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Discovering patterns in traveler behaviour using segmentation

Author

Listed:
  • Aditya Kothari

    (Ather Energy)

  • Manini Madireddy

    (Sabre Airline Solutions)

  • Ramasubramanian Sundararajan

    (Sabre Airline Solutions)

Abstract

We consider the problem of finding common behavioral patterns among travelers in an airline network through the process of clustering. Travelers can be characterized at relational or transactional level. In this article, we focus on the transactional level characterization; our unit of analysis is a single trip, rather than a customer relationship comprising multiple trips. We begin by characterizing a trip in terms of a number of features that pertain to the booking and travel behavior. Trips thus characterized are then grouped using an ensemble clustering algorithm that aims to find stable clusters as well as discover subgroup structures within groups. A multidimensional analysis of trips based on these groupings leads us to discover non-trivial patterns in traveler behaviour that can then be exploited for better revenue management.

Suggested Citation

  • Aditya Kothari & Manini Madireddy & Ramasubramanian Sundararajan, 2016. "Discovering patterns in traveler behaviour using segmentation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 334-351, October.
    • Handle: RePEc:pal:jorapm:v:15:y:2016:i:5:d:10.1057_rpm.2016.33
    DOI: 10.1057/rpm.2016.33
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    References listed on IDEAS

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    1. Teichert, Thorsten & Shehu, Edlira & von Wartburg, Iwan, 2008. "Customer segmentation revisited: The case of the airline industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 227-242, January.
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    Cited by:

    1. Grażyna Rosa, 2021. "Passenger Preferences in Rail Transport in Poland as Regards Travelling Time and Cost," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    2. Manini Madireddy & Ramasubramanian Sundararajan & Goda Doreswamy & Meisam Hejazi Nia & Amod Mital, 2017. "Constructing bundled offers for airline customers," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(6), pages 532-552, December.
    3. B. Vinod, 2021. "Artificial Intelligence in travel," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 368-375, June.
    4. Michael D. Wittman & Peter P. Belobaba, 2018. "Customized dynamic pricing of airline fare products," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(2), pages 78-90, April.

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