# Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning
## 1 Introduction
In the scope of the study conducted in this paper involving a F2P mobile game, we received feedback from the game developer that their records show that if a paying player is targeted with ads, they tend to decrease their spending amount, and are more prone to leaving the game. Nevertheless, no systematic evidence of such an effect of advertising on a user's retention was reported in a previous study on the topic [9]. In general, the more active the player, and the longer the player is in the game, the greater the chance the player will become a paying customer through completing a purchase with real money or by creating revenue through viewing ads. As a consequence, it is imperative for game designers and developers to predict which of their customers will leave the game and when, so as to optimize marketing decisions and potentially to increase overall retention and revenue. The aim of this paper is to investigate the potential of applying machine learning (ML) based models for predicting player behavior in terms of staying active in the game or leaving the game, based on features derived from previously collected gameplay event logs. ML models can provide a fast and efficient way of analyzing player behavior in such a dynamic and varying environment, and can also help developers see certain trends in their player base that would not have otherwise been noticed. The other approach is using analytical models created by experts. These expert models can be very expensive to create from the standpoint of both the expertise of people creating them, as well as from the standpoint of time. While such an approach might yield comparable results, the created models need to be adapted to specific game changes, the lifetime cycle of the game, etc., which means spending a lot of expert man-hours on repetitive tasks. Moreover, what is problematic about using expert approaches in the area of F2P games is the sheer volume of data (e.g., the game addressed in this study has had, at one point, around 200,000 daily new players). Therefore, ML algorithms fueled by fully automatic data gathering and processing functions provide a reasonable choice for study and application. From a game-provider's perspective, the developed models may have different purposes, such as to correctly identify players which will soon quit the game so that they can either be targeted with advertisements and consequently monetize them, to offer them a limited number of specific offers and discounts to keep them active in the game, or any other strategy depending on the business model. For different goals, the developed models need to have different properties. As a case study, we analyze a large dataset of anonymized player data provided by a European game developer, corresponding to a free-to-play interactive storytelling game available on the international market. The game can be categorized as a casual mobile game (or even as a hyper-casual mobile game) which makes it a useful case study, as casual games are one of the most popular in today's mobile gaming market [10]. In the game, the player can read stories comprised of chapters. To access a chapter, the player first needs to pay a type of in-game currency, which regenerates over time. If the player wants to read at a higher pace, then he/she needs to purchase in-game currency with real money. While reading each chapter, the player is offered a set of choices he/she can make, and certain choices require a second type of in-game currency which can only be obtained with real money. Additionally, the player can customize the appearance of his/her character in-game though purchasing clothing items with this second type of in-game currency. Interactive storytelling games have a huge market potential, and the game under study in this paper has spent multiple weeks in the top 100 games on the iOS app store in the USA. The available dataset comprises logs corresponding to over 80 million players, and was collected over a time frame of approximately four years (January 2018 to January 2022).