Product-Market Fit Validation: How to Know You Have It Before You Announce It

Quick answer

Product-market fit is the moment a specific product satisfies a real, repeated demand from a specific audience well enough that retention, word-of-mouth, and organic acquisition improve on their own. It is a behavioral state, not a marketing claim. You do not announce it; you observe it.

The four signals that tell you the fit is real: customers who come back without a prompt, organic word-of-mouth that brings in people who already match your ideal customer profile, retention curves that flatten instead of dropping toward zero, and rising willingness to pay at the same price. The four failure modes that tell you the fit is not real: high churn past first use, constant paid acquisition with no organic lift, customers who praise the product but never come back, and a "maybe later" pile of half-converted leads that never convert.

The 30-day validation framework is Problem → Customer → Solution → Usage → Retention → Growth Signal. Each rung must hold before the next one is meaningful. A founder who skips the retention rung and starts buying ads is buying traffic for a product that does not retain. The framework does not predict success; it surfaces the moment the evidence starts to look like fit, or the moment the evidence says you are still pre-PMF.

Key takeaways

• Product-market fit is observed in behavior, not in testimonials. A founder declaring PMF on the basis of "people said they love it" is reading the polite-yes problem. Rob Fitzpatrick's The Mom Test (2013) made this canonical: only behavior counts.
• Retention is the cheapest PMF signal. The day-1, day-7, and day-30 retention curves flatten at different rates depending on product type. A consumer app that flattens at 25% day-30 has PMF for its category; a SaaS that retains 90% past month three has PMF for its.
• Word-of-mouth without paid acquisition is the second signal. Sean Ellis's PMF survey asks "How would you feel if you could no longer use this product?" The threshold is 40% saying "very disappointed." The survey is a useful but imperfect proxy; the truer test is whether organic signups are increasing without a corresponding ad spend.
• The four pre-PMF failure modes are predictable. Build-too-early failure, false-traction failure, feature-addiction failure, and retention-curve failure each have a recognizable signature. Knowing the signature lets you diagnose the real problem before the next build cycle.
• PMF is the prerequisite for growth, not the result of growth. A common founder mistake is to hire growth before retention has flattened. The retention curve flattens first; the growth loop is what you build next.

Why this matters

I have watched two founders declare product-market fit on the same week, with very different outcomes.

The first had a B2B SaaS, $40,000 in annual contracts across nineteen customers, and 18% month-over-month growth. The retention curve dropped from 100% in month one to 71% by month four. The growth was paid acquisition: every new logo was a cold outbound campaign. By month seven, thirteen of nineteen original customers had churned. The founder had read the growth line.

The second had a consumer tool with fewer than five hundred users. The paid acquisition budget was zero. The retention curve flattened at 38% by week four. One in seven users referred someone who signed up unprompted. The growth line looked unimpressive; the retention line was telling the truth.

The two cases are not exceptions. OpenView's State of SaaS benchmarks (2022) show top-quartile retention companies growing at roughly twice the median rate. Retention predicts growth; growth does not predict retention.

This article is the smallest set of steps that lets you read the right line. Not a marketing framework. Not "ten signs of PMF." A concrete framework that turns retention, word-of-mouth, and growth from separate anecdotes into a single signal you can act on, plus a list of failure modes that show up before the founder notices them.

The deeper treatment — the six PMF measurement methods ranked, the four retention curves explained — lives in the canonical How to Validate a Startup Idea Before Building. The willingness-to-pay test that precedes PMF is in Willingness to Pay Validation and How to Test Willingness to Pay Before You Build. The conversation discipline that finds the customer is in The Mom Test Explained for Solo Founders. The vocabulary used in this article is defined in the Startup Validation glossary.

What is product-market fit?

Product-market fit is the state in which a specific product satisfies a real, repeated demand from a specific audience well enough that retention, word-of-mouth, and organic acquisition improve on their own.

Four things make this definition harder than it looks.

It is a relationship between a product, a customer, and an alternative. A product has PMF in a specific market when its customers have a real problem, the product solves it better than the alternatives they currently use, and the difference is large enough that they keep using the product without being reminded. Move the product to a different customer and the fit may disappear. The relationship, not any single piece, is what PMF names.

It is observed in behavior, not declared. The phrase "we have product-market fit" used to mean the founder had evidence. In 2026 it often means the founder wants to raise a round, and the evidence is optional. The definition above forces evidence in: retention, word-of-mouth, organic acquisition. A founder who declares PMF without those signals is reading the wrong data.

