Director / Head · E-Commerce

Your online and offline data live in different worlds.

Your e-commerce team has the data. What they don’t have is the bandwidth to find what’s buried in it. Ward delivers the findings — with root causes attached.

Retailers with unified omnichannel data see 30% higher lifetime value per customer.— Harvard Business Review
E-commerce fulfillment and digital retail operations
Fashion Home Specialty

What e-commerce finds out
too late.

  • Omnichannel inventory visibility is a dream, not reality
  • 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, not the basket

Insight cards for
head of e-com.

Before Ward
Problems surface in the quarterly review. By then, the damage is done.
  • ×Omnichannel inventory visibility is a dream, not reality
  • ×Online promo performance is measured separately from in-store
  • ×Customer behavior data is siloed by channel
  • ×BOPIS/BORIS operational complexity is growing unchecked
With Ward
Problems surface at 6:47 AM with root causes and recommended actions.
  • 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

This is what Ward
delivers to you.

Evidence trail

Every finding lists its evidence. Forecast blockers, confidence scores, goal relevance — all inspectable. Nothing is a black box.

Ward evidence panel: forecast blockers with confidence scores and investigation actions
Composable chart cards

Charts built on the fly from natural language. Revenue vs. margin, category breakdowns, store comparisons — pinnable to any dashboard.

Revenue vs gross margin chart card generated from natural language query
Ward · for Head of E-Com06:47 AM

Evening shoppers (6-9 PM) adding 22% more ready-to-eat items online vs last quarter. Click-and-collect fulfillment at Store 14 is 18 min slower than estate average.

✓ Action recommendedE-Commerce context
app.getward.ai
Ward delivering insight cards for e-commerce leaders.

The blind spots that cost
head of e-com the most.

KPIs that erode quietly when nobody’s watching. Flip to see what Ward does about each one.

Fashion
Markdown Rate
Shallower, earlier
Slow movers detected before deep clearance is the only option.
↻ Flip to see the action
Recommended Action
Slow movers detected before deep clearance is the only option.
Shallower, earlier
↻ Flip back
Fashion
Sell-Through
More at full price
Style velocity cards flag underperformers early enough to reallocate.
↻ Flip to see the action
Recommended Action
Style velocity cards flag underperformers early enough to reallocate.
More at full price
↻ Flip back
Fashion
Size Accuracy
Fewer size gaps
Size curves recalibrated by store cluster and season.
↻ Flip to see the action
Recommended Action
Size curves recalibrated by store cluster and season.
Fewer size gaps
↻ Flip back
Fashion
Return Rate
Better matching
Right size, right store means fewer returns.
↻ Flip to see the action
Recommended Action
Right size, right store means fewer returns.
Better matching
↻ Flip back
Home
Seasonal Accuracy
Weather + event driven
Pre-positioning adjusted for peak season signals.
↻ Flip to see the action
Recommended Action
Pre-positioning adjusted for peak season signals.
Weather + event driven
↻ Flip back
Home
Long-Tail Turn
Dead weight separated
Which tail SKUs serve project needs vs sit idle.
↻ Flip to see the action
Recommended Action
Which tail SKUs serve project needs vs sit idle.
Dead weight separated
↻ Flip back
Home
Project Basket Value
Cross-sell surfaced
Project purchasing patterns drive attachment.
↻ Flip to see the action
Recommended Action
Project purchasing patterns drive attachment.
Cross-sell surfaced
↻ Flip back
Home
Inventory Carrying Cost
Capital freed
Demand forecasting reduces slow-moving overstock.
↻ Flip to see the action
Recommended Action
Demand forecasting reduces slow-moving overstock.
Capital freed
↻ Flip back
Specialty
CLV
Churn risk surfaced
At-risk customers identified before they leave.
↻ Flip to see the action
Recommended Action
At-risk customers identified before they leave.
Churn risk surfaced
↻ Flip back
Specialty
Conversion Rate
Assortment + staffing
Cards that help convert high-intent browsers.
↻ Flip to see the action
Recommended Action
Cards that help convert high-intent browsers.
Assortment + staffing
↻ Flip back
Specialty
Revenue per SKU
Whitespace found
Underperformers identified, gaps in curated assortment.
↻ Flip to see the action
Recommended Action
Underperformers identified, gaps in curated assortment.
Whitespace found
↻ Flip back
Specialty
Overstock
Less capital locked
Demand matching reduces slow-moving inventory.
↻ Flip to see the action
Recommended Action
Demand matching reduces slow-moving inventory.
Less capital locked
↻ Flip back
Furniture
Inventory Carrying Cost
Aged stock flagged
Slow-moving SKUs identified before carrying costs compound.
↻ Flip to see the action
Recommended Action
Slow-moving SKUs identified before carrying costs compound.
Aged stock flagged
↻ Flip back
Furniture
Order-to-Delivery Cycle
Bottleneck visibility
Cycle time tracked by production stage against baselines.
↻ Flip to see the action
Recommended Action
Cycle time tracked by production stage against baselines.
Bottleneck visibility
↻ Flip back
Furniture
Gross Margin
Real-time by channel
Material cost drift detected as it happens, not at P&L close.
↻ Flip to see the action
Recommended Action
Material cost drift detected as it happens, not at P&L close.
Real-time by channel
↻ Flip back
Furniture
Stockout Frequency
Advance warning
POS and e-commerce signals feed back into production.
↻ Flip to see the action
Recommended Action
POS and e-commerce signals feed back into production.
Advance warning
↻ Flip back
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
0
Guarantees any single model stays on top

Your online and offline data live in different worlds.

See what Ward finds for E-Commerce leaders — with root causes and recommended actions.

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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