Refereed journal article or data article (A1)

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




List of Authors: Numminen Riikka, Viljanen Markus Juhani, Pahikkala Tapio

Publisher: Institute of Electrical and Electronics Engineers

Publication year: 2022

Journal: IEEE Transactions on Games

Volume number: 14

Issue number: 1

eISSN: 2475-1510

DOI: http://dx.doi.org/10.1109/TG.2020.3014660

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/51115088


Abstract

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.
Index Terms—Bayesian Inference, Free-to-play, Monetization,
Survival Analysis


Last updated on 2022-29-03 at 10:01