Demand Forecasting for Fashion on Snowflake, built for finance
See demand before it arrives. Ward runs it on Snowflake data over your fashion footprint, scoped to finance.
How a fashion CFO runs demand on Snowflake
Applied to Fashion & Apparel, the surface area is 15,000+ SKUs across locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Your P&L surprises are born on the store floor. Ward writes the finding at the altitude a CFO works at.
Here is demand forecasting in plain terms. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
How it runs. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
Setup: Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
What it does
- Store-SKU-day level precision
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
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 Demand matters for 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.
What Ward has eyes on.
The demand model runs daily, not on a reporting calendar. It detects the pattern, attributes the cause, and attaches a recommended move before the number reaches a review deck.
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.
The connection to Snowflake is read-only and runs on your schedule. Ward ingests any table or view in your Snowflake account, cross-database joins, and the rest of the feed, then cross-references it with external context your Data Platform does not carry: weather, local events, competitor pricing.
At the metric level. 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.
Why this combination
is its own problem.
Snowflake is the system of record for most fashion operators of your size, which means the readings Ward needs are already there. The gap is not data collection. It is that nobody has time to read any table or view in your Snowflake account and cross-database joins every morning throughout every store.
- 01 Early sell-through (weeks 1-2) is dismissed as noise when in reality it's the highest-signal indicator of full-season trajectory.
- 02 First-allocation curves use chain-average size profiles when each store cluster has a meaningfully different size mix.
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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to 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 in two days.
-
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 daily cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Findings arrive every morning 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
Your P&L surprises are born on the store floor.
- ×Margin erosion only surfaces at month-end close
- ×Inventory carrying costs are a black box
- ×Working capital tied up in slow-moving stock nobody is watching
- ×Same-store sales comps lack decomposition into actionable drivers
- ×Capex decisions for store remodels lack unit-economics evidence
- ✓GMROI tracking by category with weekly insight cards
- ✓Inventory carrying cost alerts when capital efficiency drops
- ✓Working capital optimization recommendations based on turnover trends
- ✓SSS decomposition into traffic, conversion, and basket components
- ✓Store-level unit economics cards for capex prioritization
Inventory distortion, overstock and out-of-stock combined, costs retailers $1.77 trillion globally. Source: IHL Group
Fashion KPI impact
Frequently asked questions
Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level. 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 builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
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.
Your P&L surprises are born on the store floor. Ward solves this with automated insight cards: GMROI tracking by category with weekly insight cards. Inventory carrying cost alerts when capital efficiency drops. Working capital optimization recommendations based on turnover trends.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Finance decision-making. Each card includes what changed, why it matters, and what to do next.
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.
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.
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
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Root causes, not just alerts. See it on your data.
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