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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
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    References listed on IDEAS

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    3. 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.
    4. Raphael Raymond Bar-On, 1999. "The Measurement of Seasonality and its Economic Impacts," Tourism Economics, , vol. 5(4), pages 437-458, December.
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