A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran

Nowadays Health care industry has a significant growth in using data mining techniques to discover hidden information for effective decision making. Huge amount of healthcare data is suitable to mine hidden patterns and knowledge. In this paper we traced behavior of patients during the period of 3 years in three clinics of a big public sector hospital and tried to detect special groups and their tendencies by RFML model as a customer life time value (CLV). The main goal was to detect ‘potential for loyal’ customers for strengthen relationships and ‘potential to churn’ customers for recovery of the efficiency of customer retention campaigns and reduce the costs associated with churn. This strategy helps hospital administrators to increase profit and reduce costs of customers’ loss. At first, K-means clustering algorithm was applied for identification of target customers and groups and then, decision tree classifier as churn prediction was used. We compared performance of three clinics based on the number of loyal and churn customers. Our results showed that Pediatric Hematology clinic had a better performance than that of other clinics, because of more number of loyal customers.