Which machine learning approach revolutionized retail demand forecasting by reducing prediction errors by over 30% compared to traditional time series methods?

Modern retail analytics combines data science, behavioral psychology, and machine learning to transform how stores understand customer behavior. Beyond simple sales tracking, today's analytics platforms use sophisticated algorithms to predict future buying patterns and optimize inventory. How well do you understand the cutting-edge science powering retail analytics that major retailers are implementing to stay competitive?

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Gradient-boosted decision trees with temporal features
Simple recurrent neural networks (RNNs)
K-means clustering with seasonal decomposition
Association rule mining with market basket analysis