Kano Model

Key outputs

Discrete Analysis

Through analysing every respondent's feelings toward the inclusion and exclusion of each feature, the features are categorised to represent the largest proportion of respondents.

For some features, where the proportions of responses are quite close, two categories are identified. For example, the 4K HD video feature is classified as attractive by 37% of respondents and indifferent by 28%.

Discrete Analysis is the preferred way of looking at Kano results.

Continuous Analysis

The Feature Categorisation Matrix averages the respondents' functional and dysfunctional scores to allow you to compare features across four quadrants: must-have, performance, attractive, and indifferent.

The example shows that out of the 20 features investigated, the respondents are indifferent to two features, which the company could decide to deprioritise.

Continuous Analysis is quite popular, but wу do not recommend using it because the cut-offs and numerical weights of different categories are not calibratable for each study.

Recommended further actions

The Kano Model helps you achieve product-market fit by classifying your software features according to their attractiveness to users. The following table summarises Conjoint.ly's recommendations to assist you in perfecting your software offering:

Methodology

Developed by Noriaki Kano in 1984, the Kano Model was initially extensively applied to industrial and durable goods. It seeks to describe how product feature affect customer satisfaction. As shown below, the model outlines five different relationships between customer satisfaction and performance:

The most important four categories are:

To discover users' preferences, the Kano Model survey presents each respondent with a pair of functional and dysfunctional perception questions. A functional question asks how the respondent feels when the feature is offered, whereas a dysfunctional question asks about the feeling when the feature is excluded. Take the example Kano Model survey or check out the survey flow section for the samples of Kano Model questions.

Each feature is assigned a category according to the following matrix based on the responses.

For example, when a respondent expects Feature 1 to be included, and dislikes when Feature 1 is excluded, Feature 1 is categorised as a must-have feature for the respondent.

Then, Conjoint.ly performs continuous and discrete analysis.

Benefits of the Kano Model

First, Kano helps efficiently allocate time and resources for your product development activities. Categorising features then allows you to prioritise the features that are necessary for your customers, and those that would greatly increase their satisfaction, while deprioritising features that would have little to no impact on customer satisfaction.

Second, which is more unique to the Kano Model compared to other experiment types, it can tell you which features you need to acquire customers (attractive ones) and which ones to retain customers (must-haves).

Comparing the Kano Model to other research methods

As mentioned above, the Kano Model excels at categorising your product’s features (or attributes), to discover if they are considered hard requirements, satisfaction drivers or if customers are indifferent to their inclusion. This makes the Kano Model a perfect method for exploring potential features to include in a software product, industrial goods, and food where specific nutrients are important.

However, the Kano Model does not consider the monetary trade-off customers make for potential product features. To determine what your customers are willing to pay for your particular SaaS offering, you can combine a Kano Model experiment with pricing tools like the Gabor-Granger or Van Westendorp’s Price Sensitivity Meter, which places the product attributes that you have prioritised into ideal pricing tiers. With the Feature Placement Matrix, you can determine which features to offer at every tier, which features to include in your premium offerings, and which to offer as add-ons.

The Kano Model is great for individual attributes, but it does not involve any analysis on the interaction between the attributes themselves. As such, you could benefit from conducting a conjoint analysis of the categorised attributes obtained from a Kano Model experiment to determine how they can be optimally combined, and at which levels (within those attributes) to create the best overall product. This way, you would be able to identify which set of attributes would be most desired by your customers in trade-off scenarios that mimic real purchasing experiences for your customers.

Want to discuss how combining the Kano Model with other research methods can help you get the most out of your research? Book a call with a Conjoint.ly expert today!

Survey flow

A Kano Model survey consists of the following stages:

Stage 1: Screening questions

The survey begins with screening questions to filter potential survey respondents and ensure only the desired audience is re-directed to complete the survey. Some commonly used screening questions include demographics, usages, and behaviours.

Stage 2: Functional and dysfunctional questions

Next, qualified respondents are asked a pair of functional and dysfunctional perception questions for each feature. A functional question asks how the respondent feels when the feature is offered, whereas a dysfunctional question asks about the feeling when the feature is excluded.

Stage 3: Additional diagnostic questions

Lastly, the survey ends with additional diagnostic questions, such as gender, location, and current software usage, that help you perform the subgroup analysis for more in-depth insights.

Take a sample Kano Model survey to experience how the respondents go through the decision-making process. Or book a call to discover your users' needs now!