Cracking the Code of Office Behavior: Introducing the Usage Fingerprint

In today’s workplaces, one of the most difficult questions to answer is how employees actually use office space. Traditional planning methods, like consultants’ rules of thumb, historical ratios, or static spreadsheets, fall short in an environment where employee behavior shifts on a weekly basis. That’s where Usage Fingerprints come in.

A Usage Fingerprint is a behavioral profile that predicts how employees will interact with your office. Instead of relying on assumptions, it’s powered by VergeSense’s proprietary large spatial AI model trained on over 200M+ square feet of occupancy data, the largest dataset of its kind. A Usage Fingerprint helps you accurately predict how employees will use space at a given point in time, down to the space type, based only on your floor plan information, and the number of people on the floor at any given time. This gives workplace leaders a data-backed way to plan space that balances employee experience with efficiency.

What is a Usage Fingerprint?

Think of a Usage Fingerprint as an office’s behavioral DNA. It predicts and understands  how different types of work happen in your space, from solo focus work to team collaboration, and how demand for each type of space changes as the building fills up.

“At the end of the day, we’re trying to model how space is being used, " said VergeSense CTO and Co-Founder, Kelby Green. “When one person arrives, where do they go first? What fills up next? And what runs out first? That’s what the Usage Fingerprint shows you: it’s the engine behind predicting how offices really work.”

Each fingerprint is built on four key inputs:

• Person Distribution – What percentage of employees use different types of spaces (e.g., desks, meeting rooms, phone booths) as the office fills.
  
  
• Typical Gathering Size – How many people typically use a room compared to its intended capacity (e.g., a 10-person room often hosting just 3).
  
  
• Load-Scaling Factor – How behavior changes as occupancy rises (e.g., single-person “camping” in conference rooms drops as floors fill).
  
  
• Space Popularity – The order in which rooms get claimed first, reflecting employee preferences.

Together, these metrics give workplace teams a realistic simulation of how people flow into and use an office, revealing the “hidden bottlenecks” that static headcount-to-seat ratios can’t capture.

Precision Tiers

Usage Fingerprints have different levels of precision, depending on the data you have available. At the highest tier, fingerprints generated directly from measured occupancy data (pulled from VergeSense sensors or WiFi data), give you the most accurate representation of how people are using space. 

When that level of precise measurement  isn’t possible, the model draws from VergeSense’s benchmark dataset of more than 200 million square feet to simulate usage behavior with great accuracy.

And then there’s the baseline most companies still rely on today: static  ratios provided by consultants and rules of thumb. These aren’t data-backed at all, they’re outdated shortcuts that weren’t built to take into account  hybrid patterns, and the end result is costly: masking wasted space.

The result is that no matter where you start—fully measured or just beginning to collect data—it’s now possible to build a Usage Fingerprint that’s smarter, more transparent, and more defensible than anything traditional planning methods can deliver. Instead of relying on static ratios, you can start with a data-backed hypothesis, like an 80/20 collaboration-to-focus mix, and then test, validate, and refine it based on what the data actually shows at each site.

Why it Matters

Most planning tools assume every seat is equal, or that spaces will always be used exactly as intended. Reality is far messier: desks may sit empty while all the conference rooms are full, small meetings spill into oversized rooms, and employees camp in phone booths meant for calls.

The difference comes when you ground decisions in data. With a Usage Fingerprint, workplace teams can see not just the averages but the nuances of each site—whether one office thrives on an 80/20 collaboration-to-focus mix while another skews closer to 90/10, or whether a 60% simultaneous-use threshold works better as an early warning flag than the standard 80%. That level of visibility allows leaders to right-size floorplans, adjust space types, and design spaces that employees actually want to use.

“Planning used to be about best guesses,” said VP of Product, Kanav Dhir. “Like assuming a conference room supports 0.25 people per seat. With Usage Fingerprints, those assumptions are no longer arbitrary. You can trace every number back to real behavior and defend your planning decisions with confidence.”

He also emphasized that the power of Usage Fingerprints isn’t just in accuracy, but in transparency. 

“The most exciting thing is that fingerprints aren’t a black box. You can see exactly why the model outputs what it does and decide if it reflects your people, or if you want to evolve it.”

Looking Ahead

And this is just the beginning. As VergeSense expands its predictive planning suite, leaders will soon be able to model not just supply but also demand—tying real-world attendance policies and behaviors into a unified framework. For workplace and real estate teams, that means finally moving beyond static rules and into dynamic, data-driven planning that evolves as quickly as your people do.