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RM methods for airline fare family structures

The rapid growth of low cost carriers forced many legacy airlines to simplify their fare structures and develop new pricing strategies to remain competitive. The strategy of branded fares, or “fare families”, is an increasingly popular approach for airlines to differentiate their products and services from other competitors. This thesis provides a comprehensive overview of revenue management (RM) forecasting and optimization methods developed specifically for fare family structures. These methods, collectively termed Q-Forecasting for Fare Families (QFF), provide airlines with the capability to manage branded fares from a RM perspective. The QFF methods are all constructed based on the assumed fare family passenger choice model, which accounts for both willingness-to-pay estimates as well as family preference. Each formulation makes underlying assumptions regarding passenger sell-up and buy-across. The Passenger Origin Destination Simulator is used to test and compare the performance of each QFF formulation in a dual airline competitive environment, both with leg-based RM controls as well as network RM controls. The results from the simulations indicate that substantial gains in both revenue and yield over traditional RM methods can be achieved with appropriate RM in a fare family structure. Specifically, while Hybrid Forecasting (with leg RM controls) generates a 4.0% increase in revenue over Standard Forecasting, QFF is shown to increase revenues by more than 12.5%. The benefits of QFF are greater with network RM controls, with potential revenue increases of nearly 14.0% (over Standard Forecasting). The positive results obtained with each QFF formulation are dependent upon an appropriate estimate for passenger sell-up and family preference. Consequently, this research also illustrates the importance of the estimate for passenger willingness-topay and its relationship to forecasting and optimization in airline RM. Thesis Supervisor: Peter P. Belobaba Title: Principal Research Scientist, Aeronautics and Astronautics