Specialty retailer improves forecast accuracy 50%+

Accuracy of retailer's existing forecasts was sub-optimal

Improving forecasts was made difficult by complex business hierarchies, shifting customer behavior, and high number of SKUs

Retailer had been using spreadsheet-based forecasts, which were slow and inflexible

These highly manual forecasts limited the business’s ability to adapt to market conditions in real-time

Retailer had no historical data for new products and new stores introduced each season

With limited data to inform traditional forecasting methods, this retailer was highly uncertain on how to plan for critical SKUs