It is a threshold, not a moment. There usually is no moment. There is a range of behavior, and somewhere in that range a line is crossed. The line is "retention that does not depend on acquisition." Below the line, every new customer requires a paid push. Above the line, customers come back because the product solves a recurring problem, and some of them tell other people who solve the same problem.

It changes over time. The customers who fit today may not be the customers who fit in a year; the alternatives change; the workflow shifts. PMF is a moving target. Treating it as a permanent state is one of the more common founder errors; treating it as a process you maintain is closer to right.

A useful way to think about the concept: PMF is the state in which you have stopped buying growth and started earning it. That distinction is the entire reason the validation framework exists.

Why startups need PMF validation

Four reasons, in increasing order of consequence.

First, the polite-yes problem is widespread. Rob Fitzpatrick's The Mom Test (2013) made the canonical case: when you ask customers whether they would buy, most say yes to be polite. Most would not. Sean Ellis, who coined the modern "product-market fit" framing in his 2010 essay for TechCrunch, observed the same failure mode: the polite-yes problem hides the real signal behind friendly answers. PMF validation is the discipline of testing the answer with behavior, not with conversation.

Second, building before PMF burns runway in a measurable pattern. CB Insights' Startup Failure Post-Mortem reporting consistently lists "no market need" as the most-cited reason for startup failure, named by roughly 40% of failed founders across multiple years of survey data. Building before fit is the most expensive assumption in early-stage work. Three to nine months of runway get spent on a product that has not earned retention.

Third, the growth tactics that work before PMF and after PMF are different. Before PMF, growth is paid acquisition that masks churn. After PMF, growth is organic and word-of-mouth driven, and paid acquisition becomes an accelerator rather than a substitute. A founder who hires a growth team before retention has flattened is paying a growth team to drag customers into a product they will not return to. The retention gap is the difference.

Fourth, the assumption that PMF will arrive once enough customers see the product is often wrong. Most pre-PMF products stay pre-PMF when more customers see them, because the problem the product solves is not the problem the new customers have. PMF validation forces the founder to confront whether the problem is right before scaling the channel.

How to validate product-market fit

There are six methods that produce useful signal. They run from cheapest to most expensive, and they roughly track the strength of the signal they produce.

1. Retention curve analysis

The cheapest, strongest, and most often misread method.

The right measurement is a cohort retention curve: take the users who joined in week one, log what percentage of them used the product in weeks two, three, four, and beyond. The shape of the curve tells you whether the product has a habit trigger and whether the trigger is strong enough to retain.

Three patterns are common.

• The cliff. Retention drops sharply in week one (or month one), then flattens. The cliff is the segment of users who tried the product once and never came back. If the cliff is steep (more than 70% of new users disappear in the first week) and the curve flattens low (under 15% by week four), the product has a habit trigger for a small segment and is failing to convert the rest.
• The slope. Retention declines steadily week over week with no flattening. The product does not have a habit trigger; users are dropping off at a constant rate. Most consumer apps with no recurring-need trigger look like this by month three.
• The plateau. Retention drops sharply in the first period, then flattens at a meaningful level (over 30% for a consumer app, over 80% for a B2B SaaS after the first renewal). The plateau is the signal that fit exists for the segment that reached the plateau.

The benchmark matters. Andrew Chen's cohort retention work (2015) and Lenny Rachitsky's later consumer subscription surveys show median day-30 retention in the 5–15% range across categories, with significant variance. The right benchmark is "the curve is flat at a level your business model can sustain," not "the curve is at some absolute number."

2. Sean Ellis PMF survey

The middle method, and the one most worth running this quarter.

The survey is one question: "How would you feel if you could no longer use this product?" Sean Ellis's threshold, published in 2010 and refined across many product launches since, is 40% of respondents saying "very disappointed." Below 40%, the product has not earned the kind of attachment that survives contact with alternatives.

The survey is fast (a few days to collect responses from a few hundred users), cheap (Google Forms is enough), and informative. It is also imperfect. The survey measures stated attachment, not behavior; some users say "very disappointed" and churn anyway. Treat the 40% threshold as a directional signal, not a verdict.

The survey is most useful run twice. The first time, three to six months after launch, to baseline where you are. The second time, two to three months after a major product change, to measure whether the change moved the signal. A founder who sees the score move from 22% to 38% after a redesign has evidence; a founder who sees the score stay flat has a different kind of evidence.

