mohitagw15856/user-research-synthesis

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

Reads from / Writes to the Brain

If a professional-brain (brain/) exists, use it before asking:

• Read first: open hypotheses/ (which assumptions this research can validate or invalidate) and context.md (who the users are).
• Write after: update each touched hypothesis's status, add durable insights to knowledge/users.md, and keep the raw notes in source/. Tag interview-derived claims [interview] — never launder them into [data].

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:

# 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)