Dental services marketing has become more and more crucial in Taiwan after Taiwan's entrance of the World Trade Organization and the implementation of National Health Insurance (NHI) program. This paper develops an extended RFM (recency, frequency, and monetary) model, namely LRFM (length, recency, frequency, and monetary) model, by adopting self-organizing maps (SOM) technique for a children's dental clinic in Taiwan to segment its dental patients. Twelve clusters are recommended for the overall 2258 patients. The average values of LRF are computed for each cluster and the overall patients, excluding monetary covered by NHI program. The values of LRF variables for each cluster greater than those of the overall average are identified. The results show that three clusters having the above average LRF values (454 patients) can be viewed as core patients.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence