IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v20y2021i3d10.1057_s41272-021-00318-x.html
   My bibliography  Save this article

Multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics

Author

Listed:
  • Rimo Das

    (LodgIQ)

  • Harshinder Chadha

    (LodgIQ)

  • Somnath Banerjee

    (LodgIQ)

Abstract

With the rising wave of travelers and changing market landscape, understanding marketplace dynamics in commoditized accommodations in the hotel industry has never been more important. In this research, a machine learning approach is applied to build a framework that can forecast the unconstrained and constrained market demand (aggregated and segmented) by leveraging data from disparate sources. Several machine learning algorithms are explored to learn traveler’s booking patterns and the latent progression of the booking curve. This solution can be leveraged by independent hoteliers in their revenue management strategy by comparing their behavior to the market.

Suggested Citation

  • Rimo Das & Harshinder Chadha & Somnath Banerjee, 2021. "Multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 351-367, June.
  • Handle: RePEc:pal:jorapm:v:20:y:2021:i:3:d:10.1057_s41272-021-00318-x
    DOI: 10.1057/s41272-021-00318-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-021-00318-x
    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/s41272-021-00318-x?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. Raphael Raymond Bar-On, 1999. "The Measurement of Seasonality and its Economic Impacts," Tourism Economics, , vol. 5(4), pages 437-458, December.
    2. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    3. S E Kimes, 1999. "Group forecasting accuracy in hotels," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1104-1110, November.
    4. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2006. "Models of the Spiral-Down Effect in Revenue Management," Operations Research, INFORMS, vol. 54(5), pages 968-987, October.
    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. Apostolos Ampountolas, 2019. "Forecasting hotel demand uncertainty using time series Bayesian VAR models," Tourism Economics, , vol. 25(5), pages 734-756, August.
    2. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
    3. Octavian Oancea, 2016. "Analytical framework for airline revenue management and network planning," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(1), pages 2-19, February.
    4. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    5. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2015. "Learning and Pricing with Models That Do Not Explicitly Incorporate Competition," Operations Research, INFORMS, vol. 63(1), pages 86-103, February.
    6. Soonhui Lee & Tito Homem-de-Mello & Anton Kleywegt, 2012. "Newsvendor-type models with decision-dependent uncertainty," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 76(2), pages 189-221, October.
    7. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    8. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.
    9. Hanyuan Zhang & Jiangping Lu, 2022. "Forecasting hotel room demand amid COVID-19," Tourism Economics, , vol. 28(1), pages 200-221, February.
    10. Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    11. Yunmei Bai & Chun-Hung Tang, 2021. "Determining the optimal group rate in the hotel industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(4), pages 446-456, August.
    12. E. Andrew Boyd & Ioana C. Bilegan, 2003. "Revenue Management and E-Commerce," Management Science, INFORMS, vol. 49(10), pages 1363-1386, October.
    13. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    14. Valerio Lacagnina & Davide Provenzano, 2016. "An integrated fuzzy-stochastic model for revenue management," Tourism Economics, , vol. 22(4), pages 779-792, August.
    15. Cuccia, Tiziana & Rizzo, Ilde, 2011. "Tourism seasonality in cultural destinations: Empirical evidence from Sicily," Tourism Management, Elsevier, vol. 32(3), pages 589-595.
    16. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960, July.
    17. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    18. Breffni M Noone, 2016. "Pricing for hotel revenue management: Evolution in an era of price transparency," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 264-269, July.
    19. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    20. Anna Maria Fiori & Ilaria Foroni, 2019. "Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability," Sustainability, MDPI, vol. 11(5), pages 1-24, February.

    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:20:y:2021:i:3:d:10.1057_s41272-021-00318-x. 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.