A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce
This addresses the challenge of sustainable growth and competitiveness for e-commerce platforms like Udaan in developing nations, though it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of predicting buyer order placement behavior in B2B e-commerce in India to optimize telecaller efficiency, using an ensemble of XGBoost and a modified Poisson Gamma model, resulting in a 3 times increase in customer order rates.
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.