By: Apostolos Ampountolas, Ph.D., CQF, Assistant Professor of Finance and Revenue Management, Boston University – School of Hospitality Administration
This Special Issue of the Boston Hospitality Review on Revenue Management and Pricing presents diverse research viewpoints from leading academics on the main issues that impact a hospitality industry practitioners’ ability to forecast in today’s unusual circumstances. The topics include big data analytics for efficiency analysis of revenue management strategies, machine-learning models for hotel revenue management, cancellation and no-shows policies, forecasting with dynamic booking windows, and traditional and advanced forecasting applications. Our aim is to shed light on how hotel revenue management can address many of the current challenges in the hospitality industry and provide guidance on the way forward.
Minwoo Lee, Ph.D., Assistant Professor, Jaewook Kim, Ph.D., Assistant Professor, and Agnes L. DeFranco, Ed.D., Professor, and the Conrad N. Hilton Distinguished Chair, all from Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston, discuss the need to increase operational efficiency and offer profitable services to survive and be successful in the current competitive and uncertain hospitality market. The authors employ hospitality data analytics approaches to develop a comparative operation efficiency analysis for strategic revenue management.
Machine learning has been at the leading edge of revenue management practices in the hospitality industry in recent years. Misuk Lee, Ph.D., Assistant Professor at the Albers School of Business and Economics, Seattle University, addresses the topic of machine learning for hotel revenue management using a case study example to provide a comparative analysis of machine learning and pickup models for hotel-demand forecasting. She also illustrates how hotels can take advantage of machine-learning technology for revenue management.
Professor Zvi Schwartz, Ph.D., Department of Hospitality and Sport Business Management in the Alfred Lerner College of Business and Economics at the University of Delaware, outlines the ways that the current lenient cancellation policies and no-shows present a challenge to revenue managers and negatively affect forecasting and control. These temporary looser cancellation restrictions could potentially also pose an additional challenge when policies shift back to pre-pandemic (and more restrictive) settings.
One of the most demanding elements of revenue management is anticipating consumer behavior to make optimal decisions. Timothy Webb, Ph.D., Assistant Professor in the Alfred Lerner College of Business and Economics, University of Delaware, highlights the evolution of booking behavior and the ways in which the revenue management profession has been forced to adapt forecasting with dynamic booking windows.
Nowadays, the new marketplace is becoming more competitive, which causes increased pricing pressure on the traditional service industries as the market supply increases. Therefore, overnight forecasting is key for revenue managers because of the uncertainty associated between demand and supply. In this edition, I have included an abbreviated study on the ways that traditional and advanced forecasting models predict daily demand. The article examines which methods will bring about more robust predictions and offers valuable insights into the exogenous outliers’ impact on establishing accurate daily demand forecasting.
Although the challenges in this new marketplace are many, it is our hope that this special edition on Revenue Management and Pricing will expand your thinking and equip you to uncover new opportunities to forge a productive (and prosperous) path forward!