Utilizing players’ playtime records for churn prediction: Mining playtime regularity

作     者：Yang, Wanshan Huang, Ting Zeng, Junlin Chen, Lijun Mishra, Shivakant Liu, Youjian 

作者机构：Department of Computer Science University of Colorado Boulder Boulder United States Department of Data Analytics Yoozoo Games Shanghai China Department of Electrical Computer & Energy Engineering University of Colorado Boulder Boulder United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2019年

核心收录：

主　　题：Data mining 

摘      要：—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. In particular, we measure playtime regularity using the notion of entropy and cross-entropy from information theory. After we calculate the playtime regularity of players from data sets of six free online games of different types. We leverage information from players’ playtime regularity in the form of universal features for churn prediction. Experiments show that our developed features are better at predicting churners compared to baseline features. Thus, the experiment results imply that our proposed features could utilize the information extracted from players’ playtime more effectively than related baseline playtime features. Copyright © 2019, The Authors. All rights reserved.