Predictive Analytics & ML

Granular Demand Forecasting for Proactive Staffing

Client: National home improvement retailer

Situation

Demand for field services fluctuated by geography, and staffing decisions were reactive—capacity gaps appeared after demand spikes had already hit.

Task

Forecast service volume at granular geographic levels so hiring, training, and scheduling could ramp ahead of demand.

Action

Partnered with a data scientist to build and operationalize a forecasting model at the geographic quota-group level, incorporating seasonality, promotions/marketing effects, local trend signals, and operational factors. Translated forecasts into workforce planning inputs for hiring pipeline timing, training readiness, and scheduling capacity.

Results

• Shifted from reactive to proactive capacity planning
• Improved hiring and scheduling alignment with demand patterns
• Enabled readiness for volume increases before they hit