Which mathematical innovation first enabled retailers to accurately predict seasonal shopping patterns using historical sales data?

The intersection of mathematics and retail has revolutionized how businesses understand consumer behavior. Advanced statistical models and machine learning algorithms now power retail analytics, enabling precise demand forecasting, inventory optimization, and personalized marketing. This poll explores a key milestone in the development of mathematical models for retail data analytics that changed how businesses approach consumer behavior prediction.

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Markov Chain Models (1970s)
Holt-Winters Exponential Smoothing (1960s)
Monte Carlo Simulations (1980s)
Neural Network Forecasting (1990s)