A Machine learning and Empirical Bayesian Approach for Predictive Buying
  in B2B E-commerce
Tuhin Subhra De Pranjal Singh Alok Patel
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, ecommerce enterprises frequently. Established in 2016 with a
vision to revolutionize trade in India through technology, Udaan is the
countrys largest business to business ecommerce platform. Udaan operates across
diverse product categories, including lifestyle, electronics, home and 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, show casing its potential for transformative impact in the
ecommerce industry.