Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations

TL;DR

This paper presents a heuristic dynamic pricing strategy for competitive markets with limited demand information, demonstrating its effectiveness through simulations and real-world Amazon data, outperforming traditional rule-based approaches.

Contribution

It introduces a computationally efficient heuristic for dynamic pricing in oligopoly markets, addressing the curse of dimensionality and incorporating frequent repricing to improve profits.

Findings

Heuristic pricing strategy outperforms rule-based strategies by over 20% in profit.

Price adjustment frequency impacts profits more than perfect market anticipation.

Strategy is effective even with many competitors and unknown strategies.

Abstract

Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy's applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller's historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%.