Where the data comes from
The data behind a heatmap typically comes from computer vision: cameras detect moving people in the image and their position on the floor. From that, the number of use-minutes per square meter per week or month is calculated.
People are not identified. The processed data is anonymized, and raw video is not stored beyond a short window. This sets a heatmap apart from, for example, mobile-device location data in terms of privacy.
What it tells you
A heatmap reveals three things quickly.
The busiest areas. The spots where capacity runs out first at peak hour.
The quiet areas. Spots that are underused. Underuse can be due to weak location (far from entry, poor lighting), the wrong equipment mix, or insufficient signage.
Traffic flow. Combined with time data, the heatmap shows how members move between zones across the day.
What it is used for
A heatmap is at its best as a planning tool. When the floor plan is being redesigned or the equipment mix is being moved, the heatmap tells you what was worth keeping and what to change.
The same applies to greenfield builds: heatmaps from existing gyms can teach how members use the space, and those lessons can be carried into new sites.
A heatmap is also an effective communication tool for owners and investors. It turns abstract usage data into something immediately understandable.
Common mistakes
Heatmaps are often viewed as an average over a single period, which smooths out time of day. The peak-hour heatmap can look completely different from the midday version, and is more decision-relevant.
Heatmaps also need context: high use is not always good, sometimes it signals a bottleneck that should be relieved.