Churn Prediction in MMORPGs: A Social Influence Based Approach

Massively Multiplayer Online Role Playing Games(MMORPGs) are computer based games in which players interactwith one another in the virtual world. Worldwide revenuesfor MMORPGs have seen amazing growth in last few years and itis more than a 2 billion dollars industry as per current estimates.Huge amount of revenue potential has attracted several gamingcompanies to launch online role playing games. One of the majorproblems these companies suffer apart from fierce competitionis erosion of their customer base. Churn is a big problem for thegaming companies as churners impact negatively in the wordof-mouth reports for potential and existing customers leading tofurther erosion of user base.We study the problem of player churn in the popularMMORPG EverQuest II. The problem of churn predictionhas been studied extensively in the past in various domainsand social network analysis has recently been applied to theproblem to understand the effects of the strength of social tiesand the structure and dynamics of a social network in churn.In this paper, we propose a churn prediction model based onexamining social influence among players and their personalengagement in the game. We hypothesize that social influence is avector quantity, with components negative influence and positiveinfluence. We propose a modified diffusion model to propagatethe influence vector in the players network which represents thesocial influence on the player from his network. We measure aplayers personal engagement based on his activity patterns anduse it in the modified diffusion model and churn prediction. Ourmethod for churn prediction which combines social influenceand player engagement factors has shown to improve predictionaccuracy significantly for our dataset as compared to predictionusing the conventional diffusion model or the player engagementfactor, thus validating our hypothesis that combination of boththese factors could lead to a more accurate churn prediction.

[1]  Rupesh K. Gopal,et al.  Customer Churn Time Prediction in Mobile Telecommunication Industry Using Ordinal Regression , 2008, PAKDD.

[2]  Sougata Mukherjea,et al.  Social ties and their relevance to churn in mobile telecom networks , 2008, EDBT '08.

[3]  Huan Liu,et al.  Customer Retention via Data Mining , 2000, Artificial Intelligence Review.

[4]  Bin Li,et al.  Automated Cellular Modeling and Prediction on a Large Scale , 2000, Artificial Intelligence Review.

[5]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[6]  Robert J. Moore,et al.  The life and death of online gaming communities: a look at guilds in world of warcraft , 2007, CHI.

[7]  Albert,et al.  Dynamics of complex systems: scaling laws for the period of boolean networks , 2000, Physical review letters.

[8]  Yu Zhao,et al.  Customer Churn Prediction Using Improved One-Class Support Vector Machine , 2005, ADMA.

[9]  Jacob Goldenberg,et al.  Using Complex Systems Analysis to Advance Marketing Theory Development , 2001 .

[10]  Vadlamani Ravi,et al.  Predicting credit card customer churn in banks using data mining , 2008, Int. J. Data Anal. Tech. Strateg..

[11]  Tian-Shyug Lee,et al.  Mining the customer credit using classification and regression tree and multivariate adaptive regression splines , 2006, Comput. Stat. Data Anal..

[12]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[13]  Chris GauthierDickey,et al.  A measurement study of virtual populations in massively multiplayer online games , 2007, NetGames '07.

[14]  Dirk Van den Poel,et al.  Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting , 2005, Eur. J. Oper. Res..

[15]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[16]  George Ioannou,et al.  Customer switching behaviour in Greek banking services using survival analysis , 2008 .

[17]  A. Tiwari,et al.  Churn Prediction using Complaints Data , 2006 .

[18]  Debanjan Saha,et al.  A long-term study of a popular MMORPG , 2007, NetGames '07.

[19]  Chris Volinsky,et al.  Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks , 2006, math/0606278.

[20]  Robert J. Moore,et al.  "Alone together?": exploring the social dynamics of massively multiplayer online games , 2006, CHI.

[21]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[22]  Georg Lausen,et al.  Spreading activation models for trust propagation , 2004, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004.

[23]  David L. Mothersbaugh,et al.  Switching barriers and repurchase intentions in services , 2000 .

[24]  B. Bollobás The evolution of random graphs , 1984 .

[25]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[26]  Katharina Morik,et al.  Analysing Customer Churn in Insurance Data - A Case Study , 2004, PKDD.

[27]  Kristof Coussement,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin , 2022 .

[28]  Stephen R. Garner,et al.  WEKA: The Waikato Environment for Knowledge Analysis , 1996 .

[29]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .