The Application Of Data Mining In Online Shopping

With the development of Internet, e-commerce has entered a period o frapiddevelopment.Manyof the traditional manufacturing enterprises have gradually set up their own electronic business platform to sell.In such an environment, the network shopping naturally became the largest market. The more Online shopping company have,the bigger competition.In order to enhance the core competitiveness of the company,data mining technol ogy has become a necessary tools.the strength of the company’s core com petitiveness depends largely on the effect of data mining technology used.Each link in the operation of the company,date mining has brought a lot of benefits. For example,To provide guidance for the company to dev elop the strategy;Analyze the characteristics of customers; Mining the req uirements of customers.This paper mainly analysis the characteristics of c ustomers and mining the requirements of customers.This paper depended on The real transaction data.According to custo mer’s loyalty,conducted cluster analysis for the customers. Then,on the b asis of the clustering analysis,recommended product for the different typ e of customers. After the customer clustering,described the characteristics of each types of customers and put forward the corresponding proposal.I n the process of recommended products,combined with the characteristics of customers,recommend product category,staff should possess,and the p roblems that should be paid attention to when recommending commoditie s. The main conclusions of this paper are:(1)In A company,The three index of RFM model have different wei ghts,specific for R,F,M (0.61,0.28,0.11)(2)The customers of A company is divided into eight categories and got the features of each class of customers. some customers prefer high q uality goods/while others prefer high cost of goods.(3) A company should listed face care goods as key products.(4)The customers of class 1 have the largest risk. A Company shoul d take effective measures to reduce the risk of loss of customers.