Customer-merchant shopping behavior modeling in e-commerce

The rapid advancement of e-commerce has necessitated the adoption of sophisticated analytical models to decipher customer behaviors and evaluate seller performance. In this study, we propose a novel 'Customer-Seller Shopping Behavior Model' that integrates customer preferences, purchasing patterns, and seller product offerings. The model utilizes customer metrics such as RFM (Recency, Frequency, Monetary), category preferences, and customer lifetime value, alongside seller data on category focus and sales performance, to facilitate segmentation and personalized recommendations. Employing K-Means, hierarchical clustering, and decision tree algorithms, customers and sellers are categorized based on their behavioral similarities. Furthermore, predictive models and hybrid recommendation systems are implemented to forecast customer purchasing propensities and match them with suitable sellers. The results demonstrate that the proposed model significantly enhances customer engagement, seller effectiveness, and overall platform efficiency. This research contributes to a data-driven, scalable, and dynamic analytical framework for e-commerce platforms.