Ward runs promos on your Snowflake grocery data
Ward pulls from any table or view in your Snowflake account, cross-database joins, historical data at any depth from Snowflake and delivers grocery promos cards each day. Read-only, no config changes.
The grocery promos stack on Snowflake
A Grocery & Supermarket operator is tracking 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.
Promo Effectiveness. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
How it runs. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
Getting the data in. Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
What you get
- Net lift measurement (not gross)
- Cannibalization quantification
- Pull-forward detection
- Promo ROI scorecards
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 Promos matters for Grocery retail
Most grocery chains measure promotions by gross lift, ignoring cannibalization, pantry loading, and margin erosion that destroy actual ROI. Ward isolates each effect to calculate true net promotional lift, giving category managers evidence to kill underperformers and concentrate spend where it generates real incrementality.
Why this combination
is its own problem.
Snowflake is the system of record for most grocery 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 any table or view in your Snowflake account and cross-database joins every morning throughout every store.
- 01 Vendor-funded promos look ROI-positive on margin reports because the funding offsets the visible discount, while cannibalization erodes the rest of the category.
- 02 BOGO and 2-for events drive pantry loading that shifts demand from the next 2-3 weeks, so the post-promo dip is misread as competitive pressure.
Benchmarks. Grocery promos typically show 30-80% gross lift but only 10-25% net incremental lift after cannibalization and pull-forward. The vast majority of promos are net-margin-positive only after vendor funding; remove the funding and roughly half lose money.
What Ward has eyes on.
Know which promos actually work. 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 30,000+ SKUs over 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.
Ward reads 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 daily cards. Your Data Platform is untouched.
At the metric level. Ward decomposes promo results into gross lift, cannibalization rate, pantry loading, halo effects, and true incremental margin contribution. It also tracks promo fatigue, when repeated discounts permanently shift baseline demand downward.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Snowflake. Ward reads any table or view in your Snowflake account, cross-database joins, historical data at any depth and starts building baselines. First 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
Daily cards arrive on a daily cycle 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
Grocery KPI impact
Frequently asked questions
Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading. 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 isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
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.
Ward decomposes promo results into gross lift, cannibalization rate, pantry loading, halo effects, and true incremental margin contribution. It also tracks promo fatigue, when repeated discounts permanently shift baseline demand downward.
A major snack vendor proposes a co-op BOGO program across 12 SKUs. Gross lift looks strong, but Ward shows net category lift is minimal after accounting for cannibalization and pantry-loading pull-forward. Several SKUs generate negative net category contribution. Ward provides SKU-level promo scorecards the category manager uses to restructure the deal around the SKUs with genuine incremental lift.
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 promos problems Ward catches.
Root causes, not just alerts. See it on your data.
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