Abstract
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.
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 language | English |
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Title of host publication | Proceedings of the IEEE Conference on Games |
Number of pages | 8 |
Publisher | IEEE |
Publication date | 30 Jul 2019 |
ISBN (Print) | 978-1-7281-1885-7 |
ISBN (Electronic) | 978-1-7281-1884-0 |
DOIs | |
Publication status | Published - 30 Jul 2019 |
Event | IEEE Conference on Games (2019) - Queen Mary University of London, London, United Kingdom Duration: 20 Aug 2019 → 23 Aug 2019 http://www.ieee-cog.org |
Conference
Conference | IEEE Conference on Games (2019) |
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Location | Queen Mary University of London |
Country/Territory | United Kingdom |
City | London |
Period | 20/08/2019 → 23/08/2019 |
Internet address |