Feature Article—Aggregate Advertising Models: The State of the Art

John D. C. Little
Additional contact information
John D. C. Little: Massachusetts Institute of Technology, Cambridge, Massachusetts

Operations Research, 1979, vol. 27, issue 4, 629-667

Abstract: Aggregate advertising models relate product sales to advertising spending for a market as a whole. Although many models have been built, they frequently contradict each other and considerable doubt exists as to which models best represent advertising processes. An increasingly rich literature of empirical studies helps resolve these issues by revealing major advertising phenomena that models should encompass. These include sales responding upward and downward at different rates, steady state response that can be concave or S-shaped and can have positive sales at zero advertising, sales affected by competitive advertising; and advertising dollar effectiveness that can change over time. A review of aggregate models developed on a priori grounds brings out similarities and differences among those of Vidale and Wolfe, Nerlove and Arrow, Little, and others and identifies ways in which the models agree or disagree with observed phenomena. A Lanchester-motivated structure generalizes many features of these models and conforms to some but not all of the empirical observations. Although econometric studies have revealed important empirical insights, the most frequently used structural forms do not model certain key phenomena, most notably different rise and decay rates. Future work must join better models with more powerful calibration methods.

Date: 1979
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.27.4.629 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:27:y:1979:i:4:p:629-667

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().