Narrow until a small group cannot do without you, then widen. Pick the segment where usage is strongest, talk to those people every week, and change the product until they would be very disappointed to lose it. Measure that with Sean Ellis's question, 'How would you feel if you could no longer use the product?': below 40% answering 'very disappointed' means keep iterating. Superhuman ran the survey, focused on the users who loved it, built more of what they loved, fixed what held back the near-fans, and tracked the score as it climbed. Watch behaviour as well as answers: customers who stay, usage spreading by word of mouth, and people paying without being chased. Michael Seibel's test is blunter: you have fit when demand overwhelms you. Until then, do things that do not scale for your best segment. Rule: change the product or the segment, never the definition of fit.
Segment's founder walks through failures and near-misses so you can feel the contrast between not having fit and having it. Because it shows the wrong turns as vividly as the right one, you learn to spot the difference in your own product. A grounded case study to sit alongside the frameworks.
Vohra shows a repeatable method for narrowing to the customers who most want a product that had weak initial pull. He surveys users, isolates the ones who would be "very disappointed" without the product, and builds a profile of that segment, then doubles down on them. It is one of the clearest walk-throughs of turning fuzzy early signal into a specific customer definition.
Ask users how they would feel if they could no longer use the product, and treat the "very disappointed" group as the seed of your ideal customer.
Build a profile from that high-intent segment first, then decide what to strengthen and which objections to remove.
A caution worth holding: your highest-expectation early users and your eventual broad ICP are not always the same, so revisit the definition as you grow.
Turns the vague feeling of product-market fit into a number you can move. Ask users how they would feel if they could no longer use the product, then track the share who say 'very disappointed'. Under 40 percent means keep working. A test you can run on an idea long before you scale it.
Jackson breaks fit into levels and asks how acute the problem is, how reachable the audience is, and how differentiated your answer needs to be, which lines up neatly with judging an idea. It gives you a shared vocabulary for whether your idea is worth deeper investment. Read it for a structured way to grade an idea across problem, audience, and value.
The essay that put 'product-market fit' into the startup vocabulary. Read it for the gut-level description of what PMF feels like when it's happening vs when it isn't, the intuition behind the metrics.
Seibel's whole point is that a market only counts once real customers are grabbing the product as fast as you can make it and paying for it, not because a report says the category is big. That is the exact test for your situation: a huge paper market with almost nobody paying is the opposite of the hair-on-fire demand he describes. Read it as a gut-check on whether you have found a real problem or just a large-sounding one.
The permission slip to recruit users by hand, do things manually, and deliver 'insanely great' experiences to your first few customers. The cheapest, most honest way to validate demand is to go get it one person at a time.
A grounded India-specific feature with hard-won lessons from BigBasket, Razorpay, BrowserStack and Instamojo on understanding the Indian market before building. Inc42 is the reference publication for the Indian startup ecosystem.
The free tool for running the how-would-you-feel-without-this survey that underpins the Superhuman method, giving you a concrete number to track once you have early users. It turns a fuzzy sense of whether people care into a repeatable measurement you can watch over time. Use it when you have enough users to survey.
