Assortment Planning for Convenience & C-Store, scoped to store operations
Convenience operators find assortment problems after the margin is gone. A Director Store Ops finds them on Ward the morning they start.
Assortment Planning for Convenience store operations
Managing 800 stores from a spreadsheet is insane. Ward writes the finding at the altitude a Director Store Ops works at.
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Convenience & C-Store changes the scale of the problem: 3,000+ SKUs, every one of your locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
How Ward returns Assortment daily cards: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Capabilities
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
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 Assortment matters for Convenience retail
With 3,000 SKUs on a compact selling floor, every product must earn its place, and the right assortment is hyper-local. Ward clusters stores by traffic profile, daypart mix, and surrounding demographics to recommend variations that maximize revenue per square foot at each location.
What Ward has eyes on.
The assortment model runs each day, not on a reporting calendar. It picks up the pattern, traces the cause, and attaches a recommended move before the number reaches a review deck.
Every one of your locations gets its own baseline. Ward keeps a running read on transactions/hour, attach rate, basket size against it and pulls forward only the deviations that hold up. The two that show up most in convenience retail are daypart demand variation and planogram compliance, and both are baseline problems before they are P&L problems.
At the metric level. Ward tracks revenue per facing, velocity by daypart and cluster, redundancy analysis, and attach-rate contribution. It also monitors new-item performance against the displaced SKU to measure true assortment productivity.
Why this combination
is its own problem.
Assortment Planning produces a lot of output that is technically correct and operationally useless to store operations. Ward filters on whether the finding changes a decision a Director Store Ops can actually make.
- 01 Daypart adjacency (breakfast next to coffee) is a layout decision but isn't modeled in the assortment math; it shows up as "low SKU productivity" when really it's a placement issue.
- 02 New-item performance is measured against the new item's standalone sales without accounting for what was displaced; net assortment productivity often goes backwards.
Benchmarks. C-store top-200 SKUs typically generate 50-65% of inside revenue. Cluster-aware planograms usually free 10-20% of facings without revenue loss, redirecting that space to higher-velocity items and lifting same-store inside revenue 2-5%.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward reads straight from your existing systems on a read-only connection. Nothing is written back. First daily cards arrive within 48 hours.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward sends daily cards on a daily cycle, 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 findings arrive, it is how many get acted on.
Managing 800 stores from a spreadsheet is insane.
- ×Morning check-ins rely on phone calls and email chains
- ×No single view of which stores need attention today
- ×Labor scheduling is disconnected from demand signals
- ×Planogram compliance is checked manually, quarterly
- ×Exception management is reactive and inconsistent
- ✓Morning brief delivered at 06:47 with prioritized action list
- ✓Estate-wide heat map of store performance, updated hourly
- ✓Staffing recommendations correlated with predicted traffic
- ✓Planogram compliance anomalies detected and flagged
- ✓Consistent exception handling with recommended actions
Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research
Convenience KPI impact
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Store Operations decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks revenue per facing, velocity by daypart and cluster, redundancy analysis, and attach-rate contribution. It also monitors new-item performance against the displaced SKU to measure true assortment productivity.
A standardized planogram runs across all 500 locations. Ward identifies distinct store clusters, highway/travel, urban commuter, residential, university-adjacent, each overindexing on different categories. Ward recommends reallocating shelf space per cluster to match actual demand. Pilot stores show meaningful revenue uplift from better product-location matching with zero cost increase: same SKU count, just the right ones in the right 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 assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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