Assortment Planning for Fashion on NetSuite, built for e-commerce
A fashion Head of E-Com on NetSuite should not be hunting for assortment problems. Ward brings up them on a daily cycle.
How a fashion Head of E-Com runs assortment on Oracle NetSuite
Your online and offline data live in different worlds. Ward filters to what a Head of E-Com can act on and drops the rest.
What assortment planning does: Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
In a Fashion & Apparel estate the job is 15,000+ SKUs throughout locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
The mechanism. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
How the connection works. Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.
What it does
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
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 Assortment matters for Fashion retail
Ward doesn't replace the buyer's eye, it sharpens the math behind the buy. Which store clusters need wider assortment with shallow depth? Which need narrow-deep buys with full size runs? Ward analyzes sell-through by cluster, customer segment, and style attribute to recommend architecture that matches how customers actually shop each location.
What Ward has eyes on.
The assortment model runs daily, not on a reporting calendar. It picks up the pattern, explains the driver, and attaches a recommended move before the number reaches a review deck.
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 footprint is fine and knowing which seven locations are not.
Ward ingests sales orders, inventory, purchase orders from Oracle NetSuite on a read-only connection. Nothing is written back, and your ERP configuration does not change. NetSuite stays the system of record.
At the metric level. Ward tracks assortment width vs depth by cluster, style attribute performance, size curve accuracy, and inter-style cannibalization. It measures revenue-per-option to identify when adding more styles dilutes overall performance.
Why this combination
is its own problem.
Oracle NetSuite is the system of record for most fashion operators of your size, which means the inputs Ward needs are already there. The gap is not data collection. It is that nobody has time to read sales orders and inventory every morning over every store.
- 01 Top-down option counts get applied uniformly across stores; smaller-volume locations carry too many options and starve depth where it matters.
- 02 Cannibalization between similar styles (two black bodycon dresses) is ignored, so the second option doesn't add what its standalone sell-through suggests.
Benchmarks. Fashion option counts vary 2-4x across store clusters in a healthy assortment plan. Cluster-aware planning typically lifts full-price sell-through 2-5 points and reduces end-of-season markdown depth by 100-300 bps.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Oracle NetSuite. Ward reads sales orders, inventory, purchase orders and starts building baselines. First findings land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
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.
-
03
Weeks 4 to 12: steady state
Ward delivers insight cards on a daily cycle, each with what caused it 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 Oracle NetSuite
Ward integrates with NetSuite SuiteCommerce, inventory management, and financials. Mid-market retailers get enterprise-grade insight cards.
Setup: Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance.
Data Ward reads from NetSuite
Impact metrics with NetSuite
Data lake enrichment
Ward enriches NetSuite data with: Sales orders, Weather & events, Customer segments, Vendor performance, Market pricing data
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 analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Ward connects via SuiteTalk REST or SOAP APIs. Token-based authentication. Read-only access to your NetSuite instance. Data points include: Sales orders, Inventory, Purchase orders, Customer records, Financial summaries, Item fulfillment.
Yes. Ward reads NetSuite 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 tracks assortment width vs depth by cluster, style attribute performance, size curve accuracy, and inter-style cannibalization. It measures revenue-per-option to identify when adding more styles dilutes overall performance.
A denim buyer has 200 styles to allocate across 90 stores. Ward reveals that urban flagships convert best with wide assortment at shallow depth, while suburban stores need fewer core styles with full size runs. The current uniform allocation starves variety in urban stores and creates size gaps in suburban ones. A cluster-specific matrix reduces markdown risk while lifting full-price sell-through.
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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