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

TL;DR

This paper introduces heuristic models for quick prediction of short-term player retention in free-to-play mobile games, achieving comparable performance to complex classifiers using minimal early session data.

Contribution

It presents a simple heuristic approach for rapid retention prediction that performs well with limited early player activity data.

Findings

Heuristic models perform comparably to classification algorithms.

Early session data effectively predicts short-term retention.

Simple rules can be used for quick retention estimates.

Abstract

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.