Fashion · Shopify · Head of Procurement

Ward and Shopify for Fashion procurement

Your Shopify feed covers 15,000+ SKUs over your locations. Ward reads it for procurement.

What a fashion Head of Procurement gets from Shopify / Shopify Plus

Merchandising wants Ward. You sign the contract. Ward filters to what a Head of Procurement can act on and drops the rest.

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.

The connection itself. OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events.

app.getward.ai Live demo
Acme Apparel @Merchandising: Planning Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why is fall denim sell-through behind plan?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled fall denim against last season’s curve at the same selling week. Two causes, both still fixable this week.

SignalFinding
sell_throughWeek 6 sell-through 38% vs. 52% plan, concentrated in 3 of 9 door clusters
size_curveWaist 30–32 sold out in 41 doors while 36–38 sits at 71% on hand
markdown.ladderFirst 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%.

9 parallel queries 3 sources cited confidence 0.89
Draft the size-curve transfer list.
You · 9:43 AM
Allocation Agent · drafting transfer list
Querying sell_through_weekly
Ask anything, Ward routes to the right agent. Cmd+K

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. plan
$31.7M
+3.1% WoW
Full-price mix
61.4%
−5.1pp
Sell-through, fall denim
38.2%
wk6, −9pp vs plan
Markdown rate
19.6%
+2.8pp
Revenue vs. plan 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
shopify_orders_dailyimport2m ago
shopify_returns_reasonsimport2m ago
netsuite_inventory_snapshotimport14m ago
cegid_store_salesimport1h ago
retail_size_curve_actualsimport1h ago
retail_markdown_ladderimport1h ago
retail_ga4_website_dailyimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-markdownpermitModel::"markdown_ladder"
vendor-blockedforbidModel::"labor_*"
ecom-read-returnspermitModel::"returns_reasons"
for Fashion on Shopify data, live product demo.

What Ward delivers

  • 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

Metrics Ward monitors

Sell-through rate
Markdown %
Return rate
Style velocity
Size accuracy

Fashion challenges Ward solves

  • Markdown timing
  • Size curve misallocation
  • Style velocity prediction
  • Return rate management
  • Seasonal transition

Why this combination
is its own problem.

Shopify / Shopify Plus was built for transactions, not for procurement decisions. The data is right there and it is in the wrong shape. Ward pulls from orders & line items and product catalog and rewrites them as a decision.

What Ward has eyes on.

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.

Ward pulls from orders & line items, product catalog, inventory levels from Shopify / Shopify Plus on a read-only connection. Nothing is written back, and your Commerce configuration does not change. Shopify stays the system of record.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to Shopify / Shopify Plus. Ward reads orders & line items, product catalog, inventory levels and starts building baselines. First insight cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.

  2. 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 findings are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: steady state

    Ward delivers findings daily, each with the cause and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.

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

Orders & line items
Product catalog
Inventory levels
Customer profiles
Discount usage
Fulfillment data

Impact metrics with Shopify

Sell-Through Rate
Slow movers reallocated
Order velocity tracked; underperformers flagged before markdowns.
Return Rate
Return-prone patterns spotted
Behavioral signals identify high-return product and buyer combos.
Customer LTV
Re-engagement timed right
Purchase cadence and cohort data surface lapsing customers.
Inventory Turnover
Reorder points tightened
Demand signals optimize safety stock across the catalog.

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.

Pain points
  • ×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
How Ward helps
  • 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

Markdown Rate
Shallower, earlier
Slow movers detected before deep clearance is the only option.
Sell-Through
More at full price
Style velocity cards flag underperformers early enough to reallocate.
Size Accuracy
Fewer size gaps
Size curves recalibrated by store cluster and season.
Return Rate
Better matching
Right size, right store means fewer returns.

Frequently asked questions

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.

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.

See what Fashion problems Ward catches.

Root causes, not just alerts. See it on your data.

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info