Prediction of Player Churn and Disengagement Based on User Activity Data of a Freemium Online Strategy Game

Rothmeier K., Pflanzl N., Hüllmann J. A., Preuss M.


Abstract
Churn describes customer defection from a service provider.This can be observed in online freemium games, where userscan leave without further notice. Game companies are lookingfor methods to detect and predict churn to enable managementreaction. The recorded data of games can be analyzed forthis purpose. We conducted a case study based on data fromthe freemium game The Settlers Online. Churn detection wasachieved by application of four different labeling approaches,based on common churn and disengagement definitions within thegame analytics literature. In order to model predictive classifiers,features were computed from the raw game data. Eight differentmachine learning algorithms returning binary classifications wereapplied. The results were compared for all algorithms regardingall labeling approaches. Random forests with sliding windowswere the best solution in our case, returning AUC valueshigher than 0.99, thereby enabling prediction accuracies of 97%in our data set. The results were confirmed by tests on anindependent data set and in our discussion, we offer guidanceon the interplay of feature engineering, labeling approaches-inparticular disengagement-and machine learning algorithms forchurn prediction. Our recommendations are valuable for gamecompanies and academics, who pursue similar studies.

Keywords
Player churn; Disengagement; Classification; Machine Learning



Publication type
Article in Journal

Peer reviewed
Yes

Publication status
Published

Year
2020

Journal
IEEE Transactions on Games

Volume
2020

ISSN
2475-1510

DOI

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