RFM-LIR feature framework for churn prediction in the mobile games market

Predicting player behavior and customer churn is one of the central and most common challenges in game analytics. A crucial stage in developing customer churn prediction model is feature engineering. In the mobile gaming field, features are commonly constructed from the raw behavioral telemetry data which leads to challenges related to the establishment of meaningful features and comprehensible feature frameworks. This research proposes an extended Recency, Frequency, and Monetary value (RFM) feature framework for churn prediction in the mobile gaming field by incorporating features related to user Lifetime, Intensity and Rewards (RFM-LIR). The proposed framework is verified by exploring behavioral differences between churners and non-churners within the established framework for different churn definitions and definition groups by applying robust exploratory methods and developing univariate and multivariate churn prediction models. Although feature importance varies among churn definitions, long term frequency feature stands out as the most important feature. The top five most important features distinguished by the multivariable churn prediction models include long and short term frequency features, monetary, intensity and lifetime features.