Synthesize Research

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Synthesize user research from multiple sources into structured insights and recommendations.

Usage

/synthesize-research $ARGUMENTS

Workflow

1. Gather Research Inputs

Accept research from any combination of:

Ask the user what they have:

2. Process the Research

For each source, extract:

3. Identify Themes and Patterns

Apply thematic analysis — see Research Synthesis Methodology below for detailed guidance on thematic analysis, affinity mapping, and triangulation techniques.

Group observations into themes, count frequency across participants, and assess impact severity. Note contradictions and surprises.

Create a priority matrix:

4. Generate the Synthesis

Produce a structured research synthesis:

Research Overview

Key Findings

For each major finding (aim for 5-8):

Order findings by priority (frequency x impact).

User Segments / Personas

If the research reveals distinct user segments:

Opportunity Areas

Based on the findings, identify opportunity areas:

Recommendations

Specific, actionable recommendations:

Open Questions

What the research did not answer:

5. Review and Extend

After generating the synthesis:

Research Synthesis Methodology

Thematic Analysis

The core method for synthesizing qualitative research:

  1. Familiarization: Read through all the data. Get a feel for the overall landscape before coding anything.
  2. Initial coding: Go through the data systematically. Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later.
  3. Theme development: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question.
  4. Theme review: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct from each other? Do they tell a coherent story?
  5. Theme refinement: Define and name each theme clearly. Write a 1-2 sentence description of what each theme captures.
  6. Report: Write up the themes as findings with supporting evidence.

Affinity Mapping

A collaborative method for grouping observations:

  1. Capture observations: Write each distinct observation, quote, or data point as a separate note
  2. Cluster: Group related notes together based on similarity. Do not pre-define categories — let them emerge from the data.
  3. Label clusters: Give each cluster a descriptive name that captures the common thread
  4. Organize clusters: Arrange clusters into higher-level groups if patterns emerge
  5. Identify themes: The clusters and their relationships reveal the key themes

Tips for affinity mapping:

Triangulation

Strengthen findings by combining multiple data sources:

A finding supported by multiple sources and methods is much stronger than one supported by a single source. When sources disagree, that is interesting — it may reveal different user segments or contexts.

Interview Note Analysis

Extracting Insights from Interview Notes

For each interview, identify:

Observations: What did the participant describe doing, experiencing, or feeling?

Direct quotes: Verbatim statements that powerfully illustrate a point

Behaviors vs stated preferences: What people DO often differs from what they SAY they want

Signals of intensity: How much does this matter to the participant?

Cross-Interview Analysis

After processing individual interviews:

Survey Data Interpretation

Quantitative Survey Analysis

Open-Ended Survey Response Analysis

Common Survey Analysis Mistakes

Combining Qualitative and Quantitative Insights

The Qual-Quant Feedback Loop

Integration Strategies

When Sources Disagree

Persona Development from Research

Building Evidence-Based Personas

Personas should emerge from research data, not imagination:

  1. Identify behavioral patterns: Look for clusters of similar behaviors, goals, and contexts across participants
  2. Define distinguishing variables: What dimensions differentiate one cluster from another? (e.g., company size, technical skill, usage frequency, primary use case)
  3. Create persona profiles: For each behavioral cluster:
  4. Validate with data: Can you size each persona segment using quantitative data?

Persona Template

[Persona Name] — [One-line description]

Who they are:
- Role, company type/size, experience level
- How they found/started using the product

What they are trying to accomplish:
- Primary goals and jobs to be done
- How they measure success

How they use the product:
- Frequency and depth of usage
- Key workflows and features used
- Tools they use alongside this product

Key pain points:
- Top 3 frustrations or unmet needs
- Workarounds they have developed

What they value:
- What matters most in a solution
- What would make them switch or churn

Representative quotes:
- 2-3 verbatim quotes that capture this persona's perspective

Common Persona Mistakes

Opportunity Sizing

Estimating Opportunity Size

For each research finding or opportunity area, estimate:

Opportunity Scoring

Score opportunities on a simple matrix:

Presenting Opportunity Sizing

Output Format

Use clear headers and structured formatting. Each finding should stand on its own — a reader should be able to read any single finding and understand it without reading the rest.

Tips