Fashion shrinkage from Snowflake, briefed to procurement
Merchandising wants Ward. You sign the contract. Ward detects fashion shrinkage movement in your Snowflake data early, with the cause and what to do about it attached.
Shrinkage Detection for Fashion on Snowflake, scoped to procurement
Merchandising wants Ward. You sign the contract. Ward surfaces the signals that change a procurement decision.
What shrinkage detection does: Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
For Fashion & Apparel retailers, that means watching 15,000+ SKUs over locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
What Ward does with that: Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
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
- Pattern recognition across time
- Cause-level shrinkage attribution
- Store-vs-estate benchmarking
- Receiving dock anomaly detection
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled fall denim against last season’s curve at the same selling week. Two causes, both still fixable this week.
| Signal | Finding |
|---|---|
sell_through | Week 6 sell-through 38% vs. 52% plan, concentrated in 3 of 9 door clusters |
size_curve | Waist 30–32 sold out in 41 doors while 36–38 sits at 71% on hand |
markdown.ladder | First markdown is 3 weeks later than LY, weeks-of-supply now 11.4 |
Recommend: transfer 30–32 out of the 12 overstocked doors, hold the ladder on core indigo, and take the first markdown on light wash now while it still clears at 20%.
sell_through_weekly…
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 |
|---|---|---|
shopify_orders_daily | import | 2m ago |
shopify_returns_reasons | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
cegid_store_sales | import | 1h ago |
retail_size_curve_actuals | import | 1h ago |
retail_markdown_ladder | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-markdown | permit | Model::"markdown_ladder" |
vendor-blocked | forbid | Model::"labor_*" |
ecom-read-returns | permit | Model::"returns_reasons" |
Why Shrinkage matters for Fashion retail
The biggest hidden source of fashion shrinkage isn't theft, it's administrative error in transfer-heavy operations where every handoff between stores, e-commerce, and returns is a reconciliation risk. Ward tracks inventory movements across all channels and distinguishes transfer discrepancies from return fraud and genuine theft.
Why this combination
is its own problem.
Most shrinkage projects stall at data access. This one does not, because Snowflake already exposes any table or view in your Snowflake account, cross-database joins, historical data at any depth through an API Ward reads directly.
- 01 Return fraud and wardrobing get logged as legitimate returns because store associates lack the data to challenge them in real time.
- 02 BOPIS and ship-from-store create double-counted inventory in the OMS that disappears on the next reconciliation as "shrink".
Benchmarks. Fashion shrink runs 1.4-2.5% of sales, with returns/wardrobing in premium tiers contributing 0.4-0.8% of that. High-value categories (handbags, outerwear, premium denim) account for 40-60% of total dollar shrink despite being 10-20% of unit volume.
What Ward has eyes on.
Ward pulls from 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 insight cards. Your Data Platform is untouched.
Find the leak before it drains you. 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.
Coverage is store by store, category by category. Ward scans continuously sell-through rate, markdown %, return rate throughout 15,000+ 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 locations are not.
At the metric level. Ward tracks transfer accuracy rates, return-to-sale ratios, inter-store reconciliation gaps, and high-value item movement patterns. Separating operational shrinkage from intentional loss is essential because the interventions are completely different.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward sits on top of 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 insight 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: operating rhythm
Daily cards arrive daily 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
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
Fashion KPI impact
Frequently asked questions
Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error. For Fashion retail specifically, Ward monitors 15,000+ SKUs across your locations and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Sell-through rate, Markdown %, Return rate, Style velocity, Size accuracy 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 Fashion-specific insight cards. No custom development required.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks transfer accuracy rates, return-to-sale ratios, inter-store reconciliation gaps, and high-value item movement patterns. Separating operational shrinkage from intentional loss is essential because the interventions are completely different.
Ward flags a cluster of stores where high-value item returns run well above estate average, most without original tags, with the same payment cards appearing across multiple locations. The pattern matches a wardrobing ring. LP adjusts the return policy for flagged categories and sees a significant drop in high-value returns within weeks.
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 Fashion shrinkage problems Ward catches.
Root causes, not just alerts. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
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.