/product-market-fit

name: product-market-fit
description: "Frameworks for measuring, achieving, and maintaining product-market fit. Use when validating new products, assessing readiness to scale, diagnosing retention, or measuring PMF. Trigger on: 'do I have product-market fit', 'PMF survey', 'very disappointed score', 'retention curve analysis', 'ready to scale'."

Product-Market Fit

Frameworks for measuring, achieving, and maintaining the critical milestone where your product satisfies strong market demand.

Overview

Product-Market Fit (PMF) is the degree to which a product satisfies strong market demand - the inflection point where a product becomes a "must-have" for a well-defined market segment.

**Core Principle:** PMF is not a destination, it's a milestone that gives you permission to scale. Maintaining it requires continuous attention to customer needs and market evolution.

**Key Insight:** You can't manufacture PMF through marketing or sales tactics. PMF comes from deeply understanding a specific market segment and building something they desperately need. Scaling before PMF is the number one killer of startups.

When to Use This Skill

**Auto-loaded by agents**:

• `product-strategist` - For PMF measurement, Sean Ellis survey, and retention analysis

**Use when you need**:

• Measuring product-market fit status
• Running Sean Ellis PMF surveys
• Analyzing retention curves
• Determining readiness to scale
• Diagnosing retention problems
• Planning PMF improvement strategies
• Deciding pre-PMF vs. post-PMF tactics
• Validating market expansion opportunities

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Measuring Product-Market Fit

The Sean Ellis Test (40% Rule)

The definitive method for measuring PMF through a single powerful question.

**The Question:**

> "How would you feel if you could no longer use [product]?" > > - a) Very disappointed > - b) Somewhat disappointed > - c) Not disappointed (it isn't really that useful)

**PMF Threshold:**

• **40%+ "Very disappointed" = PMF achieved**
• 25-40% = Close, keep iterating
• <25% = No PMF yet

**Why this works:**

• Measures must-have vs. nice-to-have
• Predictive of retention
• Correlates with organic growth
• Simple to administer
• Actionable results

**Complete survey methodology:** See `assets/sean-ellis-pmf-survey.md` for:

• Full survey template
• When and how to administer
• Sample size requirements
• Analysis framework
• Segment breakdowns

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The Superhuman PMF Engine

Systematic framework for measuring and improving PMF score quarter over quarter.

**Philosophy:** PMF is not binary - it's a spectrum you can measure and improve systematically.

**The 5-Step Engine:**

1. **Segment users:** Very disappointed / Somewhat / Not disappointed 2. **Analyze champions:** Who are the "very disappointed" users? What do they have in common? 3. **Find your roadmap:** Different strategies for each segment 4. **Build strategically:** 50% for champions, 50% to convert warm users, 0% for wrong-fit 5. **Measure progress:** Re-survey quarterly, track improvement

**Superhuman's Results:**

Q1 2017: 22% → Q2 2018: 58% (18 months)

**Complete framework:** See `assets/superhuman-pmf-engine.md` for:

• Detailed 5-step process
• Segment analysis worksheets
• Roadmap allocation strategy
• Progress tracking templates
• Prioritization frameworks

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Retention Curves: The Ultimate PMF Test

Retention patterns reveal if your product is truly a must-have.

**Three Patterns:**

**1. Leaky Bucket (No PMF):**

• Continuously declining curve
• Never flattens
• Users leave permanently
• Action: Find PMF before scaling

**2. Flattening Curve (PMF!):**

• Drops initially, then flattens at 30-50%
• Core users retain long-term
• Ready to scale
• Action: Prove acquisition channel, then scale

**3. Smiling Curve (Strong PMF):**

• Usage increases over time
• Network effects or habit formation
• Examples: Social networks, collaboration tools
• Action: Scale aggressively

**Complete analysis:** See `assets/retention-curve-analysis.md` for:

• How to build retention curves
• Diagnosing problems
• Industry benchmarks
• Improving retention by phase

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Leading vs. Lagging Indicators

Use both types of indicators to measure PMF comprehensively.

Leading Indicators (Feel It Now)

Early signals before metrics confirm PMF:

**1. Organic Growth:**

• Word-of-mouth referrals happening
• Unprompted social media mentions
• Inbound signup requests
• Target: >50% of growth organic

**2. User Engagement:**

• High DAU/MAU ratio (stickiness)
• Deep feature adoption
• Long session times
• Target: DAU/MAU >30-40% (B2B), >60% (B2C Social)

**3. Customer Passion:**

• "Don't take this away from me"
• Volunteering to help
• Unsolicited recommendations
• Active community forming

**4. Sales Velocity (B2B):**

• Deals closing faster over time
• Less price resistance
• Shorter sales cycles
• Higher win rates

**5. Struggle to Keep Up:**

• Natural waitlist forming
• Capacity challenges
• Can't hire fast enough
• Good problem to have

Lagging Indicators (Metrics Confirm It)

Hard metrics that retrospectively validate PMF:

**1. Retention:**

• B2C: <5% monthly churn
• B2B: <2% logo churn
• Cohort curves flattening

**2. Net Promoter Score:**

• NPS >50 (world-class)
• High promoters, low detractors

**3. Unit Economics:**

• LTV:CAC >3:1 (minimum), >5:1 (ideal)
• Payback period <12 months
• Gross margin >70% (SaaS)

**4. Growth Rate:**

• Exponential not linear
• 10%+ month-over-month
• Compounding effects visible

**5. Market Pull:**

• Inbound >50% of new customers
• PR coverage without effort
• Competitive response
• Industry recognition

**Comprehensive guide:** See `references/leading-lagging-indicators.md` for:

• Detailed metrics and benchmarks
• How to use both together
• Early warning systems
• Decision frameworks

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Dashboard and Tracking

The PMF Dashboard

Track PMF through multiple lenses for complete picture.

**Primary Metrics (The Big 3):**

1. Sean Ellis PMF Sc