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Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games
- Source :
- IEEE Transactions on Games. 14:13-22
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Free-to-play has become one of the most popular monetization models, and as a consequence game developers need to get the players to purchase in the game instead of getting players to buy the game. Game analytics and player monetization prediction are important parts in estimating the profitability of a free-to-play game. In this paper, we concentrate on predicting the fraction of monetizing players among all players. Our method is based on a survival analysis mixture cure model, and can be applied to unlabeled data collected from any free-to-play game. We formulate a statistical model and use the Expectation Maximization algorithm to solve the latent monetization percentage and the monetization rate. The original method is modified by using Bayesian inference, and the results of the versions are compared. The method can be applied as a preliminary profitability study in situations where there is no extensive historical game data available, such as game and business development scenarios that need to utilize real time analytics.
- Subjects :
- Monetization
Computer science
business.industry
ComputingMilieux_PERSONALCOMPUTING
Statistical model
Machine learning
computer.software_genre
Bayesian inference
Artificial Intelligence
Control and Systems Engineering
Expectation–maximization algorithm
Profitability index
Fraction (mathematics)
Artificial intelligence
Electrical and Electronic Engineering
Game Developer
business
Free to play
computer
Software
Subjects
Details
- ISSN :
- 24751510 and 24751502
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Games
- Accession number :
- edsair.doi...........5c7e3cac41aa2e7fc70aa131338e5d31