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Clustering ranking data in market segmentation: a case study on the Italian McDonald's customers’ preferences

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  • Eugenio Brentari
  • Livia Dancelli
  • Marica Manisera

Abstract

Cluster analysis is often used for market segmentation. When the inputs in the clustering algorithm are ranking data, the intersubject (dis)similarities must be measured by matching-type measures, able to take account of the ordinal nature of the data. Among them, we used a Weighted Spearman's rho, suitably transformed into a (dis)similarity measure, in order to emphasize the concordance on the top ranks. This allows creating clusters grouping customers that place the same items (products, services, etc.) higher in their rankings. Also the statistical instruments used to interpret the clusters must be conceived to deal with ordinal data. The median and other location measures are appropriate but not always able to clearly differentiate groups. The so-called bipolar mean, with its related variability measure, may reveal some additional features. A case study on real data from a survey carried out in the Italian McDonald's restaurants is presented.

Suggested Citation

  • Eugenio Brentari & Livia Dancelli & Marica Manisera, 2016. "Clustering ranking data in market segmentation: a case study on the Italian McDonald's customers’ preferences," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 1959-1976, August.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:1959-1976
    DOI: 10.1080/02664763.2015.1125864
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    1. Lê, Sébastien & Josse, Julie & Husson, François, 2008. "FactoMineR: An R Package for Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i01).
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    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. Etumnu, Chinonso & Gray, Allan W., 2020. "A Clustering Approach to Understanding Farmers’ Success Strategies," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 52(3), pages 335-351, August.
    3. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
    4. Andrea Bonanomi & Marta Nai Ruscone & Silvia Angela Osmetti, 2017. "Defining subjects distance in hierarchical cluster analysis by copula approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 859-872, March.

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