Ward runs pricing on your Looker fashion data
Ward ingests looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio and sends fashion pricing insight cards each day. Read-only, no config changes.
The fashion pricing stack on Looker / Looker Studio
Price Optimization is a insight card type Ward runs continuously. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.
Fashion & Apparel changes the scale of the problem: 15,000+ SKUs, every one of your locations. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
What Ward does with that: Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
How the connection works. Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.
What it does
- Competitive price monitoring
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
- Category-level price sensitivity
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 Pricing matters for Fashion retail
Fashion pricing is a one-way ratchet: mark down too early and you leave money on the table, too late and you're stuck with deep clearance. Ward monitors style-level sell-through velocity against time remaining in season and recommends optimal markdown depth and timing to maximize total margin dollars across the selling window.
What Ward has eyes on.
Coverage is store by store, category by category. Ward scans continuously 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 fleet is fine and knowing which seven locations are not.
Ward reads straight from looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio on a read-only connection. Nothing is written back, and your BI configuration does not change. Looker stays the system of record.
The pricing model runs every morning, not on a reporting calendar. It flags the pattern, traces the cause, and attaches the next step before the number reaches a review deck.
At the metric level. Ward tracks style-level sell-through vs plan, weeks-of-supply remaining, size fragmentation, competitive markdown timing, and price sensitivity by brand tier. It models the full-season margin curve, not just immediate clearance math.
Why this combination
is its own problem.
A generic pricing model applied to a fashion estate produces alerts nobody trusts. The thresholds are wrong, because fashion baselines are wrong for it. Ward learns the baseline from your own locations instead of importing one.
- 01 Competitive markdown calendars are tracked by brand, not by category, so seasonal markdown wars in adjacent categories aren't modeled.
- 02 Calendar-driven markdowns (week 8: 25% off, week 12: 50% off) ignore that hot styles still sell at full price and slow ones need a 40% cut earlier.
Benchmarks. Fashion ends-season markdown rates: 20-35% basics, 35-55% trend, 50-70% denim, 60-80% outerwear in mild winters. Operators with style-level markdown cadence typically capture 200-500 bps of additional gross margin versus chain-uniform calendars.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Looker / Looker Studio. Ward pulls from looker API for query results, underlying database (direct), lookML model metadata 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 hands you cards daily, each with root cause and what to do about it. 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.
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
Fashion KPI impact
Frequently asked questions
Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume. 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 continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.
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.
Ward tracks style-level sell-through vs plan, weeks-of-supply remaining, size fragmentation, competitive markdown timing, and price sensitivity by brand tier. It models the full-season margin curve, not just immediate clearance math.
Mid-season, Ward identifies dozens of styles selling below plan and segments them by severity: some need immediate deep markdown, others need moderate discounts, and a group should hold price because they're trending toward natural clearance. This tiered approach recovers significant margin versus the standard blanket-markdown playbook.
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 pricing problems Ward catches.
Root causes, not just alerts. See it on your data.
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