Price Optimization for Fashion on Looker, built for procurement
Ward reads straight from looker API for query results, underlying database (direct), lookML model metadata from Looker / Looker Studio, scans continuously pricing over 15,000+ fashion SKUs, and sends the procurement read daily.
Price Optimization for Fashion on Looker, scoped to procurement
Price Optimization. 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.
Merchandising wants Ward. You sign the contract. Ward writes the finding at the altitude a Head of Procurement works at.
The mechanism. 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 you get
- Category-level price sensitivity
- Competitive price monitoring
- Margin-volume tradeoff modeling
- Real-time elasticity measurement
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.
Ward reads 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 daily cards. Your BI is untouched.
The pricing model runs daily, not on a reporting calendar. It flags the pattern, traces 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 tracks 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.
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.
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 straight from what is there.
- 01 Markdown depth gets set to clear remaining units regardless of size mix; if only XS and XXL remain, no markdown depth recovers full sell-through.
- 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 reads straight 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: 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 hands back cards daily, each with the driver and a recommended action. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight 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
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
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.
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 Sell-through rate, Markdown %, Return rate, tailored for Procurement decision-making. Each card includes what changed, why it matters, and what to do next.
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.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.