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 \textit{ClusChurn}, 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, \textit{ClusChurn} 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. \textit{ClusChurn} is also able to predict user
types, which enables rapid reactions to different types of user churn.
Extensive data analysis and experiments show that \textit{ClusChurn} 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.

利用 Snapchat 的实际数据为例，本文提出了一种名为 ClusChurn 的两阶段系统框架，通过用户聚类来预测用户流失，结果表明该框架在预测用户流失方面取得了最先进的性能。