English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

As online platforms are striving to get more users, a critical challenge isuser churn, which is especially concerning for new users. In this paper, bytaking the anonymous large-scale real-world data from Snapchat as an example,we develop \textit{ClusChurn}, a systematic two-step framework forinterpretable new user clustering and churn prediction, based on the intuitionthat proper user clustering can help understand and predict user churn.Therefore, \textit{ClusChurn} firstly groups new users into interpretabletypical clusters, based on their activities on the platform and ego-networkstructures. Then we design a novel deep learning pipeline based on LSTM andattention to accurately predict user churn with very limited initial behaviordata, by leveraging the correlations among users' multi-dimensional activitiesand the underlying user types. \textit{ClusChurn} is also able to predict usertypes, which enables rapid reactions to different types of user churn.Extensive data analysis and experiments show that \textit{ClusChurn} providesvaluable insight into user behaviors, and achieves state-of-the-art churnprediction performance. The whole framework is deployed as a data analysispipeline, delivering real-time data analysis and prediction results to multiplerelevant teams for business intelligence uses. It is also general enough to bereadily adopted by any online systems with user behavior data.

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