Sean Ellis Test

What is Sean Ellis Test?

The Sean Ellis test is a single-question survey that asks existing users how they'd feel if they could no longer use your product, and treats a 40% or higher "very disappointed" response as the benchmark for product-market fit.

Sean Ellis, credited with coining the term growth hacking and co-writing Hacking Growth, built the survey after comparing results across many startups he advised. In his own account, product-market fit becomes achievable once a product reaches around 40% of users who say they'd be very disappointed to lose it. He cites one example of a team that went from 7% to 40% within a couple of weeks after reworking how they targeted and onboarded new users.

The survey itself offers four answer choices, not the two or three you'll see paraphrased elsewhere: very disappointed, somewhat disappointed, not disappointed, and an N/A option for people who no longer use the product. That last option matters. It filters out lapsed users so the percentage reflects people who are still around and still telling you the truth about how much they'd miss you.

Why it matters for product-market fit

A Produck team runs on a loop: Listen, Diagnose, Decide, Ship. The Sean Ellis test slots into the Diagnose step. It's a single number you can track over time, but the real value shows up when you read the free-text follow-up questions next to it: who the very-disappointed users are, and what's stopping the somewhat-disappointed group from moving up a tier.

That's where a raw survey score turns into direction. User feedback collected in Produck gets tagged and clustered so you can see whether your very-disappointed segment clusters around one plan or one use case. Diagnose becomes concrete instead of abstract: not "we're at 32%" but "we're at 32% because self-serve signups never reach the moment that makes the product sticky." That's the difference between a survey result and a decision about what to build next.

If you're still working out what product-market fit means for your team before you run this survey, our explainer on why PMF matters for startups walks through the broader signals worth watching alongside it.

When it works, and when it doesn't

It works when

1. You have at least 30-40 responses from users who've actually used the product enough to have an opinion, not people who signed up yesterday.
2. You send it to active or recently active users only, so the N/A option does its job of filtering out lapsed accounts before you calculate the percentage.

It falls short when

1. Your user base is too small or too new for the sample to mean anything, and a handful of enthusiastic responses skews the number either way.
2. You treat the score as a finish line instead of a diagnostic, running it once, hitting 40%, and stopping there instead of tracking it as usage and audience shift.
3. You're selling into enterprise accounts where the actual daily user and the economic buyer are different people, and surveying only one of them tells you half the story.
4. You skip the qualitative follow-up questions, because the score alone can't tell you which segment or onboarding step is dragging the average down.

How to apply it

1. Pick your survey population: users who've had a real session with the product in the last one to two weeks, not first-time signups.
2. Send the exact question, "How would you feel if you could no longer use [product]?", with the four answer options: very disappointed, somewhat disappointed, not disappointed, and N/A.
3. Add two open-ended follow-ups right after: what's the main benefit you get from the product, and what would make it better for you.
4. Wait for at least 30-40 qualified responses before calculating anything, then divide very-disappointed responses by total qualified responses, excluding N/A, to get your percentage.
5. Segment the results by plan or signup channel so you can see whether 40% is an average hiding a much stronger or weaker sub-group.
6. Feed the free-text answers into your Diagnose step so the survey turns into a ranked list of what to build or fix next, not just a number on a dashboard.

Sean Ellis test vs Net Promoter Score

The Sean Ellis test and Net Promoter Score get lumped together because both reduce a relationship to one question and one number, but they're measuring different things.

• Sean Ellis test: asks how disappointed a user would be to lose the product. It measures dependency and is meant to gauge product-market fit specifically, with a 40% "very disappointed" line as the benchmark.
• NPS: asks how likely a user is to recommend the product to a friend or colleague, scored 0 to 10, with promoters (9-10) minus detractors (0-6) giving a score from -100 to 100. It's a broader loyalty metric used well beyond the PMF stage, and it says nothing about whether a product is essential, only whether someone would speak well of it.

Use the Sean Ellis test when you're trying to answer "have we found fit yet," and use NPS as an ongoing loyalty check once you're already past that question.