Cross-Industry Statistical Law: Average Booking Curves Draw Exponential Functions

Abstract

The booking curve time series in perishable asset industries, including hotels, has been studied as a means for daily forecasting in revenue management (RM). Such studies have developed many sophisticated forecasting algorithms for RM practitioners. However, based on the opinion that timing is the key element in pricing, RM professionals have faced challenges in understanding people's booking window shift, which represents macroscopic changes in booking curves due to changing times, e.g., economy and technology. We investigate macroscopic aspects of booking curves with actual sales data across six properties in the hotel and car-rental industries for two years, considering the difference in the economic environment characterized before, middle, and after the COVID-19 epidemic. We explain a new cross-industry and cross-economic-environment universal statistical law: average booking curves draw exponential functions (the ABCDEF law). We provide a basis for the ABCDEF law from three perspectives; data confirmation, modeling in the statistical physics framework, and empirical justification for the causality of the model. The ABCDEF law provides a booking curve with its usefulness besides daily forecasting in RM; it is expected to offer informative statistics about people's booking patterns in the property and to support various industries' RM practitioners in deciding on sales strategies at an appropriate time.