Title: Instrumenting While Experimenting: An Empirical Method for Competitive Pricing at Scale
 
Abstract: We partner with a leading U.S. e-commerce retailer and develop a competitive pricing method in the context of increasing competition in online retailing. Our method allows retailers to more accurately respond to competitors' price changes at scale. First, we construct a parsimonious demand model that captures the key trade-off in competitive pricing by accounting for two types of customers heterogeneous in their "price-shopping'" behavior. Next, we design and implement a large-scale randomized price experiment on over 10,000 products. Leveraging the experiment as well as the control function approach, we are able to obtain unbiased estimates of the demand model, in particular, price elasticities of both loyal and price-shopping consumers as well as the sales lift when we undercut competitors in price. Lastly, we recommend price responses by solving a constrained optimization problem which uses the estimated demand model as an input. We test this pricing method through another large-scale controlled field experiment on over 10,000 products and demonstrate significant improvements—increasing revenue by over 15% and increasing profit by over 10%.
 
Bio:  Zhaohui (Zoey) Jiang is an assistant professor of Business Technologies at Carnegie Mellon University, Tepper School of Business. Her research interests are in new technologies, online marketplaces, and retailing. The goal of her research is to improve business decisions and policies by incorporating data-driven insights about individual-level behavior. Methodologically, Zoey applies structural empirical models, conducts causal inference analyses, analyzes game theoretical models, and combines machine learning methods. She received Ph.D. from the Ross School of Business, University of Michigan and received B.A. in Economics and B.S. in Statistics from Peking University.