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Bayesian Statistics and Marketing

  • Peter E. Rossi

    ()

    (Graduate School of Business, University of Chicago, 1101 E. 58th Street, Chicago, Illinois 60637)

  • Greg M. Allenby

    ()

    (Fisher College of Business, Ohio State University, 2100 Neil Avenue, Columbus, Ohio 43210)

Bayesian methods have become widespread in marketing literature. We review the essence of the Bayesian approach and explain why it is particularly useful for marketing problems. While the appeal of the Bayesian approach has long been noted by researchers, recent developments in computational methods and expanded availability of detailed marketplace data has fueled the growth in application of Bayesian methods in marketing. We emphasize the modularity and flexibility of modern Bayesian approaches. The usefulness of Bayesian methods in situations in which there is limited information about a large number of units or where the information comes from different sources is noted. We include an extensive discussion of open issues and directions for future research.

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File URL: http://dx.doi.org/10.1287/mksc.22.3.304.17739
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Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 22 (2003)
Issue (Month): 3 (July)
Pages: 304-328

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Handle: RePEc:inm:ormksc:v:22:y:2003:i:3:p:304-328
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  1. Allenby, Greg M & Lenk, Peter J, 1995. "Reassessing Brand Loyalty, Price Sensitivity, and Merchandising Effects on Consumer Brand Choice," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 281-89, July.
  2. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
  3. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
  4. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics, Springer, vol. 1(1), pages 5-64, March.
  5. Neeraj Arora & Greg M. Allenby & James L. Ginter, 1998. "A Hierarchical Bayes Model of Primary and Secondary Demand," Marketing Science, INFORMS, vol. 17(1), pages 29-44.
  6. Alan L. Montgomery, 1997. "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," Marketing Science, INFORMS, vol. 16(4), pages 315-337.
  7. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
  8. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
  9. Frenkel Ter Hofstede & Michel Wedel & Jan-Benedict E.M. Steenkamp, 2002. "Identifying Spatial Segments in International Markets," Marketing Science, INFORMS, vol. 21(2), pages 160-177, July.
  10. Peter J. Lenk & Ambar G. Rao, 1990. "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures," Marketing Science, INFORMS, vol. 9(1), pages 42-53.
  11. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 93-119, March.
  12. Rossi P. E & Gilula Z. & Allenby G. M, 2001. "Overcoming Scale Usage Heterogeneity: A Bayesian Hierarchical Approach," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 20-31, March.
  13. Sha Yang & Yuxin Chen & Greg Allenby, 2003. "Reply to Comments on “Bayesian Analysis of Simultaneous Demand and Supply”," Quantitative Marketing and Economics, Springer, vol. 1(3), pages 299-304, September.
  14. Kirthi Kalyanam, 1996. "Pricing Decisions Under Demand Uncertainty: A Bayesian Mixture Model Approach," Marketing Science, INFORMS, vol. 15(3), pages 207-221.
  15. Michel Wedel & Rik Pieters, 2000. "Eye Fixations on Advertisements and Memory for Brands: A Model and Findings," Marketing Science, INFORMS, vol. 19(4), pages 297-312, October.
  16. Andrew Ainslie & Peter E. Rossi, 1998. "Similarities in Choice Behavior Across Product Categories," Marketing Science, INFORMS, vol. 17(2), pages 91-106.
  17. Kwangpil Chang & S. Siddarth & Charles B. Weinberg, 1999. "The Impact of Heterogeneity in Purchase Timing and Price Responsiveness on Estimates of Sticker Shock Effects," Marketing Science, INFORMS, vol. 18(2), pages 178-192.
  18. Alan L. Montgomery & Eric T. Bradlow, 1999. "Why Analyst Overconfidence About the Functional Form of Demand Models Can Lead to Overpricing," Marketing Science, INFORMS, vol. 18(4), pages 569-583.
  19. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
  20. Sha Yang & Yuxin Chen & Greg Allenby, 2003. "Bayesian Analysis of Simultaneous Demand and Supply," Quantitative Marketing and Economics, Springer, vol. 1(3), pages 251-275, September.
  21. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
  22. Sha Yang & Gerg M. Allenby & Geraldine Fennel, 2002. "Modeling Variation in Brand Preference: The Roles of Objective Environment and Motivating Conditions," Marketing Science, INFORMS, vol. 21(1), pages 14-31, May.
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