/synthesis-frameworks

name: synthesis-frameworks
description: "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'."

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 frustr