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A Micromodeling Approach to Investigate the Advertising-Sales Relationship

Author

Listed:
  • Robert C. Blattberg

    (University of Chicago)

  • Abel P. Jeuland

    (University of Chicago)

Abstract

The purpose of this paper is to derive a model of advertising effects on the firm's sales. A micromodel is postulated and aggregated across individuals and over time to produce a macromodel of the aggregate sales-advertising relationship for a single product. The micromodel postulated is very simple. It incorporates two factors: reach of the ads and rate of decay of their effectiveness over time. This approach to modeling advertising effects is shown to be fruitful in several respects: (1) the coefficients of the aggregate equation are easily interpretable---in terms of the reach and decay parameters; (2) the model derived is nonlinear yet estimable; (3) a special case of the model is very similar to lag models that have been in use; (4) the model can be used whatever the unit of time is; (5) the carryover effect of advertising (as commonly defined) is not constant, but depends upon the previous spending levels; and (6) the model helps illustrate that the duration of advertising may be greatly overstated if aggregate lagged dependent variable models are simplistically interpreted.

Suggested Citation

  • Robert C. Blattberg & Abel P. Jeuland, 1981. "A Micromodeling Approach to Investigate the Advertising-Sales Relationship," Management Science, INFORMS, vol. 27(9), pages 988-1005, September.
    • Handle: RePEc:inm:ormnsc:v:27:y:1981:i:9:p:988-1005
    DOI: 10.1287/mnsc.27.9.988
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    Citations

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    Cited by:

    1. Norris I. Bruce, 2008. "Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters," Marketing Science, INFORMS, vol. 27(4), pages 659-673, 07-08.
    2. Xiuzhi Zhang & Ying Zhang & Zhijie Lin, 2023. "Online Advertising and Real Estate sales: evidence from the Housing Market," Electronic Commerce Research, Springer, vol. 23(1), pages 605-622, March.
    3. Jianan Wu & Victor J. Cook & Edward C. Strong, 2005. "A Two-Stage Model of the Promotional Performance of Pure Online Firms," Information Systems Research, INFORMS, vol. 16(4), pages 334-351, December.
    4. 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.
    5. (Sundar) Balakrishnan, P. V. & Hall, Nicholas G., 1995. "A maximin procedure for the optimal insertion timing of ad executions," European Journal of Operational Research, Elsevier, vol. 85(2), pages 368-382, September.
    6. Kiygi Calli, M. & Weverbergh, M. & Franses, Ph.H.B.F., 2010. "To Aggregate or Not to Aggregate: Should decisions and models have the same frequency?," ERIM Report Series Research in Management ERS-2010-046-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
    8. Mesak, Hani I. & Calloway, James A., 1995. "A pulsing model of advertising competition: A game theoretic approach, part B -- Empirical application and findings," European Journal of Operational Research, Elsevier, vol. 86(3), pages 422-433, November.
    9. Polemis, Michael L. & Stengos, Thanasis & Tzeremes, Nickolaos G., 2020. "Advertising expenses and operational performance: Evidence from the global hotel industry," Economics Letters, Elsevier, vol. 192(C).

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