in quadratic growth in the real world--consider the detail behind the earlier chart of Global Internet Users over time, a type 3 product. Every country has grown logistically, at a variety of starting-times, diffusion rates, and carrying capacities, yet the aggregate is quadratic: ![Thirty years of varied logistic growth adds up to quadratic growth](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/share-of-internet-users-simple.png) To be certain the graph at the bottom (which is the same data as the chart shown earlier) is specifically quadratic, we chart the absolute difference in online population year by year. In a quadratic, these differences should grow linearly, i.e. each year adding a constant amount more than the previous year added. Which is indeed what we find, as precisely as we could expect from data in the messy real-world: ![global internet growth is quadratic](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/absolute-change-in-global-internet-users-1-simple.png) Bringing it back down to the scale of a single company, consider Netflix, another type 3 product. While their overall growth accelerates, under the hood we can see the US was already in a phase of slow-growth by 2014, with outside-US is taking up the slack through 2019: ![A quadratic top-line, created by two roughly-linear geographies](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/d5cb88c28d1a2f8140b6cae43-simple.png) If we chart the *changes* in subscribers, rather than totals, it's even more clear that growth in the US has been in the declining phase of the Elephant Curve for a while, with outside-US is growing linearly: ![Netflix growth slows](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/d960x0-simple.png) And then, fast-forwarding to 2023, looking at total, global subscribers, we see that growth slowed outside of the US as well, and the familiar Elephant Curve returns in its entirety: ![Netflix global subscriber count](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/netflix-global-subscriber-count-simple.png) ### Logistic growth with a varying carrying capacity: Start with market-share Suppose you're Facebook, and you've saturated many markets. You might be at carrying-capacity for those markets, but more people are still coming online. The markets are growing, so your carrying-capacity is growing, so you should still be able to grow too. Indeed, recalling the charts above, Facebook's current MAU growth rate, and that of global internet users, both are currently hovering around 7% per year. Which isn't a coincidence. Let's plot Facebook's MAUs as a percentage of people online--their *market share*: ![The Elephant Curve strikes again](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-maus-as-of-global-internet-users-simple.png) Finally we have the complete answer to why Facebook's growth appears so "linear," when the theory expects an Elephant Curve. When you examine growth *relative to market size* it *is* an Elephant, complete with logistic trunk, optimized back, and declining rump (even despite a COVID bump). This is why at-scale companies are willing to spend billions of dollars increasing the size of the market--it's one of the few ways to create growth other than raising prices. So Google spent billions on Loon--a subsidized service to bring low-cost internet to remote areas of the world. Its problem-statement is the first text on [its website](https://x.company/projects/loon/): "Billions of people across the globe still don't have reliable, affordable access to the internet." Or, putting it another way, "Wifi balloons are a kooky idea but how else are we going to increase the carrying-capacity of the 'global internet user' Elephant Curve?" Or Facebook with its "Free Basics" system that ([in their words](https://www.facebook.com/connectivity/solutions/free-basics)) "Helps people discover the relevance and benefits of connectivity with free access to basic online services." Except actually it's only a few, hand-curated websites, all of which just happen to be western consumer products companies that are large Facebook advertisers, and the only available social network just happens to be Facebook. And there's no email, so I hope you like Facebook Messenger. In other words, a [digital colonialism](https://www.theguardian.com/technology/2017/jul/27/facebook-free-basics-developing-markets) whose purpose is to increase the carrying capacity of Facebook MAUs and the advertising that goes with it. Elephant Curves are more visible when we plot growth as market share, because this incorporates the idea that carrying-capacity of the underlying market can itself be a moving target. ### Logistic growth with a varying unit revenue We've largely been analyzing *users* rather than *revenue*, and for good reason: The lifeblood of any product is people who use it, regardless how much money it can extract in the process. However, when we turn to revenue, we find that curves can become perkier. Facebook's user growth might be linear, but could it be that revenue is exponential? It's certainly not linear: ![Facebook advertising revenue](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebookadrevenue-simple.png) We already know Facebook's user growth is linear, so the missing piece is Facebook's revenue *per* user: ![Logistic or Elephant yet again](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/facebook-revenue-per-user-simple.png) Perhaps by now we're not shocked to see the Elephant Curve once again. And we also know how the rest of the story goes: Because MAUs are Elephantine (which means mostly linear), and revenue-per-user is Elephantine (which means mostly linear), when you multiply them you get a quadratic, not an exponential, and that's what we see in Facebook's overall revenue growth. ## Actionable conclusions When we seek out the Elephant Curve in our marketing channels, product lines, geographies, and verticals, not just in its hopefully-explosive initial phase, but its phases of optimization and decline, we can proactively look for these phases, and take action. ### Model by component Our final discussion on the value of analyzing components of growth separate leads to a prescription for analyzing growth. 1. Estimate the growth curve for the entire market. Expect to be Elephantine (or simply logistic, in the case of trends that you can reasonably assume will not decline in the forecasted future, like global internet use or smartphone use). 