The Application Of Association Rules In Insurance Precision Marketing

In the era of massive data, the evolution of the disruptive evolution of business model and the upgrading of technology not only provide opportunities to the insurance industry, but also bring heavy competitive pressures and challenges of survival. Traditional product centric4 P or consumer centric 4C theory does not meet the increasingly stringent marketing budget,e accumulation of vast amounts of data stored queries and other needs. In order to improve the efficiency and effectiveness of the insurance industry, companies need to seek more scientific and effective of operating systems and ideas than the traditional extensive operational, Among than data mining technology which scientific and effective application that is the basis for precision marketing and technical support.Theory nPnC that combines 4P and 4C is a precision marketing concept. In this paper,on the basis of 3P3 C theory, we select the client as the object, the main research in three aspects: basic attributes of customer, including customer occupation, age, gender, education,marital status; customer value, namely the amount of customer policy, premium and frequency; customer behavior, that is the purchase of insurance. In this paper, the insurance company's financial information, transaction information, policy information, customer information and other data to achieve a precise analysis of the relationship between the insurance products and customer attributes, customer behavior. This paper describes the typical structure of distributed database platform, using correlation analysis and cluster analysis algorithm to process massive data analysis.Data mining insurance is based on the association analysis. For the three elements of customer research, we build a customer segmentation community model, association rules for each community. In this paper, the classical Apriori algorithm is used to realize the correlation analysis. And in the process of realization it is found that there is a great limit to the calculation of the actual data. This paper presents an effective method to solve the connectivity problem of Apriori algorithm on multi-dimensional data, and proposes byincreasing the sorting rules to improve the computational efficiency of the Apriori algorithm,and realizes the analysis of insurance data association.After correlation analysis,we cluster analysis data further to achieve an effective data mining process. The results reflect the group insurance products marketing rules in different types of customers in the community, which has the significance to achieve precision marketing.