FAQ About Space Utilization Analytics

Most facility teams already suspect the truth before they measure it: the Tuesday-morning walk-through shows half-empty floors, while the booking system claims 85% occupancy. That gap — between what your systems report and what your building actually does — is where lease renewals, refurbishment budgets and energy contracts quietly go wrong. Below are the questions we hear most often from facility managers, universities and public institutions once they move past the introductory articles and start asking what a real deployment looks like.

Why doesn't badge or booking data tell me enough?

Badge swipes tell you someone entered the building. They do not tell you where that person went, how long they stayed, or whether the eight-person meeting room they booked ever held more than two people. Booking data is even less reliable: a room reserved every Wednesday for a recurring meeting that was cancelled six months ago still shows as fully utilized. Space utilization analytics measures actual presence in actual spaces, which is the only data you can defend when someone challenges a decision to consolidate two floors or release a lease.

What should I measure first?

Not everything at once. The deployments that stall are almost always the ones that tried to instrument every desk on day one. Start where the money is:

• Meeting rooms — the fastest payback. Comparing booked versus actual occupancy typically exposes rooms sized wrong for how they are used.
• Whole floors or zones — this is where lease and energy decisions live. Peak versus average occupancy per zone tells you whether consolidation is realistic.
• Shared or specialist spaces — labs, lecture halls, quiet rooms. Universities in particular often discover their most contested spaces are underused at specific hours, which is a timetabling fix, not a construction project.

Desk-level measurement is valid, but treat it as phase two. It generates the most data and the most internal friction, so earn trust with zone-level wins first.

How accurate does the counting actually need to be?

More accurate than most people assume. If your data is off by 10%, a floor showing 60% peak utilization might actually be at 66% or 54% — and that range is exactly where "keep the floor" versus "release the floor" decisions sit. Vemco's AI sensors run at a contractual minimum of 96% accuracy, and in practice reach 98–99% when conditions such as lighting, layout and how people move through a space allow it. Be sceptical of any vendor quoting a flat guaranteed figure with no conditions attached; accuracy always depends on the environment.

One detail that matters more than most buyers realise: staff exclusion. In a public building or campus reception, security and service staff can pass a counting point dozens of times a day. Without exclusion, they inflate your figures and quietly corrupt every utilization ratio downstream.

Is this a privacy problem?

It doesn't have to be, but you need to handle it deliberately. Sensor-based counting measures presence, not identity — no names, no tracking of individuals across the building. That distinction is what makes deployment workable in universities and public institutions, where works councils, student bodies and data protection officers will (rightly) ask hard questions. The practical advice: involve your DPO before installation, not after, and communicate to occupants that the system counts people, it does not recognise them. Projects that skip this step end up relitigating it mid-rollout, which costs far more time than doing it upfront.

Can occupancy data actually reduce energy costs, or is that marketing?

It is real, but only if the data leaves the dashboard. A report showing that the third floor is empty after 15:00 saves nothing on its own. The saving comes when occupancy data feeds your building systems — which is what Vemco's VemFusion does, connecting live occupancy to HVAC, BMS and security so that ventilation, heating and lighting respond to actual presence rather than a fixed schedule. Offices, universities and public buildings running always-on systems for spaces that are empty half the week are paying to condition air nobody breathes. For analysis and planning — the "should we redesign this floor" questions — VemSpace handles the space utilization and facility optimization side. The two answer different questions, and knowing which one you need saves a lot of procurement confusion.

What about real-time use, not just historical reports?

Historical data drives strategy; real-time data drives operations. Real-time occupancy with alerts at a predefined limit is useful in more situations than people expect: capacity caps in lecture halls, safety limits in public buildings, or simply notifying cleaning teams which zones were actually used today so they stop cleaning untouched rooms. That last one is an unglamorous but consistently popular saving — activity-based cleaning routinely trims service contracts by a meaningful margin because you stop paying for work on spaces nobody entered.

How long until we have usable data?

Data starts flowing immediately after installation, but resist making decisions in the first weeks. Here's the practitioner's rule: collect at least one full business cycle — for offices that means several complete weeks including month-end; for universities, ideally a full teaching period, because week 3 of a semester looks nothing like exam weeks. The most common analytical mistake we see is averaging across periods that shouldn't be averaged. A lecture hall at 30% average utilization might be at 95% every Tuesday and Thursday morning — that is not an underused room, it is a scheduling constraint. Averages hide the story; peaks and patterns tell it.

What does a realistic business case look like?

Build it on three layers, in order of certainty. First, space cost avoidance: if measured peak utilization shows you can consolidate, the avoided rent or reclaimed square metres usually dwarfs the analytics investment. Second, operational savings: energy from occupancy-driven HVAC control and cleaning from activity-based scheduling. Third, the softer layer: better room availability, fewer complaints, data-backed answers when leadership asks about hybrid policy. Present the first layer as the financial case and treat the rest as upside — that framing survives scrutiny from a CFO.

If you're weighing where to start — meeting rooms, whole floors, or connecting occupancy data directly to your building systems — talk it through with people who have deployed this in offices, campuses and public buildings. Contact Vemco Group to discuss what a measurement plan for your specific portfolio should look like before you commit budget to sensors.