A utilization number has one dimension. The ways it misleads you have four. At least one of them is running in your portfolio right now.
Average utilization is 80 percent, peak is 90, and the dashboard says you have won. Standard reporting shows the same chart for all four failures. A percentage, next to a target, trending. That is why the recommendation is always the same no matter the cause. Cut footprint.
The signal reads high.
The demand is not there.
Ghosting. Bookings say yes. The sensor says no. Bookings data records intent and stops.
Camping. Space held past the need. Only a problem if someone needed it, which is why it resolves against unmet demand and not against itself.
The signal reads low.
The demand is there.
Hoarding. Badge says here. The desk says empty. Both are right. A jacket on the chair and the seat is gone. A scarcity behavior, so the vacancy is a symptom of the shortage it hides.
Unmet demand. The people who would use a room for real work if one were free. Utilization stops at 100 percent. Demand does not, and a dashboard cannot report what it never saw. This one survives headcount reduction. Take people out and the rooms are still gone.
The average is true.
The building is not.
Peak synchronization. Forty percent average. Tuesday at 11, over capacity, no room anywhere. Experience is set by the peak.
Pocket deficit. The same failure on the other axis. The floor reads fine and the reality is a hallway of vibrant stretches next to dead ones, empty cubes ten feet from a team with nowhere to sit. The supply and the demand are in the same building and never meet. Averaging across the floor erases it. People do not experience the average. They experience the pocket they are standing in.
The number is right.
The conclusion is not.
Low utilization trap. Rarely used reads as unnecessary. Some space is insurance. The question is not how often it is used. It is what happens the day it is needed and gone.
Fallacy of one. One person in a large room is a person using an empty room. Optimize against it and you get a program of closets.
Outputs
I turn the analysis into artifacts a planning team can act on. Each one below was built for a named client.
Behavioral personas, built from behavior rather than from workshops. Amazon.
Organizational segmentation. Microsoft.
Decision frameworks. Cruise.
Planning scenarios. Meta.
Impact analysis. Capital Group.
The three-way pull
Data, people, and leadership rarely want the same answer. The data says one thing. The people who use the building say another. Leadership has usually signaled what it would prefer to hear before the work starts. Most analysis fails here rather than at the math. It picks one of the three and calls it a finding. The work is holding all three in view and being explicit about which one the recommendation is asking to lose.
How engagements are shaped
Diagnosis
You have the data. You need to know what it means, and you need the answer to survive contact with finance.
Validation
You are about to commit capital against a number. Someone should check the number first.
Forecasting
You need to understand what happens next, not only what happened last quarter.
Transfer
Your in-house team is excellent at data and new to real estate. Several of the analysts now doing this work at large firms were trained by me, or trained by someone I trained.