The biggest mistake brands make when approaching the FIFA World Cup is treating it as a single media event for local marketing.

In reality, the tournament is thousands of simultaneous local moments unfolding across cities, neighborhoods, bars, restaurants, transit hubs, streaming platforms, and cultural communities. For agencies, media companies, and multi-location brands, that distinction may determine whether World Cup investments drive meaningful engagement or simply generate impressions.

While the World Cup remains one of the largest global events in media, audience behavior has become increasingly fragmented. Fans no longer gather around a single screen, platform, or viewing experience. Instead, they move fluidly between connected TV, mobile devices, public watch parties, sports bars, restaurants, and social platforms.

FAST channel Tubi’s dedicated World Cup hub is a reminder of how much viewing behavior has evolved. Live matches, highlights, creator content, and commentary are increasingly consumed across multiple environments rather than through a single broadcast experience.

For marketers, that means the World Cup should no longer be viewed as one audience.

The World Cup Audience Doesn’t Exist

“The World Cup is not one audience. It is thousands of fragmented moments happening simultaneously across different communities and environments.” That observation from Luba Giglia, COO of AdOmni, may be one of the most important lessons for marketers preparing for major cultural events.

A Brazil match in Miami creates a different environment than a Mexico match in Los Angeles. An Argentina match in New York creates different audience dynamics than an England match in Chicago. Language, culture, community identity, geography, and viewing location all influence how fans engage with the tournament.

The implication for brands is significant. National reach still matters, but performance increasingly depends on local relevance.

A restaurant chain may find success activating around sports bars in neighborhoods with concentrated fan communities. A retailer may benefit from aligning promotions with local match schedules and audience behaviors. A QSR brand may see stronger performance from dynamic creative tied to specific teams and regional fan activity rather than a single national campaign.

To better understand what this means for advertisers, Street Fight spoke with Giglia about how brands should rethink World Cup planning around local audience behavior rather than national scale.

From Global Reach to Local Relevance

With platforms like Tubi centralizing World Cup content while viewing behavior becomes more fragmented, how should brands rethink media planning to align with real-time, local audience moments rather than national scale?

The biggest shift brands should make is moving away from thinking about the World Cup as one audience experiencing one moment at the same time. Even if streaming platforms centralize access to matches, fan behavior is still highly fragmented.

People watch differently depending on market, cultural affiliation, time zone, language, and where they gather physically. For example, a Brazil match in Miami creates a different environment than a Mexico match in Los Angeles or an England match in New York.

Scale Without Relevance Doesn’t Win

What are the biggest mistakes brands make when approaching global tentpole events like the World Cup, and how can a more localized, data-driven strategy improve both relevance and performance?

The biggest mistake is overinvesting in scale while underinvesting in relevance.  Too many brands still approach global events with a mass-market mindset: one campaign, one creative direction, one static media plan designed primarily for visibility. The World Cup is not one audience. It is thousands of fragmented moments happening simultaneously across different communities and environments.

When brands ignore those differences, scale can actually work against them. Scale creates visibility, but relevance drives performance. A more localized, data-driven strategy allows brands to align messaging with where fans are gathering, how they are moving, and what emotional context they are experiencing in real time.

It also improves measurement. Advertisers increasingly want to understand how campaigns influence store visits, site traffic, app engagement, conversions, and brand lift across specific markets and audience segments.

The strongest World Cup strategies connect global scale to local execution and then measure performance at the level where consumer behavior is actually happening.

The Opportunity Lives at the Neighborhood Level

World Cup can be described as “thousands of local moments.” What does that look like in practice when it comes to activating campaigns at the neighborhood or venue level?

In practice, it means recognizing that fan energy is concentrated in very specific places and communities, not necessarily evenly distributed across the country. For one match, that could mean activating around bars and restaurant districts in heavily Colombian neighborhoods in Queens. For another, it may involve transit corridors near public watch parties in Los Angeles or entertainment districts in Miami where fans gather before kickoff.

This is where location intelligence and programmatic DOOH become especially valuable. Brands that understand where fan attention is physically concentrating gain an advantage over those relying solely on national audience forecasts.

DOOH allows advertisers to align messaging with real-world behavior in real time. Creative can shift based on matchup dynamics, local fan energy, time of day, or even what just happened in the match itself.

The World Cup may be global in scale, but fan engagement is happening block by block.

Why Cultural Intelligence Matters

What role does cultural nuance play in localizing World Cup campaigns, especially in diverse U.S. markets where fandom varies widely by community and language?

Cultural nuance is critical because World Cup fandom is driven far more by identity and allegiance than by traditional demographic categories alone.

Demographics can tell you someone’s age or income bracket. Cultural intelligence helps brands understand behavior, language preferences, viewing habits, and where communities gather during the tournament. That distinction matters because the World Cup is deeply tied to national pride and shared cultural experience. Messaging that resonates with one community may feel completely disconnected in another market.

Relevance comes from understanding the audience, not simply maximizing reach.

Connecting Real-Time Moments to Real-World Outcomes

As free streaming options like Tubi expand access to live matches, how does that shift the balance between in-home and out-of-home viewing—and what does that mean for advertisers?

