Designing a product satisfaction model using customer segmentation and information consolidation

Abstract

This study proposes a prediction model, based on Kansei Engineering, which applies the concept of consumer segmentation and information consolidation. When constructing a mutual satisfaction model for each cluster, the extracted parameters showing different levels of consumer influence were then treated as retrieval data by applying Ordinal Regression (OR). This study also tried to construct a satisfaction model for a cluster of consumers instead of just focusing on an individual satisfaction model, which is less valuable in real-life situations. The combined application of Fuzzy C-means and Ordinal Regression are considered worth using as the data needed. The combined application is less complicated compared to other forms of numerical regression analysis. It is a great benefit to designers as it lessens the time required to explore consumer satisfaction data at the early stages of the design process. © 2013 Springer-Verlag Berlin Heidelberg.