Finding and explaining clusters of consumers using the CLV approach

In consumer studies, liking scores for a set of products are usually collected from a panel of consumers. When additional information is available both on products and consumers, the data can be organized in an L-shaped structure. The CLV (Clustering around Latent Variables) approach which was originally designed to identify segments of consumers according to their preferences is extended in order to take account of product characteristics data or/and consumer background information.

Research highlights
► We aim at segmenting a panel of consumers in preference studies.
► We use the CLV approach to cluster the consumers in the context of L-shaped data.
► CLV is extended to take account of external variables on products and consumers.
► L-CLV is exemplified using a case study, with comparison to L-PLS Regression.