Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Journal of Management Information Systems
The exploration of consumers' behavior in choosing hospital by the application of neural network
Expert Systems with Applications: An International Journal
Selecting the right MBA schools - An application of self-organizing map networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Classifying the segmentation of customer value via RFM model and RS theory
Expert Systems with Applications: An International Journal
A case study of applying data mining techniques in an outfitter's customer value analysis
Expert Systems with Applications: An International Journal
Knowledge discovery on RFM model using Bernoulli sequence
Expert Systems with Applications: An International Journal
An intelligent market segmentation system using k-means and particle swarm optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A DEMATEL method in identifying key success factors of hospital service quality
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Self-organizing feature map for cluster analysis in multi-disease diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.