Modeling Airline Passenger Choice Using Booking Data-with An Analysis Of The Revenue Management Strategy Of China Airlines

Understanding passenger choice behavior is crucial to support a range of airline planning decisions such as schedule planning, pricing and revenue management. Previous studies of airline passenger choice have been based on stated preference data, thus suffer from the hypothetical nature of the experiment and are subject to a risk of response bias.In this research, we develop a methodology to analyze the choice of an airline itinerary and fare product based on booking data. Characteristics of the traveler and the trip were retrieved from the booking records and replaced trip purpose that is traditionally used to segment demand in airline markets. They were included as explanatory variables of a latent class choice model in which several factors can be used simultaneously to segment the market without necessarily dividing the bookings into many small sub-segments. In addition to an improvement in fit over previous models based on a deterministic segmentation of the market, the latent class choice model was found to provide a more intuitive segmentation of the demand between a core of time-sensitive business travelers and a mixed class of price-conscious leisure and business travelers. This model extends the range of applications of passenger choice models to additional airline planning decisions such as pricing and revenue management.In this research, we also analyzed the revenue management strategy of some Chinese Airlines(CA). As a result, we found a critical fault as CA has reckoned early morning as unattractive to passengers, which leads to a series of huge mistakes in CA's revenue management strategy.