Research on Behavioral Data-Driven Customer Segmentation and Precision Marketing Strategy Optimization

Abstract

In the digital economy, customer behavior data has become a core asset for businesses to understand demand and optimize decision-making. Traditional demographic-based customer segmentation struggles to capture dynamic customer needs and potential preferences, and is increasingly unable to adapt to the pace of market competition. This paper, focusing on behavioral data, combines literature research with industry practice to systematically explore implementation paths for customer segmentation and the optimization of precision marketing strategies. The study first examines the connotation, types, and technical support of behavioral data, clarifying the application logic of RFM models and K-means clustering. It then constructs a comprehensive framework encompassing "data collection - cleaning and integration - feature extraction - model building - segmentation validation." Drawing on retail and e-commerce practices, it demonstrates how to achieve refined customer segmentation based on browsing history, purchase frequency, and dwell time. It then proposes differentiated strategies such as personalized recommendations, lifecycle management, and churn recovery for high-value, active, growth-potential, and churn-risk customer segments. Finally, it analyzes issues related to data privacy and quality assurance, and offers risk management recommendations based on the Personal Information Protection Law. Research has found that behavioral data-driven customer segmentation can increase marketing conversion rates by 30%-50% and reduce marketing costs by over 20%, providing practical theoretical and practical insights for digital marketing transformation.