Scalable Customer Segmentation Using AI: Leveraging K-Means Clustering and Deep Learning Techniques

Abstract

This research paper explores scalable customer segmentation through the integration of K-Means clustering and deep learning techniques, offering a robust framework for businesses seeking to harness AI in understanding diverse customer bases. The study begins by highlighting the limitations of traditional segmentation methods in handling large-scale, complex datasets, emphasizing the need for more sophisticated approaches. We propose a hybrid model that combines the efficiency of K-Means clustering for initial segmentation with the nuanced analytical power of deep learning algorithms to refine and understand segment characteristics. A multi-layered architecture is developed, where K-Means provides a preliminary partitioning of customer data into distinct clusters based on key attributes, followed by a deep learning model that delves deeper into these clusters to uncover intricate patterns and insights. Our methodology is applied to a large dataset comprising various customer attributes from a multinational retail company, demonstrating scalability and accuracy. The results indicate significant improvements in segmentation granularity and predictive accuracy of customer behavior, offering actionable insights for personalized marketing strategies. This approach not only enhances the understanding of customer needs but also optimizes resource allocation in marketing initiatives. The paper concludes with discussions on the implications for real-world applications and future research directions, emphasizing the transformative potential of integrating AI techniques in customer segmentation.