Product-market fit score

“Will people actually want this?” is the question every founder dreads — partly because it feels unanswerable. idea_builder answers it with a number: the Product-Market Fit (PMF) score, a 0–100 figure built from six dimensions, plus an automatic proxy-signal view that derives evidence from your research data.

The six dimensions

Score each dimension from 0–100 using the sliders on the PMF page:

Dimension	What it measures	Default weight
Must-Have	How essential is the product to the user’s life?	30%
Disappointment if Removed	How upset would users be if it disappeared?	25%
Willingness to Pay	Are people ready to pay real money?	15%
Referral Likelihood	Would users recommend it to others?	10%
Market Size	Is the addressable market big enough?	10%
Competitive Moat	Can you defend your position?	10%

The overall score is the weighted average of the dimensions. Weights come pre-set from your idea template, and you can adjust any dimension’s value at any time — your score reflects your judgment, not a hidden formula.

Reading the score

80+ — Strong PMF. Users would be seriously disappointed without it. This is the bar to build toward.
60–79 — Moderate PMF. Real interest, but the “must-have” evidence isn’t there yet.
40–59 — Weak PMF. Interest exists in pockets; the value proposition needs work.
Under 40 — No PMF (yet). Not compelling enough — keep researching and validating before building.

The automatic proxy-signal view

A score you type in is only as good as your optimism. So the PMF page also shows what your research data says — an automatic aggregator that reads your completed research runs and computes five proxy signals, with no extra work from you:

Search demand — is anyone actively searching for a solution? (From SEO volumes and trend trajectory.)
Pain point density — are existing solutions frustrating users? (From Hacker News sentiment, app-store complaints, and competitor deep-dives.)
Market growth — is the market expanding or shrinking? (From trend trajectory, Product Hunt activity, and patent filings.)
Competitive traction — are alternatives gaining adoption? (From Product Hunt votes, app-store review counts, and patent activity.)
PMF timing — is now the right time to enter? (From red-team survivability, market maturity, and audience pain levels.)

Each signal shows a confidence level (high / medium / low) based on how much of its data came from live sources, and which research phases fed it. The five signals combine into a composite proxy score and category — the platform’s read on your fit, separate from your manual assessment.

Manual assessment alongside the auto score

You’ll see both numbers on the PMF page, deliberately side-by-side:

Your manual score — your informed judgment on the six dimensions.
The proxy composite — what the evidence suggests.

When the two disagree, that gap is the most useful thing on the page. A high manual score with weak proxy signals usually means you’re overconfident — go validate. Strong signals with a low manual score might mean you’re being too hard on yourself — or that you’ve found demand but not the right message.

Your manual score can also be updated automatically: survey responses flow through the PMF auto-bridge once you’ve collected enough of them, grounding your numbers in real user answers.

Making the score actionable

Low search demand + low pain density? That’s a “no” dressed up as data — consider killing or pivoting the idea.
High pain density but low willingness to pay? The problem is real; the pricing or packaging is wrong. Fix it before building.
Strong signals but no validation? Move to the Validate stage — the fastest way to confirm the evidence with real people.

Use the scorecards & prioritization views to apply the same rigor across all your ideas at once.

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