Analyzing Patients’ Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

Abstract

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients’ values. A two-stage approach by self-organizing maps andK-means method is applied to segment 1,462 patients into twelve clusters. The average values ofL,R, andFexcluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients withL,R, andFvalues greater than the respective averageL,R, andFvalues, while three clusters can be viewed as lost patients without any variable above the average values ofL,R, andF. When different types of patients are identified, marketing strategies can be designed to meet different patients’ needs.