Overview of Data Mining techniques for CRM management at Insurance Broker

Abstract

In today’s world, hard conditions in the market lead the companies to find new ways to be competeable. 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. By applying data mining techniques, the companies

may obtain valuable, meaningful, potentially useful and valuable information from the data analysis, which is unknown beforehand.

Among the data mining techniques we can distinguish the clustering and associative rules mining as the most used efficient techniques

for data based analysis. This paper is devoted to research and overview of the techniques and methods for the development of data

mining application in an insurance brokerage company based on effective analysis of the customer relationship management activities

meanwhile the customer master data and sales transactions can be converted to meaningful information. In this concern, data mining

application can be developed to segment processes among customers and products, and to find links between them.