Strategy
Grade real PMF signals before you pour fuel on the fire.
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- Claude, via MCP
How do you measure product-market fit?
Combine four signals: the share of users who would be "very disappointed" without the product (30 points if above 40%), a retention curve that flattens (30), organic word-of-mouth growth (20) and strong NPS and referrals (20). Strong scores full points, mixed half. 75+ is strong fit, 45 to 74 emerging, under 45 pre-fit.
Some fit, but not enough to floor the acquisition budget. Scaling now amplifies a leaky funnel. Fix the weakest signal, usually retention or organic pull, before spending harder.
Retention and the "very disappointed" test predict PMF better than growth rate, a leaky product can grow fast on paid spend and still lack fit.
About this calculator
Growth rate lies about product-market fit, a leaky product can look like it's winning while running hot on paid spend, right up until the moment spend stops and growth stops with it. This grader scores the signals that actually predict durable fit, the "very disappointed" test, retention shape, organic pull, and referral strength, so you know whether to fund acquisition or fix the product first.
How to use it
- Rate each of the four signals as strong, mixed, or weak/no: whether more than 40% of users say they'd be "very disappointed" if your product went away, whether your retention curve flattens rather than trending to zero, whether you see organic or word-of-mouth pull, and whether NPS and referrals are strong.
- Read the weighted PMF score out of 100.
- Read the verdict, strong PMF and ready to scale, emerging and worth caution, or pre-PMF and still iterating.
Methodology
Each signal carries a fixed weight reflecting how predictive it is: the "very disappointed" test and retention flattening are weighted 30 points each, organic pull and NPS/referrals 20 points each, for 100 points total.
Each signal is rated strong (full weight), mixed (half weight), or weak/no (zero), and the weighted points are summed into the overall score.
A score of 75 or above signals strong PMF, scale acquisition with confidence. 45 to 74 is emerging, real signal but not enough to floor the acquisition budget yet. Below 45 is pre-PMF, more spend here mostly buys churn.
Retention and the very-disappointed test are weighted heaviest because they're the hardest to fake, a product can grow fast on paid spend without real fit, but it can't retain users or make them genuinely upset at losing it without solving a real problem.
Product-market fit score
Score = 30 × Very disappointed + 30 × Retention + 20 × Organic pull + 20 × NPS and referrals (each: Strong = 1, Mixed = 0.5, Weak = 0) Band: 75+ strong fit, 45 to 74 emerging, under 45 pre-fit
- Very disappointed
- Share of surveyed users answering "very disappointed" to "How would you feel if you could no longer use the product?". Strong means above 40%.
- Retention
- Whether the share of a cohort still active levels off rather than falling towards zero.
- Organic pull
- Growth from word of mouth, referrals and direct search without paid acquisition.
- NPS and referrals
- Net Promoter Score and the share of customers who actually refer others.
The 40% threshold comes from Sean Ellis, who benchmarked nearly a hundred startups and found companies below 40% "very disappointed" almost always struggled to grow, as described in First Round Review. The weights and bands are this tool's. Survey users who have used the product recently and more than once.
Worked example: the default answers
A startup surveyed active users: about 30% would be very disappointed (mixed). Cohort retention flattens after month three (strong). Growth is almost all paid (weak). NPS is positive but referrals are rare (mixed).
- Very disappointed mixed = 30 × 0.5 = 15.
- Retention strong = 30.
- Organic pull weak = 0.
- NPS and referrals mixed = 20 × 0.5 = 10.
- Score = 15 + 30 + 0 + 10 = 55, emerging fit.
Retention says the product works for those who stay; the survey and organic signals say it is not yet essential to enough people. Find the users who answered "very disappointed", learn what they value and build for more people like them. Superhuman, for example, moved from 22% to 58% very disappointed over about three quarters by doing this. Check retention shape with the SaaS churn cohort visualiser.
The Sean Ellis test in practice
| Measure | Figure |
|---|---|
| Threshold for "very disappointed" | 40% |
| Superhuman, summer 2017 | 22% |
| Superhuman, after segmenting to its best-fit users | 33% |
| Superhuman, about three quarters later | 58% |
What does strong product-market fit look like?
Strong fit shows up in behaviour, not just survey answers:
- Retention flattens: a share of every cohort stays active for months.
- Users would miss it: more than 40% would be very disappointed without it.
- Growth without pushing: referrals and word of mouth bring a growing share of new users.
- Sales get easier: shorter cycles and fewer objections.
If retention does not flatten, more acquisition mostly buys churn. Fix the product first.
Measuring product-market fit in India vs the US
Indian startups selling to Indian SMBs often see strong usage but weak willingness to pay, so add a pricing signal: are users paying at the price you need? Word of mouth in India often runs through WhatsApp groups and industry communities, so ask new users where they heard about you to measure organic pull.
US startups face more competition for attention, so a high "very disappointed" share is especially telling. NPS is widely used, but referrals and expansion revenue are stronger proof. For the referral side, see the net promoter score impact modeller.
Common product-market fit mistakes
- Treating fast paid growth as proof of fit.
- Surveying all sign-ups instead of recent, active users.
- Averaging across very different user groups and missing the segment that loves the product.
- Ignoring retention because new-user numbers look good.
- Scaling acquisition before retention flattens.
Sources
- How Superhuman Built an Engine to Find Product/Market Fit, First Round Review. Accessed 2026-10-19.
FAQ
Growth rate and signups can be bought with ad spend regardless of whether the product actually fits a need. Retention curves and the disappointment test measure whether people who tried the product keep choosing it and would miss it, that's much harder to manufacture artificially and much closer to real fit.
A cohort's retention percentage should level off at some floor after the first few weeks or months rather than continuing to decay toward zero. A flat tail, even a modest one, means the users who stick around genuinely value the product; a curve that keeps sloping down means you're constantly refilling a leaking bucket.
Treat that as a warning, not a contradiction. Fast growth without underlying retention or organic pull usually means acquisition spend is masking a leaky funnel. Pause aggressive scaling, dig into where users drop off, and prioritize fixing the weakest signal before pouring more budget into the top of the funnel.
Yes, PMF isn't permanent. A new competitor, a pricing change, or a shift in your ICP can erode retention and organic pull over time. Re-run this quarterly, especially after any major product or market change, rather than treating one strong score as a settled fact.
A survey asking users how they would feel if they could no longer use the product. If more than 40% answer "very disappointed", the product is likely to have product-market fit.
Yes. Markets, competitors and customer needs change. Re-run the survey and check retention regularly.
Numbers looking off?
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