Using Space Analytics to Delay New Leases: A Butlr Guide

Step-by-step guide to delaying a new lease using space analytics

1. Start with instrumentation and baseline collection

Install privacy-first sensors across representative locations (office floors, common areas, conference rooms, amenities, and entry/circulation zones) and collect at least 8–12 weeks of data to establish baseline occupancy patterns across weekdays, weekends, and special events. Longer collections help account for variability.

2. Define the KPIs that matter

Choose measurable indicators tied to leasing decisions to make trade-offs visible and provide negotiation leverage with landlords.

• Peak utilization (per floor/zone) and peak hour windows.
• Average daily occupancy and utilization rate by space type.
• Percentage of time meeting rooms and desks are empty during core hours.
• Demand density during busiest hours (people per usable seat).
• Overtime or overflow incidents when demand exceeds available seats.

3. Identify opportunities to reclaim or repurpose space

Analyze data to find underused assets and prioritize low-disruption, modest-capex options.

• Permanently low-utilization floors or wings.
• Meeting rooms frequently reserved but unoccupied (no-shows).
• Day-to-day desks with recurring low occupancy that can be consolidated.
• Amenities that attract little usage but consume rentable area.

4. Model scenarios to test delay feasibility

Use historical patterns to model near-term demand scenarios, conservatively assume modest growth and peak surges, model redistribution strategies (consolidating teams, rotating desk assignments, relocating departments), and estimate the maximum delay your portfolio can sustain without crowding during peak periods.

5. Implement operational interventions

Combine policy changes and small investments to unlock capacity and balance loads.

• Desk hoteling and reservation rules to reduce unused assigned seating.
• Meeting room booking policies and reminders to reduce no-shows.
• Flexible schedules and staggered core hours to spread peak demand.
• Signage, wayfinding, and amenities repositioning.
• Minor fit-out changes: convert seldom-used rooms into collaborative zones.

6. Pilot and validate

Run pilots on a few floors or teams, measure pre- and post-intervention KPIs, collect employee feedback, iterate quickly, and scale evidence-driven adjustments.

7. Use data in lease negotiations and planning

Present utilization trends, modeled scenarios, and pilot results to landlords and finance teams to propose short-term extensions, phased occupancy, demand-triggered terms, or sublease options.

8. Monitor, report, and iterate

Maintain continuous monitoring with automated alerts, weekly/monthly dashboards, and quarterly reassessment of the lease timeline so space analytics becomes ongoing portfolio management.

Key KPIs to track regularly

Track granular hourly metrics and roll them up to weekly/monthly trends to distinguish structural change from noise.

• Building- and floor-level peak utilization.
• Average utilization rate per desk and meeting room.
• Percentage of reserved rooms with occupancy <50% (no-shows).
• Time-based distribution of arrivals and departures.
• Overflow events and capacity breaches during peaks.

Common pitfalls and how to avoid them

Avoid common mistakes that can undermine analytics-driven decisions.

• Relying on short-term data: use multi-week baselines to avoid seasonal or event-driven misinterpretation.
• Ignoring employee experience: communicate clearly and include conveniences to prevent morale erosion.
• Over-optimizing for cost: beware of consolidation that reduces collaboration and productivity.
• DIY analytics gaps: choose robust, privacy-preserving sensors and analytics to ensure accurate decisions.

Calculating ROI and the decision threshold

Estimate financial impact by comparing the cost of an immediate new lease (rent, fit-out, operating costs) versus the cost of operational interventions (software, minor fit-outs, change management) plus an incremental risk premium for potential crowding. Define a decision threshold such as: delay the lease if analytics show capacity can be maintained for X months with less than Y% increase in peak density and projected savings exceed costs by your target payback period.

Governance and privacy considerations

Adopt clear policies to protect privacy and build trust while enabling analytics-driven decisions.

• Use anonymous, non-identifying sensing to protect privacy and comply with regulations.
• Share aggregated metrics, not individual movement data.
• Communicate sensor purpose and data use to employees to build trust.

Checklist to get started

• Deploy privacy-first sensors in representative areas.
• Collect 8–12 weeks of baseline data.
• Define KPIs tied to lease cost and employee experience.
• Run small pilots for consolidation and hoteling.
• Model scenarios showing months of delay feasible under several demand assumptions.
• Use results in lease negotiations and update forecasts quarterly.

Final recommendations

Use space analytics as a continuous capability, not a one-off study. Instrument space, define clear KPIs, pilot operational changes, and model scenarios so decisions to delay new leases are evidence-based. A privacy-first people sensing platform provides objective, defensible data to maintain workplace effectiveness while buying time and savings. Start small, validate quickly, and scale interventions that produce measurable capacity gains. Butlr’s anonymous, thermal, camera-free sensing is designed to provide occupancy intelligence while respecting privacy.