IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v21y2022i2d10.1057_s41272-020-00273-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-020-00273-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-020-00273-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    5. M. K. Geraghty & Ernest Johnson, 1997. "Revenue Management Saves National Car Rental," Interfaces, INFORMS, vol. 27(1), pages 107-127, February.
    6. 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.
    7. 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.
    8. 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.
    9. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    10. 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.
    11. 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.
    12. 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.
    13. 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, September.
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hamed Sherafat Moula & S. Hadi Yaghoubyan & Razieh Malekhosseini & Karamollah Bagherifard, 2023. "Customer type discovery in hotel revenue management by Memetic algorithm," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 470-481, December.
    2. 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.
    3. 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.
    4. Syed Asif Raza & Rafi Ashrafi & Ali Akgunduz, 2020. "A bibliometric analysis of revenue management in airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 436-465, December.
    5. 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.
    6. Qi Feng & J. George Shanthikumar & Mengying Xue, 2022. "Consumer Choice Models and Estimation: A Review and Extension," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 847-867, February.
    7. Sierag, D.D. & Koole, G.M. & van der Mei, R.D. & van der Rest, J.I. & Zwart, B., 2015. "Revenue management under customer choice behaviour with cancellations and overbooking," European Journal of Operational Research, Elsevier, vol. 246(1), pages 170-185.
    8. Barbier, Thibault & Anjos, Miguel F. & Cirinei, Fabien & Savard, Gilles, 2020. "Product-closing approximation for ranking-based choice network revenue management," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1002-1017.
    9. Meissner, Joern & Strauss, Arne, 2012. "Network revenue management with inventory-sensitive bid prices and customer choice," European Journal of Operational Research, Elsevier, vol. 216(2), pages 459-468.
    10. Flores, Alvaro & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2019. "Assortment optimization under the Sequential Multinomial Logit Model," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1052-1064.
    11. Hu, Qiying & Wei, Yihua & Xia, Yusen, 2010. "Revenue management for a supply chain with two streams of customers," European Journal of Operational Research, Elsevier, vol. 200(2), pages 582-598, January.
    12. Kavitha Balaiyan & R. K. Amit & Atul Kumar Malik & Xiaodong Luo & Amit Agarwal, 2019. "Joint forecasting for airline pricing and revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(6), pages 465-482, December.
    13. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    14. 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.
    15. Ødegaard, Fredrik & Wilson, John G., 2016. "Dynamic pricing of primary products and ancillary services," European Journal of Operational Research, Elsevier, vol. 251(2), pages 586-599.
    16. Maclean, K.D.S. & Ødegaard, F., 2020. "Dynamic capacity allocation for group bookings in live entertainment," European Journal of Operational Research, Elsevier, vol. 287(3), pages 975-988.
    17. Ruxian Wang & Ozge Sahin, 2018. "The Impact of Consumer Search Cost on Assortment Planning and Pricing," Management Science, INFORMS, vol. 64(8), pages 3649-3666, August.
    18. Meissner, Joern & Strauss, Arne, 2012. "Improved bid prices for choice-based network revenue management," European Journal of Operational Research, Elsevier, vol. 217(2), pages 417-427.
    19. Xiang Zhao & Xinghua Shan & Jinfei Wu, 2023. "The Impact of Seat Resource Fragmentation on Railway Network Revenue Management," Networks and Spatial Economics, Springer, vol. 23(1), pages 135-177, March.
    20. Kevin R. Williams, 2017. "The Welfare Effects of Dynamic Pricing: Evidence from Airline Markets," Cowles Foundation Discussion Papers 2103R2, Cowles Foundation for Research in Economics, Yale University, revised Jun 2021.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:21:y:2022:i:2:d:10.1057_s41272-020-00273-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.