Kano Model of Feature Selection
Kano Model of Feature Selection (with Free Excel Template)
Published on
22 October 2020
Harrigan Davenport image
Harrigan Davenport
Market Researcher
Nurul Zainal image
Nurul Zainal
Insights Writer
Kano Model

Ensure product-market fit and maximise your user acquisition and expansion, by differentiating software features according to your users' needs.

The Kano Model is used to analyse consumer preferences for different features and group features into multiple categories. This helps product managers to prioritise development efforts.

Features and claims
Feature and Pricing Suite for SaaS

Kano Model

Ensure product-market fit and maximise your user acquisition and expansion, by differentiating software features according to your users' needs.

While the Kano Model can be an extremely helpful tool in classifying potential features of your software, it can sometimes be confusing to understand exactly what the model represents and where the value of the model lies. In this guide, we will take you through the Kano Model step by step using the free Excel template in order to demystify the model. But of course you don’t have it do it manually anymore because it is available as an easy-to-use tool on Conjointly.

Conjointly Kano Model Excel template

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

Overview of 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.

Attractive NeedsSeen 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 NeedsThese 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 NeedsThe 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.
IndifferentCustomers 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 graph

It is worth emphasising 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.

Questions to ask in a Kano Model survey

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:

Like itExpect itNeutralTolerateDislike
Like it4QuestionableAttractiveAttractiveAttractivePerformance
Expect it2ReverseQuestionableIndifferentIndifferentMust-be

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.

QuestionableThis 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.
ReverseThese 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.

Conducting a Kano Model analysis on Conjointly

The easiest way to conduct a Kano Model analysis is by running a Kano Model experiment on the Conjointly platform.

But if you really want to do it manually, you can run a series of multiple choice questions on the platform.

In this case, 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 Conjointly 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 Delighted to 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.

Using Kano Model Excel template

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

Once your Conjointly experiment is complete, you can use the following steps to input your results into the Conjointly 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 Conjointly 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 of a Kano Model analysis

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 the 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.

Try the simple Kano Model tool on Conjointly instead

The Excel template may be free, but your time is not. Try the Kano Model tool on Conjointly instead to get results easily.

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