Price Optimization for Fashion on Shopify, built for procurement
Price on elasticity you can measure. Ward runs it on Shopify data throughout your fashion estate, scoped to procurement.
How a fashion Head of Procurement runs pricing on Shopify / Shopify Plus
Applied to Fashion & Apparel, the surface area is 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.
Merchandising wants Ward. You sign the contract. Ward writes the finding at the altitude a Head of Procurement works at.
Price Optimization is a insight card type Ward runs continuously. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
How it runs. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
How the connection works. OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events.
The short list
- Category-level price sensitivity
- Competitive price monitoring
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
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.
Why this combination
is its own problem.
Running Ward on Shopify in a fashion fleet skips the usual first step. There is no ingestion project, because Shopify / Shopify Plus already holds orders & line items, product catalog, inventory levels. Ward reads straight from what is there.
- 01 Competitive markdown calendars are tracked by brand, not by category, so seasonal markdown wars in adjacent categories aren't modeled.
- 02 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.
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 Ward has eyes on.
Every one of your locations gets its own baseline. Ward keeps a running read on sell-through rate, markdown %, return rate against it and pulls forward only the deviations that hold up. The two that show up most in fashion retail are markdown timing and size curve misallocation, and both are baseline problems before they are P&L problems.
Ward ingests Shopify rather than replacing it. Orders & line items, product catalog, inventory levels come across on a read-only connection, get enriched with contextual data, and come back as cards. Your Commerce is untouched.
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.
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.
What the first 90 days
actually look like.
-
01
Week 1: read-only connection
Ward connects to Shopify / Shopify Plus with read-only credentials and begins ingesting orders & line items and product catalog. No config changes on your side. First insight 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
Insight 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 Shopify / Shopify Plus
Ward connects to Shopify and Shopify Plus via the Admin API. Orders, products, inventory, and customer data power Ward insight cards for omnichannel retailers.
Setup: OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events.
Data Ward reads from Shopify
Impact metrics with Shopify
Data lake enrichment
Ward enriches Shopify data with: Order & line items, Customer behavior, Marketing attribution, Returns & exchanges, Competitor pricing
Merchandising wants Ward. You sign the contract.
- ×Business sponsor saw the demo. You have a week to vet a vendor you didn't pick
- ×AI vendors price by seats and tokens. Total cost is unknowable until invoice three
- ×Multi-year commits with auto-renew. No exit if the pilot stalls
- ×Renewals come back 30% higher, with no room to push back and no benchmark to cite
- ×Security and DPA reviews start after the team has already committed
- ✓MSA, DPA, SOC 2 Type II underway, and architecture review available before signature
- ✓Month-to-month contracts. No multi-year lock-in. No auto-renew traps
- ✓Transparent pricing set by scope and store count
- ✓14-day insight guarantee. If Ward doesn't deliver, month two is on us
- ✓AI strategy, orchestration and reporting-layer work in enterprise grocery at nine-figure revenue. Reference calls available before signature
Enterprise SaaS spend grew 18% YoY. 53% of subscriptions are underused or duplicative. Source: Gartner
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.
OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events. Data points include: Orders & line items, Product catalog, Inventory levels, Customer profiles, Discount usage, Fulfillment data.
Yes. Ward reads Shopify data and combines it with contextual signals (weather, events, demographics) to generate Fashion-specific insight cards. No custom development required.
Merchandising wants Ward. You sign the contract. Ward solves this with automated insight cards: MSA, DPA, SOC 2 Type II underway, and architecture review available before signature. Month-to-month contracts. No multi-year lock-in. No auto-renew traps. Transparent pricing set by scope and store count.
Ward delivers daily insight cards covering Sell-through rate, Markdown %, Return rate, tailored for Procurement 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
See what Fashion pricing problems Ward catches.
Root causes, not just alerts. See it on your data.
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