# I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application

arXiv: https://arxiv.org/abs/1910.01447 · HTML: https://mlsanity.com/paper/1910.01447/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 10 of 10)

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- [An Estimation of Online Video User Engagement from Features of Continuous Emotions](https://mlsanity.com/paper/2105.01633/) (2021)
- [DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://mlsanity.com/paper/2210.12402/) (2022)
- [TIM: Temporal Interaction Model in Notification System](https://mlsanity.com/paper/2406.07067/) (2024)
- [New User Event Prediction Through the Lens of Causal Inference](https://mlsanity.com/paper/2407.05625/) (2024)
- [Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network](https://mlsanity.com/paper/2103.01300/) (2021)
- [BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models](https://mlsanity.com/paper/2203.16155/) (2022)

## Abstract (arXiv)

As online platforms are striving to get more users, a critical challenge is user churn, which is especially concerning for new users. In this paper, by taking the anonymous large-scale real-world data from Snapchat as an example, we develop textitClusChurn, a systematic two-step framework for interpretable new user clustering and churn prediction, based on the intuition that proper user clustering can help understand and predict user churn. Therefore, textitClusChurn firstly groups new users into interpretable typical clusters, based on their activities on the platform and ego-network structures. Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi-dimensional activities and the underlying user types. textitClusChurn is also able to predict user types, which enables rapid reactions to different types of user churn. Extensive data analysis and experiments show that textitClusChurn provides valuable insight into user behaviors, and achieves state-of-the-art churn prediction performance. The whole framework is deployed as a data analysis pipeline, delivering real-time data analysis and prediction results to multiple relevant teams for business intelligence uses. It is also general enough to be readily adopted by any online systems with user behavior data.