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Promoter: An Automated Promotion Evaluation System

Author

Listed:
  • Magid M. Abraham

    (Information Resources, Inc.)

  • Leonard M. Lodish

    (The Wharton School, University of Pennsylvania)

Abstract

A system and methodology for evaluating manufacturers' trade promotions which may be combined with consumer promotions is described. The methodology incorporates concepts from expert systems in order to evaluate promotions on a mass scale (i.e., by geographical area and size or flavor) with a minimum of analyst intervention. The system uses available data (shipments, warehouse withdrawal or store level scanner data) and contains a knowledge base to recognize and adjust for data irregularities. The system estimates a baseline of what sales would have been had a promotion not been run. The three available real cases where promotions were discontinued are shown as partial validation for the procedure. The large potential biases in using only factory shipment data for evaluating promotions are also explored.

Suggested Citation

  • Magid M. Abraham & Leonard M. Lodish, 1987. "Promoter: An Automated Promotion Evaluation System," Marketing Science, INFORMS, vol. 6(2), pages 101-123.
  • Handle: RePEc:inm:ormksc:v:6:y:1987:i:2:p:101-123
    DOI: 10.1287/mksc.6.2.101
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    Cited by:

    1. Goic, Marcel & Álvarez, Rodolfo & Montoya, Ricardo, 2018. "The Effect of House Ads on Multichannel Sales," Journal of Interactive Marketing, Elsevier, vol. 42(C), pages 32-45.
    2. Bandyopadhyay, Subir, 2009. "A Dynamic Model of Cross-Category Competition: Theory, Tests and Applications," Journal of Retailing, Elsevier, vol. 85(4), pages 468-479.
    3. Goodwin, Paul, 2000. "Correct or combine? Mechanically integrating judgmental forecasts with statistical methods," International Journal of Forecasting, Elsevier, vol. 16(2), pages 261-275.
    4. Epstein, Leonardo D. & Inostroza-Quezada, Ignacio E. & Goodstein, Ronald C. & Choi, S. Chan, 2021. "Dynamic effects of store promotions on purchase conversion: Expanding technology applications with innovative analytics," Journal of Business Research, Elsevier, vol. 128(C), pages 279-289.
    5. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
    6. Raju, Jagmohan S., 1995. "Theoretical models of sales promotions: Contributions, limitations, and a future research agenda," European Journal of Operational Research, Elsevier, vol. 85(1), pages 1-17, August.
    7. Matsatsinis, Nikolaos F. & Siskos, Yannis, 1999. "MARKEX: An intelligent decision support system for product development decisions," European Journal of Operational Research, Elsevier, vol. 113(2), pages 336-354, March.
    8. Ehrenberg, Andrew S. C. & Uncles, Mark D. & Goodhardt, Gerald J., 2004. "Understanding brand performance measures: using Dirichlet benchmarks," Journal of Business Research, Elsevier, vol. 57(12), pages 1307-1325, December.
    9. Leeflang, Peter S.H. & Parreño Selva, Josefa & Van Dijk, Albert & Wittink, Dick R., 2008. "Decomposing the sales promotion bump accounting for cross-category effects," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 201-214.
    10. Jorge Silva-Risso & Irina Ionova, 2008. "—A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions," Marketing Science, INFORMS, vol. 27(4), pages 545-566, 07-08.
    11. Jorge Silva-Risso & William V. Shearin & Irina Ionova & Alexei Khavaev & Deirdre Borrego, 2008. "Chrysler and J. D. Power: Pioneering Scientific Price Customization in the Automobile Industry," Interfaces, INFORMS, vol. 38(1), pages 26-39, February.
    12. Harald J. van Heerde & Shuba Srinivasan & Marnik G. Dekimpe, 2010. "Estimating Cannibalization Rates for Pioneering Innovations," Marketing Science, INFORMS, vol. 29(6), pages 1024-1039, 11-12.
    13. Koen Pauwels & Shuba Srinivasan, 2004. "Who Benefits from Store Brand Entry?," Marketing Science, INFORMS, vol. 23(3), pages 364-390, July.
    14. Rutger D. van Oest & Harald J. van Heerde & Marnik G. Dekimpe, 2010. "Return on Roller Coasters: A Model to Guide Investments in Theme Park Attractions," Marketing Science, INFORMS, vol. 29(4), pages 721-737, 07-08.
    15. Michel Wedel & Jie Zhang & Fred Feinberg, 2015. "Implementing Retail Category Management: a Model-Based Approach to Setting Optimal Markups," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(2), pages 165-176, June.
    16. Goodwin, Paul, 2000. "Improving the voluntary integration of statistical forecasts and judgment," International Journal of Forecasting, Elsevier, vol. 16(1), pages 85-99.
    17. Praveen K. Kopalle & Carl F. Mela & Lawrence Marsh, 1999. "The Dynamic Effect of Discounting on Sales: Empirical Analysis and Normative Pricing Implications," Marketing Science, INFORMS, vol. 18(3), pages 317-332.
    18. Xavier Drèze & David R. Bell, 2003. "Creating Win–Win Trade Promotions: Theory and Empirical Analysis of Scan-Back Trade Deals," Marketing Science, INFORMS, vol. 22(1), pages 16-39, November.
    19. Jorge M. Silva-Risso & Randolph E. Bucklin & Donald G. Morrison, 1999. "A Decision Support System for Planning Manufacturers' Sales Promotion Calendars," Marketing Science, INFORMS, vol. 18(3), pages 274-300.
    20. Little, John D. C. & Management in the 1990s (Program), 2003. "Information technology in marketing," Working papers 86-033. Working paper (Sl, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    21. Ramanathan, Usha & Muyldermans, Luc, 2010. "Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK," International Journal of Production Economics, Elsevier, vol. 128(2), pages 538-545, December.
    22. Pinçe, Çerağ, 2021. "Forward Buying and Strategic Stockouts," European Journal of Operational Research, Elsevier, vol. 289(1), pages 118-131.
    23. Suresh Divakar & Brian T. Ratchford & Venkatesh Shankar, 2005. "Practice Prize Article—: A Multichannel, Multiregion Sales Forecasting Model and Decision Support System for Consumer Packaged Goods," Marketing Science, INFORMS, vol. 24(3), pages 334-350, July.
    24. Geoffrey A. Chua & Yan Liu, 2019. "Sensitivity analysis on responsive pricing and production under imperfect demand updating," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(7), pages 529-546, October.

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