Machine Learning-Driven Customer Segmentation in Retail Cloud Data

This manuscript explores how machine learning (ML) techniques can optimize customer segmentation in retail, particularly when integrated with cloud-based data architectures. The study analyzes various ML algorithms, including clustering and classification methods, to identify consumer patterns, enabling personalized marketing strategies. Key findings highlight the effectiveness of cloud-based segmentation in enhancing customer engagement, optimizing sales, and improving decision-making in real-time. This research demonstrates that ML-driven segmentation empowers retail businesses to adapt dynamically to market demands, ensuring sustainable customer relationships and boosting profitability.