Measure and optimize your product-market fit using the Superhuman framework from First Round Review.

1. Collect survey responses with the 4 proven PMF questions
2. AI automatically analyzes responses and assigns personas
3. Calculate your PMF Score (benchmark: 40%)
4. Generate actionable insights:
   • High-Expectation Customer (HXC) profile
   • What users love (from "very disappointed" responses)
   • What holds users back (from "somewhat disappointed" responses)
   • Roadmap recommendations (50% double down / 50% address gaps)

The Superhuman PMF engine uses Sean Ellis's leading indicator: % of users who would be "very disappointed" without your product.

Benchmark: Companies with strong traction exceed 40%. Companies struggling to grow are below 40%.

1. How would you feel if you could no longer use [product]?

   • Very disappointed
   • Somewhat disappointed
   • Not disappointed

2. What type of people do you think would most benefit from [product]?

3. What is the main benefit you receive from [product]?

4. How can we improve [product] for you?

1. Segment to find supporters and paint a picture of your high-expectation customers
2. Analyze feedback to convert on-the-fence users into fanatics
3. Build roadmap by doubling down on what users love AND addressing what holds others back
4. Repeat and track your PMF score over time

1. Click Remix
2. Store your OPENAI_API_KEY as an environment variable
3. Customize PROMPT.txt with your product name and context
4. Share the survey link with your users
5. Watch insights populate in your dashboard

Survey

• Users complete the 4-question survey
• Responses are stored with timestamp

AI Analysis

• Each response is analyzed to assign a persona
• Personas are inferred from the "type of people" answer (Q2)
• Main benefits and improvement requests are extracted

Dashboard Insights

• PMF Score: % of "very disappointed" responses
• Segmented PMF: Score filtered to personas from the "very disappointed" group
• HXC Profile: AI-generated High-Expectation Customer description
• Benefits Word Cloud: What your happiest users love (from Q3)
• Improvements Word Cloud: What holds "somewhat disappointed" users back (from Q4)
• Roadmap Recommendations: Split 50/50 between doubling down and addressing gaps

From the original Superhuman article:

"If you only double down on what users love, your product-market fit score won't increase. If you only address what holds users back, your competition will likely overtake you."

The framework teaches you to:

• Ignore "not disappointed" users entirely - they won't convert
• Focus on "very disappointed" users to understand what to double down on
• Convert "somewhat disappointed" users who value your main benefit