Churn prediction for high-value players in casual social games

Julian Runge · Peng Gao · Florent Garcin · Boi V. Faltings

147 citations0/0 files storedIEEE Conference on Computational Intelligence and Games (CIG)

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Customer churn and segmentation Online Learning and Analytics AI and HR Technologies

Abstract

Predicting when players will leave a game creates a unique opportunity to increase players' lifetime and revenue contribution. Players can be incentivized to stay, strategically cross-linked to other games in the company's portfolio or, as a last resort, be passed on to other companies through in-game advertisement. This paper focuses on predicting churn for highvalue players of casual social games and attempts to assess the business impact that can be derived from a predictive churn model. We compare the prediction performance of four common classification algorithms over two casual social games, each with millions of players. Furthermore, we implement a hidden Markov model to explicitly address temporal dynamics. We find that a neural network achieves the best prediction performance in terms of area under curve (AUC). In addition, to assess the business value of churn prediction, we design and implement an A/B test on one of the games, using free in-game currency as an incentive to retain players. Test results indicate that contacting players shortly before the predicted churn event substantially improves the effectiveness of communication with players. They further show that giving out free in-game currency does not significantly impact the churn rate or monetization of players. This suggests that players can only be retained by remarkably changing their gameplay experience ahead of the churn event and that cross-linking may be the more effective measure to deal with churning players.

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References

2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2008Social ties and their relevance to churn in mobile telecom networks 2006CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services 2009Churn Prediction in MMORPGs: A Social Influence Based Approach 2014Predicting player churn in the wild 2013A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services

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2020A Survey on Churn Analysis in Various Business Domains 2016Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles 2017Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior 2015Predicting Purchase Decisions in Mobile Free-to-Play Games 2017Early churn prediction with personalized targeting in mobile social games 2017Churn prediction of mobile and online casual games using play log data 2015

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