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
Article

Abstract

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.

Keywords

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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References

  1. Allenby, Greg M. and Peter E. Rossi. (1998). “Marketing Models of Consumer Heterogeneity,” Journal of Econometrics 89, 57–78.CrossRefGoogle Scholar
  2. Anderson S. P. and A. de Palma. (1988). “Spatial Price Discrimination with Heterogeneous Products,” The Review of Economic Studies 55, 573–592.Google Scholar
  3. Anselin, Luc. (1988). Spatial Econometrics: Methods and Models, Dorddrecht: Kluwer Academic Publishers.Google Scholar
  4. Anselin, Luc. (2001). “Spatial Econometrics,” in B. Baltagi (ed.), A Companion to Theoretical Econometrics, Oxford: Basil Blackwell, 310–330.Google Scholar
  5. Anselin, Luc. (2002). “Under the Hood: Issues in the Specification and Interpretation of Spatial Regression Models,” Agricultural Economics 17(3), 247–267.Google Scholar
  6. Aribarg, A., N. Arora, and H. Onur Bodur. (2002). “Understanding the Role of Preference Revision and Concession in Group Decisions,” Journal of Marketing Research, 39(August), 336–349.Google Scholar
  7. Arora, Neeraj. (2004). “Joint Decision Making and Spatial Models,” Position Paper, Session on Spatial Models in Marketing, Invitational Choice Symposium.Google Scholar
  8. Arora, Neeraj and Greg M. Allenby. (1999). “Measuring the Influence ofIndividual Preference Structures in Group Decision Making,” Journal of Marketing Research 36(November), 476–487.Google Scholar
  9. Bell,David R. and Sangyoung Song. (2004). “Social Contagion and Trial on the Internet:Evidence from Online Grocery Retailing,” Working Paper, Wharton School of Management, University of Pennsylvania.Google Scholar
  10. Besag, Julian. (1974). “Spatial Interaction and the Statistical Analysis of Lattice Systems,” Journal of the Royal Statistical Society B 36, 192–236.Google Scholar
  11. Besag, Julian. (1975). “Statistical Analysis of Non-Latice Data,” The Statistician 24, 179–195.Google Scholar
  12. Bronnenberg, Bart J. (2004). “Spatial Models in Marketing Research and Practice,” Applied Stochastic Models in Business and Industry (forthcoming).Google Scholar
  13. Bronnenberg, Bart J. and Vijay Mahajan. (2001). “Unobserved Retailer Behavior in Multimarket Data: Joint Spatial Dependence in Market Shares and Promotion Variables,” Marketing Science 20(Summer), 284–299Google Scholar
  14. Bronnenberg, Bart J. and Catarina Sismeiro. (2002). “Using Multimarket Data to Predict Brand Performance in Markets for Which No or Poor Data Exist,” Journal of Marketing Research 39(February), 1–17.Google Scholar
  15. Bronnenberg, Bart J., Sanjay Dhar and Jean-Pierre Dube. (2005), “Market Structure and the Geographic Distribution of Brand Shares in Consumer Packaged Goods Industries,” Working Paper, Anderson School of Management, UCLA.Google Scholar
  16. Brunsdon, Chris, Stewart Fotheringham, and Martin Charlton. (1998). “Geographically Weighted Regression: Modeling Spatial Non-Stationarity,” The Statistician 47(3), 431–443.Google Scholar
  17. Chintagunta, Pradeep, Dube, Jean-Pierre, and Goh, Kim Yong. (2004). “Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models,” Management Science (forthcoming).Google Scholar
  18. Cressie, Noel A. C. (1993). Statistics for Spatial Data, New York: John Wiley and Sons.Google Scholar
  19. Duvvuri, Sri Devi, Tom Gruca, and Gary J. Russell. (2004). “Modeling Household Response Heterogeneity Using Spatial Measures of Similarity,” Presentation at the MRSIG Special Session, AMA Educators' Conference.Google Scholar
  20. DeSarbo, Wayne S. and J. Wu. (2001). “The Joint Spatial Representation of Multiple Variable Batteries Collected in Marketing Research,” Journal of Marketing Research 38(May), 244–253.Google Scholar
  21. Fotheringham, A. S., C. A. Brunsdon, and M. Charlton. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, New York: John Wiley and Sons.Google Scholar
  22. Haining, Robert. (1997). Spatial Data Analysis in the Social and Environmental Sciences, New York: Cambridge University Press.Google Scholar
  23. Jank, Wolfgang, and P. K. Kannan. (2003). “Understanding Geographical Markets of Online Firms Using Spatial Models of Consumer Choice,” Working Paper, Maryland Business School, University of Maryland.Google Scholar
  24. Larson, J. S., E. T. Bradlow, and P. Fader. (2005). “An Exploratory Look at In-Store Supermarket Shopping Paths,” International Journal of Research in Marketing (forthcoming).Google Scholar
