Research On Application Of Customer Segmentation Model In Retailing Based On Data Mining

Along with the rapid development of information network, the strategy centering on customer and aiming on service in retailing are becoming important. It is the key that successfully mastering the trend of customers'requirement, strengthening the connection with them, efficiently digging and managing their information. Thus, the customer relation management becomes the focus in the research of retail domain. The customer subdivision is the chief task of the customer relation management. Only the good subdivision model is build, then the customers can be effectively identified, and the customer keeping and the customer attracting can be put into practice.A lot of sellers believe that behavior is the best jumping-off point in building a customer subdivision model, the paper chooses the method which basing on customer behavior to build a subdivision model. For selecting subdivision variable, the customer's value and quality of relationship should be considerate simultaneously; however, it is hard to live up to. Among the behavior-based subdivision methods, although both the classical RFM analysis, and customer value matrix analysis are relatively efficient subdivision methods, they neglect a point, that's the customer's loyalty, the profit brought by loyal customers is also very important for the corporation. Hence, after selecting the average monetary and the frequency of purchase basing on the value matrix analysis, another variable is selected to embody the stability of customer's existing time.In this paper, the data mining technology is used, the average monetary and frequency of purchase and existing time is used as the behavior variables to build the retailing customer subdivision model. The k-means algorithmic is widely used in clustering analysis, but the number of clustering needs to be appointed by beforehand, and the initial clustering centers is selected in random, the last clustering effect is turn up trumps unnecessarily. The SOM (self-organizing feature map) neural networks arithmetic divides the sample data into different clusters by self adaptively, and the clustering number is needn't to be pre-established, but the accurate clustering information can not be offered. Upon then, a combinative arithmetic is brought forward. The clustering analysis process is composed of two steps: At the first step, using SOM neural networks to get clustering number and the clustering centers; at the second step, the output of the first step is used as input of the second step which is the k-means.After dividing the customers into different classes, picking up the characteristics of each class of customers, that is helpful for boosting the pertinence and the validity of sell activity, help for bring into effect of customer relation management, the decision tree in data mining is used for picking up customers'characteristics. At last, purchase consult analysis is carried through, that is analyzing the association of customers'characteristics and the commodities purchased. Pick-up customers'characteristics and purchase consult analysis are the application of the customer subdivision model just constructed.