Fashion · Looker

Looker + Fashion Retail

Fashion retailers have 15,000+ SKUs and blind spots hiding in every store. Ward watches them all and delivers the findings your team doesn't have bandwidth to find. Your Looker / Looker Studio data holds answers nobody has time to extract. Ward reads it via read-only APIs.

Ward + Looker for Fashion & Apparel

Fashion & Apparel retailers running Looker / Looker Studio get AI-powered insight cards without custom development. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.

How it works: Ward can query Looker via API or connect directly to the underlying database. Either way, Ward monitors while your team browses.

Ward monitors 15,000+ SKUs across your locations and delivers automated insight cards covering Sell-through rate, Markdown %, Return rate, and more.

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Live product demo — Ward analyzing retail data in real time.

Metrics Ward monitors

Sell-through rate
Markdown %
Return rate
Style velocity
Size accuracy

Fashion challenges Ward solves

  • Markdown timing
  • Size curve misallocation
  • Style velocity prediction
  • Return rate management
  • Seasonal transition

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

Looker API for query results
Underlying database (direct)
LookML model metadata

Impact metrics with Looker

Time to Insight
Proactive, no login
Explains why metrics moved before anyone checks a dashboard.
Anomaly Detection
Inter-refresh coverage
Catches deviations between Looker dashboard refresh cycles.
Decision Velocity
Root cause attached
Every anomaly card includes cause analysis; no drill-down needed.
Data Utilization
Unused models activated
LookML dimensions and measures queried beyond built dashboards.

Data lake enrichment

Ward enriches Looker data with: Looker query results, Underlying database, Weather & events, Competitor data, Customer segments

Fashion KPI impact

Markdown Rate
Shallower, earlier
Slow movers detected before deep clearance is the only option.
Sell-Through
More at full price
Style velocity cards flag underperformers early enough to reallocate.
Size Accuracy
Fewer size gaps
Size curves recalibrated by store cluster and season.
Return Rate
Better matching
Right size, right store means fewer returns.

Frequently asked questions

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.

First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust 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.

Ward
Insight
Dispatch
Feedback
Evaluate
Learn
01

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.

Real-time detection Root cause + recommendation
02

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.

Tickets created automatically Dispatched to the right person
03

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached
04

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored
05

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

Cycle repeats, sharper each time
$1.8T
Projected global AI market by 2030
0
×
Customer acquisition lift for data‑driven orgs
0
+
Foundation models shipped since 2022
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Guarantees any single model stays on top

See what Fashion problems Ward catches.

Root causes, not just alerts. See it on your data.

Get a demo

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.

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About your operation
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