Retail Marketing Strategy Optimization: Customer Segmentation with Artificial Intelligence Integration and K-Means Clustering
DOI:
10.33395/sinkron.v8i4.14000Keywords:
Customer Segmentation, K-Means Clustering, RFM Analytics, Retail Marketing Optimization, Artificial IntelligenceAbstract
This study aims to optimize retail marketing strategies through customer segmentation using the K-Means clustering method and RFM (Recency, Frequency, Monetary) analysis. By utilizing transaction data from a large retail company, customers are categorized into six segments: VIP Customers, Loyal Customers, Potential Loyalists, New Customers, At-Risk Customers, and Dormant Customers. This segmentation allows for the implementation of more targeted marketing strategies for each customer group. For example, VIP Customers who represent 3.0% of total customers are very active with significant spending, so they deserve exclusive offers and premium services. Loyal Customers, which account for 7.0% of total customers, show high transaction frequency and loyalty, suitable for loyalty programs and recurring discounts. Potential Loyalists, which comprise 15.0%, show the potential for increased loyalty through retention campaigns. New customers representing 16.3% need a brand recognition and promotion strategy to increase their initial engagement. At-Risk Customers covering 30.7% indicated a decrease in transaction activity and required intervention to prevent churn, while Dormant Customers covering 28.1% required a strong reactivation strategy. The clustering evaluation showed an average Silhouette score of 0.3115, which indicates that the clusters that are formed are quite well defined, although there is still room for improvement. This research provides valuable insights to develop more effective and efficient marketing strategies, as well as increase customer satisfaction and loyalty.
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