Fashion pricing from Snowflake, briefed to merchandising
Your category managers are drowning in spreadsheets. Ward picks up fashion pricing movement in your Snowflake data before it compounds, with the driver and a recommended action attached.
Price Optimization for Fashion on Snowflake, scoped to merchandising
Your category managers are drowning in spreadsheets. Ward filters to what a VP Merchandising can act on and drops the rest.
What price optimization does: Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
Fashion & Apparel changes the scale of the problem: 15,000+ SKUs, every one of your locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
Under the hood. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
The connection itself. Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
Capabilities
- Competitive price monitoring
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
- Category-level price sensitivity
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 Pricing matters for Fashion retail
Fashion pricing is a one-way ratchet: mark down too early and you leave money on the table, too late and you're stuck with deep clearance. Ward monitors style-level sell-through velocity against time remaining in season and recommends optimal markdown depth and timing to maximize total margin dollars across the selling window.
What Ward has eyes on.
The connection to Snowflake is read-only and runs on your schedule. Ward pulls from any table or view in your Snowflake account, cross-database joins, and the rest of the feed, then joins it with external context your Data Platform does not carry: weather, local events, competitor pricing.
Price on elasticity you can measure. 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 tracks sell-through rate, markdown %, return rate throughout 15,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the estate is fine and knowing which seven locations are not.
At the metric level. Ward tracks style-level sell-through vs plan, weeks-of-supply remaining, size fragmentation, competitive markdown timing, and price sensitivity by brand tier. It models the full-season margin curve, not just immediate clearance math.
Why this combination
is its own problem.
A VP Merchandising rarely logs into Snowflake. They read what someone else pulled out of it, two days later. Ward removes the two days and the someone else.
- 01 Markdown depth gets set to clear remaining units regardless of size mix; if only XS and XXL remain, no markdown depth recovers full sell-through.
- 02 Calendar-driven markdowns (week 8: 25% off, week 12: 50% off) ignore that hot styles still sell at full price and slow ones need a 40% cut earlier.
Benchmarks. Fashion ends-season markdown rates: 20-35% basics, 35-55% trend, 50-70% denim, 60-80% outerwear in mild winters. Operators with style-level markdown cadence typically capture 200-500 bps of additional gross margin versus chain-uniform calendars.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward plugs into 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 daily cards arrive within 48 hours.
-
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 cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward hands back insight cards on a daily cycle, 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 cards arrive, it is how many get acted on.
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 category managers are drowning in spreadsheets.
- ×Promo planning still runs off last year's playbook
- ×Assortment reviews happen quarterly when they should happen daily
- ×Price changes chase the market a week behind it
- ×No visibility into true cannibalization across categories
- ×Vendor negotiations lack real-time sell-through evidence
- ✓Insight cards flag promo cannibalization the day it happens
- ✓Assortment gaps and whitespace opportunities surface automatically
- ✓Price elasticity shifts detected before margin erosion compounds
- ✓Category-level performance cards replace manual spreadsheet reviews
- ✓Vendor scorecards generated from actual fill rate and quality data
Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. Source: McKinsey & Company
Fashion KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. 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 continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
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 category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Merchandising decision-making. Each card includes what changed, why it matters, and what to do next.
Ward tracks style-level sell-through vs plan, weeks-of-supply remaining, size fragmentation, competitive markdown timing, and price sensitivity by brand tier. It models the full-season margin curve, not just immediate clearance math.
Mid-season, Ward identifies dozens of styles selling below plan and segments them by severity: some need immediate deep markdown, others need moderate discounts, and a group should hold price because they're trending toward natural clearance. This tiered approach recovers significant margin versus the standard blanket-markdown playbook.
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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