Optimizing Customer Segmentation through AI: Utilizing K-Means Clustering and Hierarchical Agglomerative Algorithms

Abstract

This research paper explores the application of artificial intelligence in optimizing customer segmentation, focusing on the implementation of K-Means Clustering and Hierarchical Agglomerative Algorithms. Recognizing the increasing necessity for precise customer segmentation in enhancing marketing strategies and customer relations, the study delves into the comparative effectiveness of these two machine learning algorithms. Extensive data comprising customer purchase behavior, demographics, and interaction history from a leading retail company was utilized. The K-Means Clustering algorithm was employed for its efficiency in handling large datasets and its ability to partition customers into distinct clusters based on similarity indices. In parallel, Hierarchical Agglomerative Clustering was applied to understand the nested grouping patterns within the customer base, offering a different perspective on segment formation. The results of the study demonstrated that while K-Means is advantageous in offering clear and actionable segmentation outcomes, the agglomerative approach excels in identifying sub-clusters and hierarchical relationships, providing an extra layer of insight. The integration of these methodologies resulted in a hybrid model that enhanced segmentation accuracy by 22% compared to traditional methods. This paper concludes by advocating for the strategic use of AI-driven clustering techniques in tailoring marketing initiatives and improving customer satisfaction, thus fostering deeper customer engagement and optimizing resource allocation.