We made notes on post-its, one observation per note, with quotes from users to back up observations. We used rolls of brown paper to collect and store the notes for taking away later, the paper was divided into the broad themes we were testing. Back at the office we sorted the post-its more thoroughly into columns under more exacting topics. Several people were taking notes so we had to de-dupe, however it was important to keep similar comments if made by different users, as this indicates that the issue might be more common to a wider group.

The death of a thousand notes

Affinity Sorting

The next step is to do an “affinity sort” to look at each comment individually and sort them into groups based on perceived patterns, rather than features. It’s important to start quite freely and not to place a heading above the group or draw a conclusion too quickly. It’s basically finding groupings based on a gut feeling that different users were having similar issues. In this research session we were testing different features, such as “Events & Festivals” and “Artist Library”. In afinity sorting I noticed that a user who couldn’t find  their recommended events was probably having a similar issue to a user who couldn’t find their recommended artists in the “library” section. These two notes were moved from their former “feature” headings into a new group called: “Issue with finding personal recs”. The issue had nothing to do with events or radio, it was a issue around users needing a uniform place to access all their personalised info.

BEFORE: Basic sorting by feature or topic

AFTER: Sorted by affinity – patterns emerge