Affinity Mapping Template for User Research

Card sorting is a technique for testing information architecture. Participants sort content labels into categories to reveal how users expect navigation to work. Affinity mapping is broader. It clusters any qualitative data (observations, quotes, pain points) to find patterns. The mechanics are similar (silent sorting of cards into groups), but the inputs and outputs are different.

You can, but the results will be weaker. Solo affinity mapping tends to produce clusters that confirm your existing assumptions. The value of the group exercise is that multiple perspectives create clusters you would not see alone. If you must work solo, write the cards one day and sort them the next to create some cognitive distance.

Label disagreements usually mean the cluster is too broad and should be split. Try breaking the disputed cluster into two sub-clusters and labeling each one separately. If the team still disagrees, it often means the data is ambiguous and more research is needed for that theme.

Duplicate the card and place it in both clusters. This happens with 5-10% of cards and is normal. During the labeling phase, note which cards are duplicated. They often represent connections between themes that are worth exploring.

Yes. Affinity mapping is a synthesis technique, not a data collection method. Pair it with [customer interviews](/templates/customer-interview-template) and usability tests for data collection. Feed the high-priority clusters into an [Opportunity Solution Tree](/frameworks/opportunity-solution-tree) to map opportunities to solutions. The [discovery guide](/discovery-guide) covers the full process from research planning through solution validation. ---