The number of users attracted and engaged in a system dictates the value of the system itself. In gamification, timely detection of churners can produce more successful applications by informing both designers and algorithms. While churn prediction has been extensively studied in entertainment games, gamified systems often implement simpler mechanics, leading to a limited set of features compared to full-featured games. In this work, we studied whether limited players’ telemetry data describing in-game activity can be used to train a Random Forest model for churn prediction in a gamified application. Specifically, we analyzed different approaches for data preprocessing and sampling. Then, data from an online free-to-play (F2P) game was used as a validation set. Results show how in-game activity can be successfully used to predict churn. Moreover, from the tree’s visualization and interpretation, we found how players’ likelihood of abandoning the game is proportional to their time investment, both in the game and gamified system.

吸引和参与系统的用户数量决定了系统本身的价值。在游戏化中，通过通知设计者和算法，及时发现搅动者可以产生更多成功的应用程序。尽管娱乐游戏中的流失预测已得到广泛研究，但游戏化系统通常采用更简单的机制，与功能齐全的游戏相比，功能有限。在这项工作中，我们研究了描述游戏活动的有限玩家的遥测数据是否可以用于训练随机森林模型，以在游戏化应用中预测用户流失。具体来说，我们分析了数据预处理和采样的不同方法。然后，将在线免费游戏（F2P）中的数据用作验证集。结果显示了如何将游戏中的活动成功地用于预测用户流失。而且，