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Sales and Market Share Response Models

  • Dominique M. Hanssens
  • Leonard J. Parsons
  • Randall L. Schultz
Part of the International Series in Quantitative Marketing book series (ISQM, volume 2)

Abstract

The relation between sales response and marketing variables is the core of the theory and practice of marketing management. In making decisions, marketing managers must have some ideas about how their actions will influence sales and profits. Usually these ideas concerning the link between apparent causes (marketing decision variables, the actions of competitors, and certain environmental factors) and measurable market responses (sales or market share) are based on experience—a “feel” for the implications of a firm’s marketing decisions. Such casual interpretations of market response may be expedient, serving managers as guides to marketing planning, but they are severely limited in their ability to provide managers with more objective evidence on how to improve the quality of their decisions. Sales response models are formal ways of describing the complex relation between a firm and its market. They are designed to overcome as much uncertainty as possible regarding the nature of sales response and, in addition, to provide the behavioral mechanism in a decision model that allows management to explore optimal policies.

Keywords

Market Share Price Elasticity Product Life Cycle Reaction Function Advertising Effort 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.
    Lancaster misclassified deodorants as primary demand only.Google Scholar
  2. 2.
    We talk about “scale economies of advertising” in the sense of the production function (sales response function) for an individual brand and do not address the purported advantages that large firms derive from advertising. With regard to the latter issue, Boyer and Lancaster (1986, p. 524) found no static relation between advertising costs per dollar of sales and the size of the advertiser.Google Scholar
  3. 3.
    This implies that primary demand for the products studied has not been affected by changes in the absolute price levels. Simon (1979) reports a similar result.Google Scholar
  4. 4.
    These articles build upon earlier work of McGuire et al. (1968), Beckwith (1972; 1973), Bultez and Naert (1975), and McGuire and Weiss (1976).Google Scholar
  5. 5.
    Farley and Lehmann (1986) discuss how to conduct meta-analysis in marketing.Google Scholar
  6. 6.
    If market share is the dependent variable, the estimation method should address the issue of logical consistency. Our interest in this section is in the empirical results rather than in the methodological issues.Google Scholar
  7. 7.
    Modeling relative price by sinh was also done by Albach (1979). Albach displayed figures showing the change in own and cross-elasticities over time.Google Scholar
  8. 8.
    Also see Nelson (1976).Google Scholar
  9. 9.
    The quality of estimated reaction functions is also related to their use in decision analysis or simulation. See the discussion of the Schultz and Dodson model in chapter 8.Google Scholar

Copyright information

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • Dominique M. Hanssens
    • 1
  • Leonard J. Parsons
    • 2
  • Randall L. Schultz
    • 3
  1. 1.University of CaliforniaLos AngelesUSA
  2. 2.Georgia Institute of TechnologyUSA
  3. 3.University of IowaUSA

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