Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIG.2014.6932876 | Computational Intelligence and Games |
Keywords | DocType | ISSN |
advertising,computer games,mobile computing,public domain software,behavioral telemetry,business models,free-to-play games,freemium games,future player behavior prediction,game development company,game industry,game-design specific features,global marketplace,in-game advertising,in-game purchases,industry standards,marketing strategies,mobile platforms,online distribution platforms,player chum prediction,session intervals,session length,behavior,behavior modeling,churn,churn prediction,free-to-play,freemium,game analytics,game data mining,games | Conference | 2325-4270 |
Citations | PageRank | References |
31 | 1.58 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Hadiji | 1 | 99 | 8.45 |
| Rafet Sifa | 2 | 133 | 30.03 |
| Anders Drachen | 3 | 514 | 53.24 |
| Christian Thurau | 4 | 478 | 34.19 |
| Kristian Kersting | 5 | 1932 | 154.03 |
| Christian Bauckhage | 6 | 1979 | 195.86 |