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Application of online booking data to hotel revenue management

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  • Saito, Taiga
  • Takahashi, Akihiko
  • Koide, Noriaki
  • Ichifuji, Yu

Abstract

This paper presents an application of online booking data, comprised of big data crawled from a hotel booking website to hotel revenue management. It is important to build a quantitative revenue management method for online hotel booking systems incorporating overbooking strategies, because of increasing numbers of bookings through online booking websites and last-minute cancellations, which cause serious damage to hotel management. We construct a quantitative overbooking model for online booking systems combined with customers’ choice behaviors estimated from the data. Firstly, we present the overbooking model for online booking systems. Secondly, we estimate the choice behaviors of the customers from the online booking data by a discrete choice model. Thirdly, combining the estimated discrete choice model with the theoretical overbooking model, we investigate the expected sales maximization problem where we numerically solve the optimal overbooking level and room charge. Finally, we provide numerical examples of the optimal overbooking strategies and room charges using online booking data of two major luxury hotels in Shinjuku ward, Tokyo. This method, which utilizes online booking data available by crawling from booking websites, helps hotels obtain an optimal room charge and overbooking level maximizing the expected sales.

Suggested Citation

  • Saito, Taiga & Takahashi, Akihiko & Koide, Noriaki & Ichifuji, Yu, 2019. "Application of online booking data to hotel revenue management," International Journal of Information Management, Elsevier, vol. 46(C), pages 37-53.
    • Handle: RePEc:eee:ininma:v:46:y:2019:i:c:p:37-53
    DOI: 10.1016/j.ijinfomgt.2018.11.003
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    3. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. 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.

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