The Kano ("Kah-no") model is a framework for identifying which attributes should be included in a product or service. This article describes the:
Logic of the Kano model
The central idea of the Kano model is to categorize attributes that could be offered in a product according to the extent to which attributes are required and the extent to which attributes create delight. The interplay of these two dimensions leads to a 3 by 3 matrix, shown below, which places attributes into five categories:
- Performance: Attributes that are both required and create delight. For example, entertainment on a long-haul flight.
- Attractive: Attributes that are delightful, but not required. For example, a glass of champagne when seated on a plane.
- Must-be: Attributes that are required, but don't create delight. For example, safety in air travel.
- Indifferent: Attributes that are neither required nor create delight.
- Questionable: These are theoretically impossible combines of delight and requirement, which if identified in a study suggest data integrity problems.
- Reverse: Attributes that detract from a product. For example, delays in a plane flight.
Required data for the Kano Model
The Kano Model is typically implemented by asking two survey questions for each attribute:
- The functional question measures the extent to which an attribute creates or reduces delight.
- The dysfunctional question measures the extent to which it is required that an attribute be present or absent.
For more information, see Data Requirements for the Kano Model.
Analysis of the Kano Model data
The end goal of the Kano Model analysis is to create a Kano Diagram, where attributes are categorized. For example:
There are a variety of ways of creating such conclusions:
- Categorization Analysis of the Kano Model, which categorizes each attribute for each case (person) in the data set into the six categories.
- Satisfaction Coefficients Analysis of the Kano Model, which creates a scatterplot from the categorization, focusing on the first four categories.
- Continuous Scale Analysis of the Kano Model, which creates a scatterplot from weighted averages of the functional and dysfunctional data.
- Segmentation Analysis of the Kano Model
Performing data cleaning of Kano Model data is advisable.