2. Estimate the product market-share curve. Expect to be Elephantine, and don't be so bold as to assume your product will never decline relative to the market--are you better at execution than Facebook? 3. Estimate monetization, i.e. revenue per customer.[^11] This curve *might* be Elephantine, but not necessarily. It is highly dependent on the product and market, on how distinct the product is competitively, on the budgets of the customers, and more. Facebook has a strong [moat](https://longform.asmartbear.com/moats/) (network effect) and doesn't charge end-users, so they (like Google) can raise prices consistently. A product in a commoditized market might never be able to raise prices, and thus must find growth in avenues like increasing usage, the introduction of companion products, expanding to other verticals or geographies, or by applying their technology to new markets. [^11]: The definition of "customer" should match whatever activity is most highly correlated with growth; this is also what "market share" should mean. For normal products people pay for, this is simply "paying customers," but for example in the case of Facebook, this is MAUs at least, perhaps even DAUs. You get better models by predicting each of these components separately, then multiplying for a final growth prediction. You're also better able to track the model against reality, as more data becomes available. --- Besides this break-down, there are many operational ideas suggested by the results above, especially for managing marketing campaigns. This might be expanded in a future article, but for now, these probing questions might lead to better ideas on how to analyze and affect growth: ### Advice for Marketing teams * Is our AdWords campaign topped out? Are we fooling ourselves into thinking there's more inventory to access? How much more optimization is there to be had, and how would know? Are we hitting a decline due to uneconomical auctions, and if so, what is our reaction? When should we start experimenting with new channels, rather than continue to flog the AdWords channel for results that don't exist? * Is it OK to be less cost-effective if it means we can stave off decline? Should we be that "irrational bidder" who bids "too much" because we're wise enough to see value beyond immediate cash pay-back? If so, how do we quantify that value, so we know just how "irrational" to be? * To hit our growth goals for the year, what would have to be true of the growth of existing campaigns? Which can be reasonably expected to grow, hold steady, or shrink, based on their phase? How many additional, successful campaigns do we need, and how soon? Since not all that we attempt will succeed, how many do we need to start to yield the final quantity we need? * Should we lean into newer channels before others figure them out, saturating the channel and cause clicks to be [both expensive and more rare](https://taylorpearson.me/ctr/)? * Rather than stack up small, limited campaigns, is there something more substantial that could generate more total growth? A single, large new geography instead of many smaller ones? A single, substantial new sales mechanism (e.g. reselling) rather than more advertising? A different pricing model instead of an additional sales model? Even if it takes 10x the effort, and possible even if it takes 10x the time, it might have 10x the results. * Or the reverse--do we pull funds when we smell decline, rather than spending our time and money fighting a losing battle, accepting a short-term hit on top-line growth in exchange for more efficient growth? Do we try to stack up many smaller, more efficient campaigns, generating growth as a bulk effort? Each effort affects the top line only marginally, but conversely our growth is less sensitive to the decline of any one campaign. ### Advice for Product Managers * It's great to add a feature to an existing product, but significant additional growth comes from increasing carrying capacity or creating a new avenue of growth. Early on you should focus on winning market share in one space, creating the first Elephant Curve, but after the product matures, something more drastic is required: Wholly new products, or updates significant enough to address new markets. * It's well-known that companies need to add additional products to continue fast growth after achieving scale. However the second product is highly unlikely to achieve same market share and monetary scale as the first, so there needs to be multiple, not just one.[^12] This requires serious investment, parallel efforts, and the chutzpah to kill off the ideas that aren't working. * Because word-of-mouth-driven growth is so much more effective than marketing-driven growth (both in cost-per-customer and in that unlike direct advertising it grows automatically as the company grows), it is worth a great deal of time trying to figure out how to build that into the product, rather than relying only on the marketing team. [^12]: This is true at any scale--advertising is still 82% of Google's revenue; of that 71% is advertising from search alone (i.e. excluding YouTube and other properties). Apple revenue is 60% iPhone. Even at smaller scales: Basecamp (neé 37signals) built multiple products over nearly two decades but only their first was successful enough to be worth working on; the company divested itself of the rest and rebranded to be identical to that product. It *is* possible for second products to eclipse the first; the iPhone was of course not Apple's first product; The Tesla model 3 outsells the earlier model X, And at my own company Smart Bear our second product ended up being 95% of sales, and we essentially did the same as 37signals and went to a single product model. --- > "Half my advertising is wasted. I just don't know which half." > --John Wanamaker Mr. Wanamaker made his famous complaint more than a hundred years ago; even with modern analytics, today [it's worse](https://www.mediavillage.com/article/which-half-of-my-advertising-is-wasted-and-it-is-only-half). The quadratic growth model won't solve that puzzle, but the better you understand the mechanisms of growth, the more it is under your control.
![The elephant curve at the end](https://public.asmartbear.com/books/anthology/chapters/exponential-growth/theelephantcurve2-simple.png)

