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Comparing performance of feed-forward neural nets and k-means for cluster-based market segmentation


Hruschka, Harald; Natter, Martin (1999). Comparing performance of feed-forward neural nets and k-means for cluster-based market segmentation. European Journal of Operational Research, 114(2):346-353.

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.

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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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
Dewey Decimal Classification:330 Economics
Language:English
Date:1999
Deposited On:09 Dec 2016 10:37
Last Modified:17 Nov 2017 12:31
Publisher:Elsevier
ISSN:0377-2217
Publisher DOI:https://doi.org/10.1016/S0377-2217(98)00170-2
Other Identification Number:merlin-id:14215

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