Occupancy Intelligence with WiFi Data explores how existing WiFi infrastructure can be leveraged to estimate how many people are present in a building, floor, or neighborhood at any given time. Instead of requiring new hardware installations, VergeSense ingests WiFi data from supported providers to deliver occupancy insights at scale. This makes it easier to monitor usage trends across a portfolio, identify underutilized spaces, and make data-driven workplace strategy decisions—especially in areas where installing sensors may not be practical or cost-effective.

VergeSense uses people count trends to help workplace teams with:

• Portfolio right-sizing
• Neighborhood planning
• Workplace policy evaluation

WiFi Occupancy data is especially valuable in spaces not covered by sensors, enabling broader coverage without expanding hardware investments.

Supported Infrastructure and Requirements

VergeSense currently supports the following WiFi vendors and licenses:

• Cisco Spaces (Act and Extend Licenses)
• Cisco Meraki
• HPE Aruba Networks

To use the WiFi Occupancy service, customers must have an active integration with one of the above systems. Juniper Mist integration is planned for release before the end of 2025.

Minimum Requirements:

• At least three access points (APs) per floor for neighborhood-level data
• Neighborhoods must be a minimum of 5,000 square feet
• For Cisco Meraki: Active floorplans with APs mapped in the Meraki dashboard
• For Cisco Spaces: Mapped floorplans required only for neighborhood-level data (not for floor-level)

Accuracy and Granularity Overview

WiFi data provides 80–85% accuracy in estimating occupancy. This makes it a great option when:

• Cost and scalability are higher priorities than pinpoint accuracy
• You need building- or floor-level data rather than detailed person-level metrics

Considerations:

• Duplicate devices and non-WiFi users can cause margin of error. VergeSense automatically deduplicates devices based on user authentication to count only one device per person
• As you zoom in (e.g., to neighborhoods), accuracy may decline due to variance in access point layout and strength

Use Cases: Portfolio Right-Sizing vs Neighborhood Planning

WiFi Occupancy data supports different types of strategic decision-making:

Portfolio Right-Sizing

Use building and floor-level occupancy trends to:

• Identify consistently underused space
• Eliminate excess capacity
• Justify expansion decisions

Neighborhood Planning

Use large-zone (5,000+ sq ft) neighborhood-level data to:

• Balance space allocation based on team needs
• Respond to evolving work patterns
• Optimize workplace layout

For high-granularity use cases, VergeSense recommends using Area Sensors for better precision at the room or small-zone level.

Updated June 16, 2025 21:49