Enhancing segmentation systems

Abstract

Market segmentation analysis and market segmentation systems, both geodemographic and household-level, have played an important role in marketing strategy, specifically in direct marketing. Geodemographic segmentation has been successfully used in the commercial sector for more than 30 years. During this brief time period, changes have been made to increase the efficiency and effectiveness of these systems. A great deal of emphasis has been directed towards investigating different model types, for example mixed models, clusters, CHAID and CART, with the goal of separating consumers in terms of their heterogeneous purchasing behaviour. The purpose of this paper is to demonstrate how segmentation systems can be enhanced by modifying the systems with additional data dimensions. We propose that generic segmentation systems, built a priori to a general consumer behaviour analysis, can be enhanced by augmenting the systems with data variables known to be correlated with the behaviour of interest, which are not currently utilised in the segmentation system. Three sample files, representing three different industries, are examined. The files are appended with generic segmentation systems and then augmented with exographic variables (data descriptors that represent environmental phenomena beyond one's immediate being and neighborhood). These exographic variables are added to the pool of candidate variables from which the segmentation system is augmented. The results obtained in this study indicate that the proposed strategy of incorporating relevant data with current segmentation systems has potential value with respect to increasing separation between segments with regard to a consumer behaviour of interest. The Gini coefficient is described in this paper as a useful method for assessing segmentation system performance