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Forecasting cancellation rates for services booking revenue management using data mining

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  • Romero Morales, Dolores
  • Wang, Jingbo

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.

Suggested Citation

  • Romero Morales, Dolores & Wang, Jingbo, 2010. "Forecasting cancellation rates for services booking revenue management using data mining," European Journal of Operational Research, Elsevier, vol. 202(2), pages 554-562, April.
    • Handle: RePEc:eee:ejores:v:202:y:2010:i:2:p:554-562
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    References listed on IDEAS

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    1. Iliescu, Dan C. & Garrow, Laurie A. & Parker, Roger A., 2008. "A hazard model of US airline passengers' refund and exchange behavior," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 229-242, March.
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    Cited by:

    1. Rennie, Nicola & Cleophas, Catherine & Sykulski, Adam M. & Dost, Florian, 2021. "Identifying and responding to outlier demand in revenue management," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1015-1030.
    2. Zhi-Hua Hu & Qing Li & Xian-Juan Chen & Yan-Feng Wang, 2014. "Sustainable Rent-Based Closed-Loop Supply Chain for Fashion Products," Sustainability, MDPI, vol. 6(10), pages 1-26, October.
    3. Carrizosa, Emilio & Nogales-Gómez, Amaya & Romero Morales, Dolores, 2017. "Clustering categories in support vector machines," Omega, Elsevier, vol. 66(PA), pages 28-37.
    4. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.
    5. Onur Bas & Tamer Aksoy, 2022. "Examining the impact of cargo and ancillary revenues on net profit for full service carrier airlines," International Journal of Business Ecosystem & Strategy (2687-2293), Bussecon International Academy, vol. 4(3), pages 48-72, July.
    6. Debjit Roy & Eirini Spiliotopoulou & Jelle de Vries, 2022. "Restaurant analytics: Emerging practice and research opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3687-3709, October.
    7. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
    8. Sebastian Vock & Laurie A. Garrow & Catherine Cleophas, 2022. "Clustering as an approach for creating data-driven perspectives on air travel itineraries," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 212-227, April.
    9. Shuqair, Saleh & Costa Pinto, Diego & Cruz-Jesus, Frederico & Mattila, Anna S. & da Fonseca Guerreiro, Patricia & Kam Fung So, Kevin, 2022. "Can customer relationships backfire? How relationship norms shape moral obligation in cancelation behavior," Journal of Business Research, Elsevier, vol. 151(C), pages 463-472.

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