Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity
This work addresses churn prediction for game operators to retain players, but it appears incremental as it builds on existing playtime features.
The paper tackled churn prediction for long-term players in free online games by developing new universal features based on playtime regularity, but no concrete results or numbers are provided in the abstract.
In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime.