Fuzzy decision tree based online precision marketing method for brand products on the internet

In order to solve the problems of poor user satisfaction, low user purchase rate and low recommendation accuracy in current brand product marketing methods, this paper proposes an online precision marketing method for brand products based on fuzzy decision trees. Firstly, collect and obtain user characteristics of brand products driven by internet data; secondly, construct a user influence relationship model and a user preference and interest model for the product; again, classify brand product data features based on fuzzy decision trees; finally, precise online marketing of brand product users is achieved through cosine similarity calculation. The experimental outcomes demonstrate that the marketing satisfaction achieved by the approach introduced in this article consistently exceeds 92%, reaching a peak user purchase rate of 52.18% and attaining a maximum accuracy of 95.08% in product recommendations. The approach outlined in this article can significantly enhance the efficacy of data-driven online precision marketing strategies for brand products.