Convenience & C-Store · 3,000+ SKUs

Ward for
Convenience retail.

Daypart demand variation. Planogram compliance. High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.

Inside-store sales account for only 33% of c-store revenue but 70% of gross profit. Optimizing that mix is the margin game.— NACS State of the Industry Report
Convenience store checkout counter with product displays
Transactions/hour Attach rate Basket size

Problems Convenience operators
find too late.

  • Daypart demand variation
  • Planogram compliance
  • Impulse category optimization
  • Fuel attach rate
  • Labor scheduling
app.getward.ai
Ward analyzing Convenience retail data. Live product, real data lake.

Where Convenience operators
leave money on the table.

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

Attach Rate
Impulse adjacencies
Daypart-specific cross-sell opportunities surfaced.
Daypart Revenue
Weak hours identified
Which hours and categories underperform, and why.
Planogram Compliance
Sales-correlated flags
Deviations flagged when they affect revenue, not just visuals.
Shrinkage
Slow-bleed detection
Transaction-level anomalies that periodic audits miss.
Important caveats

Value compounds across multi-site operators. Chains with 100+ locations see the strongest returns. Fuel-dominant locations should expect impact concentrated on forecourt-to-store attach rate.

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

See what Convenience 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
Your contact info