  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.