Who's lying?

Jason Cohen — Sat, 26 Feb 2022 00:00:00 +0000

![i know its wrong im just waiting for the autocorrect](https://public.asmartbear.com/books/anthology/chapters/whos-lying/d6782print-simple.png) We had been flying for four hours, but both gas gauges still read "full." I didn't need a pilot's license to know that couldn't be right, nor to feel the rush of adrenaline in my gums at the thought of the engine sputtering to an eerie quiet death, propeller blades windmilling as we scream "mayday mayday" and "set it down over there" like in the movies, hopefully including the part where the heroes confidently stride away while the wreckage ignites in a fireball, such a banal event in their exhilarating life that they can't even be bothered to glance back at the carnage. "Umm, this can't be right" I said to Gerry, the real pilot. "Yeah," he said, "the needles get stuck to the glass." He flicked the glass. The needle didn't move. "So... do we have enough gas?" "Yeah, we have another hour left, I stuck the tanks before we left." "Sticking" means plumbing a wooden dowl through top of the wing, into the gas tank, judging the gas level by the height of the resulting wetness. Sometimes the simplest technology is best. Wooden sticks don't run out of batteries or make you wait forty-seven minutes for a security update. > "Trust, but verify." > --Ronald Regan, repeating a Russian rhyming proverb: Доверяй, но проверяй It's not even good enough to just have "two of everything." If two things both rely on electricity, and the electricity goes out, you lose both. There were two gas gauges; both failed for the same reason. It needs to be differently-implemented as well, like a stick versus a gauge. For example, there's a normal magnetic ball compass floating in liquid that will work even if other power sources fail, but it's hard to read as it bounces around from the vibration so it's good to have another one that runs off suction--air pressure differential between the outside and inside of the cabin--which is stable even when you're turning the plane in turbulence. Gerry would say: **"Who's lying?"** Usually your instruments are correct, but sometimes one is lying. Maybe the suction system isn't working, so you double-checking suction-based dials with the electric-based dials. You stick the tank, in case the gas gauge is lying. The same lesson applies to our daily life of data and metrics. You think you understand what each number means, and usually you're correct. But sometimes you're running out of gas and don't realize it. I've seen this happen for all sorts of reasons: The spreadsheet had a subtle bug in a formula, the analytics JavaScript code was accidentally left off one page, the survey email didn't get sent to all the customers in the cohort, the database query did/didn't filter something important, a nightly update script hasn't been running for three months. A good way to check for bad data is to replicate the airplane dashboard method of deriving the same information in two different ways. Revenue from your billing system compared to cash flows from your bank statements. (Once I discovered our credit card processor was delaying our cash receipts.) Number of active customers from Stripe, and from your User Portal. (Because sometimes a cancellation in one system fails to cancel in another system.) Web traffic from Google Analytics but also another analytics system, or your raw web logs. (If you use five web analytics tools, they'll all give you different numbers; this *could* be due to differences in definitions of things like "visit" and "session," but is that truly all it is?) Besides paranoia, I've found another advantage in computing the same data twice: a better understanding of the forces behind that data, and therefore better analysis of how the company operates and how the market is changing. Consider web traffic. There are analytics that tell you where traffic originates (imperfectly, especially with the latest browsers and extensions intentionally obscuring or blocking data), data about your advertising click-throughs, your own raw web server logs, and broad industry data (e.g. Google Trends on how search-traffic for your keywords is changing). They all tell a different story. None has the full picture; all are biased. But taken together, your picture of the world is more complete, and biases might be cancelled through averaging or by paying heed only to the clearest, most consistent trends. If four different sources agree that a trend is happening, then it's definitely happening. If a metric is important enough to watch it every day, and to act if its behavior deviates from expectation, then it's important enough to be double-checked. Both for accuracy, and for completeness of comprehension. If your dashboard isn't redundant, you'll never know... who's lying?

