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

Authors

  • Anders Drachen Aalborg University
  • Eric Lundquist Northwestern University
  • Yungjen Kung Northwestern University
  • Pranav Rao Northwestern University
  • Rafet Sifa Fraunhofer IAIS
  • Julian Runge Wooga GmBH and Humboldt University
  • Diego Klabjan Northwestern University

DOI:

https://doi.org/10.1609/aiide.v12i1.12856

Keywords:

game analytics, prediction, machine learning, monetization, heuristics

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.

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Published

2021-06-25

How to Cite

Drachen, A., Lundquist, E., Kung, Y., Rao, P., Sifa, R., Runge, J., & Klabjan, D. (2021). Rapid Prediction of Player Retention in Free-to-Play Mobile Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 12(1), 23-29. https://doi.org/10.1609/aiide.v12i1.12856

Issue

Vol. 12 No. 1 (2016): Twelfth Artificial Intelligence and Interactive Digital Entertainment Conference

Section

Full Oral Papers