# Rapid Prediction of Player Retention in Free-to-Play Mobile Games

arXiv: https://arxiv.org/abs/1607.03202 · HTML: https://mlsanity.com/paper/1607.03202/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2016.

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

- [How Early Is Early Enough? Design-Dependent Observation-Window Sufficiency in Subscription Churn Prediction](https://mlsanity.com/paper/2607.00473/) (2026)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Personalized Game Difficulty Prediction Using Factorization Machines](https://mlsanity.com/paper/2209.13495/) (2022)

## Abstract (arXiv)

Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.