Comparing performance of feedforward neural nets and K-means for cluster-based market segmentation

We compare the performance of a speci®cally designed feedforward arti®cial neural network with one layer of hidden units to the K-means clustering technique in solving the problem of cluster-based market segmentation. The data set analyzed consists of usages of brands (product category: household cleaners) in di€erent usage situations. The proposed feedforward neural network model results in a two segment solution that is con®rmed by appropriate tests. On the other hand, the K-means algorithm fails in discovering any somewhat stronger cluster structure. Classi®cation of respondents on the basis of external criteria is better for the neural network solution. We also demonstrate the managerial interpretability of the network results. Ó 1999 Elsevier Science B.V. All rights reserved.