Method and a system for optimizing e-commerce markdown price based on contextual bandit technique

Abstract

This disclosure relates generally to optimizing markdown price and, more particularly, to a method and a system for optimizing E-commerce markdown price based on contextual bandit technique. E-commerce and retail industries employ several strategies to boost business, of which markdown pricing is popular. The online markdown pricing problem is particularly challenging due to the high variability in demand. The existing state-of-art CB based techniques to optimize the markdown price, are designed to either clear off maximum inventory or as a revenue maximization problem and do not explicitly consider contextual features. The disclosed techniques optimize E-commerce markdown price based on contextual bandit technique focusing on both margin optimization and inventory reduction, while considering contextual features by employing a suite of Contextual Bandit (CB) algorithms, including LinUCB, Mini-monster in Vowpal Wabbit (VW), Contextual Thompson Sampling (CTS), and Bayes Upper Confidence Bounds (UCB), which tackle the dynamic nature of e-commerce.