Ward runs customer on your NCR convenience data
Convenience retailers on NCR run customer through Ward. 3,000+ SKUs over your locations, keeps a running read on around the clock.
Customer Behavior for Convenience & C-Store, running on NCR
For Convenience & C-Store retailers, that means monitoring 3,000+ SKUs across locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
Here is customer behavior in plain terms. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
How it runs. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
Getting the data in. Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
The short list
- Daypart behavior modeling
- Customer segment migration
- Cross-sell opportunity detection
- Basket composition trends
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…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
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" |
Why Customer matters for Convenience retail
The 6:30 AM coffee buyer and the 9 PM snack buyer are fundamentally different shoppers, even when they're the same person. Ward analyzes transaction patterns by daypart to identify mission-based behaviors and cross-sell opportunities within each mission, focusing on basket-level patterns rather than individual customer tracking.
Why this combination
is its own problem.
Most customer projects stall at data access. This one does not, because NCR already exposes POS transactions, item-level sales, tender data through an API Ward reads directly.
- 01 Daypart attach rates get reported as chain averages, hiding that the morning coffee-to-food attach varies 2-3x across stores due to layout and execution.
- 02 Loyalty programs cover under 30% of c-store transactions, so customer-level analysis misses most of the volume; basket-mission analysis catches what loyalty data can't.
Benchmarks. C-store morning rush coffee-to-food attach: 20-35% chain average, with top performers above 50%. Fuel-to-inside conversion: 25-45% with wide variation by canopy promotion and inside merchandising. Each percentage point of attach gain is typically worth 0.5-1.5% same-store inside revenue.
What Ward has eyes on.
Ward monitors 3,000+ SKUs over your locations, at the store-category level rather than the chain roll-up. The metrics under watch include transactions/hour, attach rate, basket size. A roll-up hides a single-store problem inside a healthy average, which is how daypart demand variation stays invisible for a quarter.
Ward reads straight from POS transactions, item-level sales, tender data from NCR Voyix on a read-only connection. Nothing is written back, and your POS configuration does not change. NCR stays the system of record.
The customer model runs daily, not on a reporting calendar. It catches the pattern, traces the cause, and attaches the next step before the number reaches a review deck.
At the metric level. Ward segments by daypart mission, tracks attach rates within each mission, measures layout and adjacency effects on cross-purchase, and monitors fuel-to-inside conversion as a key traffic metric.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of NCR Voyix with read-only credentials and begins ingesting POS transactions and item-level sales. No config changes on your side. First insight cards arrive within 48 hours.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the daily cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Findings arrive each day and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
How Ward connects to NCR Voyix
Ward integrates with NCR Voyix POS and Aloha for convenience and restaurant retail. Transaction-level data powers daypart analysis and impulse optimization.
Setup: Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration.
Data Ward reads from NCR
Impact metrics with NCR
Data lake enrichment
Ward enriches NCR data with: POS transactions, Weather & events, Loyalty data, Competitor proximity, Demographic data
Convenience KPI impact
Frequently asked questions
Ward tracks basket composition shifts, daypart patterns, and customer segment migration. For Convenience retail specifically, Ward monitors 3,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Transactions/hour, Attach rate, Basket size, Planogram compliance, Daypart mix at the store-category level. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
Ward reads NCR transaction data via API or data export. Real-time or batch, depending on your NCR configuration. Data points include: POS transactions, Item-level sales, Tender data, Daypart summaries, Loyalty data.
Yes. Ward reads NCR data and combines it with contextual signals (weather, events, demographics) to generate Convenience-specific insight cards. No custom development required.
Ward segments by daypart mission, tracks attach rates within each mission, measures layout and adjacency effects on cross-purchase, and monitors fuel-to-inside conversion as a key traffic metric.
Ward reveals a clear split in morning rush transactions: most are coffee-only with low basket value, while the minority adding food have baskets several times larger. Stores with breakfast displayed adjacent to the coffee station convert significantly more coffee-only customers to coffee-plus-food than stores requiring a separate trip down an aisle. Ward recommends a layout test moving grab-and-go breakfast next to the coffee bar at the lowest-converting stores.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
See what Convenience customer problems Ward catches.
Root causes, not just alerts. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
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.