The Important Thing⁠--powerful enough to override all your deficiencies

Jason Cohen — Mon, 04 Nov 2019 00:00:00 +0000

![I don't care whose time it is, we're going to have a nice dinner for once!](https://public.asmartbear.com/books/anthology/chapters/the-important-thing/d7817print-simple.png) Do you feel the crushing weight of the disadvantages facing every new company? No brand, no features, no customers, no money, no distribution, no search engine rankings, no efficient advertising, no incredible executive team, no NPS, no strategy. How do the successful startups win anyway? Do they solve all those problems at once, or at least quickly? No. One answer is they pick their battles where they have [advantages over incumbents](https://longform.asmartbear.com/startup-beats-incumbent/). The other answer is that they need one thing to go really _really_ right, to overcome the torrent of things that are going wrong. --- ![First iPhone](https://public.asmartbear.com/books/anthology/chapters/the-important-thing/image-simple.png) I apologize in advance for using the dang iPhone as an example but... The iPhone is one of the most successful and important products of the past few decades. But the first version launched with a mountain of issues. It was a terrible phone, ironically. The whole idea was that it was a "smart *phone*," yet everyone agreed their cheap-o Nokia flip-phone was ten times better at being, you know, a *phone*. Also, imagine launching an operating system that didn't include "copy/paste." Terrible! But, the iPhone did something so well, that people wanted so badly, they would put up with all the other crap: You could use the internet. The *real* Internet with full websites and everything. The web actually *worked* (even if slowly). Email actually worked. In your pocket. It's hard to explain the magic and excitement to a Gen-Z'er who takes it for granted. This was so compelling, all the other problems didn't matter. --- ![Screenshot of Heroku home page with command-line commands](https://public.asmartbear.com/books/anthology/chapters/the-important-thing/herokuscreenshot-500x424-simple.png) For more than ten years--an eon in tech--Heroku has been the dominate way that Ruby on Rails developers launch public applications. When it first came out, it was rife with "deal-breakers" that developers continually winged about. "What do you mean I have to use Bundler--it's broken half the time!" "What do you mean I can't change the filesystem at run-time--I'll have to change my algorithms!" "What do you mean it doesn't support MySQL--everyone uses MySQL! My queries are going to break." "Wow these websites are really slow." On and on with the complaints, and all quite valid. But, Heroku did something so well, that people wanted to badly, they would put up with all the other crap. You could type `git push production` and your site went live. You could use a knob on a web page to determine how scalable the site was. (Don't worry, that knob is also connected to your wallet.) You never saw a server. You never thought about backups. You never worried how to securely stash your API keys. You always had a staging area to test things in a real server environment before pushing code live. DevOps became a thing of the past for a large class of applications. This was a revolution so important, so compelling, all the other problems didn't matter. Developers changed their workflows and their code around Heroku and "[12-Factor](https://www.12factor.net/)" apps; Heroku did not change to suit developers. --- This is a universal pattern I call **The Important Thing**. Break-out products deliver something so fantastic, so game-changing, so important to the customer, that this one thing is sufficient to override the otherwise-overwhelming deficiencies of the rest of the product and company. So great that people tell their friends or force their colleagues to