A guide to using the Kano model in B2B markets

What is the Kano model?

When a company builds a product, there are typically many features/attributes that they can include. For example, a logistics company developing a new product might consider offering 24-hour delivery or real-time tracking.

If companies want to be profitable, they cannot just include every possible feature in their product. Similarly, they cannot just maximize functionality for each feature they include. Over-delivery makes it even harder to be profitable.

Technology companies face additional product development challenges due to capacity constraints. Adding or improving features requires development resources. There is never enough resource to develop every feature, so changes are added to a product roadmap.

As a result, any B2B company building or developing a product needs to make decisions about:

• Which features to include or exclude
• Which features they are going to ‘maximize’ and which they are just going to offer at a basic level

These decisions are made somewhat easier by the fact that:

• Some features are more important to users than others. Prioritizing the most important ones can lead to better results
• Some features don’t need to be maximized. Offering these features at a basic level will lead to the same level of satisfaction as if you maximize them

When making product development decisions, it can be helpful to have a robust method to identify which features/attributes should be prioritized and maximized.

Over the years, several different models have been developed with this purpose in mind. One of the first of these models, the two-factor theory, was introduced in 1959. This theory puts attributes into two groups:

• Hygiene factors – attributes or features that don’t increase satisfaction when offered but cause dissatisfaction when absent. These attributes should be included in a product but not ‘maximized’
• Motivators – attributes or features that increase satisfaction when offered. The more these features are maximized, the higher the satisfaction

The models that have been introduced since 1959 all try to add more depth to the two-factor theory. One of the most popular was introduced by Noriaki Kano in 1984.

The Kano model assigns attributes and features to five core categories: Mandatory, Performance, Excitement, Indifferent, Reverse.

These categories can be plotted on a chart with two axes. The vertical axis is based on the extent to which an attribute satisfies customers.

Kano practitioners differ in what they put on the horizontal axis:

• Some use ‘functionality,’ which considers how much of a feature the customer gets
• Others consider how much a company invests in the feature or attribute, i.e., how much time, resources, or budget is put into the feature

Of course, these two options are linked. Generally, the more you invest in a feature, the greater functionality it will have. We tend to favor ‘functionality’ when doing a Kano analysis.

Let’s look at each factor in turn.

Mandatory (also called must-have, threshold, or must-be factors)

Mandatory attributes are essentially the same as hygiene factors in the two-factor model.

Mandatory features are expected; customers aren’t impressed if they are offered but become dissatisfied if they are not.

To put this in terms of the Kano chart, a small amount of functionality significantly increases satisfaction, but satisfaction hits a ceiling. No matter how much of that attribute you deliver, customer satisfaction doesn’t improve.

In other words, once you have hit customers’ basic expectations, you don’t need to invest any more in these features or attributes.

Let’s take the example of a company building a B2B fintech product. Customers will expect the product to have 99% uptime. The fintech company can invest resources to exceed these expectations, but there is no real benefit. Customers will never praise the product for its uptime, only criticize it when there’s downtime.

Performance (also called one dimensional or satisfied factors)

The better a Performance feature or attribute, the more satisfied a customer becomes.

Unlike Mandatory factors, there is no ceiling on satisfaction. If you continue to improve functionality, you’ll continue to be rewarded.

This is reflected on the Kano chart, where Performance is a straight line to demonstrate the linear relationship between functionality and satisfaction.

Let’s return to the example of the B2B fintech company. A Performance attribute might be ‘ease of use,’ as each UX improvement will boost satisfaction.

Excitement (also called attractive, differentiators or delighters)

In the two-factor model, Excitement and Performance factors are grouped. The Kano model separates them because there is a critical distinction between the two.

Excitement factors are unexpected features that cause positive reactions and increase a company’s competitive edge. As a result, they are attributes that a company should prioritize.

Excitement factors only require a small amount of functionality to increase customer satisfaction. Once you increase the functionality further, there are even more significant improvements in happiness. However, there can become a point at which further boosts see diminishing returns.

