Research On Customer Preference Of Mushroom Street E-commerce Platform Based On System Clustering

With the rapid development of electronic information technology,big data,cloud computing,artificial intelligence and other emerging technologies have infiltrated into the operation of contemporary enterprises.Since the Internet entered China in the 90 s of last century,e-commerce enterprises have sprung up like mushrooms.More and more traditional businesses have launched e-commerce business.The market environment which has been slightly convergent has been dominated by many consumer groups.The era of e-commerce enterprise competition is a customer focus form of competition,how to attract customers,and establish a good customer relationship and customer needs analysis is key enterprises to develop marketing strategies,customer preferences and individual differences and become an important factor affecting the modern enterprise marketing.In China e-commerce platform in the context of rapid development,in order to better understand and grasp the customer preference influence on modern enterprise marketing,this paper based on the theory of clustering algorithm,using the improved AP+K-means algorithm based on affinity propagation algorithm,establish the customer preference analysis model,data acquisition and analysis for mogujie.com business platform,for a case study of mogujie.com business platform customer preferences,and it aims to complement the existing research of clustering algorithm in the field of application of e-commerce platform for customer preference and puts forward some suggestions for the development of enterprises,help enterprises to predict customer behavior,customer classification effectively,provide information support for the enterprise to develop the next step of the marketing strategy formulation the more perfect marketing plan,to improve enterprise efficiency results.