Customer Behavior for Convenience on Oracle, built for loss prevention
A convenience Head of LP on Oracle should not be hunting for customer problems. Ward raises them every morning.
Customer Behavior for Convenience on Oracle, scoped to loss prevention
Shrinkage costs you more than you think. Ward finds out where. Ward filters to what a Head of LP can act on and drops the rest.
Customer Behavior, in one sentence. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
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.
How it runs. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
The connection itself. Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Key capabilities
- Basket composition trends
- Daypart behavior modeling
- Customer segment migration
- Cross-sell opportunity detection
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.
What Ward has eyes on.
The connection to Oracle Retail is read-only and runs on your schedule. Ward reads straight from sales audit, inventory positions, and the rest of the feed, then stitches together it with external context your ERP does not carry: weather, local events, competitor pricing.
The customer model runs each day, not on a reporting calendar. It picks up the pattern, attributes the driver, and attaches a recommended action before the number reaches a review deck.
Ward keeps a running read on 3,000+ SKUs throughout 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.
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.
Why this combination
is its own problem.
Oracle Retail is the system of record for most convenience operators of your size, which means the readings Ward needs are already there. The gap is not data collection. It is that nobody has time to read sales audit and inventory positions every morning across every store.
- 01 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.
- 02 Fuel-to-inside conversion is treated as a fixed location attribute when it actually moves with canopy promotion, store cleanliness, and inside merchandising.
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 the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Oracle Retail. Ward reads straight from sales audit, inventory positions, allocation and starts building baselines. First daily cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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 findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands you cards each day, each with the driver and the next step. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.
How Ward connects to Oracle Retail
Ward integrates with Oracle Retail Merchandising (RMFCS), Oracle Retail Demand Forecasting, and Oracle Retail Analytics. Full stack visibility.
Setup: Ward reads from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments.
Data Ward reads from Oracle
Impact metrics with Oracle
Data lake enrichment
Ward enriches Oracle data with: Sales audit data, Weather & events, Competitor pricing, Demographic data, Supplier scorecards
Shrinkage costs you more than you think. Ward finds out where.
- ×Shrinkage lands as a year-end surprise
- ×Cannot distinguish theft from spoilage from admin error
- ×High-shrinkage stores only identified during audits
- ×No correlation between operational changes and loss patterns
- ×Exception-based reporting misses slow-bleed patterns
- ✓Store-level shrinkage tracking with cause attribution
- ✓Anomaly detection flags stores deviating from estate average
- ✓Receiving dock discrepancy patterns identified automatically
- ✓Correlation analysis links operational changes to loss shifts
- ✓Trend analysis catches slow-bleed patterns audits miss
US retail shrinkage hit $112.1 billion in 2022. Up 19.4% year over year. Source: National Retail Federation
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 from Oracle Retail via REST APIs or direct database views. Compatible with Oracle Cloud and on-premise deployments. Data points include: Sales audit, Inventory positions, Allocation, Replenishment, Demand forecasts, Price management.
Yes. Ward reads Oracle data and combines it with contextual signals (weather, events, demographics) to generate Convenience-specific insight cards. No custom development required.
Shrinkage costs you more than you think. Ward finds out where. Ward solves this with automated insight cards: Store-level shrinkage tracking with cause attribution. Anomaly detection flags stores deviating from estate average. Receiving dock discrepancy patterns identified automatically.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
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
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