Find Product-Market Fit
20 weeks · 3 milestones
Reach a 40%+ 'very disappointed' score on Sean Ellis test with real users.
Milestone map
Milestone map
3 milestones
Define, before you measure it, what product-market fit will look like for your specific business. PMF is often described as a feeling ('you know it when you have it') but that description is not actionable. The most operationalised PMF signals are: Sean Ellis's '40% of users would be very disappointed if the product disappeared', NPS above 50, retention curves that flatten rather than trend to zero, or a specific cohort-based retention benchmark for your category. The choice of PMF signal matters because different signals are appropriate for different business types — a consumer app and a B2B enterprise product have fundamentally different PMF indicators. Defining your signal before measuring it prevents post-hoc rationalisation.
Proof required
Write a PMF hypothesis document covering: (1) the PMF signal you are targeting and why it is the right signal for your specific business model; (2) the current measurement result for that signal (even if below target); (3) the leading indicators you are watching that might predict PMF before the lagging signal confirms it.
What gets checked
- PMF signal is specific and pre-defined — not 'when it feels right' but a named quantitative signal with a target threshold.
- Current measurement is shared honestly — if you are below the target, that is the most valuable data in this document.
- Leading indicators are named — what do you watch day-to-day that predicts PMF before the signal confirms it?