What is user research synthesis?

User research synthesis is the process of organizing and interpreting raw data from users—such as interviews, surveys, and support tickets—to identify patterns, themes, and actionable product insights.

Can this skill help with quantitative data?

Yes, it helps interpret survey distributions, identify non-response bias, and triangulate quantitative metrics with qualitative findings to provide a holistic view of user behavior.

How does this skill handle qualitative interview notes?

It applies thematic analysis and affinity mapping to extract observations, categorize behaviors versus stated preferences, and identify signals of intensity to ensure insights are backed by evidence.

How does it assist in creating personas?

The skill guides the creation of evidence-based personas by identifying behavioral patterns and distinguishing variables from research data rather than relying on fictional demographics.

Does it provide a prioritization framework?

Yes, it includes methodology for opportunity sizing and scoring based on addressable users, frequency of the issue, severity, and strategic alignment.

User Research Synthesis

Name: User Research Synthesis
Author: nsalvacao

bynsalvacao

0•

Productivity & Workflow

Synthesizes raw qualitative and quantitative user data into structured insights, evidence-based personas, and prioritized product opportunities.

The User Research Synthesis skill transforms fragmented data from interviews, surveys, and support tickets into actionable product strategy. By applying rigorous methodologies like thematic analysis and affinity mapping, it helps product teams identify recurring themes, validate hypotheses through triangulation, and build data-driven personas. It is an essential tool for product managers and researchers who need to bridge the gap between raw observations and high-confidence opportunity sizing, ensuring every product decision is backed by user evidence.

Key Features

01Automated extraction of behavioral signals and insights from interview notes

02Quantitative survey data interpretation including distribution and segment analysis

030 GitHub stars

04Evidence-based persona development based on behavioral clusters rather than demographics

05Thematic analysis and affinity mapping for qualitative data synthesis

06Opportunity sizing and scoring based on impact, frequency, and severity

Use Cases

01Synthesizing open-ended survey responses alongside quantitative usage data to validate product hypotheses

02Creating and prioritizing a product opportunity backlog based on research-driven impact scores

03Analyzing a batch of user interview transcripts to identify common pain points and workarounds

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nsalvacao/nsalvacao-claude-code-plugins user-research-synthesis

For use in Claude.ai and ChatGPT

Key Features

01Automated extraction of behavioral signals and insights from interview notes

02Quantitative survey data interpretation including distribution and segment analysis

030 GitHub stars

04Evidence-based persona development based on behavioral clusters rather than demographics

05Thematic analysis and affinity mapping for qualitative data synthesis

06Opportunity sizing and scoring based on impact, frequency, and severity

Use Cases

01Synthesizing open-ended survey responses alongside quantitative usage data to validate product hypotheses

02Creating and prioritizing a product opportunity backlog based on research-driven impact scores

03Analyzing a batch of user interview transcripts to identify common pain points and workarounds

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nsalvacao/nsalvacao-claude-code-plugins user-research-synthesis

For use in Claude.ai and ChatGPT