Case Study · Enterprise Retail Brand
Mark & Save
AI Vision that turned shelves into real time retail intelligence
How Mark & Save deployed computer vision across hypermarket floors, detecting shelf gaps, measuring shopper behavior, and giving ops teams visibility before revenue is lost.
The Journey
From challenge to measurable impact
How we delivered measurable impact for Mark & Save.
01 · Challenge
What needed to change
Limited real time shelf and floor visibility across large hypermarket formats, out of stocks, planogram gaps, and shopper flow insights were hard to catch before they hurt sales.
02 · Strategy
How we approached it
An AI Vision stack trained on Mark & Save’s assortment and store layouts, edge and cloud inference, ops alerts, and dashboards that turn cameras into operational intelligence.
03 · Results
What was achieved
- Real time shelf and inventory visibility across live store cameras
- Faster exception handling for out of stocks and planogram issues
- Shopper journey analytics that inform layout, staffing, and promotions
Strategy in detail
- Shelf gap and planogram compliance detection across priority categories
- Shopper journey, dwell, and zone analytics for layout and staffing decisions
- Edge inference with cloud sync for multi store rollout
- Exception alerts routed to store ops with image evidence
- Integration hooks into replenishment and store operations workflows
AI Vision
Shelf & floor intelligence
Real time
Gap & planogram detection
Ops alerts
Exception routing to stores
Performance Metrics
Data driven outcomes
Real results from the campaign for Mark & Save.
Campaign results
Capability
AI Vision / Computer Vision
Primary Use Cases
Shelf · Shopper · Ops alerts
Deployment Model
Edge + cloud inference
Store Format
Large format hypermarkets
Outcome Focus
OOS reduction & floor visibility
Shelf Exception Response
Time from shelf gap detection to store ops awareness
Before
360 min
Uplift
-96%
After
15 min
Vision Signal Coverage Rollout
Store camera coverage expanded as models were trained and validated
Start
25%
Peak
100%
Change
4X coverage
Vision Operations Funnel
From camera coverage to actionable store exceptions
Stage 1
Live floors
Camera Coverage
Edge Inference
Stage 2
Continuous
Shelf Detections
Gaps Planograms
Stage 3
Prioritized
Ops Exceptions
Alerts Store teams
Stage 4
Same day
Action Taken
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