Enhancing Customer Segmentation Through AI: Analyzing Clustering Algorithms and Deep Learning Techniques

Abstract

This research paper investigates the potential of artificial intelligence, specifically focusing on clustering algorithms and deep learning techniques, to enhance customer segmentation in marketing strategies. It begins by examining traditional segmentation methods, identifying their limitations in handling large and complex datasets. Building on this, the study explores clustering algorithms such as K-means, hierarchical clustering, and DBSCAN, evaluating their effectiveness in grouping customers based on distinct behavioral patterns and demographic characteristics. Simultaneously, the research delves into advanced deep learning techniques, including neural networks and autoencoders, to understand their capacity for identifying nuanced customer segments through unsupervised learning. By conducting experiments on diverse datasets obtained from various industries, the study assesses the accuracy, scalability, and execution speed of each AI method. Results demonstrate that while clustering algorithms offer simplicity and interpretability, deep learning techniques provide superior precision in revealing complex, non-linear relationships among customer data. The paper concludes by presenting a hybrid model that combines the strengths of both approaches, recommending its deployment for businesses seeking to optimize marketing efforts and personalize customer experiences. Furthermore, the implications of these findings are discussed in the context of ethical considerations and customer privacy, highlighting the need for responsible AI utilization in consumer analytics.