Customer Churn Analysis In E-Commerce Based On Data Mining

Nowadays, With the domestic high-speed development of e-commerce, every operation companies have spread drastic competition in many areas, such as big customers of enterprise, Customer Relationship Management, Maintain customer, and so on. Traditional market stratagem, which is focus on technique and production sell, has been replaced by new tactic, which concentrates on customer and service. It means that customer resource has become the key of enterprises' competition.Furious competition leads to the unstable state of customer, and operation companies almost all confront with such a serious problem--customer churn. Just for a great quantity of customers' vanishment, it has brought great loss to many companies. In such a severe circumstance, it has become one of the focuses of operation corporations that how to avoid customer churn and carry out retainment. Then managers find it important for operation enterprises in e-commerce to predict customer churn based on customer character and customer behavior, and make effective measures to realize customer retainment and customer attainment, which is also one of the most important projects on customer research and consultation in e-commerce.Author discusses customer value primarily first, and applies it to customer churn. At the same time, according to customers' investigated data, this paper applies many technique of data mining to the research of customer churn, such as decision tree, clustering, etc. As for those problems exist in corresponding research at present, author gives a detailed scheme to solve them, such as customer profile, attribute contraction, churn reason analysis, churn prediction and control tactics, and so on. At last, this paper succeeds to establish customer churn prediction model, and generate the rule of the customers which may loss.By collecting the e-business environment of customer churn analysis questionnaire data, this paper made specific analysis of customer churn, and combined marketing and consumer psychology and other related knowledge, the paper put forward analysis of the reasons for the loss and the loss control strategy.