Early churn prediction with personalized targeting in mobile social games

Abstract

A model for predicting churn one day after registration in games is proposed.The model is generalized and applicable to various types of free-to-play games.Two approaches for retaining churned users via push notifications are presented.The proposed system is deployed and used in the production of a large-scale game. Customer churn is a widely known term in many industries, including banking, telecommunications and gaming. By definition, churn represents the act of a customer leaving a product for good. Most commonly, late customer churn is addressed. In the dynamics of free to play games, most of newly registered users abandon the game in the first few days, so the main focus is on early customer churn. Therefore, successful early churn prevention methodology is vital to having a successful business in free to play gaming industry. To tackle this problem, we introduce a two stage intelligent system. It employs early churn prediction, formulated as a binary classification task, followed by a churn prevention technique using personalized push notifications. For early churn prediction, common machine learning models are trained and compared using a data set obtained from two million players of Top Eleven - Be A Football Manager online mobile game. To prevent churn, we track user activity, identify the game features that are potentially interesting to the user and then use that data to tailor personalized push notifications with a purpose to attract users back into the game. Using this approach, we are able to reduce churn up to 28%, which, at the scale of millions of users, represents a significant positive impact to business.