Demand Forecasting for Fashion on Shopify, built for merchandising
A fashion VP Merchandising on Shopify should not be hunting for demand problems. Ward raises them every morning.
How a fashion VP Merchandising runs demand on Shopify / Shopify Plus
Demand Forecasting is a card type Ward runs continuously. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
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.
Your category managers are drowning in spreadsheets. Ward hands back cards scoped to merchandising decision-making.
The mechanism. Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
Getting the data in. OAuth-based connection. Ward reads via Shopify Admin GraphQL API. Real-time webhooks for order and inventory events.
Key capabilities
- 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 every morning, not on a reporting calendar. It detects the pattern, traces what caused it, and attaches a recommended action before the number reaches a review deck.
Ward scans continuously 15,000+ SKUs throughout 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 reads 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 findings. Your Commerce is untouched.
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.
Running Ward on Shopify in a fashion estate 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 what is there.
- 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 Shopify / Shopify Plus with read-only credentials and begins ingesting orders & line items and product catalog. No config changes on your side. First 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 returns insight cards every morning, each with root cause and a recommended move. 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 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
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 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.
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.
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 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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