The demands of users are increasing. In times of "minimum viable products" (MVP) and agile development, users no longer accept long development cycles like they did just a few years ago. Decision makers face difficult decisions in the face of tight budgets and limited resources:

• Which functions are important for my users at all?
• With which functions can I inspire my users?
• Which functions should be implemented with priority, which functions last?

In order to set the right priorities for development, the Kano model can help. The model describes the connection between the fulfilment of customer wishes and their satisfaction with the product. Four categories are distinguished here:

• Basic factors: Of course, great dissatisfaction in the case of non-fulfillment. Does not contribute significantly to satisfaction.
• Performance factors: Lower degree of dissatisfaction in the case of non-fulfillment, but at the same time contributes to satisfaction during implementation.
• Enthusiasm factors: Beneficial features that the user does not necessarily expect. Implementation leads to high satisfaction.
• Neutral factors: No importance for customers, do not lead to dissatisfaction in case of lack, nor does implementation lead to satisfaction. These can therefore rather be neglected.

Getting the right ideas in advance

To obtain valid data, it is important that the features are all queried at the same level of abstraction. So it is not useful to query "function that informs me about nearby gas station when I am running low on fuel" and "on-board computer" together, because the on-board computer itself has different functions again. It is equally important that the features are understandable for the end user. Therefore, abbreviations and anglicisms should be avoided. However, it is also possible to dispense with the name of the feature itself and instead describe it in easily understandable words such as "a function that can xyz". Only if the features are thoroughly elaborated at the beginning of the study can the results be translated into appropriate recommendations for action.

Recognize trends in time with Kano

In the course of time the expectations of the users shift. An enthusiasm feature can first develop into a performance feature and later into a basic feature, i.e. it has a life cycle like a product itself. As a result, satisfaction can decrease while the product remains the same. In order to map this habituation effect, it makes sense to divide the sample into several groups based on criteria such as the technical affinity of the users. If it turns out that a feature is classified as a performance factor by people with little technical affinity, but is already considered a base factor by people with more technical affinity, it can be concluded that a stronger habituation effect sets in for this feature.

Conclusion

Kano is not only an excellent tool to help decision-makers by quantifying results, but in addition, with the right data, it is possible to predict future user expectations.