​Why Granular Retail Demand Forecasting Needs Multi-Tower Neural Networks

James Theuerkauf explains that granular retail demand forecasting benefits from multi-tower neural networks using learned embeddings to represent products and stores, improving predictions for intermittent sales at the item-store level. Traditional item-by-item methods fail to leverage shared patterns across products and stores, while embedding-based models capture similarities and hidden factors like climate or traffic.