  World of Warcraft is a massively multiplayer online video game released on
November 23, 2004, by Blizzard Entertainment. In contrast with traditional
games only having a single upfront fee to play, WoW also has a monthly
subscription to play the game. With customer subscriptions in mind, we can
apply the use of churn prediction to not only predict whether a customer will
unsubscribe from the service but explore the user's playing behavior to obtain
more insight into user playing patterns. The churn problem is somewhat complex
due to the nature of not having a one size fits all solution, as different
services define churn in a variety of ways. In this paper, we explore a dataset
that focuses on one year from January 1, 2008, until December 31, 2008, as it
highlights the release of a major content update in the game. Machine learning
is used in two aspects of this paper: Survival Analysis and Binary
Classification. Firstly, we explore the dataset using the Kaplan Meier
estimator to predict the duration until a customer churns, and lastly predict
whether a person will churn in six months using traditional machine learning
algorithms such as Logistic Regression, Support Vector Machine, KNN Classifier,
and Random Forests. From the survival analysis results, WoW customers have a
relatively long duration until churn, which solidifies the addictiveness of the
game. Lastly, the binary classification performed in the best performing
algorithm having a 96% ROC AUC score in predicting whether a customer will
churn in six months.