A classic example of an Excitement factor was free WiFi in hotels. In 2011, free WiFi was not standard. Hotels that offered free WiFi saw a significant increase in their online ratings. Academic studies found that free WiFi was the biggest driver of hotels’ ratings on TripAdvisor. Those that invested in fast, reliable WiFi saw particularly positive reviews.

Of course, in 2021, free hotel WiFi is no longer an Excitement factor (see below).

Indifferent

These factors are neither positive nor negative. Customers’ indifference towards these features is the same regardless of their functionality.

In other words, they are a waste of money.

Reverse

In simple terms, these are attributes or features that decrease satisfaction when you offer them more.

This category doesn’t always appear in descriptions of the Kano model. There are two reasons for this:

• Reverse features or attributes don’t tend to be included in the Kano analysis process because they don’t make sense to offer. For example, B2B fintech would not introduce a feature that exposed customers’ data to hackers. You don’t need to include this feature in the analysis process to work that out
• In some instances, a feature might appear to be a Reverse factor because the individual participating in the research was distracted or disengaged when answering the questions. Responses like this should be removed from the analysis process to improve the quality of the research

Benefits and drawbacks of conducting Kano analysis

Kano analysis provides a framework for exploring customer needs that offers clear guidance about product development:

• Eliminate features that customers are indifferent towards
• Avoid wasting unnecessary time on attributes that will provide the same level of satisfaction no matter how many resources you invest in them
• Prioritize features and attributes that will boost satisfaction

There are, however, some drawbacks to using the Kano model:

• Kano analysis typically requires a survey which can be quite dull for respondents
• The model has a bias towards features that customers already know and understand. Features or attributes that are genuinely innovative may not perform well in the analysis because survey participants are unable to grasp how unique and beneficial they are
• The model doesn’t consider price. Some features may be desirable to a customer but cost so much to deliver that they lead to a significantly higher price. Customers may be unwilling to pay that price for the product. If you do need guidance on pricing, you may need to use some B2B pricing research techniques, such as conjoint

How to conduct Kano analysis in B2B markets

#1. Start with which features you want to test. Don’t pick too many

Kano analysis is generally based on a quantitative survey of the target audience. When designing a Kano analysis questionnaire, you need to start by creating a list of features or attributes to test.

Most B2B market research practitioners are familiar with the concept of MECE, which is the idea that when you are designing a list of answer options, it should be Mutually Exclusive and Collectively Exhaustive.

The ‘Collectively Exhaustive’ part can be particularly problematic when conducting a Kano analysis. An ‘exhaustive’ list of features can quickly become very long. The result is a survey that is fatiguing for respondents and a dataset that is overwhelming to analyze.

When doing a Kano analysis, try to avoid testing more than 15-20 features.

#2. When describing features, don’t skimp on the detail

The Kano analysis process assumes that participants have a detailed understanding of what each feature does. If you don’t spend the time properly explaining a feature, there may be some fundamental issues with your data.

Kano practitioners recommend a ‘show not tell’ approach. In other words, try to represent the feature visually with a prototype or screenshot.

If visualization is not possible, then make sure to explain the feature in detail. This description should emphasize how the user benefits.

#3. For each feature, you will need to ask 2-3 questions

For each feature, you will need to ask at least two questions.

The first is called the ‘functional’ question. It asks, ‘if the product has [feature/attribute], how do you feel?’

The second is called the ‘dysfunctional’ question. It asks, ‘if the product doesn’t have [feature/attribute], how do you feel?’

Both questions have the same answer options:

• I like it
• I expect it
• I am neutral
• I can tolerate it
• I dislike it

These two questions allow you to determine each feature’s importance to users by assigning each feature into one of the five categories (see below for an explanation of how to analyze the data).

But a third question can add more context. Some practitioners ask participants how important each feature is to them, using a scale from ‘not at all important’ to ‘extremely important.’

