Analysis of AI-Powered User Marketing Portraits and Conversion Optimization Paths for Cross-Border E-Commerce Supported by Commercial Big Data
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Abstract
The cross-border e-commerce industry has exited the era of traffic dividends, with intensified competition among domestic and overseas merchants entering a stage of refined operation. The combination of Commercial big data and artificial intelligence algorithms precise identification of the real demands of overseas consumers and optimizes the complete transaction chain from exposure to repurchase. Based on theories of big data governance, user portraits, cross-border consumer behavior, and e-commerce conversion funnels, this paper defines the scope of big data collection for cross-border scenarios, sorts out the complete workflow of AI portrait modeling, and constructs a implementable indicator system for cross- border user portraits. This study conducts empirical analysis using operational data from 50 domestic cross- border enterprises of varying scales, sets up five groups of data comparison tables, and calculates the magnitude of changes in store click-through rate, add-to-cart rate, order conversion rate, and repurchase rate before and after the application of AI portraits. The research summarizes four core obstacles encountered by enterprises in implementing AI portrait systems: data silos, poor algorithm adaptability, overseas data compliance risks, and fragmented marketing conversion chains. Optimization paths are proposed across five dimensions: construction of data platforms, iterative updating of AI models, design of hierarchical marketing strategies, full-link conversion transformation, and cross-border data compliance management. This research forms a complete practical framework, providing a reference for various cross-border e- commerce enterprises to carry out digital precision marketing, helping merchants cut invalid marketing costs and boost overall store transaction conversion performance.
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