Diagnosis and Management of Customer Churn in Social Commerce Scenarios

Social commerce is an emerging e-commerce paradigm that integrates social networks and online transactions. Customer churn is one of the important issues faced by social e-commerce. Traditional e-commerce customer churn diagnosis focuses on mining and modeling transactional data, which makes it difficult to fully depict the driving factors and evolution patterns of customer churn in social commerce scenarios. In view of this, this study systematically combs the reasons of customer churn in social e-commerce, and develops a logistic regression-based customer churn diagnosis model incorporating multidimensional features from customer transactions, social interactions, and review behaviors. The proposed model enables the identification of customer churn status and further recognizes seven types of churned customers, offering targeted retention strategies.This study enriches the theories and methods of customer churn prediction, addresses the limitations of traditional e-commerce customer churn diagnosis models, and enhances the accuracy and robustness of churn diagnosis in social commerce. Furthermore, the findings provide guidance for customer relationship management and operational practices in social commerce platforms.