What is the Kano Model Prioritization Framework? Overview, Guide, and Templates
The Kano model (“Kano analysis”) has become one of the best-known frameworks for prioritizing features for a reason: it works.
The sweet taste of frameworks supported by research.
Kano also stands out because—unlike many other frameworks—it’s customer-centric. It focuses on your user experience and expectations.
But—and it’s a big but—not every implementation of Kano is equally good.
Read on to learn the basics of the method and the best way to use it.
The Kano model works by categorizing features into categories based on how your users view them: Must-be qualities, one-dimensional qualities, attractive qualities, indifferent qualities, and reverse qualities.
PMs typically prioritize the Must-be qualities early and then focus on the one-dimensional quality features.
Attractive quality features make good options for filling in the cracks of your dev budget. You would prioritize Indifferent qualities only when there’s a good business case. And you would usually identify the reverse quality features to remove them.
The model gets complicated when you try to figure out what method to use to actually do the categorization. There are several ways to do it, and only some are supported by empirical research.
If you’re going to use the Kano method, stick to the original way specified by Noriaki Kano and colleagues.
What is the Kano model?
The Kano model of customer (or consumer) satisfaction is a method for prioritizing new product features based on how your customers perceive them. The classifications help product managers understand how each feature will impact customer satisfaction and user experience. Those classifications can then help PMs make prioritization decisions.
The model is named after Noriaki Kano, a Japanese consultant who defined the model in the 1980s in a paper published in the Journal of the Japanese Society of Quality Control.
What are the Kano model quality types and categories?
The model classifies features into 5 categories. Those categories are typically translated into English as:
Must-be qualities. Also called threshold or basic attributes. These are the features that consumers take for granted and expect. Without them, a product will have difficulty being successful. For example, brakes on a car fall into this category. In SaaS, it could be a way for your users to log in to their accounts.
One-dimensional qualities. Also called performance attributes. These are the features that contribute to customer satisfaction when they’re there and detract from customer satisfaction when they’re absent. For example, a car’s fuel economy falls into this category. In SaaS, integrations with other tools fall in this category.
Attractive qualities. Also called exciters or delighters. These are features that customers won’t miss if they’re absent but that create delight when they’re present. For example, seat warmers might fall into this category. In a SaaS product, confetti cannon moments are an example of this.
Indifferent qualities. These are qualities that have no significant effect on your consumer’s experience of the product either good or bad. For example, many cars have a plate listing part numbers under the hoods, which is useful for repairpeople and mechanics. But including them won’t affect the experience of most car buyers. Your software data infrastructure is an example of this in a SaaS product.
Reverse qualities. These are product features that hurt customer experience when they’re present. For example, pushy sales tactics might fall into this category. In a SaaS product, this might be a complicated process to complete a transaction. Typically, you want to identify these and then remove them from your product or streamline them.
Remember that the Kano model is a dynamic framework, and customer expectations and preferences can change over time. For example, in the car example, automatic windows at one time were an attractive quality, but are now probably a must-be quality for most people. Cup holders, too, were originally attractive qualities and now are expected.
Strengths of the Kano model analysis
The Kano model has several crucial strengths.
1. Customer-centric prioritization
This is probably the most important strength of the method. Kano is fundamentally about understanding how your customers use your product or service, and what they need to have a good experience. It seeks to align your product development with those needs.
We also love that it does this by actually asking customers what they think.
Ultimately, by classifying features based on how customers perceive them, product managers can make data-driven decisions that directly impact customer satisfaction and retention.
2. Balancing innovation and essentials
The Kano model empowers product managers to strike a balance between the must-have features that customers expect and the innovative features that can set a product apart from its competitors.
By identifying attractive qualities, PMs can prioritize resources to create delightful experiences while still addressing the must-be and one-dimensional qualities that drive satisfaction.
3. Holistic view of features
The Kano model encourages a comprehensive approach to feature evaluation because it accounts for the absence of features, not just their presence. This perspective helps product managers understand the full range of customer reactions to various new features, allowing them to make more informed prioritization decisions.
4. A dynamic framework
As customer preferences evolve, so do their expectations of product features. The Kano model is a dynamic framework that allows for continuous reassessment and adjustment of feature prioritization, ensuring that product development remains aligned with shifting customer needs.
Weaknesses of the Kano model analysis
The model does have some weaknesses. They include the following.
1. Not easy to prioritize within categories
Kano is great for figuring out how customers perceive features in the five categories. But for most SaaS companies, you’ll get a ton of customer requests that mostly fall in the “one-dimensional quality” bucket. Depending on your product, you might have hundreds or thousands of these.
