Dynamic Pricing under Operational Frictions

Abstract

This dissertation investigates the tactical dynamic pricing decisions in industries where sellers sell multiple types of capacity-constrained products/services to their customers. Motivated by operational frictions posed by business considerations, I develop dynamic pricing heuristics that have both provably good revenue performance and nice features which can address these operational frictions. The first essay studies how to do effective dynamic pricing without too many price changes. In practice, many sellers have concerns about dynamic pricing due to the computational complexity of frequent re-optimizations, the negative perception of excessive price adjustments, and the lack of flexibility caused by existing business constraints. To address these concerns, I develop a pricing heuristic which is computationally easy to implement and only needs to adjust a small number of prices and do so infrequently to guarantee a strong revenue performance. In addition, when not all products are equally admissible to price adjustment, my heuristic can replace the price adjustment of some products by their similar products and maintain an equivalent revenue performance. These features allow the sellers to achieve most of the benefit of dynamic pricing with much fewer price changes and provide extra flexibility to manage prices. While the first essay assumes that the sellers know the underlying demand function, this information is sometimes unavailable to the sellers in practice. The second and the third essays study how to jointly learn the demand and dynamically price the products to minimize revenue loss compared to a standard revenue upper bound in the literature. The second essay addresses the parametric case where the seller knows the functional form of the demand but not the parameters; the third essay addresses the nonparametric case where the seller does not even know the functional form of the demand. There is a considerable gap between the revenue loss lower bound under any pricing policy and the performance bound of the best known heuristic in the literature. To close the gap, in my second essay, I propose a heuristic that exactly match the lower bound for the parametric case, and show that under a demand separation condition, a much sharper revenue loss bound can be obtained; in my third essay, I propose a heuristic whose performance is arbitrarily close to the lower bound for the nonparametric case. All the proposed heuristics are computationally very efficient and can be used as a baseline for developing more sophisticated heuristics for large-scale problems.