Ward catches Fashion demand issues before they reach the P&L.
Insight cards surface the demand patterns your Fashion dashboards leave buried.
Why demand matters
in fashion retail.
Most fashion SKUs have zero sales history, they're new every season, so time-series models fail. Ward takes an attribute-based approach, clustering new styles against historical analogues by silhouette, colorway, price point, and fabric weight, then calibrating in real time as early sell-through data arrives.
Benchmarks. Fashion forecast accuracy: 30-45% MAPE pre-season, dropping to 18-28% by week 4 of selling. Operators using attribute-based modeling typically reduce week-1 first-allocation error by 25-40% and recover 1-3 points of full-price sell-through.
Pre-season buy planning, fall collection
The buying team is finalizing quantities for hundreds of new fall styles with no sell-through history. Ward maps each to attribute clusters from prior seasons and adjusts for current trend velocity. The result is store-cluster-level buy recommendations that materially reduce first-allocation error, meaning fewer stockouts on winners and less dead inventory on misses.
Three pitfalls Ward catches
in fashion demand.
- 01 Pre-season buys are sized off prior-year category totals, ignoring that the trend mix has shifted (more elevated denim, less basic tee) within the category.
- 02 First-allocation curves use chain-average size profiles when each store cluster has a meaningfully different size mix.
- 03 Early sell-through (weeks 1-2) is dismissed as noise when in reality it's the highest-signal indicator of full-season trajectory.
How Ward runs demand
for fashion retailers.
-
01
Build attribute-based analog mapping
Every new style gets matched to 3-5 historical analogues by silhouette, colorway, fabric, and price point, producing a probabilistic demand curve rather than a single point estimate.
-
02
Calibrate on early sell-through
After weeks 1-2, Ward updates the trajectory using the actual signal and re-allocates remaining inventory across stores.
-
03
Feed misses back into the next pre-season
Categorical biases (overforecasting basics, underforecasting trend) are surfaced as recurring patterns to inform next-season planning.
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 demand:
the shift.
- ×Markdown timing
- ×Size curve misallocation
- ×Style velocity prediction
- ✓Store-SKU-day level precision
- ✓Weather-driven adjustment
- ✓Event and holiday modeling
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 demand.
Most fashion SKUs have zero sales history, they're new every season, so time-series models fail. Ward takes an attribute-based approach, clustering new styles against historical analogues by silhouette, colorway, price point, and fabric weight, then calibrating in real time as early sell-through data arrives.
The buying team is finalizing quantities for hundreds of new fall styles with no sell-through history. Ward maps each to attribute clusters from prior seasons and adjusts for current trend velocity.
Ward uses attribute-based similarity models, trend velocity indicators, store cluster demand profiles, and early-signal calibration from the first weeks of sell-through. It also tracks fashion cycle timing to anticipate when trends peak and decay.
First demand insight cards arrive within 48 hours. Stable fashion baselines form within two weeks.
More Fashion insight cards.
Fashion retailers: see what demand 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.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.