The Startup Drake Equation

source

Most startups fail, even when the founders are smart, driven, passionate, capable, and are solving a problem that at least a few people would pay to have solved. Why?

We already explored the primary causes of startup failure and how to combat those.

Since failure is the primary result, however, perhaps “why do startups fail” is the wrong question. Startups fail by default; the question is: How can they possibly survive?

And: Why specifically are they default-dead? Is there something we can learn from that?

There is a reason, and yes there are things we can do about it.

The Drake explanation

source

Frank Drake created his eponymous 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 aliens we should expect to see in the night sky:

Where…

\(N\) = the number of civilizations in our galaxy with which communication might be possible
\(R_*\) = average rate of star formation
\(f_\mathrm{p}\) = fraction of those stars that have planets
\(n_\mathrm{e}\) = average number of planets per star that potentially has life
\(f_\mathrm{l}\) = fraction of those planets where life actually develops
\(f_\mathrm{i}\) = fraction of those planets where life reaches intelligent civilization
\(f_\mathrm{c}\) = fraction of those that transmit signs of their existence into space
\(L\) = how long those civilizations have been sending detectable signals

source

Paraphrased in marketing language:

The galaxy produces many warm leads, but every step of the conversion funnel is brutally leaky, so it’s hard to get a conversion to sale.

Of course the most salient fact about detecting alien civilizations is: We haven’t detected any alien civilizations. We know the that first few values of the Drake Equation (star formation rate, number of planets in habitable zones) are astronomically large. So that means one or perhaps all of those conversion steps are vanishingly rare.

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 fail the chain of probabilities, not resulting in a venture where the owner can quit their day job and brag to outwardly-supportive-but-inwardly-jealous Twitter “followers” about achieving Product/Market Fit.

Startups face a chain of risks, or as I like to say, a chain of “ands”—many things all have to go right. Of course “all things” rarely go right simultaneously; this is why startups typically fail.

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:

• Having an idea for something people actually want to pay for
• Able to build that something
• Able to get those people’s attention
• Pricing that those people will accept (and that is greater than costs)
• Enough of those people to sustain the organization
• Competitive enough to be chosen by enough people
• Able to attract and retain talent to grow the organization
• Able to psychologically handle multiple years of deep effort and stress and pain

It’s easy to find examples of failures due to each factor. A classic example is the non-technical founder who unsuccessfully manages a consultant to build the product. Another is the technical founder who builds forever without talking to customers, and as a result doesn’t sell anything (or build the right thing). Another is the “I had the problem myself, so I built it” origin story, but it turns out not enough other people have the problem and the budget and desire to have it solved. Another is that it was too hard to push through the pain of iteration and pivoting, and 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 category is fatal, so we should spend more time identifying and addressing the biggest risk-areas.
2. We can think in terms of “reducing risk” or “increasing chances” rather than “best ideas” or “unique strategy” or “changing the world”

Here’s how we can use these insights to increase the chance of success:

Completely crush a few areas to overcome risks and weaknesses

No organization will be low-risk on all areas. But perhaps some areas can be 100%, or even greater.

For example, a top-1% engineer might satisfy the question of whether we can build it, but having the founder of the open source project around which the company is built becomes a “unique competitive and marketing advantage,” which could overcome deficiencies like not having unique features in the product or not having special abilities in advertising.

I call this The Important Thing, and you gain both focus and a higher probability of success when you have one.

source

Or, if you’re an renowned expert in some market, that decreases market risk, both because that’s a marketing advantage and because you probably have insights that others lack¹. Whereas if you’re entering a market you know nothing about, your education might prove fatal.

¹ although you should be wary that your experience could be blinding you.

Select product/market/customer to decrease risk

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 also smart, and sometimes easier to answer:

What would be the easiest customer segment for us to target?

Part of the answer is inherent in the segment. For example, indie hackers² like selling to their peer indie hackers, but indie hackers have no money and usually go out of business, so that’s a very risky segment. Instead, stable small businesses like dentist offices have large budgets, rarely change software, and last for decades; this is a better market. Large, growing markets are better still, because “large” means there’s lots of niches in which to get started, and lots of adjacencies to expand into later, and incumbents are fighting over new customers, not focussed on new entrants who aren’t big enough yet to be worrisome.

² Found mainly on Twitter, the quintessential “indie hacker” is a solo founder, who wants no employees, who can build software without assistance, who values freedom, flexibility, and autonomy over money or prestige. They therefore build simple (but hopefully delightful) products, at low prices, designed to be profitable without scaling.

The other part is in the phrase “for us.” An Enterprise segment of some market might be terrific in terms of size and willingness-to-pay-gobs-of-money-for-a-decade, but a brand 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 this would be a risky market for them.

You must select low-risk markets, which 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, it will be harder than you think, but still possible. But if you already think it will be difficult, it’s even worse than you fear: impossible.

Founding team with key skills

When a co-founder is a top-1% engineer, there’s almost no risk that the company will fail only because the product couldn’t be built. When a co-founder is a top-1% growth marketer, and if the market exists, there’s almost no risk that the company will fail only because it was impossible to get attention.

This is one reason why investors like two founders—“one to build it, one to get rid of it.” It’s not just “getting twice the work done,” and not just “someone to commiserate with,” but also because you dramatically improve at least two of the variables in the Startup Drake equation.

Design out weaknesses

You have weaknesses, which increase risks in the Startup Drake equation. But, you can make choices that make your weaknesses irrelevant, thus also removing the risks.

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 support tickets taking 48 hours for a response. That might have implications on how complex the product is, what customers expect of it, and what the price is. You might see those as negative constraints, but instead see those as point the way towards the best strategy. In this case, your profit margin is higher (because you don’t have support costs) and you can sell around the world (because language isn’t a barrier). 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, 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 with infrastructure engineers or backend enterprise systems.

source

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 going, it means even poor marketing can result in a growing, healthy company. (It’s weird that a “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 listing your weaknesses as fervently as you list your strengths, you can increase the chance of success by avoiding them, and embracing the knock-on implications.

I cover this in more detail in my article about Pivot Points.

Attack the high-risk variables first

With complex projects, the Risk-First Heuristic 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 tasks, you might discover the entire venture is unworkable. Even if it’s not fatal, new insights means rework, which means you wasted time. It’s better to address the risky tasks first, learn from them, and proceed with the rest of the project informed by those lessons.

The Startup Drake Equation identifies those 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 into the product, make sure that works first, because usually it doesn’t (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 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.

All startups are risky, and even with this model in mind, most startups will 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!

source

Printed from: A Smart Bear
https://longform.asmartbear.com/startup-drake-equation/
© 2007-2024 Jason Cohen      @asmartbear