Early Churn User Classification in Social Networking Service Using Attention-Based Long Short-Term Memory

Social networking services (SNSs) see much early churn of new users. SNSs can provide effective interventions by identifying potential early churn users and important factors leading to early churn. The long short-term memory (LSTM) model, whose input is the user behavior event sequence binned at constant intervals, is proposed for this purpose. This model better classifies early churn users than previous machine learning models. We hypothesized that the importance of each temporal part in the event sequence is different for classifying early churn users because user behavior is known to consist of coarse and dense parts and initial behavior influences long-term behavior. To treat this, we proposed attention-based LSTM for classifying early churn users. In an experiment conducted on RoomClip, a general SNS, the proposed model achieved higher classification performance compared to baseline models, thus confirming its effectiveness. We also analyzed the importance of each temporal part and each event. We revealed that the initial temporal part and users' actions have high importance for classifying early churn users. These results should contribute to providing effective interventions for preventing early churn.