use it too (defeating the lack of marketing). So great that they'll use it even if support is slow and releases have bugs (defeating the lack of operational excellence). So great that they're excited to support a promising new company instead of worried about creating a dependency on a wobbly new company (defeating the lack of brand). In short, despite the startup not having the positive attributes of large companies (brand, service, features, stability, integrations, social proof), [the startup can still win](https://longform.asmartbear.com/startup-beats-incumbent/) because their Important Thing is so compelling. The Important Thing isn't always a feature or technology. Fogbugz was never the leading bug-tracking system, but I employed it for most of the 2000s because I was a huge fan of [Joel Spolsky's blog](https://longform.asmartbear.com/letters-to-joel-spolsky/), so it felt good to use a product made by a company whose values and behaviors I respected and learned from. The same happened with Basecamp and the [37signals blog](https://m.signalvnoise.com/).[^1] [^1]: You might be tempted to say Basecamp was successful because the work-style espoused by 37signals leads to successful products, but the contrary evidence is that all of their many subsequent products--built with the same work-style, by the same people, and even with the same code base--were all dramatically less successful, to the point that all of them have now been discontinued, and the company has been renamed "Basecamp" to emphasize that only the first of those experiments was ultimately successful. _Editor's Note:_ Now in 2024, they seem to be successful with hey.com. Here's how to apply this to your own business: It's easy to get overwhelmed by the myriad of inadequacies you undoubtedly have. It's tempting to attack them all, but worrying about everything and attacking simultaneously on all fronts with no weapons just leads to burn-out, and does not result in a company that is excellent on any front. Fortunately, you don't need to solve all those problems. You need to solve almost none of them. Instead, you need to focus on the one thing (maybe two) which is your Important Thing. The thing where, if you're extraordinarily good at it, customers will overlook everything else. It could be a feature (e.g. disappearing messages with Snapchat), but you can look beyond features. It could be enabling a lifestyle (such as remote-work or with-kids-work). It could be your online reputation (e.g. Joel Spolsky for me). It could be that you're solving a problem in an industry that others overlook; having "any solution, even with problems" is better than having no solution. It could be that your culture resonates with an audience (e.g. 37signals), maybe due to an informal voice in an otherwise formal market, or because you have a cause--a higher purpose--so that people aren't just buying a shirt or some software but rather they are supporting a movement (e.g. Patagonia who cares so much about the environment that there's a company policy that they will bail employees out of jail if they're arrested for peaceful protest, and by the way one of the results is that they have only 4% annual employee turn-over). You should select something that you *want* to obsess over for the next five to ten years, that gets your customers excited, and which you at least have a possibility of executing. And then do that. Maybe *only* that. If you let all the other fires burn, maybe you have a shot at actually being excellent at that Important Thing. If you do, all those other disadvantages exist, but aren't fatal. That's all you can hope for, [at the beginning](https://longform.asmartbear.com/chaos-at-start/). And all you need. ![Any way we can hurry this up?](https://public.asmartbear.com/books/anthology/chapters/the-important-thing/d1705print-simple.png)

Capturing luck with "or" instead of "and"

