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Carlson Rezidor Hotel Group Maximizes Revenue Through Improved Demand Management and Price Optimization

Author

Listed:
  • Pelin Pekgün

    (JDA Software Group, Marietta, Georgia 30067; currently at Moore School of Business, University of South Carolina, Columbia, South Carolina 29208)

  • Ronald P. Menich

    (JDA Software Group, Marietta, Georgia 30067)

  • Suresh Acharya

    (JDA Software Group, Marietta, Georgia 30067)

  • Phillip G. Finch

    (JDA Software Group, Marietta, Georgia 30067)

  • Frederic Deschamps

    (Carlson Rezidor Hotel Group, Minnetonka, Minnesota 55305)

  • Kathleen Mallery

    (Carlson Rezidor Hotel Group, Minnetonka, Minnesota 55305)

  • Jim Van Sistine

    (Carlson Rezidor Hotel Group, Minnetonka, Minnesota 55305)

  • Kyle Christianson

    (Carlson Rezidor Hotel Group, Minnetonka, Minnesota 55305)

  • James Fuller

    (Carlson Rezidor Hotel Group, Omaha, Nebraska 68164)

Abstract

Under changing market conditions for the hospitality industry, the Carlson Rezidor Hotel Group (CRHG) collaborated with JDA Software Group to use operations research to drive higher revenue for its hoteliers and to stay ahead of the competition. This highly innovative revenue optimization project, Stay Night Automated Pricing (SNAP), started with enterprise demand forecasting across 600 US hotels in 2007. It was followed by a large-scale network optimization solution to dynamically optimize hotel room rates based on price elasticity of demand, competitor rates, availability of remaining inventory, demand forecasts, and business rules. All North American hotels were operational in SNAP by March 2011. Starting from the optimization prototyping results in 2008, CRHG consistently measured a 2–4 percent revenue improvement in compliant hotels over noncompliant ones. To date, compliant hotels have increased revenue by more than $16 million annually. After a successful deployment in the Americas, CRHG extended the partnership with JDA to globally roll out SNAP, with an initial focus on Europe, the Middle East, Africa, and the Asia Pacific region. CRHG anticipates that the worldwide incremental revenue from this solution will exceed $30 million annually.

Suggested Citation

  • Pelin Pekgün & Ronald P. Menich & Suresh Acharya & Phillip G. Finch & Frederic Deschamps & Kathleen Mallery & Jim Van Sistine & Kyle Christianson & James Fuller, 2013. "Carlson Rezidor Hotel Group Maximizes Revenue Through Improved Demand Management and Price Optimization," Interfaces, INFORMS, vol. 43(1), pages 21-36, February.
    • Handle: RePEc:inm:orinte:v:43:y:2013:i:1:p:21-36
    DOI: 10.1287/inte.1120.0660
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    References listed on IDEAS

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    1. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    2. Dev Koushik & Jon A. Higbie & Craig Eister, 2012. "Retail Price Optimization at InterContinental Hotels Group," Interfaces, INFORMS, vol. 42(1), pages 45-57, February.
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    Cited by:

    1. Qi (George) Chen & Stefanus Jasin & Izak Duenyas, 2016. "Real-Time Dynamic Pricing with Minimal and Flexible Price Adjustment," Management Science, INFORMS, vol. 62(8), pages 2437-2455, August.
    2. 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.
    3. Maxime C. Cohen, & Georgia Perakis & Robert S. Pindyck, 2021. "A Simple Rule for Pricing with Limited Knowledge of Demand," Management Science, INFORMS, vol. 67(3), pages 1608-1621, March.
    4. Michael Murimi & Billy Wadongo & Tom Olielo, 2021. "Determinants of revenue management practices and their impacts on the financial performance of hotels in Kenya: a proposed theoretical framework," Future Business Journal, Springer, vol. 7(1), pages 1-7, December.
    5. Stanislav Hristov Ivanov & Craig Webster & Elitza Stoilova & Daniel Slobodskoy, 2022. "Biosecurity, crisis management, automation technologies and economic performance of travel, tourism and hospitality companies – A conceptual framework," Tourism Economics, , vol. 28(1), pages 3-26, February.
    6. Martin Petricek & Stepan Chalupa & David Melas, 2021. "Model of Price Optimization as a Part of Hotel Revenue Management—Stochastic Approach," Mathematics, MDPI, vol. 9(13), pages 1-16, July.
    7. Stefanus Jasin, 2014. "Reoptimization and Self-Adjusting Price Control for Network Revenue Management," Operations Research, INFORMS, vol. 62(5), pages 1168-1178, October.
    8. Andrei M. Bandalouski & Natalja G. Egorova & Mikhail Y. Kovalyov & Erwin Pesch & S. Armagan Tarim, 2021. "Dynamic pricing with demand disaggregation for hotel revenue management," Journal of Heuristics, Springer, vol. 27(5), pages 869-885, October.
    9. Övünç Yılmaz & Pelin Pekgün & Mark Ferguson, 2017. "Would You Like to Upgrade to a Premium Room? Evaluating the Benefit of Offering Standby Upgrades," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 1-18, February.
    10. Arenoe, Bjorn & van der Rest, Jean-Pierre I. & Kattuman, Paul, 2015. "Game theoretic pricing models in hotel revenue management: An equilibrium choice-based conjoint analysis approach," Tourism Management, Elsevier, vol. 51(C), pages 96-102.
    11. Ivanov, Stanislav Hristov & Webster, Craig & Stoilova, Elitza & Slobodskoy, Daniel, 2020. "Biosecurity, crisis management, automation technologies, and economic performance of travel, tourism and hospitality companies – a conceptual framework," SocArXiv 2hx6f, Center for Open Science.
    12. Qi (George) Chen & Stefanus Jasin & Izak Duenyas, 2021. "Technical Note—Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters," Operations Research, INFORMS, vol. 69(2), pages 560-573, March.
    13. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.

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