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Constructing bundled offers for airline customers

Author

Listed:
  • Manini Madireddy

    (Sabre Airline Solutions)

  • Ramasubramanian Sundararajan

    (Sabre Airline Solutions)

  • Goda Doreswamy

    (ANI Technologies Pvt. Ltd.)

  • Meisam Hejazi Nia

    (Staples Innovation Lab (SparX))

  • Amod Mital

    (Sabre Airline Solutions)

Abstract

We consider the problem of product bundling (seats and ancillaries) in the context of offering the right products to airline customers at the right price and in the right manner, so as to best satisfy customer needs and maximize airline revenue. This problem falls on the cusp of airline revenue management (apropos controlling price and availability) and retail e-commerce (apropos bundle design and shopping session management); therefore, we synthesize ideas from both domains to devise a solution framework. Our proposed solution is designed in a modular manner, so as to allow incremental and independent improvements to product design, pricing, and shopping session management. In this paper, we specifically focus on methodologies for offer construction: creating product bundles and estimating willingness to pay. We demonstrate the utility of these methodologies through illustrative results on real and simulated datasets.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorapm:v:16:y:2017:i:6:d:10.1057_s41272-017-0096-y
    DOI: 10.1057/s41272-017-0096-y
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    References listed on IDEAS

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    1. Steven L. Scott, 2010. "A modern Bayesian look at the multi‐armed bandit," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(6), pages 639-658, November.
    2. Shen, Huawei & Cheng, Xueqi & Cai, Kai & Hu, Mao-Bin, 2009. "Detect overlapping and hierarchical community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1706-1712.
    3. 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.
    4. 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.
    5. Ding, Chris & Li, Tao & Peng, Wei, 2008. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3913-3927, April.
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    Citations

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    Cited by:

    1. Stacey Mumbower & Susan Hotle & Laurie A. Garrow, 2023. "Highly debated but still unbundled: The evolution of U.S. airline ancillary products and pricing strategies," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(4), pages 276-293, August.
    2. Kevin K. Wang & Michael D. Wittman & Adam Bockelie, 2021. "Dynamic offer generation in airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 654-668, December.
    3. Daniel Schubert & Christa Sys & Rosário Macário, 2022. "Customized airline offer management: a conceptual architecture," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(5), pages 553-563, October.
    4. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    5. Muzaffer Buyruk & Ertan Güner, 2022. "Personalization in airline revenue management: an overview and future outlook," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 129-139, April.
    6. 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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