# Synthesis Frameworks > Research synthesis frameworks including affinity mapping, thematic analysis, and insight generation. Use when synthesizing interview data, identifying patterns across research, extracting actionable insights, or building personas from findings. Trigger on: 'synthesize my research', 'what themes emerged from interviews', 'affinity mapping', 'turn research into insights', 'build a persona from this data'. - Skill: `slgoodrich/synthesis-frameworks` (Agent Skill, multi-file: 12 files) - Install (CLI): `npx skillmds@latest add slgoodrich/synthesis-frameworks` - Raw SKILL.md: https://api.skillmd.com/api/skills/slgoodrich/synthesis-frameworks/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: slgoodrich (https://skillmd.com/u/slgoodrich) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/slgoodrich/synthesis-frameworks --- # Synthesis Frameworks Frameworks for synthesizing qualitative research data to identify patterns, extract insights, and drive product decisions. ## Overview Research synthesis transforms raw observations into actionable insights. Good synthesis reveals hidden patterns, validates assumptions, and informs strategic product decisions through structured analysis and collaborative sense-making. ## When to Use This Skill **Auto-loaded by agents**: - `research-ops` - For thematic analysis, insight generation, and research reports **Use when you need**: - Analyzing interview transcripts - Synthesizing usability test findings - Identifying user needs and pain points - Creating research deliverables - Documenting and communicating insights - Running team synthesis workshops - Pattern recognition across research data - Translating research into actionable recommendations ## Core Synthesis Methods ### The Synthesis Process Research synthesis follows a structured five-step process from raw data to actionable insights: **The five steps:** 1. Immersion: Consume all raw data without judgment 2. Extraction: Identify individual observations atomically 3. Grouping: Find patterns through affinity mapping 4. Insight Generation: Ask "so what?" and connect to implications 5. Communication: Share findings in actionable formats **Detailed template:** See `assets/synthesis-process-template.md` for step-by-step instructions, time allocation, and specific techniques for each phase. **Time allocation:** Expect 8-16 hours total for a typical research project, with most time in grouping (30-40%) and insight generation phases. ### Affinity Mapping Collaborative technique for organizing observations into themes through physical or digital clustering. **When to use:** - Team synthesis workshops - Large amounts of qualitative data - Need for shared understanding - Pattern discovery (not testing hypothesis) **Basic process:** 1. Silent note writing: Individual observation capture 2. Wall posting: Share all observations 3. Silent grouping: Cluster related notes 4. Name clusters: Identify themes 5. Generate insights: Discuss implications **Complete guide:** See `assets/affinity-mapping-guide.md` for: - Setup instructions (physical and digital) - Step-by-step process with timings - Color-coding strategies - Tips for effective clustering - Remote and solo variations ### Pattern Identification Systematic approach to recognizing recurring themes across qualitative data. **Five types of patterns to look for:** 1. **Frequency patterns:** How often something occurred (e.g., "8/10 users mentioned pricing") 2. **Behavioral patterns:** What users actually did vs. said (e.g., "All users skipped tutorial") 3. **Sentiment patterns:** Emotional responses (e.g., frustration, delight, confusion) 4. **Segment patterns:** Differences by user type (e.g., new users vs. power users) 5. **Journey patterns:** When issues occur (e.g., "70% dropped off at onboarding step 3") **Deep dive:** See `references/pattern-identification-guide.md` for: - Detailed examples of each pattern type - How to interpret patterns - Pattern validation techniques - Common pitfalls to avoid - Advanced pattern recognition methods ### Thematic Analysis Formal method for coding qualitative data and identifying themes. **Three coding levels:** - **Level 1 - Descriptive codes:** What was said/done (e.g., "Mentioned pricing") - **Level 2 - Interpretive codes:** What it means (e.g., "Price sensitivity") - **Level 3 - Pattern codes:** Big picture themes (e.g., "Value perception issue") **Four-phase process:** 1. Open coding: Generate