Snowflake plus Ward: assortment planning for convenience retail
Your Snowflake data already carries the assortment indicator. Ward ingests it, explains the driver, and attaches a recommended action.
Assortment Planning for Convenience & C-Store, running on Snowflake
Assortment Planning is a card type Ward runs continuously. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
For Convenience & C-Store retailers, that means tracking 3,000+ SKUs throughout locations. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
How Ward hands you Assortment daily cards: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Getting the data in. Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
The short list
- 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.
Why this combination
is its own problem.
Assortment Planning behaves differently in convenience retail than it does anywhere else. The footprint shape, the SKU count, and the speed of the category all change what counts as a real data point and what is noise. Ward is tuned to the convenience version.
- 01 New-item performance is measured against the new item's standalone sales without accounting for what was displaced; net assortment productivity often goes backwards.
- 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 Ward has eyes on.
Stock what sells. Cut what doesn't. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
Ward monitors 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.
Ward pulls from Snowflake rather than replacing it. Any table or view in your Snowflake account, cross-database joins, historical data at any depth come across on a read-only connection, get enriched with contextual data, and come back as findings. Your Data Platform is untouched.
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.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Snowflake. Ward ingests any table or view in your Snowflake account, cross-database joins, historical data at any depth 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: 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
Insight cards arrive daily 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 Snowflake
Ward queries your Snowflake data warehouse directly. If your retail data lives in Snowflake, Ward reads it without moving or copying anything.
Setup: Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
Data Ward reads from Snowflake
Impact metrics with Snowflake
Data lake enrichment
Ward enriches Snowflake data with: Any Snowflake table, Weather & events, Demographics, Competitor data, Custom feeds
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.
Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake. Data points include: Any table or view in your Snowflake account, Cross-database joins, Historical data at any depth.
Yes. Ward reads Snowflake data and combines it with contextual signals (weather, events, demographics) to generate Convenience-specific insight cards. No custom development required.
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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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.