This article originally appeared in TechCrunch.
The mean case can cost a founder up to $200K in self-funded investment and up to 24 months before they are ready to test outside funding. It is a nontrivial investment, in both capital and time, to test a hypothesis of product-market fit. And in many instances, a founder can fail to give themselves enough time and runway to reach product-market fit.
A founder’s capital investment consists of the cost to launch and the indirect cost of foregone salaries. This foregone salary is often overlooked and often higher than the launch costs. A 12-month period for a founder could mean foregoing a salary of more than $100K.
To truly test product-market fit, a founder may need to budget for up to 24 months before a strong signal emerges. Uber for example, founded in March-2009, was self-funded with $200K (in August 2009) before receiving a $1.3 million seed round in October 2010. It arguably had product-market fit from launch, yet still took 19 months to reach the first outside round.
Most founders don’t seem to map this out before diving in. The risk can be having a small signal emerge after, say, 6 to 12 months that suggests there could be something there. But a founder then has to give up due to lack of runway (or mindset) to push on to a greater degree of product-market fit.
Even if a founder hits product-market fit in 12 months and is ready for an outside round, their investment will still be large. Say $50K-100K in working capital costs and the opportunity cost of a foregone salary, which can easily equate to more than $200K.
That is the mean case, though; startups are compelling not due to the mean case but due to anomalies. Some founders will discover product-market fit incredibly fast with as little as $50K.
Here are two illustrative examples. Last year, we stumbled on a vertically integrated furniture e-commerce startup. Three founders brought different but complementary expertise – warehouse operations, marketing and supply relationships. They pooled this and $10k to test their hypothesis around product-market fit.
The business model benefitted from customers paying up front and fulfilment taking place six to eight weeks later. They would batch-manufacture each week’s orders, usually equating to a container’s worth of product. Every week a container would arrive that they then unpacked and delivered nationally.
They used third-party and daily-deal sites to capture early sales (and manufacturing volume) with a plan to convert them into repeats. In fewer than six months, the team had built a $2 million revenue run rate and had $200K in the bank.
Another example: In July, a team that previously built a $10 million revenue line business invested $20K to test product-market fit in an on-demand space. They used the marketing playbook from their last startup, which focused on capturing email lists through partnerships and other growth hacks. In January, they did $40K in revenue and were cash-flow positive in two out of their first six months of trade (albeit they weren’t paying founder salaries for part of that). The business benefitted from an anomaly in high referral rates creating a good organic channel early on. We have actually seen this early product-market fit in other on-demand startups.
These are anomalies to the mean case. They don’t make sense and we rarely come across them. In fact, in the last set of 250 companies we’ve met with, we’ve discovered seven anomalies. In venture, we seek to invest in anomalies, where there is some signal displaying abnormal strength — as the mean case in venture is negative returns.
So, the mean case can cost a founder up to $200K in self-funded investment and up to 24 months before they’re ready to test outside funding. At this point, outside investors will pass on the majority of founders as there are only a handful of anomalies. A common response from a founder is to see this as failure, or feel disgruntled toward these passes.
A sunk-cost fallacy can occur where a person makes a decision about a current situation based on what they have previously invested in that situation. This fallacy and the noise of the startup ecosystem can cloud a rational mindset. Most will anchor on some data point encouraging them to keep pushing forward until someone, anyone, will fund them. Usually, they hope they can work it out post funding. But receiving a “no” from an outside investor can be a valuable insight for a founder — if they think like an investor.
A founder doesn’t realize it, but they are investors themselves. And their investment thesis is their startup. An outside investor invests some time and capital. A founder invests capital, salaries foregone and career capital. The career capital can be substantial as they set aside up to two years to test an investment thesis.
As an investor, the rational approach for a founder should be to seek the best information to determine whether to proceed. But a founder will suffer from information asymmetry.
In contract theory, information asymmetry deals with transactions where one party has more or better information than the other. This creates an imbalance which can cause a kind of market failure or inefficiency in the system. Some say, the venture industry has a history of poor returns due to this information asymmetry.
In simple terms, though, a founder doesn’t get access to what an investor is assessing in the market. Professional investors see the underlying workings of upwards of 250 startups a quarter. This gives them valuable insights on KPIs, team capabilities and competitive position, for example, across the opportunity set. Professional investors have access to the information required to determine whether an anomaly really exists. That’s an unfair advantage. A professional investor passing on an opportunity is not a roadblock but a risk filter for a founder.
The caveat here is that the wisdom of the no (or the yes) is in the beholder of the investor. A professional investor is spending the majority of their time assessing opportunities. An angel investor may not be. They may only see those opportunities that surface in their network which can be much smaller. The risk is that an angel is not a qualified no or more worryingly not a qualified yes. A founder should want a yes from the investor who’s assessed 250 startups in the last quarter versus the investor who’s assessed maybe 20.
The problem with the current funding environment is we’re seeing not only the anomalies being funded but a good portion of the mean cases, as well.
There is an adage that founders have to struggle receiving no after no after no from investors before getting to yes and success. But the right investor saying no can be a good thing. And getting a yes from the wrong investor can be risky as a founder could be investing more of their career in a startup that has a lower deemed probability of success. A founder can measure their risk by counting the qualified no’s.
To think like an investor, a founder should view it akin to a chess match. Stand up from the board and have a more experienced player review the position. If they say no then the right answer may be for a founder to sit back down and topple their king.