Analysis of E-Commerce Marketing Strategy Based on Xgboost Algorithm

Abstract

With the development and popularization of multimedia technology, people’s communication and information dissemination become more and more convenient. The use of new media technology in marketing accelerates the enterprise’s adaptation to market changes. With the help of new marketing technology, the quality of marketing can be improved and the effect of marketing can be maximized. This paper reviews the current literature on e-commerce marketing and then analyzes the feasibility of precision marketing in e-commerce market in the new media era. In order to screen potential consumers and improve the success rate of precision marketing, this paper establishes a prediction model for precision marketing of bank credit cards. A public dataset selected in this paper contains the basic characteristics of the customer, such as age, gender, monthly income, monthly consumption, and whether the customer has agreed to process the credit card after personalized recommendation. After data cleaning and preprocessing, XGboost algorithm is used to predict whether the customer will process the credit card. The calculation shows that the XGboost algorithm can still maintain a high accuracy when dealing with fewer characteristic variables. This study is helpful to better predict users’ consumption intention, further explore potential customers, and improve the success rate of precision marketing.