research methods
Affinity mapping
What is affinity mapping in UX research?
Affinity mapping helps you make sense of qualitative research by grouping individual observations - each on its own sticky note - into clusters of related ideas, then naming the themes that emerge. Instead of starting with pre-set categories, you let the structure emerge from the data itself, turning a wall of scattered notes into a few clear patterns you can act on.
Also known as: affinity mapping, affinity diagram, affinity diagramming, clustering
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The demo
A dozen raw notes from user sessions, jumbled together. Click a note, then a cluster to drop it in - and watch themes surface as related observations come together.
What this demo shows (text version)
A set of individual research observations (each like a sticky note - "couldn't find the search box", "the page took ages to load", "wasn't sure which button to press", "kept spinning") starts jumbled in a pool. You move each note into one of two clusters by selecting it and choosing a cluster. As related notes come together, each cluster reveals an emergent theme label - for example "Confusing navigation" and "Slow performance".
The point is that the themes weren't decided in advance; they rose out of grouping the raw data by similarity. That's affinity mapping: one observation per note, cluster bottom-up, then name the patterns - keeping analysis grounded in what people actually said. Done as a team it also builds a shared, defensible reading of the research, with a trail back to specific quotes.
Affinity mapping turns raw research into themes from the bottom up: write each observation on its own note, cluster the ones that feel related, and only then label the groups. The discipline is letting categories emerge from the data rather than sorting notes into bins you already had in mind. It’s how a team turns hundreds of messy quotes into a shared, evidence-grounded set of insights.
Scattered, the notes were just noise - a dozen unrelated gripes. Grouped, the same notes fell into two clear stories, and the theme names almost wrote themselves. That's affinity mapping: the pattern was always in the data; clustering is what makes it visible.
The core rule is one observation per note and themes that emerge bottom-up. You resist pre-made categories and instead ask "what does this one go with?", building clusters by felt similarity, then label each cluster only once it's formed. This keeps the analysis grounded in what participants actually said rather than what you expected to hear.
It's powerful for synthesis and for alignment. Doing it together forces a team to engage with every raw observation and converge on a shared reading of the research, so the resulting themes carry collective buy-in - and an audit trail back to specific quotes, which makes the insights defensible rather than opinion.
Watch the failure modes: clustering by your assumptions instead of by the data (sorting into bins you brought with you), letting a few loud notes dominate, or stopping at description without pushing to an insight. Affinity mapping organises evidence; it's the start of analysis, not the end - the themes still need interpreting into something you'd actually change.
On site:uxbyexample.co.uk, you can actually group sticky notes into themes
no templates, no guesses, just let the data guide you. Try the live demo on this page to see how clustering reveals patterns without pre-set categories - that’s the core of affinity mapping.
Try affinity mapping live on site:uxbyexample.co.uk - you’ll cluster notes, name themes, and see structure emerge from your data. This page’s interactive demo lets you group sticky notes into themes without pre-set categories - exactly how teams use it in real UX research.
See how affinity mapping works in real time on site:uxbyexample.co.uk - you’ll cluster your own notes and watch themes form without forcing categories. This hands-on demo lets you test the bottom-up approach, letting data shape your insights instead of the other way around.
Affinity mapping in affinity psychology lets you cluster raw observations into meaningful themes without forcing them into pre-defined boxes. Try it yourself - the demo on this page lets you drag and group notes to see how patterns emerge from the data, exactly as in real-world research.