Grocery & FMCG retail
In-store analytics & shelf intelligence for grocery & FMCG retail
Computer-vision people-counting, queue and checkout optimization, and FMCG shelf analytics — unified by the Y AI reasoning engine. GDPR-compliant, anonymised, and live on your existing cameras in 2–4 weeks.
95–98%
People-counting accuracy from anonymised vision sensors
2–4 wks
Typical time to live on existing camera infrastructure
150+
Store metrics ingested by the Y AI reasoning engine
€4,200/wk
Recoverable revenue Y has flagged from a single staffed checkout
The grocery challenge
Why grocery operations need more than POS data
Point-of-sale data tells a grocery chain what sold — not who walked past an empty shelf, abandoned a queue at the lunch peak, or never reached the promotion at the back of the store. Peak hours shift seasonally while staffing models stay fixed, so supermarkets are either overstaffed or underwater. On-shelf availability gaps quietly convert demand into lost baskets.
Pygmalios closes that gap with anonymised computer vision across the store — entrance, aisle, shelf, and checkout — and turns the signals into decisions through the In-Store Analytics platform and the Y AI reasoning engine.
From entrance to checkout
What Pygmalios measures across the grocery journey
Footfall & capture rate
Count shoppers per entrance, measure passer-by capture, and break traffic down by zone and daypart.
Queue & checkout
Predict queue length and wait time, monitor lane utilization, and trigger alerts before checkout abandonment spikes.
Shelf & category
Track on-shelf availability, planogram compliance, and dwell at the category level to cut out-of-stocks and lost sales.
Staffing & layout
Align staffing to real peak hours and A/B-test store layout and aisle flow against conversion.
How does shelf analytics work in a grocery store?
Computer-vision shelf analytics uses existing ceiling and shelf-edge cameras to monitor on-shelf availability, detect out-of-stocks and planogram compliance, and measure shopper dwell and pick-up at category level. Anonymised vision feeds are processed at the edge — no facial recognition — and the results stream into the Y AI reasoning engine, which correlates gaps with lost conversion and alerts the store team in near real time.
How accurate is computer-vision people-counting in supermarkets?
Pygmalios’ vision sensors count shoppers with 95–98% accuracy, separating staff from customers, filtering re-entries, and reporting counts per entrance and per zone rather than a single store total. Accuracy is validated during onboarding by calibrating sensors against manual counts, so footfall, capture rate, and conversion are reliable enough to staff and merchandise against.
What ROI can a grocery chain expect from in-store analytics?
Grocery and FMCG retailers typically see returns from three levers: reducing queue abandonment at checkout, improving on-shelf availability, and aligning staffing to true peak hours. The Y engine has flagged single fixes worth ~€4,200/week (one staffed checkout during the lunch peak) and ~€1,800/month in staffing savings from realigning shifts to a shifted Saturday peak. Use the ROI calculator to model the impact across your own store count and basket size.
Is grocery shopper tracking GDPR-compliant?
Yes. Pygmalios processes anonymised sensor data only — no personal data, facial recognition, or individual tracking. Data stays within EU infrastructure with enterprise-grade encryption at rest and in transit, which is why grocery operators across 20+ countries can deploy it without a heavy privacy review.
Does it work across a large supermarket network?
Yes. Pygmalios runs across 2,000+ retail locations and 250M+ shopper sessions, with cross-store benchmarking that surfaces underperformers — for example, a store 23% below its regional conversion average despite higher traffic. Each site keeps its own calibration while regional managers get one comparable view.
Explore the products
The Pygmalios stack for grocery & FMCG
In-Store Analytics →
Footfall, queues, customer journeys, conversion, and shelf signals — with the Y engine on top.
Market Intelligence →
Track competitor pricing, promotions, reviews, and FMCG market moves around each store.
Digital Signage →
Run in-store screens and promotions, and measure how shoppers actually engage with them.
Not sure where the upside is? Model it with the ROI calculator → · Or read the 2026 shelf-availability & foot-traffic benchmark →
Talk to us
See what your stores are trying to tell you.
Book a 30-minute demo and we'll walk through your own store data — live, no slides.