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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.
Originalsprog | Engelsk |
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Titel | Proceedings of the IEEE Conference on Games |
Antal sider | 8 |
Forlag | IEEE |
Publikationsdato | 30 jul. 2019 |
ISBN (Trykt) | 978-1-7281-1885-7 |
ISBN (Elektronisk) | 978-1-7281-1884-0 |
DOI | |
Status | Udgivet - 30 jul. 2019 |
Begivenhed | IEEE Conference on Games (2019) - Queen Mary University of London, London, Storbritannien Varighed: 20 aug. 2019 → 23 aug. 2019 http://www.ieee-cog.org |
Konference
Konference | IEEE Conference on Games (2019) |
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Lokation | Queen Mary University of London |
Land/Område | Storbritannien |
By | London |
Periode | 20/08/2019 → 23/08/2019 |
Internetadresse |
Emneord
- Freemium games
- Churn prediction
- Neural network architectures
- Sequential data
- Aggregate data
Fingeraftryk
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