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Prediction and profitability in market segmentation typing tools

Author

Listed:
  • Marco Vriens

    (Kwantum Analytics)

  • Nathan Bosch

    (Kwantum Analytics)

  • Chad Vidden

    (University of Wisconsin, La Crosse)

  • Jason Talwar

    (Brown University School of Engineering)

Abstract

A vital component in strategic segmentation is the typing tool. Little is known about their prediction performance. Even less is known how well they perform at the segment-level, in imbalanced situations, and how well they predict the smallest (minority) segment. We investigate using simulated and real-life data, how well typing tools perform overall and at the specific segment-level and we show the following. One, even when overall prediction accuracy is good, specific segments may be predicted poorly. Two, for valuable (minority) segments with high targeting costs misclassification can have a substantial impact on the profitability of the segmentation strategy. Poor prediction of a minority segment can happen in high and mildly imbalanced segments. Three, prediction of minority segments can vary substantially across different base classifiers and across imbalance correction methods. We find that performance can vary substantially across base classifiers and that support vector machines, overall, perform best. Four, the prediction of a (minority) segment can always be improved by using imbalance correction methods, and overall random under-sampling performs best.

Suggested Citation

  • Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.
  • Handle: RePEc:pal:jmarka:v:10:y:2022:i:4:d:10.1057_s41270-021-00145-4
    DOI: 10.1057/s41270-021-00145-4
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    1. Pires, Guilherme D. & Stanton, John & Stanton, Patricia, 2011. "Revisiting the substantiality criterion: From ethnic marketing to market segmentation," Journal of Business Research, Elsevier, vol. 64(9), pages 988-996, September.
    2. Mora Cortez, Roberto & Højbjerg Clarke, Ann & Freytag, Per Vagn, 2021. "B2B market segmentation: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 126(C), pages 415-428.
    3. Georgios Marinakos & Sophia Daskalaki, 2017. "Imbalanced customer classification for bank direct marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 5(1), pages 14-30, March.
    4. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    5. Gilseung Ahn & You-Jin Park & Sun Hur, 2021. "A Membership Probability–Based Undersampling Algorithm for Imbalanced Data," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 2-15, April.
    6. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
    7. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    8. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    9. Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
    10. Paul H. Lee, 2014. "Resampling Methods Improve the Predictive Power of Modeling in Class-Imbalanced Datasets," IJERPH, MDPI, vol. 11(9), pages 1-14, September.
    11. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
    12. Susan Brudvig & Michael J. Brusco & J. Dennis Cradit, 2019. "Joint selection of variables and clusters: recovering the underlying structure of marketing data," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(1), pages 1-12, March.
    13. Ying Liu & Sudha Ram & Robert F. Lusch & Michael Brusco, 2010. "Multicriterion Market Segmentation: A New Model, Implementation, and Evaluation," Marketing Science, INFORMS, vol. 29(5), pages 880-894, 09-10.
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    1. Maria Petrescu & Anjala S. Krishen, 2023. "Mapping 2022 in Journal of Marketing Analytics: what lies ahead?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(1), pages 1-4, March.

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