Research On Membership Grouping And Feature Extraction From The Perspective Of Accurate Marketing

The development of information technology and fierce competition in the market have prompted the retail industry to continuously improve its operating efficiency,especially its digital marketing capabilities.Precision marketing is the focus of digital marketing in the retail industry at present.Its purpose is to provide customers with more accurate and convenient products by using efficient tools,exploiting customer information,and understanding customers better.The use of the membership system helps companies continuously accumulate customer data and manage customer lifecycles better.Companies can adopt different incentives to improve conversion rate and customer loyalty due to different stages and different people.In the company's marketing work,if we can find the characteristics of different groups of people,not only can the subsequent marketing activities be more targeted,but also provide opportunities for business development and reduce the cost of work,Besides this,feature extraction can also provide further analysis for subsequent data analysis.Good feature variables can improve predictive performance.This article explores the opportunities for precision marketing from the perspective of membership segmentation group feature extraction,and take the member data of a maternal-and-child retail enterprise as a case,considering the preference of category purchase as a basis for membership segmentation.Firstly we chooses a group of members with a clear preference for category purchase.The main method to extract the characteristics of the group is the decision tree method and we hope to discover whether groups of members with different purchase catogary preference have intrinsic characteristics in terms of activity level,channel preference,etc.The case analysis of this paper finds that although the decision tree can be used to extract the characteristics of the membership group,but because the decision tree is very sensitive to the data imbalance problem,so in the process of overcoming the data imbalance problem,many data information will be sacrificed.The analysis process brings about obstacles and also leads to questions about the results.Therefore,the work of extracting the characteristics of the membership needs to be combined with a variety of methods such as decision tree,rule learning,contingency table analysis,and descriptive statistics.And then based on the understanding of the business background,effective analysis results can be determined.