Customer Churn Prevention For E-Commerce Platforms Using Machine Learning-Based Business Intelligence

Abstract

Businesses in the E-Commerce sector, especially those in the business-to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. The main objective of this research is to detect probable churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-Commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle.