Applications of machine learning in the hospitality industry for maximizing revenue

Demand forecasting is one of the key components of a successful revenue management (RM) tool in the hospitality industry because it is a building block of hotel’s strategic decisions, as their accuracy determines the efficacy of pricing and rooms inventory optimization decisions. Several scholars have explored different forecasting methods for tourism and hotel demand, but research on hotel demand forecasting is not as abundant as those in tourism demand forecasting and research on demand forecasting method in the midst of uncertainty is rare. Furthermore, a few revenue management solutions (RMS) in the market claim that machine learning has been applied to their system, but the forecasting models of most RMS are still mainly based on combined forecasting models which use historical booking records and advanced booking data (e.g., pickup methods based on trailing periods). Although there has been more and more literature using machine learning to make predictions, lack of interpretability in predictive models has been raised as one of the key concerns and undermine trust in those models. Therefore, this project aims to propose a hotel demand forecasting method using an interpretable machine learning approach so that we can understand how it arrived at a specific estimation.