Affinity Mapping

Overview

Affinity mapping is how you go from "we have a ton of data" to "here's what it means." You take individual pieces of information (research notes, user quotes, ideas from a brainstorm) and physically group them by similarity until themes start to surface.

What makes it work isn't the sorting itself. It's that the sorting forces conversation. When two people disagree about whether a note belongs in one group or another, that's where the real insight happens. The method traces back to Jiro Kawakita's KJ Method in the 1960s, but its staying power comes from how naturally it fits any process where you need to find the signal in qualitative noise.

The trap most teams fall into is treating it like a categorization exercise, slotting things into buckets that already exist in their heads. The whole point is to let structure emerge from the data, not impose one on top of it. If you already know your categories, you don't need this method.

When to Use It

• After a round of user interviews, when you have pages of notes and no clear narrative yet.
• Following a brainstorm that produced more ideas than anyone can hold in their head.
• When synthesizing feedback from multiple stakeholders who each see the problem differently.
• During a retrospective or debrief where the team needs to identify recurring themes.
• Any time your team is staring at a wall of sticky notes and asking "so what does this all mean?"

Skip it when you already have a clear hypothesis or when the data set is small enough to reason through in conversation. Affinity mapping shines with volume and ambiguity. Without those, it's ceremony for ceremony's sake.

How It Works

Start with a clear question. Not "sort these by theme" but something specific, like "what are the biggest friction points in the onboarding flow?" Post this where everyone can see it.

Each participant writes observations, quotes, or ideas on individual notes, one thought per note. Spread everything out on a wall, table, or digital whiteboard.

Then sort silently. Have everyone move notes into groups based on gut-level similarity without talking. Notes can be moved more than once. This silent phase prevents the loudest voice from defining the structure too early.

Once initial clusters form, step back and discuss. Name each cluster with a phrase that captures its essence. Not a single word, but something that reflects what the group actually means. Split clusters that are too broad. Pay attention to outliers sitting alone; they sometimes point to the most interesting insights.

Tips

Don't label clusters too early. The moment you name a group, people start sorting into that label rather than letting the data guide them. Wait until things stabilize.

Keep your input material clean. A raw interview transcript isn't ready for this. You need discrete, digestible observations. Spend time preparing your notes before the session.

Watch for power dynamics. If a senior stakeholder starts narrating the sort, quieter team members will defer. Protect the silent phase.

Resist the urge to make it neat. If it looks tidy after twenty minutes, you probably haven't pushed hard enough.

The Output

A set of named theme clusters that represent the patterns in your data, plus the shared understanding your team built while creating them. The conversations during sorting are often more valuable than the final arrangement.

This typically feeds into prioritization, problem framing, or journey mapping. It's the bridge between raw research and actionable direction.

Related Methods

• Interviewing: Comes before. Affinity mapping is one of the best ways to synthesize what you heard across multiple interviews.
• Rose, Thorn, Bud: Comes before. Sort the output of an RTB session with affinity mapping to find deeper patterns.
• How Might We: Comes after. Turn your cluster themes into opportunity statements for ideation.
• Impact/Effort Matrix: Comes after. Once you've identified themes, prioritize them by plotting impact against effort.
• Journey Mapping: Runs alongside. Themed clusters often map onto stages of a user journey, making these two natural companions.