Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers.
ASJC Scopus subject areas
- Computer Science(all)