User Research Synthesis Skill
Synthesize qualitative user research data into actionable product insights with thematic analysis and evidence-based recommendations.
Overview
This skill provides comprehensive capabilities for transforming raw user research data into structured insights. It supports interview transcript analysis, survey response synthesis, support ticket mining, and cross-source research aggregation.
Capabilities
Transcript Analysis
- •Analyze interview transcripts for patterns and themes
- •Extract key quotes and supporting evidence
- •Identify user pain points, needs, and goals
- •Tag and categorize research findings
- •Calculate insight confidence levels based on evidence
Thematic Analysis
- •Create affinity diagrams from research data
- •Build thematic maps showing relationships
- •Identify emerging patterns across participants
- •Cluster related findings into themes
- •Prioritize themes by frequency and impact
Persona Development
- •Generate persona attributes from research data
- •Identify user segments and archetypes
- •Map behaviors, motivations, and frustrations
- •Create Jobs-to-be-Done statements per persona
- •Validate personas against quantitative data
Research Aggregation
- •Synthesize research across multiple sources
- •Combine surveys, interviews, and support tickets
- •Track sentiment trends over time
- •Calculate statistical confidence in findings
- •Generate research repository documentation
Prerequisites
Required Tools
- •Text processing and NLP capabilities
- •Spreadsheet or structured data handling
- •Document generation for reports
Input Data Formats
code
Supported formats: - Interview transcripts (.txt, .md, .docx) - Survey exports (.csv, .xlsx) - Support ticket exports (.csv, .json) - User feedback logs (.json, .csv)
Usage Patterns
Interview Transcript Analysis
markdown
## Analysis Framework ### Step 1: Initial Coding For each transcript: 1. Read through completely for context 2. Highlight significant statements 3. Apply initial codes (open coding) 4. Note participant metadata ### Step 2: Pattern Recognition Across transcripts: 1. Group similar codes 2. Identify recurring themes 3. Note frequency of mentions 4. Track contradicting evidence ### Step 3: Insight Generation For each theme: 1. Define the insight clearly 2. List supporting evidence (3+ quotes) 3. Assess confidence level 4. Note actionable implications
Structured Coding Template
json
{
"transcript_id": "INT-001",
"participant": {
"id": "P001",
"segment": "power_user",
"tenure": "2_years"
},
"findings": [
{
"code": "onboarding_friction",
"theme": "First-time experience",
"quote": "I had no idea where to start...",
"timestamp": "00:12:34",
"sentiment": "negative",
"intensity": "high"
}
],
"summary": {
"key_pain_points": [],
"unmet_needs": [],
"positive_experiences": [],
"feature_requests": []
}
}
Affinity Diagram Generation
markdown
## Affinity Diagram Process ### 1. Capture Observations - One observation per note - Include source attribution - Maintain original language ### 2. Group Bottom-Up - Cluster similar observations - Name each cluster - Create hierarchy of clusters ### 3. Output Format # Theme: [Theme Name] ## Subtheme: [Subtheme Name] - Observation 1 (P001, INT-001) - Observation 2 (P003, INT-003) - Observation 3 (P007, INT-007) ### Evidence Strength - Strong: 5+ supporting observations - Moderate: 3-4 supporting observations - Emerging: 2 supporting observations
Insight Documentation
markdown
## Insight Template ### Insight ID: INS-001 **Statement**: [Clear, actionable insight statement] **Theme**: [Parent theme] **Confidence**: [High/Medium/Low] **Evidence Count**: [Number of supporting data points] ### Supporting Evidence | Source | Quote | Participant | |--------|-------|-------------| | INT-001 | "..." | P001 | | INT-003 | "..." | P003 | | SUR-045 | "..." | R045 | ### Implications - Product: [Product implications] - Design: [Design implications] - Engineering: [Technical considerations] ### Recommendations 1. [Specific recommendation] 2. [Specific recommendation] ### Contradicting Evidence - [Note any contradicting findings]
Integration with Babysitter SDK
Task Definition Example
javascript
const researchSynthesisTask = defineTask({
name: 'research-synthesis',
description: 'Synthesize user research into actionable insights',
inputs: {
transcriptPaths: { type: 'array', required: true },
researchQuestion: { type: 'string', required: true },
outputFormat: { type: 'string', default: 'markdown' },
minEvidenceThreshold: { type: 'number', default: 3 }
},
outputs: {
themes: { type: 'array' },
insights: { type: 'array' },
personas: { type: 'array' },
recommendations: { type: 'array' }
},
async run(inputs, taskCtx) {
return {
kind: 'skill',
title: 'Synthesize user research findings',
skill: {
name: 'user-research-synthesis',
context: {
operation: 'full_synthesis',
transcriptPaths: inputs.transcriptPaths,
researchQuestion: inputs.researchQuestion,
outputFormat: inputs.outputFormat,
minEvidenceThreshold: inputs.minEvidenceThreshold
}
},
io: {
inputJsonPath: `tasks/${taskCtx.effectId}/input.json`,
outputJsonPath: `tasks/${taskCtx.effectId}/result.json`
}
};
}
});
Analysis Frameworks
Jobs-to-be-Done (JTBD) Extraction
markdown
## JTBD Statement Format When [situation/context], I want to [motivation/goal], So I can [expected outcome]. ### Extraction Process 1. Identify triggering situations in transcripts 2. Extract stated and unstated motivations 3. Map to desired outcomes 4. Categorize: Functional, Emotional, Social jobs
Pain Point Severity Matrix
| Severity | Frequency | Impact | Priority |
|---|---|---|---|
| Critical | 80%+ users | Blocks core task | P0 |
| High | 50-80% users | Significant friction | P1 |
| Medium | 25-50% users | Noticeable issue | P2 |
| Low | <25% users | Minor annoyance | P3 |
Output Formats
Research Summary Report
markdown
# Research Synthesis Report ## Executive Summary [2-3 sentence overview] ## Research Methodology - **Method**: [Interviews/Surveys/etc.] - **Participants**: [N participants] - **Duration**: [Date range] - **Research Questions**: [Key questions] ## Key Themes ### Theme 1: [Name] [Description and evidence] ### Theme 2: [Name] [Description and evidence] ## Top Insights 1. **Insight**: [Statement] - Evidence: [Count] - Confidence: [Level] - Recommendation: [Action] ## Persona Implications [How findings affect personas] ## Recommended Actions 1. [Action item] 2. [Action item] ## Appendix - Full coding scheme - Participant demographics - Raw data references
Best Practices
- •Maintain Participant Anonymity: Use consistent IDs, not names
- •Preserve Original Language: Quote users verbatim when possible
- •Triangulate Sources: Seek confirmation across multiple sources
- •Note Outliers: Document contradicting evidence, don't dismiss
- •Quantify Where Possible: Count frequency of themes
- •Separate Observation from Interpretation: Clearly distinguish facts from analysis