Space Management Software for CRE: Data-Driven Strategies to Optimize Occupancy

Core features of space management software powered by Butlr

Integrated solutions typically include real-time dashboards, zone-level analytics, and automation to translate occupancy data into operational actions.

• Real-time occupancy dashboards showing current and historical use.
• Zone-level analytics for rooms, desks, corridors, and common areas.
• AI-driven people counting and dwell-time analysis.
• Scheduling and desk hoteling optimization based on actual demand.
• Alerts and automation triggers for cleaning, HVAC, or security.
• Integration capabilities with BMS, workplace apps, and IWMS.

Common CRE use cases and benefits

Butlr-enabled space management supports objectives from right-sizing to tenant analytics and delivers measurable financial and operational benefits.

• Space right-sizing: identify underused areas to reduce leased footprint or repurpose space for revenue-generating uses.
• Hybrid workplace management: analyze in-office attendance to tailor desk allocations and meeting room provisioning.
• Energy optimization: use occupancy schedules to drive HVAC and lighting control, cutting utility bills.
• Operational efficiency: schedule cleaning and maintenance when spaces are actually occupied.
• Tenant analytics: provide dashboards or reports to show value and justify rent structures.

Benefits are measurable: reduced operating costs, increased utilization rates, and improved tenant satisfaction.

Implementation roadmap: from pilot to portfolio rollout

A practical phased approach helps validate results and scale successfully across a portfolio.

• Assess and prioritize: map key spaces and define goals, identify integration points (BMS, IWMS, CAFM).
• Pilot deployment: choose a representative floor or segment, install wired and wireless sensors, validate placement and calibration.
• Data validation and model tuning: run the system for several weeks to collect baseline data and adjust AI models to align counts with known events.
• Action planning: translate insights into changes such as desk reconfiguration or HVAC scheduling, set KPIs and reporting cadence.
• Scale and integrate: roll out across additional floors/buildings and integrate with desk-booking systems and facilities workflows.
• Continuous improvement: periodically review sensor coverage, data quality, and business outcomes and adjust thresholds and automation rules.

Deployment checklist

• Clearly defined objectives and KPIs
• Site survey for sensor placement
• Connectivity (Wi-Fi, LoRa, or wired backhaul)
• Integration plan for downstream systems
• Privacy and communications plan for tenants/staff
• Training for facilities and workplace teams

Metrics and KPIs to track

Track the metrics that drive decisions and ROI and link them to financial outcomes such as cost per square foot saved and payback period.

• Occupancy rate: average percentage of capacity occupied over time.
• Peak density: maximum people per zone at peak times for safety and ventilation planning.
• Utilization rate: proportion of spaces used relative to availability.
• Dwell time: average duration a person spends in a space.
• Turnover/churn: frequency of occupant changes to inform cleaning schedules.
• Energy per occupied hour: energy used normalized to occupancy.
• Space cost per user: real estate and operating cost divided by active occupants.

Link these metrics to financial outcomes (e.g., cost per square foot saved, payback period on sensor deployment).

Privacy, compliance, and tenant trust

Thermal sensing measures heat rather than images, making it inherently privacy-preserving; follow best practices to maintain trust and compliance.

• Publish a clear privacy policy describing data types collected and retention schedules.
• Anonymize and aggregate data before sharing with tenants.
• Limit access to raw sensor data and provide role-based dashboards.
• Follow local data protection regulations and consult legal counsel if needed.
• Communicate transparently to tenants and employees about what the sensors do and don’t collect.

These steps protect occupants and make adoption smoother across the portfolio.

Measuring ROI and common challenges

ROI drivers include reduced energy bills, lower cleaning costs, lease savings from right-sizing, and improved tenant retention; real-world programs often see payback within 12–24 months.

Estimating ROI

• Baseline current costs (energy, cleaning, unused lease area).
• Forecast savings from occupancy-driven changes (e.g., 10–30% cleaning cost reduction; 5–20% energy savings).
• Include sensor and analytics cost, installation, and integration and calculate payback period and three-year ROI.

Common challenges and mitigations

• Sensor blind spots or occlusion: conduct thorough site surveys and use multiple sensors for larger areas.
• Building materials affecting thermal readings: calibrate sensors and train AI with site-specific data.
• Integrating with legacy systems: prioritize APIs and middleware and phase integrations.
• Stakeholder resistance: run a small pilot, share quick wins, and communicate privacy and benefits clearly.

Conclusion — actionable next steps

To start optimizing occupancy with Butlr’s AI sensors, define one or two clear objectives, run a focused pilot, track core KPIs, translate insights into operational changes, and scale gradually while maintaining tenant communication and privacy safeguards.

• Define objectives (e.g., reduce energy costs, improve desk utilization).
• Run a focused pilot to validate assumptions.
• Track core KPIs and translate insights into operational changes.
• Scale gradually with clear communication and privacy safeguards.

Combined, space management software and anonymous heat-based sensing enable faster, smarter decisions that reduce cost, increase utilization, and improve tenant experience.