Jason Cohen — Tue, 08 Oct 2019 00:00:00 +0000

![stocks rose slightly in early trading then plummeted on news that stocks rose slightly in early trading](https://public.asmartbear.com/books/anthology/chapters/capturing-luck/d0710print-simple.png) I won a fake stock market competition in elementary school. I put all my money in a few penny stocks--those are stocks whose prices are less than a dollar. Back then, stock prices could only be multiples of 1/8. Their small denomination meant that any fluctuation was a large (as a percentage) fluctuation in value. Some days I had the worst portfolio, other days I had the best. The competition happened to end on an up-day. This was an example of "high risk, high reward." Like startups, but more random. (But are startups less random?) Startups need luck too, in [finding advertisement channels](https://longform.asmartbear.com/low-budget-marketing/) that work, in the right mix of [features](https://longform.asmartbear.com/your-idea-sucks-do-it-anyway/) and usability that [triggers product/market fit](https://longform.asmartbear.com/product-market-fit/), in cultivating a [useful social media presence](https://longform.asmartbear.com/startup-beats-incumbent/), on [employee number one](https://longform.asmartbear.com/startup-hiring-advice/) working out well, on a [competitor](https://blog.asmartbear.com/competitive-positioning.html) not making a fatal move, on there being enough money in the [market](https://longform.asmartbear.com/problem/), on [appropriate pricing](https://longform.asmartbear.com/pricing-determines-your-business-model/), on market forces not shifting the rules of the game, and the list goes on. And that's after the luck of where and when you were born, the color of your skin, your gender, and that list goes on too. When you put it that way, it's obvious why startups fail so frequently! They need a lot of success in a lot of areas, which is [a lot of "good luck"](https://longform.asmartbear.com/lucky/) to string together. How can you reduce this effect, and maybe even turn luck to work in your favor? ![serendipity is up fluke doing well concerned about dumb luck](https://public.asmartbear.com/books/anthology/chapters/capturing-luck/d7231print-simple.png) The list above is a bunch of "and's." That is, you need a good marketing channel *and* you need a few killer features *and* you need great initial employees *and* you need a healthy market, etc.. **"And" is bad!** It's bad because each one has a probability of success, and you compute the total probability of success by multiplying them. No matter how optimistic you are about those probabilities, the end product is a small number. 70% multiplied by itself six times is only 12%; most of these things we're listing have a probability less than 70%, and there are more than six things that have to go right. You can increase the chance that some of those things go right by making explicit choices. For example, you can pick a [large, growing, and healthy market](https://longform.asmartbear.com/problem/) so that the risk that "no one wants to solve this problem" goes to zero. You can compete in a [niche where incumbents don't care](https://longform.asmartbear.com/startup-beats-incumbent/) or cannot move quickly. You can hire someone you've worked with before, or build something sustainable without hiring. You can [generate Love](https://longform.asmartbear.com/willingness-to-pay/) rather than only Utility or Coercion to overcome your deficiencies. Here's a [long list of those things and how to mitigate them](https://longform.asmartbear.com/avoid-blundering/). Even so, there will be plenty of challenges, so we need a second technique for boosting probability: **Leverage "or" instead of "and."** Consider marketing channels. You could get your first few hundred customers through GoogleAds, or Facebook ads, or affiliate sales, or targeted outbound sales, or SEO, or guesting on podcasts and newsletters, or partnering with a high-profile reseller, or great press about your unique brand and message. Only one of these needs to work! So although the probability of success for each one of these is low, the probability that *something* will work is higher. This is true of everything from product features to website copy for conversions to avenues to exiting the company years from now. The general rule is **optionality is strength.** When there are lots of ways for things to go right, that is a strong position even if you haven't actualized one of those ways. The converse of this is a business that has extra "and" clauses--even more than usual. Marketplaces, for example, [almost never succeed](https://blog.asmartbear.com/marketplace-business-model.html). When they do succeed, they are often durable and profitable, which makes them a smart bet for a Venture Capitalist that can maintain a diversified portfolio of attempts, but [for the individual business it's a tough road](https://longform.asmartbear.com/investment/). A marketplace has to thrive both with the sellers *and* the buyers, *and* it can't be too expensive to build up both sides, *and* both sides need to realize value, *and* it must not be easier/cheaper/preferable to transact outside the marketplace, ... otherwise the marketplace fails. Also, many marketplaces often only deliver value once they are at scale; so another "and" is that they have to also "scale down" so the first 100 buyers and sellers also see value. By accumulating "and" requirements, you are lowering the probability of success. By stringing together possible solutions with "or," you are increasing the number of ways that luck could smile upon you. Set yourself up for luck!

Distributed Logical Time

Jason Cohen — Sat, 23 Feb 2019 00:00:00 +0000