NDLI: Predicting player churn in the wild

Content Provider	IEEE Xplore Digital Library
Author	Hadiji, F. Sifa, R. Drachen, A. Thurau, C. Kersting, K. Bauckhage, C.
Copyright Year	2014
Description	Author affiliation: Aalborg Univ., Aalborg, Denmark (Drachen, A.) \|\| Fraunhofer IAIS, St. Augustin, Germany (Sifa, R.; Bauckhage, C.) \|\| Tech. Univ. Dortmund, Dortmund, Germany (Hadiji, F.; Kersting, K.) \|\| Game Analytics, Berlin, Germany (Thurau, C.)
Abstract	Free-to-Play or “freemium” games represent a fundamental shift in the business models of the game industry, facilitated by the increasing use of online distribution platforms and the introduction of increasingly powerful mobile platforms. The ability of a game development company to analyze and derive insights from behavioral telemetry is crucial to the success of these games which rely on in-game purchases and in-game advertising to generate revenue, and for the company to remain competitive in a global marketplace. The ability to model, understand and predict future player behavior has a crucial value, allowing developers to obtain data-driven insights to inform design, development and marketing strategies. One of the key challenges is modeling and predicting player churn. This paper presents the first cross-game study of churn prediction in Free-to-Play games. Churn in games is discussed and thoroughly defined as a formal problem, aligning with industry standards. Furthermore, a range of features which are generic to games are defined and evaluated for their usefulness in predicting player churn, e.g. playtime, session length and session intervals. Using these behavioral features, combined with the individual retention model for each game in the dataset used, we develop a broadly applicable churn prediction model, which does not rely on game-design specific features. The presented classifiers are applied on a dataset covering five free-to-play games resulting in high accuracy churn prediction.
Starting Page	1
Ending Page	8
File Size	702950
Page Count	8
File Format	PDF
ISBN	9781479935475
ISSN	23254289
DOI	10.1109/CIG.2014.6932876
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	game data mining Games churn prediction churn games Mobile communication freemium Data mining game analytics free-to-play behavior behavior modeling
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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