The out-of-home advertising landscape is undergoing its most significant transformation in decades, moving away from the era of “buying boards and crossing fingers.” For years, the industry relied on static metrics that treated giant billboards as blunt instruments of reach, but the launch of MOVE 2.0 has fundamentally rewritten those rules. With a massive twenty-million-dollar investment and five years of rigorous development, the industry is transitioning from simply counting physical panels to a sophisticated understanding of how human beings navigate their world. This shift represents a leap into precision, where data analytics and mobility tracking allow planners to see exactly how audiences flow through urban centers and suburban neighborhoods. By integrating vast datasets and modeling synthetic populations, the sector is finally achieving a level of transparency and rigor that matches digital channels. This conversation explores how the movement from “screens to people” is changing the strategic core of media buying and what it means for the future of the physical advertising space.
How does shifting from counting physical billboard panels to tracking human mobility change the way you value specific ad spaces? What specific metrics are you now prioritizing to understand how various audiences move through their daily routines across different urban and suburban environments?
The shift essentially moves us from a world of counting objects to a world of understanding human life. In the past, we valued a billboard based on the road it sat next to, but now we are prioritizing the actual movement patterns of the people passing it, which changes the valuation of every single asset. We are looking at how audiences transition between different formats, from classic roadside billboards to digital screens in transit hubs or even small screens in local cafes. By focusing on mobility, we can see the rhythm of a person’s day, which allows us to value an ad space not just by the volume of traffic, but by the relevance of the moment. It gives agencies a newfound confidence that their investment is reaching the right person at a time when they are most likely to engage with the message.
With hundreds of billions of rows of data used to model synthetic populations, how do you validate these simulations against real-world behavior? What steps are involved in layering public transport taps and census data to ensure regional and seasonal accuracy for advertisers?
Validating a model built on roughly six hundred billion rows of data requires an incredible layering process to ensure that the “synthetic Australians” we model actually behave like real people. We start with the foundational data from the Australian Bureau of Statistics to establish a demographic baseline and then weave in actual public transport tap data to see how people move through transit systems in real-time. This layered approach allows us to account for regional nuances and, for the first time, seasonal shifts that drastically alter audience density. For example, we can now track how a seaside town’s audience swells during the summer holidays compared to a quiet winter Tuesday, giving planners the ability to test and learn with high-fidelity data. It’s about creating a living map of the population that reflects the ebb and flow of real life rather than a static snapshot.
Now that measurement covers digital screens in niche locations like gyms and doctor surgeries, how do you weigh the impact of these high-dwell environments against traditional roadside billboards? What nuances in audience interaction are you seeing during these specific daily touchpoints?
The inclusion of high-dwell environments like gyms, cafes, and doctor surgeries changes the strategic weight we give to “dwell time” versus “glance reach.” While a massive roadside billboard offers undeniable impact and scale, a screen in a doctor’s surgery captures an audience in a high-attention, low-distraction state where they might stay for twenty minutes or more. We are seeing that these niche locations provide a unique intimacy, allowing for more complex messaging that wouldn’t work on a highway where a driver has only a few seconds to look. This allows us to build campaigns that follow a consumer’s journey, using roadside for brand fame and gym or cafe screens for deeper engagement. The nuance lies in recognizing that a person’s mindset changes depending on their environment, and our measurement now finally reflects that psychological shift.
Investing $20 million into a unified measurement system requires significant industry-wide collaboration. How did competing providers align their interests to build this platform, and what does this level of transparency mean for the long-term credibility and rigor of the out-of-home advertising sector?
It is a rare and impressive feat to see fierce competitors in a crowded market decide to pool their resources for five years to build a single, unified system. The providers realized that for the out-of-home sector to grow, they needed to stop arguing about whose data was better and instead agree on a transparent, standardized language for the entire industry. This twenty-million-dollar investment creates a “gold standard” that eliminates the “black box” nature of proprietary metrics, which historically made some planners skeptical of the medium. By being transparent about how we track mobility and reach, we are bringing a level of rigor to the channel that matches the most sophisticated digital platforms. This collective move doesn’t just benefit the media owners; it provides the transparency and accountability that modern brands demand before they commit significant portions of their budget.
Agencies are now integrating massive mobility datasets directly into their own proprietary planning tools. How does this shift campaign strategy away from simple location-based buying, and what kind of specific business outcomes can clients expect from this more granular, data-driven approach?
Agencies are no longer just picking dots on a map; they are plugging this massive mobility data directly into their internal systems to drive specific business outcomes like foot traffic or digital conversions. This shift moves us away from buying a “location” and toward buying an “audience behavior,” which allows for much more granular targeting. Clients can expect to see less waste in their spend because we can now identify the specific times of day and formats that align with their target demographic’s actual routine. For instance, instead of buying every board in a suburb, a brand might only buy screens along the specific commute path of high-income professionals. This level of precision fundamentally changes the conversation from “how many people saw this?” to “how did this movement-driven strategy influence our bottom line?”
What is your forecast for the out-of-home advertising industry?
I forecast that the out-of-home industry will move from being a secondary “support” channel to becoming the primary driver of integrated brand campaigns. As we move away from the “blunt instrument” era, the ability to layer mobility data with real-time digital triggers will lead to a surge in creative experimentation. We will see brands using these six hundred billion rows of data to create hyper-local, time-sensitive advertisements that feel personal rather than intrusive. Because we can now measure regional and seasonal shifts with such accuracy, I expect to see a significant increase in ad spend outside of major metropolitan hubs. Ultimately, the industry will achieve a level of trust and sophistication that allows it to compete directly with online search and social media for performance-based marketing budgets.
