手法を比較

	Importance-Performance Analysis	Perceptual Mapping
分野	Marketing Science	Marketing Science
系統	Process / pipeline	Process / pipeline
提唱年	1977	1997
提唱者	John A. Martilla & John C. James	J. Douglas Carroll & Paul E. Green (multidimensional scaling in marketing)
種類	Two-dimensional diagnostic grid for prioritizing attribute improvements	Dimension-reduction pipeline for visualizing brand positions in a low-dimensional perceptual space
原典	Martilla, J. A., & James, J. C. (1977). Importance-Performance Analysis. Journal of Marketing, 41(1), 77-79.	Carroll, J. D., & Green, P. E. (1997). Psychometric Methods in Marketing Research: Part II, Multidimensional Scaling. Journal of Marketing Research, 34(2), 193-204.
別名	IPA, Importance-Performance Mapping, Action Grid Analysis, Quadrant Analysis	Brand Mapping, Positioning Maps, Product Space Maps, Perceptual Space Analysis
関連	3	3
概要	Importance-Performance Analysis (IPA) is a simple, durable diagnostic for deciding where to focus improvement effort by combining how much customers care about each attribute with how well the offering performs on it. John Martilla and John James introduced it in a 1977 Journal of Marketing note, using automobile-dealer service data to show that satisfaction depends jointly on the salience of attributes and judgments of actual performance. The technique plots each attribute as a point on a two-dimensional grid — importance on one axis, performance on the other — divided into four quadrants by crosshairs, and reads off a managerial action for each quadrant. The headline insight is that high-importance, low-performance attributes are where to 'concentrate here,' while resources poured into low-importance, high-performance attributes represent 'possible overkill.' Because it rests on a clear conceptual link between salient-attribute importance and performance, IPA pairs naturally with structured customer-needs work such as the Voice of the Customer. Its visual action grid makes priorities legible to managers without statistical training, which is why it has spread far beyond its original marketing context.	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.