# Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning
## 6 Conclusions and Outlook  
This paper presents our efforts to apply machine learning techniques to predict player churn of a globally popular mobile F2P storytelling game. Following the collection of a very large dataset over the course of a 4-year period, we discuss the process of data collection and cleaning in preparation for training various ML models. Our initial statistical analysis of the data shows significant user churn within 1-7 days of initial game installment, and thus motivates the need for developing ML-based models to accurately predict such events. This challenge is even more pronounced when considering our specific studied game as compared to related work, due to the very dynamic player behavior in the studied game. Timely reactions can prevent the potential departure of players, for example, by providing special offers, additional tickets, etc. The statistical review of the data also justifies the use of simple end-of-interest assumptions as valid tags for user feature vectors (churnLabel tags), as the accuracy of the simple assumption ranges from 83.65% to more, as interest-end observation days increase. Furthermore, we notice the problems in the implementation of tracking a large number of players concurrently, and when determining how this can affect the validity of logged information. We perform empirically based data preparation to alleviate these issues. The most important implications of the study are as follows. The ML models can predict, with significant accuracy, the departure of players from the game even at very short learning and prediction periods (i.e., one day). The data analysis suggests that, for this game, and possibly for this game genre, even shorter time frames need to be considered (i.e., hours). The prediction for shorter time frames will enable developers to react quickly to players potentially leaving the game through specific offers or rewards in an effort to try to keep the players engaged. We find the use of features which specify the distribution of player activity during the observed time period to be of the highest significance for the performance of ML models. Thus, we encourage future research to encompass such features in their models. From the tested ML algorithms, the random forest model stood out as the best model in the general analysis, while the logistic regression resulted with the poorest performance. Considering a period of one day of user observation and one day of churn period AUC, metrics for the algorithm of random noise of 0.77 and average AP accuracy of 94% are good results for short time periods of observation, and the reliability of this algorithm is satisfactory. Nevertheless, when considering the presented results, the behaviors of the players and behavior variations over time need to be taken into account. To illustrate, in the first day, over 60% of the players were found to become churners, while, when considering periods after the 10 th day, only 5% of the remaining players will churn. The results of this study should be considered with several limitations. First, the study was conducted on a casual interactive storytelling game. Further study is needed to confirm whether similar behavior is observed in other games of this genre and whether similar methodologies and models can be applied within this game's genre and other game genres. Additionally, the event-logging system of the game was flawed, so the measures used for the reconstruction of the players' sessions may have impacted the data. Another limitation of the research was in the computational power required for processing such large datasets, so the initial dataset consisting of 4 years of data needed to be broken down into smaller ones for faster and more convenient ML model iterations. The study was conducted during a period of the game's decline, so that specific point in the lifecycle of the game also needs to be taken into account.