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Passenger Name Record Data Mining Based Cancellation Forecasting for Revenue Management

Dolores Romero Morales(dolores.romero-morales***at***sbs.ox.ac.uk)
Jingbo Wang(jingbo.wang***at***sbs.ox.ac.uk)

Abstract: Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we examine the performance of the existing models and propose new promising ones based on Support Vector Machines. We also illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which confirms the dynamic aspect of this problem.

Keywords: revenue management, cancellation forecasting, PNR data mining, two-class probability estimation, support vector machines

Category 1: Applications -- OR and Management Sciences (Yield Management )

Category 2: Applications -- Science and Engineering (Data-Mining )

Citation: Technical Report, Said Business School, University of Oxford, Park End Street, Oxford OX1 1HP, United Kingdom, April 2008

Download: [PDF]

Entry Submitted: 04/16/2008
Entry Accepted: 04/16/2008
Entry Last Modified: 04/16/2008

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