AI-based early detection to prevent user churn in MMORPG

Massive multiplayer online role playing game (MMORPG) is a common type of game these days.Predicting user churn in MMORPG is a crucial task.The retention rate of users is deeply associated with the lifespan and revenue of the service.If the churn of a specific user can be predicted in advance, targeted promotions can be used to encourage their stay.Therefore, not only the accuracy of churn prediction but also the speed at which signs of churn can be detected is important.In this paper, we propose methods to identify early signs of churn by utilizing the daily predicted user retention probabilities.We train various deep learning and machine learning models using log data and estimate user retention probabilities.By analyzing the change patterns in these probabilities, we provide empirical rules for early identification of users at high risk of churn.Performance evaluations confirm that our methodology is more effective at detecting high risk users than existing methods based on login days.Finally, we suggest novel methods for customized marketing strategies.For this purpose, we provide guidelines of the percentage of accessed users who are at risk of churn.

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Publication Details

Journal
Korean Journal of Applied Statistics
Published
2024-08-26
DOI
https://doi.org/10.5351/kjas.2024.37.4.525
Citations
1
Primary Topic
Persona Design and Applications
Type
article
Field-Weighted Citation Impact
0.37

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AI-based early detection to prevent user churn in MMORPG

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1 citations
Korean Journal of Applied Statistics
Persona Design and Applications
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article

AI-based early detection to prevent user churn in MMORPG

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article en
1 citations

Abstract

Massive multiplayer online role playing game (MMORPG) is a common type of game these days.Predicting user churn in MMORPG is a crucial task.The retention rate of users is deeply associated with the lifespan and revenue of the service.If the churn of a specific user can be predicted in advance, targeted promotions can be used to encourage their stay.Therefore, not only the accuracy of churn prediction but also the speed at which signs of churn can be detected is important.In this paper, we propose methods to identify early signs of churn by utilizing the daily predicted user retention probabilities.We train various deep learning and machine learning models using log data and estimate user retention probabilities.By analyzing the change patterns in these probabilities, we provide empirical rules for early identification of users at high risk of churn.Performance evaluations confirm that our methodology is more effective at detecting high risk users than existing methods based on login days.Finally, we suggest novel methods for customized marketing strategies.For this purpose, we provide guidelines of the percentage of accessed users who are at risk of churn.

Korean Journal of Applied StatisticsVol. 37(4)
Statistics Korea (KR), Korea University (KR), Kao Corporation (Japan) (JP)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 40%
Persona Design and Applications
0.37
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