Confronta i metodi

	Modello di Kano	Voice of Customer Analysis
Campo	Interazione uomo-macchina	Marketing Science
Famiglia	Hypothesis test	Process / pipeline
Anno di origine	1984	1993
Ideatore	Noriaki Kano	Abbie Griffin & John R. Hauser
Tipo	Two-dimensional model categorizing product/service features by satisfaction impact	Structured qualitative-to-structured pipeline for eliciting, organizing, and prioritizing customer needs
Fonte seminale	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.
Alias	Kano Analysis, Attractive-Performance-Basic Model	VoC Analysis, Voice of the Customer, Customer-Needs Elicitation, VoC for Quality Function Deployment
Correlati	3	3
Sintesi	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.