Predicting Customer Churn in World of Warcraft

by   Sulman Khan, et al.

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 predicting whether a customer will churn in six months.



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