International Research Journal of Education and Technology

This study focuses on understanding customer behavior by using the K-Means clustering algorithm, combined with Business Intelligence (BI) tools, to create actionable insights. The dataset includes customer demographics, purchase history, and engagement data. Before applying K-Means, we clean and prepare the data, selecting the most relevant features for meaningful segmentation.To determine the optimal number of clusters, we use techniques like the Elbow Method and Silhouette Score. The results are then visualized in interactive BI dashboards (Power BI/Tableau), allowing businesses to track customer trends and adjust marketing strategies in real time. This approach helps improve customer targeting, boost retention, and optimize resource allocation.We also address challenges such as maintaining data quality, ensuring the model scales effectively, and adapting to changing customer behavior. Looking ahead, future research will explore AI- powered clustering and predictive models for even deeper insights.

Keywords: Customer Segmentation, K- Means Clustering, Business Intelligence, Data Analytics, Machine Learning