A Comparison of Methods for Player Clustering via Behavioral Telemetry

The analysis of user behavior in digital games has been aided by the
introduction of user telemetry in game development, which provides
unprecedented access to quantitative data on user behavior from the
installed game clients of the entire population of players. Player
behavior telemetry datasets can be exceptionally complex, with
features recorded for a varying population of users over a temporal
segment that can reach years in duration. Categorization of behaviors,
whether through descriptive methods (e.g. segmentation) or
unsupervised/supervised learning techniques, is valuable for finding
patterns in the behavioral data, and developing profiles that are
actionable to game developers. There are numerous methods for
unsupervised clustering of user behavior, e.g. k-means/c-means, Nonnegative
Matrix Factorization, or Principal Component Analysis.
Although all yield behavior categorizations, interpretation of the
resulting categories in terms of actual play behavior can be difficult if
not impossible. In this paper, a range of unsupervised techniques are
applied together with Archetypal Analysis to develop behavioral
clusters from playtime data of 70,014 World of Warcraft players,
covering a five year interval. The techniques are evaluated with
respect to their ability to develop actionable behavioral profiles from
the dataset.