Demand forecasting in hospitality using smoothed demand curves

TL;DR

This paper introduces a new demand forecasting model for hospitality that uses cubic smoothing splines to create smooth demand curves, improving accuracy and revenue prediction by incorporating industry constraints.

Contribution

The paper presents a novel demand forecasting model based on cubic smoothing splines, integrating industry knowledge through linear programming for better accuracy and revenue outcomes.

Findings

Lower forecasting error achieved

13.3% increase in revenue

Effective modeling of guest behavior

Abstract

Forecasting demand is one of the fundamental components of a successful revenue management system in hospitality. The industry requires understandable models that contribute to adaptability by a revenue management department to make data-driven decisions. Data analysis and forecasts prove an essential role for the time until the check-in date, which differs per day of week. This paper aims to provide a new model, which is inspired by cubic smoothing splines, resulting in smooth demand curves per rate class over time until the check-in date. This model regulates the error between data points and a smooth curve, and is therefore able to capture natural guest behavior. The forecast is obtained by solving a linear programming model, which enables the incorporation of industry knowledge in the form of constraints. Using data from a major hotel chain, a lower error and 13.3% more revenue is obtained.