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Personalization @ scale in airlines: combining the power of rich customer data, experiential learning, and revenue management

Author

Listed:
  • Alberto Guerrini

    (Boston Consulting Group)

  • Gabriele Ferri

    (Boston Consulting Group)

  • Stefano Rocchi

    (Boston Consulting Group)

  • Marcelo Cirelli

    (Boston Consulting Group)

  • Vicente Piña

    (Boston Consulting Group)

  • Antoine Grieszmann

    (Boston Consulting Group)

Abstract

Recently, several macro trends have converged to provide airlines new opportunities for one-to-one digital customer engagement and personalization. Airlines have more types and volumes of data available than ever before: shopping-behavior data, data providing context on booking decisions, social media data enriching the information available on travel trends, and more. All of these can play a critical role in defining the right offers and setting the right prices for each shopping request. A plethora of advanced AI and ML techniques have become available on open-source platforms, letting players generate actionable customer insights and leverage vast amounts of existing data. New distribution technology is being deployed to allow airlines to implement real-time retailing capabilities. Consumers have been trained by the likes of Amazon, Netflix, Alibaba, and Starbucks to expect products and services tailored to their individual needs along with superior and engaging content. This paper presents different approaches to price-product personalization that have been tested in airline cases globally. It also explores how the concept of experiential learning is nicely suited to tackling scenarios in which the purchaser is well-identified as well as cases in which not much is known about the visitor except the context of the shopping session.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorapm:v:22:y:2023:i:2:d:10.1057_s41272-022-00404-8
    DOI: 10.1057/s41272-022-00404-8
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    References listed on IDEAS

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    1. 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.
    2. B. Vinod, 2021. "Artificial Intelligence in travel," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 368-375, June.
    3. Ben Vinod, 2021. "Artificial Intelligence and Emerging Technologies in Travel," Management for Professionals, in: The Evolution of Yield Management in the Airline Industry, edition 1, chapter 11, pages 313-337, Springer.
    4. B. Vinod, 2021. "The age of intelligent retailing: personalized offers in travel for a segment of ONE," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(4), pages 473-479, August.
    Full references (including those not matched with items on IDEAS)

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