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.
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- Churn prediction
- Cost-benefit analysis
- Customer lifetime value
- Data mining
- Game analytics
ASJC Scopus subject areas
- Artificial Intelligence
- Electrical and Electronic Engineering
- Control and Systems Engineering