Research On User Refinement Operation Based On Clustering And Prediction Algorithm

With the rapid development of the Internet industry in these years,the number of users has soared.A number of Internet products that used the bonus express have risen rapidly.During this period,even if there are not enough user operations,as long as there are enough traffic sources,they can continue to grow.User size.Nowadays,the traffic dividend is gradually disappearing.The simple and extensive operation and management mode of relying solely on expanding traffic is no longer suitable for the times.However,the development of big data analysis technology in these years has gradually become the water and electricity coal for enterprises to survive.With sufficient technical support,more and more companies have begun to pay attention to the application of data mining technology to study user behavior and user preferences,through research to discover patterns and predict trends,and assist platform operators to develop precise operational strategies to protect themselves healthier.Lasting development.Many Internet platforms today have put forward the demand for refined operation.It is hoped that through data mining and analysis,users will be firmly stuck on the platform,effectively improving user stickiness and reducing user churn rate.In view of this,this paper mainly does the following work:Firstly,in the introduction part,the development status and current problems of user operations in the Internet platform are analyzed.The gradual maturity of data mining technology and its application in enterprises are introduced.This paper lays a foundation for the use of data mining technology to carry out user refined operations.Analytical basis.Secondly,taking a financial company user as an example,it completed data collection,data extraction,data cleaning,and combined with the actual business situation of the industry,developed user indicators that conform to industry characteristics,and formed basic data for subsequent analysis.Then,the cleaned data is characterized by multiple angles to form a basic understanding of the user's situation.Then,it introduces the importance of user stratification in refined operation,clarifies the value brought by tiering to the company,then compares two commonly used clustering algorithms and selects two-step clustering method as the experimental clustering algorithm.Next,design the experiment,layer the users through the clustering model,and propose a specific proposal for the refined operation based on the layered results.Finally,the user's stratification is low-active users,and the loss warning is carried out.The problem of user loss of P-finance company based on decision tree,neural network and naive Bayesian algorithm is studied.According to the characteristics of lost users and non-loss users.The user churn prediction model is built,and the decision tree is used as the final selection model by evaluation,and the recall proposal with users with loss tendency is given.