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Continuous pricing algorithms for airline RM: revenue gains and competitive impacts

Author

Listed:
  • Bazyli Szymański

    (Massachusetts Institute of Technology)

  • Peter P. Belobaba

    (Massachusetts Institute of Technology)

  • Alexander Papen

    (Massachusetts Institute of Technology
    Amadeus IT Group)

Abstract

Traditionally, airlines have been limited to a set of fixed fare classes and, in turn, price points, to distribute their fare products. The advent of IATA’s new distribution capability (NDC) will soon enable airlines to quote any fare from a continuous range. In theory, such continuous pricing could increase revenues by extracting more of the consumer surplus, through its ability to offer more granular fares, closer to the customer’s willingness-to-pay (WTP). In this article, we describe several algorithms that lead to the quotation of a single fare from a continuous range. These algorithms either rely on traditional fare classes for the purpose of forecasting and optimization (class-based), or completely abandon the notion of fare classes, instead assuming different WTP distributions within each booking period prior to departure (classless). We describe how these algorithms build upon and differ from their traditional RM counterparts. Performance of these heuristics is then benchmarked against traditional class-based RM, and competitive impacts are analyzed when continuous pricing is adopted by one airline asymmetrically or both airlines symmetrically in a hypothetical 2-carrier network in the passenger origin–destination simulator (PODS). We find that continuous pricing is generally revenue-positive, and the revenue gains can be as high as 2.0% for the first-mover and reach up to 1.2% when both airlines adopt the new method. In addition, we show that these gains depend on the number of fare classes in the traditional fare structure used as a baseline, and that they are smaller under lower demand-to-capacity ratios.

Suggested Citation

  • Bazyli Szymański & Peter P. Belobaba & Alexander Papen, 2021. "Continuous pricing algorithms for airline RM: revenue gains and competitive impacts," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 669-688, December.
  • Handle: RePEc:pal:jorapm:v:20:y:2021:i:6:d:10.1057_s41272-021-00350-x
    DOI: 10.1057/s41272-021-00350-x
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    References listed on IDEAS

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    1. Guillermo Gallego & Garrett van Ryzin, 1997. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management," Operations Research, INFORMS, vol. 45(1), pages 24-41, February.
    2. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    3. Ming Chen & Zhi-Long Chen, 2015. "Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information," Production and Operations Management, Production and Operations Management Society, vol. 24(5), pages 704-731, May.
    4. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    5. Michael D. Wittman & Peter P. Belobaba, 2019. "Dynamic pricing mechanisms for the airline industry: a definitional framework," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(2), pages 100-106, April.
    6. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
    7. Dan Zhang & Zhaosong Lu, 2013. "Assessing the Value of Dynamic Pricing in Network Revenue Management," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 102-115, February.
    8. Thomas Fiig & Oriana Goyons & Robin Adelving & Barry Smith, 2016. "Dynamic pricing – The next revolution in RM?," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 360-379, October.
    9. Peter P. Belobaba, 1989. "OR Practice—Application of a Probabilistic Decision Model to Airline Seat Inventory Control," Operations Research, INFORMS, vol. 37(2), pages 183-197, April.
    10. Conrad J. Lautenbacher & Shaler Stidham, 1999. "The Underlying Markov Decision Process in the Single-Leg Airline Yield-Management Problem," Transportation Science, INFORMS, vol. 33(2), pages 136-146, May.
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    Cited by:

    1. Resul Aydemir & Mehmet Melih Değirmenci & Abdullah Bilgin, 2023. "Estimation of passenger sell-up rates in airline revenue management by considering the effect of fare class availability," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 501-513, December.
    2. Yanbin Long & Peter Belobaba, 2024. "Airline revenue management with segmented continuous pricing: methods and competitive effects," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(1), pages 14-27, February.

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