AI-Powered Business Intelligence for Smarter Decision-Making and Growth

Business intelligence (BI) has become a cornerstone for data-driven decision-making in modern enterprises. With the increasing complexity of data, traditional methods often fall short in providing actionable insights. Machine learning techniques, such as Gradient Boosting Machines (GBMs), offer advanced predictive capabilities that can significantly enhance BI outcomes. This paper explores the application of Gradient Boosting Machines (GBMs) in enhancing BI for better decision-making and operational efficiency. GBMs, a powerful ensemble learning technique, are utilized to predict customer churn based on a comprehensive retail dataset encompassing customer demographics, purchase history, engagement scores, and monetary values. The Proposed Method is implemented using Python Software and demonstrates that GBMs achieve exceptional predictive accuracy of 99.2%, significantly outperforming traditional methods like Decision Tree, SVM, BDMS. Feature importance analysis reveals that ‘Recency’ (days since last purchase) is the most influential predictor of churn, followed by ‘Engagement Score’ and ‘Monetary Value’. The findings emphasize the model's capability to enhance customer retention strategies, optimize resource allocation, and improve overall business outcomes. Utilizing GBMs, high-risk customers (top 10% predicted probabilities of churn) were targeted with a specialized retention campaign, which led to a 15% reduction in churn rates among this group. The effectiveness of the campaign validates the model's predictions and highlights the practical benefits of integrating GBMs into BI systems. This research underscores the transformative potential of AI-driven techniques in business intelligence, offering actionable insights for businesses seeking to leverage predictive analytics for competitive advantage.

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