  25. LeSage, James P. (1999). Spatial Econometrics, Morgantown: Regional Research Institute, University of West Virginia.Google Scholar
  26. LeSage, James P. (2000). “Bayesian Estimation of Limited Dependent-Variable Spatial Autoregressive Models,” Geographical Analysis 32(1), 19–35.Google Scholar
  27. LeSage, James P. (2003). “A Family of Geographically Weighted Regression Models,” Working Paper, Department of Economics, University of Toledo.Google Scholar
  28. LeSage, James P. and R. K. Pace. (2000). “Using Matrix Exponentials to Explore Spatial Structure in Regression Relationships,” Working Paper, Department of Economics, University of Toledo.Google Scholar
  29. Manchanda, P., A. Ansari, and S. Gupta. (1999). “The Shopping Basket: A Model for Multicategory Purchase Incidence Decisions,” Marketing Science 18(2), 95–114.Google Scholar
  30. Marshall, P. and E. T. Bradlow. (2002). “A Unified Approach to Conjoint Analysis Models,” Journal of the American Statistical Association 97(459), 674–682.CrossRefGoogle Scholar
  31. Mittal, Vikas, Wagner A. Kamakura, and Rahul Govind. (2004). “Geographic Patterns in Customer Service and Satisfaction: An Empirical Investigation,” Journal of Marketing 68, 48–62.CrossRefGoogle Scholar
  32. Moon, Sangkil and Gary J. Russell. (2004). “A Spatial Choice Model for Product Recommendations,” Working Paper, Tippie School of Business, University of Iowa.Google Scholar
  33. Murphy, Edward D. (2004). “Tracking Grocery Hot Spots,” Portland Press Herald, Tuesday, January 27, 2004 edition (http://www.MaineToday.com/).
  34. Pace, R. K. and Barry. (1997). “Quick Computation of Regressions with a Spatially Autoregressive Dependent Variable,” Geographical Analysis 29(1).Google Scholar
  35. Pace, R. K. and Barry. (1999), “Monte Carlo Estimates of the Log Determinant of Large Spatial Matrices,” Linear Algebra and its Applications 289(1–3), 41–54.Google Scholar
  36. Pace, R. K. and Dongya Zou. (2000). “Closed-Form Maximum Likelihood Estimates of Nearest Neighbor Spatial Dependence,” Geographical Analysis 32(2), 154–172.Google Scholar
  37. Ripley, B. D. (1988). Statistical Inference for Spatial Processes, New York: Cambridge University Press.Google Scholar
  38. Rigaux, Philippe, Michel Scholl, and Agnes Voisard. (2002), Spatial Databases with Application to GIS, New York: Academic Press.Google Scholar
  39. Russell, Gary J. and Ann Petersen. (2000). “Analysis of Cross-Category Dependence in Market Basket Selection,” Journal of Retailing 76, 367–392.CrossRefGoogle Scholar
  40. Sismeiro, Catarina. (2004). “Microlevel Spatial Data: Challenges and the Effects of Macrolevel Structure,” Position Paper, Session on Spatial Models in Marketing, Invitational Choice Symposium.Google Scholar
  41. Sorensen Associates. (2004). Introducing Path Tracker, Product Brochure, http://www.sorensen-associates.com/.
  42. Tanner, Martin A. (1996). Tools for Statistical Inference: Methods for the Exploration of Poseterior Distributions and Likelihood Functions, New York: Springer-Verlag.Google Scholar
  43. Ter Hofstede, Frenkel, Michel Wedel, and Jan-Benedict E. M. Steenkamp. (2002). “Identifying Spatial Segments in International Markets,” Marketing Science 21, 160–177.CrossRefGoogle Scholar
  44. Ter Hofstede, Frenkel. (2004). “On the Spatial Organization of Consumer Needs,” Position Paper, Session on Spatial Models in Marketing, Invitational Choice Symposium.Google Scholar
  45. Thomadsen, Raphael. (2004). “Optimal Location Choice: Which Products Should Firms Offer?,” Presentation, Session on Spatial Models in Marketing, Invitational Choice Symposium.Google Scholar
  46. Train, Kenneth E. (2003). Discrete Choice Methods with Simulation, New York: Cambridge University Press.Google Scholar
  47. Waller, Lance A., Bradley P. Carlin and Hong Xia. (1997). “Structuring Correlation Within Hierarchical Spatio-temporal Models for Disease Rates,” in Timothy G. Gergoire et al. (eds.), Modelling Longitudinal and Spatially Correlated Data, New York: Springer-Verlag, 309–319.Google Scholar
  48. Wedel, Michel and Rik Pieters. (2000). “Eye Fixations on Advertisements and Memory for Brands: A Model and Findings,” Marketing Science 19, 297–312.CrossRefGoogle Scholar
  49. Yang, Sha and Greg M. Allenby. (2003). “Modeling Interdependent Consumer Preferences,” Journal of Marketing Research 40, 282–294.CrossRefGoogle Scholar
  50. Yang, Sha. (2004). “Understanding the Interdependence in Consumer Preferences via Spatial Modeling,” Position Paper, Session on Spatial Models in Marketing, Invitational Choice Symposium.Google Scholar

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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