Ward runs demand across your entire Convenience estate.
Your Convenience data already holds the answers. Ward finds them and hands you the next step.
Why demand matters
in convenience retail.
C-store demand is the most volatile in retail, weather, construction detours, school schedules, and local events can swing traffic dramatically in the same store. Ward builds location-specific models incorporating real-time traffic data, weather forecasts, and event calendars to help operators order precisely for each delivery window.
Benchmarks. C-store daypart accuracy: 18-30% MAPE for morning rush is healthy; over 35% indicates the model isn't capturing the operational signal. A 5-point daypart accuracy gain typically reduces fresh waste by 20-35% and lifts coffee-attach revenue by 1-3%.
Construction detour impact, fuel site cluster
A highway on-ramp closure reroutes commuters past some of your stores and away from others. Within 48 hours, Ward detects the shift: stores on the new route are depleting morning coffee and breakfast by mid-morning while stores that lost traffic are over-ordering and generating waste. Ward issues demand adjustment cards for all affected locations with revised quantities for the construction period.
Three pitfalls Ward catches
in convenience demand.
- 01 Daily forecasts miss the daypart shift that drives c-store P&L; a store can hit daily volume but stockout coffee at 9 AM and waste fresh food at 9 PM.
- 02 Traffic count data is often state-DOT level and weeks delayed; operational decisions need real-time or near-real-time signals.
- 03 Construction and event impact lasts beyond the chain's standard demand-curve lookback, so the system relearns slowly and over-orders for weeks after the cause clears.
How Ward runs demand
for convenience retailers.
-
01
Forecast at the hour-store grain
Ward fits demand by hour, with weather and traffic features tuned to each store's specific catchment and daypart mix.
-
02
Detect signal shifts in real time
Construction, events, and weather extremes trigger immediate model overrides rather than waiting for the next planning cycle.
-
03
Push order adjustments to DSD and central
Recommendations land directly in the order interface or as a daily card to the store manager, with the math exposed.
What a Ward card looks like.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled the 6–9a daypart for the 14 Route 9 sites against the chain baseline. Two causes, one of them scheduling.
| Signal | Finding |
|---|---|
daypart_sales | 6–9a revenue −11% vs. chain, coffee units −18% |
labor_scheduling | Second associate clocks in at 7:30a, peak starts 6:40a at 9 of 14 sites |
foodservice.waste | Breakfast sandwich waste 14%, hold times past 4 hours at 6 sites |
Recommend: move the second open to 6:15a at those nine sites, cut the breakfast batch by one tray, and re-check attach in two weeks.
daypart_sales…
Dashboards
Pinned views built from saved data-lake queries.
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
ncr_pos_transactions | import | 2m ago |
pdi_fuel_transactions | import | 2m ago |
verifone_forecourt_events | import | 14m ago |
ncr_planogram_audit | import | 1h ago |
retail_daypart_sales | import | 1h ago |
retail_foodservice_waste | import | 1h ago |
retail_labor_scheduling | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
ops-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fuel-team-forecourt | permit | Model::"fuel_transactions" |
Convenience demand:
the shift.
- ×Daypart demand variation
- ×Planogram compliance
- ×Impulse category optimization
- ✓Store-SKU-day level precision
- ✓Weather-driven adjustment
- ✓Event and holiday modeling
Questions about convenience demand.
C-store demand is the most volatile in retail, weather, construction detours, school schedules, and local events can swing traffic dramatically in the same store. Ward builds location-specific models incorporating real-time traffic data, weather forecasts, and event calendars to help operators order precisely for each delivery window.
A highway on-ramp closure reroutes commuters past some of your stores and away from others. Within 48 hours, Ward detects the shift: stores on the new route are depleting morning coffee and breakfast by mid-morning while stores that lost traffic are over-ordering and generating waste.
Ward depends on traffic-correlated models, hourly weather impact curves, local event detection, and delivery-window-aware order recommendations. Forecast accuracy is measured by daypart because a model that nails the daily total but misses the morning-to-evening split is useless for a c-store.
First demand insight cards arrive within 48 hours. Stable convenience baselines form within two weeks.
Convenience demand
by data source.
More Convenience insight cards.
Convenience retailers: see what demand problems Ward catches.
Root causes, not just alerts. See it on your data.
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Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.