Affinity Mapping 🧠 Prompts

SUMMARY

Purpose: Affinity Mapping helps synthesise qualitative research findings into meaningful clusters by identifying patterns, themes, and insights from user data.

Design Thinking Phase: Define

Time: 45–60 min session + 1–2 hours analysis

Difficulty: ⭐⭐

When to use:After conducting user interviews or usability testsDuring early-stage discovery to define problem spacesTo align team perspectives around core user needs

What it is

Affinity Mapping is a method used by UX teams to organise a large volume of qualitative data—such as interview notes, observations, or survey responses—into clusters of related insights. The goal is to reveal recurring themes, patterns, or unmet needs that inform strategic direction and design decisions.

📺 Video by NNgroup. Embedded for educational reference.

Why it matters

Affinity Mapping turns raw data into structured knowledge. It allows product teams to extract insight from open-ended responses, work across silos, and align on user needs. It’s especially valuable when research produces a wide array of feedback, emotions, or anecdotes that don’t fit neatly into charts or KPIs.

When to use

• After generative research to make sense of diverse input
• When surfacing themes during a team workshop or design sprint
• To collaboratively prioritise which user problems to solve first

Benefits

• Rich Insights: Helps uncover user needs that aren’t visible in metrics.
• Flexibility: Works across various project types and timelines.
• User Empathy: Deepens understanding of behaviours and motivations.

How to use it

1. Prepare your input: Collect all qualitative data sources—transcripts, voice notes, chat logs, or observations.
2. Write data points: Break down findings into individual observations written on sticky notes (physical or virtual).
3. Cluster by theme: Group observations based on similarity in topics, sentiments, behaviours, or challenges.
4. Label clusters: Name each group based on the insight it represents (e.g., “Confusion during onboarding” or “Motivated by saving time”).
5. Discuss and align: Collaborate with the team to prioritise which clusters represent key user pain points or opportunities.

Example Output

After synthesising 10 interviews on a food delivery app, these user themes emerged:

• “Unclear wait time estimates” — Mentioned by 7/10 users
• “Preferences not saved” — Frustration around app not remembering prior customisations
• “Lack of local recommendations” — Some users expect context-aware suggestions based on location

Common Pitfalls

• Too much summarising upfront: Don’t paraphrase too early—use raw user expressions at the start to avoid biasing analysis.
• Over-clustering: Avoid creating mega-groups that cover everything but say nothing specific.
• Lack of prioritisation: Once insights are clustered, teams often skip prioritising them—always ask “What does this imply for design?”

10 Design-Ready AI Prompts for Affinity Mapping – UX/UI Edition

How These Prompts Work (C.S.I.R. Framework)

Each of the templates below follows the C.S.I.R. method — a proven structure for writing clear, effective prompts that get better results from ChatGPT, Claude, Copilot, or any other LLM.

C.S.I.R. stands for:

• Context: Who you are and the UX situation you're working in
• Specific Info: Key design inputs, tasks, or constraints the AI should consider
• Intent: What you want the AI to help you achieve
• Response Format: The structure or format you want the AI to return (e.g. checklist, table, journey map)

Level up your career with smarter AI prompts.Get templates used by UX leaders — no guesswork, just results.Design faster, research smarter, and ship with confidence.First one’s free. Unlock all 10 by becoming a member.

Prompt Template 1: “Cluster Interview Data into Thematic Insights”

Cluster Interview Data into Thematic Insights

Context: You are a UX researcher reviewing qualitative data from 8 user interviews focused on [a core task or product area].  
Specific Info: User notes cover [specific behaviours, frustrations, quotes, goals, etc.].  
Intent: Identify clusters of insights that represent consistent user expectations, pain points, or confusion areas.  
Response Format: Present a grouped list of 4–6 key themes with a one-sentence summary and a sample user quote for each.

If the behaviours or quotes provided are unclear, ask for clarification. Suggest a related affinity theme we might have missed.