Churn describes customer defection from a service provider.This can be observed in online freemium games, where users can leave without further notice. Game companies are looking for methods to detect and predict churn to enable management reaction. The recorded data of games can be analyzed for this purpose. We conducted a case study based on data from the freemium game The Settlers Online. Churn detection was achieved by application of four different labeling approaches,based on common churn and disengagement definitions within the game analytics literature. In order to model predictive classifiers,features were computed from the raw game data. Eight different machine learning algorithms returning binary classifications were applied. The results were compared for all algorithms regarding all labeling approaches. Random forests with sliding windows were the best solution in our case, returning AUC values higher than 0.99, thereby enabling prediction accuracies of 97%. The results were confirmed by tests on an independent data set and in our discussion, we offer guidance on the interplay of feature engineering,labeling approaches—in particular disengagement—and machine learning algorithms for churn prediction. Our recommendations are valuable for game companies and academics, who pursue similar studies.

中文翻译：

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基于免费增值在线策略游戏用户活动数据的玩家流失和脱离预测

Churn 描述了客户从服务提供商处叛逃。这可以在在线免费增值游戏中观察到，用户可以在不另行通知的情况下离开。游戏公司正在寻找检测和预测流失的方法，以实现管理层的反应。为此可以分析记录的游戏数据。我们根据免费增值游戏 The Settlers Online 的数据进行了案例研究。基于游戏分析文献中常见的流失和脱离定义，通过应用四种不同的标记方法来实现流失检测。为了对预测分类器进行建模，从原始游戏数据中计算出特征。应用了八种不同的机器学习算法，返回二进制分类。对所有标记方法的所有算法的结果进行了比较。在我们的案例中，带有滑动窗口的随机森林是最好的解决方案，返回的 AUC 值高于 0.99，从而使预测准确率达到 97%。结果通过对独立数据集的测试得到证实，在我们的讨论中，我们提供了关于特征工程、标记方法（尤其是脱离）和用于流失预测的机器学习算法之间相互作用的指导。我们的建议对从事类似研究的游戏公司和学者很有价值。标记方法——尤其是脱离——以及用于流失预测的机器学习算法。我们的建议对从事类似研究的游戏公司和学者很有价值。标记方法——尤其是脱离——以及用于流失预测的机器学习算法。我们的建议对从事类似研究的游戏公司和学者很有价值。