Case Study: Office Occupancy Sensor Cuts Costs & Preserves Privacy

The solution

Core features included

• Anonymous presence detection — counts and motion detection without capturing images or personal identifiers.
• High sensitivity to small groups and single occupants, enabling desk- and room-level insights.
• Integration with building management systems (BMS) for automated HVAC and lighting control.
• Real-time dashboards and historical reports for space planning and cost analysis.

Benefits the organization targeted

• Reduce energy use by matching HVAC and lighting to actual occupancy.
• Improve space allocation and reduce wasted desk and meeting-room inventory.
• Preserve employee privacy to maintain trust and comply with internal policies.

Implementation approach

• Baseline audit

  Measured existing energy usage patterns, desk and room reservation data, and cleaning schedules to establish a baseline.

• Pilot phase (4–8 weeks)

  Installed sensors in a representative subset of floors: open-plan areas, a mix of meeting rooms, and a lunchroom. Connected sensors to an analytics platform and integrated select automation rules with the BMS.

• Calibration and validation

  Compared sensor counts to manual spot checks and reservation logs to validate accuracy. Tuned sensitivity to minimize false positives and false negatives.

• Policy and communications

  Shared clear privacy documentation with staff describing camera-free sensing and anonymized data use and offered Q&A sessions to address concerns.

• Scale-up and automation

  Extended sensors across the building, implemented automated HVAC setback rules, and adjusted cleaning schedules to usage.

• Continuous monitoring

  Set KPIs and a cadence for reviewing energy savings, utilization, and anomalies.

Results (typical outcomes)

After full roll-out, the organization observed the following within 3–6 months:

• Energy reduction: HVAC and lighting runtime decreased by 15–30% depending on zone and baseline schedules.
• Space utilization: Average meeting-room occupancy increased, while desk utilization revealed opportunities to consolidate seating and repurpose underused areas.
• Cleaning efficiency: Custodial routes were optimized to focus on high-traffic areas, reducing cleaning hours and supplies cost.
• Cost savings and payback: Combined operational savings and deferred real-estate costs produced a payback period often under 24 months for typical mid-sized deployments.
• Employee trust: Transparent communications and camera-free technology reduced privacy complaints and increased acceptance of the system.

Note: Actual savings depend on baseline inefficiencies, local energy costs, and the extent of automation.

Savings breakdown (where reductions come from)

• HVAC and chilled water: Reduced runtime during low-occupancy periods; targeted zone control avoids conditioning empty spaces.
• Lighting: Occupancy-driven lighting control and daylight harvesting integration.
• Janitorial and supplies: Move from fixed schedules to usage-based cleaning.
• Real estate and space costs: Improved utilization can lead to reduced need for leased space or enable repurposing.
• Labor efficiency: Facility staff redeployed from repetitive tasks to higher-value activities.

Privacy and compliance

Preserving privacy was a key requirement. Best practices the team followed:

• Use camera-free, thermal sensors that detect heat patterns but do not produce images.
• Anonymize data at the source: avoid storing personally identifiable information and refrain from occupant tracking across time.
• Limit data retention and publish retention policies.
• Publish clear, accessible privacy statements and FAQs for employees.
• Engage legal and HR early to align the deployment with internal policies and applicable regulations.
• Provide opt-out mechanisms or physical signage where required.

These measures increased user trust and reduced resistance to the sensors.

Lessons learned and best practices

• Start with a pilot that covers representative spaces to validate accuracy and refine automation rules.
• Integrate occupancy data with facility systems incrementally and begin with conservative automation.
• Use data to inform policy changes, for example shifting from fixed cleaning schedules to needs-based cleaning.
• Focus on communication: explain the technology, data uses, and privacy safeguards before installation.
• Monitor and iterate: occupancy patterns change with seasons and organizational rhythms.
• Combine occupancy data with calendar and reservation systems (anonymized) to reconcile differences and improve room allocation policies.

Conclusion

A privacy-first office occupancy sensing approach can deliver meaningful cost savings and space optimization while maintaining employee trust. By using thermal, camera-free sensors and treating the deployment as a change-management program — starting small, validating results, communicating transparently, and scaling thoughtfully — organizations can reduce energy and operational costs, get more value from existing real estate, and preserve a respectful workplace culture.

If your organization is wrestling with underutilized space, rising facility costs, or privacy concerns about monitoring, consider a staged pilot of camera-free occupancy sensors tied to measurable KPIs. The results in this case study illustrate how careful selection, clear communication, and disciplined measurement can produce both financial and cultural benefits.