A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce
Tuhin Subhra De, Pranjal Singh, and Alok Patel
In Proceedings of the 2024 8th International Conference on Machine Learning and Soft Computing, Singapore, Singapore, 2024
In the context of developing nations like India, traditional business-to-business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, e-commerce enterprises frequently employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, showcasing its potential for transformative impact in the e-commerce industry.