IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v38y2008i2p103-111.html
   My bibliography  Save this article

NBC-Universal Uses a Novel Qualitative Forecasting Technique to Predict Advertising Demand

Author

Listed:
  • Srinivas Bollapragada

    (GE Global Research Center, Niskayuna, New York 12309)

  • Salil Gupta

    (GE Global Research Center, Niskayuna, New York 12309)

  • Brett Hurwitz

    (ESPN Media Networks, New York, New York 10023)

  • Paul Miles

    (GE Global Research Center, Niskayuna, New York 12309)

  • Rajesh Tyagi

    (GE Global Research Center, Niskayuna, New York 12309)

Abstract

NBC-Universal (NBCU), a subsidiary of the General Electric Company (GE), implemented a novel demand prediction and analysis system to support its annual upfront market. The upfront market is a brief period in late May when the television networks sell a majority of their on-air advertising inventory. The system uses an innovative combination of the Delphi method and the Grass Roots forecasting methodology to estimate demand for television commercial time. We embedded this forecasting methodology within a workflow system that automates the demand estimates gathering process and seamlessly integrates into NBCU's existing sales systems. Since 2004, over 200 sales and finance personnel at NBCU have been using the system to support sales decisions during the upfront market when NBCU signs advertising deals worth over $4.5 billion. The system enables NBCU to sell and analyze pricing scenarios across all of NBCU's television properties with ease and sophistication, while predicting demand with a high accuracy. NBCU's sales leaders credit the system with having given them a unique competitive advantage.

Suggested Citation

  • Srinivas Bollapragada & Salil Gupta & Brett Hurwitz & Paul Miles & Rajesh Tyagi, 2008. "NBC-Universal Uses a Novel Qualitative Forecasting Technique to Predict Advertising Demand," Interfaces, INFORMS, vol. 38(2), pages 103-111, April.
    • Handle: RePEc:inm:orinte:v:38:y:2008:i:2:p:103-111
    DOI: 10.1287/inte.1080.0346
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.1080.0346
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.1080.0346?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. Srinivas Bollapragada & Hong Cheng & Mary Phillips & Marc Garbiras & Michael Scholes & Tim Gibbs & Mark Humphreville, 2002. "NBC's Optimization Systems Increase Revenues and Productivity," Interfaces, INFORMS, vol. 32(1), pages 47-60, February.
    2. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    3. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    4. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    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. Chidambaram Subbiah & Andrea C. Hupman & Haitao Li & Joseph Simonis, 2023. "Improving Software Development Effort Estimation with a Novel Design Pattern Model," Interfaces, INFORMS, vol. 53(3), pages 192-206, May.
    2. Sylvia Hristakeva & Julie Holland Mortimer, 2023. "Price Dispersion and Legacy Discounts in the National Television Advertising Market," Marketing Science, INFORMS, vol. 42(6), pages 1162-1183, November.

    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. Fifić, Mario & Gigerenzer, Gerd, 2014. "Are two interviewers better than one?," Journal of Business Research, Elsevier, vol. 67(8), pages 1771-1779.
    2. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    3. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    4. Karine Bouthevillain, 1993. "La prévision macro-économique : précision relative et consensus," Économie et Prévision, Programme National Persée, vol. 108(2), pages 97-126.
    5. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
    6. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.
    7. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    8. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    9. Julia A. Minson & Jennifer S. Mueller & Richard P. Larrick, 2018. "The Contingent Wisdom of Dyads: When Discussion Enhances vs. Undermines the Accuracy of Collaborative Judgments," Management Science, INFORMS, vol. 64(9), pages 4177-4192, September.
    10. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    11. Cem Peker, 2023. "Extracting the collective wisdom in probabilistic judgments," Theory and Decision, Springer, vol. 94(3), pages 467-501, April.
    12. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    13. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    14. P. J. Lamberson & Scott E. Page, 2012. "Optimal Forecasting Groups," Management Science, INFORMS, vol. 58(4), pages 805-810, April.
    15. Vokurka, Robert J. & Flores, Benito E. & Pearce, Stephen L., 1996. "Automatic feature identification and graphical support in rule-based forecasting: a comparison," International Journal of Forecasting, Elsevier, vol. 12(4), pages 495-512, December.
    16. Antonis Michis, 2012. "Monitoring Forecasting Combinations with Semiparametric Regression Models," Working Papers 2012-02, Central Bank of Cyprus.
    17. Pierre Dodin & Jingyi Xiao & Yossiri Adulyasak & Neda Etebari Alamdari & Lea Gauthier & Philippe Grangier & Paul Lemaitre & William L. Hamilton, 2023. "Bombardier Aftermarket Demand Forecast with Machine Learning," Interfaces, INFORMS, vol. 53(6), pages 425-445, November.
    18. Chan, Chi Kin & Kingsman, Brian G. & Wong, H., 1999. "The value of combining forecasts in inventory management - a case study in banking," European Journal of Operational Research, Elsevier, vol. 117(2), pages 199-210, September.
    19. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2001. "Using investment portfolio return to combine forecasts: A multiobjective approach," European Journal of Operational Research, Elsevier, vol. 134(1), pages 84-102, October.
    20. Lisheng He & Pantelis P. Analytis & Sudeep Bhatia, 2022. "The Wisdom of Model Crowds," Management Science, INFORMS, vol. 68(5), pages 3635-3659, May.

    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:38:y:2008:i:2:p:103-111. 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.