Using a combination of RFM model and cluster analysis to analyze customers' values of a veterinary hospital

Jo Ting Wei, Shih Yen Lin, You Zhen Yang, Hsin Hung Wu

Research output: Contribution to journalArticle

Abstract

The purpose of this study is to identify customers with different behaviors and then develop adequate marketing strategies to maintain good relationships with its existing customers and attract new customers for a veterinary hospital. A two-stage clustering method, the combination of self-organizing maps and K-means method, and RFM model are used to analyze customers' values from the transactions data focusing solely on dogs of a veterinary hospital in Taichung City, Taiwan in 2014. The results show that 4,472 customers are classified into twelve clusters, and seven out of twelve clusters are found to be the best or loyal customers. However, the other five clusters are uncertain customers. Among the five clusters, three clusters are lost customers and two clusters with relatively higher recency values than the average value can be viewed as new customers.

Original languageEnglish
Pages (from-to)442-448
Number of pages7
JournalIAENG International Journal of Computer Science
Volume47
Issue number3
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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