# User Research Synthesis Skill This skill helps analyze user research data and transform it into actionable insights following a structured methodology. ## Required Inputs Ask the user for these if not provided: - **Research data** (transcripts, notes, survey results, or summary bullets) - **Research method** (interviews, surveys, usability tests, etc.) - **Number of participants** and their profiles (role, context) - **Research questions** the study aimed to answer ## Synthesis Framework ### 1. Data Collection Overview - **Research Type**: Interviews, surveys, usability tests, etc. - **Participant Profile**: Demographics, segments, sample size - **Research Questions**: What we sought to learn - **Methodology**: How data was collected ### 2. Key Themes Identification Organize findings into themes using this structure: **Theme Name** - **Description**: What this theme represents - **Prevalence**: How many participants mentioned this (e.g., "8 out of 12 participants") - **Supporting Quotes**: 2-3 representative quotes - **Implication**: What this means for our product Aim for 4-8 major themes per research effort. ### 3. Pain Points Analysis For each identified pain point: - **Pain Point**: Clear description - **Severity**: High/Medium/Low (based on impact and frequency) - **Current Workaround**: How users deal with it today - **Evidence**: Specific examples from research ### 4. Feature Requests Categorize requests: - **Must-Have**: Critical needs blocking user success - **High Value**: Would significantly improve experience - **Nice-to-Have**: Incremental improvements For each request: - **Request**: What users asked for - **Frequency**: How often it came up - **User Quote**: Representative example - **Underlying Need**: Why they want this (dig deeper than surface request) ### 5. User Workflow Insights Document actual workflows observed: - **Current State**: How users accomplish tasks today - **Pain Points**: Where they struggle - **Ideal State**: What they wish they could do - **Opportunities**: Where we can add value ### 6. Segmentation Insights If research reveals distinct user segments: - **Segment Name**: Descriptive label - **Characteristics**: What defines this segment - **Unique Needs**: How their needs differ - **Size/Importance**: Relative weight for prioritization ### 7. Competitive Insights If users mentioned competitors or alternatives: - **Competitor/Alternative**: What they use - **Why They Use It**: What it does well - **Gaps**: What it doesn't do - **Switching Barriers**: Why they don't switch fully ### 8. Recommendations Prioritized recommendations based on insights: **High Priority** - Recommendation with supporting evidence - Expected impact **Medium Priority** - Recommendation with supporting evidence - Expected impact **Low Priority / Future Consideration** - Recommendation with supporting evidence - Expected impact ### 9. Open Questions Research gaps identified: - What we still need to understand - Suggested follow-up research - Uncertainties requiring validation ## Analysis Guidelines **When synthesizing interviews:** - Look for patterns across multiple participants - Note both what users say AND what they do - Pay attention to emotional reactions - Identify jobs-to-be-done, not just feature requests **When analyzing quotes:** - Use verbatim quotes in "quotation marks" - Attribute quotes: [Participant ID, Role, Context] - Select quotes that illustrate patterns, not outliers - Include both positive and negative feedback **When identifying themes:** - Use descriptive names, not generic labels - Provide evidence for each theme - Quantify when possible ("7 out of 10 users...") - Connect themes to business objectives ## Quality Checks - [ ] Themes identify patterns across multiple participants, not individual responses - [ ] Insights connect to specific product decisions, not just observations - [ ] Each claim includes supporting evidence (quotes, counts, or examples) - [ ] Observations and interpretations are clearly separated - [ ] Findings are prioritised by impact, not just listed ## Anti-Patterns - [ ] Do not list every individual comment — synthesis must identify patterns across participants - [ ] Do not make interpretive leaps without supporting evidence from the data - [ ] Do not focus on feature requests before understanding the underlying problem — always identify the job-to-be-done first - [ ] Do not ignore contradictory data — conflicting findings must be surfaced and noted - [ ] Do not present results without quantifying prevalence — state how many participants held each view ## Example Theme ``` **Theme: Information Overload During Onboarding** **Description**: Users consistently expressed feeling overwhelmed by the amount of information presented during initial setup, leading to incomplete onboarding and delayed time-to-value. **Prevalence**: 9 out of 12 participants mentioned this issue unprompted **Supporting Quotes**: - "I just wanted to get started, but it felt like I needed to read a manual first" [P3, Marketing Manager] - "By the third screen of instructions, I started clicking 'Next' without reading" [P7, Sales Rep] - "I wish there was a 'quick start' option for people like me who just want to try it" [P11, Product Designer] **Implication**: Our current onboarding flow prioritizes completeness over engagement. We should consider a progressive disclosure approach where users can start using the product quickly and learn advanced features contextually. **Recommended Action**: - Design a "Quick Start" path that gets users to first value in <3 minutes - Move advanced configuration to contextual help within the app - Test with 5-10 new users before full rollout - Expected impact: +20-30% activation rate improvement ``` ## Template Output Structure When synthesizing research, use this structure: ```markdown # User Research Synthesis: [Research Topic] ## Research Overview - **Date**: [Date range] - **Methodology**: [Interview/Survey/Testing] - **Participants**: [Number] [User types] - **Research Questions**: 1. [Question 1] 2. [Question 2] 3. [Question 3] ## Executive Summary [2-3 sentence overview of key findings and implications] ## Key Themes ### Theme 1: [Theme Name] [Full theme documentation as shown in example above] ### Theme 2: [Theme Name] [Full theme documentation] [Continue with 4-8 themes] ## Pain Points Summary | Pain Point | Severity | Frequency | Current Workaround | |------------|----------|-----------|-------------------| | [Pain 1] | High | 10/12 users | [How they cope] | | [Pain 2] | Medium | 7/12 users | [How they cope] | ## Feature Requests ### Must-Have 1. **[Request]** - Mentioned by [X] participants - Quote: "[Representative quote]" - Underlying need: [Why they want this] ### High Value [Similar structure] ### Nice-to-Have [Similar structure] ## Recommendations ### High Priority (0-3 months) 1. **[Recommendation]** - Supporting evidence: [Data from research] - Expected impact: [What will improve] - Effort estimate: [Rough sizing] ### Medium Priority (3-6 months) [Similar structure] ### Future Consideration (6+ months) [Similar structure] ## Open Questions 1. [Question requiring more research] 2. [Uncertainty to validate] 3. [Follow-up study needed] ## Appendix - Interview guide used - Full participant demographics - Raw notes/transcripts (link) ```