<![CDATA[The rapid growth of e-commerce platforms presents challenges in understanding customer behaviour to apply targeted marketing strategies. This research combines the K-Means clustering method and Artificial Intelligence (AI) techniques for customer segmentation, enabling personalized marketing. A publicly available e-commerce customer transaction dataset was processed using the Recency, Frequency, Monetary (RFM) model and additional behavioural features. The Elbow and Silhouette methods were applied to determine the optimal number of clusters. The findings identified three main customer segments: “Premium Customers”, “Potential Customers”, and “New/Transient Customers”. AI was used to predict the segment membership of new customers based on historical data. The results are implemented through a simple blue-and-white web interface that provides personalized marketing campaign recommendations for each segment. This research contributes an end-to-end framework from data analysis to practical AI-based segmentation implementation for e-commerce. Keywords: customer segmentation, K-Means clustering, artificial intelligence, e-commerce, personalized marketing.]]>
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