To successfully operate online games, gaming companies are introducing the systematic customer relationship management model. Particularly, churn analysis is one of the most important issues, because preventing a customer from churning is often more cost-efficient than acquiring a new customer. Churn prediction models should, thus, consider maximizing not only accuracy but also the expected profit derived from the churn prevention. We, thus, propose a churn prediction method for optimizing profit consisting of two main steps: first, selecting prediction target, second, tuning threshold of the model. In online games, the distribution of a user's customer lifetime value is very biased that a few users contribute to most of the sales, and most of the churners are no-paying users. Consequently, it is cost-effective to focus on churn prediction to loyal customers who have sufficient benefits. Furthermore, it is more profitable to adjust the threshold of the prediction model so that the expected profit is maximized rather than maximizing the accuracy. We applied the proposed method to real-world online game service, Aion, one of the most popular online games in South Korea, and then show that our method has more cost-effectiveness than the prediction model for total users when the campaign cost and the conversion rate are considered.

中文翻译：

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在线游戏中长期忠诚客户的利润优化流失预测

为了成功运营网络游戏，游戏公司正在引入系统的客户关系管理模式。特别是，流失分析是最重要的问题之一，因为防止客户流失通常比获得新客户更具成本效益。因此，流失预测模型不仅要考虑最大化准确性，还要考虑最大化从流失预防中获得的预期利润。因此，我们提出了一种优化利润的流失预测方法，包括两个主要步骤：第一，选择预测目标，第二，模型的调整阈值。在网络游戏中，用户的客户终生价值分布非常偏，少数用户贡献了大部分销售额，而流失的大部分是免费用户。最后，专注于对拥有足够收益的忠诚客户进行流失预测是具有成本效益的。此外，调整预测模型的阈值使预期利润最大化而不是准确性最大化更有利可图。我们将所提出的方法应用于现实世界的在线游戏服务 Aion，它是韩国最受欢迎的在线游戏之一，然后表明我们的方法比针对总用户的预测模型具有更高的成本效益，当活动成本和考虑转化率。