E-Commerce Business Model Analysis in Urban Area Using Machine Learning

In today's highly competitive B2C e-commerce landscape, companies are in a relentless battle to both retain their existing customers and attract those of competitors. The escalating cost of customer acquisition due to increased market saturation and aggressive marketing tactics necessitates a focus on customer retention. The paper outlines a methodology for predicting customer attrition by leveraging machine learning techniques, specifically Support Vector Machines (SVM). Coupled with a hybrid recommendation approach tailored for retention strategies, this predictive framework helps e-commerce businesses offer targeted incentives to potential churn customers. Our empirical analysis confirms the effectiveness of the integrated model across various performance indicators such as coverage ratio, hit ratio, and precision rate. The paper further employs the RFM (Recency, Frequency, Monetary) model to segment at-risk customers, aiding in the formulation of more personalized churn prevention tactics.