The Evolution of People-Counting Technology for Space Management

people working in an office space

There are four people sitting in a room designed to hold fifty. Does it matter? Strategic Space Planners responsible for multi-building corporate and college campuses say yes. Leasing agreements and building maintenance are major budget items for large employers and universities. But how do Strategic Space planners validate building, floor, and room utilization?

Organizations started experimenting with manual clicker studies and sensor-based data for campus space planning, but that only got them so far. Today’s software-based solutions and spatial intelligence can harness predictive analytics that is scalable, cheaper, and faster to get started.


For many years, corporations have been utilizing clicker studies to gather employee data and occupancy analytics. They were used to shed light on where you need to increase collaborative spaces and the average number of people in a meeting room. The problem is that these manual studies become instantly outdated, capturing a moment in time not a true representative of use. 

Facilities leaders found that manual counts lack the precision required to meet the transformational needs of today’s office space. Also, because these analytics aren’t connected to other types of data such as meeting room scheduling tools, what you have is occupancy data without context. Context becomes a driver in terms of what to change in order to maximize your space. Manual clicker studies don’t provide facilities leaders the ability to track patterns or visualize their spaces and how it’s being used. 


Facilities leaders began looking to sensors to gather employee data and space utilization analytics. Sensors were able to provide granular data about how many people may have occupied space, such as a conference room. 

Sensors were able to do what clicker studies never could, which is to allow operation teams the ability to visualize their space in a format that could be easily understood. 

Although sensors appeared to be a better way to measure occupancy, they came with many challenges. Like all on-premise hardware solutions, sensors require installation and maintenance which drives costs up. And if you’re managing a sprawling campus, scaling a sensor-based occupancy strategy becomes costly quickly.


The emergence of the coronavirus resulted in people focusing on occupancy more than ever before. Overcrowded spaces soon became corporations’ biggest concern. Hot topics included occupancy monitoring and counting the number of people entering and exiting a building. 

Apps such as the Doorman app which was originally marketed towards security staff at clubs and bars can now be used for routine occupancy monitoring. Occupancy data is no longer just a security concern, but can be crucial when making leasing decisions and designing office spaces for employees.

Corporations had to reduce their office space or restructure their office spaces to accommodate a workforce that was not likely to return to a 5-day office. They had to get creative. This meant turning unused conference rooms into new common areas for employees to reduce vacant space.


The evolution of technology relied on more sophisticated views that leveraged historical data for predictive future usage. A new campus experience combined with a return to office plan meant Senior Leadership Teams were reconsidering why they had to build or expand. For example, The University of Iowa is converting residence hall lounges into dorm rooms as enrollment and occupancy spikes. 

This requires predictive analytics and software that can leverage existing infrastructures to collect space utilization information. Predictive analytics allows companies to understand patterns of life—how employees use office spaces over time—to inform major spatial decisions. The information is anonymized, so visual representations are provided without PII.  

“​​If CREs can focus on utilizing data to look at employee behaviors on those data insight platforms, more so than chasing the next shiny technology object that’s being thrown at them, then I think that can be really helpful because there’s such an abundance of technology” says Robert Teed, Founder and CCO of Integri Group, quoted in an Lambent Fireside Chat about the evolving role of CRE leaders

The Lambent Spaces platform allows users to uncover occupancy analytics and track patterns over time, relying on historical views to predict how a space will be used in the future.

Want to learn more about Lambent space analytics solution? You can visit our website or reach out directly to for a quick demo.

Alex Trotto contributes to the Blog and Social Media channels for Lambent. She is currently a Northeastern University student in her sophomore year.

Why DIY? Reasons to Buy or Build

people at office workspace

CIOs and other organizational decision-makers are looking past the so-called digital sprints of 2020 and planning on transformation technology efforts, according to industry analysts. But when it comes to transformational efforts, does it make sense to go it alone–that is, do it yourself?

Often, companies favor building software projects in-house, fearing that third-party solutions may not meet their specialized needs or be compatible with existing systems. The challenge is two fold: determining whether some of your IT staff has specialty skills, such as data mining or data visualization. And if you are lucky enough to have a large enough team to tackle a transformational technology, does it make sense to tie them up with one project?

When considering the age-old problem of whether to build or buy, you’ll get a different answer depending on the business landscape and project urgency. To determine whether to keep a software project in-house or search for a collaborative technology partner, it makes sense to look at the following four major factors: cost, control, connectivity, and maintenance. 


According to Gartner, global enterprise software spending is projected to climb 9% in 2021 to a total of around 4.2 trillion dollars. According to John-David Lovelock, research vice president at Gartner, “This means building technologies that don’t yet exist, and further differentiating their organization in an already crowded market”.  CEOs are much more willing to invest in technology that has a clear tie to business outcomes, and less so for everything else, according to Gartner.

