Sean Ellis test
40%+ = PMF
Source: Sean Ellis, Hiten Shah benchmark across ~100 startups
Free PMF measurement tool
Free Product-Market Fit Scorecard
A product-market fit scorecard is a structured way to score whether a product has reached PMF using the Sean Ellis "very disappointed" test alongside retention, NPS, churn, and growth signals. This free PMF calculator returns a 0-100 score, compares each input to SaaS benchmarks, and gives you 3-5 prioritized action items.
Track customer feedback that drives PMF - try FeatureVote free to capture feature requests, run continuous user surveys, and prioritize what to ship next.
Enter your PMF signals
Fill in what you have. Score updates live as inputs change - no API calls, no signup.
Top must-have drivers (multi-select)What did your "very disappointed" users mention most? Used for qualitative insight.
Monthly churn %
Willingness-to-pay confidence (1-5)Confidence that users would pay if it were no longer free.
Time to value
Your PMF score
Weighted from all signals you provided. Live updates as you type.
Enter at least one signal to see your PMF score.
How to measure product-market fit
- 1
Run the Sean Ellis 40% survey
Ask active users how they would feel if they could no longer use the product. The percentage of 'very disappointed' responses is your headline PMF signal.
- 2
Pull retention and growth data
Add Week 4 retention, monthly growth, churn, activation, and the share of organic and word-of-mouth signups. These behavioral signals confirm or contradict the survey result.
- 3
Layer in qualitative signals
Capture NPS, willingness-to-pay confidence, and time to value. Together they tell you whether intensity matches the headline numbers.
- 4
Read the score and act on the gaps
Use the prioritized action items as a one-quarter focus list. Re-run the scorecard quarterly to track movement.
SaaS PMF benchmarks
Industry-typical values for each dimension. Use them to translate raw numbers into a verdict.
Week 4 retention
30-50% healthy
Source: Lenny's Newsletter SaaS retention benchmarks
NPS (SaaS)
30-40 median
Source: SaaS Capital and Retently industry medians
Monthly growth
5-7% good, 10%+ great
Source: Y Combinator default growth targets
Monthly churn
Logo <3%, Revenue <1%
Source: OpenView SaaS benchmarks
Activation rate
25-40% reach aha
Source: Mixpanel and Amplitude product analytics studies
Product-market fit scorecard FAQ
Common questions about measuring PMF, the Sean Ellis test, and how to interpret a PMF score.
What is the Sean Ellis product-market fit test?
The Sean Ellis test asks users one question: how would you feel if you could no longer use this product? If at least 40% answer 'very disappointed', the product has reached early product-market fit. Sean Ellis developed the benchmark after running it across nearly 100 startups and finding that the 40% threshold consistently predicted which products could sustain growth.
What's a good PMF score?
On this scorecard, 0-39 is Pre-PMF, 40-59 is Approaching PMF, 60-79 indicates PMF, and 80-100 is Strong PMF. The score blends the Sean Ellis result with retention, NPS, organic growth, churn, activation, and growth rate, so a high score requires multiple signals to align, not just one survey.
How is product-market fit measured?
There is no single PMF metric. The most reliable approach combines a leading indicator (Sean Ellis 'very disappointed' percentage), a behavioral indicator (cohort retention curves that flatten), and growth signals (organic acquisition, low churn, healthy NPS). This scorecard weights all three so a single strong number cannot mask weakness elsewhere.
What percentage of 'very disappointed' users indicates product-market fit?
The benchmark is 40%. At or above 40%, you typically have early PMF and growth tends to be sustainable. Between 25% and 40% you are approaching PMF and should focus on the must-have drivers users mention. Below 25% indicates you have not yet reached PMF and should iterate on the core value proposition.
How often should I run the PMF survey?
Run the Sean Ellis survey at least quarterly, and re-run it after any major product or positioning change. The Superhuman PMF engine recommends resurveying after each release cycle so you can watch the 'very disappointed' percentage trend up over time. Survey users who have experienced the product at least twice in the last two weeks for the most accurate signal.
Track customer feedback that drives PMF
Use FeatureVote to collect feature requests, prioritize what your most engaged users actually want, and ship the changes that move the "very disappointed" number up.
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