Affinity Mapping 🧠 Prompts

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.

Prompt Template 2: “Generate Hypothesis Statements from Themes”

Generate Hypothesis Statements from Themes

Context: You are a UX strategist reviewing affinity map clusters after discovery research.  
Specific Info: Clusters include topics like [e.g. checkout confusion, inconsistent preferences, misaligned search expectations].  
Intent: Draft hypothesis statements to guide problem framing and ideation.  
Response Format: Provide a table with two columns — "Theme" and "Hypothesis Statement" (formatted as: “We believe that [user group] struggles with [X] because [Y]. We’ll know this is true when [evidence].”).

Ask for clarification if the themes are broad or undefined. Recommend next steps for validation.

Prompt Template 3: “Map Themes to Design Opportunities”

Map Themes to Design Opportunities

Context: You are a UX designer preparing for a design workshop after synthesising interview insights.  
Specific Info: Affinity themes identified include [list themes].  
Intent: Translate thematic insights into design prompts or opportunity areas.  
Response Format: Return a list of design prompts phrased as “How might we…” questions, grouped by related themes.

Ask for clarification if the themes cover multiple user types or journeys.

Prompt Template 4: “Compare Stakeholder Assumptions to User Data”

Compare Stakeholder Assumptions to User Data

Context: You’re a UX lead facilitating a research debrief with cross-functional stakeholders.  
Specific Info: You have a list of stakeholder assumptions and thematic user feedback clusters from research.  
Intent: Align or contrast each assumption with actual user insight.  
Response Format: Create a comparison table with columns “Assumption,” “User Insight Cluster,” and “Aligned or Misaligned?”

If assumptions are vague, request clarification. Suggest which misalignments should prompt further discussion.

Prompt Template 5: “Structure a Research Readout Slide Deck”

Structure a Research Readout Slide Deck

Context: You are writing a deck to summarise key findings from user interviews using affinity mapping.  
Specific Info: The themes identified include [X, Y, Z], affecting [target users or journey stages].  
Intent: Create a clear narrative to share insights with product and design teams.  
Response Format: Provide a slide outline with titles, slide objectives, and any suggested visuals.

Ask how long the deck should be. Recommend a story arc that leads to action.

Prompt Template 6: “Analyse Emotional Tone in User Quotes”

Analyse Emotional Tone in User Quotes

Context: You are a UX researcher looking for emotional drivers behind key behaviours.  
Specific Info: Provide 10–15 raw user quotes from discovery interviews.  
Intent: Classify emotional tones and highlight underlying motivations.  
Response Format: Return a list of quotes with associated emotions and inferred needs or fears.

If quote context is missing, prompt the user to clarify. Suggest additional emotional categories if relevant.

Prompt Template 7: “Standardise Research Tags for Better Clustering”

Standardise Research Tags for Better Clustering

Context: You are managing a growing research repository using tags to organise insights.  
Specific Info: Current tags are inconsistent, overlapping, or unclear.  
Intent: Refine and consolidate tags based on affinity group logic.  
Response Format: Return a cleaned list of tags with reasoning for consolidation and category suggestions.

Request sample tags before attempting the output.

Prompt Template 8: “Create a Visual Affinity Diagram (Markdown or Miro-style)”

Create a Visual Affinity Diagram (Markdown or Miro-style)

Context: You are prototyping a remote synthesis session in Miro/FigJam for affinity mapping interviews.  
Specific Info: You’ve extracted 40 data points grouped into 5 themes.  
Intent: Represent clusters visually in a format that is copy-pastable or importable.  
Response Format: Use nested bullet points denoting Theme > Observations.

Ask for number of themes or platform preference (e.g. Miro or Markdown) if missing.

Prompt Template 9: “Extract Theme-Based Personas”

Extract Theme-Based Personas

Context: You are developing early user personas based on clustered themes from qualitative data.  
Specific Info: Each theme represents a user mindset or behaviour pattern.  
Intent: Turn themes into actionable proto-personas.  
Response Format: Output 2–3 detailed persona drafts including name, trait summary, behaviours, goals, and a representative quote.

Confirm which themes should turn into personas — not all clusters represent a full persona.

Prompt Template 10: “Identify Gaps in Your Synthesis”

Identify Gaps in Your Synthesis

Context: You just completed an affinity mapping session and want to validate coverage.  
Specific Info: You’ve mapped 6 key clusters from recent interviews.  
Intent: Spot potential blind spots or overlooked user needs.  
Response Format: List follow-up research questions based on analysis gaps or low-represented user behaviours.

Ask for the target user group and product context to suggest specific gaps.

Recommended Tools

• FigJam – Collaborative whiteboarding for affinity mapping
• Dovetail – Qualitative research and tagging tool
• Miro – Visual collaboration board
• Obsidian – For qualitative note structuring and theme tracking

Learn More

• NNGroup: UX Research & Design Articles
• Interaction Design Foundation – Topics
• Google Design Sprint Kit