How Superhuman Built an Engine to Find Product Market Fit
The product-market fit definitions I had found were vivid and compelling, but they were lagging indicators — by the time investment bankers are staking out your house, you already have product-market fit. Instead, Ellis had found a leading indicator: just ask users “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed.”
After benchmarking nearly a hundred startups with his customer development survey, Ellis found that the magic number was 40%. Companies that struggled to find growth almost always had less than 40% of users respond “very disappointed,” whereas companies with strong traction almost always exceeded that threshold.
A helpful example comes from Hiten Shah, who posed Ellis’ question to 731 Slack users in a 2015 open research project. 51% of these users responded that they would be very disappointed without Slack, revealing that the product had indeed reached product-market fit when it had around half a million paying users. Today, this isn’t too surprising, given Slack’s legendary success story. Truly, this example shows just how hard it is to beat the 40% benchmark.
With only 22% opting for the “very disappointed” answer, it was clear that Superhuman had not reached product-market fit. And while this result may seem disheartening, I was instead energized. I had a tool to explain our situation to the team and — most excitingly — a plan to boost our product-market fit
As an early-stage team, you could just narrow the market with preconceived notions of who you think the product is for, but that won’t teach you anything new. If you instead use the "very disappointed" group of survey respondents as a lens to narrow the market, the data can speak for itself — and you may even uncover different markets where your product resonates very strongly.
To start, we grouped the survey responses by their answer to the first question (“How would you feel if you could no longer use Superhuman?”):
We then assigned a persona to each person who filled out a survey.
Next, we looked at the personas that appeared in the very disappointed group — the 22% that were our biggest supporters — and used those to narrow the market. In this simplified example, you can see we focused on founders, managers, executives and business development — temporarily ignoring all other personas.
With this more segmented view of our data, the numbers shifted. By segmenting down to the very disappointed group that loved our product most, our product-market fit score jumped by 10%. We weren’t quite at that coveted 40% yet, but we were a lot closer with minimal effort.