Kano Model
This product is coming out soon.
The Kano Model is used to analyse and identify consumer preferences for features and group them into multiple categories. Categorise up to 15 features according to the relationship between a product's attributes and customer satisfaction to help identify which features you should focus on when developing your product. Features are categorised into the following categories:
- Must-be needs: These features are expected to be delivered well. Exclusion or poor implementation of these features will cause consumers to view the product as a failure
- Attractive needs: These features are not expected by consumers, but will lead to high satisfaction from consumers if included
- Performance needs: These are features that contribute to satisfaction proportionally. The more performance features a product has, the greater the overall satisfaction
- Indifferent, Questionable, or Indifferent needs: These are features that consumers are indifferent to, have mixed preference for, or reduce overall satisfaction when included. These are the features that you should ignore when developing your product
The Kano Model is a module of the Pricing page optimisation methodology developed by Conjoint.ly
Respondents are asked to choose between one of four claims, arranged in various combinations.
Kano Model surveys can be automatically translated to more than 30 languages.
Bring your own respondents or buy quality-assured panel respondents from us.
Key outputs of the Kano Model
Feature categorisation matrix
By plotting the average score for functional and dysfunctional scores, features can be categorised into the four main categories - Attractive, Performance, Must-be, and Indifferent. The feature categorisation matrix provides a quick at-a-glance summary of how features are categorised by the average respondent
Feature breakdown showing consumer categorisation of features
The core output of the Kano Model, the feature breakdown table shows a detailed analysis of the categories consumers consider each of the features. Each cell shows the proportion of respondents who consider the feature to be part of that category. The overall category of the feature is determined by the largest proportion. This output allows for a more nuanced view of how features are seen by respondents, showing which features may be considered important for some respondents but not for others.