Customer Relationship Management (CRM) is an infrastructure that helps companies to manage customer relationships in a structured way. One important goal of CRM is applying data mining techniques effectively on existing customer transaction data and profiles to improve customer value and loyalty.
This research uses public insurance data to design a CRM cross-selling model. Kohonen Self-Organizing Maps (SOM) neural network, based on selected socio-demographic attributes, successfully segmented the above customers into three or more (depends on similarity of customer profiles) clusters. The association rule engine finds some useful and interesting cross-selling rules for each cluster of customers. A supervised clustering data mining model is built to identify prospective customers for a new product. The attributes on the significance splits in decision trees are also discovered for predicting potential buyers.
The major result of this research is design and development of a more efficient cross-selling model; using a cost-effective way to have the ability to understand different customer needs and to turn that understanding into customized cross-selling products to customers, every time, everywhere – enough to increase customer satisfaction, value, and loyalty, and therefore, yield profitable business advantage.

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