Machine learning approach to market behavior estimation with applications in revenue management

Abstract

Demand forecasting models used in airline revenue management are primarily based on airline’s own sales data. These models have limited visibility into overall market conditions and competitive landscape. Market factors significantly influence customer behavior and hence should be considered for determining optimal control policy. We discuss data sources available to airlines that provide visibility into the future competitive schedule, market size forecast, and market share estimation. We also describe methodologies based on Machine Learning algorithms that can use to forecast these quantities and explain how they can be leveraged to improve pricing and revenue management practices.