Accurate demand forecasting is essential for retailers to minimize the risk of stores running out of a product, or not having enough of a popular brand, color or style. Preseason and in-season forecast errors account for 20 to 25 percent of losses in sales. Traditional demand forecasting methods for all stock-keeping units (SKUs) across all stores and all geographies have an inherent weakness of no ability to data mine the volumes of time-series data at the SKU-level. The purpose of this article is to present a machine learning approach – the GenIQ Model© – for demand forecasting that has demonstrated superior results compared to the traditional techniques.