Once you have that bucket, Kano doesn’t do much. You’d need another framework for prioritizing within that bucket.
2. Time-consuming data collection
One main barrier to this method is that it requires you to gather customer feedback through surveys and interviews. That can be time-consuming.
Implementing the Kano model requires a significant investment of resources and effort to collect accurate data, which may be challenging for smaller teams or those working under tight deadlines. (That’s why, I think, lots of young SaaS companies just kind of use a quick Value vs. Effort matrix, guess on value, and move on).
3. Categorization can be challenging
As I’ll show you, there are a bunch of different ways to do the categorization that the Kano method requires. Some ways are better than others.
But even the best way can be kind of subjective because it’s based on customer opinions. Since customers have different opinions, you kind of end up just going with the majority. That subjectivity can make it difficult to achieve a consistent understanding of customer priorities across the entire product team.
How to use the Kano model
At a superficial level, the Kano model is pretty simple. First, you classify your features into one of the above 5 categories. Then, you prioritize features from each category.
Typically, you’ll prioritize like this:
Build the must-be/basic features first
Then build as many one-dimensional/performance features as possible, layering in attractive qualities when there is space and budget
Build indifferent features when there is a good business case, like to reduce tech debt
Identify reverse features to avoid them or remove them from your product
Not so fast…
Important: Don’t use the wrong classification method!
The categorization step is where the Kano model gets complex—and where it can go wrong if you’re not careful.
Few other articles go into this detail, but it’s actually critical.
There are good and not-as-good ways to categorize your features—pick the right method (I can’t stress this enough).
There are several ways you can do the classification. Research shows that some are valid and reliable… but some aren’t. Here are 5 common classification methods, which one to use, and which ones to avoid.
1. The original Kano questionnaire method
TL;DR: Use this model ✅
In their original paper, the creators of the Kano method specified an approach to classifying quality attributes (features) using a structured questionnaire. It consists of pairs of questions for each feature of a product or service. In each pair:
One question asks customers how they would feel if the feature was present (the authors called this a “functional” question)
The other question asks customers how they would feel if the feature was absent (a “dysfunctional” question)
Results are tallied in a table, like the example below.
In this example question, a consumer responds to a Kano questionnaire about airline services. The consumer says that it would be pleasing to buy airline tickets online, but that they can accept it if they can’t buy tickets online. Using the scoring card, that pattern of responses suggests “buying tickets online” is an “attractive” quality for that customer—it is a feature that delights the customers when present but wouldn’t cause dissatisfaction when it’s absent. (Who remembers the days when buying plane tickets online was a “nice-to-have?” 🙋♂️) Source: Mikulić, J. and Prebežac, D. (2011).
Responses are tallied up for each customer, and then you would look at the frequency for each response. If most people said a feature was “attractive”, then it gets assigned to that category.
(For a full guide on using this method, check out this article.)
What research says: This method works—it appears to be reliable and valid*.* It’s one you can use.
2. A penalty-reward contrast analysis
TL;DR: Don’t use this model ❌
A second method for classifying features is the penalty-reward method, originally developed by D. Randall Brant in the 1980s. The idea is that, rather than look at how customers feel about the presence or absence of features, you can look at each feature’s varying effect on customer ratings of overall satisfaction.
Roughly, you do the penalty-regard contrast analysis by:
Getting customer ratings of features. Customers rate each attribute on whether they think a feature performs well, poorly, or about average. They also give their overall satisfaction rating.
Do some fancy stats. Then, you do some multiple regressions for each feature (out of scope for this article, but see here for more guidance). In the end, you get two regression coefficients for each attribute: one quantifies the negative effect that the feature has on overall satisfaction (penalty); the other quantifies the positive effect that it has on satisfaction (reward).
Classify the features. You can use those coefficients to classify the features in the Kano model because you know how people feel if those features are performing well or performing poorly.
What research says: This has questionable validity because it looks at a feature’s performance on overall satisfaction, rather than how people feel about the presence or absence of the feature. In other words, it’s not doing the thing as the Kano model.
This method can provide insight into how features impact customer satisfaction with the product, but it’s not the best for classifying into the Kano model.
3. The Importance grid
TL;DR: Don’t use this model ❌
The importance grid method was developed by an IBM consultant as a tool for classifying new features into the Kano model. It works by comparing, for each feature, implicit importance and explicit importance.
The explicit importance of a feature is the importance customers directly assign to a feature when asked about it. This is typically measured through surveys or interviews. It typically goes on the x-axis of the grid.
The implicit importance of a feature is a quantity that you derive through regression or correlation that represents the relationship between that feature and some global score (like overall satisfaction). It typically goes on the y-axis.
