# Classic Kano survey analysis

The original way Noriaki Kano devised to analyse a survey was to use a lookup table to classify each pair of responses per feature and then tally the results:

![Source: Kano et al. (1984), translated](/files/3wHq7AUmiaCkbnimuh7n)

## Using the lookup table

In the classic analysis, you look up the category of the combination of a respondent's functional and dysfunctional answers.

| **Functional** ↓ | **Dysfunctional** → |        |         |        |         |
| :--------------: | :-----------------: | :----: | :-----: | :----: | :-----: |
|                  |         Like        | Expect | Neutral | Accept | Dislike |
|       Like       |          Q          |    A   |    A    |    A   |    O    |
|      Expect      |          R          |    I   |    I    |    I   |    N    |
|      Neutral     |          R          |    I   |    I    |    I   |    N    |
|      Accept      |          R          |    I   |    I    |    I   |    N    |
|      Dislike     |          R          |    R   |    R    |    R   |    Q    |

## Tallying the results

When you have determined the category of each response, tally them up. For each feature, count the number of times they have been attributed the Natural, One-Dimensional, Attractive, Indifferent, Reverse and Questionable categories.

For a survey with 25 respondents, the results could be:

|            |   N   |    O   |  A  |  I  |  R  |  Q  |
| ---------- | :---: | :----: | :-: | :-: | :-: | :-: |
| Feature #1 |   9   | **10** |  1  |  2  |  3  |  0  |
| Feature #2 | **9** |    5   |  4  |  2  |  4  |  1  |
| ...        |       |        |     |     |     |     |

The classic way to determine the final category of the feature is to simply look at what category has the majority. For feature 1, that’d be One-Dimensional. For the second feature, that would be that it’s a Natural.

When you're ready with your analysis, you can begin [interpreting the results](/applying-your-kano-study-results/prioritizing.md).


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