Analytics + Intelligence / Study 04
Why does the office feel full when average use is low?
Look beneath the office-wide average to find local pressure, explain the limits of the data, and identify what to test.
Illustrative study · fictional scenario and sample data. This demonstrates a consulting approach. It is not a completed client engagement or an actual ITO result.
The brief
People experience the space they can actually use.
A fictional 120-workpoint office appears lightly used overall. Yet one neighborhood reaches capacity while dozens of seats remain available elsewhere. Leadership wants to understand the discrepancy before deciding whether the office has too much or too little space.
The office contains three 40-workpoint neighborhoods. Its hypothetical roster assigns 64 employees to East, 40 to West, and 40 to North. A modeled access rule asks people to use their assigned neighborhood unless they obtain approval to work elsewhere.
What the data represent
Five synthetic observations of simultaneous seated workpoint use at 11:00, one on each weekday. Each occupied primary workpoint counts once. Empty desks holding belongings, meeting rooms, visitors, and people elsewhere in the building are outside this measure.
What they do not represent
These five snapshots are explanatory, not representative daily averages or a reliable estimate of utilization across an entire week.
Executive readout
An allocation problem can hide inside spare capacity.
The office averages 52 occupied workpoints across the observations. East is full on Tuesday and 95% occupied on Thursday. The restriction on sharing makes the distribution of use as important as the total.
Show the distribution
One office average. Three different experiences.
| Neighborhood / capacity | Mon | Tue | Wed | Thu | Fri |
|---|---|---|---|---|---|
| East / 40 | 2050% used | 40100% used | 1640% used | 3895% used | 820% used |
| West / 40 | 820% used | 1845% used | 1025% used | 2050% used | 615% used |
| North / 40 | 1230% used | 2050% used | 1435% used | 2255% used | 820% used |
| Office-wide measure | Mon | Tue | Wed | Thu | Fri |
|---|---|---|---|---|---|
| Occupied workpoints | 40 | 78 | 40 | 80 | 22 |
| Share of 120 workpoints | 33.3% | 65.0% | 33.3% | 66.7% | 18.3% |
Tuesday: full locally, spare elsewhere.
East uses all 40 seats, while West and North together leave 42 unused. On Thursday, East uses 38 seats, while the other neighborhoods leave 38 unused.
The missing question is suitability.
The sample establishes uneven use and local saturation. It does not show whether alternative seats were suitable, how many people failed to find a seat, or how long the pattern lasted. Use at capacity can conceal additional demand without quantifying it.
Measures with a purpose
Define the denominator before making the claim.
| Measure | Calculation | Result |
|---|---|---|
| Mean observed office use | 260 occupied-workpoint observations ÷ 5 snapshots | 52 workpoints |
| Mean observed office utilization | 260 ÷ (120 workpoints × 5 snapshots) | 43.3% |
| Highest observed office utilization | 80 ÷ 120 workpoints | 66.7% |
| East: mean observed use | 122 ÷ (40 workpoints × 5 snapshots) | 61.0% |
| West: mean observed use | 62 ÷ (40 workpoints × 5 snapshots) | 31.0% |
| North: mean observed use | 76 ÷ (40 workpoints × 5 snapshots) | 38.0% |
| East observations at or above 95% | 2 ÷ 5 snapshots | 40% of observations, not working hours |
| East assignment ratio | 64 assigned employees ÷ 40 workpoints | 1.60 assigned employees per workpoint |
Keep presence, assignment, and seat use distinct.
Assigned headcount describes the roster allocated to a workplace. Concurrent workpoint use describes people using primary workpoints at one time. Neither gives a complete view of all people present in the office.
A count of badge entries records arrivals over an interval. On its own, it cannot replace a simultaneous seated-use count. Booking data describe reservations and should be checked against actual use.
The recommendation
Test access to existing capacity before changing the footprint.
Illustrative recommendation
Validate the pattern, then pilot shared overflow access.
Collect a broader baseline before making a footprint decision. If the pattern holds and suitable workpoints are available, test 12 shared overflow workpoints: six in West and six in North, with clear access and release rules.
This changes booking eligibility; it does not guarantee 12 empty seats. Compare local saturation, unsuccessful seat searches, overflow uptake, and availability for West and North. Any improvement would need to be measured during the pilot.
The five explanatory snapshots do not support a definitive decision to reduce the office or the wider property portfolio.
- 01
Build a representative baseline
Workplace analytics + facilities
For this proposed study, collect concurrent observations across several times of day for six to eight ordinary working weeks, including a known busy cycle. Agree coverage before starting.
- 02
Check why seats are not interchangeable
Planning + local teams
Confirm usable inventory, access rules, equipment, accessibility, adjacencies, reservation releases, and dedicated-seat requirements.
- 03
Test the access change
Facilities + team representatives
Pilot the 12-workpoint overflow pool, record unsuccessful searches separately, and check the effect on each neighborhood.
- 04
Review the options
Leadership + workplace team
If sharing is impractical, compare neighborhood reassignment and coordinated team schedules before considering physical changes.
What the engagement could provide
A practical package for the next decision.
- Data definitions and quality review
- Neighborhood heatmap and observation summaries
- Clearly defined workplace measures
- Executive findings with evidence limits
- Pilot scope, monitoring measures, and decision criteria
Your question. Your context.
Put this thinking to work for your team.
Start with the decision you need to make. Michael can help define the right scope, information, and deliverables.
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