Dynamic Pricing Under Competition : A Heuristic Approach to Break the Curse of Dimensionality

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 oligopoly competition for the sale of perishable goods. We propose a novel heuristic approach to efficiently compute dynamic pricing strategies by circumventing the curse of dimensionality. To demonstrate that the strategy is applicable even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently adjust their prices. Finally, we show that our heuristic feedback strategy yields excellent results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of price anticipations. We find that adjustment frequencies can easily have a greater impact on expected profits than perfect price anticipations. Our results are illustrated by numerical examples based on real-life data. Data-driven demand estimations, which can be easily applied by practitioners are also provided