Customer Segmentation for Targeted Marketing: A Comparative Analysis of Clustering Techniques

In the contemporary landscape of data-driven decision-making, businesses are increasingly harnessing customer segmentation as a strategic tool for tailoring their marketing endeavors. his research employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to investigate customer segmentation for targeted marketing. It encompasses phases such as data understanding, preprocessing, modeling, evaluation, deployment, and monitoring. Our study applies K-Means and Hierarchical Clustering algorithms to create customer segments. While Hierarchical Clustering provides a visually insightful segmentation structure, K-Means excels in terms of the Silhouette score, a crucial clustering metric. Overall, K-Means Clustering emerges as the superior choice due to its interpretability and comprehensive utility. This research contributes to data-driven marketing by offering insights for businesses seeking to enhance marketing strategies, elevate customer engagement, and boost revenue.