Then you plot each feature on the grid using those values and match them to Kano categories like so:
Must-be qualities have high explicit importance but low implicit importance.
Attractive qualities have high implicit importance but low explicit importance.
One-dimensional qualities have implicit and explicit importance that go together: both are low together or both are high together.
Example of an importance grid using features of an airline’s service. Source: Mikulić, J. and Prebežac, D. (2011).
What research says: This method has questionable validity because it’s not clear that implicit and explicit importance can be used to identify Kano model categories.
Also, there are some technical issues with it. Because it uses regression, the relative position of one feature (and so its Kano category) is influenced by the position of all the others. So a feature’s Kano category can change if the scores of the other features change. In the Kano model, each feature’s category should be independent of the others.
As a Kano classifying tool, it’s not recommended.
4. Qualitative data methods
TL;DR: Don’t use this model ❌
Some practitioners have used qualitative data analysis of critical incidents or customer complaints and compliments to understand how customers feel about a feature. Basically, the idea is that the relative frequency of a feature’s complaints or compliments can tell you about its Kano category:
Must-be attributes: If you receive many more negative incidents or complaints about a feature than you do compliments, that’s may suggest it’s a must-be/basic feature.
Attractive attributes: If you receive many more positive incidents or compliments about a feature than complaints, it might be an attractive/excitement feature.
One-dimensional attributes: If you receive roughly equal numbers of complaints and compliments about a feature, it might be a one-dimensional/performance feature.
What research says: This method is probably valid in that it is consistent with how the original Kano model worked. However, the reliability of actually classifying features into categories is questionable.
More importantly, if you’re already receiving complaints or compliments about a feature, then you don’t need to prioritize it on your roadmap—you’ve already built it.
Thank you, next.
5. The direct classification method
TL;DR: Don’t use this model ❌
Finally, some people just explain the Kano categories to customers and get them to decide directly. Customers are given a survey with a list of features. They’re asked to classify the features into each category.
What research says: This method can be unreliable because respondents may not properly understand the categories, and so your results can be distorted. The original Kano method is preferable.
Basically, skip it.
Alternative prioritization frameworks
There are lots of other feature prioritization methods you could choose to use instead of Kano. Here are some of the other most popular:
Value vs. Effort matrix. A quick and dirty way to find quick wins by scoring each feature on the value it would generate and the effort it would take to build.
ICE scoring model. Very similar to value vs. effort, but also takes into account your confidence in your scoring.
RICE scoring model. Very similar to the Value vs. Effort matrix, but with value separated into two metrics: reach and impact. It also considers your confidence in your scoring as well.
Weighted scoring. Similar to the other three, but more flexible because you can include whatever criteria you like, not just value, effort, reach, and confidence.
The MoSCoW model. This method categorizes features into must-haves, should-haves, could-haves, and won’t-haves. I’m not a huge fan, but some people like it.
User story mapping. Prioritizes based on how customers use the product and what comes next in the story.
The Savio model. First, keep track of what your customers are asking you, along with customer data. Then look for the features that best accomplish your specific business goals.
Read more about the other prioritization frameworks here.
So after all that—what’s up with the Kano model?
The Kano model can be super useful to PMs:
It encourages you to actually talk to your customers and understand their expectations
Done well, it’ll give you a tidy set of categories for your features that can help you prioritize
You’ll end up with a better sense of which features your customers consider essential, which are nice to have, and which you want to avoid to increase customer satisfaction
The categorization process is non-trivial—it’ll take some work getting talking to users to get the data you need
There are some right ways and some wrong ways to categorize (unless you have some good reasons not to, use the original Kano survey methodology)
You might get the results and realize you need another framework to prioritize from within a category—say, to choose between all the “one-dimensional” performance features.
In that last case, try an alternative or check out the strategy we use at Savio.
- Read the complete Guide to Product Roadmaps
- How to create a product roadmap with your prioritized features
- What type of product roadmap should you use?
Brandt, R.D. (1987). A procedure for identifying value-enhancing service components using customer satisfaction survey data, in Surprenant, C.F. (Ed.), Add Value to Your Service: The Key to Success, AMA, Chicago, IL, pp. 61-4.
Engineering Design & Communications (2002). Kano Model Analysis. The University of Calgary.
Mikulić, J., & Prebežac, D. (2011). A critical review of techniques for classifying quality attributes in the Kano model. Managing Service Quality: An International Journal, 21(1), 46–66. doi: http://dx.doi.org/10.1108/09604521111100243
Kareem is a co-founder at Savio. He's been prioritizing customer feedback professionally since 2001. He likes tea and tea snacks, and dislikes refraining from eating lots of tea snacks.
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