An Evaluation of Different Hotel Revenue Management Techniques

Abstract

The field of hotel revenue management is inherently multi-dimensional, forcing a modern revenue manager to synthesize information about past prices, current forecasts, competitor pricing, and pricing disruptions to determine optimal prices. While there recently has been a proliferation of quantitative pricing policies that do not rely on human input, however, there has been little comparison to the performance of traditional revenue managers. This thesis aims to model the Knowledge Base of Zak Ali, an award-winning Revenue Manager, creating a binary representation of over a decade of real-life industry experience. Once this Ali Policy is formalized, this thesis examines both whether the Ali Policy is more effective in a Simulator than a set of a quantitative policies, and whether adjusting the Simulator to reflect a more real-world representation changes the e cacy of these policies.