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A Comparison of Self-organising Maps and Principal Components Analysis

This paper attempts to compare self-organising maps (SOM) and principal components analysis (CPA) by applying them to the marketing construct ‘retail store personality’. Data were collected for the retail store personality construct via a validated scale from previous studies that had used the mall intercept technique. A total of 367 people responded, of whom 353 were found to be valid for data analysis. Data were analysed using CPA and SOM; both methods gave comparable clustering results, although the results for SOM were quite conclusive. In addition, we found that SOM complemented PCA by providing visual clustering results far superior to those of PCA. SOM can be used to further analyse PCA data using visual clustering features; both could be used in tandem. Although SOM have been used in a number of studies in marketing, this is the first paper to compare PCA and SOM on terms of application to the marketing construct ‘retail store personality’.