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Using segment level stability to select target segments in data-driven market segmentation studies

Author

Listed:
  • Sara Dolnicar

    (The University of Queensland)

  • Friedrich Leisch

    (University of Natural Resources and Life Sciences)

Abstract

Market segmentation is widely used by industry to select the most promising target segment. Most organisations are interested in finding one or a small number of target segments to focus on. Yet, traditional criteria used to select a segmentation solution assess the global quality of the segmentation solution. This approach comes at the risk of selecting a segmentation solution with good overall quality criteria which, however, does not contain groups of consumers representing particularly attractive target segments. The approach we propose helps managers to identify segmentation solutions containing attractive individual segments (e.g., more profitable), irrespective of the quality of the global segmentation solution. We demonstrate the functioning of the newly proposed criteria using two empirical data sets. The new criteria prove to be able to identify segmentation solutions containing individual attractive segments which are not detected using traditional quality criteria for the overall segmentation solution.

Suggested Citation

  • Sara Dolnicar & Friedrich Leisch, 2017. "Using segment level stability to select target segments in data-driven market segmentation studies," Marketing Letters, Springer, vol. 28(3), pages 423-436, September.
    • Handle: RePEc:kap:mktlet:v:28:y:2017:i:3:d:10.1007_s11002-017-9423-8
    DOI: 10.1007/s11002-017-9423-8
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    References listed on IDEAS

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    1. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    2. Sara Dolnicar & Friedrich Leisch, 2010. "Evaluation of structure and reproducibility of cluster solutions using the bootstrap," Marketing Letters, Springer, vol. 21(1), pages 83-101, March.
    3. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    4. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    5. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.
    6. Mark Chiang & Boris Mirkin, 2010. "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 3-40, March.
    Full references (including those not matched with items on IDEAS)

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