Convenience assortment, briefed to technology
Ward raises assortment findings scoped to technology, throughout every one of your convenience locations.
Assortment Planning for Convenience technology
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.
The business wants AI. You sign off on the architecture. Ward filters to what a Head of IT can act on and drops the rest.
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Under the hood. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
What you get
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace 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 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.
Coverage is store by store, category by category. Ward keeps a running read on transactions/hour, attach rate, basket size over 3,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven locations are not.
The assortment model runs daily, not on a reporting calendar. It picks up the pattern, accounts for the cause, and attaches what to do about it before the number reaches a review deck.
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.
The technology problem in convenience retail is not missing data. It is that transactions/hour, attach rate, basket size live in different systems on different refresh schedules, and reconciling them is a person-week. Ward does the reconciliation and sends the two-line version.
- 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 Chain-wide planograms over-allocate space to slow tail SKUs in high-volume stores and starve depth on the items that drive 60% of basket starts.
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 findings arrive in two days.
-
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: operating rhythm
Daily cards 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.
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
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.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
Ward delivers daily insight cards covering Transactions/hour, Attach rate, Basket size, tailored for Technology 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.
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.