The value of asking this question is that it can help with prioritization. For example, if the initial analysis identifies three Performance attributes, you can refer to the ‘self-stated importance’ question to see which of the three matters most to users.

#4. Use the Kano analysis framework to assign features to categories

Once you have collected the data, you need to analyze it. The good news is that there are some existing frameworks that you can leverage.

The most common framework is the table below. It allows you to assign each feature to one of the core five categories.

To start, you need to calculate the overall result for each feature’s functional and dysfunctional questions. For example, you might find that the target audience ‘expects’ to have Feature X and dislikes it when Feature X is absent.

There are two ways of doing this – for details on how to do this, contact us.

Once you have the results, you use the table below to assign a category to each feature. For example, Feature X would be ‘Mandatory.’

You’ll notice that the table below has a sixth category: Questionable. These factors are the result of contradictory responses – how can you like having a feature and like not having it? – and respondents who give such conflicting answers should probably be removed from the data.

#5. Overlaying data about respondents’ relationship with you can add more context

Kano analysis provides clear guidance about what the target audience thinks you should prioritize.

However, should each customer or prospect be treated equally? For example, if the only people who care about Feature X are prospects who have little intention of working with you, should you be focusing on that feature? Similarly, if your most loyal customers see Feature Y as ‘attractive,’ but it’s a ‘dissatisfier’ for everyone else, should you prioritize it?

Overlaying data about participants’ relationships with you can provide meaningful context and ensure you aren’t distracted by features that won’t benefit you.

Specifically, you can overlay:

• Whether they’re a customer or prospect
• How likely they are to work with you in future
• Their affinity towards your brand. Some will suggest using NPS for this, but we believe that NPS is not suitable in B2B markets

Doing this extra bit of analysis allows you to isolate market segments whose preferences should be prioritized. Follow our link to see more about B2B market segmentation.

#6. Consider qualitative research techniques to add further context

If you want to conduct a Kano analysis, quantitative research is essential. However, it’s worth considering additional research techniques to add extra context.

A survey will tell you which features are ‘attractive’ or ‘expected,’ but not why. B2B qualitative research allow you to explore the ‘why’ in more detail, providing additional context.

#7. Consider using a Jobs-to-be-Done approach to Kano

Jobs-to-be-Done (JTBD) research allows you to unlock a better understanding of the target audience’s needs and attitudes.

The basic premise of JTBD is that when businesses are launching a product or trying to acquire customers, they often focus on the wrong thing. Specifically, they focus on who their existing customers are and on which products those customers are currently buying

The JTBD framework suggests that you think more broadly and focus on the ‘job’ that customers are hiring a product for.

The JTBD framework can be incorporated into the Kano model by adapting the functional and dysfunctional questions to focus on ‘jobs,’ not features:

• Rather than asking, ‘if the product has [feature/attribute], how do you feel?’, ask, ‘if you were able to do [job-to-be-done], how do you feel?’
• Rather than asking, ‘‘if the product doesn’t have [feature/attribute], how do you feel?’, ask, ‘if you could not do [job-to-be-done], how do you feel?’

#8. Regularly review the analysis

Needs and expectations change over time. Features that were once ‘Excitement’ factors can become ‘Mandatory’ (and vice versa).

Take the example mentioned earlier in this article of hotels offering free WiFi. In 2011, this was an Excitement factor. In 2021, free WiFi may still impact hotels’ ratings, but differently.

Offering free WiFi doesn’t positively impact hotel ratings because it is expected. But not offering free WiFi is likely to harm ratings.

Customer needs shift for a variety of reasons:

• As in the example above, adoption becoming more widespread can turn something from ‘Excitement’ into ‘Mandatory’
• As technology evolves and matures, customers’ needs change too. For example, in the early days of smartphones, device storage was one of the most important features. Once cloud storage arrived, device storage no longer mattered
• The emergence of competitors who disrupt the market – to learn how to spot potential disruptors, see our guide on B2B competitor research

Ultimately, a Kano analysis only measures a moment in time. It needs to regularly be repeated so that you can track changes in needs.