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10.1109/CSE.2009.80guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Churn Prediction in MMORPGs: A Social Influence Based Approach

Published: 29 August 2009 Publication History

Abstract

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.

Cited By

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  • (2023)“Better Dead than a Damsel”: Gender Representation and Player ChurnCompanion Proceedings of the Annual Symposium on Computer-Human Interaction in Play10.1145/3573382.3616083(102-107)Online publication date: 6-Oct-2023
  • (2022)Quantifying Proactive and Reactive Button InputProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501913(1-18)Online publication date: 29-Apr-2022
  • (2020)A Trajectory-based Deep Sequential Method for Customer Churn PredictionProceedings of the 2020 5th International Conference on Machine Learning Technologies10.1145/3409073.3409083(114-118)Online publication date: 19-Jun-2020
  • Show More Cited By

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Information

Published In

cover image Guide Proceedings
CSE '09: Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
August 2009
1217 pages
ISBN:9780769538235

Publisher

IEEE Computer Society

United States

Publication History

Published: 29 August 2009

Author Tags

  1. Churn prediction
  2. Diffusion
  3. Social Influence

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Cited By

View all
  • (2023)“Better Dead than a Damsel”: Gender Representation and Player ChurnCompanion Proceedings of the Annual Symposium on Computer-Human Interaction in Play10.1145/3573382.3616083(102-107)Online publication date: 6-Oct-2023
  • (2022)Quantifying Proactive and Reactive Button InputProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501913(1-18)Online publication date: 29-Apr-2022
  • (2020)A Trajectory-based Deep Sequential Method for Customer Churn PredictionProceedings of the 2020 5th International Conference on Machine Learning Technologies10.1145/3409073.3409083(114-118)Online publication date: 19-Jun-2020
  • (2020)Knowing your FATEProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403276(2269-2279)Online publication date: 23-Aug-2020
  • (2019)On churn and social contagionProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3344378(833-841)Online publication date: 27-Aug-2019
  • (2019)From non-paying to premiumProceedings of the 14th International Conference on the Foundations of Digital Games10.1145/3337722.3341855(1-9)Online publication date: 26-Aug-2019
  • (2019)Characterizing and Forecasting User Engagement with In-App Action GraphProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330750(2023-2031)Online publication date: 25-Jul-2019
  • (2019)Uncovering the Co-driven Mechanism of Social and Content Links in User Churn PhenomenaProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330736(3093-3101)Online publication date: 25-Jul-2019
  • (2018)I Know You'll Be BackProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219821(914-922)Online publication date: 19-Jul-2018
  • (2018)Toward Personalized Activity Level Prediction in Community Question Answering WebsitesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/318701114:2s(1-15)Online publication date: 25-Apr-2018
  • Show More Cited By

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