Calibration cut error by ~24%
A national monthly multiplier applied on top of the raw model pulled MAE from the pre-calibration baseline down to 1.87. A second-pass residual correction takes it lower still — the model knows its own systematic miss.
A nationwide retail-movement forecasting model — county-granular, channel-aware, with a calibrated baseline and a residual correction that delivers sub-2.0 MAE across more than fifty thousand tracked accounts.
A national rollup hides as much as it reveals. County-level forecasting surfaces where growth is real, where it’s softening, and which accounts deserve a rep’s next visit.
A national monthly multiplier applied on top of the raw model pulled MAE from the pre-calibration baseline down to 1.87. A second-pass residual correction takes it lower still — the model knows its own systematic miss.
Forecasting at the county level surfaces growth pockets a state-level model averages away. Across 2,142 active counties, neighboring counties routinely diverge by 10+ percentage points of YoY movement.
Per-channel error ranges from 1.62 in the tightest slice to 3.77 in the loosest. Useful both as an honesty signal and as guidance for where additional data would actually move the number.
Roughly 19,000 accounts have a forward forecast that sits below their most recent actual movement. That’s a prioritized call-list for sales without any extra modeling work — it falls out of the same forecast.
A deliberate pipeline. Each step is a choice about what to model and what to leave to a follow-up data investment.
Twelve months of account-level movement across every modeled county and channel.
Each retail channel gets its own baseline — high-frequency channels behave nothing like long-tail ones.
National monthly multiplier, then a residual correction pass to pull MAE down further.
Three months forward, ranked by predicted movement vs current actuals.
The model knows its own gaps. The data investments below were scoped against actual MAE deltas observed when each signal was introduced to a slice of the model.
Third-party point-of-sale across a set of priority markets, monthly refresh.
Neighborhood-level affluence, traffic, and visitor signals layered onto each account location.
Daily movement feeds from channel partners — closing the gap between actuals and the next forecast pass.
Per-channel MAE tells you both how much to trust each slice of the forecast and where the next data investment is most likely to move the number.