Segmentation of a panel of consumers using clustering of variables around latent directions of preference

A procedure of clustering of variables is discussed and applied for the purpose of segmenting a panel of consumers. The underlying principle of the method is to find K groups of variables (i.e. the consumers) and K latent components such that the consumers in each group are as much correlated as possible with the corresponding latent component. The procedure involves running, in a first step, a hierarchical clustering algorithm to determine the appropriate number of clusters and an initial partition of consumers. In a second step, a partitioning algorithm is carried out in order to improve the solution thus obtained. This clustering approach is illustrated using two real data sets. On these data sets, the procedure MD-PREF is also performed and it is shown how it can be complemented by the outcomes of the cluster analysis. In particular, indication about the number of clusters among consumers is given.