Kris Ferreira is an assistant professor of business administration in the Technology and Operations Management (TOM) Unit. She teaches the Supply Chain Management course in the MBA elective curriculum, the Operations & Supply Chain Management course in the Harvard Business Analytics Program, and in various Executive Education programs. She has previously taught the core TOM course in the first year MBA required curriculum.
In her research, Professor Ferreira focuses on helping e-commerce companies make more profitable revenue-management decisions, including pricing, assortment planning, ranking, etc. For much of this work, she partners with online retailers to design new machine learning and optimization algorithms that use the massive amount of data at their fingertips to improve their decision making in this domain. Most recently, Professor Ferreira has started studying the characteristics that are critical in the success or failure of an analytics tool’s development and implementation. Her work has been awarded first place for the Revenue Management and Pricing Section Practice Award, and two-time finalist for the M&SOM Best Paper Award and Innovative Applications in Analytics Award.
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.
There has been a recent trend by both brick-and-mortar and online retailers to change their assortment of products offered to customers more and more frequently. Several factors likely go into this decision: limited shelf space, enabling trend detection, etc. We introduce and study another important factor – the value of concealment – that should play a role in this decision. Namely, by distributing its seasonal catalog of products over multiple assortments rotated throughout the season – as opposed to selling all products in a single, fixed assortment – a retailer effectively conceals a portion of its full product catalog from consumers, injecting uncertainty into the consumer’s relative product valuations. Consumers may respond by purchasing more products, thereby generating additional sales for the retailer: the value of concealment.
Brick-and-mortar retailers in shopping malls are well aware of the impact of a snazzy window display case, enticing customers walking through the mall to enter the store. Online retailers can rank (sort) their products to have a similar effect: when customers see the first several products in the ranking, they decide whether or not to continue browsing. We design online learning algorithms for product ranking that maximize the number of customers who browse/engage with the site. We develop a class of online learning-then-earning algorithms that prescribe a ranking to offer each customer, learning from preceding customers’ clickstream data to offer better rankings to subsequent customers. Our algorithms balance product popularity with diversity: the notion of appealing to a large variety of heterogeneous customers. We partner with Wayfair and find that our algorithm yields a significant increase (5-30%) in the number of customers that engage with the site.
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.
