Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games

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.