Deep Learning-Driven Dynamic Clustering for Intelligent Customer Segmentation

Abstract

Translate Article

E-commerce businesses acquire considerable competitive edge with successful segmentation of customers, allowing for focused marketing campaigns for particular customer groups. This study suggests a new deep learning-based dynamic clustering method that addresses the shortcomings of traditional clustering techniques in dealing with high-dimensional transaction data. Our approach combines autoencoders for non-linear feature extraction and Gaussian Mixture Models (GMM) for probabilistic clustering in feature space with application to the Online Retail Dataset. Experimental results show significant clustering quality improvement in terms of Silhouette Score (0.392 for GMM in latent space) and Davies-Bouldin Index (0.914 for GMM), while enabling business decision-making with actionable customer insights. Performance measurements show that autoencoders capture non-linear relationships more effectively than PCA, even though they use more computational resources (0.367s vs. 0.025s). Our comparison with K-Means, DBSCAN, and Hierarchical Clustering identifies competitive advantages and trade-offs, particularly in dealing with intricate, high-dimensional data. The suggested framework adds to customer analytics by providing an efficient, scalable clustering framework that improves segmentation quality and facilitates business growth strategies. This research also emphasizes the capability of deep learning in solving real-world data segmentation problems, laying the groundwork for future optimization studies.