Understanding customer value using data mining applications: A case study of an insurance broker

In today's world, difficult conditions in the market lead the companies to find new ways to compete better. With the intensive global competition and rapidly changing technological environments, meeting customers' various needs and maximizing the value of profitable customers are becoming the only viable option for many contemporary companies. Together with technological developments, companies and institutions constantly store customer and sales data. Data mining is a process to obtain useful, hidden, meaningful, unknown and valuable information in the way of reaching the aims of the companies in this continuously stored data. Clustering analysis is a method of data mining that is used to group similarities within these data, while Associative Rules identifies rules for similar events. Clustering analysis is also called segmentation analysis or taxonomy analysis. More specifically, it tries to identify homogenous groups of cases if the grouping is not previously known. Association rule is a procedure, which is meant to find frequent patterns, correlations, associations, or causal structures from data sets found in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories. The aim of this study is to propose a base for the Customer Relationship Management activities by using data mining tools and applications for a company in insurance brokerage sector. Customer master data and sales transactions of customers are converted to meaningful information that can be used for Customer Relationship Management activities. In this concern, an application was made use of to conduct a segmentation of customers and products, and to find relationships between them.