Research and optimization of cross-border e-commerce marketing mode based on big data technology

Abstract

The development of mobile Internet promotes the updating of cross-border e-commerce models, and the precision marketing realized by relying on big data technology better meets the all-around demand of users for content, socialization, and transactions. The article establishes a cross-border e-commerce marketing process on the basis of STP marketing management and builds a cross-border e-commerce precision marketing model by combining the STP marketing model. The user behavior characteristics of cross-border e-commerce users are extracted based on the RFM model, and the user behavior model is established by combining the user’s interest in purchasing goods. Then, the K-Means clustering algorithm is used to process the subgroups of cross-border e-commerce customer samples so as to construct a precise portrait of users. The cross-border e-commerce enterprise Z is selected as the research object, and the impact of precision marketing strategy on its user growth, merchandise sales, click-to-purchase conversion rate, and marketing optimization effect is analyzed. The number of effective users grew from 10,516 in 2020 to 16,804 in 2022, and the click-to-purchase conversion rate of products improved by 20%~46%, and different types of customers have various degrees of improvement under the precision marketing strategy. Based on big data technology, cross-border e-commerce users can be accurately portrayed, marketing products can be provided to users with more accuracy, and cross-border e-commerce enterprises can effectively enhance their marketing capabilities.

Keywords

• rfm model
• user behavior model
• k-means clustering algorithm
• precision marketing
• cross-border e-commerce
• 97r50