Research And Application Of A/B Testing In User Behavior Analysis Scenario

User behavior analysis is the analysis of user behaviors on products or touchpoints and the data behind them.By building a user behavior data analysis system and user portraits,it can change product,marketing,and operational decisions,achieve refined operationsvand guide business,increase.In the Internet field,user behavior data is the basis for most analyses,and various analyses are carried out around user behavior data.However,under the huge user base,if any changes are launched without data-driven methods for objective verification,it may bring huge disasters.To verify the impact of various changes on users.A/B experiments are common data-driven methods whose rationale is based on hypothesis testing,which is the most widely used set of methods.However,in the process of the rapid development of the Internet,there are still many problems between theory and practice that need to be solved.The application of A/B experiments in this scenario is also greatly challenged.For example,most Internet practitioners lack statistical knowledge,and their understanding of the P value in hypothesis testing is often vague or even wrong.A/B experiments based on hypothesis testing are counterintuitive,and the information given is limited and cannot be satisfied.Due to the need for rapid iteration in the Internet field,researchers have begun to conduct A/B experiments based on Bayesian inference.In order to adapt to the scenario of Internet user behavior analysis,some disadvantages of A/B experiments based on traditional hypothesis testing include interpretability and quantifiable expected loss,and high efficiency.In this paper,Bayesian inference is introduced into A/B experiments,instead of traditional hypothesis testing.of A/B experiments and demonstrated its multiple advantages.The specific method is to use public data sets and compare the differences in user retention rates of various churn prediction models in three scenarios,and analyze the expected loss of different schemes,and finally prove that the Bayesian A/B method is better than the traditional A/B method.Intuitive advantage of method B and advantage of quantifiable loss.At the same time,this paper also expounds the efficiency advantages of the Bayesian A/B method in the process of rapid development of product iteration,as well as the applicability advantages in the case of small differences between groups.Finally,the research results of the article are summarized,and further potential research directions are proposed for the problem that in the A/B experiment,the distribution method of traffic between groups does not give full play to the business value.