No more shrinkage surprises. Ward sees them first.
Most Specialty retailers discover shrinkage issues after damage. Ward finds them before.
Why shrinkage matters
in specialty retail.
With manageable SKU counts, specialty retail can track inventory discrepancies at the individual item level, revealing patterns tied to specific shelf positions, staffing configurations, or time windows that aggregate reporting would never surface. Per-unit loss is high enough that each incident matters.
Industry benchmarks
Specialty retail shrink: 1.0-2.0% standalone, 1.8-3.5% mall and tourist locations. High-value, easily concealable categories (premium fragrance, jewelry, small electronics) drive 40-65% of dollar shrink despite 5-15% of unit volume.
High-value cosmetics loss pattern, beauty retailer
Ward flags premium fragrance shrinkage running far above average at high-traffic mall locations. The loss concentrates on tester-adjacent units during weekend afternoons when staff-to-customer ratios drop. Ward recommends relocating premium fragrances behind the counter and adding floor coverage during peak windows. Implementation brings shrinkage back toward standalone-store levels.
What Ward actually tracks
Ward leverages item-level tracking feasible at the 5K-SKU scale, maps loss to store layout and traffic flow, and monitors high-value item movement between floor and backroom.
Data signals
POS at SKU-store-shift, inventory snapshots and physical reconciliation, store layout metadata, traffic counts, and staffing schedules.
Three pitfalls Ward catches
in specialty shrinkage.
- 01 Specialty per-unit shrink dollar exposure is high enough that loss patterns affecting just 5-15 units per store per quarter still represent meaningful margin loss, but periodic counts can't detect at that frequency.
- 02 High-traffic mall and tourist locations have fundamentally different staffing-to-traffic ratios than standalone stores; loss patterns track to the staffing density rather than the brand or category.
- 03 Tester-adjacent units in beauty and fragrance categories have 3-7x the standard shrink rate; this isn't modeled in chain-wide shrink expectations.
How Ward runs shrinkage
for specialty retailers.
-
01
Track shrinkage at item level
At specialty's smaller SKU count, Ward tracks variance at the individual unit level, surfacing patterns periodic counts miss.
-
02
Correlate with layout and staffing
Each shrink event is tagged with shelf position, time window, and staffing density, exposing the operational levers that drive loss.
-
03
Pilot interventions with ROI tracking
Cards recommend specific actions (counter relocation, floor coverage, RFID tagging) and track shrink trajectory in 30/60/90-day windows.
What a Ward card looks like.
Store #37 showing 4.2% shrinkage vs 1.8% estate average. Pattern suggests receiving dock discrepancy, not shoplifting.
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…
Dashboards
Pinned views built from saved data-lake queries.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
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 |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
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Specialty shrinkage:
the shift.
- ×Assortment curation
- ×Customer lifetime value
- ×Staff selling effectiveness
- ✓Cause-level shrinkage attribution
- ✓Store-vs-estate benchmarking
- ✓Receiving dock anomaly detection
Specialty KPI impact.
Ward needs 3\u20136 months to reach statistical confidence at the individual store level. High-ticket, low-frequency retailers should expect longer baselines than replenishment-oriented specialty.
Questions about specialty shrinkage.
With manageable SKU counts, specialty retail can track inventory discrepancies at the individual item level, revealing patterns tied to specific shelf positions, staffing configurations, or time windows that aggregate reporting would never surface. Per-unit loss is high enough that each incident matters.
Ward flags premium fragrance shrinkage running far above average at high-traffic mall locations. The loss concentrates on tester-adjacent units during weekend afternoons when staff-to-customer ratios drop. Ward recommends relocating premium fragrances behind the counter and adding floor coverage during peak windows. Implementation brings shrinkage back toward standalone-store levels.
Ward leverages item-level tracking feasible at the 5K-SKU scale, maps loss to store layout and traffic flow, and monitors high-value item movement between floor and backroom.
First shrinkage insight cards arrive within 48 hours. Robust specialty baselines form within two weeks. Ward needs 3\u20136 months to reach statistical confidence at the individual store level. High-ticket, low-frequency retailers should expect longer baselines than replenishment-oriented specialty.
More Specialty insight cards.
Specialty retailers: see what shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
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.