Assortment Planning: Fashion retail, Looker data, E-Commerce decisions
Your online and offline data live in different worlds. Ward detects fashion assortment movement in your Looker data while it is still fixable, with the cause and the next step attached.
The full picture: fashion assortment, Looker data, E-Commerce decisions
For Fashion & Apparel retailers, that means monitoring 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 online and offline data live in different worlds. Ward delivers insight cards scoped to e-commerce decision-making.
Here is assortment planning in plain terms. Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate.
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 can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
The short list
- 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 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.
Why this combination
is its own problem.
Running Ward on Looker in a fashion fleet skips the usual first step. There is no ingestion project, because Looker / Looker Studio already holds looker API for query results, underlying database (direct), lookML model metadata. Ward reads what is there.
- 01 Top-down option counts get applied uniformly across stores; smaller-volume locations carry too many options and starve depth where it matters.
- 02 Cluster definitions don't account for online inventory pooling; a store next to a strong DC has different effective assortment math than a remote one.
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 Ward has eyes on.
Coverage is store by store, category by category. Ward monitors sell-through rate, markdown %, return rate over 15,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the estate is fine and knowing which seven locations are not.
Ward pulls from Looker rather than replacing it. Looker API for query results, underlying database (direct), lookML model metadata come across on a read-only connection, get enriched with contextual data, and come back as findings. Your BI is untouched.
The assortment model runs each day, not on a reporting calendar. It spots the pattern, explains what caused it, and attaches a recommended move before the number reaches a review deck.
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.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Looker / Looker Studio. Ward reads looker API for query results, underlying database (direct), lookML model metadata 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: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward sends findings daily, each with root cause and the next step. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.
How Ward connects to Looker / Looker Studio
Ward does not replace Looker. Ward watches the same data Looker visualizes and proactively alerts when something changes. Your dashboards stay. Ward adds intelligence.
Setup: Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
Data Ward reads from Looker
Impact metrics with Looker
Data lake enrichment
Ward enriches Looker data with: Looker query results, Underlying database, Weather & events, Competitor data, Customer segments
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 can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses. Data points include: Looker API for query results, Underlying database (direct), LookML model metadata.
Yes. Ward reads Looker 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
See what Fashion assortment problems Ward catches.
Root causes, not just alerts. See it on your data.
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