The Literature Of Apply Data Mining Technology For Customer Relationship Management

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A Review of the Literature of Apply Data Mining Technology for Customer Relationship Management and Customer Privacy in Banking Introduction The concept of customer relationship management (CRM) was developed in the mid- 1990s, when the information technology was being used to ‘track multiple activities of customers’ (Chieko Minamia, 2008). After many years, the CRM is considered the ‘underlying tool’ for business, because mining the customer value is the key to success for each company. CRM was defined as ‘helping organizations to better discriminate and more effectively allocate resources to the most profitable group of customers through the cycle of customer identification, customer attraction, customer retention and customer development’ (E.W.T. Ngai 2009). CRM applications such as customer segmentation, prospecting and acquisition, affinity and cross sell, profitability, retention and attrition, risk analyses, and so on. (Femina Bahari T, 2015). One of these applications is the credit scoring which is the most important part in the banking and financing industry. With development of computer system, the data quantity generated quickly in world wide. Nejad (2011) pointed ‘the rapid growth of the collection, processing, distributing and manipulating the data is made easy. In recent years, production technology and data collection has been growing rapidly’. And the largest dataset of the world is in the banking system. Moro (2015) stated ‘banking has been a prolific industry for innovation concerning information systems and technologies’. Further more, according to McKinsey Global Institute analysis, finance include banking and insurance have the highest big data potential value. For banks, the data mining technique can be ap... ... middle of paper ... ...ning and these four models of CRM data mining can guide to the future data modeling in the CRM data mining. In addition, the literature reviewed the reason and effect of customer privacy issue of CRM data mining in banking industry. This literature can be a guide to help banking CRM data mining department to protect customer data. For future, as Chris Rygielski (2002) stated ‘businesses must also bear in mind that they have to use technology responsibly in order to achieve a balance between privacy rights and economic benefits’. Banking CRM data mining department should also find a balance between customer privacy and business benefits. Furthermore, the government should do more on the regulation to protect customer privacy. As Linda Christiansen (2011) pointed ‘improper uses of data must be restricted and punished while beneficial uses are concurrently managed’.

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