Booking data is the most abundant and most misread evidence in the workplace. Read naively, it reports demand; read properly, it reports behaviour under the current rules — hoarding under scarcity anxiety, padding under meeting culture, no-shows under zero-cost reservation. Analysed against presence, it becomes a continuous diagnostic: where friction is building, which settings the booking system misallocates, and where policy artefacts masquerade as demand.
- Continuously, with periodic structured analysis
- When scarcity complaints rise — the analysis distinguishes real scarcity from allocation failure
- Before capacity or ratio revisions: booking patterns test whether pressure is demand or behaviour
- After rule changes, to measure the behavioural response
- Analyst running the analysis
- FM as owner of booking rules and primary consumer of findings
- Strategist interpreting against the demand model
- Privacy governance: booking data is person-linked; report in aggregate above cohort thresholds
- Reservation data: bookings, durations, lead times, cancellations, no-shows, recurrences, by setting type
- Presence evidence for the booked-versus-used triangulation
- Current booking rules: rights, horizons, auto-release, recurrence permissions
- Sharing ratios and overflow protocols as designed
- Establish the baseline per setting type: booking rates, realisation rates, durations, lead times, recurrence loads.
- Run the signature analyses: hoarding (recurring blocks, low realisation), padding (booked exceeds used), ghost demand (high booking, high no-show), avoidance (persistently unbooked), squeeze (short-lead bookings failing — genuine scarcity).
- Separate policy artefacts from demand: test whether the rules produce the pattern before reading it as demand — no-show rates follow auto-release settings; hoarding follows recurrence rights.
- Triangulate with presence: 90% booked at 55% present is an allocation problem in a capacity costume.
- Locate friction by population, setting and time: the squeeze map is the overflow protocol's early warning.
- Model rule interventions before capacity interventions — rules are cheaper than construction.
- Recommend layered: rules first, norms second, capacity last.
- The scarcity spiral: perceived shortage → defensive booking → real shortage — broken by rules, fed by capacity
- Realisation collapse in specific teams: local friction or exemption culture
- Recurrence concentration: a small population holding prime capacity
- Avoided-but-bookable settings confirming a kit failure
- Reading booking behaviour as preference when it is adaptation to rules
- Behavioural signature analysis per setting type
- Policy-artefact register
- Friction and squeeze map
- Layered intervention recommendations
- Continuous indicators for the monitoring framework
Rule findings go to FM; norm findings to change via the Workplace Behaviour Review; capacity findings to the Spatial Performance Review and, where assumptions breach, the register. The squeeze map operationalises the Sharing Ratio's overflow protocol.
The platform computes signatures; judgement assigns causes and chooses intervention layers. Whether hoarding reflects scarcity anxiety, status or genuine continuous need — and whether the response is a rule, a conversation or capacity — are interpretive and political calls.
- Reporting booking rates as utilisation — the original sin of workplace analytics
- Adding capacity to an allocation problem, feeding the spiral
- Changing rules without measuring the response
- Reading individual booking behaviour — a privacy and analytical failure
- Ignoring avoided settings because complaints concentrate on squeezed ones
An insurer's meeting-room crisis showed 94% booking, 51% realisation. Analysis found recurrence hoarding: 12% of staff held 61% of prime slots at 30% realisation. The intervention was a rule: recurrence capped with realisation-based renewal, auto-release at ten minutes. Squeeze incidents fell 70% in eight weeks; the €400k business case for six new rooms was archived.