Developing Personalized Marketing Strategies Based on Customer Behavior Analysis Using Clustering and Machine Learning

This study introduces a data-driven approach to customer segmentation using clustering algorithms like K-Means, DBSCAN, hierarchical clustering, and PCA. Customers are segmented based on purchasing patterns, website navigation, and demographics, identifying groups such as loyal customers, price-sensitive buyers, and disengaged consumers. These insights enable targeted marketing strategies, including personalized loyalty programs and re-engagement campaigns. The key innovation lies in the comprehensive multi-method approach, which enhances segmentation accuracy, uncovers nuanced customer insights and helps businesses optimize customer engagement and profitability.