# Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning
## 2 Related Work
They also received a certain amount of free in-game currency. The results showed that providing a substantial amount of free in-game currency to high-value players affected by churning did not significantly affect the churn rate. Different genres of F2P mobile games have been studied in terms of player churn--role-playing games [22], puzzle games [23], social games [20], etc. It is challenging to generalize research findings, considering that many games have unique actions that a user can perform, and the habits of players often differ across different games. Studies that try to generalize findings have been performed across multiple games, such as [24], in which the authors create classifiers for churning players and remaining players on a dataset comprising five different games, and using only non-game-specific features (e.g., time between sessions). Different types of mobile games have also been combined in certain studies for the evaluation of player churn. In [25], the authors created a dataset from a player simulation game, a racing game, and a music game to predict player disengagement (i.e., players spending less time in the game or logging in less frequently) in those games using specific features related to game and event frequency features, which can be generalized over different games. The results indicated that biased datasets can easily lead to false classifiers, and that disengagement over varying dates can yield better performance over other approaches. Disengagement alongside churn has been a topic of research in [26], where the authors also looked at the freemium strategy game and evaluated different machine learning algorithms, concluding that the random forest model is the best for predicting churn when implementing the \"two windows\" approach (i.e., defining time windows in which players do and do not appear to be active). Different approaches towards churn prediction and towards metrics which describe churn have been presented in two subsequent studies by [PERSON] et al. [27; 28]. The authorsuse survival ensembles (a group of survival tree models similar to the random forest approach). Whilst first working with binary classification [27], in the second paper [28], the authors extend the models by predicting the level obtained in the game and the playtime. While the dataset is large, the specific prediction analysis has been conducted on thousands of players--primarily whales--while the short-term players who quit the game quickly were not addressed. Another approach for predicting player churn [29] is using a recency, frequency, and monetary value framework which takes into account features related to user lifetime, intensity, and rewards (RFM-LIR model). The authors define different churn types based on the windowed approach, starting absence, engagement related to average engagement, and switching behavior (i.e., decreased trend in engagement). AUC values are between 0.53 and 0.71 for developed models. The research in this paper differs from previous work, as it focuses on a highly dynamic game in terms of player acquisition and player churn. This paper assesses a game in which approximately 60% of the players leave the game after only one day of gameplay, and the influx of new players in the game is counted in the tens of thousands per day. Therefore, because developers have a very short time to react to players leaving their game with any in-game action, we focus on very short time frames for the machine learning algorithms--up to one day--and the results of this research show that developer reactions are needed on even shorter time frames (i.e., several hours), which will be addressed in our future work. With a high churn percentage observed after a single day, there is a need for rapid prediction models, such as that presented in [23], while the majority of related work focuses on much longer time frames (e.g., weeks or months). In addition, our analysis has been conducted on samples of hundreds of thousands of users, as opposed to samples of thousands of users prevalent in the related work.