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

Shu-Hui Chao, Mu-Kuan Chen and Hsin-Hung Wu

SAGE Open, 2021, vol. 11, issue 3, 21582440211031899

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.

Keywords: LRFM model; patient loyalty; self-organizing maps; K-means method; cluster analysis (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:11:y:2021:i:3:p:21582440211031899

DOI: 10.1177/21582440211031899

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