Shrinkage Detection: Fashion retail, BigCommerce data, Merchandising decisions
A fashion VP Merchandising on BigCommerce should not be hunting for shrinkage problems. Ward pulls forward them each day.
Shrinkage Detection for Fashion on BigCommerce, scoped to merchandising
Your category managers are drowning in spreadsheets. Ward writes the finding at the altitude a VP Merchandising works at.
Here is shrinkage detection in plain terms. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
Fashion & Apparel changes the scale of the problem: 15,000+ SKUs, every one of your locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
The mechanism. Ward compares expected inventory against actual counts, segments loss by cause category, and flags store-level anomalies against your estate baseline.
How the connection works. Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.
Capabilities
- 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.
What Ward has eyes on.
The shrinkage model runs every morning, not on a reporting calendar. It picks up the pattern, explains the cause, and attaches the next step before the number reaches a review deck.
Every one of your locations gets its own baseline. Ward watches sell-through rate, markdown %, return rate against it and pulls forward only the deviations that hold up. The two that show up most in fashion retail are markdown timing and size curve misallocation, and both are baseline problems before they are P&L problems.
Ward reads straight from orders, products & variants, customers from BigCommerce on a read-only connection. Nothing is written back, and your Commerce configuration does not change. BigCommerce stays the system of record.
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.
Why this combination
is its own problem.
The merchandising problem in fashion retail is not missing data. It is that sell-through rate, markdown %, return rate live in different systems on different refresh schedules, and reconciling them is a person-week. Ward does the reconciliation and hands you the two-line version.
- 01 BOPIS and ship-from-store create double-counted inventory in the OMS that disappears on the next reconciliation as "shrink".
- 02 Return fraud and wardrobing get logged as legitimate returns because store associates lack the data to challenge them in real time.
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 the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to BigCommerce. Ward pulls from orders, products & variants, customers 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: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward delivers insight cards each day, each with the driver and a recommended action. 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 BigCommerce
Ward connects to BigCommerce for omnichannel retailers running headless or traditional storefronts. Orders, catalog, and customer data drive insight cards.
Setup: Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.
Data Ward reads from BigCommerce
Impact metrics with BigCommerce
Data lake enrichment
Ward enriches BigCommerce data with: Orders & variants, Customer behavior, Marketing data, Returns & exchanges, Competitor pricing
Your category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
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 BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events. Data points include: Orders, Products & variants, Customers, Inventory, Promotions, Storefront analytics.
Yes. Ward reads BigCommerce data and combines it with contextual signals (weather, events, demographics) to generate Fashion-specific insight cards. No custom development required.
Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Merchandising 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.
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