<![CDATA[The increasing complexity of market competition and the rapid growth of enterprise data have made traditional marketing decision-making approaches inadequate in addressing information asymmetry and dynamic market changes. Conventional decision support systems (DSS) are often limited to data-level reporting and lack advanced analytical capabilities to uncover hidden patterns and strategic insights. This study aims to design and evaluate an intelligent marketing Decision Support System by integrating data warehousing, Online Analytical Processing (OLAP), and data mining techniques to enhance the quality and effectiveness of marketing decisions. The proposed method adopts a data-driven DSS architecture that performs extract–transform–load (ETL) processes to build a unified subject-oriented data warehouse, followed by multidimensional analysis and knowledge discovery using decision tree classification and neural network models. Experimental validation was conducted using FoodMart 2000 sales data to assess the predictive performance and decision support capability of the system. The results demonstrate that the three-layer BP neural network model achieved a mean absolute percentage error of 15.13% in sales prediction, indicating satisfactory forecasting accuracy, while simulation and sensitivity analyses reveal a positive correlation between promotional investment and corporate profit growth. These findings confirm that the proposed marketing DSS can effectively reduce information asymmetry, improve forecasting reliability, and support strategic marketing decisions related to pricing, promotion, and market expansion. The study implies that integrating data mining with DSS provides a robust analytical foundation for data-driven marketing management and sustainable enterprise competitiveness in complex market environments.]]>
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