Turn product-market fit from a lagging gut feeling into a single leading metric you can survey, segment, and deliberately raise, then engineer the roadmap around moving that number upward.
Superhuman
Superhuman is a premium email client founded by Rahul Vohra in 2014, but its most influential contribution to product strategy was a method rather than a mailbox: it turned product-market fit from a gut feeling into a number the team could measure and deliberately raise. The through-line is instrumentation, replacing lagging, after-the-fact signals of fit with a single leading indicator the team optimized on purpose.
PMF: turning "do we have fit?" into a metric you can engineer
The problem. By 2017 Superhuman had a working product and enthusiastic early users, but Vohra could not tell whether it had genuine product-market fit or was fooling itself. The usual signals (revenue, word of mouth, "you'll just know") were lagging and unactionable: they told you fit had arrived only after the fact, and gave no roadmap for getting there. Vohra worried the company was scaling on a feeling rather than proof.
The approach. Vohra adopted Sean Ellis's survey question, "How would you feel if you could no longer use Superhuman?", and treated the percentage answering "very disappointed" as the metric to optimize, using Ellis's 40% threshold as the bar for real product-market fit. He then built a four-step engine around it: segment to isolate high-expectation users, analyze feedback to double down on what fans loved (speed) while converting on-the-fence users, split the roadmap between those two goals, and repeat with the score as the product team's sole OKR. He even defined a concrete target persona, "Nicole," a hard-working professional who reads roughly 100 to 200 emails and sends 15 to 40 on a typical day, and deliberately ignored users who would never be disappointed so they would not dilute the roadmap.
How it solved it. The metric proved both measurable and movable. Superhuman started at just 22% "very disappointed," well below the 40% benchmark; focusing on the high-expectation segment alone lifted the score to 33%, and continued execution pushed it past 40% into the high 50s. The 2018 First Round Review write-up, "How Superhuman Built an Engine to Find Product/Market Fit," became one of the publication's most-read pieces precisely because it made fit reproducible rather than lucky, and Vohra credited the engine with making everything from hiring to fundraising significantly easier.