CUSTOMER PROFILING AND SEGMENTATION IN RETAIL BANKS USING DATA MINING TECHNIQUES
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Abstract
The objective of achieving profitability is one of the main targets of any banking sector for longer sustainable existence. The customer satisfaction index determines the longevity in relation of customer-bank and thereby provides the idea of devising new policies and strategies for healthy connection of customers with the bank. Offering the services and products to the customer based on his choice and needs requires understanding the customer. The customer data available in the bank can provide the deep insights to the bank for designing the customized service and products. Deriving useful information from customer data using data mining techniques is of paramount importance in these days. Leveraging existing granular customer data can help banks gain deep actionable customer insights useful to understand the customers and to reveal and unlock opportunities for increasing profitability .customer segmentation and profiling are vital in achieving two main objectives of CRM(Customer Relationship Management)i.e.; customer retention and customer development. The main aims of customer profiling and segmentation include expanding customer base, design of tailor made products, micro targeting of sales, aligning right channels for right products, increasing effectiveness of cross selling and up-selling, enhanced customer experience by focused customer relationship, prioritizing relationship with high value customers, effectively managing cost with low value customers based on the profiling and segmentation of customers. In this paper we are using data mining techniques i.e. Naïve Bayes classification algorithm for customer profiling and BIRCH clustering algorithm for customer segmentation
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