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BRANDAID: A Marketing-Mix Model, Part 1: Structure

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  • John D. C. Little

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

Marketing managers make decisions about price, advertising, promotion, and other marketing variables on the basis of factual data, judgments, and assumptions about how the market works. BRANDAID is a flexible, on-line model for assembling these elements to describe the market and evaluate strategies. This paper motivates the model and presents its mathematics. The structure is modular so that individual decision areas can be added or deleted at will. The model is of the aggregate response type, in which decision variables relate closely to specific sales performance measures. The major submodels are advertising, promotion, price, salesmen, and retail distribution. The advertising submodel employs a long-run sales response to advertising function and a linear lag process. Promotional effects are built up from a characteristic time pattern for the type of promotion and a response curve. Salesman affect sales through a response process structurally similar to that for advertising. Retail distribution variables are intermediaries that the company affects and that in turn affect customer response. Submodel outputs combine multiplicatively. Competition enters in a modular, symmetric way through a matrix of competitive coefficients that determine the source of sales for each brand as it seeks to increase its market position.

Suggested Citation

  • John D. C. Little, 1975. "BRANDAID: A Marketing-Mix Model, Part 1: Structure," Operations Research, INFORMS, vol. 23(4), pages 628-655, August.
    • Handle: RePEc:inm:oropre:v:23:y:1975:i:4:p:628-655
    DOI: 10.1287/opre.23.4.628
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    Citations

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    Cited by:

    1. Davis, Joseph G. & Sundaram, David, 1995. "PETAPS: A prototype decision support system for consumer product marketing and promotion," European Journal of Operational Research, Elsevier, vol. 87(2), pages 247-256, December.
    2. Purohit, Basant Kumar, 2010. "Leveraging the Integration of Sales Career Cycle with Brand Life Cycle in Indian Pharmaceutical Firms," IIMA Working Papers WP2010-07-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    3. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
    4. C. Clark & Ulrich Doraszelski & Michaela Draganska, 2009. "The effect of advertising on brand awareness and perceived quality: An empirical investigation using panel data," Quantitative Marketing and Economics (QME), Springer, vol. 7(2), pages 207-236, June.
    5. Olivier Rubel & Prasad A. Naik, 2017. "Robust Dynamic Estimation," Marketing Science, INFORMS, vol. 36(3), pages 453-467, May.
    6. Basant Kumar Purohit, 2010. "Leveraging the Integration of Sales Career Cycle with Brand Life Cycle in Indian Pharmaceutical Firms," Working Papers id:2725, eSocialSciences.
    7. Little, John D. C. & Anderson, Eric T., 1994. "A product choice model with marketing, filtering and purchase feedback," Working papers 3670-94., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    8. John D. C. Little, 2004. "Comments on ÜModels and Managers: The Concept of a Decision CalculusÝ," Management Science, INFORMS, vol. 50(12_supple), pages 1854-1860, December.
    9. Dong, Weimin & Swain, Scott D. & Berger, Paul D., 2007. "The role of channel quality in customer equity management," Journal of Business Research, Elsevier, vol. 60(12), pages 1243-1252, December.
    10. Yong Tan & Vijay S. Mookerjee, 2005. "Allocating Spending Between Advertising and Information Technology in Electronic Retailing," Management Science, INFORMS, vol. 51(8), pages 1236-1249, August.

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