• KARTHIGA M
• SOUNDARARAJAN K
• RAMYA R
• SALINI S
• SARANYA A

This project analyzes a UK-based online retail dataset from the UCI Machine Learning Repository to optimize marketing strategies through customer segmentation. Using RFM (Recency, Frequency, Monetary) analysis and the K-means clustering algorithm, customers are grouped based on purchasing behavior. The optimal number of clusters is determined using the elbow method for higher accuracy. Segmented customer groups help tailor targeted marketing campaigns. A recommendation system is developed to suggest top- selling products within each cluster, enhancing cross-selling and upselling. The model's performance is evaluated using precision and recall metrics. This project highlights the value of data-driven insights in boosting customer engagement, sales, and long- term business growth, and sets the stage for future enhancements like collaborative filtering.

Customer Segmentation, K-means Clustering, Recommendation System, Recency, Frequency, Monetary (RFM), Data-Driven Marketing.

"STRATEGIC CUSTOMER DATA DRIVEN INSIGHTS FOR ONLINE RETAIL THROUGH CUSTOMER SEGMENTATION AND PERSONALIZED RECOMMENDATION ENGINE USING K-MEANS CLUSTERING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.e425-e432, May-2025, Available :http://www.jetir.org/papers/JETIR2505518.pdf

"STRATEGIC CUSTOMER DATA DRIVEN INSIGHTS FOR ONLINE RETAIL THROUGH CUSTOMER SEGMENTATION AND PERSONALIZED RECOMMENDATION ENGINE USING K-MEANS CLUSTERING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppe425-e432, May-2025, Available at : http://www.jetir.org/papers/JETIR2505518.pdf