Kano Model
The Kano model classifies product attributes by how their fulfilment affects customer satisfaction: basic attributes are taken for granted and generate only dissatisfaction when missing; performance attributes increase satisfaction proportionally; delight attributes work disproportionately, without customers expecting them or being able to name them.
Origin
The model was formulated by Noriaki Kano and colleagues (Kano/Seraku/Takahashi/Tsuji, “Attractive Quality and Must-Be Quality”, Journal of the Japanese Society for Quality Control, 1984). The paper replaced the one-dimensional assumption that more fulfilment always produces more satisfaction with a two-dimensional picture: degree of fulfilment and satisfaction relate differently for each attribute class. A further central idea is attribute drift — delight attributes turn into performance attributes over time, and eventually into basic ones.
Typical use
The Kano model serves requirements prioritisation in product development and quality management: in roadmap decisions, in the design of variants and trim lines, and in the question of where additional development budgets still generate satisfaction. It prevents watering-can improvement, in which all attributes are upgraded evenly — even though customers have long ticked off part of them as self-evident.
Procedure
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Collect candidate attributes
From customer interviews, complaints, competitive comparison and internal development, compile a list of concrete attributes — precise enough that customers can imagine their presence or absence.
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Run the Kano survey
Each attribute is tested with a pair of questions: how would you feel if the attribute were present — and how if it were not? Only the combination of the functional and the dysfunctional answer allows assignment to an attribute class.
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Classify and segment
The answer pairs are mapped to classes via an evaluation table — including indifferent and reverse attributes. Evaluate each customer segment separately, since the same attribute can delight one segment and leave another cold.
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Derive consequences
Fulfil basic attributes completely, calibrate performance attributes against the competition, and invest deliberately in a few delight attributes. Because of attribute drift, the measurement should be repeated at regular intervals.
Limits and typical mistakes
The Kano model delivers a snapshot: because of attribute drift, classifications age, and whoever bases decisions on a survey from three years ago is optimising for a market that no longer exists. The survey itself is hypothetical — customers rate described rather than experienced attributes — and delight attributes in particular are the ones they can hardly name, precisely because they do not expect them. Aggregated evaluations across heterogeneous segments blur the classes beyond recognition. And finally, the model prioritises attributes but says nothing about cost or technical feasibility — it is an instrument of demand analysis, not a roadmap.
Relation to the Innovator's Dilemma
The Kano model gives the dilemma's central mechanism — overshoot — a measuring instrument: overshoot means continuing to raise performance attributes beyond what customers can use. In customers' perception these attributes drift towards the basic class; additional performance no longer generates additional willingness to pay, and differentiation shifts to price and convenience. It is exactly in this position that established providers become attackable from below. Moreover, the delight attributes of disruptive solutions typically lie on different dimensions — simplicity, accessibility, price — and remain invisible as long as only the established attribute catalogue is surveyed. Where the performance limits of the technology itself lie is shown, complementarily, by S-curve analysis.