Shrinkage Detection for Convenience on Snowflake, built for loss prevention
Find the leak before it drains you. Ward runs it on Snowflake data throughout your convenience estate, scoped to loss prevention.
Shrinkage Detection for Convenience on Snowflake, scoped to loss prevention
What shrinkage detection does: Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
Applied to Convenience & C-Store, the surface area is 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.
Shrinkage costs you more than you think. Ward finds out where. Ward writes the finding at the altitude a Head of LP works at.
The mechanism. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
The connection itself. Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
Capabilities
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
- Pattern recognition across time
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 Shrinkage matters for Convenience retail
C-store shrinkage is dominated by slow-bleed employee theft and scan avoidance, small per-transaction losses that compound across thousands of daily transactions. Ward monitors voids, no-sales, and scan-rate deviations, then correlates them with shift patterns and employee schedules to surface risk that audit cycles miss.
What Ward has eyes on.
The shrinkage model runs every morning, not on a reporting calendar. It catches the pattern, accounts for what caused it, and attaches the next step before the number reaches a review deck.
Coverage is store by store, category by category. Ward monitors transactions/hour, attach rate, basket size throughout 3,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven locations are not.
The connection to Snowflake is read-only and runs on your schedule. Ward reads straight from any table or view in your Snowflake account, cross-database joins, and the rest of the feed, then combines it with external context your Data Platform does not carry: weather, local events, competitor pricing.
At the metric level. Ward focuses on transaction anomaly rates (voids, no-sales, manual overrides), shift-correlated patterns, high-theft category velocity gaps, and receiving accuracy on high-value items, benchmarking each store against its own history and the estate average.
Why this combination
is its own problem.
Snowflake is the system of record for most convenience operators of your size, which means the inputs 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 over every store.
- 01 High-margin impulse items (candy, gum) get under-counted because shrink rates are reported as percentages of large category totals.
- 02 Vendor-direct receiving for tobacco and beer happens outside the POS; shrink in those categories surfaces only at periodic counts.
Benchmarks. C-store shrink runs 0.8-1.8% of inside-store sales, with tobacco and high-margin impulse categories driving disproportionate dollar loss. A small per-transaction void pattern (under $5) on tobacco can cost $15K-40K per store per year before it triggers traditional threshold alerts.
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 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: steady state
Ward hands you insight cards daily, each with the driver and what to do about it. 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
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
Convenience KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. 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 compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
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.
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 Transactions/hour, Attach rate, Basket size, tailored for Loss Prevention decision-making. Each card includes what changed, why it matters, and what to do next.
Ward focuses on transaction anomaly rates (voids, no-sales, manual overrides), shift-correlated patterns, high-theft category velocity gaps, and receiving accuracy on high-value items, benchmarking each store against its own history and the estate average.
Ward flags multiple locations with a consistent pattern: tobacco void rates spike during a specific overnight shift window. The amounts are small enough to evade threshold-based alerts but consistent enough to represent significant annual loss per store. Ward attributes the pattern to specific shift schedules, and investigation confirms scan avoidance by a ring of night-shift employees across the affected 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
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