Art der Publikation: Beitrag in Zeitschrift

Multiunit Dynamic Pricing with Different Types of Observable Customer Information

Autor(en):

Schur, R.

Titel der Zeitschrift:

OR Spectrum

Veröffentlichung:

2024

Ort(e):

Universität Duisburg-Essen

Sprache:

Englisch

Volltext:

Multiunit Dynamic Pricing with Different Types of Observable Customer Information (0.95 MB)

Link zum Volltext:

https://doi.org/10.1007/s00291-024-00759-x

Zitation:

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Kurzfassung

Dynamic Pricing, enabled by technological developments, is gaining more importance in fields beyond the airline industry, including retail, where neglecting multiunit demand leads to suboptimal prices and lost revenues. In these fields, nonlinear pricing is a common static pricing strategy that explicitly takes multiunit demand into account but lacks the possibility to dynamically adapt prices. In this paper, we bring the strengths of both pricing strategies together by combining them to multiunit dynamic pricing. We formulate the corresponding stage-wise optimization problem. To account for customers' preferences regarding batch size, we adapt an adequate customer choice model based on (random) willingness-to-pay. The willingness-to-pay is defined by a combination of customer’s attraction to and consumption of the product. These two aspects of customers’ preferences are private information, but the firm may have (partial) access to the information of the current customer. The firm is monopolistic and can price-discriminate between different order sizes by quoting nonlinear batch prices. This work investigates three cases of what information is observable: attraction to the product, consumption of the product, or both. We solve the resulting optimization model analytically and derive closed-form expressions of the optimal solution in two of the cases. Moreover, we proof the desirable monotonicity in time and capacity is still intact. Building on this monotonicity, we show dynamics of the optimal pricing policy. Finally, we examine the value of information in a numerical study to gain managerial insights regarding the importance of knowing customers’ preferences.