Kano Model of Feature Selection (with Free Excel Template)

The Kano Model is used to analyse consumer preferences for different features and group them into multiple categories. This allows firms to identify which features they should focus on when developing a new product.

While the Kano model can be an extremely helpful tool in classifying the attributes of a potential NPD, it can sometimes be confusing to understand exactly what the model represents and where the value of the model lies. In this guide we are going to take you through the Kano model step by step, to demystify the model and show you how it can be used in your upcoming product launches.

Conjoint.ly Kano Model Excel template

The template is already set up with an example experiment for classifying attributes for a new smartphone.

What is the Kano Model?

Developed by Noriaki Kano in 1984, the Kano model is a method of describing the relationships between a product’s attributes and customer satisfaction. The relationships that the model produces allows the needs of a customer to be categorised into different groups.

Category Explanation Example
Attractive Needs Seen as delighters, these are never expected but cause joy when they occur. Checking in at a hotel and finding that you have been randomly upgraded to the penthouse suite - for free!
Must-be Needs These are the hard requirements. Your product will fail if these are not met, but won't receive praise for including them. What would happen if you purchased a pair of shoes and they didn’t come with shoelaces? You would be very disappointed as you expected them to be included.
Performance Needs The more of these, the better. The more of these needs that are met, the higher the overall satisfaction. Imagine you are going to buy a soda from the store. If the 2L bottle was the same price as the 1.5L bottle, the 2L will leave you more satisfied.
Indifferent Customers are indifferent to this attribute, the level of functionality does not affect satisfaction at all. Your new $30,000 car comes with a free branded drink bottle.

For each of these attributes, the relationship between satisfaction and functionality is shown on the graph below:

Kano model categories graph

It is worth emphasizing that while the above chart describes the kano model, the chart is not the output of the kano model. Instead the output of the model is the classification of attributes into the groups mentioned above.

How is a Kano model survey run?

Running a survey for Kano model analysis is relatively simple. The first step is to find the attributes that you wish to classify. It is useful to have a wide range of attributes, to classify into each of the possible categories.

For each of the attributes, we want to ask both a functional and a dysfunctional question. A functional question asks about the customers perceptions of when an attribute is included in a product, whereas a dysfunctional question asks about when an attribute is not included. The typical format of questions are as follows:


How would you feel if [attribute] was included in [our product]?

  1. Love it
  2. Expect it
  3. Indifferent
  4. Tolerate it
  5. Unhappy


How would you feel if [attribute] was not included in [our product]?

  1. Love it
  2. Expect it
  3. Indifferent
  4. Tolerate it
  5. Unhappy

Asking the questions in this format allows us to compare consumers' perceptions of when a feature is included compared to when it is excluded. This can be done through the following matrix:

Kano model functional dysfunctional matrix

For example, we can see if consumers like when an attribute is present, and don’t care when it is not present, the attribute can be classified as an attractive attribute. When an attribute is strictly liked when included and disliked when excluded, we can see it is a performance attribute.

Here we see two more categories that we did not originally include - questionable and reverse.

Category Explanation
Questionable This category is for responses that don’t make logical sense. For example, consumers liking when an attribute is present and liking when it is excluded is not logically consistent.
Reverse These can be seen as negative attributes, as they are disliked when they are present and are liked when they are excluded. When negative attributes occur, they can be fixed by swapping the functional and dysfunctional questions. This now makes it so that the functional question is when an attribute is not present whereas the dysfunctional will now be when an attribute is present.

How can I use Kano model analysis on Conjoint.ly?

On Conjoint.ly, the easiest way to perform Kano model analysis is through using a series of multiple choice questions.

For each of your attributes, two questions need to be asked - both a functional and dysfunctional question. It is important that the options are in order from positive to negative, and that the order is functional, then dysfunctional.

Here we have an example of a functional question for testing the attribute wireless charging

Functional kano model question example

Note: When using the Conjoint.ly Kano model excel template, it is important that your survey has the following format

  • Questions ordered by functional question first, dysfunctional second.

  • Five multiple choice options, ordered from Very Happy at the to Very Unhappy.


  • Be sure to use formatting such as bolding to make the questions clear and easy to read.

  • Consider using randomisation blocks to randomise the order that respondents see questions in.

How to use the Conjoint.ly Kano Model template

The Conjoint.ly Kano model template is designed to easily take the output from a Conjoint.ly experiment and perform Kano analysis. The model supports up to 30 features and 5000 respondents.

Once your Conjoint.ly experiment is complete, you can use the following steps to input your results into the Conjoint.ly Kano Model Template

  1. Export your experiment results, using the Excel Export button, available under the Market Overview tab of your report.

  2. Open your exported results. Under the Respondent Overview sheet copy only the results to your Kano model questions.

  3. Download the Conjoint.ly Kano Model Template. Paste the results into the sheets.

  4. At the top of each group of questions, write in the appropriate attribute name.

Just like that you’re done!

Key Outputs

It is important to keep in mind that the output for the Kano model is simply the categorisation of attributes. The Kano model generates output from both discrete analysis, as well as continuous analysis.

The results from discrete analysis are calculated by looking at each individual’s responses to the questions to find how they categorise each attribute. From here we can see how each attribute is categorised on average.

Categorising features and attributes with kano model

For example, looking at Feature 4: Free Smart Watch we see that the most common category is Attractive, as this is how it was categorised by 41% of respondents.

One issue with discrete analysis is that information is often lost along the way. A potential solution to this is to use continuous analysis to take an average of the results. This can be found in the Kano Model template under the Continuous Results tab.

Continuous analysis of average kano results

Both the continuous and discrete output are valuable and should both be considered when classifying attributes. The discrete output indicates which classification is the most common among respondents, while the continuous takes an averaged view.

Conjoint.ly Kano Model Excel template

The template is already set up with an example experiment for classifying attributes for a new smartphone.

Written on 22 October 2020 by:
Harrigan Davonport image
Harrigan Davonport
Market Researcher

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