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

2019/12/15 by Yang, Wanshan, Huang, Ting, Zeng, Junlin +4
#FOS: Computer and information sciences #Human-Computer Interaction (cs.HC)

paper · doi:10.48550/arxiv.1912.06972

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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