Empirical Analysis Of Customer Churn In Online Shopping Environment

In recent years, with the continuous improvement of productivity, the developing of the information technology, Internet has become an important strategic resource in today’s society. Along with the arrival of the era of Internet, the enterprise business environment has changed dramatically, e-commerce platform has been built.The simplicity,immediacy and convenient of the Electronic business model has attracted the attention of a large number of customers. Many customers turned to the emerging markets, joining the growing ranks of online shopping.The enterprise on the e-commerce platform which only attracting new customers to increase market share is not strong enough to win the commercial war.Electricity companies must also do a good job in defense of customer churn, solve the problem of customer attraction and churn, then achieve the purpose of the effective management of customer for electric business enterprise.The researchers who study on customer churn pay more attention to the traditional enterprise, and less to new business environment,such as B2C or C2C platform. In this years, online shopping has become a way, of life for people, traditional research is not suitable to be used in the field of e-commerce. Therefore, in this paper, we combined the traditional customer churn prediction model with e-commerce mode, in order to achieve the latest requirements of electric business enterprise.Electric business enterprise produce vast amounts of customer purchase data every day.It is vital for companies to analyse the customer purchase behavior to predict customer churn,which also produce the application of the data mining techniques in business. Using data mining techniques to analyse and research the massive amounts of customer data in e-commerce sites can draw churn prediction model of online customer,then,then it will provide valuable information to e-commerce business. Data mining technology is a kind of process,which integrates mathematics, statistics, artificial intelligence and machine learning techniques, extract and identify useful information from large databases,and get useful information for the enterprise. Application of data mining techniques in customer relationship management (CRM) has become a inevitable trend in the Times of global economization. Data mining technology is a kind of effective tools of analyzing customer relationship management (CRM), it can help enterprise storage and integration the huge amounts of data between enterprises and customers, analysis the hidden information under the huge amounts of data, and help enterprises analyze existing customers, finding out the potential customers who are the high value customers and the low value customers who waste enterprises resources and not profit to the enterprise. The integration of the information can help the enterprise dominate the information advantage in the process of globalization,and help enterprises to improve efficiency of resource utilization and the effect of enterprise marketing policy.In this paper, we combined the traditional customer chum prediction model which based on data mining technology and the RFM theory which descripted the historical customer buying behavior,made a correction to online customer churn prediction model, which can only be used a few key indicators.