Clustering in Data-Driven Marketing: Customer Behavior Analysis and Its Applications

This study investigates the application of customer clustering techniques in data-driven marketing, focusing on customer behavior analysis to enhance segmentation and personalization strategies. With the increasing availability of customer data from diverse sources, businesses have an unprecedented opportunity to gain deeper insights into consumer preferences and develop tailored marketing approaches. This research employs multiple machine learning clustering methods, including K-Means clustering, DBSCAN, hierarchical clustering, and principal component analysis (PCA), to analyze a comprehensive dataset of Amazon customer interactions. The findings reveal that K-Means clustering effectively categorizes broad customer segments, such as loyal customers and price-sensitive shoppers. At the same time, DBSCAN identifies smaller, more distinct groups, and hierarchical clustering provides a granular view of customer relationships. The results underscore the role of customer clustering in optimizing marketing efforts, enhancing customer engagement, and maximizing return on investment (ROI) by enabling businesses to target relevant customer segments with personalized offers precisely. Furthermore, this study highlights the strategic importance of clustering in formulating data-driven marketing strategies and optimizing resource allocation. Ultimately, the research demonstrates how machine learning-based clustering techniques can facilitate a more targeted, customer-centric, and analytically driven approach to marketing.

کلیدواژه ها:

Customer Clustering ، Data-driven Marketing ، Customer behavior analysis ، K-Means Clustering ، Machine learning