CHAPTER 1Why Prioritization Will Make or Break Your Product
CHAPTER 2Why Insights Are Essential and How to Source Them
CHAPTER 3How to Select the Right Prioritization Framework for Your Product
CHAPTER 4The 7 Most Popular Prioritization Frameworks
CHAPTER 5Roadmaps: The (Visual) Result of Your Prioritization Process
The Kano model is a framework used to prioritize features on the roadmap based on how likely they will satisfy or delight users. Your team should pull together a list of features to be considered, and plot them on a chart that visualizes satisfaction versus functionality. It points towards how desired or needed a feature can be, or if users are simply indifferent to it.
Whenever your team is considering the list of features to work on for your next release, and would like to figure out what mix is the best. This will also result in the best allocation of limited resources and time.
Kano allows you to work out the right combination of:
Minimum basic features that must be included
Performance features to start working or investing in
Which delight features will impress users the most
Here’s where things get interesting; to identify how satisfied or even delighted users will be with a product, we have to consider the two dimensions (or plotted axes), satisfaction versus functionality. How will users react to the level of functionality of our features?
Let’s break it down:
Satisfaction (Y-axis) :
The vertical axis measures the level of satisfaction, and it ranges from frustration (or complete dissatisfaction), to delight (or complete satisfaction).
Please note: This doesn’t always work as a linear scale, as you’ll come to learn in the following section, and it’s impossible to always stay at the top of the scale.
Also called investment or sophistication by some, this represents how well we’ve implemented a certain feature, how much has been invested in its development, or how much of a particular feature the user gets. It ranges from None to Best (or Very Well Done)
Looking at the graph we can quickly identify and classify the features into four different buckets:
Basic Features: also known as must-be features. These are expected by your users but they won’t satisfy them more. Without them, they won’t even consider your product. For example, we expect our email to be able to import or look up contacts, or a messenger app to send messages. If they don’t have this or the feature doesn’t work, users will simply go elsewhere.
We expect these features to be there and work, and therefore we see that as our product team puts more effort or money into making them more functional, our satisfaction grows. Yet it will never reach the positive quadrant.
Once it reaches its maximum potential, the team can stop investing effort into it.
Performance features (one-dimensional): The more of these we give users, the more satisfied they’ll be, therefore moving in a linear direction. As we increase functionality, so does our investment. Examples could be storage space on your cloud service or faster internet from your provider.
Attractive features: also known as delighters. These are pleasant surprises that the user isn’t expecting, but as the name suggests, once introduced they immediately generate excitement. Introducing these features can be what differentiates you. Think about the time Apple introduced Apple Pay from your iPhone, or the first time you were able to collaborate on Google Docs.
These are the kind of features that make you go, “Wow! How cool!”, and if plotted on the graph, it’s easy to see how the slightest increase of functionality will rapidly increase satisfaction.
Certain features simply don’t make much of a difference. The user feels indifferent towards their presence or absence, and they, therefore, don’t have an impact on the interaction or reaction. In other words, you should avoid working on these.
Categories change over time in a dynamic environment, just like user expectations. Our users will change their perception of product attributes in the future. What now seems to be a game-changer to you, might become a standard or expected a year from now. That’s why Attractive features will eventually transform into Performance and Basic features in time.
What our customers feel about some product attribute now is not what they’ll feel in the future. Attractive features turn into Performance and Must-be features as time goes by.
In order to discover customer insights about your product’s features, we must deploy a Kano questionnaire followed by an evaluation of the different combinations.
The questionnaire consists of questions about the feature we’d like to assess, and the questions are termed functional or dysfunctional forms:
If you have this feature, how do you feel?
If you don’t have the feature, how do you feel?
The possible answers for these questions are:
I like it
I expect it
I am neutral
I can tolerate it
The Kano model is undoubtedly one of the best models to highlight market fit, being customer-centric allowing immediate identification of product advantages and weaknesses through its features.
In order to discover customer insights about your product’s features, we must deploy a Kano questionnaire followed by an evaluation table of the different combinations, that we will then plot on the graph.
In practice, you should also consider adding an extra question, asking how important they consider a certain feature to be.
This answer allows you to distinguish which features are most important to users. It’s a tool to differentiate big from small features and the impact they have on your user’s view on the product.
The beauty of the Kano model is that when we pair up our functional and dysfunctional answers, we uncover how much a feature is wanted, needed or if our users are indifferent to it.
We use an evaluation table to uncover in which categories our features fit, by pairing functional answers with dysfunctional ones in rows and columns.
Before we proceed: You’ll notice that there are two new categories in the table - Questionable and Reverse.
Questionable suggests that someone didn’t quite understand the questions or feature being described.
Reverse suggests that what we suggest is the opposite of what the user wants.
First, you need to fully understand the table and what each pairing means in terms of categories.
Questionable: they are contradictory so you’ll always see them in a diagonal pattern across the table.
Performance features: these are features that users like having and dislike not having. The more performance features, the better.
Must-be (basic): Users dislike not having them, and when present they range from tolerating to expecting them.
Attractive: Users like having features they don’t expect to have. The dysfunctional range will therefore go from ‘expect it’, to ‘tolerate it’.
Indifferent features: Will always be in the middle of the table (as in the graph), as users are always neutral or can tolerate them, in both their functional and dysfunctional answers.
Now you know how each pairing is categorized you’ll want to get all your answers together and organize your data to see where the features go. The two approaches to organizing your data are called discrete and continuous analysis.
If you are new to this and don’t have much time, we recommend using the simpler approach- discrete.
The discrete approach gathers all of your respondents’ answers from the evaluation table. You should then count the total responses per category or demographic, and designate a feature’s category based on the most recurrent response. This will allow you to also place them on the quadrant.
In order to prioritize your results, use the following order:
You’ll end up with a table like this:
It highlights market fit:
fully customer-centric, it allows immediate identification of product advantages and weaknesses through its features.
It tailors a product to the needs of current and target users.
This allows for predictions about features and audiences based on expectations.
Subject to inherent limitations caused by survey delivery
It solely focuses on customer opinion:
this means it ****fails to account for a level of knowledge about the product and individual bias.
Prone to delayed time-to-market :
delays are due to surveying, data collection and processing time.
Would you like to apply the Kano model to your own product? You can download a copy and replicate it yourself.