# Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games

arXiv: https://arxiv.org/abs/2209.03184 · HTML: https://mlsanity.com/paper/2209.03184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles](https://mlsanity.com/paper/1710.02264/) (2017)
- [Combining Static and Dynamic Features for Multivariate Sequence Classification](https://mlsanity.com/paper/1712.08160/) (2017)
- [Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data](https://mlsanity.com/paper/1802.02301/) (2018)
- [A Recurrent Neural Network Survival Model: Predicting Web User Return Time](https://mlsanity.com/paper/1807.04098/) (2018)

## Built on by (most cited first, 1 of 1)

- [Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data](https://mlsanity.com/paper/2312.01301/) (2023)

## Abstract (arXiv)

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.