Abstract
ABSTRACTThe primary aim of market segmentation is to identify relevant groups of consumers that can be addressed efficiently by marketing or advertising campaigns. This paper addresses the issue whether consumer groups can be identified from background variables that are not brand-related, and how much personality vs. socio-demographic variables contribute to the identification of consumer clusters. This is done by clustering aggregated preferences for 25 brands across 5 different product categories, and by relating socio-demographic and personality variables to the clusters using logistic regression and random forests over a range of different numbers of clusters. Results indicate that some personality variables contribute significantly to the identification of consumer groups in one sample. However, these results were not replicated on a second sample that was more heterogeneous in terms of socio-demographic characteristics and not representative of the brands target audience.
Highlights
The primary aim of market segmentation is to identify relevant groups of consumers that can be addressed efficiently by marketing or advertising campaigns
We address the issue whether consumer groups can be identified from background variables that are not brand-related, and how much personality vs. socio-demographic variables contribute to the identification of consumer clusters
Our empirical results derived from the data of a sample of young adults living in a metropolitan area (Study 1) show that personality variables become more important for market segmentation as the number of target segments increases
Summary
The primary aim of market segmentation is to identify relevant groups of consumers that can be addressed efficiently by marketing or advertising campaigns. We address the issue whether consumer groups can be identified from background variables that are not brand-related, and how much personality vs socio-demographic variables contribute to the identification of consumer clusters. We do not make any assumptions about a ‘true’ or ‘natural’ number of consumer clusters, but rather to try to identify ‘constructive’, pragmatic clusters [34]. We start from the presumption that the underlying multivariate distribution of brand preferences is continuous and use clustering as a tool to segment this continuous distribution into meaningful groups of individuals who differ in their brand preferences and can be targeted differently with marketing communication. We take into consideration that in most practical applications the number of different target groups or
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