Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity
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 operation...
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Published in | IEEE transactions on games Vol. 14; no. 2; p. 1 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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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. 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. |
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AbstractList | Churn prediction is an important topic in the free online game industry. 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 article, we consider developing new universal features for churn predictions for long-term players based on playtime. In particular, we measure playtime regularity using the notion of entropy and cross-entropy from information theory. After computing playtime regularity of players from the datasets of six free online games of different types, we leverage information from the playtime regularity in the form of universal features for churn prediction. Experiments show that the proposed features are better at predicting churners compared to the baseline features, implying that the proposed features could utilize the information extracted from playtime more effectively than the related baseline playtime features. 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. |
Author | Zeng, Junlin Huang, Ting Chen, Lijun Liu, Youjian Yang, Wanshan Mishra, Shivakant |
Author_xml | – sequence: 1 givenname: Wanshan surname: Yang fullname: Yang, Wanshan email: wanshan.yang@colorado.edu organization: Department of Computer Science, University of Colorado Boulder, 1877 Boulder, Colorado United States (e-mail: wanshan.yang@colorado.edu) – sequence: 2 givenname: Ting surname: Huang fullname: Huang, Ting email: thuang@yoozoo.com organization: Department of Data Analytics, Yoozoo Games, Shanghai China (e-mail: thuang@yoozoo.com) – sequence: 3 givenname: Junlin surname: Zeng fullname: Zeng, Junlin email: zengjl@yoozoo.com organization: Department of Data Analytics, Yoozoo Games, Shanghai China (e-mail: zengjl@yoozoo.com) – sequence: 4 givenname: Lijun surname: Chen fullname: Chen, Lijun email: lijun.chen@colorado.edu organization: Department of Computer Science, University of Colorado Boulder, 1877 Boulder, Colorado United States (e-mail: lijun.chen@colorado.edu) – sequence: 5 givenname: Shivakant surname: Mishra fullname: Mishra, Shivakant email: mishras@colorado.edu organization: Department of Computer Science, University of Colorado Boulder, 1877 Boulder, Colorado United States (e-mail: mishras@colorado.edu) – sequence: 6 givenname: Youjian surname: Liu fullname: Liu, Youjian email: eugeneliu@ieee.org organization: Department of Electrical, Computer, & Energy Engineering, University of Colorado Boulder, 1877 Boulder, Colorado United States (e-mail: eugeneliu@ieee.org) |
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Snippet | 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... Churn prediction is an important topic in the free online game industry. Reducing the churn rate of a game significantly helps with the success of the game.... |
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SubjectTerms | Churn prediction Computer & video games Data mining Electronic mail Entropy Entropy (Information theory) feature engineering Feature extraction free-to-play games Games Incentives Industries Information theory Players Prediction models Predictions Predictive models Regularity supervised learning |
Title | Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity |
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