Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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 rate forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which not only confirms the dynamic aspect of this problem, but will also help revenue managers better understand the drivers of cancellation. Finally, we examine the performance of the state-of-the-art data mining methods when applied to Passenger Name Record based cancellation rate forecasting.
OriginalsprogEngelsk
TidsskriftEuropean Journal of Operational Research
Vol/bind202
Udgave nummer2
Sider (fra-til)554–562
ISSN0377-2217
DOI
StatusUdgivet - 2010
Udgivet eksterntJa

Emneord

  • Revenue management
  • Cancellation rate forecasting
  • PNR data mining
  • Two-class probability estimation
  • Time-dependency

Citer dette

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Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining. / Romero Morales, Dolores ; Wang, Jingbo.

I: European Journal of Operational Research, Bind 202, Nr. 2, 2010, s. 554–562.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

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