Assortment Planning for Grocery on Snowflake, built for technology
Stock what sells. Cut what doesn't. Ward runs it on Snowflake data across your grocery store base, scoped to technology.
How a grocery Head of IT runs assortment on Snowflake
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
In a Grocery & Supermarket estate the job is 30,000+ SKUs across stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
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.
The mechanism. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
The connection itself. Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
What you get
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
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 |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
relex_replenishment_plan | import | 1h ago |
blue_yonder_forecast_daily | import | 1h ago |
retail_fresh_waste_daily | import | 1h ago |
retail_promo_calendar | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
lp-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
fresh-team-west | permit | Model::"fresh_waste_daily" |
Why Assortment matters for Grocery retail
Every assortment addition displaces something else, so the real question is incremental contribution after cannibalization and basket effects. Ward clusters stores by demographics, traffic, and competitive set, then benchmarks SKU performance at the cluster level to produce assortment recommendations that go beyond national planograms.
Why this combination
is its own problem.
Assortment Planning behaves differently in grocery retail than it does anywhere else. The estate shape, the SKU count, and the speed of the category all change what counts as a real indicator and what is noise. Ward is tuned to the grocery version.
- 01 Cluster definitions based on store size or banner miss the demographic signal; two same-size stores can have completely different shopper profiles.
- 02 Slot fees from CPG vendors lock in SKUs that don't earn their facing, Ward exposes the cost-of-inclusion versus actual revenue per linear foot.
Benchmarks. A typical grocery store carries 30,000-45,000 SKUs; the bottom 20% generate under 2% of revenue. Cluster-aware assortment usually frees 5-12% of facings without a revenue drop, which translates to space for 1,500-3,000 better-fit SKUs per store cluster.
What Ward has eyes on.
Ward reads straight from any table or view in your Snowflake account, cross-database joins, historical data at any depth from Snowflake on a read-only connection. Nothing is written back, and your Data Platform configuration does not change. Snowflake stays the system of record.
The assortment model runs on a daily cycle, not on a reporting calendar. It picks up the pattern, attributes what caused it, and attaches a recommended move before the number reaches a review deck.
Ward watches 30,000+ SKUs throughout your stores, at the store-category level rather than the chain roll-up. The metrics under watch include fill rate, shrinkage %, fresh waste %. A roll-up hides a single-store problem inside a healthy average, which is how fresh waste & spoilage stays invisible for a quarter.
At the metric level. Ward tracks SKU productivity (revenue per facing), incremental contribution, substitution patterns, and cluster-level demand elasticity, all weighted against supplier fill rates and promotional obligations.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to Snowflake with read-only credentials and begins ingesting any table or view in your Snowflake account and cross-database joins. No config changes on your side. 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 delivers cards each day, each with root cause and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.
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
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
Grocery KPI impact
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. For Grocery retail specifically, Ward monitors 30,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Fill rate, Shrinkage %, Fresh waste %, Promo lift, Basket size 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 Grocery-specific insight cards. No custom development required.
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 Fill rate, Shrinkage %, Fresh waste %, tailored for Technology decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks SKU productivity (revenue per facing), incremental contribution, substitution patterns, and cluster-level demand elasticity, all weighted against supplier fill rates and promotional obligations.
A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores that match the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram leaves revenue on the table in urban stores while it ties up slow-moving inventory in rural ones.
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 Grocery assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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