IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v201y2010i2p608-618.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(09)00155-6
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    3. Douglas Steinley & Michael Brusco, 2008. "Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 125-144, March.
    4. Buratto, Alessandra & Grosset, Luca & Viscolani, Bruno, 2006. "Advertising a new product in a segmented market," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1262-1267, December.
    5. Mizuno, Makoto & Saji, Akira & Sumita, Ushio & Suzuki, Hideo, 2008. "Optimal threshold analysis of segmentation methods for identifying target customers," European Journal of Operational Research, Elsevier, vol. 186(1), pages 358-379, April.
    6. Michael Brusco & Hans-Friedrich Köhn, 2008. "Optimal Partitioning of a Data Set Based on the p-Median Model," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 89-105, March.
    7. Zhang, Michael & Bell, Peter C., 2007. "The effect of market segmentation with demand leakage between market segments on a firm's price and inventory decisions," European Journal of Operational Research, Elsevier, vol. 182(2), pages 738-754, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bagirov, Adil M. & Ugon, Julien & Mirzayeva, Hijran, 2013. "Nonsmooth nonconvex optimization approach to clusterwise linear regression problems," European Journal of Operational Research, Elsevier, vol. 229(1), pages 132-142.
    2. 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.
    3. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:201:y:2010:i:2:p:608-618. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/locate/eor .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.