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User Research Synthesis: From Raw Data to Actionable Insight

Complete framework for analyzing and synthesizing qualitative research data (interviews, usability tests, diaries) into prioritized insights and actionable design recommendations for the product team.

Transform raw qualitative research data into clear, prioritized insights that drive concrete product decisions, using affinity diagramming, job stories, and impact vs. effort techniques to align design, product, and business.

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Prompt objective

Transform raw qualitative research data into clear, prioritized insights that drive concrete product decisions, using affinity diagramming, job stories, and impact vs. effort techniques to align design, product, and business.

Real use case

The product team at a fintech investment app conducted 12 in-depth interviews and 6 usability tests, generating 8 hours of recordings and 140 pages of transcripts. The problem: the team doesn't know how to turn all this into product decisions—insights got stuck in reports and never made it to the backlog.

Customize these fields first

COMPANY NAMERESEARCH TYPENUMBERRESEARCH TOPICTIMETYPE — e.g., analytics, NPS surveyDATES

Replace the placeholders with your own context before you run the prompt. That usually improves the first output more than adding more instructions later.

Prompt

Create a complete research synthesis framework for the [COMPANY NAME] team, which conducted [RESEARCH TYPE] with [NUMBER] participants on [RESEARCH TOPIC].\\\\\\\\n\\\\\\\\n**Available raw data:**\\\\\\\\n- Interviews: [NUMBER] with average duration of [TIME]\\\\\\\\n- Usability tests: [NUMBER] sessions\\\\\\\\n- Supporting quantitative data: [TYPE — e.g., analytics, NPS survey]\\\\\\\\n- Collection period: [DATES]\\\\\\\\n- Researcher(s): [NUMBER]\\\\\\\\n\\\\\\\\n**Part 1 — Pre-Synthesis Preparation:**\\\\\\\\n\\\\\\\\n**Transcription and organization:**\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\nFor each session, create a document with:\\\\\\\\n- Metadata: participant name, date, moderator, profile\\\\\\\\n- Full transcription (or summary if no recording)\\\\\\\\n- Raw highlights: direct quotes that stood out\\\\\\\\n- Moderator observations: behaviors, emotions, hesitations\\\\\\\\n- Screenshots of critical moments (usability tests)\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\n\\\\\\\\n**Prepare stickies (analog or digital — FigJam/Miro):**\\\\\\\\n- 1 observation per sticky\\\\\\\\n- Color by participant (for traceability)\\\\\\\\n- Quote directly (in quotes) when possible\\\\\\\\n- Separate facts from interpretations\\\\\\\\n\\\\\\\\n**Part 2 — Four-Stage Synthesis:**\\\\\\\\n\\\\\\\\n**Stage 1 — Individual Download (2 hours):**\\\\\\\\nEach researcher downloads their observations alone, without group influence.\\\\\\\\n- Write stickies for everything observed (don't filter yet)\\\\\\\\n- Highlight 3 most critical moments from each session\\\\\\\\n- Don't group yet — just download\\\\\\\\n\\\\\\\\n**Stage 2 — Affinity Diagramming as a Group (3-4 hours):**\\\\\\\\nFull research team grouping in silence:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\nProtocol:\\\\\\\\n1. Everyone places stickies on the board simultaneously (no presenting)\\\\\\\\n2. In silence: move similar stickies near each other\\\\\\\\n3. When movement stops: no more than 7-10 clusters\\\\\\\\n4. Name each cluster with a header (verb + subject — e.g., \\\\\\\\

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How to use this prompt

  1. 1Replace the key placeholders first: COMPANY NAME, RESEARCH TYPE, NUMBER, RESEARCH TOPIC.
  2. 2Replace any bracketed placeholders like [this] with your own context.
  3. 3Add extra background information when you want more tailored results.
  4. 4Combine multiple prompts in one conversation when you need a richer output.
  5. 5Save your best-performing prompts so they are easy to reuse later.

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