Research on the Construction of Precision Marketing Strategy of E-commerce Platform Based on User Profile

In order to improve the marketing accuracy and user response efficiency of e-commerce platforms, a multi-level feature model is built using static and dynamic user profiles, and the nonlinear mapping mechanism of user behavioral embedding and lifecycle value prediction is investigated, so as to construct a strategy matching algorithm incorporating the attention mechanism. Analyzing 218,542 real user data, 22 static attributes and 128-dimensional dynamic behavioral features are extracted, and the MLP model is used for CLV estimation, which is combined with the quantile value division to realize user value stratification. In the strategy generation stage, multi-channel attention network is introduced to improve the accuracy and sorting quality of strategy matching for different value groups. The experimental results show that the fusion feature model reaches 0.479 on NDCG@5, and the Precision@5 of high-value users reaches 0.576, and the recommendation stability is significantly enhanced with the increase of the sliding window length, which verifies the effectiveness and robustness of the user profile-driven precision marketing strategy system.