IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v51y2021i1p26-41.html
   My bibliography  Save this article

Carnival Optimizes Revenue and Inventory Across Heterogenous Cruise Line Brands

Author

Listed:
  • Justin Beck

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • John Harvey

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • Kristina Kaylen

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • Corrado Sala

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • Melinda Urban

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • Peter Vermeulen

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • Norman Wilken

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • Wei Xie

    (Carnival Corporation & plc, Miami, Florida 33178;)

  • Dan Iliescu

    (Revenue Analytics Inc., Atlanta, Georgia 30339)

  • Pratik Mital

    (Revenue Analytics Inc., Atlanta, Georgia 30339)

Abstract

Carnival Corporation & plc identified the need for a cutting-edge revenue management system; however, existing solutions from the airline and hospitality industries were not compatible with the idiosyncrasies of the cruise domain. As such, the company partnered with revenue analytics to build a complete revenue and inventory management system to meet its requirements. Yield optimization and demand analytics (YODA) is a system that leverages a unique quadratic programming model to jointly determine cruise prices and allocate cabin inventory to multiple cruises (e.g., 14-day and 7-day lengths) offered simultaneously on a given ship. The optimization inputs come from several machine learning algorithms that predict demand. YODA combines these algorithms with an elasticity model derived from an exponential curve to represent the unique price-sensitivity behavior observed in the cruise industry. The system generates millions of price recommendations each day and has been used to price voyages on 65 Carnival ships, approximately one quarter of the ships in the entire cruise industry, since December 2017. During A/B testing, YODA generated a 1.5%–2.5% incremental uplift in net ticket revenue, which is a significant revenue increase because Carnival was a Fortune 300 company in 2019.

Suggested Citation

  • Justin Beck & John Harvey & Kristina Kaylen & Corrado Sala & Melinda Urban & Peter Vermeulen & Norman Wilken & Wei Xie & Dan Iliescu & Pratik Mital, 2021. "Carnival Optimizes Revenue and Inventory Across Heterogenous Cruise Line Brands," Interfaces, INFORMS, vol. 51(1), pages 26-41, February.
    • Handle: RePEc:inm:orinte:v:51:y:2021:i:1:p:26-41
    DOI: 10.1287/inte.2020.1062
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2020.1062
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2020.1062?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sharon Hormby & Julia Morrison & Prashant Dave & Michele Meyers & Tim Tenca, 2010. "Marriott International Increases Revenue by Implementing a Group Pricing Optimizer," Interfaces, INFORMS, vol. 40(1), pages 47-57, February.
    2. Barry C. Smith & John F. Leimkuhler & Ross M. Darrow, 1992. "Yield Management at American Airlines," Interfaces, INFORMS, vol. 22(1), pages 8-31, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mihai Banciu & Andreas Hinterhuber & Fredrik Ødegaard, 2023. "Revenue management in sports, live entertainment and arts," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(3), pages 185-187, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aldric Vives & Marta Jacob & Marga Payeras, 2018. "Revenue management and price optimization techniques in the hotel sector," Tourism Economics, , vol. 24(6), pages 720-752, September.
    2. Dominik Schreyer, 2019. "Football spectator no-show behaviour in the German Bundesliga," Applied Economics, Taylor & Francis Journals, vol. 51(45), pages 4882-4901, September.
    3. Sridharan, Sri V., 1998. "Managing capacity in tightly constrained systems," International Journal of Production Economics, Elsevier, vol. 56(1), pages 601-610, September.
    4. Chatwin, Richard E., 2000. "Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices," European Journal of Operational Research, Elsevier, vol. 125(1), pages 149-174, August.
    5. Ali Hortaçsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," NBER Working Papers 29508, National Bureau of Economic Research, Inc.
    6. Gerlach, Max & Kliewer, Natalia & Cleophas, Catherine, 2016. "A note on effective code-share management in practice," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 202-205.
    7. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    8. Chen, Lijian, 2020. "Determine the cost of denying boarding to passengers: An optimization-based approach," International Journal of Production Economics, Elsevier, vol. 220(C).
    9. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    10. Luo, Sirong & Çakany?ld?r?m, Metin & Kasilingam, Raja G., 2009. "Two-dimensional cargo overbooking models," European Journal of Operational Research, Elsevier, vol. 197(3), pages 862-883, September.
    11. Feng, Youyi & Xiao, Baichun, 2006. "A continuous-time seat control model for single-leg flights with no-shows and optimal overbooking upper bound," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1298-1316, October.
    12. Chatarin Subying & Chanin Yoopetch, 2023. "A Bibliometric Review of Revenue Management in the Tourism and Hospitality Industry, 1989–2021," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    13. Shaban, I.A. & Wang, Z.X. & Chan, F.T.S. & Chung, S.H. & Eltoukhy, A.E.E. & Qu, T., 2019. "Price setting for extra-baggage service for a combination carrier using the newsvendor setup," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 1-14.
    14. Naragain Phumchusri & Phatsakorn Sangsukiam & Nannapat Chariyasethapong, 2020. "Optimal overbooking model for car rental business with two levels of prices having stochastic joint booking and show-up levels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(3), pages 190-209, June.
    15. Benjamin Marcus & Chris K. Anderson, 2006. "Online Low-Price Guarantees---A Real Options Analysis," Operations Research, INFORMS, vol. 54(6), pages 1041-1050, December.
    16. Peter C. Bell & Jing Chen, 2017. "Close integration of pricing and supply chain decisions has strategic as well as operations level benefits," Annals of Operations Research, Springer, vol. 257(1), pages 77-93, October.
    17. C.K. Anderson & M. Davison & H. Rasmussen, 2004. "Revenue management: A real options approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(5), pages 686-703, August.
    18. Barut, M. & Sridharan, V, 2004. "Design and evaluation of a dynamic capacity apportionment procedure," European Journal of Operational Research, Elsevier, vol. 155(1), pages 112-133, May.
    19. Wei Shi Lim, 2009. "Overselling in a Competitive Environment: Boon or Bane?," Marketing Science, INFORMS, vol. 28(6), pages 1129-1143, 11-12.
    20. Alavi Fard, Farzad & Sy, Malick & Ivanov, Dmitry, 2019. "Optimal overbooking strategies in the airlines using dynamic programming approach in continuous time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 384-399.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:51:y:2021:i:1:p:26-41. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.