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

Rimo Das (), Harshinder Chadha () and Somnath Banerjee ()
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Rimo Das: LodgIQ, 440 N Wolfe Rd
Harshinder Chadha: LodgIQ, 440 N Wolfe Rd
Somnath Banerjee: LodgIQ, 440 N Wolfe Rd

A chapter in Artificial Intelligence and Machine Learning in the Travel Industry, 2023, pp 145-161 from Springer

Abstract: 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.

Keywords: Revenue management; Forecast; Unconstrained demand; Constrained demand; Market; Machine learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-25456-7_12

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DOI: 10.1007/978-3-031-25456-7_12

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