product-market fit

Also called: PMF, product market fit, measuring product-market fit, PMF survey

The state where a product satisfies a real market's need well enough that users keep using it and recommend it without being pushed. Seen in retention, not in sign-ups.

Product-market fit (PMF) means that a specific group of people needs the product enough to keep using it, and would miss it if it were gone. It is not a feeling, a funding round or a spike in downloads. Sign-ups show that the promise is attractive; PMF shows that the product keeps it.

No single number proves PMF, so teams combine several signals, strongest first:

Retention plateau. Cohort retention curves flatten instead of sliding to zero, and newer cohorts flatten at the same level or higher. This is the most reliable signal because it is based on behavior.
The "disappointment" survey. Ask active users who have used the product recently: "How would you feel if you could no longer use it?" (very disappointed / somewhat disappointed / not disappointed). The share answering "very disappointed" is widely compared with a rule of thumb of about 40%. Treat it as a guide, not a law.
Organic pull. A growing share of new users comes from word of mouth, invites and search rather than paid ads, and paid users retain about as well as organic ones.
Engagement and money. Usage frequency that matches the product's natural rhythm, willingness to pay, and low churn among paying users.

PMF is always for a segment: a product can fit couples sharing rent and miss travelers entirely. Measure every signal per segment and per acquisition channel, then decide where to double down. PMF can also be lost when the market or competition shifts, so these signals are worth tracking continuously, not just once.

Example

The Halves CEO asks: "Do we have product-market fit?" Maya puts three signals side by side for the three group types:

Segment	Monthly retention curve	"Very disappointed"	Share
Couples	plateau at ~40%	150 of 300	150 / 300 = 50.0%
Flats	plateau at ~38%	180 of 400	180 / 400 = 45.0%
Trips	falls to ~2% by month 6	70 of 250	70 / 250 = 28.0%
All respondents	—	400 of 950	400 / 950 = 42.1%

The overall survey result (42.1%) clears the 40% rule of thumb, but only because couples and flats carry it. Both have flat retention curves and around half their active users would be very disappointed without Halves: strong signs of fit. Trips show neither. Maya's answer: "Yes for people who share a home, not yet for travel. Focus acquisition and roadmap on couples and flats, and treat trips as an acquisition channel for them, not as a separate market."

Common mistakes

Reading growth as fit. A spike in sign-ups from ads or a partner promo says nothing about whether people stay.
One number for everyone. Blended retention or survey results can pass while most segments fail, or fail while one segment fits strongly.
Surveying the wrong people. Ask users who have used the product recently and more than once; surveying everyone who ever signed up, or only fans, distorts the share.
Treating 40% as a pass mark. The threshold is a rule of thumb; the retention curve and its trend across cohorts matter more.
Declaring PMF once and forever. Re-check the signals on new cohorts as the product, market and acquisition mix change.