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Sprinter Mod III: A Model for the Analysis of New Frequently Purchased Consumer Products

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  • Glen L. Urban

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

This paper presents a model-based information system designed to analyze test-market results, to assist decision-making for a new frequently purchased consumer product, and to serve as an adaptive control mechanism during national introduction. The model, called SPRINTER, is based on the behavioral process of the diffusion of innovation and can be used normatively in an interactive search mode to find the best marketing strategy for a new product. The input is obtained from test-market data analyzed by statistics and combined with subjective judgments. A Go, On, or No decision is made on the basis of the estimated profit and risk produced by the best marketing strategy. During national introduction the model serves as a “problem-finding” mechanism. It uses early national sales and micro-level behavioral data to diagnose problems in the introduction. It also can be used to search for solutions to these problems as they are recognized. Finally, an application of this model to a real product is reported, this application using an on-line computer program that allows convenient man-system communication.

Suggested Citation

  • Glen L. Urban, 1970. "Sprinter Mod III: A Model for the Analysis of New Frequently Purchased Consumer Products," Operations Research, INFORMS, vol. 18(5), pages 805-854, October.
  • Handle: RePEc:inm:oropre:v:18:y:1970:i:5:p:805-854
    DOI: 10.1287/opre.18.5.805
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    Citations

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    Cited by:

    1. Silk, Alvin J. & Urban, Glen L., 1976. "Pre-test market evaluation of new packaged goods : a model and measurement methodology," Working papers 834-76., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Ralf van der Lans & Gerrit van Bruggen & Jehoshua Eliashberg & Berend Wierenga, 2010. "A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth," Marketing Science, INFORMS, vol. 29(2), pages 348-365, 03-04.
    3. Kamrad, Bardia & Lele, Shreevardhan S. & Siddique, Akhtar & Thomas, Robert J., 2005. "Innovation diffusion uncertainty, advertising and pricing policies," European Journal of Operational Research, Elsevier, vol. 164(3), pages 829-850, August.
    4. Scott Morton, Michael S., 2003. "Some perspectives on computerized management decision making systems," Working papers no. 33., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Hauser, John R. & Urban, Glen L., 1975. "A normative methodology for modeling consumer response to innovation," Working papers 785-75., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Chi Chiang & Tsui-Yii Shih, 2015. "Establishment Decision of Experience Stores: Insights into Marketing Effect," Journal of Social Science Studies, Macrothink Institute, vol. 2(1), pages 165-185, January.
    7. Jehoshua Eliashberg & Jedid-Jah Jonker & Mohanbir S. Sawhney & Berend Wierenga, 2000. "MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures," Marketing Science, INFORMS, vol. 19(3), pages 226-243, January.
    8. Urban, Glen L. & Hulland, John S. & Weinberg, Bruce., 1990. "Modeling, categorization, elimination, and consideration for new product forecasting of consumer durables," Working papers 3206-90., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    9. Little, John D. C., 1973. "Brandaid II," Working papers 687-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    10. Berend Wierenga & Gerrit H. Van Bruggen & Richard Staelin, 1999. "The Success of Marketing Management Support Systems," Marketing Science, INFORMS, vol. 18(3), pages 196-207.

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