Kano Model

Survey users about features two ways (what if you had it, what if you didn't), then bucket each feature by how the answers cluster.

When to use this

Use paired survey questions to explore whether customers view a feature as expected, performance-related, attractive, indifferent, or unwanted. Review the distribution and respondent count before using the categories in prioritization.

Limits

A small sample can give an unstable picture. Kano also does not rank features by effort, business value, or delivery priority.

Inputs

Functional question: "How would you feel if the product had [feature]?" Five answers: I like it, I expect it, I'm neutral, I can live with it, I dislike it.
Dysfunctional question: "How would you feel if the product did NOT have [feature]?" Same five answers.
A list of features to test (keep the survey short enough for respondents to answer carefully).

The math

There's no single formula. Each user's pair of answers maps to a category in Kano's standard matrix:

Must-have: Users expect it; its absence causes dissatisfaction, while its presence may add little satisfaction.
Performance: Better fulfillment increases satisfaction; poorer fulfillment increases dissatisfaction.
Delighter: Its presence adds satisfaction, while its absence causes little dissatisfaction.
Indifferent: They don't care either way.
Reverse: The responses indicate the feature is unwanted.
Questionable: The answer pair is contradictory and needs review.

Aggregate across respondents. The category with the highest percentage wins, but the full distribution matters -- a feature that's 45% Must-have and 40% Indifferent is a different bet than one that's 80% Must-have.

A worked example

Say you're building a note-taking app. You survey 50 users about 4 features.

Auto-save: 38 of 50 respondents (76%) give the paired answers “I expect it” when present and “I dislike it” when absent. Those 38 pairs classify as Must-have. Classify the other 12 pairs before reporting the full distribution.

Dark mode: 22 users (44%) like it when present, and 24 (48%) are neutral when absent. These separate totals do not tell us how the answers pair up. They cannot establish a Kano category.

AI summarization: 30 users (60%) like it when present, while 28 (56%) are neutral when absent. Again, retain each respondent's pair before classifying.

Version history: 35 users (70%) like it when present, and 32 (64%) dislike its absence. A respondent who gives both answers is classified as Performance, but the separate totals do not show how many did so.

The lesson is to store paired responses, not just totals for each question. Then examine categories alongside customer needs, effort, and other evidence.

How PM Toolkit handles Kano

The MCP tool classifies each supplied functional/dysfunctional answer pair with the 5×5 Kano table and summarizes categories across the submitted entries. That portfolio summary is not automatically a survey distribution for each feature.

The interactive calculator uses a different model: implementation and satisfaction sliders mapped to category curves. Its categories are not calculated from respondent-level survey pairs. Use the MCP table when you need that paired-answer classification.

Common mistakes

Inferring pairs from separate answer totals.
Treating a category as a delivery order or a churn prediction.
Ignoring minority or Reverse responses that may reveal different customer needs.
Assuming categories stay fixed as expectations and the product change.