Kris Ferreira is an assistant professor of business administration in the Technology and Operations Management (TOM) Unit and teaches the TOM course in the MBA required curriculum.
In her research, Professor Ferreira focuses on online markets and retail, seeking to build operations management models that inform practice, as well as to provide theoretical insights. In particular, she employs a combination of machine learning and optimization techniques to help companies use their data to make better tactical and strategic decisions. Her work with the online retailer Rue La La received the 2014 INFORMS Revenue Management and Pricing Section Practice Award and was named a finalist in the 2015 Innovative Applications in Analytics competition.
Professor Ferreira earned her PhD in operations research at the Massachusetts Institute of Technology and her BS in industrial and systems engineering at the Georgia Institute of Technology. Before entering graduate school, she was a supply chain consultant for Alvarez & Marsal and a project manager for UPS Supply Chain Solutions.
For many fashion retailers, prices of new products are typically based on some combination of merchants’ intuition, competitors' pricing, and percentage markup on cost; given the difficulty of forecasting demand for fashion products, though, these approaches often lead to either stock-outs early in the season or large amounts of unsold inventory at the end of the season. We worked in partnership with the online flash sales retailer Rue La La to explore a data-driven approach to pricing new fashion products, developing machine learning and optimization algorithms that use Rue La La’s abundance of data to better predict demand and set prices. We conducted a five-month field experiment to test the impact of our algorithms’ price recommendations; results show an increase in revenue by approximately 10% with minimal impact on cost.
Online retailers have several advantages over traditional brick-and-mortar retailers; for example, online retailers have the ability to view real-time consumer purchase decisions, and they can typically change prices frequently at negligible cost. Given these capabilities, we develop a novel machine learning algorithm that changes product prices over the course of a selling season in order to maximize total revenue.
Since the retailer does not know consumer purchase probabilities at different prices, they face a tradeoff commonly referred to as the exploration-exploitation tradeoff. Towards the beginning of the selling season, the retailer may offer several different prices to try to learn the purchase probability at each price (“exploration” objective). Over time, the retailer can use estimates of these purchase probabilities to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). To further complicate matters, the retailer is constrained by limited inventory and thus pursuing the exploration objective comes at the cost of diminishing valuable inventory. Our algorithm addresses these challenges and optimally sets prices to balance exploration and exploitation in order to maximize revenue.
The Digital Initiative is a cross-unit venture that unites scholars and practitioners to explore and impact the transformation of business in today’s digital, networked, and media-rich environment.
