Revenue management forecasting: The resiliency of advanced booking methods given dynamic booking windows

Abstract Forecasting is the initial component of the hospitality revenue management (RM) cycle. The accuracy of the forecast is critical for RM systems to make appropriate recommendations to optimize revenue. Over recent years the industry has cited shifting booking windows due to a variety of macro (e.g., technology and economy) and micro (e.g., promotion) factors. These shifts pose challenges for RM forecasting algorithms particularly in the domain of pick-up based techniques. In this paper, we review the literature on hotel RM forecasting, particularly with respect to popular techniques used in practice. We then introduce a neural network approach to the advance booking environment to address issues related to booking window shifts. The models are estimated and tested for accuracy, and then re-tested years later after the booking window has shifted. The results are synthesized with discussion as to which models are more suitable for forecasting in dynamic booking windows.

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