3. Organic growth tracking

The second behavior signal, and the one that catches founders who scale paid acquisition before retention has flattened.

The right measurement is the ratio of organic signups (or signups from non-paid channels: SEO, direct navigation, word-of-mouth referral) to total signups over a rolling 90-day window. Before PMF, the ratio is low or zero. After PMF, the ratio grows steadily because existing customers bring in new customers.

Three signals are diagnostic. Paid acquisition with no organic lift — every new customer is the result of a paid push; churn offsets new acquisitions. Word-of-mouth without a referral program — existing customers are telling other potential customers without being incentivized. Organic search growth without SEO investment — new visitors arrive from search terms the founder did not target.

The Sean Ellis "must-have" score — the 40% threshold — is correlated with, but distinct from, organic growth. A founder who hits 40% on the survey but sees no organic lift has a product that is liked but not told about. A founder who has organic lift but is under 40% on the survey has a product that is told about but not universally liked. The two together are the cleanest signal.

4. Cohort analysis by acquisition channel

A founder who has been running paid acquisition should run cohort analysis segmented by the channel that brought each cohort in. The question is whether retention is consistent across channels or whether one channel is producing customers who retain and another is producing customers who churn.

The pattern is diagnostic. A founder whose SEO cohort retains at 60% past month three and whose paid social cohort retains at 12% past month three does not have a product problem; they have a channel-product-fit problem. The fix is to scale the channel where the fit is real, not to scale all channels equally.

5. Willingness to pay at a higher price

A product with PMF supports a price-elasticity test. The test: can the founder raise prices by 20–50% without a meaningful drop in conversion? A product without PMF cannot, because customers who are choosing the product because it is cheaper will leave when it stops being cheaper. A product with PMF can, because customers who are choosing the product because it solves a problem will pay what the problem is worth.

The test is informative either way. A founder who raises prices and sees conversion drop by 5% has a product with elasticity that fits. A founder who raises prices and sees conversion drop by 50% has a product without fit, regardless of what the retention curve says.

6. Cohort LTV / CAC ratio

The most expensive, most diagnostic method, and the one that closes the PMF loop.

The right measurement is a 12-month cohort LTV (lifetime value, the cumulative revenue per customer over 12 months) divided by the CAC (customer acquisition cost) for the same cohort. For most SaaS businesses the threshold is LTV:CAC above 3:1 with payback under 12 months.

A product with PMF hits this ratio organically, because retention is high enough that the cohort generates revenue over time. A product without PMF does not, because churn offsets the new revenue. The ratio is the financial translation of fit.

The PMF validation framework: Problem → Customer → Solution → Usage → Retention → Growth Signal

The six-step framework below is what every PMF validation method is a sub-test of. Each rung must hold for the next rung to be meaningful.

Problem. A specific person has a specific problem often enough that solving it is worth their time. The cheapest test is a customer interview that asks "what did you do last time?" Without this rung, the rest is irrelevant.

Customer. A specific, reachable group of people have the problem and are not currently well-served by alternatives. The cheapest test is a customer-discovery conversation that asks "what are you using today, and what do you hate about it?" Without this rung, the product is being built for an abstract audience.

Solution. A proposed solution would meaningfully improve the customer's situation. The cheapest test is a solution interview or a clickable prototype that asks "if a tool did X, would that change what you do?"

Usage. Real customers use the product enough times for the usage pattern to be measurable. The cheapest test is a manual concierge or a no-code prototype used by ten to twenty real people for one to two weeks.

Retention. Customers keep using the product past the first use, past the first week, past the first month. The cheapest test is a cohort retention curve over 30 days. Without this rung, growth tactics do not compound.

Growth Signal. Existing customers bring in new customers without paid acquisition. The cheapest test is the ratio of organic signups to paid signups over a 90-day window. A meaningful organic ratio is the moment PMF starts to feel real.

The framework is sequential. A founder who has not tested the customer rung should not run a retention test, because they will not know whether the curve is bad because of the product or because of the audience. A founder who has not tested the retention rung should not run a growth test, because they will not know whether the growth is real or paid.

The framework is also cumulative. Each rung produces evidence that the next rung depends on. A founder who skips the interview rung and jumps straight to a cohort retention curve can still learn something — they will learn the shape of the curve — but they will not know whether the curve shape is high because the audience is right or because the channel is selecting for the right audience.

