Abstract: With the e-commerce industry shifting from scale expansion to refined operation, traditional undifferentiated marketing strategies face challenges such as high costs and low conversion rates, making precision marketing a key direction for enterprises to enhance competitiveness. This study focuses on solving the problem of effective user segmentation and personalized marketing adaptation in e-commerce, taking clustering algorithms as the core technical support. First, it systematically combs the core theories of clustering algorithms and key theories of e-commerce precision marketing. Then, it designs a complete e-commerce precision marketing model based on clustering algorithms: it clarifies the sources of user data and conducts preprocessing steps such as data cleaning, integration, transformation, and reduction; it optimizes the K-means algorithm by determining the optimal number of clusters through the elbow method and improving initial cluster center selection via K-means++ algorithm.

Keywords: clustering algorithm, e-commerce, precision marketing, K-means algorithm, user segmentation, marketing effect verification, data preprocessing

Pages: 443-451