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