Product-market fit is not a feeling, it is a pattern you can see in the data. Before you spend on ads or hire a sales team, you need honest evidence that the users you already have keep coming back, and that they would miss you if you disappeared. This lesson gives you three practical tools, retention cohorts, the Sean Ellis 40 percent test, and NPS, plus the leading indicators that tell you PMF is getting close.
Retention cohorts: what users actually do
A cohort is a group of users who joined in the same period, for example everyone who signed up in a given week or month. Instead of looking at total active users, which can hide problems, you track each cohort on its own: what share is still active 1 week later, 4 weeks later, 12 weeks later. Plot this and you get a retention curve. Every curve eventually falls, so the real question is where it flattens. A curve that flattens into a stable plateau means a genuine group of users keeps finding value, and that plateau is the single strongest signal of product-market fit. Then compare cohorts over time: if your March cohort retains better than your January cohort, your product is improving.
Match the cadence to how often people should use you. A payments or food delivery app should watch weekly or even daily cohorts, while a B2B SaaS or insurance product is better judged on monthly cohorts. In India, be careful with cohorts acquired during a festival push or a heavy discount: a Diwali sale or a cashback offer can inflate signups that never return, so separate incentive-driven cohorts from organic ones before you judge retention.
The Sean Ellis 40 percent test
Growth expert Sean Ellis, who studied product-market fit across many early startups, popularised a single survey question: "How would you feel if you could no longer use [product]?" with three answers, very disappointed, somewhat disappointed, and not disappointed. Your score is the percentage who answer "very disappointed". Ellis found that startups above roughly 40 percent tended to grow strongly, while those below that mark usually struggled. Ask only users who have genuinely experienced your core value recently, not people who signed up and vanished, and aim for at least about 40 responses so the number is meaningful. Then ask the "very disappointed" group what they love, and the "somewhat" group what is missing. That free text often points straight at what to build next.
NPS: a loyalty signal, not a PMF verdict
Net Promoter Score was created by Fred Reichheld with Bain and Company and popularised in a 2003 Harvard Business Review article, "The One Number You Need to Grow". You ask, "How likely are you to recommend us to a friend or colleague?" on a scale of 0 to 10. Scores of 9 to 10 are promoters, 7 to 8 are passives, and 0 to 6 are detractors. NPS equals the percentage of promoters minus the percentage of detractors, so it ranges from -100 to +100. NPS is useful for tracking loyalty and word of mouth over time, which matters in India where referrals travel fast on WhatsApp. But treat it as a supporting metric: it measures stated sentiment, not whether people keep paying. Never let a good NPS override weak retention.
Leading indicators that you are close
Surveys tell you what people say, cohorts tell you what they do, and the two should agree. You are getting close to PMF when several of these line up: newer cohorts retain better than older ones, the retention curve flattens instead of falling to zero, organic and word-of-mouth signups rise without extra spend, your Sean Ellis score climbs toward 40 percent, and support conversations shift from "I do not get it" to "please add this feature".
A few practical notes for Indian founders. Run these surveys inside the app or over WhatsApp rather than email, since messaging here sees far higher open and reply rates. Segment metro users against tier 2 and tier 3 users, since they often retain very differently and an aggregate number can mislead you. Above all, fix retention first. Only once a cohort truly sticks should you pour money into acquisition, otherwise you are simply paying to fill a leaking bucket.