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A customer type discovery algorithm in hotel revenue management systems

Author

Listed:
  • Milad HajMirzaei

    (Shiraz University)

  • Koorush Ziarati

    (Shiraz University)

  • Alireza Nikseresht

    (Shiraz University)

Abstract

Knowing customer types and their purchase behavior helps revenue management experts to estimate the demand and finally devise a better sales strategy to improve the revenue. Inferring customer types from sales transactions and availability data is a challenging topic in RM. In this paper, we proposed an approach to discover customer types using a classic linear ordering problem. Our linear ordering-based market discovery approach (LMD) comprises three steps: generation of an initial solution, evaluation of the solution by a choice-based model, and finally creation and addition of a new customer type. The number of different customer types is factorial in the number of alternatives and should be pruned. Here, the customer types are pruned based on observed sales and offered-sets, instead of business assumptions or applications. To evaluate the proposed method, a real publicly available dataset of five hotels is used. The results show that LMD outperforms the other available approaches in the literature and improves the log-value results of all datasets by approximately 6%.

Suggested Citation

  • Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 2022. "A customer type discovery algorithm in hotel revenue management systems," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 200-211, April.
  • Handle: RePEc:pal:jorapm:v:21:y:2022:i:2:d:10.1057_s41272-020-00273-z
    DOI: 10.1057/s41272-020-00273-z
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    References listed on IDEAS

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    1. Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 0. "Discovering customer types using sales transactions and product availability data of 5 hotel datasets with genetic algorithm," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 0, pages 1-15.
    2. Guillermo Gallego & Huseyin Topaloglu, 2019. "Revenue Management and Pricing Analytics," International Series in Operations Research and Management Science, Springer, number 978-1-4939-9606-3, April.
    3. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    4. Garrett van Ryzin & Gustavo Vulcano, 2015. "A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models," Management Science, INFORMS, vol. 61(2), pages 281-300, February.
    5. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    6. M. K. Geraghty & Ernest Johnson, 1997. "Revenue Management Saves National Car Rental," Interfaces, INFORMS, vol. 27(1), pages 107-127, February.
    7. Maddah, Bacel & Moussawi-Haidar, Lama & El-Taha, Muhammad & Rida, Hussein, 2010. "Dynamic cruise ship revenue management," European Journal of Operational Research, Elsevier, vol. 207(1), pages 445-455, November.
    8. Sumit Kunnumkal, 2014. "Randomization Approaches for Network Revenue Management with Customer Choice Behavior," Production and Operations Management, Production and Operations Management Society, vol. 23(9), pages 1617-1633, September.
    9. Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 2020. "Discovering customer types using sales transactions and product availability data of 5 hotel datasets with genetic algorithm," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 386-400, December.
    10. Lee, Haengju & Eun, Yongsoon, 2020. "Discovering heterogeneous consumer groups from sales transaction data," European Journal of Operational Research, Elsevier, vol. 280(1), pages 338-350.
    11. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    12. Guerriero, Francesca & Miglionico, Giovanna & Olivito, Filomena, 2014. "Strategic and operational decisions in restaurant revenue management," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1119-1132.
    13. Garrett van Ryzin & Gustavo Vulcano, 2017. "Technical Note—An Expectation-Maximization Method to Estimate a Rank-Based Choice Model of Demand," Operations Research, INFORMS, vol. 65(2), pages 396-407, April.
    14. Chen, Lijian & Homem-de-Mello, Tito, 2010. "Mathematical programming models for revenue management under customer choice," European Journal of Operational Research, Elsevier, vol. 203(2), pages 294-305, June.
    15. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
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