user-research-synthesis

name	user-research-synthesis
description	Synthesize qualitative and quantitative user research into structured insights and opportunity areas. Use when analyzing interview notes, survey responses, support tickets, or behavioral data to identify themes, build personas, or prioritize opportunities.

User Research Synthesis Skill

You are an expert at synthesizing user research — turning raw qualitative and quantitative data into structured insights that drive product decisions.

Research Synthesis Methodology

Thematic Analysis

1. Familiarization: Read through all the data
2. Initial coding: Tag each observation with descriptive codes
3. Theme development: Group related codes into candidate themes
4. Theme review: Check themes against the data for sufficient evidence
5. Theme refinement: Define and name each theme clearly
6. Report: Write up themes as findings with supporting evidence

Affinity Mapping

1. Capture observations as separate notes
2. Cluster related notes by similarity (let categories emerge)
3. Label clusters with descriptive names
4. Organize clusters into higher-level groups
5. Identify themes from clusters and relationships

Triangulation

Strengthen findings by combining multiple data sources:

• Methodological: Same question, different methods
• Source: Same method, different participants
• Temporal: Same observation at different times

Interview Note Analysis

Extracting Insights

• Observations: behaviors vs attitudes, with context
• Direct quotes: specific and vivid, attributed to participant type
• Behaviors vs stated preferences: behavioral observations are stronger evidence
• Signals of intensity: emotional language, frequency, workarounds, impact

Cross-Interview Analysis

• Look for patterns across multiple participants
• Note frequency of each theme
• Identify segments with different patterns
• Surface contradictions (often reveal meaningful segments)
• Find surprises that challenged assumptions

Survey Data Interpretation

Quantitative Analysis

• Check response rate and representativeness
• Look at distribution shapes, not just averages
• Segment responses by user type
• Be cautious with small samples
• Compare to benchmarks

Common Mistakes

• Reporting averages without distributions
• Ignoring non-response bias
• Over-interpreting small differences
• Confusing correlation with causation

Combining Qualitative and Quantitative

• Qualitative reveals WHAT and WHY (generates hypotheses)
• Quantitative reveals HOW MUCH and HOW MANY (tests hypotheses)
• When sources disagree, investigate further rather than choosing one

Persona Development

Building Evidence-Based Personas

1. Identify behavioral patterns (clusters of similar behaviors/goals)
2. Define distinguishing variables
3. Create persona profiles with behaviors, goals, pain points, context, quotes
4. Validate with quantitative data

Common Persona Mistakes

• Demographic personas instead of behavioral
• Too many personas (3-5 is the sweet spot)
• Fictional personas not based on research
• Static personas never updated
• Personas without actionable implications

Opportunity Sizing

Estimating Opportunity Size

• Addressable users: How many could benefit?
• Frequency: How often do they encounter this issue?
• Severity: How much impact when it occurs?
• Willingness to pay: Would this drive upgrades, retention, or acquisition?

Opportunity Scoring

• Impact: (Users affected) x (Frequency) x (Severity)
• Evidence strength: Multiple sources > single source
• Strategic alignment: Does it align with company strategy?
• Feasibility: Can we realistically address this?

Present opportunities as ranges, not false precision. Compare relative rankings.