Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining

Research output: Contribution to journalJournal articleResearchpeer-review

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 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.
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.
LanguageEnglish
JournalEuropean Journal of Operational Research
Volume202
Issue number2
Pages554–562
ISSN0377-2217
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

    Cite this

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    title = "Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining",
    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 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.",
    keywords = "Revenue management, Cancellation rate forecasting, PNR data mining, Two-class probability estimation, Time-dependency",
    author = "Morales, {Dolores Romero} and Jingbo Wang",
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    Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining. / Morales, Dolores Romero; Wang, Jingbo.

    In: European Journal of Operational Research, Vol. 202, No. 2, 2010, p. 554–562.

    Research output: Contribution to journalJournal articleResearchpeer-review

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    T1 - Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining

    AU - Morales,Dolores Romero

    AU - Wang, Jingbo

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    AB - 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.

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    KW - Cancellation rate forecasting

    KW - PNR data mining

    KW - Two-class probability estimation

    KW - Time-dependency

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