User Behavior Modelling Approach for Churn Prediction in Online Games

Abstract

Massively Multiplayer Online Role-Playing Games (MMORPGs) are persistent virtual environments where millions of players interact in an online manner. Game logs capture player activities in great detail and user behavior modeling approaches can help to build accurate models of player behavior from these logs. We are interested in modeling player churn behavior and we use a lifecycle-based approach for this purpose. In a player lifecycle-based approach, we analyze the activity traits of churners in the weeks leading up to their point of leaving the game and compare it with the activity traits of a regular player. We identify several intuitive yet distinct behavioral profiles associated with churners and active players which can discriminate between the two populations. We use these insights to propose three semantic dimensions of engagement, enthusiasm and persistence to construct derived features. Using three session-related variables and the features derived from them, we are able to achieve good classification performance with the churn prediction models. Finally, we propose a distance-based classification scheme, which we call wClusterDist, which benefits from these distinct behavioral profiles of the two populations. Experimental results show that the proposed classification scheme is well-suited for this problem formulation and its performance is better than or comparable to other traditional classification schemes.