Performance Evaluation Of Kmeans clustering, Agglomerative hierarchical clustering And Affinity Propagation for Customer Segmentation

Customer segmentation is a critical factor of e-commerce marketing strategies, enabling groups to better apprehend their customer base and tailor personalised studies to beautify client satisfaction and pressure sales. The technique involves categorizing customers into wonderful groups based on similar characteristics and behaviours, permitting e-commerce systems to optimize product tips, marketing campaigns, and ordinary consumer enjoy. This research focuses on comparing the effectiveness of commonly used clustering algorithms, namely K-Means, Agglomerative Hierarchical Clustering, and Affinity Propagation, in handling challenges such as large-scale, dynamic customer data and high-dimensional feature spaces. Real-world e-commerce data is utilized for performance analysis, offering valuable insights into the suitability of these algorithms. By understanding the strengths and limitations of each method, businesses can make informed decisions when selecting a clustering approach that aligns with their specific needs and dataset characteristics, ultimately improving customer experiences and driving sales.