The Kano Model is a popular customer satisfaction framework used by businesses to understand the needs and expectations of their customers. It helps businesses to identify what features or attributes of their products or services are essential for customer satisfaction and which ones are merely nice to have.

But what is the Kano Model, and how did it come to be? Let's dive into its history and key concepts to better understand this essential business tool. Also, we will show you how to build Kano Model to analyze customer satisfaction effectively by establishing a Kano Model Example.

The Kano Model, pronounced as "Kah-no," is a product roadmap prioritization approach that considers the degree to which features can satisfy customers. It helps product teams weigh the benefits of high-satisfaction features against their implementation costs to determine their strategic value. The Kano Model is one of several prioritization frameworks that product teams can use to rank their initiatives.

Unlike other prioritization frameworks, the Kano Model's main focus is on how much new features will satisfy users. It groups potential new features into various categories, ranging from those that could disappoint customers to those likely to satisfy or even delight customers. This customer-centric approach makes the Kano Model unique and helps product managers prioritize new features to improve customer satisfaction.

The Benefits vs. Cost Model, for example, might use customer satisfaction among its scoring criteria but might also use different criteria, such as increased revenue. However, with the Kano Model, the key consideration for any new feature is how much it will satisfy users. This approach ensures that the product team's efforts are directed toward creating features that truly delight customers, ultimately leading to greater success for the product.

The Kano Model was developed by Dr. Noriaki Kano, a Tokyo University of Science professor of quality management, in 1984. According to author Dave Verduyn, Dr. Noriaki created this framework while researching factors that contribute to customer satisfaction and loyalty.

The Kano Model divides potential customer reactions to a new feature into five categories, which range from dissatisfaction to indifference, all the way up to what many consider as customer-attractive or exciting features.

When applying the Kano Model, product teams compile a roster of potential new features that are in contention for resources and a place on the roadmap. The team will then evaluate these features based on two opposing standards:

• Their capacity to please customers; and
• The cost required to implement them.

The Kano Model can also be regarded as the "Customer Delight vs. Implementation Investment" strategy.

The Kano Model helps teams understand the degree to which features on a product roadmap will satisfy customers. The model divides potential customer reactions to a new feature into five categories. Here are the five Feature Categories of the Kano Model, with detailed descriptions of each:

Must-Have Features are the basic requirements that customers expect in a product. If these features are not present, the product is considered incomplete and unsatisfactory. For example, a car must have a steering wheel, seats, and an engine to be considered functional.

Performance Features are the features that customers directly associate with their satisfaction. These are the features that customers usually compare when choosing a product over another. A car's acceleration, braking distance, and fuel economy are examples of performance features.

Attractive Features are not expected by customers but add extra value and can enhance customer satisfaction. These features can differentiate a product from its competitors and can lead to increased customer loyalty. A car's sunroof, heated seats, and ambient lighting are examples of attractive features.

Indifferent Features are those that do not affect customer satisfaction or dissatisfaction. They are not considered necessary, nor do they add any extra value. A car's antenna or tire pressure monitoring system may be considered indifferent features.

Reverse Features are the features that initially seem attractive to customers but, when added, can lead to dissatisfaction. These features can be counterproductive and negatively affect customer satisfaction. An example of a reverse feature is a car's navigation system that is difficult to use and leads to frustration.

Out of the five customer reactions identified in the Kano Model, two should be avoided: indifference and reverse quality. The remaining three categories, which include attractive, one-dimensional, and must-be quality, are considered desirable to incorporate. Prioritizing these three categories in a project, service, or product can lead to a positive association and the intended satisfaction. Conversely, if any of these categories are missing, dissatisfaction can gradually set in over time.

The Kano Model is an effective tool for product teams to use when determining which features to prioritize on a product roadmap. By analyzing customer needs and preferences, the team can build products that meet customer expectations, enhance satisfaction, and increase loyalty.

So how can we use the Kano Model to assist with business decisions? Next, we will provide a practical Kano Model example to explain it.

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Company A's product is scheduled for a version update next month. The Product Manager has collected product feature update requirements from various sources. To determine which features need to be updated, PM decides to use the Kano Model for requirements analysis.

The data analysis tool used for this practical analysis is the self-service business intelligence tool FineBI.

FineBI is a modern big data analytics & BI software designed for everyone who requires data analysis. Everyone of any level can use FineBI to easily establish professional business reports and BI dashboards, perform self-service data analysis, gain useful insights, and thus drive your business forward.

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In FineBI, data processing is also very convenient. Through functions such as filtering and merging calculations in FineBI, data can be processed quickly without writing SQL statements or other codes.

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Moreover, using FineBI makes it easy to build various classic data analysis models, such as the KANO analysis model discussed in this article, as well as other models like the BCG Matrix (Growth Share Matrix), RFM Model, DuPont Analysis Model, etc., to help businesses gain insights.

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FineBI can liberate business professionals from the quagmire of data processing and visualization, allowing them to focus more on data analysis, management, and business communication.

Let's now demonstrate how to use FineBI for quick Kano Model analysis.

