Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games
This work addresses churn prediction for game developers to retain players and increase revenue, but it is incremental as it builds on existing methods by combining data types.
The paper tackled the problem of predicting player churn in casual freemium games by combining sequential and aggregated data using neural networks, resulting in improved prediction accuracy compared to using either data type alone.
In freemium games, the revenue from a player comes from the in-app purchases made and the advertisement to which that player is exposed. The longer a player is playing the game, the higher will be the chances that he or she will generate a revenue within the game. Within this scenario, it is extremely important to be able to detect promptly when a player is about to quit playing (churn) in order to react and attempt to retain the player within the game, thus prolonging his or her game lifetime. In this article we investigate how to improve the current state-of-the-art in churn prediction by combining sequential and aggregate data using different neural network architectures. The results of the comparative analysis show that the combination of the two data types grants an improvement in the prediction accuracy over predictors based on either purely sequential or purely aggregated data.