The Kano model in a nutshell

A ten minute guide to understanding the Kano model

(This is an excerpt from my complete guide to Kano model).

The subjective component of perceived quality

In the 1980s, Dr. Noriaki Kano, a Japanese professor and business consultant, devised a theory and method to gauge how customers perceive the value of a product feature. That subjective perception determines whether and how a feature’s performance influences customer satisfaction.

The satisfaction with a feature can be directly related to its degree of performance. Think of the mileage of an electrical vehicle: the further the car can go with a fully charged battery, the more satisfied the customer will be.

But there are also features that do not cause increased satisfaction with increased performance. Customers can be “not dissatisfied but not more satisfied” with increased performance. A restaurant where each table has two salt cellars instead of one will not satisfy customers more. In this case, there is no linear relation between performance and customer satisfaction. Either it’s good enough (one salt cellar), and the customers are satisfied. But if there are no salt cellars, customers are dissatisfied.

How a feature is perceived differs between customers. A race car driver will feel differently about the time it takes for a car to reach top speeds than a regular driver, even though the car’s objective performance is the same. The race car driver’s satisfaction will be linear to the car’s performance while the regular driver will be “not dissatisfied” when the car’s acceleration is above a certain threshold of expectation. Kano categories of customer satisfaction

The Kano model categorises how a feature’s performance (including its presence and absence) can impact customer satisfaction.

Suppose you are a product manager at DropBox, the cloud file storage service. Management wants you to draft the roadmap for future development. The more impact you can have on customer satisfaction, the better.

You were handed five ideas to investigate. You must report back with your recommendations.

  • Automatic file synchronisation across devices;
  • Decide whether to keep part of the storage free;
  • Invest more in the automatic creation of photo albums;
  • Decide whether files should be sortable by filename length;
  • Decide whether files should be automatically deleted after 30 days.

Now you have to decide what amount of effort to spend on each feature. You don’t want to waste any precious effort. Will increasing a feature’s performance improve customer satisfaction? If so, how?

The relation between performance and customer satisfaction is different for each of these five features. By studying this relation and attributing each feature to a Kano category, you will be able to decide confidently on how to move ahead with them.

Let’s suppose your Kano study (we’ll get to how you do these studies later) leads to this result:

Automatic syncingMust-Be
Part of the storage is freePerform
Automatic photo albumsAttract
Sort by filename lengthIndifferent
Delete files after 30 daysReverse

The “Must-Be” category

Dropbox customers are satisfied when automatic synchronisation of files between devices performs above a certain threshold. Customers think It’s only natural that their files are synchronised automatically. If things work as expected, all’s well.

Features such as these are categorised as Must-Be features. Without these kinds of features, customers will not even consider using your product. It will feel incomplete. The feature’s absence (or underperformance) is cause for dissatisfaction.

(Must-Be is the most common moniker for this category, but in my own studies I prefer to call it Natural because that’s closer to the original Japanese concept. I’ll keep using Must-Be in this guide, as that has become the common term.)

Must-Be features must be on your product roadmap, no matter what. Take care not to overengineer this kind of features; there’s a threshold of performance above which customer satisfaction no longer improves.

Remember the salt cellar example:

[I]f a salt cellar on a restaurant dining table is a Must-Be factor, then guests will be dissatisfied if they find none. The restaurant manager can eliminate this dissatisfaction by providing one (…). However, he or she cannot generate ever-increasing amounts of customer satisfaction by placing more and more salt cellars on each table. (Horton & Goers, 2019)

Your suggestion to management would therefore be: allocate enough resources to make sure syncing never fails. But don’t spend time and effort on speeding things up by a few extra milliseconds.

Satisfaction increases with the performance of Must-Be features, but only up until a certain level.
Satisfaction increases with the performance of Must-Be features, but only up until a certain level.

The “One-Dimensional” (or “Perform”) category

Your study shows that the second feature, the amount of free storage provided, elicits a different customer feeling than the file syncing feature. It influences customer satisfaction differently than the file syncing feature: customer satisfaction increases together with the amount of free storage. The more free storage, the better.

These are called One-Dimensional (or Perform) features. They are what products typically compete on. Most marketing campaigns focus on these types of features.

Your suggestion to management here would be to keep an eye on the competition. Either stay ahead by offering more free storage than anyone else, or at least stay on par with them.

 Satisfaction with one-dimensional features is directly related to their performance.
Satisfaction with one-dimensional features is directly related to their performance.

The “Attract” category

Dropbox automatically creates a shareable album of the photos in your Dropbox folder. Your study shows that customers find this feature an unexpected delightful surprise. Whether the album is the best kind of digital photo album you can find on the market is less important to them.

An automatically generated album increases customer satisfaction, but its absence will not create dissatisfaction. Customers will not be dissatisfied if the feature is missing (as would be the case with a Must-Be feature), nor will their satisfaction increase together with the performance of the feature (like with a One-Dimensional feature). Above a certain threshold, the feature’s performance (how well it is executed) has no influence on customer satisfaction. This feature therefore falls in the Attract category.

Attract features are typically product differentiators. They set your product apart from the competition by their presence, not by their performance.

Your recommendation to your management would be that as long as automatic album creation is enough of a differentiator and as long as the competition is not catching up, they should not invest too much in making it perform better.

The presence of an Attract feature generates satisfaction, but after a certain point their performance has little influence on customer satisfaction.
The presence of an Attract feature generates satisfaction, but after a certain point their performance has little influence on customer satisfaction.

