The Kano model classifies features by how customers react to having them and to not having them. Must-be features do not delight, but their absence drags satisfaction down; performance features raise satisfaction as they improve; attractive features delight and their absence does not hurt; indifferent features change nothing. The classification comes from a two-question survey per feature and a standard evaluation table, and it helps decide scope.
How to use
- Enter the product name (optional) so the tool can build the ready-to-use questionnaire.
- For each feature, enter the answer pairs (if it has it, if it lacks it) with the number of people, or one row per person.
- See the dominant category, the better and worse coefficients and the satisfaction chart.
- Read the warnings about few answers, mixed classifications and contradictory answers.
- Export the table as PNG, CSV or Markdown and use the questionnaire in the next round.
What each field asks for
| Field | What to enter |
|---|---|
| Product (optional) | Used in the questionnaire wording. |
| Feature | E.g. two-step sign-in verification |
| If it has it (functional) | Answer to the question with the feature |
| If it lacks it (dysfunctional) | Answer to the question without the feature |
| People | Empty counts as 1 |
Example
A personal finance app surveyed 18 people about four features. Two-step sign-in verification got 12 must-be answers: better 0.17 and worse −0.83, so it has to be in the launch. Automatic bank sync came out as performance (9 out of 18), with better 0.67 and worse −0.72. Weekly savings challenges are attractive (11 out of 18, better 0.72). Custom colour themes are indifferent (13 out of 18) and become candidates to leave out of scope.
When to use
- When defining the scope of an MVP, to separate what is mandatory from what sets you apart.
- When the team debates whether a feature customers asked for is worth the investment.
- To review a mature product: yesterday’s attractive features tend to become must-be.
- After interviews, to test the value hypotheses that came up with more people.
Frequently asked questions
What is the Kano model?
It is a customer satisfaction model created by quality researchers in Japan in the 1980s. It shows that different features affect satisfaction in different ways: some only prevent frustration, others delight, others are indifferent.
How do I build the questionnaire?
For each feature, ask two separate questions: how the person would feel if the product had the feature and if it did not. The answers are the same five for both: I like it, I expect it, I am neutral, I can live with it and I dislike it. The tool writes the wording for you.
How many answers do I need?
With fewer than 10 answers per feature, the category is a hypothesis, and the tool warns you. With 20 to 30 people from the same segment, the pattern usually settles. Mixing very different segments can hide different categories in each one.
What are the better and worse coefficients?
The better coefficient, from 0 to 1, shows how much having the feature raises satisfaction. The worse coefficient, from 0 to −1, shows how much lacking it lowers satisfaction. Both are calculated over the attractive, performance, must-be and indifferent answers.
What should I do with reverse and questionable answers?
Reverse means some people prefer the product without the feature: consider making it optional or reviewing the segment. Questionable means a contradictory answer, such as liking both having it and not having it. Many questionable answers call for clearer wording.
What is a mixed classification?
When the two most voted categories are less than 6 percentage points apart, the feature has no clear profile. It often points to different segments in the sample: split the answers by profile and classify again.
How do I use Kano alongside prioritisation?
Use Kano to decide what goes into scope and RICE or MoSCoW to set the order. A common rule: must-be first, then the performance features with the best return, and at least one attractive feature to make the offer stand out.