Big Data-Driven Cross-Border E-commerce Platform Operation Strategy Based on Data Mining

Abstract

In the context of the big data era, cross-border e-commerce enterprises are confronting significant challenges. The traditional marketing model finds it challenging to adapt to the evolving needs, making precision marketing for cross-border e-commerce platforms particularly crucial. This paper, based on the big data-driven path of operating cross-border e-commerce platforms, designs the operation strategy of these platforms from the perspective of precision marketing and empirically analyzes its impact. The RFM model is first used to design user value labels, and the K-means algorithm then uses the clustered labels. Combined with mining the three types of key data—user basic attributes, user value labels, and user consumption behaviors—of Company A’s Amazon store to construct user profiles, analyze them, and further design precise marketing strategies based on user profiles and analyze their effects, This paper classifies customer groups into three categories: high-value premium types, dynamic premium types, and growth types. High-value, quality customers account for most of Company A’s business, and through personalized marketing, their sales show a certain growth trend. Vitality-quality customers stimulate the desire to buy by recommending new products and activating old ones, and their sales increased to $32,527 in the fourth quarter. The impact of growth-type customers using consumption coupons and discount codes to stimulate consumption and purchases is flat, with no significant growth. This indicates that the operation strategy in this paper is more obvious and can be used as a precursor for further optimization.

Keywords

• rfm model
• k-means clustering
• data mining
• user profiling
• cross-border e-commerce platforms
• 62-07