Forecast when customers actually arrive.
Accurate rosters start with an accurate forecast. We use machine learning to predict demand and footfall for every store and daypart, learning the patterns a weekly template never captures.
A rainy Tuesday in the school holidays behaves nothing like a sunny payday Friday. The model learns both, store by store.
What is demand forecasting in retail?
Retail demand forecasting predicts how many customers will arrive, and how much they will buy, at a given store and hour. Done well it accounts for seasonality, paydays, weather, promotions and local events, so the roster built on it matches reality instead of a calendar.
A forecast is only useful if it is accurate at the level you actually staff: the store and the daypart. Chain-level or daily averages hide the peaks and troughs that decide whether a shift is over- or under-resourced.
Our machine-learning models learn each store’s own rhythm from your historical data and the signals that move it, then keep improving as more data arrives. The output feeds directly into demand-matched rosters, so the forecast does real work rather than sitting in a dashboard.
Built for how stores really trade.
Store-level, not chain-level
Every store gets its own model. Chain averages hide the peaks and troughs that decide whether a shift is over- or under-resourced.
Hour-by-hour granularity
Forecasts at the resolution you actually staff, so the roster built on them matches the trading day.
Driver-aware
The model weighs seasonality, paydays, weather, promotions and local events where the data supports it.
Accuracy you can see
Forecast accuracy is measured against actuals, so you know how far to trust it before you staff to it.
A rainy Tuesday in the school holidays draws a different crowd, at different hours, than a sunny payday Friday. A flat template treats them the same. A learned model staffs each correctly.
What the no-obligation scan gives you.
The free first step puts a number on your current forecast accuracy and the uplift on the table.
A forecast-accuracy read
How accurate your current planning assumption is against what actually happened, for a sample of stores.
A real demand profile
What demand looks like hour by hour, versus the flat assumption most rosters are built on.
The signals that move your demand
Which factors most affect your footfall, and roughly how much each one matters.
Indicative uplift
The accuracy gain a learned model could add, and what that is worth in roster terms.
What good looks like.
Illustrative move in forecast accuracy from a flat assumption to a learned store model (placeholder).
Granularity of the forecast, matched to how you actually schedule (illustrative).
Every store gets its own model rather than a single chain-wide curve (illustrative).
Demand forecasting: common questions
Find your labour-cost gap in one conversation.
A no-obligation efficiency scan gives you a numbers-first picture of where your roster is leaking margin, and what it's worth to fix. No software to buy. No commitment.