Optimal Pricing and Demand Learning With Strategic Customers

We study the optimal pricing of a finite quantity of seasonal goods in the presence of forward-looking strategic customers. The arrival rate of customers is unknown, and we assume the retailer and customers have the same initial belief about the arrival rate based on the information from previous performances. We distinguish between two pricing strategies: 1) inventory contingent dynamic pricing with demand learning and 2) announced pricing at the beginning of the sale period. We conducted a numerical study to explore the impact of strategic consumers on pricing policies and expected revenue performance. We find inventory contingent dynamic pricing with demand learning outperforms in some situations