Accuracy is Good, Precision is Best
July 5, 2018
Directionally correct startups could be considered successful depending on the amount of capital raised, but ultimately fail to live up to their potential. For most, the pull of the market ends early and the next phase of growth requires precise allocation of capital internally.
Subsequently, growth expectations begin to outpace what funding alone can accomplish and a ceiling in valuation is created. This leaves the company unable to raise more capital, eventually leading to an exit that leaves investors clamoring for what could have been.
My hypothesis is that there are several companies which are acquired for somewhere between $20-$50M that fall into the category of directionally correct (accurate), but did not operate with precision during their early days.
Accuracy (n): the degree to which the result of a measurement, calculation, or specification conforms to the correct value or a standard.
Precision (n): refinement in a measurement, calculation, or specification, especially as represented by the number of digits given.
I'm admittedly still in the early innings and need to see more startups at this stage before coming to a valid conclusion, but I want to have a few key points in writing for reference moving forward. In no particular order they are:1) The difference between these two groups happens during the time period between traction and scale. That is, 95% (or some number larger than 80%) of the time, scale happens when startups execute with precision in product and marketing after their initial customers are onboarded.
2) The first few customers - the early adopters - were going to love the product and be the easiest to find regardless of how well the company executed. The result is a lower bar than what most of the market will consider “viable” in an MVP and marketing costs were artificially lowered by initial consumer demand. Counterintuitively, acquisition costs will actually go up after the early adopter market is completely exhausted.
3) Startups that scale don't over-estimate the fidelity of the data created by early adopters. Instead, they create a framework for discovering core product value for users which will be key to both growth and preventing customer churn in the future as they enter new markets or segments.
4) Market size (need) is correlated to the length of time a startup has to build a scalable customer acquisition strategy which is more than finding a blended CAC. Precise startups understand how to achieve a sustainable ROI and focus on LTV (ex. bookings) acquisition instead of purely growth metrics (ex. customer count). For example, at CE we knew a customer in TX had a substantially higher LTV than one in CT and adjusted accordingly.
5) Precision is defined as a repeatable process in the most vital parts of the startup like sales, marketing, and product. Often, investors talk about "playbooks" and this is where they really punch above their weight. If the market is X then we do Y or if we do A then the result is B are powerful indicators of precise execution. Chamath Palihapitiya highlights the importance of this in a talk on "growth hacking". During his time at Facebook, they discovered if a new user hit 7 friends in 10 days they were hooked and built product focused on hitting this metric.
6) Once a startup crosses this threshold, the solution will have seemed obvious. The reason, getting there requires measuring and testing over and over and in hindsight, it's easy to feel the data revealed a straight-forward conclusion and to discount the decisions needed to arrive at the right answer.I'm looking forward to having more opportunities to help growing startups bridge the gap from consistency to precisision in the coming years while measuring the level of truth in the insights to refine refine them moving forward.