Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity
Human-Computer Interaction
2019-12-30 v2
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.
Keywords
Cite
@article{arxiv.1912.06972,
title = {Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity},
author = {Wanshan Yang and Ting Huang and Junlin Zeng and Lijun Chen and Shivakant Mishra and Youjian and Liu},
journal= {arXiv preprint arXiv:1912.06972},
year = {2019}
}