Comparar métodos

	Voice of Customer Analysis	Importance-Performance Analysis	Modelo de Kano	Perceptual Mapping
Campo	Marketing Science	Marketing Science	Interacción persona-ordenador	Marketing Science
Familia	Process / pipeline	Process / pipeline	Hypothesis test	Process / pipeline
Año de origen	1993	1977	1984	1997
Autor original	Abbie Griffin & John R. Hauser	John A. Martilla & John C. James	Noriaki Kano	J. Douglas Carroll & Paul E. Green (multidimensional scaling in marketing)
Tipo	Structured qualitative-to-structured pipeline for eliciting, organizing, and prioritizing customer needs	Two-dimensional diagnostic grid for prioritizing attribute improvements	Two-dimensional model categorizing product/service features by satisfaction impact	Dimension-reduction pipeline for visualizing brand positions in a low-dimensional perceptual space
Fuente seminal	Griffin, A., & Hauser, J. R. (1993). The Voice of the Customer. Marketing Science, 12(1), 1-27.	Martilla, J. A., & James, J. C. (1977). Importance-Performance Analysis. Journal of Marketing, 41(1), 77-79.	Kano, N., Seraku, N., Takahashi, F., & Tsjui, S. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control, 14(2), 147–156.	Carroll, J. D., & Green, P. E. (1997). Psychometric Methods in Marketing Research: Part II, Multidimensional Scaling. Journal of Marketing Research, 34(2), 193-204.
Alias	VoC Analysis, Voice of the Customer, Customer-Needs Elicitation, VoC for Quality Function Deployment	IPA, Importance-Performance Mapping, Action Grid Analysis, Quadrant Analysis	Kano Analysis, Attractive-Performance-Basic Model	Brand Mapping, Positioning Maps, Product Space Maps, Perceptual Space Analysis
Relacionados	3	3	3	3
Resumen	Voice of Customer (VoC) analysis is a structured method for hearing what customers actually need, in their own words, and turning that into a prioritized, organized set of requirements for product development. Abbie Griffin and John Hauser established its modern foundations in their 1993 Marketing Science article, which examined the customer-needs component of Quality Function Deployment and answered practical questions: how many customers to interview, how to extract needs from verbatims, how to structure them, and whether one-on-one interviews or focus groups are more efficient. Their key empirical findings — that needs accumulate toward saturation, that a modest number of interviews uncovers most needs, and that one-on-one interviews are at least as productive per dollar as focus groups — turned VoC from an art into a repeatable research process. The method distills raw customer statements into solution-free need statements, organizes them into a primary-secondary-tertiary hierarchy through customer sorting, and assigns importance weights using survey priorities, an idea closely tied to importance-performance thinking. Those weighted, structured needs then feed Quality Function Deployment, where they are mapped onto engineering attributes to drive design decisions.	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.	The Kano Model is a framework for categorizing product or service features based on their impact on customer satisfaction. Developed by Noriaki Kano, this model distinguishes three types of features: basic (must-have) features that satisfy minimally but cause significant dissatisfaction if absent; performance features that increase satisfaction proportionally with their level; and attractive (delightful) features that exceed expectations and generate disproportionate satisfaction. By classifying features using the Kano Model, product teams prioritize development efforts, balance risk and innovation, and design experiences that delight rather than merely satisfy.	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.