Kano Model Analyzer
Classify product features as must-be, performance, attractive, indifferent or reverse from paired functional and dysfunctional survey answers, using the standard Kano evaluation table.
Last reviewed by the Radiatus Cloud team
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Not every feature affects satisfaction the same way
Noriaki Kano's insight in 1984 was that the relationship between a feature and customer satisfaction is not linear and is not the same for every feature. Some features only cause dissatisfaction by their absence and produce no delight when present: nobody praises a hotel for having hot water, but its absence ruins the stay. Others scale: more of them is proportionally better. And some produce delight when present but are not missed when absent, because customers did not know to want them.
The two question technique
Each feature is tested with a pair of questions. The functional form asks how you would feel if the product had this feature; the dysfunctional form asks how you would feel if it did not. Both are answered on the same five point scale from "I like it" to "I dislike it". The combination of the two answers places the feature in a category through a fixed evaluation table, and the fact that the categorisation comes from a pair rather than a single rating is what makes it resistant to the usual survey problem where every feature is rated important.
Categories move over time
Today's attractive feature becomes tomorrow's performance feature and eventually a must-be. A car with air conditioning was delightful, then a differentiator, and is now expected. That decay is the strategic point: a roadmap built only on must-be features never differentiates, and one built only on attractive features ships something delightful with a missing basic. The distribution across categories matters more than any single classification.
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Frequently Asked Questions
What are the Kano categories?
Must-be features cause dissatisfaction when absent and no satisfaction when present. Performance features scale linearly with satisfaction. Attractive features delight when present and are not missed when absent. Indifferent features do not affect satisfaction either way, and reverse features actively reduce it.
How many respondents do I need?
Twenty to thirty per segment gives a usable distribution for a directional decision. Fewer than about fifteen makes the category assignment unstable, since one or two respondents can flip the modal answer.
What does a questionable result mean?
A logically inconsistent answer pair, such as liking both the presence and the absence of a feature. A few are normal. A high rate usually means the question wording was ambiguous or respondents did not understand the feature.
Should I build the attractive features first?
Not before the must-be ones. A missing basic causes dissatisfaction no amount of delight compensates for. The usual sequence is to secure the must-be set, compete on performance features and use attractive features to differentiate.
Do the categories change?
Yes, and quite quickly in software. Attractive features become expected once competitors match them. Re-running the survey annually on the same feature set shows the drift, which is more informative than any single snapshot.
Privacy & Security
Everything runs in your browser; nothing is uploaded.
How to Use
For each feature, record how customers answered the functional and dysfunctional questions.
Disclaimer: This tool is provided "as is" without warranty of any kind. Results are for educational and utility purposes.
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