The Kohonen Self-organising Map as an Alternative to Cluster Analysis: An Application to Direct Marketing

Abstract

This paper examines the potential of the Kohonen self-organising map (SOM) in a marketing context. It deals specifically with consumer attitudes towards direct marketing. The SOM belongs to the general class of neural network (NN) models, but differs from the now orthodox way in which NNs are implemented. The major difference is that network learning is ‘unsupervised’, in which case the SOM is related to clustering methods. The result of an SOM is a two-dimensional grid of related ‘prototypes’ rather than non-overlapping clusters. The method involves iterative adjustment of the prototypes in such a way as to capture and preserve the properties of the data. We show how the resulting maps offer useful new perspectives.