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

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  • Daniel Müllensiefen
  • Christian Hennig
  • Hedie Howells

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.

Suggested Citation

  • Daniel Müllensiefen & Christian Hennig & Hedie Howells, 2018. "Using clustering of rankings to explain brand preferences with personality and socio-demographic variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(6), pages 1009-1029, April.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1009-1029
    DOI: 10.1080/02664763.2017.1339025
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    Cited by:

    1. Pierpaolo D’Urso & Vincenzina Vitale, 2022. "A Kemeny Distance-Based Robust Fuzzy Clustering for Preference Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 600-647, November.
    2. Ferreira, Diogo Cunha & Marques, Rui Cunha & Nunes, Alexandre Morais & Figueira, José Rui, 2021. "Customers satisfaction in pediatric inpatient services: A multiple criteria satisfaction analysis," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    3. Antonella Plaia & Simona Buscemi & Johannes Fürnkranz & Eneldo Loza Mencía, 2022. "Comparing Boosting and Bagging for Decision Trees of Rankings," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 78-99, March.
    4. Dongyun Nie & Michael Scriney & Xiaoning Liang & Mark Roantree, 2024. "From data acquisition to validation: a complete workflow for predicting individual customer lifetime value," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 321-341, June.

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