Analyzing patients' values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan

Hsin-Hung Wu, Shih Yen Lin, Chih Wei Liu

Research output: Contribution to journalArticle

8 Citations (Scopus)

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 and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding 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 with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs.

Original languageEnglish
Article number685495
JournalScientific World Journal
Volume2014
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Health insurance
Dental Clinics
Pediatrics
Self organizing maps
Cluster analysis
Taiwan
Cluster Analysis
cluster analysis
Marketing
health insurance
matrix
marketing
National Health Programs
programme
need
method

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)
  • Medicine(all)

Cite this

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Analyzing patients' values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan. / Wu, Hsin-Hung; Lin, Shih Yen; Liu, Chih Wei.

In: Scientific World Journal, Vol. 2014, 685495, 01.01.2014.

Research output: Contribution to journalArticle

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