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.

翻译：在印度等发展中国家的背景下，传统的企业对企业（B2B）商务高度依赖于买卖双方之间建立牢固的关系、信任和信贷安排。因此，电子商务企业经常面临挑战。成立于2016年，旨在通过技术革新印度贸易，Udaan是该国最大的B2B电子商务平台。Udaan运营涵盖多种产品类别，包括生活方式、电子产品、家居等，并雇佣电话销售员来培养买家关系、简化订单流程以及推广特别促销活动。准确预测买家的下单行为成为实现可持续增长、提升竞争力及优化这些电话销售员效率的关键因素。为了应对这一挑战，我们采用了一种集成方法，结合XGBoost和改进的泊松伽马模型来精确预测客户订单模式。本文深入探讨了机器学习与经验贝叶斯方法的战略融合，并辅以对相关特征的审慎选择。这一创新方法使客户下单率提高了3倍，展示了其在电子商务行业中产生变革性影响的潜力。