Heatmaps
Measure where attention concentrates - not where it is assumed to happen
KSI turns every camera into a spatial behavior sensor. Every square meter of your space gets a traffic and interest score, in real time, with no new hardware.
Request my demo→Layout decisions are made without seeing where customers stop
Store layouts are often designed around a hypothesis: that customers will walk the aisles in a certain order, stop at specific key points, and react to merchandising as expected.
Reality almost never matches the hypothesis. There are dead zones no customer touches, hot spots nobody mapped, and merchandising in aisles where people only pass through quickly. Without measurement, it cannot be corrected.
Without visibility into attention, layout gets optimized blindly
From pixels to layout decisions
1. Calibrated grid division
The space is divided into a calibrated metric grid. Each cell maps to a real, measurable floor area.
2. Traffic map
Every time a person crosses a cell, +1 traffic. Result: a precise circulation map.
3. Interest map
If someone stays in a cell longer than a configurable threshold (default 10s), +1 interest. Distinguishes between walking past and looking.
Decisions that become obvious
Layout optimization
Detect dead aisles and reallocate space where people actually circulate.
Merchandising planning
Place high-margin products in zones with real interest, not assumed ones.
Dead zone detection
Identify areas that get traffic but no attention -and figure out why.
A/B testing of changes
Measure before/after impact of every layout change or visual merchandising intervention.
More than a pretty map
Evidence-based layout decisions
Every store layout change is validated with behavioral data, not assumptions.
More than 99% accuracy
Algorithm validated in real deployments. Works with any existing camera brand.
Heatmaps FAQ
The space is divided into a calibrated metric grid. Every time a person crosses a cell it adds traffic; if they stay in that cell beyond a configurable threshold, that cell adds interest. This distinguishes between a zone people just walk through and one that actually captures attention.
It detects dead aisles and reallocates space where people actually move; it lets you place higher-margin products in zones with real (not assumed) interest, identify cold zones that get traffic but no attention, and measure the impact of a layout change with before-and-after data.
More than 99% accuracy, validated in real-world implementations.