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An LRFM Model to Analyze Outpatient Loyalty From a Medical Center in Taiwan

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  • Shu-Hui Chao
  • Mu-Kuan Chen
  • Hsin-Hung Wu

Abstract

This research is intended to study the behaviors of outpatients in a medical center and constructs a set of data exploration procedures such that hospital management can deal with patient relationship management more effectively. This study adopts LRFM (length, recency, frequency, and monetary) model and cluster analysis, including self-organizing maps and K-means method, to categorize 321,908 outpatients of the medical center into 12 groups and then uses the multidimensional customer clustering philosophy to classify the outpatients. Outpatients can be categorized into five different types of groups, namely, core customer groups, potential customer groups, new customer groups, lost customer groups, and resource-consuming customer groups. In addition, seven types of outpatients based on five types of categories are identified. The similarities and differences of each group based on the patients’ characteristics are analyzed to give differentiation strategy advices for hospital management. Hospital management thus can design the optimal service strategies, provide the best care services, enhance hospital’s performance, and reduce the overall cost to establish quality relationships with outpatients.

Suggested Citation

  • Shu-Hui Chao & Mu-Kuan Chen & Hsin-Hung Wu, 2021. "An LRFM Model to Analyze Outpatient Loyalty From a Medical Center in Taiwan," SAGE Open, , vol. 11(3), pages 21582440211, July.
    • Handle: RePEc:sae:sagope:v:11:y:2021:i:3:p:21582440211031899
    DOI: 10.1177/21582440211031899
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    References listed on IDEAS

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    1. En-Chi Chang & Shian-Chang Huang & Hsin-Hung Wu, 2010. "Using K-means method and spectral clustering technique in an outfitter’s value analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(4), pages 807-815, June.
    2. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
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