# Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning
## 2 Related Work  
The topic of analyzing player behavior and its application in business intelligence has evolved over the years, as have the games' business models. During the prevalence of the pay-to-play (P2P) model, and especially the subscription-based model that was popular amongst MMORPGs, _player churn rate_ (i.e., a measure indicating how many players stop playing a game over a certain time period) and _player retention rate_ (i.e., a measure indicating how many players continued playing a game over a certain time period) were the most important metrics investigated. In [11], the authors measured player churn through the GameSpy service, and identified that it follows a power-law distribution. Similar results were confirmed in [12], where findings indicate that an average player's interest in playing these games evolves according to a non-homogeneous Poisson process, whose intensity function is given by a power law, with the Weibull distribution being the most appropriate to describe playing time per player. Churn in the MMORPG _Everquest II_ was analyzed in [13], with a focus on social networks and their impact on churn. The authors present churn-prediction models, but their precision and recall are relatively low--around 50%. The dataset from _Everquest II_ was also used to predict churn using supervised and unsupervised learning methods in [14], achieving precision and recall up to 75%. The same authors extended their previous work in [15] by closely looking at player behavior and formulating four types of churner behavior and four types of active player behavior. Their results indicate top accuracy results of 92.9%, whilst most of the models have values of performance metrics around 70%. Some researchers have also investigated the effects of network parameters (i.e., latency and jitter) on player departure in MMORPGs [16]. It should be noted that the examined player departure does not refer to players leaving the game forever, but rather players ending the specific session. The authors created a model which can assess when the players will end the session based on network quality--high delay and high packet loss result in very short sessions. The results show that delay jitters are less tolerable than absolute delays and that packet loss is more tolerable than packet delay. Churn and retention prediction was conducted for other major AAA games, such as _World of Warcraft_[17], _Just Cause 2_[18], and _Destiny_[19], but also for popular F2P games such as _Top Eleven--Be A Football Manager_[20], as the F2P model increased in popularity. The generation of revenue in F2P games is a highly discussed topic among game developers and publishers, given that these processes are much more complex than in P2P models and are driven by advertising and in-game purchases. In [21], the authors focus on predicting when a player will leave the game, with the goal being to open unique opportunities for companies to increase revenue contribution and provide incentives for players to stay or to initiate a new lifetime in another game. The chosen game genre was casual social games. Three core terms for solving the problem are high-value players, activity, and churn. To be classified as high-value, a player needs to be in the top 10% of players in terms of generated revenue. An active high-value player is considered to be a high-value player who has played the game at least once in the past two weeks. In this paper, the churn of an active high-value player corresponds to the player being inactive during a specific period. The churn prediction was modeled as a binary classification problem, classifying players according to the categories _churn_ or _no churn_. After the classification, the high-value churning players were divided into three different test groups. In two of the groups, the players were already churning, and one of the groups received a free amount of in-game currency, while the other did not. The last group was composed of players who were still active, but who were showing signs of churning.