What is the Sean Ellis Product-Market Fit Survey?
The Sean Ellis product-market fit survey asks your active users one main question: “How would you feel if you could no longer use [product]?”, with the answers very disappointed, somewhat disappointed and not disappointed. Product-market fit (PMF) means you have built something a defined group of people would genuinely miss. The survey turns that fuzzy idea into a percentage you can track.
Growth adviser Sean Ellis benchmarked nearly a hundred startups with this survey and found a line at 40%: companies where fewer than 40% of users answered “very disappointed” almost always struggled to grow, and companies above it almost always had strong traction. Rahul Vohra, founder of the email app Superhuman, described this benchmark and how his team turned the survey into a repeatable process in a 2018 First Round Review article.
When should you use it?
Use it once you have at least 40 people who use the product regularly. Vohra writes that results start to become directionally correct around 40 respondents.
- Fits: apps, SaaS, subscriptions, newsletters, communities, consumer products people use repeatedly.
- Fits B2B: survey the daily users inside client companies, and keep the buyer’s opinion separate.
- Too early: before people have used the product enough to miss it. A pre-launch waitlist can’t answer this question; use a pre-sale offer to test demand instead.
- Poor fit: one-off purchases. Nobody misses a product they were never going to use again.
How do you do it, step by step?
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Pick the right people. Survey only users who have used the main part of your product recently. Superhuman followed Ellis’s advice to survey people who used the product at least twice in the last two weeks. Leave out people who signed up and never activated; they tell you about your sign-up flow, a different problem.
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Use the four questions. Superhuman’s version, which you can copy: (1) “How would you feel if you could no longer use [product]?” Very disappointed / Somewhat disappointed / Not disappointed. (2) “What type of people do you think would most benefit from [product]?” (3) “What is the main benefit you receive from [product]?” (4) “How can we improve [product] for you?” We suggest adding a fourth answer to question 1, “I no longer use it”, and leaving those replies out of the score.
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Send it personally. Email from your own name with one line of context: “You’re one of our most active users. Four questions, two minutes, and I read every answer.” Use a Tally or Google Forms link, or show it in-app with PostHog surveys. Send one reminder after 3 days.
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Calculate your score. Very disappointed answers divided by all answers (minus “I no longer use it”). 25 out of 60 is 42%. Ask each person only once, so repeat answers don’t inflate the result.
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Segment the very disappointed group. Add columns to your sheet for role, company size, use case or whatever describes your users. Look at who the very disappointed people are, then recalculate the score for that segment alone. Superhuman did exactly this: by focusing on the personas who loved the product most, its score rose from 22% to 33% before any product change.
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Read the benefit answers. Question 3 from very disappointed users tells you what to double down on, in their words. Use those words on your landing page.
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Win over the “somewhat disappointed” who share that benefit. Among somewhat disappointed users, find those whose main benefit matches the very disappointed group. Their answers to question 4 show what holds them back. Ignore requests from people who don’t value your main benefit. Vohra reports that after three quarters of this work, Superhuman’s score nearly doubled to 58%.
What does a worked example look like?
Say Noor runs an app that helps freelance illustrators manage commissions: quotes, deposits, deadlines and payment reminders. She has 380 sign-ups, and 140 used the app at least twice in the last two weeks. She emails those 140 and gets 62 answers.
Her overall score: 19 very disappointed out of 62, or 31%. Below the line.
Then she segments. Illustrators who sell to private clients (pet portraits, wedding illustrations) answered very disappointed 13 times out of 26, or 50%. Illustrators who work for agencies and publishers answered very disappointed 6 times out of 36, or 17%. The private-client group names the same main benefit again and again: “I stopped chasing deposits.”
So Noor narrows her focus to private-client illustrators. Among the somewhat disappointed people in that group who also love the deposit reminders, the top request in question 4 is a way to send a payment link from a phone. She builds that next, rewrites her homepage around “Stop chasing deposits”, and plans to re-run the survey in 3 months for new active users.
Which free tools help?
| Tool | What you use it for | Free plan |
|---|---|---|
| Tally | The survey form, sent by email | Yes, free plan |
| Google Forms | Simple version with answers in Sheets | Free |
| PostHog Surveys | Showing the survey inside your app | Yes, 1,500 responses a month |
| Google Sheets | Scoring and segmenting answers | Free |
What mistakes should you avoid?
- Surveying everyone. Inactive sign-ups will drag the score down and hide the group that loves you. Ask recent, repeat users only.
- Treating 40% as a pass/fail exam. The number is a benchmark from Ellis’s comparisons, a signal to read alongside retention and revenue.
- Stopping at the overall score. The value is in segmenting. A 30% overall score can hide a 55% segment.
- Building every request. Requests from people who don’t value your main benefit pull you toward a different product. Filter by benefit first.
- Surveying the same people repeatedly. People answer more kindly the second time. Survey new active users each round.
How do you know it worked?
The survey worked when you can name one segment, its score, and the main benefit they would miss, in one sentence. Say: “Private-client illustrators, 50% very disappointed, because we stop them chasing deposits.”
Our rule of thumb for go / no-go: at 40% or more in a segment of at least 40 people, focus your marketing on that segment and grow. Between 25% and 40%, you are close; follow steps 5 to 7 and re-run in a quarter. Below 25% across every segment, the product isn’t yet something people would miss; go back to churn interviews and your cohort retention curves.
What should you do next?
Turn the winning segment into a clear customer persona and check that it matches your beachhead segment. Keep the benefit answers flowing with a customer feedback loop. If you want to test product-market fit with guidance from an AI co-founder, the F/MS startup game walks you through it step by step.
FAQ
What is the 40% rule for product-market fit?
It is Sean Ellis’s benchmark: if at least 40% of surveyed active users say they would be very disappointed without your product, you likely have product-market fit. He arrived at it by comparing results across nearly a hundred startups. Treat it as a strong signal next to retention, and segment before you judge.
How many responses do I need for the Sean Ellis test?
Rahul Vohra of Superhuman writes that results become directionally correct around 40 respondents. Fewer than that and a couple of answers swing the score by several points. More is better, especially if you plan to segment.
Who created the product-market fit survey?
Sean Ellis, a growth adviser who worked with early-stage startups, developed the survey and the 40% benchmark. Superhuman’s Rahul Vohra later made it popular with a detailed write-up of how his team used it as a product engine.
Updated 26 October 2026 · Written by the Fe/male Switch team