Customer Behavior Analysis System For E-Shopping Platform

At present,the competition of e-shopping platform in China is increasingly fierce.Facing such a competitive e-shopping environment,it will become particularly important to obtain customers and improve customer viscosity through behavior analysis,as well as improve the company's service ability and obtain the real needs of customers.In this paper,a set of customer behavior analysis system for electronic shopping platform was designed.According to the actual demand,four modules of customer behavior analysis module,page jump statistics module,commodity analysis module and real-time advertising statistics module were analyzed,designed and implemented.In the customer behavior analysis module,the functions of session filter by condition,aggregate statistics,session extraction,hot categories obtaining and active session obtaining were mainly implemented.The stratified sampling algorithm was used to implement the session extraction function.In the page jump statistics module,the function of page jump rate calculation was implemented.In the commodity analysis module,the function of regional statistics of hot commodities was implemented.In the real-time advertising statistics module,the relevant statistical calculation function of advertising flow was implemented.Based on the algorithm of fetching Top N in groups,the functions of obtaining active session,hot goods and popularity of advertisment were implemented respectively.Finally,through system test,the system was proved to meet the usage requirements.Based on the above system,decision makers such as product managers,data analysts and executives can analyze the behavior of customers,understand the customer satisfaction,page conversion rate,product popularity and advertising effect of the shopping platform,so as to improve the product design,adjust the business segment,research the development strategy,and finally,improve the company's performance,increase the turnover,and increase the market share.