Implementation of Data Mining for Customer Segmentation Using the K Means Clustering Algorithm Based on Annual Income and Spending Score

Background: This research is motivated by the dynamics of the retail industry, which requires a deep understanding of consumer behavior in order to compete effectively in an increasingly competitive market. Many marketing strategies fail to achieve optimal results because they overlook variations in individual shopping behavior within large customer populations. Understanding these behavioral differences is important for developing more targeted and effective marketing strategies. Objective: This study aims to group customers into homogeneous segments in order to support more precise strategic decision-making in marketing activities. Method: The study applies a data mining approach using the K-Means clustering algorithm to analyze a dataset consisting of 200 customers. The clustering process is conducted based on two main variables, namely annual income and spending score, to identify patterns of consumer behavior. Findings and Implications: The results reveal five distinct consumer clusters with different behavioral characteristics. The Target group represents the majority with 81 customers, followed by the Sultan group (39 customers), the Thrifty group (35 customers), the Passive group (23 customers), and the Impulsive group (22 customers). The findings indicate that income level does not always correlate linearly with consumption intensity, implying that behavioral-based segmentation provides more accurate insights for marketing strategy development. Conclusion: Customer segmentation using the K-Means clustering algorithm enables clearer identification of target markets through well-defined cluster separation. Therefore, marketing strategies should emphasize lifestyle orientation rather than focusing solely on purchasing power to optimize customer loyalty and engagement.