Use caseValidate product-market fit
Describe the users who would be very disappointed to lose your product, what they use it for and who they are, and set them against occasional and lapsed users to see where fit stops.
Fit shows in the contrast between people who rely on a product and people who tried it and drifted, so the study hears both from the start.
The team exports heavy, occasional and lapsed users from its own product analytics and invites them through a connection to its existing tools or a list upload, so every interview can be read against how often that person uses the product.
A short survey asks how people would feel if they could no longer use the product, very disappointed, somewhat or not at all, and the share in the first group becomes the fit signal the rest of the study explains.
Interviews ask what people use the product for, what they used before and what they would do without it, which shows whether it has become part of how they work or stays a nice extra.
The world's most ambitious teams research with Pulse.
GrowwProduct, growth and leadership read the result for one decision: which customers to serve better before trying to reach more of them.
The very-disappointed group is described in full, their role, their situation, the job they hire the product for and what they would switch to, which sharpens targeting and the positioning built on it.
Occasional and lapsed users say what stopped them relying on the product, a missing step, a slow setup or a better habit elsewhere, which points to the gaps between them and the core group.
Needs raised by the core group are weighed first on the roadmap, and requests from users the product serves less well are marked as a separate bet, so the team protects what already works.
Product-market fit is the question of whether a product satisfies a strong market demand. Behavioral signals, such as retention, frequency of use and organic growth, give one view. Customer research gives the other: who relies on the product, for what job, and what they would do without it. A widely used survey question, associated with Sean Ellis, asks users how they would feel if they could no longer use the product, and the share who say very disappointed is read as a signal of fit, especially when combined with the reasons behind the answers. Interviews with occasional and lapsed users show what limits adoption. Segmenting customers finds the group with the strongest fit, and the usual advice is to serve that group better before expanding. Jobs-to-be-done interviews then describe the progress that group seeks and the alternatives it compares against, which is where the next round of product decisions starts.
If the very-disappointed group shares a role and a situation, people in that role who have never tried the product are the next test. Recruited to that profile, they show whether the fit reaches beyond the people already using it.
Users who would be very disappointed often recommend the product unprompted. The AI moderator asks whom they told and what they said, which shows growth where referrals start and which roles pass the product on.
Core users may have found the product's value by a route nobody designed. Their transcripts show the first task that made it indispensable, and growth can steer new sign-ups toward that task in their first session.