The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines

In this article a methodology, based on the Kano model, to prioritize the features of a product or service is proposed. Instead of using detailed, lengthy, burdensome, time-demanding, and biased-prone questionnaires, enquiring the user satisfaction with each product feature, a simplified survey asking for the overall satisfaction with the product is used. The proposed method starts by training a machine learning (ML) model using a dataset of different instances of the product and the corresponding perceived quality. This model is then employed to derive the relationship between each feature and the satisfaction associated with them. The shape of this relationship is interpreted according to a Kano model placing each attribute in a bidimensional Kano map which is later partitioned using ML clustering techniques. This methodology has been applied to an open dataset containing the physicochemical characteristics of hundreds of wines and the corresponding scores obtained in a blind tasting evaluation. The research has shown that ML models get very remarkable results predicting the perceived quality of a wine and is able to build a Kano map of the wine features. The ML clustering techniques employed partitioning this Kano map has clearly overperformed conventional rectangular or polar segmentation. It has also been shown that using four categories of features, as it is proposed in the Kano model, is the most reasonable partition from an ML clustering perspective.