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

^†This paper is dedicated to the memory of Livia Dancelli.

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.

KEYWORDS:

• Hierarchical cluster analysis
• matching-type measure
• weighted rank-based correlation coefficient
• bipolar mean
• mean deviation about the bipolar mean

SUBJECT CLASSIFICATION CODES:

• 62P25
• 91B08
• 62H30

Acknowledgements

We wish to thank Prof. Paola Zuccolotto (University of Brescia), promoter of the survey, and the Corporate Relations Manager of McDonald's Italia for making the data available.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

^† This paper is dedicated to the memory of Livia Dancelli.