Customer Segmentation for Targeted Campaigns Using RFM Analysis and K-Means Clustering

Customer segmentation is critical for tailoring marketing campaigns to specific consumer needs, enhancing engagement, and maximizing business profitability. This study integrates Recency-Frequency (RF) analysis with the K-Means clustering algorithm to classify customers of "Ramallah Outfit" into distinct behavioral segments. The Monetary (M) component was excluded because spending levels can sometimes be misleading high or low expenditures do not always reflect true loyalty or engagement. While monetary data may indicate how much a customer spends, it does not necessarily correlate with the frequency or recency of purchases, which are more reliable indicators of customer behavior and engagement. Excluding the Monetary component allows for a clearer focus on customer retention and loyalty. A high spender might make only occasional large purchases, whereas a customer with frequent smaller purchases could be more engaged and loyal. By focusing on Recency and Frequency, the analysis more accurately captures ongoing engagement, providing actionable insights for marketing. Using a unique first-hand dataset, the model achieved a clustering accuracy of 99.47%, effectively segmenting customers into actionable clusters: Gold (34.4%), Bronze (35.2%), and Silver (30.4%). The results demonstrate the effectiveness of this refined approach in optimizing marketing efforts and fostering customer loyalty.