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Platform / Pulse / Business Analytics

The cameras already know. Now they tell you.

Retail footfall analytics on the cameras you already have, counts, demographics, dwell time, peak hours. Daily store briefs and regional rollups. Same cameras that catch shoplifters, telling you when to staff, what to promote, and which stores need attention.

Business Intelligence

Northgate · last 30 days

Illustrative
Hourly footfallIndividuals / hour
08:0012:0016:0020:00
Peak hour
17:00
712 visitors / hr · +12% vs avg
Gender split
8,954VISITORS
F 51.6%M 48.4%
What it does

Four lenses on the day, the week, the estate.

Not just a number. A number with context, vs yesterday, vs last week, vs the regional benchmark.

01

Footfall & conversion

Counts at the door, at the till, at the high-margin aisle. Conversion from 'in the store' to 'in the basket', without integrating with EPOS.

02

Demographics & peak profile

Anonymous age bands and gender splits. Who's in your store at 11am vs 6pm. Is the product mix in your endcaps actually targeted at them?

03

Daily & regional brief

Morning of, on your phone. Your store yesterday vs your region. Three things to act on today. No dashboards to log into.

How it works

Anonymous by default. Aggregated by design.

QuantumEye watches the floor anonymously when no face is matched. No identities stored. No profiles built. Just the patterns of the day.

COUNT

Anonymous counting

Per-camera footfall tallies. Aggregated per zone. No identity attached unless a face matches your watchlist or whitelist.

PROFILE

Demographic estimation

Age band and gender estimated at the frame, then aggregated. The individual estimate is discarded, only the aggregate persists.

DELIVER

Brief at 08:00

Yesterday rolled up overnight. Your store, your region, your action items, waiting on your phone before the store opens.

Live example

Morning brief, yesterday at a glance.

Illustrative scenario. The manager opens the brief: visitor count, peak hour against the daily average, demographic mix, and the surge windows that are under- or over-staffed. Three actions ranked at the bottom.

app.quantumeye.io/analytics/business-intelligence

Business Intelligence

Customer demographics and behavioural insights

Illustrative
Last 30 daysAll storesAll cameras
Total Visitors
8,954
+12.4% vs last period
Female
51.6%
4,621 detected
Male
48.4%
4,333 detected
Peak Hour
17:00
712 visitors/hr
Hourly Footfall
Individuals detected per hour
08:0012:0016:0020:00
Gender Split
8,954VISITORS
Female 51.6%Male 48.4%
Age Distribution
Core demographic: 25–34 · Avg age 38
18%
18–24
34%
25–34
24%
35–44
16%
45–54
8%
55+

Pairs well with

GDPR & accuracy

Counted, not catalogued.

Business Analytics is built on aggregate data. Individual demographic estimates are computed and discarded, only the aggregate persists.

01

Aggregate-only retention

Per-frame estimates are computed and discarded. Only aggregate counts (per hour, per zone, per demographic band) persist.

02

No identity in the analytics graph

The analytics aggregate is technically and architecturally separate from the security face graph.

03

Sensitive classifications are protected

Ethnicity classification codes are never surfaced in the daily brief or in any standard report.

Your security cameras as your ops brain.

You already have the cameras. You already have the footage. You're just not yet asking them the second question. QuantumEye does.