The challenge is that IT projects tend to exceed both time and cost estimates. In short, IT teams often find themselves wishing they had found a reliable software or solutions partner.  While purchasing software from a third party can sometimes have a higher upfront cost, it’s also a known cost for a product that is ready to use immediately or by a set date. As one Forester report put it, technology implementations – even those “really easy” software-as-a-service based implementations – are no different than the DIY home improvement project gone totally awry with surprise time and cost requirements.


One of the biggest appeals of building in-house is that the software can be customized. On the other hand, that approach can leave a company entirely dependent on its coders and developers to deliver a perfect product. And companies are often left with unusable code bases created by developers who no longer work at the company, meaning they might need to hire new developers to rebuild code from scratch or maintain a legacy codebase. When you build, you have 100% control of the software’s function. This comes, however, with weaknesses, as it creates a burden on IT teams, and also leaves them without the benefit of collaboration with dedicated developers who are focused solely on the type of software they are deploying or coding.


Each company has its own unique ecosystem of applications that all need to be compatible with systems beyond the company. Building your own solutions should help ensure total compatibility, something that isn’t guaranteed with all third-party vendors. It’s important to assess if a third party has pre-built APIs or an open-source platform that allows for integrations, or a way to leverage your existing systems.


When you buy software, SaaS vendors handle all the maintenance behind the scenes and usually roll the costs onto a subscription fee. It’s important to understand that these external vendors have hundreds of hours of experience setting up and maintaining their software. If you choose to build, you will be responsible for all the maintenance of your new software: managing the launch, resolving any bugs, training people to use the software, setting up passwords, etc. All of this maintenance will require increased bandwidth, and possibly additional staff. 


The Lambent AI-powered platform provides facilities and security teams with an accurate, real-time understanding of how many, and how often people are utilizing different spaces. The software enables smarter decisions related to crowd density, space utilization, safety, maintenance, and guest experience while also providing easy access to valuable data trends for ROI related to space management.  If you’re unfamiliar with spatial utilization and the terms surrounding it, check out our blog on some key definitions. If you are considering installing a space utilization software. For more on how spatial representation software can bring density data to life, check out our blog on spatial representations.

Layering our Lambent software over existing infrastructure turns data into actionable intelligence. Our software monitors occupancy and provided predictive analytics for space planning. We’re helping transform corporate and higher education campuses into smart spaces.

To learn more about how Lambent helps facilities teams deliver on priorities like this, schedule time with one of our experts today.

Three Considerations For Creating Flexible Architecture

colleagues hanging out

Flexible architecture design allows buildings and spaces to evolve over time. As technology and the way we work shifts changes, the spaces we use need to keep up with changes or risk becoming obsolete. 

Facilities management, design, and architecture teams traditionally bucket flexible architecture into three categories: adaptability, transformability, and convertibility. But knowing when to adapt or entirely convert can be a guessing game without data-validated, full-venue transparency. 

Knowing when to adapt or entirely convert can be a guessing game without data-validated, full-venue transparency.


Adaptability is defined as the ability to change and evolve as needed. In a more spatial context, adaptability is a building’s ability to service a multitude of its occupant’s needs without altering the architecture. Some spatial adaptations are intuitive, a designer or office manager reconfigures desks to fit more seats. Room to grow has always equaled more space requirements. However, by adding capabilities to track room, floor, or area occupancy and harnessing machine learning tools, you can identify underutilized spaces which can inform and validate your redesign


Transformability, in a similar vein to adaptability, has to do with interior and exterior changes without the need for construction. Unlike adaptability, however, these changes have the potential to be permanent. Key components of a transformable structure include movability and responsiveness. Moveable objects, like fabric wall partitions, can be repositioned to better accommodate health guidelines, and increase efficiency. Responsive structures are able to react to external stimuli like the weather. Data and machine learning can help identify underused spaces and transform them into something useful. For example, your data may show a cafeteria is underused, prompting a redesign into a conference room in only a few steps.


Unlike its predecessors, convertibility involves constructing and altering the physical appearance of a building. Moreover, the changes are almost always permanent. This might include erecting a new building from a vacant lot on campus or converting a rooftop into a dining space. As these changes are costly and require heavy construction, validating decisions with dependable data will help stakeholders sign off on large-scale changes. 


When designing flexible workspaces, cross-departmental teams are envisioning not just the near future but the capacity for change. Full-venue transparency with data validation lets you see the spaces you manage in a whole new way. Space analytics software – which provides real-time occupancy data with historical overlays – helps you predict future utilization and design. 

Layering our Lambent software over existing infrastructure turns data into actionable intelligence. Our software enhances decision-making for the reallocation of existing spaces and the construction of new ones. Lambent Spaces will only aid in ensuring a given space are being used to its fullest potential.

To learn more about how Lambent helps facilities teams deliver on priorities like this, schedule time with one of our experts today.