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

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

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Article ID: iaor20001757
Country: Netherlands
Volume: 114
Issue: 2
Start Page Number: 346
End Page Number: 353
Publication Date: Apr 1999
Journal: European Journal of Operational Research
Authors: ,
Keywords: marketing
Abstract:

We compare the performance of a specifically designed feedforward artificial 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 different usage situations. The proposed feedforward neural network model results in a two segment solution that is confirmed by appropriate tests. On the other hand, the K-means algorithm fails in discovering any somewhat stronger cluster structure. Classification 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.

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