Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity

Wanshan Yang; Ting Huang; Junlin Zeng; Lijun Chen; Shivakant Mishra; Youjian; Liu

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arXiv · 1912.06972

Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity

Wanshan Yang·Ting Huang·Junlin Zeng·Lijun Chen·Shivakant Mishra·Youjian·Liu

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Abstract

In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime.

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Wanshan Yang, Ting Huang, Junlin Zeng, Lijun Chen, Shivakant Mishra, Youjian, Liu. 2019-12-15. Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity. https://arxiv.org/abs/1912.06972

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