What a grocery Head of LP pulls out of BigQuery
Your BigQuery feed covers 30,000+ SKUs throughout your stores. Ward reads it for loss prevention.
What a grocery Head of LP gets from Google BigQuery
Applied to Grocery & Supermarket, the surface area 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.
Shrinkage costs you more than you think. Ward finds out where. Ward delivers findings scoped to loss prevention decision-making.
How the connection works. Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
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" |
What Ward delivers
- Store-level shrinkage tracking with cause attribution
- Anomaly detection flags stores deviating from estate average
- Receiving dock discrepancy patterns identified automatically
- Correlation analysis links operational changes to loss shifts
- Trend analysis catches slow-bleed patterns audits miss
Metrics Ward monitors
Grocery challenges Ward solves
- Fresh waste & spoilage
- On-shelf availability gaps
- Promo cannibalization
- Supplier fill rate variance
- Seasonal demand spikes
Why this combination
is its own problem.
Running Ward on BigQuery in a grocery fleet skips the usual first step. There is no ingestion project, because Google BigQuery already holds any BigQuery dataset, gA4 event exports, ads data transfers. Ward ingests what is there.
What Ward has eyes on.
Coverage is store by store, category by category. Ward tracks fill rate, shrinkage %, fresh waste % across 30,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the footprint is fine and knowing which seven stores are not.
Ward pulls from BigQuery rather than replacing it. Any BigQuery dataset, gA4 event exports, ads data transfers come across on a read-only connection, get enriched with contextual data, and come back as insight cards. Your Data Platform is untouched.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Google BigQuery. Ward pulls from any BigQuery dataset, gA4 event exports, ads data transfers and starts building baselines. First findings 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
Cards arrive every morning 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 Google BigQuery
Ward queries BigQuery using your existing datasets. GA4 exports, POS data, CRM exports. Ward reads it where it lives.
Setup: Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule.
Data Ward reads from BigQuery
Impact metrics with BigQuery
Data lake enrichment
Ward enriches BigQuery data with: Any BigQuery dataset, GA4 event exports, Weather & events, Demographics, Custom feeds
Shrinkage costs you more than you think. Ward finds out where.
- ×Shrinkage lands as a year-end surprise
- ×Cannot distinguish theft from spoilage from admin error
- ×High-shrinkage stores only identified during audits
- ×No correlation between operational changes and loss patterns
- ×Exception-based reporting misses slow-bleed patterns
- ✓Store-level shrinkage tracking with cause attribution
- ✓Anomaly detection flags stores deviating from estate average
- ✓Receiving dock discrepancy patterns identified automatically
- ✓Correlation analysis links operational changes to loss shifts
- ✓Trend analysis catches slow-bleed patterns audits miss
US retail shrinkage hit $112.1 billion in 2022. Up 19.4% year over year. Source: National Retail Federation
Grocery KPI impact
Frequently asked questions
Service account with BigQuery Data Viewer role. Ward runs read-only SQL queries on your schedule. Data points include: Any BigQuery dataset, GA4 event exports, Ads data transfers, Custom ETL outputs.
Yes. Ward reads BigQuery data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.
Shrinkage costs you more than you think. Ward finds out where. Ward solves this with automated insight cards: Store-level shrinkage tracking with cause attribution. Anomaly detection flags stores deviating from estate average. Receiving dock discrepancy patterns identified automatically.
Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste %, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
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 problems Ward catches.
Root causes, not just alerts. See it on your data.
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