Real-time assortment for multi-store Fashion & Apparel.
Assortment Planning at scale, running across your whole Fashion estate without another dashboard to check.
Why assortment matters
in fashion retail.
Ward doesn't replace the buyer's eye, it sharpens the math behind the buy. Which store clusters need wider assortment with shallow depth? Which need narrow-deep buys with full size runs? Ward analyzes sell-through by cluster, customer segment, and style attribute to recommend architecture that matches how customers actually shop each location.
Benchmarks. Fashion option counts vary 2-4x across store clusters in a healthy assortment plan. Cluster-aware planning typically lifts full-price sell-through 2-5 points and reduces end-of-season markdown depth by 100-300 bps.
Assortment architecture, denim category
A denim buyer has 200 styles to allocate across 90 stores. Ward reveals that urban flagships convert best with wide assortment at shallow depth, while suburban stores need fewer core styles with full size runs. The current uniform allocation starves variety in urban stores and creates size gaps in suburban ones. A cluster-specific matrix reduces markdown risk while lifting full-price sell-through.
Three pitfalls Ward catches
in fashion assortment.
- 01 Top-down option counts get applied uniformly across stores; smaller-volume locations carry too many options and starve depth where it matters.
- 02 Cannibalization between similar styles (two black bodycon dresses) is ignored, so the second option doesn't add what its standalone sell-through suggests.
- 03 Cluster definitions don't account for online inventory pooling; a store next to a strong DC has different effective assortment math than a remote one.
How Ward runs assortment
for fashion retailers.
-
01
Cluster stores on shopper signal
Ward uses basket affinity, brand-tier mix, and category penetration to produce 4-8 actionable clusters, regardless of square footage or banner.
-
02
Score every option per cluster
Productivity, incremental contribution, cannibalization, and size-curve fit are computed per cluster, exposing which styles deserve depth, breadth, or removal.
-
03
Architect the seasonal plan
Ward outputs cluster-specific option counts, depth, and size curves; the buying team uses these as inputs to the open-to-buy.
What a Ward card looks like.
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…
Dashboards
Pinned views built from saved data-lake queries.
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" |
Fashion assortment:
the shift.
- ×Markdown timing
- ×Size curve misallocation
- ×Style velocity prediction
- ✓Store cluster segmentation
- ✓SKU rationalization recommendations
- ✓Whitespace opportunity detection
Fashion KPI impact.
Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.
Questions about fashion assortment.
Ward doesn't replace the buyer's eye, it sharpens the math behind the buy. Which store clusters need wider assortment with shallow depth?
A denim buyer has 200 styles to allocate across 90 stores. Ward reveals that urban flagships convert best with wide assortment at shallow depth, while suburban stores need fewer core styles with full size runs.
Ward tracks assortment width vs depth by cluster, style attribute performance, size curve accuracy, and inter-style cannibalization. It measures revenue-per-option to identify when adding more styles dilutes overall performance.
First assortment insight cards arrive within 48 hours. Stable fashion baselines form within two weeks.
Fashion assortment
by data source.
More Fashion insight cards.
Fashion retailers: see what assortment 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.