As a product manager, a lot of product requirements are often encountered. The software engineers are already very occupied, but the users seem to want everything. With limited software development resources, how can we find out the real user needs? Give high priority to really important needs.

PM decided to introduce the "KANO model" to sort out the needs of the system, analyze and refine the needs, and improve efficiency.

PM surveyed about 100 users and used the KANO model to draw a four-quadrant diagram.

1.  Survey questionnaire design

Each function in the KANO questionnaire must have forward and reverse questions, for example:

 2. Process data

1) Upload "KANO raw data" to FineBI. Add a self-service dataset and check all the fields of "KANO raw data", as shown in the figure below:

2) Add a new column "Merge Attitude", merge "Add Function Attitude" and "Not Add Function Attitude", as shown in the figure below:

According to the user's "Add Function Attitude" and "Not Add Function Attitude", we can finally use the following table to locate what a function is for users.

• M: Must-be /basic demand;
• O: One-dimensional/performance demand;
• A: Attractive/excitement demand;
• I: Indifferent demand;
• R: Reverse demand;
• Q: Questionable result.

3) In the last step, we already know how to locate the demand type, the next thing to do is to locate the judgment in the analysis table and add the type column, as shown in the following figure:

Since the formula is very long, the user can directly copy the following to the formula column, and replace the "Merge Attitude" with their fields, using the switch function:

SWITCH (Merger Attitude, "like very much like very much","Q","like very much should be so","A","like very much it doesn't matter","A","like very much reluctantly accept","A","like very much dislike very much","O","should be so like very much","R","should be so should be so","I","should be so it doesn't matter","I","should be so reluctantly accept","I","should be so dislike very much","M","it doesn't matter like very much","R","it doesn't matter should be so","I","it doesn't matter it doesn't matter","I","it doesn't matter reluctantly accept","I","it doesn't matter dislike very much","M","reluctantly accept like very much","R","reluctantly accept should be so","I","reluctantly accept it doesn't matter","I","reluctantly accept reluctantly accept","I","reluctantly accept dislike very much","M","dislike very much like very much","R","dislike very much should be so","R","dislike very much it doesn't matter","R","dislike very much reluctantly accept","R","dislike very much dislike very much","Q")

Results as shown below:

4) Add "Group Summary" to get the number of people with various demand types for each function, as shown in the following figure.

For example, among the number of people participating in the survey, 48 people think that "function1" has no different needs.

5) Because some users skip questions during the survey process, the number of people participating in each function survey is different. Add a new column "Number of people participating", and select "within the group all values", as shown in the figure below, to find the number of people participating in each function survey.

6) Calculate the proportion and find the proportion of each demand type in the number of people participating in the survey.

For example, the proportion of type "I" people of "function1" to the number of people participating in the "function1" survey is 0.48, as shown below:

3. Make the component

1) Copy 5 "proportion" fields, as shown in the figure below:

2) Perform detail filtering on the copied "proportion1" field, and the filter condition is: the "type" is in "A". And rename it to "A proportion", as shown below:

In the same way, perform detail filtering on the other copied "proportion" fields, respectively filter the types, and rename them, as shown in the following figure:

3) Use the better-worse coefficient, as shown in the figure below.

• better-Satisfaction coefficient improved after adding a certain function: better=(A proportion + O proportion)/(A proportion + O proportion + M proportion + I proportion), the closer to 1, the user satisfaction The stronger the effect of improvement, the faster the increase in satisfaction.
• worse-Dissatisfaction coefficient if not adding a certain function: worst=-1*(O proportion +M proportion)/(A proportion +O proportion +M proportion +I proportion), the closer it is to -1, It means that it has the greatest impact on user dissatisfaction. The stronger the effect of reducing satisfaction, the faster the decline.

According to the above formulas for "better" and "worst", create a new calculation field for "better" and "worse absolute value", as shown in the following figure:

4) Select "Scatter Chart" and drag in the "better" and "worse absolute value" fields. And drag the "function" field into the label bar and color bar of the "Graphic Properties", as shown below:

5) Add "cordon" and "vertical warning line" respectively, which are better average and worse average respectively, as shown below:

And then, the scatter chart of the Kano Model is done!

FineBI is a powerful data analysis tool that allows users to create their dashboards by combining various charts and graphs. As you can see the Kano Model Example in the below picture: it combine two different charts to illustrate the Kano Model Analysis.

In addition to data analysis, users can share their dashboards with colleagues or superiors, making it easier to collaborate on projects using FineBI's data sharing and management function.

FineBI's advanced data management features are designed for enterprise-level use. One key feature is the ability to manage data and analytic reports with enterprise-level permission controls. FineBI enables administrators to manage user permissions, granting or restricting access to data and reports based on the user's role or level of authorization. With FineBI's data management capabilities, organizations can ensure that only authorized personnel can access sensitive or confidential data, improving data security and compliance. This feature provides organizations with a robust data management solution that enables them to manage their data with greater control and efficiency.

FineBI is free for personal use. For enterprises, it offers a quote-based plan that charges according to different situations. In a word, FineBI is price-friendly to all customers.

Click the button below to try FineBI for free. Give it a try today!