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Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods

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  • Andrews, Rick L.
  • Brusco, Michael J.
  • Currim, Imran S.

Abstract

The segmentation of customers on multiple bases is a pervasive problem in marketing research. For example, segmentation service providers partition customers using a variety of demographic and psychographic characteristics, as well as an array of consumption attributes such as brand loyalty, switching behavior, and product/service satisfaction. Unfortunately, the partitions obtained from multiple bases are often not in good agreement with one another, making effective segmentation a difficult managerial task. Therefore, the construction of segments using multiple independent bases often results in a need to establish a partition that represents an amalgamation or consensus of the individual partitions. In this paper, we compare three methods for finding a consensus partition. The first two methods are deterministic, do not use a statistical model in the development of the consensus partition, and are representative of methods used in commercial settings, whereas the third method is based on finite mixture modeling. In a large-scale simulation experiment the finite mixture model yielded better average recovery of holdout (validation) partitions than its non-model-based competitors. This result calls for important changes in the current practice of segmentation service providers that group customers for a variety of managerial goals related to the design and marketing of products and services.

Suggested Citation

  • Andrews, Rick L. & Brusco, Michael J. & Currim, Imran S., 2010. "Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods," European Journal of Operational Research, Elsevier, vol. 201(2), pages 608-618, March.
    • Handle: RePEc:eee:ejores:v:201:y:2010:i:2:p:608-618
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    References listed on IDEAS

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    4. Schlittgen, Rainer & Ringle, Christian M. & Sarstedt, Marko & Becker, Jan-Michael, 2016. "Segmentation of PLS path models by iterative reweighted regressions," Journal of Business Research, Elsevier, vol. 69(10), pages 4583-4592.
    5. Yao Jiao & Yu Yang & Hongshan Zhang, 2019. "An integration model for generating and selecting product configuration plans," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1291-1302, March.

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