A Prescriptive Analytics Approach to Markdown Pricing for an E-Commerce Retailer
The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically
(ISSN 1558-884X) immediately upon acceptance.
A Prescriptive Analytics Approach to Markdown Pricing for an E-Commerce Retailer
Andrew Vakhutinsky, Kresimir Mihic, Su-Ming Wu
JPRR Vol 14, No 1 (2019); doi:10.13176/11.842 
Download
Andrew Vakhutinsky, Kresimir Mihic, Su-Ming Wu
Abstract
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.
JPRR Vol 14, No 1 (2019); doi:10.13176/11.842 | Full Text  | Share this paper: