Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments

A retailer following a competition-based dynamic pricing strategy tracks competitors' price changes and then must answer the following questions: (1) Should we respond? (2) If so, respond to whom? (3) How much of a response? (4) And on which products? The answers require unbiased measures of price elasticity as well as accurate estimates of competitor significance and the extent to which consumers compare prices across retailers. There are two key challenges to quantify these factors empirically: first, the endogeneity associated with almost any type of observational data, where prices are correlated with demand shocks observable to pricing managers but not to researchers, and second, the absence of competitor sales information, which prevents efficient estimation of a full consumer-choice model. We address the first issue by conducting a field experiment with randomized prices. We resolve the second issue by exploiting the retailer's own and competitors' stockouts as a source of variation to the consumer choice set, in addition to variations in competitors' prices. We estimate an empirical model capturing consumer choices among substitutable products from multiple retailers. Based on the estimates, we propose a best-response pricing strategy that takes into account consumer choice behavior, competitors' actions, and supply parameters (procurement costs, margin target, and manufacturer price restrictions). We test our algorithm through a carefully controlled live experiment that lasts five weeks. The experiment documents an 11 percent revenue increase while maintaining a margin above a retailer-specified target.