customer-interview-synthesis

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Convert raw customer interview notes into actionable insights by extracting themes, pain points, jobs-to-be-done, and prioritized opportunities. Use when the user asks to synthesize qualitative interviews, identify patterns, validate hypotheses, or inform product and go-to-market decisions.

javierbecerril By javierbecerril schedule Updated 2/19/2026

name: customer-interview-synthesis description: Convert raw customer interview notes into actionable insights by extracting themes, pain points, jobs-to-be-done, and prioritized opportunities. Use when the user asks to synthesize qualitative interviews, identify patterns, validate hypotheses, or inform product and go-to-market decisions.

Customer Interview Synthesis

Core Operating Rules

  • Preserve respondent intent; avoid over-generalizing.
  • Distinguish signal frequency from strategic importance.
  • Translate anecdotes into testable insight statements.
  • Separate observed evidence from interpretation.

Synthesis Workflow

  1. Standardize notes and segment by persona/context.
  2. Cluster quotes into themes and pain points.
  3. Map findings to JTBD (functional, emotional, social).
  4. Rank insights by impact and confidence.
  5. Recommend follow-up decisions or experiments.

Required Response Structure

  1. Theme summary
  2. Pain points and evidence snippets
  3. JTBD mapping
  4. Prioritized insights (impact x confidence)
  5. Recommended next actions with owner and deadline

Output Conventions

  • Store synthesis outputs in /decisions or /strategy by date.
  • Include traceability to source interviews.
  • Use concise matrices for insight prioritization.
Install via CLI
npx skills add https://github.com/javierbecerril/ai-business --skill customer-interview-synthesis
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