Decoding E-commerce Customer Churn: Harnessing Data Science to Combat Negative Experiences

Abstract

The dynamic landscape of e-commerce presents both opportunities and challenges, with customer churn emerging as a critical issue impacting business sustainability. This paper explores the application of data science techniques to decode and mitigate customer churn, focusing on the identification and analysis of negative experiences that drive customers away. By leveraging advanced analytics, machine learning algorithms, and big data, we develop predictive models to pinpoint at-risk customers and uncover key churn indicators. Our findings demonstrate the effectiveness of data-driven strategies in pre-emptively addressing customer dissatisfaction, thereby enhancing retention rates. The study provides actionable insights for e-commerce businesses aiming to foster long-term customer loyalty and improve overall customer satisfaction through the strategic use of data science