Prediction accuracy for reservation-based forecasting methods applied in Revenue Management

• Published in: International journal of hospitality management Vol. 84; p. 102332
• Main Authors: Fiori, Anna Maria, Foroni, Ilaria
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2020
• Subjects: Hotel demand forecasting; Multiplicative model; Pickup forecasting technique; Prediction intervals; Hotel demand forecasting; Prediction intervals; Multiplicative model; Pickup forecasting technique
• ISSN: 0278-4319; 1873-4693
• DOI: 10.1016/j.ijhm.2019.102332

•Interval forecasting of hotel bookings is key to the success of Revenue Management.•We propose a stochastic framework for a class of pickup forecasting methods.•This framework leads to prediction intervals representing possible booking scenarios.•The approach is tested on real reservation data from a luxury hotel in Italy. With a few notable exceptions, airlines and hospitality forecasting research has been focused so far on point predictions of customers’ bookings. However, Revenue Management decisions are subject to a much greater risk when based exclusively on point predictions. To overcome this drawback, we propose a stochastic framework that allows the construction of prediction intervals for reservation-based (pickup) forecasting methods, which are widely used in the industry. Moreover, we introduce an extension of the multiplicative pickup technique based on Generalized Linear Models. We test the proposed framework with real reservation data from a medium-sized hotel on Lake Maggiore (Italy) and we obtain more efficient prediction intervals relative to classical time series methods. Our approach can be useful to hotel revenue managers that wish to make more informed decisions, planning alternative pricing and room allocation strategies for a range of possible demand scenarios.