retail analytics

Customer flow analytics that trace the route, not just the total

In short

Customer flow analytics combines Ayonix directional counting, dwell measurement and heatmap accumulation to describe how visitors move through a space. It shows which routes carry most traffic, which zones are reached and which are bypassed, as aggregate patterns rather than individual journeys.

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Conceptual illustration of shop-floor movement used for heatmap analysis. AI-generated conceptual image, not a customer deployment.

Scope

What this analytic does, and what it does not

What it detects

Directional flow volumes along defined routes
Relative activity across zones of a floor
Zone reach — the share of arrivals that reach a given area
Change in flow pattern between comparable periods

What it does not guarantee

An individual customer journey from entrance to exit
Cross-camera tracking of a specific person
Attribution of sales to a particular route
Accurate flow where coverage between zones has gaps

The problem

What customers are actually dealing with

These are the situations that lead teams to look at this analytic in the first place.

A mall knows total footfall but not which routes people take between anchors.
Upper floors underperform and nobody can show whether the problem is reach or conversion.
Wayfinding is redesigned without measuring how people actually move now.
Flow assumptions in the original store design were never tested after opening.

How it works

Detection workflow

Every event carries the rule that produced it, so an operator can see why they were alerted.

1

Define the network

Counting lines are placed on the routes between zones, and zones are defined for activity accumulation.
2

Measure

Directional counts, dwell and accumulated activity are collected concurrently across the covered area.
3

Aggregate

Route volumes and zone reach are computed as aggregate proportions per interval.
4

Compare

The current period is compared against a stored baseline to show change.
5

Publish

Flow volumes and zone reach are published to the dashboard and exported for planning.

Customer flow analytics workflow diagram

Configuration options

Route counting lines between zones, with consistent direction labelling
Zone definitions aligned to commercially meaningful areas
Reporting intervals and the baseline period used for comparison
Exclusions for staff routes and service corridors
Trading-hours schedule
Whether reach is expressed against total arrivals or against a specific entrance

Alert quality

How irrelevant and duplicate alerts are reduced

No video analytic eliminates false alerts. These are the mechanisms that reduce them, and the residual rate is measured on your own cameras.

Consistent direction labelling across all lines is what makes route volumes comparable rather than merely present.
Aggregation across a multi-week period removes single-day effects such as weather or a one-off event.
Coverage gaps between zones are documented explicitly so reach figures are not over-interpreted.
Excluding staff routes prevents back-of-house movement from distorting the flow picture.
Flow is reported as aggregate proportions, not as reconstructed individual journeys.
Counting error at each measurement point propagates into every derived proportion, so route volumes remain estimates whose confidence depends on coverage completeness rather than on the reporting period.

Prerequisites

Camera requirements and environmental limits

Camera suitability decides more of the outcome than any software setting. These are assessed per camera before commitment.

Camera requirements

Counting-suitable views at every route between zones that matters to the analysis
Elevated views over zones used for activity accumulation
Enough coverage that a route is not inferred from an unmeasured gap
Consistent lighting across the covered area and through trading hours
Stable mounting across all cameras contributing to a comparison baseline

Environmental limitations

Any unmeasured route weakens every derived proportion, so coverage design matters more than tuning.
Flow is aggregate; it does not reconstruct where an individual went.
Comparability across periods requires that no camera was moved or re-aimed in between.
Very large open spaces may need several cameras to describe a single route.

Delivery

Alerts, evidence and where they land

Event and evidence fields

Route or line name and direction
Interval volumes per route
Zone reach proportion
Baseline reference for comparison
Coverage completeness indicator
Camera availability across contributing cameras

VMS integration

Flow analytics is a planning output consumed on dashboards and in BI tools. It does not generate operator alarms and is not normally surfaced inside the VMS.

See compatibility states →

Deployment options

On-premise processing where a single site has many contributing cameras
Edge processing per building in a multi-building campus or centre
Hybrid deployment so estate-level comparison runs centrally on aggregate series only

Compare architectures →

Measurement

Dashboard metrics and pilot acceptance criteria

Acceptance thresholds are agreed with you before the pilot starts. This site publishes no benchmark figures, because they do not transfer between sites.

Dashboard metrics

Route volumes by direction and interval
Zone reach as a share of arrivals
Flow change against baseline
Top and bottom routes by volume
Coverage completeness per analysis
Contributing camera availability

Measurable pilot criteria

Route volume accuracy against manual counts at sampled lines and times
Stability of reach proportions across two comparable weeks
Documented coverage completeness, with gaps explicitly listed
Sensitivity check that a deliberate change produces the expected directional shift
Data completeness across trading hours for all contributing cameras

Governance

Privacy and governance

All outputs are aggregate proportions and volumes; no individual journey is stored or reconstructed.
No cross-camera re-identification is performed.
Aggregate series can be retained for planning without retaining imagery.
Zones covering staff areas should be excluded or handled under employee monitoring obligations.

Frequently asked questions

Can you show me one shopper’s path through the centre?

No. This site does not offer cross-camera re-identification. Flow analytics reports aggregate volumes and proportions across routes and zones, which answers layout and reach questions without building individual journeys.

What if some routes have no camera?

Those routes are recorded as coverage gaps and the resulting proportions are qualified accordingly. It is better to state the gap than to infer a volume that was never measured, and the readiness review identifies gaps before the analysis is designed.

How is this different from a heatmap?

A heatmap shows where activity accumulates within one camera view. Flow analytics links measurements across several cameras into route volumes and zone reach, which answers questions about movement between areas rather than within one.

Keep reading

A controlled pilot establishes what this analytic actually does in your environment, against acceptance criteria we agree before it starts. Send a sample video first if you would rather see an assessment before committing to a pilot.