initial codes liberally (30-100+) 2. Axial coding: Group related codes into categories (10-30) 3. Selective coding: Refine into core themes (5-10) 4. Validation: Ensure themes are supported and distinct **Complete methodology:** See `references/thematic-analysis-guide.md` for: - Detailed coding process with examples - Tools and techniques (manual and digital) - Quality checks and validation - Common coding patterns - Worked examples from transcripts ### Jobs-to-be-Done (JTBD) Synthesis Framework for understanding customer motivations and the "jobs" users hire products to do. **Job statement format:** ``` When [situation], I want to [motivation], So I can [expected outcome] ``` **The Forces Framework:** Analyze four forces that drive or prevent adoption: - **Push:** Why change from current solution? (pain, frustration) - **Pull:** Why choose new solution? (benefits, aspirations) - **Anxieties:** What holds users back? (fear, risk, learning curve) - **Habits:** Why stay put? (familiarity, switching costs) **Switching equation:** Push + Pull > Anxieties + Habits = Switch **Full framework:** See `references/jtbd-synthesis-guide.md` for: - How to identify jobs in research data - Finding jobs through struggle and workarounds - Complete forces analysis with examples - Strategy recommendations for each force - JTBD vs traditional research approaches ## Insight Quality Not all findings are insights. Good insights are specific, surprising, actionable, evidence-based, and prioritized. ### The Five Characteristics **1. Specific (not vague):** - Bad: "Users don't like the UI" - Good: "6/8 users couldn't find Settings in hamburger menu - move to top nav" **2. Surprising (not obvious):** - Bad: "Users want fast loading" - Good: "Users prefer slow load with progress over fast load with no feedback" **3. Actionable (clear implications):** - Bad: "Users are frustrated" - Good: "8-field signup causes 60% abandonment - reduce to 3 fields to match competitors" **4. Evidence-based (not speculation):** - Bad: "I think users would like dark mode" - Good: "5/10 users requested dark mode unprompted, citing eye strain from all-day use" **5. Prioritized (impact + feasibility):** - Bad: List of 50 equal findings - Good: Top 5 insights ranked by impact/effort with quick wins called out ### Insight Formula **Template:** "[X%/number] of users [did/said specific thing] because [reason], suggesting we should [action] to [expected outcome]" **Example:** "8/10 users abandoned onboarding at 'Invite Team' step because they didn't have emails ready, suggesting we should make this optional to reduce abandonment from 60% to 30%" **Quality guide:** See `references/insight-quality-guide.md` for: - Detailed breakdown of each characteristic - Bad vs. good insight examples - How to strengthen weak insights - Insight quality checklist - Teaching teams to recognize quality ## Synthesis Deliverables ### Executive Summary One-page research summary for the team who need highlights without detail. **Standard sections:** - Research goals and methodology (brief) - Top 3-5 key findings with evidence - Prioritized recommendations (quick wins vs. strategic) - Next steps and owners **Template:** See `assets/executive-summary-template.md` ### Full Research Report Comprehensive documentation (5-10 pages) for complete findings and recommendations. **Structure:** 1. Executive summary 2. Background and research questions 3. Methodology and participants 4. Findings by theme (with quotes and data) 5. Recommendations (prioritized) 6. Appendix (full participant list, materials, extra quotes) **Template:** See `assets/research-report-template.md` ### Insight Cards One-page format for individual insights that can be shared independently. **Includes:** - Insight statement and evidence (quotes + data) - Impact and why it matters - Specific recommendation with expected outcome - Priority, effort, and owner - Visual (screenshot, diagram, or clip) **Template:** See `assets/insight-card-template.md` ### Data-Driven Personas User archetypes based on behavioral patterns discovered in research. **Creation process:** 1. Identify behavioral patterns in research 2. Cluster users with similar behaviors 3. Define 3-5 distinct archetypes 4. Validate against real users **Persona template includes:** - Goals and motivations - Behaviors and workflows - Pain points and needs - Representative quote - When to use: Prioritization, design decisions, communication **Template:** See `assets/persona-template.md` ## Collaborative Synthesis ### Team Synthesis Workshop 2-hour structured workshop for collaborative insight generation with shared understanding. **Workshop structure:** - 0:00-0:15: Context setting (research goals, questions, methodology) - 0:15-0:45: Affinity mapping (silent note writing + clustering) - 0:45-1:15: Grouping and naming themes - 1:15-1:45: Insight generation and prioritization - 1:45-2:00: Action planning with owners **Benefits:** - Shared understanding across team - Multiple perspectives improve pattern recognition - Buy-in through participation - Faster insight to action **Complete guide:** See `references/synthesis-workshop-guide.md` for: - Pre-workshop preparation checklist - Detailed agenda with timings - Facilitation tips and techniques - Workshop variations (remote, async, executive) - Post-workshop documentation ## Impact vs. Effort Prioritization Use 2x2 matrix to prioritize insights: ``` Low Effort High Effort High Quick Wins Major Projects Impact (Do First) (Do Next) Low Fill-ins Money Pits Impact (Do Later) (Avoid) ``` **Scoring insights:** - **Impact (1-5):** Frequency × Severity × Business impact - **Effort (1-5):** Technical complexity + Time + Dependencies - **Priority score:** Impact / Effort **Quick wins** (high impact, low effort) should be shipped within 1-2 sprints to build momentum and demonstrate research value. ## Synthesis Best Practices **DO:** - Involve team in synthesis (shared understanding, multiple perspectives) - Use participant quotes liberally (evidence, authenticity) - **Evidence Standards:** Use only actual quotes from transcripts - never invent or fabricate - When paraphrasing: mark as "[Paraphrased from participant]" not as direct quote - Each quote must be verbatim from transcript and attributed to specific participant - Quantify when possible ("8/10 users", "60% abandoned") - Look for surprising patterns (not just confirmation bias) - Prioritize insights ruthlessly (top 3-5 most important) - Connect to business goals explicitly (why it matters) - Make recommendations actionable (clear next steps) - Document process (photos of affinity map, save artifacts) **DON'T:** - Cherry-pick data to support existing beliefs - Present all findings equally (overwhelming, no guidance) - Use research jargon (be clear and accessible) - Bury insights in long reports (lead with key findings) - Synthesize alone when team synthesis is possible - Ignore contradictions (explore them, they're interesting) - Stop at observations (push through to insights and implications) - Skip validation (check themes across multiple participants) ## Common Synthesis Pitfalls **Confirmation bias:** Only seeing patterns that confirm beliefs - Solution: Actively look for disconfirming evidence **Overgeneralization:** "One user did X, therefore all users..." - Solution: Require multiple examples to claim pattern **Too many themes:** More than 10 themes = probably too granular - Solution: Look for meta-themes, consolidate **Generic insights:** "Users want better UX" (too vague) - Solution: Apply the five characteristics of quality insights **No prioritization:** Treating all findings equally - Solution: Use impact/effort matrix, call out quick wins **Insight fatigue:** So many insights team doesn't act - Solution: Limit to top 3-5, focus on actionable quick wins ## Troubleshooting **"I have 50 interview notes and don't know where to start"**: Start with affinity mapping. Pull one observation per sticky note, group by similarity, then name the groups. Don't try to find themes in your head -- let the clusters emerge from the data. **"My insights are just restated observations"**: An insight connects observation to implication. "Users struggle with onboarding" is an observation. "Users need guided setup because they can't discover core features on their own, leading to 60% Day-1 churn" is an insight. Add the "so what" and "because." **"Stakeholders don't trust qualitative findings"**: Quantify where you can. "7 of 10 participants mentioned X" is more persuasive than "participants mentioned X." Pair qualitative themes with any available quantitative data. --- ## Related Skills - `interview-frameworks` - Conducting interviews that produce quality data - `usability-frameworks` - Usability testing methods and analysis - `validation-frameworks` - Solution validation and experiment design - `user-research-techniques` - Methods for gathering research data