Predicting player churn in the wild
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.
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By multiplying the number of customers planning to churn by the incentive cost, the cost of business loss that can incur when the customers are not retained can be calculated through the churn prediction model.
“In addition, by multiplying the number of customers planning to churn by the incentive cost to be provided to those customers, the cost of business loss that can incur when the customers are not retained can be calculated through the churn prediction model [45] [53].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [53]
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Self-citation
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Cited by
Sorted by citations
Churn prediction for high-value players in casual social games
Runge, J., Gao, P., Garcin, F., & Faltings, B. (2014) · 2014 IEEE Conference on Computational Intelligence and Games · doi:10.1109/cig.2014.6932875 · Cited by 144 · see citing works
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- Authors
- Hadiji, F., Sifa, R., Drachen, A., Thurau, C., Kersting, K., & Bauckhage, C.
- Affiliations
- TU Dortmund University (DE)
- Fraunhofer Institute for Intelligent Analysis and Information Systems (DE)
- Game Analytics, Berlin, Germany
- Venue
- 2014 IEEE Conference on Computational Intelligence and Games
- Publisher
- IEEE
- Published
- 2014-08
- Pages
- 1-8
- OpenAlex
- W2003528492
- Semantic Scholar
- 8f2dda206af379a82b2cc1bfc0494155f71dd50c
- Cited by
- 154
- References
- 27
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 8 · 2023: 12 · 2022: 7 · 2021: 18 · 2020: 17 · 2019: 18 · 2018: 21 · 2017: 14 · 2016: 14 · 2015: 10 · 2014: 2
- FWCI
- 54.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Computational Intelligence and Games (CIG) (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- free-to-play games, player retention, behavioral telemetry, in-game purchases, session duration, video game industry
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