Müllensiefen, D;
Hennig, C;
Howells, H;
(2017)
Using clustering of rankings to explain brand preferences with personality and socio-demographic variables.
Journal of Applied Statistics
10.1080/02664763.2017.1339025.
(In press).
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Using clustering of rankings to explain brand preferences with personality and socio-demographic variables |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/02664763.2017.1339025 |
| Publisher version: | http://doi.org/10.1080/02664763.2017.1339025 |
| Language: | English |
| Additional information: | Copyright © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Market segmentation, personality, demographics, cluster analysis, distance, ranking data, logistic regression, random forests |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1563390 |
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