Impact of Datamining Clustering Methods on Decision Support System - Concerning Customer Segmentation

Effective customer segmentation is crucial for personalized marketing and improving customer satisfaction. This study examines the impact of data mining clustering methods, particularly agglomerative hierarchical clustering, on customer segmentation in decision support systems (DSS). We investigate how it can improve marketing personalization and customer satisfaction, ultimately leading to increased sales. The study explores the theoretical foundations of data mining clustering and its application to customer segmentation in DSS frameworks. It then moves into popular clustering algorithms, focusing on agglomerative hierarchical clustering for customer segmentation tasks. A series of experiments using real datasets evaluate the practical implications of using this method for customer segmentation in DSS. The findings demonstrate the effectiveness of agglomerate hierarchical clustering for customer segmentation in DSS. The advantage of this approach is the identification of nested or overlapping customer segments, which provides valuable information for targeted marketing strategies and product development. By allowing companies to customize their approach to different customer groups, the agglomerate hierarchical cluster of DSS can significantly improve customer loyalty and increase sales.