Dynamic Pricing Under Competition: A Data-Driven Approach

Abstract

Most sales applications are characterized by competitive settings and limited demand information. Due to the complexity of such markets, smart pricing strategies are hard to derive. We analyze stochastic dynamic pricing models under competition for the sale of durable goods. In a first step, a data-driven approach is used to estimate sales probabilities in competitive markets. In a second step, we use a dynamic model to compute powerful feedback pricing strategies efficiently, which are even applicable if the number of competitors is large and their strategies are unknown. In markets with sticky prices, our strategy performs close to optimal. In the case of liquid markets, in which competitors frequently adjust their prices, we verify that our heuristic feedback strategy also yields excellent results. To be able to compare expected profits, we compute optimal response strategies in a duopoly market where the competitor's price adjustments can be anticipated. We also show that the lack of information can be (over)compensated by adjusting prices slightly more frequently than the competitor does.