Leveraging Data Mining Techniques For Strategic Business Intelligence In Retail

Abstract

Retail companies produce an abundance of transactional and customer-oriented data that demands smart analyses. Most existing business intelligence tools are limited to descriptive statistics and do not provide necessary insights for dynamic retail decision-making. The aim of this research was to develop an innovative data mining-based strategic business intelligence architecture for customer analytics, sales forecasting, merchandising strategy, and retail decision support through the use of the UCI Online Retail Dataset. This study outlines four data mining techniques such as K-Means clustering, Apriori association rule mining, Random Forest classification, and LSTM forecasting incorporated in business intelligence architecture. Missing data imputation, normalization, feature engineering, and outlier detection methods were used to process input data. From an experimental perspective, the Random Forest algorithm yielded 94.28% accuracy, 92.64% precision, and 92.25% F1 score for customer behavior pattern analysis. In addition, the advantage of LSTM-based prediction compared to ARIMA is manifested by the fact that the former approach yielded better forecast accuracy (93.81%) and a smaller RMSE. In turn, customer segmentation helped identify the profitable customers, whereas the association rule mining process, implemented through the Apriori algorithm, played an important role in identifying the relationships between products for recommending them. The developed business intelligence dashboard significantly enhanced KPI monitoring, inventory management, visualization of sales trends, and retail strategy planning. The suggested framework revealed substantial improvements in predictive intelligence, efficiency gains, and evidence-based business decisions in modern retail settings.