What a fashion Head of E-Com sees in shrinkage first
Fashion operators find shrinkage problems too late to act. A Head of E-Com finds them on Ward the morning they start.
What a fashion Head of E-Com sees in shrinkage
Here is shrinkage detection in plain terms. Ward identifies abnormal inventory loss patterns and distinguishes between theft, damage, spoilage, and administrative error.
Applied to Fashion & Apparel, the surface area is 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.
Your online and offline data live in different worlds. Ward surfaces the signals that change a e-commerce decision.
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.
The short list
- 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.
Ward keeps a running read on 15,000+ SKUs across your locations, at the store-category level rather than the chain roll-up. The metrics under watch include sell-through rate, markdown %, return rate. A roll-up hides a single-store problem inside a healthy average, which is how markdown timing stays invisible for a quarter.
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.
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 e-commerce 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: read-only connection
Ward ingests your existing systems on a read-only connection. Nothing is written back. First cards arrive in two days.
-
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 delivers 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 daily cards arrive, it is how many get acted on.
Your online and offline data live in different worlds.
- ×Nobody can see one inventory position across every channel
- ×Online promo performance is measured separately from in-store
- ×Customer behavior data is siloed by channel
- ×BOPIS/BORIS operational complexity is growing unchecked
- ×Digital marketing attribution stops at the click
- ✓Unified insight cards across online and in-store channels
- ✓Cross-channel promo effectiveness with true attribution
- ✓Customer journey tracking across digital and physical touchpoints
- ✓BOPIS fulfillment performance monitoring with exception cards
- ✓Full-funnel marketing attribution to in-store conversion
Retailers with unified omnichannel data see 30% higher lifetime value per customer. Source: Harvard Business Review
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.
Your online and offline data live in different worlds. Ward solves this with automated insight cards: Unified insight cards across online and in-store channels. Cross-channel promo effectiveness with true attribution. Customer journey tracking across digital and physical touchpoints.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for E-Commerce 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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Find out what your data has been hiding.
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