Discrete choice models are appealing for airline revenue management (RM) because they offer a means to profitably exploit preferences for attributes such as time of day, routing, brand, and price. They are also good at modeling demand for unrestricted fare class structures, which are widespread throughout the industry. However, there is little empirical research on the practicality and effectiveness of choice-based RM models. Toward this end, we report the results of a study of choice-based RM conducted with a major U.S. airline. Our study had two main objectives: (1) to assess the extent to which choice models can be estimated well using readily available airline data, and (2) to gauge the potential impact that choice-based RM could have on a sample of test markets.
We developed a maximum likelihood estimation algorithm that uses a variation of the expectation-maximization method to account for unobservable data. The procedure was applied to data for a test market from New York City to a destination in Florida. The outputs are promising in terms of the quality of the computed estimates, although a large number of departure instances may be necessary to achieve highly accurate results. These choice model estimates were then used in a simulation study to assess the revenue performance of the EMSR-b (expected marginal seat revenue, version b) capacity control policies and the current controls used by the airline relative to controls optimized to account for choice behavior. Our simulation results show 1%–5% average revenue improvements using choice-based RM. Although such simulated results must be taken with caution, overall our study suggests that choice-based revenue management is both feasible to execute and economically significant in real-world airline environments.