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Digging into Kano model: conducting study and analyzing results

The first thing to remember is that Kano study could be a highly needed touch point with a customer. During the study, you can ask key questions and get to the essence of customer needs, perceptions, and motivations. Asking "why?" is super powerful.


Kano analysis helps you uncover personas 

When analyzing responses to a Kano study questions - you might spot major differences in a customer reaction to a certain topic. This might mean that you have uncovered a persona. Every product has different user and customer personas.
In user-centered design and marketing, personas are fictional characters created to represent the different user types that might use a site, brand, or product in a similar way.
Personas are multidimensional. The following persona types could be applicable to almost any product: early adopter, early majority, late majority, laggards. Such archetypes might have a significantly different emotional reactions to certain features of your product. Moreover, when features change type - that could be spotted in relation to a persona. Analyzing results of a Kano study could reveal you this insight.


Prioritizing results and comparing product features

How to analyze Kano study results to find out what features are the most preferable by customers?

Add importance question

One way is to add a feature importance question to a pair of functional\dysfunctional questions. For every feature, you ask customer one additional question: "how important this feature is?" An answer should be provided on a scale from 1 to 9, from "Not at all important" to "Extremely important".

Another way is to do a statistical analysis of Kano study results. Standard deviation will help  you spot the significance of results. This method has been described by Bill DuMouchel.

Jan Moorman in her great UXmag article suggests another, visual way to present Kano study results. It lists product features and satisfaction\dissatisfaction scales against them.
Features sorted by the most potentially dissatisfying, if not included, on top. By looking at such picture you immediately see what features you should be focused on. This is a great way to present results of a Kano study to the dev team and other parts of an organization.

How to discover "Attractive" features

This kind of features by definition is not expected by customers, therefore those are hard to uncover by just asking the Kano questions. What can you do to discover these? The best way is to do a qualitative research: observation, interviews, focus groups. You need to understand how customers solving their problems currently and then be really creative to figure out how your solution might help them to be more effective\satisfied.

Improvement of existing features has a limit

Kano model clearly shows that your product will not be hugely successful if you'll focus only on the optimization of existing features. Improving "must-have" features might reduce customer dissatisfaction, but it will not help you make a leap forward.  Without attractive features, without delighters, your product will be "just another one".

How many customers you should ask?

You are ready to do your first Kano study, how many  people should you ask?
The answer is pretty simple (in theory): you need a number of people to get statistically confident data. For some studies 6 respondents are enough, for some it could be 12, 24, 48, 64...etc. The following basic rules apply: you need a representable group of participants. To spot the bigger difference you need fewer participants, while more granular needs more people. When defining your participants' list check how chosen people correspond to your personas.

Best practices to conduct a Kano study


  • Ensure the chosen participants represent your target group (persona definition)
  • Let participants experience features rather than just hear about them (present your prototype or see them using your product)
  • Record participants reactions to features straight away
  • Do statistical analysis on result to make sure you have statistically significant results


Prioritization rule: M > O > A > U


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