Sammenlign metoder

	Perceptual Mapping	Latent-Class Choice Segmentation
Fagfelt	Marketing Science	Marketing Science
Familie	Process / pipeline	Latent structure
Opprinnelsesår	1997	1989
Opphavsperson	J. Douglas Carroll & Paul E. Green (multidimensional scaling in marketing)	Wagner A. Kamakura & Gary J. Russell
Type	Dimension-reduction pipeline for visualizing brand positions in a low-dimensional perceptual space	Finite-mixture choice model for simultaneous segmentation and response estimation
Opprinnelig kilde	Carroll, J. D., & Green, P. E. (1997). Psychometric Methods in Marketing Research: Part II, Multidimensional Scaling. Journal of Marketing Research, 34(2), 193-204.	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.
Alias	Brand Mapping, Positioning Maps, Product Space Maps, Perceptual Space Analysis	Finite-Mixture Logit Segmentation, Latent-Class MNL, Mixture Choice Model, Concomitant-Variable Latent-Class Choice Model
Relaterte	3	3
Sammendrag	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.	Latent-class choice segmentation estimates consumer market segments and their preferences at the same time, by fitting a finite mixture of discrete-choice models to individual purchase or choice data. Wagner Kamakura and Gary Russell introduced the approach in their 1989 Journal of Marketing Research paper, which fit a probabilistic choice model whose latent segments differ in both brand preference and price sensitivity, yielding a unified picture of market structure and elasticities. Rather than clustering consumers first and modeling choice afterward, the method treats segment membership as an unobserved (latent) variable and recovers it jointly with the segment-level choice parameters by maximum likelihood. Each segment is a multinomial logit model with its own coefficient vector, and the mixing proportions describe how large each segment is. Michel Wedel and Wagner Kamakura's authoritative monograph later codified the finite-mixture framework as the methodological backbone of model-based market segmentation. The result links the pattern of brand switching to the magnitudes of own- and cross-price elasticities, giving managers a behaviorally grounded segmentation tied directly to demand response.