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Perceptual Mapping/Ushahidi
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Perceptual Mapping

Perceptual mapping turns how consumers see a set of brands into a picture: a low-dimensional space in which nearby brands are perceived as similar and the axes summarize the perceptual dimensions that organize the category. Two families of techniques produce these maps. Attribute-based mapping starts from brand-by-attribute ratings and uses dimension reduction — principal components, factor analysis, or correspondence analysis — to place brands and overlay attribute directions as a biplot. Similarity-based mapping starts from consumers' direct judgments of how similar brands are and uses multidimensional scaling (MDS) to recover the space, requiring no attribute list. J. Douglas Carroll and Paul Green's 1997 Journal of Marketing Research review codified MDS as a marketing tool, and Green is widely regarded as a central figure in bringing scaling and clustering to marketing research. Adding consumers' ideal points or preference vectors converts a perceptual map into a positioning tool that reveals where demand concentrates and where white-space gaps lie. Because the map summarizes competitive structure, it complements choice-based views of market structure such as those from latent-class choice models. The result is a single diagram managers use to diagnose positioning, spot competitors, and find opportunities.

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Perceptual Mapping (Attribute-Based and MDS-Based Brand Maps)
Rekodi ya mbinu ya kiajenda · process-pipeline / marketing-science
  • Carroll, J. D., & Green, P. E. (1997). Psychometric Methods in Marketing Research: Part II, Multidimensional Scaling. Journal of Marketing Research, 34(2), 193-204. · DOI 10.1177/002224379703400201
  • Kamakura, W. A., & Russell, G. J. (1989). A Probabilistic Choice Model for Market Segmentation and Elasticity Structure. Journal of Marketing Research, 26(4), 379-390. · DOI 10.1177/002224378902600401
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Same method familyImportance-Performance Analysismachine-suggested · Relational suggestion, not evidence.Used in the same domainLatent-Class Choice Segmentationmachine-suggested · Relational suggestion, not evidence.Same method familyVoice of Customer Analysismachine-suggested · Relational suggestion, not evidence.

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