Rapid Prediction of Player Retention in Free-to-Play Mobile Games

Drachen, Anders, Lundquist, Eric Thurston, Kung, Yungjen, Rao, Pranav Simha, Klabjan, Diego, Sifa, Rafet, Runge, Julian

arXiv.org Machine Learning 

Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.

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