In this talk, I will present a series of theoretical and applied work in competitive pricing. A
In this talk, I will present a series of theoretical and applied work in competitive pricing. A retailer following a competition-based dynamic-pricing strategy tracks competitors’ price changes and then must decide whether and how to respond. The answers require modeling of consumer decisions, unbiased measures of self- and cross-price elasticity as well as competitor impacts. I will discuss how we achieve them through a combination of consumer modeling, experimentation, causal inference, as well as high-dimensional statistics. I will highlight two implementations that each led to 10-20 percent revenue and profit improvement through close collaborations with leading US and international retailers, and a theoretical development that scales up choice models through high dimensional regularization.
Meeting Link: https://utsa.zoom.us/j/92171200398
Speaker Biography:Jun Li is an Associate Professor of Technology and Operations at Stephen M. Ross School of Business, University of Michigan. She conducts research in empirical operations management and business analytics spanning areas across revenue management and pricing, healthcare management, supply chain risks, and public sector operations as well as improving the wellbeing of children and young adults. She won the 2015 INFORMS Revenue Management and Pricing Practice Award, the 2020 INFORMS Revenue Management and Pricing Section Award, the 2022 MSOM Young Scholar Prize, the Management Science Best Publication award and the Responsible Operations Management Best Publication award, etc. She serves as Associate Editors at Management Science, Manufacturing and Service Operations Management, Operations Research and Production and Operations Management. She holds a Ph.D. in Managerial Economics and Management Science from the Wharton School, University of Pennsylvania, and a Bachelor in Operations Research and Industrial Engineering from Tsinghua University.
(Friday) 2:00 pm - 3:00 pm
Department of Management Science and Statistics