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Assesses product-market fit using Sean Ellis survey, retention curves, and segment analysis. Guides PMF measurement, user engagement evaluation, and scaling decisions.
How this skill is triggered — by the user, by Claude, or both
Slash command
/gtm-skills:product-market-fit-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Read bootstrap context before asking questions: `strategy/brand.md` for brand, audience, offer, channels, tools, constraints, and metrics; `about/me.md` for personal voice; `content/ideas.md` and `content/calendar.md` for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to `content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md`, and route d...
Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.
This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.
Comprehensive framework for assessing, achieving, and scaling product-market fit.
| Situation | Use This Skill For |
|---|---|
| Measuring PMF | Sean Ellis Survey |
| Retention analysis | Retention Curves |
| PMF validation | Leading Indicators |
| Segment-specific PMF | Segment Analysis |
| Scaling decisions | Post-PMF Strategy |
Product-market fit is the condition where a product satisfies a strong market demand. It's not binary — it's a spectrum.
PMF is obvious when you have it.
Matt MacInnis: "Product market fit is something where you absolutely know it when you see it. Therefore if you don't absolutely know it, you don't have it."
| Level | Customers | Focus |
|---|---|---|
| Nascent | 3-5 | Satisfaction |
| Developing | 5-25 | Demand |
| Strong | 25-100 | Efficiency |
| Extreme | 100+ | Scaling |
The Question:
"How would you feel if you could no longer use this product?"
- Very disappointed
- Somewhat disappointed
- Not disappointed
The Benchmark:
40% "very disappointed" = on the right track
Focus on the "very disappointed" segment as the core value indicator.
Uri Levine's Definition:
"Product market fit has one metric. Retention. If you create value, they will come back. If they're not coming back, you're not creating value."
What to look for:
Key retention points:
Christian Idiodi:
"The holy grail is really a reference customer - somebody who loves it enough to tell people about it."
| Market | Target References |
|---|---|
| B2B | 6-8 reference customers |
| B2C | 15-25 reference customers |
| Indicator | What It Means |
|---|---|
| "Very disappointed" > 40% | Strong core value |
| Retention curve flattening | Product creates ongoing value |
| Customer "pull" | Market is pulling product |
| Outrage during outages | Product is mission-critical |
| Customer driving next steps | Intent, not polite interest |
Raaz Herzberg:
"We felt the questions change — 'How are you pricing this? When can we start a POV?' That's real intent."
True pull is characterized by:
Jeff Weinstein:
"During those 20 minutes our customers weren't furious. That was the signal we did not have product market fit."
If your product goes down and nobody notices or complains, you haven't solved a mission-critical problem.
Karri Saarinen: "The way we think about it is, 'Do we have the fit in specific segments?' and how strong that fit is."
PMF exists in segments, not universally.
Start narrow, then expand:
Casey Winters: "If you have a product that retains well and you can't find more users for it, I don't think that's product market fit."
True PMF requires:
Without both, you don't have true PMF.
Casey Winters: "Protecting what you've built is increasingly important once you build scale. You might fall out of product market fit in a year or five years if you're not continually making your product better."
Markets shift, competitors improve, user expectations rise. Continuously monitor and protect PMF.
| Mistake | Reality |
|---|---|
| Confusing launch spikes with PMF | Sustained organic growth matters |
| Ignoring retention data | If they don't come back, no PMF |
| Scaling too early | Paid growth before PMF burns cash |
| Conflating TAM with PMF | Large market ≠ fit within it |
| Listening to "somewhat disappointed" | Focus on "very disappointed" |
| Signal | Action |
|---|---|
| 40%+ "very disappointed" + flattening retention | Ready to scale |
| < 40% "very disappointed" | Keep iterating |
| No reference customers | Build them first |
| No distribution mechanism | Find channels first |
| Factor | Scale | Keep Iterating |
|---|---|---|
| PMF survey | > 40% very disappointed | < 40% |
| Retention | Curve flattening | Decaying to zero |
| References | Target achieved | Not yet |
| Distribution | Channels identified | Unknown |
npx claudepluginhub manojbajaj95/claude-gtm-plugin --plugin gtm-skillsMeasures product-market fit with Sean Ellis surveys (40% very disappointed rule), Superhuman engine, retention curves, and scaling indicators. For validating products, diagnosing retention, and planning growth.
Define and measure product-market fit indicators specific to your customer segment.
Generates PMF surveys and analyzes results using Rahul Vohra's Superhuman framework. Measures 'very disappointed' score against 40% benchmark for product-market fit.