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Journal of Revenue and Pricing Management

, Volume 17, Issue 2, pp 78–90 | Cite as

Customized dynamic pricing of airline fare products

Research Article

Abstract

Firms practice dynamic pricing when they charge different customers different prices for the same products, as a function of an observable state of nature. In the airline industry, dynamic pricing has historically been limited by the capabilities of distribution systems, which require filing a finite set of fare products with fixed prices. However, advancements in airline distribution technology will soon allow the generation of “customized offers,” which could include a dynamically generated price. In this paper, we propose a heuristic for customized dynamic pricing of airfares when the observable state of nature includes an observation of passenger characteristics. We first introduce a general model for decoupling the customized offer generation problem from the well-studied airline revenue management problem. After generating a baseline assortment of fare products and observing the characteristics of a passenger’s request, an airline can choose to customize that passenger’s offer by dynamically incrementing or discounting the prices that would ordinarily be offered. The methodology could be applied with existing airline revenue management methods, and would be compatible with IATA’s New Distribution Capability (NDC). For implementation, we propose a straightforward heuristic approach based on simple estimates of passenger willingness-to-pay distributions. The heuristics are simulated in the Passenger Origin–Destination Simulator, a complex airline revenue management simulation environment that takes into account passenger choice and competition. The results show that the heuristics can lead to revenue gains of up to 3–4% when practiced by one airline. Furthermore, the heuristics remain revenue positive in competitive environments.

Keywords

Dynamic pricing Airline distribution New Distribution Capability Airline revenue management Customized offers 

Notes

Acknowledgements

The authors are indebted to the members of the MIT PODS Consortium for financial support and for their invaluable feedback and discussion of previous versions of this paper, and to Craig Hopperstad and Matthew Berge for excellent programming and development assistance. We also thank two anonymous referees who provided constructive and helpful comments.

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Copyright information

© Macmillan Publishers Ltd 2017

Authors and Affiliations

  1. 1.International Center for Air TransportationMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.International Center for Air TransportationMassachusetts Institute of TechnologyCambridgeUSA

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