Research On The Application Of Data Mining's Classification Technology In The Insurance CRM Of China

With the computer technology and the rapid economic development, the global database of all line services have changed tremendously in number and scale .Data Mining is in the context of this application needs to generate and rapidly becomes an important area of research. Data Mining technology as a multi-disciplinary integration, has become the most important information-processing techniques and methods.As a kind of management strategy of customer centered, Customer Relationship Man- agement (CRM) can retain and obtain customers well. Data Mining, as a tool of analyses, can be used to analyze mass data and mine customer values in CRM.Therefore,building effective DM applications in CRM and researching which can improve decision-making support functions of data mining techniques and theory is a topic of great significance.In this thesis, I research the application of DM's Classification technology in the insurance CRM of China, mainly using decision-tree and neural network algorithms to analyze the particularly customers distinguish, and so on. In the end, After studying the relevant CRM system, I build a new CRM system through the experiments based on weka platform and give part of the mining results.In customers distinguish, I put forward an improved algorithm based on ID3 algorithm. In general, in order to accelerate the speed of spanning tree algorithm, we must choose properties accuratelly and quickly. Finally, I choose age, driving experience, the average payment rate of 5 attributes as condition attributes and select decision-making attributes in order to obtain decision tree, which features a detailed breakdown with a client with a high risk.In Customers loss analysis, I use neural network algorithm and choose the age, education level, income, work area, occupation, insurance and other properties as the relevant customer data indicators. These indicators will enter the genetic neural network and be trained to identify our customer loss prediction model which can guide decision-makers whether a specific customer base to take the necessary measures to reduce loss and retention measures to be taken about which kinds of customer.