<![CDATA[The exponential growth of customer data within Management Information Systems (MIS) has generated an urgent need for structured analytical approaches capable of transforming raw information into valuable insights that support decision-making across various organizational processes. This study aims to develop a comprehensive and systematic framework for mining customer data in MIS by integrating preprocessing procedures, machine learning algorithms, and model evaluation techniques into a unified analytical workflow. Using the Design Science Research methodology, the framework was designed based on existing data mining standards, developed through iterative refinement, and demonstrated using a customer-behavior dataset processed with clustering, classification, and association rule mining techniques. The findings reveal that the proposed framework improves data quality, enhances segmentation accuracy, and strengthens predictive capability, enabling MIS to deliver deeper insights into customer behavior, purchasing tendencies, and potential churn risks. Experimental results show that combining K-Means, Random Forest, and Apriori algorithms yields more comprehensive and reliable patterns compared to using a single analytical technique. The outcomes of this research highlight the practical significance of applying an integrated data mining approach in MIS, allowing organizations to optimize marketing strategies, personalize services, and make more informed managerial decisions. Overall, this study contributes to the field by offering a scalable, adaptable, and effective framework for implementing customer data mining within real-world MIS environments.]]>
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