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A heuristic for incorporating ancillaries into air choice models with personalization (Part 1: estimating preferences using hedonic regression)

Author

Listed:
  • Michal Sznajder

    (Sabre Research)

  • Richard Ratliff

    (Sabre Research)

  • Cuneyd Kaya

    (Sabre Research)

Abstract

In recent years, airlines have begun selling “branded fares” which are bundles of a travel “right-to-fly” plus assorted amenities (e.g., first bag included, extra legroom, priority boarding, extra loyalty miles, etc.). Branded fares allow airlines to further differentiate their products and have proven popular with customers. However, the explanatory features considered in previous air choice models in use at Sabre were limited to price and itinerary schedule attributes only; they did not consider the value-added utility of the new amenities included in airline branded fares. This paper is the first of a two-part series about incorporating ancillaries into air choice models. In Part 1, the authors describe a hedonic regression (HR) heuristic to measure the additional customer value-added utility from bundling ancillary amenities into airline branded fares. We found the estimated market pricing from HR models to be useful in determining travelers’ valuation and preferences across different categories of popular air ancillaries. Furthermore, we will show how to extend this approach to include personalization by customer segment.

Suggested Citation

  • Michal Sznajder & Richard Ratliff & Cuneyd Kaya, 2023. "A heuristic for incorporating ancillaries into air choice models with personalization (Part 1: estimating preferences using hedonic regression)," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 122-139, April.
    • Handle: RePEc:pal:jorapm:v:22:y:2023:i:2:d:10.1057_s41272-022-00399-2
    DOI: 10.1057/s41272-022-00399-2
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    References listed on IDEAS

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    1. Diana, Tony, 2010. "`Discrete Choice Modelling and Air Travel Demand: Theory and Applications` by Laurie A. Garrow," Journal of Airport Management, Henry Stewart Publications, vol. 5(1), pages 88-89, September.
    2. Warnock-Smith, David & O'Connell, John F. & Maleki, Mahnaz, 2017. "An analysis of ongoing trends in airline ancillary revenues," Journal of Air Transport Management, Elsevier, vol. 64(PA), pages 42-54.
    3. 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.
    4. Octavian Oancea, 2021. "The implications of behavioural economics for pricing in a world of offer optimisation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 626-633, December.
    5. Rodrigo Acuna-Agost & Eoin Thomas & Alix Lhéritier, 2021. "Price elasticity estimation for deep learning-based choice models: an application to air itinerary choices," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 213-226, June.
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