Data for demand forecasting

Demand forecasting predicts how much customers will buy, where and when, so planners can set inventory, staffing and production. External data improves forecasts most when it explains drivers that your own history cannot.

Retail sales data shows market demand and share by product, store and region, beyond what your own sales reveal. Weather data provides forecasts and history for temperature, rain and other conditions that drive demand in categories from drinks to heating. Foot traffic data shows visits to stores and areas, which helps forecast footfall, staff rotas and local demand shifts.

A typical workflow

1. Build a baseline from your own history.
2. Add external drivers one at a time and measure the improvement.
3. Use forecasts of those drivers, not actuals, when testing.
4. Monitor forecast error and retrain as patterns change.

What to check before you buy

Ask about history length, granularity and latency, because a driver that arrives too late cannot help a forecast. For weather, check whether archived historical forecasts are available, since testing with observed weather overstates the gain. Confirm that location and product mappings line up with your hierarchy.