Extended RFM logit model for churn prediction in the mobile gaming market

Abstract

As markets are becoming increasingly saturated, many businesses are shifting their focus to customer retention. In their need to understand and predict future customer behavior, businesses across sectors are adopting data-driven business intelligence to deal with churn prediction. A good example of this approach to retention management is the mobile game industry. This business sector usually relies on a considerable amount of behavioral telemetry data that allows them to understand how users interact with games. This high-resolution information enables game companies to develop and adopt accurate models for detecting customers with a high attrition propensity. This paper focuses on building a churn prediction model for the mobile gaming market by utilizing logistic regression analysis in the extended recency, frequency and monetary (RFM) framework. The model relies on a large set of raw telemetry data that was transformed into interpretable game-independent features. Robust statistical measures and dominance analysis were applied in order to assess feature importance. Established features are used to develop a logistic model for churn prediction and to classify potential churners in a population of users, regardless of their lifetime.