The “Indifferent” category

Customers don’t care about the ability to sort the files in their Dropbox account based on filename length. Neither the feature’s presence or performance influence customer satisfaction. Sorting files by file length is a feature that falls in the Indifferent category.

Your recommendation to management is simple: don’t invest in this feature.

Don’t jump to this conclusion too quickly though. Customers can be indifferent about a feature because they don’t understand its value yet.

Neither presence or absence, nor performance have an influence on satisfaction.
Neither presence or absence, nor performance have an influence on satisfaction.

The “Reverse” category

Deleting customer files that are older than 30 days would generate dissatisfaction (to put it mildly). Features such as these are in the Reverse category. Their presence or performance contribute to customer dissatisfaction.

Turning these types of features on their head may lead to the discovery of better ideas. You could for instance suggest that Dropbox provide a service that guarantees 100% safekeeping of files, indefinitely. Features that turn out to be in the Reverse category can be a source of innovative solutions.

Customer satisfaction is inversely related to Reverse features’ presence and performance.
Customer satisfaction is inversely related to Reverse features’ presence and performance.

Basing product decisions on feature categories

The Dropbox example shows that a feature’s category informs a team on what actions to take:

FeatureCustomer attitudeCategoryRoadmap priority
Automatic syncing of files across devicesNot dissatisfied if present, dissatisfied if absentMust-BeMust be part of the product, but don’t spend effort in surpassing expectations
Part of storage is freeThe more, the betterOne-DimensionalIf it’s possible to beat the competition, put it on the roadmap and make it best in class
Automatic creation of photo albumsNot dissatisfied if absent, satisfied if presentAttractIf it makes your product stand out from the rest, do it.
Files can be sorted by filename lengthNeither performance nor presence have impact on satisfactionIndifferentDon’t bother or check whether value is clear to the customer.
Files are automatically deleted after 30 daysThe less, the better.ReverseAbsolutely don’t do this. Maybe try and turn the feature around?

For this Dropbox example, the feature categories are quite predictable.

Or are they? Are the categories really that clear-cut? Feature categories evolve over time. It’s therefore important not to assume you know what category a feature will be in.

For example, automatic synchronisation of files once was a distinguishing feature that Attracted customers to the Dropbox service. Now it is a Must-Be feature. Between these two phases, it was a One-Dimensional feature, where companies competed on performing the synchronisation better (across more platforms, faster, etcetera).

And the photo album feature may very well evolve into a One-Dimensional or even Must-Be feature too one day. Customers may come to expect its presence. And who but the actual customer is to say sorting by filename length falls under the Indifferent category? Maybe there’s a segment that attaches a lot of value to this feature.

Rule number one in product development is that you should never assume you know what your customers are thinking.

Luckily, Noriaki Kano did not only develop a theory for feature classification. He also devised a practical method for surveying customers and determining the categories of features based on that survey’s outcome. Surveying customers to categorize features

The Kano method uses a specific survey format and evaluation method to categorize features.

For each feature, customers answer how they feel about a feature’s presence (or performance) and how they feel about its absence (or underperformance). For each question, their answer must be one of:

  • I like it
  • I take that for granted
  • I don’t care
  • I can live with it
  • I dislike it

The wording for each answer can be different, but the underlying intention must be: Like, Expect, Neutral, Accept, Dislike.

Let’s say you’re building a voice-controlled TV. In order to save costs, you are considering leaving out the remote control. Of course you’re wondering how customers will react.

This is how you ask them using the Kano survey format and how one customer may have answered:

The voice-controlled TV comes with a remote control
( ) I like it
(x) I take that for granted
( ) I don’t care
( ) I can tolerate it
( ) I dislike it

The voice-controlled TV does not come with a remote control
( ) I like it
( ) I take that for granted
( ) I don’t care
( ) I can tolerate it
(x) I dislike it

The customer’s answer (Expect for functional, Dislike for dysfunctional) is then evaluated and categorised using this standard lookup table Dr. Kano created:

Feature presenceFeature absence

The participant in the survey takes the remote control’s presence for granted (Expect, row 2) and dislikes its absence (Dislike, column 5). According to the evaluation table, this makes the feature a Must-Be type of feature.

(The Q-category stands for “Questionable”, indicating the response makes no logical sense. For instance, you cannot like that the TV comes with a remote and at the same time like the absence of the remote).

If you have heard of the Kano model before, you’ll have encountered alternatives to the original lookup table. I stick to the original because I believe many of the alternatives developed over the years stem from a fundamental misunderstanding about the meaning of the answer options and the Kano categories.

Of course, you should not take one customer’s response as the basis for your decisions.

Determining a feature’s category is done by tallying all responses. A survey with 80 customers could for example result in the feature being a Must-Be feature because the majority of answers categorise it as such:

FeatureMOAIQFinal category
Remote control5412842Must-Be

That’s it?

The Kano model is an easy method to apply, and once you have internalised it, it’s hard not to see products and services through the lens of Kano categories.

But there’s a lot hidden under the surface. You are probably already wondering what you can use the method for, how to interpret less clear-cut results than the one in the example given, whether you are allowed to change the survey format, how to formulate your questions and the answer labels, whether features can change categories over time, and so much more.

The complete guide to Kano model I’m writing aims to answer all the questions you may have about this method, based on existing research, my own experience using the model and helping other people use it.