Churn Prediction in MMORPGs: A Social Influence Based Approach

Date of Submission: 
May 20, 2009
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Massively Multiplayer Online Role Playing Games (MMORPGs) are computer based games in which players interact with one another in the virtual world. Worldwide revenues for MMORPGs have seen amazing growth in last few years and it is more than a 2 billion dollars industry as per current estimates. Huge amount of revenue potential has attracted several gaming companies to launch online role playing games. One of the major problems these companies suffer apart from fierce competition is erosion of their customer base. Churn is a big problem for the gaming companies as churners impact negatively in the "word-of-mouth" reports for potential and existing customers leading to further erosion of user base.

We study the problem of player churn in the popular MMORPG EverQuest II. The problem of churn prediction has been studied extensively in the past in various domains and social network analysis has recently been applied to the problem to understand the effects of the strength of social ties and the structure and dynamics of a social network in churn. In this paper, we propose a churn prediction model based on examining social influence among players and their personal engagement in the game. We hypothesize that social influence is a vector quantity, with components negative influence and positive influence. We propose a modified diffusion model to propagate the influence vector in the player's network which represents the social influence on the player from his network. We measure a player's personal engagement based on his activity patterns and use it in the modified diffusion model and churn prediction. Our method for churn prediction which combines social influence and player engagement factors has shown to improve prediction accuracy significantly for our dataset as compared to prediction using the conventional diffusion model or the player engagement factor, thus validating our hypothesis that combination of both these factors could lead to a more accurate churn prediction.