Assistant Professor of Business Administration at Harvard Business School
Kris Ferreira is an Assistant Professor of Business Administration in the Technology and Operations Management (TOM) Unit at Harvard Business School. Much of her research is in partnership with online retailers, where she employs a combination of machine learning and optimization techniques to help them make better revenue management decisions. Her work has been awarded first place for the Revenue Management and Pricing Section Practice Award, and finalist for the M&SOM Best Paper Award and Innovative Applications in Analytics Award. Prior to joining HBS, she earned her PhD in Operations Research from MIT and worked as a supply chain consultant for Alvarez & Marsal.
Track: Supply Chain
Monday, April 15, 9:10–10:00am
Dynamic Pricing for Varying Assortments
Most demand learning and price optimization approaches in academia and practice rely on learning the demand of each product in an assortment over time via price experimentation. Although these approaches may work well when the retailer offers a static assortment, the approaches fail to learn demand and optimally price when retailers change their assortment frequently. With the growth of e-commerce as well as fast fashion business models, retailers are changing their assortments more frequently. In this research, we develop a demand learning and price optimization approach for retailers whose assortments change frequently. Our approach can be described as a “learning-then-earning” approach that uses conjoint analysis and optimal experimental design to learn attribute-based demand, and subsequently uses this information to price optimally. We test our algorithm in a field experiment at an e-commerce company, which demonstrates that our algorithm quickly learns demand and sets prices that significantly increase revenue.