A Prescriptive Analytics Approach to Markdown Pricing for a in E-Commerce Retailer

This paper introduces a prescriptive analytics approach to solving markdown-pricing optimization for an e-commerce retailer capable of price differentiation based on customer demand elasticity and the cost of delivery or other services. We consider a situation when the retailer has a limited but potentially large amount of inventory that is stored at multiple fulfillment centers and must be sold by a certain exit date. The objective is to maximize the gross profit, defined as the total revenue minus total shipping cost. We propose a model which predicts, based on historical data, the demand from each customer group as a function of price. Then we formulate the optimization using non-linear objective function and constraints and describe a so-called randomized decomposition approach to finding a near-optimal solution. Finally, we discuss the results of our computational experiments.