Unsupervised Learning for Customer Behavior Analysis: A Clustering Approach
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P128Keywords:
Customer Segmentation, Unsupervised Learning, Clustering, RFM Analysis, K-Means Clustering, DBSCAN, Retail Analytics, Customer Behavior Analysis, E-commerceAbstract
Understanding customer purchasing behavior is essential for businesses to optimize marketing strategies and improve customer retention. This study employs machine learning based clustering techniques to segment customers based on transactional data. By leveraging Recency, Frequency, and Monetary (RFM) analysis, the study compares multiple clustering algorithms to identify distinct customer groups. Experimental results demonstrate that the proposed approach effectively categorizes customers, enabling data-driven decision-making for targeted marketing. These findings highlight the potential of unsupervised learning methods in enhancing business intelligence and customer relationship management.
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