With Kano Driver analysis, product features rather than being measured on a single dimension of impact (e.g., low impact, high impact), they are classified into four categories on two dimensions.
The two dimensions on which the attributes are charted on are delight and dissatisfaction, while the categories are:
- Dissatisfiers: these attributes are taken for granted when fulfilled (they won’t produce satisfaction) but result in dissatisfaction when not fulfilled. These attributes are expected and viewed as basic. Example: clear communication about the product.
- Delighters: these attributes provide delight when delivered, but do not cause dissatisfaction when not fulfilled. These attributes are not normally expected and are considered strategic as they can help product differentiation. Example: free extended warranty.
- Performance attributes: these attributes result in satisfaction when delivered and dissatisfaction when not fulfilled. Both their presence and absence are noticed. Example: battery life of a mobile phone.
- Indifferent attributes: these attributes refer to aspects that are not perceived as positive or negative, and they do not result in customer delight or dissatisfaction. Example: size of the package of a mobile phone.
Kano Driver Analysis allows classifying the drivers into the 4 Kano’s categories, and therefore provides additional insight over more traditional approaches.
Categories are not Forever
Over time customer expectations change, leading to a change in the attributes category:
- delighters will become performance attributes and
- performance attributes will turn into dissatisfiers.
Because the position of attributes on the Kano Square evolves over time, it is important for a company to:
- monitor the attributes on a regular basis;
- use a robust approach for the estimation of the attributes’ importance and location on the map; variations in the attributes scores should reflect actual market changes and not noise in the data/algorithm.
The functions kano.importance.analysis available in R-sw Drivers allows Kano analysis. Based on standard variables (i.e., product features assessed for one or more brands and an overall metric such as overall satisfaction assessed for the same brands) the algorithm allows:
- assessing each product feature against the two Kano dimensions;
- assigning the product features to the 4 Kano categories.