Combining Sequential and Aggregated Data for Churn Prediction inCasual Freemium Games

Jeppe Theiss Kristensen, Paolo Burelli

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


In freemium games, the revenue from a player comes from the in-app purchases made and the advertisement to which that player is exposed. The longer a player is playing the game, the higher will be the chances that he or she will generate a revenue within the game.
Within this scenario, it is extremely important to be able to detect promptly when a player is about to quit playing (churn) in order to react and attempt to retain the player within the game, thus prolonging his or her game lifetime.
In this article we investigate how to improve the current state-of-the-art in churn prediction by combining sequential and aggregate data using different neural network architectures.
The results of the comparative analysis show that the combination of the two data types grants an improvement in the prediction accuracy over predictors based on either purely sequential or purely aggregated data.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Games
Number of pages8
Publication date30 Jul 2019
ISBN (Print)978-1-7281-1885-7
ISBN (Electronic) 978-1-7281-1884-0
Publication statusPublished - 30 Jul 2019
EventIEEE Conference on Games (2019) - Queen Mary University of London, London, United Kingdom
Duration: 20 Aug 201923 Aug 2019


ConferenceIEEE Conference on Games (2019)
LocationQueen Mary University of London
Country/TerritoryUnited Kingdom
Internet address


  • Freemium games
  • Churn prediction
  • Neural network architectures
  • Sequential data
  • Aggregate data


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