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Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation

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  • Kessara Kanchanapoom

    (Silpakorn University)

  • Jongsawas Chongwatpol

    (National Institute of Development Administration)

Abstract

Cluster analysis and RFM model are widely used to gain a deeper understanding of customers’ characteristics and needs due to its simplicity and applicability in analyzing customer purchasing behavior. However, the lack of considering the future value of customers or whether current customers exhibit a pattern of likely attrition or switching to a competitor into the original segmentation models is a big concern in segmenting customers for further strategic and personalized campaigns. How can organizations integrate their customers’ lifetime value and customer migration, which refers to the probability that their customers will likely return in the future, as parts of the RFM and cluster analysis to improve marketing decisions? Our modified segmentation models are then validated in the context of complementary and alternative medicine in the healthcare industry to demonstrate the practical validity of our proposed methods.

Suggested Citation

  • Kessara Kanchanapoom & Jongsawas Chongwatpol, 2023. "Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 172-185, June.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:2:d:10.1057_s41270-022-00158-7
    DOI: 10.1057/s41270-022-00158-7
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    References listed on IDEAS

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    Cited by:

    1. Stephan Curiskis & Xiaojing Dong & Fan Jiang & Mark Scarr, 2023. "A novel approach to predicting customer lifetime value in B2B SaaS companies," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 587-601, December.

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