Assortment Planning for Specialty Retail on Lightspeed
Specialty retailers on Lightspeed run assortment through Ward. 5,000+ SKUs throughout your boutiques, scans continuously around the clock.
How Ward turns Lightspeed data into specialty assortment
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.
Assortment Planning. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
Under the hood. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.
Getting the data in. Ward connects via Lightspeed Retail API. OAuth-based authentication. Read-only access.
The short list
- Planogram optimization inputs
- Store cluster segmentation
- SKU rationalization recommendations
- Whitespace opportunity detection
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.
Ward ingests Lightspeed rather than replacing it. Sales transactions, inventory counts, customer profiles come across on a read-only connection, get enriched with contextual data, and come back as insight cards. Your POS is untouched.
Stock what sells. Cut what doesn't. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
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 footprint 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 generic assortment model applied to a specialty footprint 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 Assortment additions get evaluated on standalone projected margin without modeling brand-coherence dilution, wrong-customer-segment items can damage long-term equity even when they sell well short-term.
- 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: read-only connection
Ward connects to Lightspeed Retail with read-only credentials and begins ingesting sales transactions and inventory counts. No config changes on your side. First findings arrive inside the first 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: operating rhythm
Daily cards arrive on a daily cycle 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 Lightspeed Retail
Ward integrates with Lightspeed Retail POS for specialty and boutique retailers. In-store transaction data powers customer behavior and assortment insight cards.
Setup: Ward connects via Lightspeed Retail API. OAuth-based authentication. Read-only access.
Data Ward reads from Lightspeed
Impact metrics with Lightspeed
Data lake enrichment
Ward enriches Lightspeed data with: Sales transactions, Customer profiles, Weather & events, Competitor data, Demographic data
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 Lightspeed Retail API. OAuth-based authentication. Read-only access. Data points include: Sales transactions, Inventory counts, Customer profiles, Purchase orders, Employee performance.
Yes. Ward reads Lightspeed 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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