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Using clustering of rankings to explain brand preferences with personality and socio-demographic variables

Müllensiefen, Daniel; Hennig, Christian and Howells, Hedie. 2017. Using clustering of rankings to explain brand preferences with personality and socio-demographic variables. Journal of Applied Statistics, pp. 1-21. ISSN 0266-4763 [Article]

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Abstract or Description

The 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.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1080/02664763.2017.1339025

Keywords:

Market segmentation, personality, demographics, cluster analysis, distance, ranking data, logistic regression, random forests

Departments, Centres and Research Units:

Psychology

Dates:

DateEvent
15 June 2017Published Online
26 April 2017Accepted
11 October 2016Submitted

Item ID:

21076

Date Deposited:

19 Sep 2017 16:55

Last Modified:

29 Apr 2020 16:34

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/21076

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