Study On The Customer Group Characteristics Based On The Method Of Clustering Results Explanation

The importance of Customer Relationship Management (CRM) is growing in the company Management because of the accelerated development of the market economy. As the primary method of CRM, customer segmentation can help enterprises do customers division effectively, thus enterprises can make corresponding management strategy according to different customer group characteristics. In the situation of customer demand with personalization and diversification, the extraction of customer group characteristics has become an important research subject.A method of feature extraction based on explanation of clustering results is proposed, due to the problem of having difficulty in extracting customer group characteristics after subdivision. Combined with factor analysis and cluster analysis and taking advantage of one point that factor analysis can extract explained factors, this method can extract characteristic factors from customer segmentation indexes, and calculate the average factor score of each customer group after customer clustering, then show calculation results by tables and charts. In this way, customer group characteristics can be analysed and interpreted in the light of different factor value in each customer group characteristics and customer indexes included in characteristic factors. After comparing and analysing its applicability and superiority,the method is proved reasonable and scientific in the study of the customer group characteristics.When the method is applied to empirical analysis of telecom customer relationship management, we can get optimum customer groups and their characteristics' factor value. Through analysing the results, we can not only know better each customer group characteristics, solving the problem of having difficulty in extracting customer group characteristics that is brought about by the complication of customer indexes; but also analyse each customer group characteristics according to the factor value and information contained in factors highly correlated with customer indexes, providing valuable information for enterprises to make cross-selling, bundling selling and develop new products. Compared with the previous methods, this method doing customer group characteristics' extraction and analysis has more practical value.