Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model

TL;DR

This paper introduces a scalable survival ensemble model for predicting player churn in mobile games, enabling accurate, real-time analysis of player retention across large datasets.

Contribution

The paper presents a novel, robust survival ensemble approach for multi-dimensional churn prediction that scales efficiently and handles diverse data distributions.

Findings

Accurate prediction of player churn levels and playtime.

Model is robust to different data distributions.

Suitable for real-time analysis of millions of users.

Abstract

The emergence of mobile games has caused a paradigm shift in the video-game industry. Game developers now have at their disposal a plethora of information on their players, and thus can take advantage of reliable models that can accurately predict player behavior and scale to huge datasets. Churn prediction, a challenge common to a variety of sectors, is particularly relevant for the mobile game industry, as player retention is crucial for the successful monetization of a game. In this article, we present an approach to predicting game abandon based on survival ensembles. Our method provides accurate predictions on both the level at which each player will leave the game and their accumulated playtime until that moment. Further, it is robust to different data distributions and applicable to a wide range of response variables, while also allowing for efficient parallelization of the algorithm. This makes our model well suited to perform real-time analyses of churners, even for games with millions of daily active users.