Signs you have not found PMF

The failure modes that show up before the founder notices them. Each is recognizable, named, and preventable.

Build-too-early failure. The product was launched before the customer rung held. Customers exist but do not have the problem the product solves; the retention curve drops to zero because the product does not address the actual recurring need. The fix is to revisit the problem and customer rungs, not to rebuild the product.

False-traction failure. The product has signups, downloads, social media followers, or pre-orders. None of it converts to retained users. The conversion funnel has volume at the top and empty seats at the bottom. The fix is to test the solution and retention rungs before adding more top-of-funnel volume.

Feature-addiction failure. The founder keeps adding features in response to individual customer requests. The product gets larger; the retention curve does not move. The fix is to talk to customers about what they actually do, not what they wish the product did.

Retention-curve failure. The product has signups, customers, and revenue. The retention curve declines steadily. The founder is paying to replace churned customers with new ones. The fix is to identify the cliff or slope and rebuild the product around the segment that did reach the plateau.

No-organic-lift failure. The product has customers. The growth is paid. The product may have customers; it does not have customers who tell other customers. The fix is to revisit the solution rung, not to spend more on channels.

Acquisition-channel-mismatch failure. The product has customers in one channel (typically one the founder has privileged access to: a community, an audience, a network). The audience for which the product has fit is narrow; the founder is treating it as broad. The fix is to scale the channel where the fit is real.

Pricing-mismatch failure. The product has customers. The willingness to pay at the planned price is low; the willingness to pay at a lower price is higher. The product has fit at a different price point than the founder is charging. The fix is to test the price. The full method lives in Willingness to Pay Validation.

Audience-mismatch failure. The product has customers. The customers do not match the founder's ideal customer profile. The product has fit for an audience the founder is not optimizing for. The fix is to redefine the ICP and the channel together.

Common mistakes

The mistakes I see founders make in roughly the order they make them.

Treating compliments as evidence. "This is great!" is not the same as "I will keep using this." Compliments measure enthusiasm. PMF measures behavior. Rob Fitzpatrick's The Mom Test (2013) made this canonical.

Confusing growth with fit. A growing user count with no retention curve improvement is paid acquisition masking churn. The growth rate and the retention rate are independent metrics; a founder who tracks only growth will read the wrong data.

Hiring growth before retention has flattened. A growth team hired before retention is a growth team scaling a leaky bucket. The first three months of growth work should focus on fixing the bucket. After the bucket holds water, growth work compounds.

Ignoring the cohort LTV/CAC ratio. A founder who does not know the 12-month LTV/CAC for their product does not know whether they have PMF. The ratio is the financial translation of fit.

Treating PMF as a destination. PMF is a state, not a moment. A founder who declares PMF once and never revisits the assumption will eventually find the fit has slipped.

Skipping the problem rung. A founder who has customers, retention, and revenue can still have built the wrong product if the problem the product solves is not the problem the customers have. The cheapest test of the problem rung is a customer interview.

Treating PMF as the end of validation. PMF is the end of pre-PMF work. It is the beginning of post-PMF work, which is different in kind. Post-PMF work is about scaling, retention at larger cohorts, and protecting the fit from new competitors.

FAQ

What is product-market fit?

Product-market fit is the state in which a specific product satisfies a real, repeated demand from a specific audience well enough that retention, word-of-mouth, and organic acquisition improve on their own. It is a behavioral state, not a marketing claim. The full definition and the four signals are in the Startup Validation glossary.

How do startups find product-market fit?

By testing the six-step framework — Problem → Customer → Solution → Usage → Retention → Growth Signal — and stopping to fix each rung before moving to the next. The most common error is skipping the retention rung and starting to scale before the retention curve has flattened. The method is explained in the canonical How to Validate a Startup Idea Before Building.

How long does product-market fit take?

There is no honest answer. PMF takes as long as it takes to find a problem, customer, and solution that hold across the retention rung. For most early-stage teams, the median is between nine and eighteen months from idea to PMF; the variance is enormous. The right answer is "however long it takes to flatten the retention curve," not "however long it takes to feel ready."

Can AI predict product-market fit?

No. AI is useful for summarizing patterns in customer feedback, drafting the Sean Ellis survey, and analyzing cohort retention curves. It cannot predict whether a product will achieve PMF, because PMF is a behavioral state that depends on customer behavior, alternatives, and market dynamics the model cannot observe. Treat AI as a tool for the analysis; treat PMF as a state the founder must earn. The validation report that Startup MRI produces surfaces the assumptions that most often prevent PMF; it does not predict whether PMF will be achieved.

What metrics indicate product-market fit?

The four diagnostic metrics: cohort retention curve shape (cliff, slope, or plateau), Sean Ellis score (40%+ "very disappointed"), organic signup ratio (meaningful share of signups from non-paid channels), and cohort LTV/CAC ratio (above 3:1 with payback under 12 months for SaaS). The four together are more informative than any single one; a founder who hits three of four has a strong PMF signal; a founder who hits two of four has partial fit; a founder who hits zero of four has no PMF.

How do you measure product-market fit for a SaaS product?

For a SaaS product, the right metrics are: logo retention at month three (above 90% indicates strong fit; below 70% indicates weak fit), net revenue retention above 100% (expansion revenue exceeds churn), the Sean Ellis score at 40%+, and cohort LTV/CAC above 3:1 with payback under 12 months. The SaaS-specific assumption stack is recurring willingness to pay, churn past first renewal, ICP narrowness, pricing-tier fit, and distribution-channel reachability. The full SaaS playbook lives in How to Validate a SaaS Idea Before You Build It.

How do you measure product-market fit for a mobile app?

For a mobile app, the right metrics are: day-1 retention above 40% (a strong habit signal), day-7 retention above 20% (the trigger is real), day-30 retention above 10% (the cliff is survivable), and a Sean Ellis score at 40%+. The mobile-specific assumption stack includes install intent, app-store discoverability, monetization at the chosen price tier, and the offline-usage assumption. The full mobile playbook is in How to Validate a Mobile App Idea Before You Build It.

What is the Sean Ellis survey?

The Sean Ellis survey is a one-question survey — "How would you feel if you could no longer use this product?" — with response options "very disappointed," "somewhat disappointed," "not disappointed," or "not applicable." The threshold Sean Ellis proposed in 2010 is 40% of respondents saying "very disappointed." The survey is fast, cheap, and imperfect: it measures stated attachment, not behavior. Run it twice — once to baseline, once after a major change — and use the change in the score, not the absolute number, as the primary signal.

What is the difference between product-market fit and traction?

Traction is growth in usage; PMF is the engine that produces sustainable traction. A product can have traction without PMF (viral spread without retention) and PMF without visible traction (a small base of highly retained users). The interesting state is PMF, because PMF compounds. A founder who tracks only traction will scale paid acquisition before the engine exists; a founder who tracks PMF will fix retention before scaling acquisition.

Can product-market fit be lost?

Yes. PMF is a state that depends on the relationship between a product, a customer, and an alternative. Move any of the three — change the product, change the customer, change the alternative — and the fit may disappear. Most products lose PMF as the market evolves, competitors arrive, and the customer's workflow shifts. A founder who treats PMF as a destination rather than a process will eventually find the fit has slipped. The discipline of maintaining PMF is the same as the discipline of finding it: measure retention, word-of-mouth, and organic acquisition on a rolling basis.

How does PMF relate to willingness to pay?

PMF requires willingness to pay; willingness to pay does not guarantee PMF. A product that has willingness to pay has customers who would pay, in a transaction, for the solution. A product that has PMF has customers who are paying, who are staying, and who are bringing in new customers without paid acquisition. Willingness to pay is one of the four PMF signals; the other three (retention curve, organic growth, repeated use) are what convert willingness to pay into PMF. The full willingness-to-pay method is in Willingness to Pay Validation.

What are the most common PMF failure modes?

The seven most common are: build-too-early failure (product solves the wrong problem), false-traction failure (volume at the top, churn at the bottom), feature-addiction failure (adding features without addressing the recurring need), retention-curve failure (the curve never flattens), no-organic-lift failure (paid acquisition with no word-of-mouth), acquisition-channel-mismatch failure (fit exists for one channel only), and pricing-mismatch failure (fit exists at a price the founder is not charging). Each has a recognizable signature; each has a fix that starts with the framework rung that broke.

How is PMF different for B2B vs B2C products?

