Specialty assortment, briefed to procurement
Ward pulls forward assortment findings scoped to procurement, over every one of your specialty boutiques.
Assortment Planning on a specialty fleet, scoped to procurement
Merchandising wants Ward. You sign the contract. Ward filters to what a Head of Procurement can act on and drops the rest.
Assortment Planning. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Specialty Retail changes the scale of the problem: 5,000+ SKUs, every one of your boutiques. High-consideration purchases, curated assortments, and customer lifetime value. Ward tracks the metrics that matter for margin-rich retail.
How it runs. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
What it does
- SKU rationalization recommendations
- Whitespace opportunity detection
- Planogram optimization inputs
- Store cluster segmentation
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.
The assortment model runs on a daily cycle, not on a reporting calendar. It detects the pattern, accounts for the cause, and attaches what to do about it before the number reaches a review deck.
Coverage is store by store, category by category. Ward keeps a running read on CLV, conversion rate, units per transaction over 5,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the fleet is fine and knowing which seven boutiques are not.
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 Head of Procurement in specialty retail owns numbers that move faster than the reporting cycle that covers them. CLV, conversion rate, units per transaction shift store by store, on a daily cycle. A monthly pack cannot represent that. Merchandising wants Ward. You sign the contract.
- 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 whatever holds your transaction and inventory data. Ward starts building baselines the same day. First cards land in two days, 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 daily cards are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: steady state
Ward sends insight cards every morning, 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 findings arrive, it is how many get acted on.
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
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.
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 CLV, Conversion rate, Units per transaction, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
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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