Product-Market Fit Measurement

Purpose

This recipe produces a quantitative PMF Score Dashboard combining four measurement signals — Sean Ellis survey (40%+ threshold), cohort retention curves, organic growth share, and engagement depth — into a single trackable score, plus a PMF Improvement Roadmap using the Superhuman 50/50 methodology. Superhuman used this framework to increase their PMF score from 22% to over 58%. [src1]

Prerequisites

• Active user base — minimum 30 users who used the core product at least twice in last 2 weeks
• User email addresses — for survey distribution (100+ addresses to get 40+ responses)
• Analytics platform with cohort retention data — Mixpanel, Amplitude, PostHog, or equivalent
• Acquisition channel data — paid vs. organic vs. referral breakdown
• Survey tool — Google Forms, Typeform, or PMFSurvey.com

Constraints

• Survey only users who experienced the core product at least twice in the last 2 weeks. [src3]
• Minimum 40 survey responses required for statistical reliability.
• Cohort retention must be analyzed separately by segment — do not combine B2B and B2C.
• PMF measurement is continuous, not one-time. Run quarterly at minimum.
• Do not treat 40% as binary pass/fail — track direction and velocity of change. [src4]

Tool Selection Decision

Which path?
├── Early stage (30-100 users) AND first-time measurement
│   └── PATH A: Minimal — Google Forms + Sheets (Sean Ellis + retention)
├── Growth stage (100-500 users) AND analytics instrumented
│   └── PATH B: Standard — PMFSurvey.com + analytics + Sheets
├── Scale stage (500+ users) AND want full framework
│   └── PATH C: Comprehensive — Full Superhuman engine + all 4 signals
└── Enterprise / investor reporting
    └── PATH D: Board-Grade — C + segmented analysis + trend tracking

Execution Flow

Step 1: Design and Deploy the Sean Ellis Survey

Duration: 30-45 minutes · Tool: Google Forms / PMFSurvey.com

Create survey with core question (“How would you feel if you could no longer use [product]?”) plus 3 follow-ups (ideal user type, main benefit, improvement suggestions). Send to users with 2+ uses in last 2 weeks. Aim for 40+ responses. [src3]

Verify: Survey sent to qualifying user list · If failed: Try in-app survey for higher response rate

Step 2: Calculate the Sean Ellis Score

Duration: 30 minutes · Tool: Spreadsheet

Score = “Very Disappointed” count / (Total − N/A) × 100. Benchmarks: < 25% = no PMF; 25-40% = weak; 40-60% = good; > 60% = exceptional. Segment by channel, persona, and tenure. [src1]

Verify: Score calculated with 40+ qualifying responses · If failed: Extend survey window, send reminder

Step 3: Analyze Cohort Retention Curves

Duration: 45-60 minutes · Tool: Analytics platform + spreadsheet

Pull monthly cohort retention. Key: Month-3 retention is the critical inflection. Flattening curve = PMF signal. Continuously declining = no PMF. B2B target: > 40% at Month 3 (score 3). B2C target: > 20% at Week 4 (score 3). [src4]

Verify: At least 3 monthly cohorts analyzed · If failed: Use payment recurrence as proxy

Step 4: Measure Organic Growth Share

Duration: 30 minutes · Tool: Analytics platform

Calculate organic channels (direct + organic search + referral + WOM) / total new users. Benchmarks: < 20% = score 0; 20-35% = score 1; 35-55% = score 2; > 55% = score 3.

Verify: Channel attribution configured correctly · If failed: Use “How did you hear about us?” survey as proxy

Step 5: Measure Engagement Depth

Duration: 30 minutes · Tool: Analytics platform

Calculate DAU/MAU (daily products) or WAU/MAU (weekly products). Daily: < 10% = score 0, 10-20% = 1, 20-40% = 2, > 40% = 3. Weekly: < 30% = score 0, 30-50% = 1, 50-70% = 2, > 70% = 3. [src6]

Verify: Engagement ratio calculated for appropriate frequency · If failed: Use session frequency as proxy

Step 6: Calculate Composite Score & Generate Improvement Roadmap

Duration: 45-60 minutes · Tool: Spreadsheet

Weight signals: Sean Ellis 40%, Retention 25%, Organic Growth 20%, Engagement 15%. Generate roadmap using Superhuman method: identify High-Expectation Customer from “very disappointed” users, analyze “somewhat disappointed” blockers, build 50/50 roadmap (50% strengthen core, 50% remove blockers). [src2]

Verify: Composite score calculated, HEC profile defined, 50/50 roadmap generated · If failed: If survey data insufficient for roadmap, prioritize by retention cohort analysis

Output Schema

{
  "output_type": "pmf_score_dashboard",
  "format": "XLSX",
  "sections": [
    {"name": "sean_ellis_score", "type": "number", "description": "% very disappointed responses"},
    {"name": "retention_score", "type": "number", "description": "Cohort retention shape score 0-3"},
    {"name": "organic_growth_share", "type": "number", "description": "% users from organic channels"},
    {"name": "engagement_ratio", "type": "number", "description": "DAU/MAU or WAU/MAU ratio"},
    {"name": "composite_pmf_score", "type": "number", "description": "Weighted composite 0-100"},
    {"name": "hec_profile", "type": "string", "description": "High-Expectation Customer profile"},
    {"name": "top_blockers", "type": "array", "description": "Top 5 blockers from somewhat disappointed users"}
  ]
}

Quality Benchmarks

If below minimum: PMF score is directional only. Schedule rerun in 4-6 weeks after expanding user base.

Error Handling

Cost Breakdown

Anti-Patterns

Wrong: Surveying all users including churned

Sending PMF survey to entire email list including inactive users deflates the score and provides no actionable insight. [src3]

Correct: Survey only qualified active users

Survey users who experienced the core product at least twice in the last two weeks. This is Sean Ellis’s original methodology.

Wrong: Treating 40% as hard pass/fail

A single reading is noisy. The direction and velocity of change matter more than crossing an exact threshold.

Correct: Track trend over time

Measure quarterly. A score moving from 25% to 35% to 42% tells a different story than a one-time 42%. Superhuman tracked their score continuously and optimized systematically. [src1]

When This Matters

Use this recipe when a startup has at least 30 active users and needs to quantitatively measure product-market fit before scaling spend. The PMF score should be the primary metric guiding scaling decisions. Without this measurement, 74% of startups scale prematurely.