Knowledge discovery from patients' behavior via clustering-classification algorithms based on weighted eRFM and CLV model: an empirical study in public health care services

ABSTRACT

The rapid growing of information technology [IT] motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management [CRM] to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML [added parameter Length] based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value [CLV] for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree [CHAID] as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients [estimated by RFML] is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers