Our Twitter reports provide unsupervised clustering, which breaks users out into clusters based on their unbiased interest behaviors.
Individual users are grouped into a single best cluster based on the handles they choose to connect with.
Here is an example of how this works for Twitter: when a group of users has an established pattern of all following a handful of the same accounts, and another user has a similar pattern, they are matched to a particular cluster. When the patterns of multiple clusters are observed for a given user, the pattern they match with most closely will become their assigned cluster. Users can only be a member of one cluster.
Affinio offers quantifiable and accurate results with every report. Audience segmentation is central to Affinio’s analysis. You’ll see that we provide several metadata metrics to help you further evaluate clusters, such as engagement, shared interests, and interconnectivity. Throughout this guide, you will learn more about how to use our tool to better understand your target audiences.