Occupancy analytics and space utilisation

Occupancy analytics is the practice of measuring how many people use a space and when, then turning that into insight about how the space performs. For universities and smart buildings it answers questions like: which rooms are actually used, how full are they when occupied, and how does real use compare with what was booked? Crowd Connected produces this from a wire-free Bluetooth mesh that counts people anonymously, with no cameras and no personal data.

Space utilisation is the share of available time that a space is in use, often combined with how full it is when occupied. Crowd Connected measures it by counting the number of Bluetooth devices in each room continuously, converting those counts into people counts with machine learning, and comparing them against room capacity and operating hours. This replaces one-off manual surveys and booking assumptions with continuous, hour-by-hour data. Unlike space utilisation software that works only from room-booking records, the figures reflect measured presence.

The Bluetooth mesh counts the wireless signals that phones and other devices broadcast as people move through a building. It counts unique devices per zone and uses machine learning to estimate the real headcount, then discards the rest. No images are captured, no Wi-Fi MAC addresses are stored, and no individual is identified. This makes it a privacy-first alternative to camera-based people counting, with no biometric data to govern.

Yes. By importing your timetable or room-booking data, the platform compares planned use with measured use. That reveals timetabled sessions with no attendance, rooms in use with no booking, and rooms that are consistently larger than the groups using them. The timetable is imported from your existing scheduling system.

Sector benchmarks for teaching-space frequency and occupancy have long sat well below half of available capacity, and continuous sensing usually confirms the real figure is lower than booking data implies. In one multi-campus study, rooms were in active use for under 40% of operating hours and were around a third full when occupied. The value of measuring it is less the single headline number and more seeing which specific rooms and hours drive the average, so you can act on them.

Occupancy counting produces aggregate counts, not records about people. Because no images, no device identifiers, and no personal data are stored, there is generally no personal data being processed in counting mode, which keeps it straightforward under UK GDPR. This is a key difference from camera-based and Wi-Fi-tracking approaches.