Decision Support in Customer Relationship Management Using Data Mining Techniques

With a unbridled increase in international and domestic forms of business, Customer Relationship Management (CRM) has become one of the matters of concern to the enterprise and the entrepreneurs. CRM takes customer as the center and it enchants a new life to the organization system and optimizes its business process increasing its profitability. In order to help enterprises understand the “Product Purchasing Psychology (PPP)” and ways to retain the valued customers we propose data mining techniques. Clustering of customers provide in depth knowledge of their behavior. Clustering is one of the most useful and traditional technique used in data mining. The scope of this paper is to understand and predict the behaviors of the customer with behavior segmentation methodology. The result of the analysis results into enhancing of the customer support and targeting sales of the right product to the customers with better concentration on campaigning product promotion. The policy holders claim dataset for the health insurance company is taken for consideration. This behavior segmentation methodology with clustering is applied here to predict distinct customer segments which help in the production of customized products which takes care of the priorities and preferences of the customers. Apriori association rule which is performed on clusters of claim dataset gives the association amongst the attributes. It is derived from Clustering Based Association Rule Mining (CBARM) model. Association rule is applied on claim dataset which predicts the claim cost and association amongst the attributes that influences the claim cost of the policy holder.