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

arXiv: https://arxiv.org/abs/1710.02262 · HTML: https://mlsanity.com/paper/1710.02262/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Actionable Recourse via GANs for Mobile Health](https://mlsanity.com/paper/2211.06525/) (2022)
- [AI in (and for) Games](https://mlsanity.com/paper/2105.03123/) (2021)
- [BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models](https://mlsanity.com/paper/2203.16155/) (2022)

## Abstract (arXiv)

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.