手法を比較

	Importance-Performance Analysis	カノモデル	Voice of Customer Analysis
分野	Marketing Science	ヒューマンコンピュータインタラクション	Marketing Science
系統	Process / pipeline	Hypothesis test	Process / pipeline
提唱年	1977	1984	1993
提唱者	John A. Martilla & John C. James	Noriaki Kano	Abbie Griffin & John R. Hauser
種類	Two-dimensional diagnostic grid for prioritizing attribute improvements	Two-dimensional model categorizing product/service features by satisfaction impact	Structured qualitative-to-structured pipeline for eliciting, organizing, and prioritizing customer needs
原典	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.	Griffin, A., & Hauser, J. R. (1993). The Voice of the Customer. Marketing Science, 12(1), 1-27.
別名	IPA, Importance-Performance Mapping, Action Grid Analysis, Quadrant Analysis	Kano Analysis, Attractive-Performance-Basic Model	VoC Analysis, Voice of the Customer, Customer-Needs Elicitation, VoC for Quality Function Deployment
関連	3	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.	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.	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.