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Dynamic pricing of ancillaries using machine learning: one step closer to full offer optimization

Author

Listed:
  • MadhuSudan Rao Kummara

    (Etihad Airways)

  • Bhaskara Rao Guntreddy

    (Etihad Airways)

  • Ines Garcia Vega

    (Etihad Airways)

  • Yun Hsuan Tai

    (Etihad Airways)

Abstract

Today airlines’ ancillary pricing decision-making is mostly manual, where prices are generally determined by analysts through competitor benchmarking and historical data analysis. After manual computation, ancillary prices are filed in ATPCO (Airline Tariff Publishing Company) or Merchandising systems and these prices can be further tailored to the characteristics of the ancillary request through merchandising rules. Using airline ancillary and itinerary data, we built a gradient boosting machine algorithm that can understand the intricate relations between numerous attributes such as passenger type, itinerary, aircraft type, ancillary product, or season and can make an automated pricing decision based on science. The analysts are relieved from manual work and have the flexibility to change the machine learning (ML) algorithm’s input and output to suit business strategies. The ML algorithm learns the new trends and patterns as part of its training, and analysts can track its performance periodically. The output of the ML algorithm seamlessly integrates with merchandising platforms to implement the dynamic pricing of ancillaries and offers in the direct, indirect, and channels with new distribution capability. The ML algorithm is extendable to airline and third-party ancillary products and ticket bundles. It can suggest an optimal mix of products and price points that have the highest propensity to purchase for a given customer and travel itinerary.

Suggested Citation

  • MadhuSudan Rao Kummara & Bhaskara Rao Guntreddy & Ines Garcia Vega & Yun Hsuan Tai, 2021. "Dynamic pricing of ancillaries using machine learning: one step closer to full offer optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 646-653, December.
    • Handle: RePEc:pal:jorapm:v:20:y:2021:i:6:d:10.1057_s41272-021-00347-6
    DOI: 10.1057/s41272-021-00347-6
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    References listed on IDEAS

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    1. Thomas Fiig & Remy Guen & Mathilde Gauchet, 2018. "Dynamic pricing of airline offers," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(6), pages 381-393, December.
    2. Ben Vinod & Richard Ratliff & Vikram Jayaram, 2018. "An approach to offer management: maximizing sales with fare products and ancillaries," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(2), pages 91-101, April.
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    Cited by:

    1. Pardo González, Germán & Tabares Pozos, Alejandra & Quiroga, Camilo & à lvarez-Martínez, David, 2024. "Seat assignment recommendation in airlines purchase flow to increase ancillary revenue considering weight and balance constraints," Journal of Air Transport Management, Elsevier, vol. 117(C).
    2. Kevin K. Wang & Michael D. Wittman & Thomas Fiig, 2023. "Dynamic offer creation for airline ancillaries using a Markov chain choice model," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 103-121, April.

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