Specialty assortment without leaving BigCommerce
Ward pulls from orders, products & variants, customers from BigCommerce and returns specialty assortment cards every morning. Read-only, no config changes.
The specialty assortment stack on BigCommerce
Assortment Planning, in one sentence. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Applied to Specialty Retail, the surface area is 5,000+ SKUs over boutiques. High-consideration purchases, curated assortments, and customer lifetime value. Ward tracks the metrics that matter for margin-rich retail.
What Ward does with that: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Getting the data in. Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.
What you get
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled traffic against conversion by hour for the four flagships. Traffic is fine. The gap is coverage and depth.
| Signal | Finding |
|---|---|
traffic_conversion | Conversion 18.4% vs. 23.1% chain, all of the gap in 12–2p and 5–7p |
labor.coverage | One associate on the floor through both peaks at 3 of 4 doors |
inventory.depth | Top 20 styles at 1.4 units per size, walk-away rate +9% |
Recommend: add floor coverage to both peak windows, deepen the top 20 styles to three per size at the flagships, and route the walk-away list to clienteling.
traffic_conversion…
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 |
lightspeed_store_sales | import | 2m ago |
netsuite_inventory_snapshot | import | 14m ago |
retail_customer_ltv | import | 1h ago |
retail_traffic_conversion | import | 1h ago |
retail_clienteling_log | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
ops-read-default | permit | Model::* |
crm-read-ltv | permit | Model::"customer_ltv" |
associate-pii-blocked | forbid | Model::"customer_pii" |
merch-read-assortment | permit | Model::"sales_by_tier" |
Why Assortment matters for Specialty retail
In specialty, curation is the product, adding the wrong item dilutes the brand. Ward quantifies the curatorial instinct by scoring which items reinforce the store's point of view through customer fit and companion purchase patterns, and which are dilutive.
What Ward has eyes on.
Every one of your boutiques gets its own baseline. Ward keeps a running read on CLV, conversion rate, units per transaction against it and surfaces only the deviations that hold up. The two that show up most in specialty retail are assortment curation and customer lifetime value, and both are baseline problems before they are P&L problems.
Ward reads straight from BigCommerce rather than replacing it. Orders, products & variants, customers come across on a read-only connection, get enriched with contextual data, and come back as insight cards. Your Commerce is untouched.
The assortment model runs every morning, not on a reporting calendar. It spots the pattern, accounts for root cause, and attaches the next step before the number reaches a review deck.
At the metric level. Ward tracks assortment coherence, customer-fit scoring, incremental contribution beyond existing assortment, and curatorial dilution risk, the danger of adding items that weaken brand positioning.
Why this combination
is its own problem.
A generic assortment model applied to a specialty estate produces alerts nobody trusts. The thresholds are wrong, because specialty baselines are wrong for it. Ward learns the baseline from your own boutiques instead of importing one.
- 01 Cannibalization between near-identical items in the existing assortment isn't modeled, so the "new addition" sometimes just shifts revenue from another SKU.
- 02 Customer-fit scoring requires loyalty data many specialty chains under-utilize; without segment-level signal, brand coherence becomes a guess.
Benchmarks. Specialty assortment-coherence-aware planning typically delivers 15-30% higher full-price sell-through on new-item additions and reduces end-of-season markdown by 200-400 bps.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to BigCommerce. Ward ingests orders, products & variants, customers and starts building baselines. First cards 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 insight cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Cards arrive daily 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 BigCommerce
Ward connects to BigCommerce for omnichannel retailers running headless or traditional storefronts. Orders, catalog, and customer data drive insight cards.
Setup: Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events.
Data Ward reads from BigCommerce
Impact metrics with BigCommerce
Data lake enrichment
Ward enriches BigCommerce data with: Orders & variants, Customer behavior, Marketing data, Returns & exchanges, Competitor pricing
Specialty KPI impact
Frequently asked questions
Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. For Specialty retail specifically, Ward monitors 5,000+ SKUs across your boutiques and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks CLV, Conversion rate, Units per transaction, Repeat purchase rate, Sell-through by tier at the store-category level. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Ward connects via BigCommerce REST API with OAuth. Webhooks for real-time order and inventory events. Data points include: Orders, Products & variants, Customers, Inventory, Promotions, Storefront analytics.
Yes. Ward reads BigCommerce data and combines it with contextual signals (weather, events, demographics) to generate Specialty-specific insight cards. No custom development required.
Ward tracks assortment coherence, customer-fit scoring, incremental contribution beyond existing assortment, and curatorial dilution risk, the danger of adding items that weaken brand positioning.
A buyer evaluates 60 new SKUs for the fall assortment. Ward scores each on customer fit, basket affinity, and margin contribution after displacement. It separates high-coherence items from those that score well on margin but would attract the wrong customer segment. The buyer selects the high-coherence group and sees meaningfully higher sell-through than prior season additions.
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 Specialty assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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