Identifying Patterns in User Behavior in aMusic Streaming Service: A Cluster AnalysisApproach

Abstract: Logged user data has become a highly valued asset to many Internet based services with large user bases. Being able to draw insight from this data is considered a key to gaining competitive advantages for the companies behind the services. This study aims to identify patterns in the behavior of users when interacting with Spotify, a music streaming service, by studying automatically logged data. In the study, we examine several methods to perform such analyses using machine learning techniques. We identify six different types of behavior through k-means cluster analysis, each representing between 51.4% and 0.5% of all user sessions. We also identify five factors partly explaining the differences in behavior between different sessions. These are found through factor analysis and account for 39% of the variance in the data. Finally, we demonstrate how factors and clusters can be translated from numeric representations to linguistic interpretations.