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Perceptual maps via enhanced correspondence analysis: representing confidence regions to clarify brand positions

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Listed:
  • Dawn Iacobucci

    (Vanderbilt University)

  • Doug Grisaffe

    (University of Texas at Arlington)

Abstract

Brand positioning is frequently facilitated by the use of perceptual maps. Several approaches exist for deriving such maps. This research uses the variability inherent in customer data to build confidence regions around brands and attributes in perceptual maps. Doing so generalizes the typical descriptive approach to a truer, statistical inferential approach to mapping. The resulting visualizations clarify the interpretations regarding which brands are similar, with overlapping confidence regions, and which brands are distinct, given non-overlapping confidence ellipses. The modeling is first demonstrated on a small, synthetic dataset and then on real consumer data. The model extension is shown to be useful, and it is relatively straightforward in implementation. It is hoped that this extension to this frequently used market mapping approach should enhance interpretive precision, and therefore, lead to more accurate and successful strategic positioning decisions.

Suggested Citation

  • Dawn Iacobucci & Doug Grisaffe, 2018. "Perceptual maps via enhanced correspondence analysis: representing confidence regions to clarify brand positions," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(3), pages 72-83, September.
    • Handle: RePEc:pal:jmarka:v:6:y:2018:i:3:d:10.1057_s41270-018-0037-7
    DOI: 10.1057/s41270-018-0037-7
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    1. SCHMALENSEE, Richard & THISSE, Jacques-François, 1988. "Perceptual maps and the optimal location of new products: an integrative essay," LIDAM Reprints CORE 840, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. DeSarbo, Wayne S. & De Soete, Geert & Eliashberg, Jehoshua, 1987. "A new stochastic multidimensional unfolding model for the investigation of paired comparison consumer preference/choice data," Journal of Economic Psychology, Elsevier, vol. 8(3), pages 357-384, September.
    3. Norman Cliff, 1966. "Orthogonal rotation to congruence," Psychometrika, Springer;The Psychometric Society, vol. 31(1), pages 33-42, March.
    4. Edmund Peay, 1988. "Multidimensional rotation and scaling of configurations to optimal agreement," Psychometrika, Springer;The Psychometric Society, vol. 53(2), pages 199-208, June.
    5. Heungsun Hwang & Hec Montréal & William Dillon & Yoshio Takane, 2006. "An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 161-171, March.
    6. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
    7. Glen L. Urban, 1975. "Perceptor: A Model for Product Positioning," Management Science, INFORMS, vol. 21(8), pages 858-871, April.
    8. Shizuhiko Nishisato, 1996. "Gleaning in the field of dual scaling," Psychometrika, Springer;The Psychometric Society, vol. 61(4), pages 559-599, December.
    9. Gregory S. Carpenter, 1989. "Perceptual Position and Competitive Brand Strategy in a Two-Dimensional, Two-Brand Market," Management Science, INFORMS, vol. 35(9), pages 1029-1044, September.
    10. Michel Velden & Henk A.L. Kiers, 2005. "Rotation in Correspondence Analysis," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 251-271, September.
    11. M. O. Hill, 1974. "Correspondence Analysis: A Neglected Multivariate Method," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(3), pages 340-354, November.
    12. Aaker, David A. & Shansby, J. Gary, 1982. "Positioning your product," Business Horizons, Elsevier, vol. 25(3), pages 56-62.
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