The signals are similar; the benchmarks are different. B2B products typically have smaller customer counts, longer sales cycles, and higher willingness to pay per customer. The PMF threshold for a B2B SaaS is usually a logo retention rate above 90% past the first renewal and net revenue retention above 100%. B2C products typically have larger customer counts, shorter consideration cycles, and lower willingness to pay per customer. The PMF threshold for a B2C consumer app is usually day-30 retention above 10% and a Sean Ellis score at 40%+. The framework is the same; the benchmarks are different.

Can a startup have multiple product-market fits?

Yes. A founder can find fit for a narrow segment first, then expand to adjacent segments, each with its own retention curve and Sean Ellis score. The discipline is to treat each expansion as a separate PMF test, not as a continuation of the original fit. A founder whose first product has fit with solo founders may or may not have fit with enterprise teams; the answer is empirical, not assumed.

Summary

Product-market fit is the state in which a specific product satisfies a real, repeated demand from a specific audience well enough that retention, word-of-mouth, and organic acquisition improve on their own. It is observed in behavior, not declared in conversation. The four signals are cohort retention curve shape, the Sean Ellis score, organic signup ratio, and cohort LTV/CAC. The seven failure modes are predictable: build-too-early, false-traction, feature-addiction, retention-curve, no-organic-lift, acquisition-channel-mismatch, and pricing-mismatch.

The six-step validation framework is Problem → Customer → Solution → Usage → Retention → Growth Signal. Each rung must hold before the next is meaningful. A founder who has not flattened the retention curve should not be hiring growth; a founder who has not tested the customer should not be measuring retention.

PMF is the prerequisite for growth, not the result of growth. The retention curve flattens first; the growth loop is what you build next. The polite-yes problem is real; behavior is the only honest test. The Sean Ellis 40% threshold is a useful but imperfect proxy; the cohort LTV/CAC ratio is the financial translation of fit.

The goal is not to declare PMF. The goal is to find out, in 30 days and for under $500 in tooling, whether the product is producing the kind of retention and organic growth that compounds. If it is, scale. If it is not, fix the rung that broke.

Related reading

• How to Validate a Startup Idea Before Building — the canonical four-stage framework this article sits inside.
• Problem-Solution Fit Validation — the rung before PMF; confirms the proposed solution addresses a real, recurring problem before any product is built.
• Willingness to Pay Validation — the payment signal that precedes PMF; PMF requires willingness to pay.
• How to Test Willingness to Pay Before You Build — the deeper playbook on the six pricing experiments and the seven-day payment plan.
• The Mom Test Explained for Solo Founders — the conversation discipline that finds the customer before the retention curve can flatten.
• How to Find Your First Customers Before You Build — where the future customers actually gather, and the weekly plan to reach the first 10.
• Startup Validation Checklist — the operational checklist that turns the framework into a 30-day plan.
• How to Validate a SaaS Idea Before You Build It — the SaaS-specific assumption stack, including the recurring-revenue cliff.
• How to Validate a Mobile App Idea Before You Build It — the day-1, day-7, day-30 retention floors and the app-store distribution surface.
• Distribution Channels Ranked for Solo Founders — the eleven channels that move a stranger from first contact to paying customer, ranked by solo-founder fit.
• The Startup Validation hub — the full reading path and topical taxonomy.
• The Startup Validation glossary — plain-language definitions of PMF, retention, organic growth, customer lifetime value, and the rest of the validation vocabulary.

What to do next

If you have read this far, the question is what you will do in the next 30 days.

The smallest useful action is a Sean Ellis survey sent to the 50 customers most likely to answer honestly. The next is a cohort retention curve over 30 days, segmented by acquisition channel. The third is the ratio of organic to paid signups over a 90-day window. None of these requires a finished product. None requires permission. None requires a team.

If you would like a structured second opinion on which assumptions in your idea carry the most PMF risk before you run the survey or pull the curves, Startup MRI's validation analysis helps. It takes about five minutes and surfaces the parts of your idea most likely to break under the retention rung, so the cohort curve you measure is the one with the highest signal.

For a worked example of what a real validation report looks like, see the SaaS validation report example, the AI startup validation report example, or the mobile app validation report example. Each shows every section your own report will contain, including the retention-assumption callout.

The right validator landing depends on the audience: the Startup Idea Validator for general ideas, the Startup Idea Evaluator for founders who use "evaluate" as the verb, and the Startup Score Calculator for a numeric 0–100 read on the same six dimensions.

Behavior is stronger than compliments. Run the framework. Measure the rungs. Read the retention line.