Customer based time-to-event models for cancellation behavior: a revenue management integrated approach

Time-to-event models;Cancellation behavior;Revenue management

Iliescu, Dan Cristian ; Civil and Environmental Engineering

University:Georgia Institute of Technology

Department:Civil and Environmental Engineering

关键词: Time-to-event models;    Cancellation behavior;    Revenue management;   

Others  :  https://smartech.gatech.edu/bitstream/1853/26537/1/iliescu_dan_c_200810_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Low-cost carriers and escalading fuel costs are placing increased pressure on US legacy carriers to reposition traditional revenue management techniques towards more customer-centric approaches. In this context, recent critiques of revenue management models question the validity of assumptions used to describe passenger cancellation and no-show behavior. Since forecasts of cancellation and no-shows are used to determine overbooking levels, i.e, authorization levels in excess of capacity, concerns related to possible missed revenue opportunities are justifiable.The goal of this research is to explore the impact of time-to-event forecasts of cancellations on airlines' revenue streams. To determine the intensity of the cancellation process, a discrete time proportional odds (DTPO) model with a prospective time scale was estimated for a sample of tickets provided by the Airline Reporting Corporation. Empirical results based on 2004 data from eight domestic US markets indicate that the intensity of the cancellation process is strongly influenced both by the time from ticket purchase and the time before flight departure, as well as several other covariates, including departure day of week, market, and group size. In order to assess potential revenue benefits associated with the DTPO formulation, a modified simulation experiment of a "single-resource capacity control" was designed. Simulation results indicate that time-to-event cancellation forecasts can generate revenue gains up to 2%. Overall, this research provides new insights into the transitional properties associated with the cancellation process, which will help airlines to improve their overbooking strategies.