Abstract

In the cutthroat modern retail environment, strategic decision-making is increasingly driven by intelligent data usage. This research investigates how data science and ML can be leveraged to enhance critical business processes in multi-channel retail settings that include online, in-store, and social commerce platforms. An integrated framework comprising predictive modeling, customer segmentation, promotion response analysis, and dynamic pricing has been used in this research to demonstrate how machine learning enhances business intelligence and improves operational performance. A thorough analysis pipeline in Python was developed with models such as Random Forest, XGBoost, K-Means, LSTM, and Q-learning. Results showed significant enhancement in the accuracy of forecasts, R² = 0.93; efficiency of marketing, AUC = 0.91; and inventory optimization, MAPE = 6.2%. Feature importance analysis further showed that customer engagement and discount sensitivity are key drivers of revenue performance. The study concludes that integrating analytics driven by ML into strategic retail management empowers better-informed, agile, and profitable decision-making, placing data-driven intelligence at the heart of sustainable retail competitiveness.

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