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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
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    References listed on IDEAS

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    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.
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    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.
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    Cited by:

    1. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
    2. 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.
    3. 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.

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