Marketing Letters

, Volume 16, Issue 3–4, pp 267–278 | Cite as

Spatial Models in Marketing

  • Eric T. Bradlow
  • Bart Bronnenberg
  • Gary J. Russell
  • Neeraj Arora
  • David R. Bell
  • Sri Devi Duvvuri
  • Frankel Ter Hofstede
  • Catarina Sismeiro
  • Raphael Thomadsen
  • Sha Yang


Marketing science models typically assume that responses of one entity (firm or consumer) are unrelated to responses of other entities. In contrast, models constructed using tools from spatial statistics allow for cross-sectional and longitudinal correlations among responses to be explicitly modeled by locating entities on some type of map. By generalizing the notion of a map to include demographic and psychometric representations, spatial models can capture a variety of effects (spatial lags, spatial autocorrelation, and spatial drift) that impact firm or consumer decision behavior. Marketing science applications of spatial models and important research opportunities are discussed.


Autocorrelation Spatial Autocorrelation Important Research Spatial Model Science Application 
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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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Eric T. Bradlow
    • 1
  • Bart Bronnenberg
    • 2
  • Gary J. Russell
    • 3
  • Neeraj Arora
    • 4
  • David R. Bell
    • 1
  • Sri Devi Duvvuri
    • 3
  • Frankel Ter Hofstede
    • 5
  • Catarina Sismeiro
    • 6
  • Raphael Thomadsen
    • 7
  • Sha Yang
    • 8
  1. 1.University of Pennsylvania
  2. 2.UCLALos Angeles
  3. 3.University of Iowa
  4. 4.University of Wisconsin
  5. 5.University of TexasAustin
  6. 6.Imperial CollegeLondon
  7. 7.Columbia UniversityColumbia
  8. 8.New York University

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