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Machine learning approach to market behavior estimation with applications in revenue management

Author

Listed:
  • Nitin Gautam

    (Sabre Airline Solutions)

  • Shriguru Nayak

    (Sabre Airline Solutions)

  • Sergey Shebalov

    (Sabre Airline Solutions)

Abstract

Demand forecasting models used in airline revenue management are primarily based on airline’s own sales data. These models have limited visibility into overall market conditions and competitive landscape. Market factors significantly influence customer behavior and hence should be considered for determining optimal control policy. We discuss data sources available to airlines that provide visibility into the future competitive schedule, market size forecast, and market share estimation. We also describe methodologies based on Machine Learning algorithms that can use to forecast these quantities and explain how they can be leveraged to improve pricing and revenue management practices.

Suggested Citation

  • Nitin Gautam & Shriguru Nayak & Sergey Shebalov, 2021. "Machine learning approach to market behavior estimation with applications in revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 344-350, June.
    • Handle: RePEc:pal:jorapm:v:20:y:2021:i:3:d:10.1057_s41272-021-00317-y
    DOI: 10.1057/s41272-021-00317-y
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    References listed on IDEAS

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    1. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
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    Cited by:

    1. Ezgi C. Eren & Zhaoyang Zhang & Jonas Rauch & Ravi Kumar & Royce Kallesen, 2024. "Revenue management without demand forecasting: a data-driven approach for bid price generation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(6), pages 499-516, December.
    2. Alberto Guerrini & Gabriele Ferri & Stefano Rocchi & Marcelo Cirelli & Vicente Piña & Antoine Grieszmann, 2023. "Personalization @ scale in airlines: combining the power of rich customer data, experiential learning, and revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 171-180, April.

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