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

Croatian Operational Research Review

Ana Perišić¹  Marko Pahor² 

[1] Polytechnic of Šibenik, Šibenik, Croatia;University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia;

关键词: churn prediction;    logistic regression;    mobile games;    RFM;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown