Fashion & Apparel · 15,000+ SKUs

Ward for
Fashion retail.

Markdown timing. Size curve misallocation. Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.

The average fashion retailer marks down 30–40% of inventory, erasing up to $150 billion in margin industry-wide each year.— McKinsey State of Fashion Report
Fashion retail storefront with curated clothing displays
Sell-through rate Markdown % Return rate

Problems Fashion operators
find too late.

  • Markdown timing
  • Size curve misallocation
  • Style velocity prediction
  • Return rate management
  • Seasonal transition
app.getward.ai
Ward analyzing Fashion retail data. Live product, real data lake.

Where Fashion operators
leave money on the table.

These are the KPIs Ward monitors — and what changes when someone is actually watching.

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.
Important caveats

Ward requires at least 2 full selling cycles to baseline style velocity and markdown timing. Results vary between basics and trend-driven categories.

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
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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 operators are missing.

Ward finds the margin leaks, shrinkage patterns, and promo misfires your reports don’t surface.

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.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
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