Fashion demand from Snowflake, briefed to e-commerce
See demand before it arrives. Ward runs it on Snowflake data throughout your fashion fleet, scoped to e-commerce.
The full picture: fashion demand, Snowflake data, E-Commerce decisions
For Fashion & Apparel retailers, that means watching 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 delivers findings scoped to e-commerce decision-making.
Demand Forecasting, in one sentence. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
Under the hood. 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
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
- Store-SKU-day level precision
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.
See demand before it arrives. 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.
Coverage is store by store, category by category. Ward keeps a running read on sell-through rate, markdown %, return rate over 15,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the store base is fine and knowing which seven locations are not.
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 combines 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.
A generic demand model applied to a fashion footprint produces alerts nobody trusts. The thresholds are wrong, because fashion baselines are wrong for it. Ward learns the baseline from your own locations instead of importing one.
- 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.
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 sits on top of 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 cards arrive within 48 hours.
-
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: operating rhythm
Cards arrive each day 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 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 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 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 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
See what Fashion demand problems Ward catches.
Root causes, not just alerts. See it on your data.
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