Predicting Churn in Online Games by Quantifying Diversity of Engagement

Understanding engagement patterns of users in online platforms, may it be games, online social networks, or academic websites, is a widely studied topic with many real-world applications and economic consequences. A holy grail in this area of research is to develop an automatic prediction algorithm for when a user is going to leave the platform and devise proper intervention. In this work, we study online recreational games and propose to model the engagement patterns of players through an unsupervised learning framework. We think of engagement as a continuous temporal process, measured along specific axes derived from gaming users' data using principal component analysis. We track the overall trend of the projection of the data along the significant principal components. We find that the geometric variability of the trajectory is a good predictor of the users' engagement level. Users characterized by a time series with large variability are users with higher engagement; namely, they will continue playing the game for prolonged periods of time. We evaluated our methodology on two data sets of very different game types and compared the performance of our method with state-of-the-art black-box machine learning algorithms. Our results were fairly competitive with these methods, and we conclude that churn can be predicted using an explainable, intuitive, and white-box decision-rule algorithm.