The Precise Operational Practices Enabled by User Behavior Analysis Supported by Clustering Algorithms in the Rapid Digital Transformation of Enterprises

This study proposes a clustering-based user behavior analysis framework to support precise operational practices in rapidly transforming digital enterprises. By integrating K-means and DBSCAN algorithms, the framework identifies behavioral patterns from multidimensional user activity data, including transaction behavior, service utilization, engagement frequency, and interaction characteristics. A Precision Operation Framework (POF) is developed to transform clustering outcomes into actionable operational strategies through data acquisition, behavioral analysis, insight extraction, and decision-support modules. Experimental results based on 86,000 user records demonstrate that the proposed approach improves operational precision by 27.5%, increases targeted marketing effectiveness by 22.1%, and reduces user churn risk by 19.3% compared with traditional rule-based segmentation methods. The framework also exhibits strong robustness, interpretability, and adaptability under evolving behavioral conditions. The proposed methodology is particularly applicable to communication-intensive digital environments supported by wireless communication infrastructures and antenna-enabled mobile access networks, where continuous user interaction data can be leveraged to enhance real-time decision-making and service optimization. This study provides an effective engineering solution for intelligent enterprise operation, user-centric management, and data-driven digital transformation.