Labeling Customers Using Discovered Knowledge Case Study: Automobile Insurance Industry

Abstract

In this paper, we used the knowledge discovery in databases and data mining, one of the data-based decision support techniques to help labeling customers in the automobile insurance industry. In most data mining application cases, major tasks including data preparation, data preprocessing, data transformation, data mining, interpretation, application and evaluation, are Keywords labeling customers, discovered knowledge, decision tree, ID3 IntroductionCustomer relationship management (CRM) includes a set of processes and it enables systems to support a business strategy to build long term, profitable relationships with specific customers [11]. Customer data and information technology (IT) tools form the foundation of any successful CRM strategy. Also, the rapid growth of the Internet and its connected technologies has extremely increased the opportunities for marketing and transformed the way that relationships between companies and their customers are managed [14].From the architecture point of view, the CRM framework can be classified into operational and analytical ([1]; [8]; [21]). Operational CRM apply to the automation of business processes, while analytical CRM refers to the analysis of customer characteristics and behaviors to support the organization's customer management strategies. Analytical CRM could help an organization to better separate and more effectively allocate resources to the most profitable group of customers. Data mining tools are a popular means of analyzing customer data within the analytical CRM framework. Many organizations have collected and stored a wealth of data about their current customers, potential customers, suppliers and business partners. However, the inability to explore valuable information hidden in the data prevents the organizations from transforming these data into valuable and useful knowledge [1]. In order to effectively reveal information in handling data, information technology has introduced the development and application of the techniques of knowledge discovery (KD) and data mining (DM). KD/DM is basically a dataoriented method that merges database management, knowledge representation, and machine