Free streaming expands access, but it does not eliminate communal viewing behavior. In many ways, it amplifies it.

As access barriers drop, viewing behavior becomes more fluid. Fans may start watching at home, continue following matches on mobile while commuting, and then gather in bars or public spaces for key moments later in the tournament.

For marketers, this creates opportunities to connect connected TV, mobile, social, retail media, and out-of-home channels around the same audience journey.

Building Newsroom-Style Marketing

How can brands better integrate real-time triggers such as match events, upsets, or player moments into localized campaigns without losing consistency at scale?

The brands that perform best operate with newsroom-style responsiveness. They prepare systems that allow creative, messaging, and targeting to evolve as the tournament evolves. Brands can develop templated creative that adapts dynamically to regional team momentum, match outcomes, weather conditions, time of day, or live fan sentiment while still maintaining brand consistency.

Consistency comes from maintaining a clear strategy while adapting messaging to the moment.

The Bottom Line

The lesson for marketers is that global events no longer create a single audience. They create thousands of localized opportunities.

The brands that win the World Cup won’t necessarily be the ones with the biggest budgets. They’ll be the ones that connect global scale with local execution, adapting creative, media placement, and measurement to the communities where fan behavior is actually occurring.

In an era of fragmented viewing, localized audience intelligence may be the most valuable World Cup asset a marketer can have.

For years, advanced audience targeting has been one of the biggest promises in TV advertising. The challenge has never been defining sophisticated audience segments—it has been carrying those audiences consistently from planning and buying through measurement and reporting.

That is the problem Nielsen and Mediaocean are now attempting to solve.

The companies announced a new integration that expands Nielsen’s audience measurement capabilities directly within Mediaocean’s Prisma platform, one of the advertising industry’s most widely used media management systems. The initiative allows agencies and advertisers to define advanced audience segments and evaluate performance against those same audiences within a unified workflow. For agencies and multi-location brands, the announcement highlights a broader shift underway in TV advertising as the industry moves beyond age-and-gender demographics toward audience definitions built around consumer behavior, purchase intent, lifestyle characteristics, and first-party data.

Just as importantly, it signals a future where those audience definitions can remain consistent throughout the campaign lifecycle.

The End of the Audience Translation Problem

One of the longstanding frustrations in TV advertising has been audience inconsistency. A campaign might be planned against one audience definition, purchased against another, and measured against something entirely different. The result is a fragmented process that makes it difficult to understand whether campaigns are truly reaching the consumers advertisers intended to target.

The new Nielsen-Prisma integration addresses that challenge by allowing advertisers to define advanced audience segments within Nielsen Audience Builder and then view corresponding measurement data directly inside Prisma. The goal is to maintain a consistent audience definition from planning through reporting rather than forcing marketers to continually translate audiences across different systems.

According to Nielsen, advertisers will be able to work with first-party audiences, third-party audiences, and Nielsen-owned audience segments while maintaining greater continuity throughout the campaign process. What may appear to be a technical workflow improvement could ultimately have meaningful implications for campaign performance, optimization, and accountability.

For agencies managing campaigns across multiple clients, media channels, and markets, reducing audience fragmentation can improve reporting consistency and campaign optimization. For brands, it creates a clearer picture of which audiences are actually driving performance and business outcomes.

Why Mediaocean Matters

While Nielsen provides the audience and measurement layer, Mediaocean may be the more strategic story for many agency leaders. Prisma already sits at the center of media operations for many of the world’s largest agencies and brands, serving as a system of record for planning, buying, stewardship, and reconciliation. By embedding Nielsen’s audience capabilities directly into that workflow, Mediaocean is helping address one of the biggest friction points in advanced audience advertising: the need to move between multiple planning, buying, and reporting environments.

The move reflects a broader strategy that has increasingly defined Mediaocean’s role in the marketplace. Rather than focusing solely on media transactions, the company has positioned itself as an operational backbone that connects planning, execution, financial management, measurement, and optimization across the advertising ecosystem.

The Nielsen partnership extends that vision into advanced audience advertising. Instead of asking agencies to adapt to new tools or workflows, the integration brings audience intelligence directly into the systems buyers already use every day. For agency teams under constant pressure to improve efficiency while managing increasingly complex media plans, that workflow simplification may be as valuable as the audience data itself.

Linear TV Continues Its Transformation

The announcement also reflects how rapidly linear TV is evolving. Historically, national TV advertising relied heavily on broad demographic segments such as adults 18-49 or adults 25-54. While those measurements remain important, marketers increasingly want audiences tied to real consumer behaviors, purchase intent, and business outcomes.

Nielsen’s integration brings advanced audience measurement directly into the operational systems agencies already rely upon. That matters because the future of TV advertising is increasingly dependent on making advanced audiences practical to activate, measure, and optimize at scale.

The move mirrors broader changes happening across the TV ecosystem. Media buyers are demanding the same precision and accountability from linear TV that they have become accustomed to in digital channels. Advanced audience planning and measurement have emerged as one of the industry’s primary responses to that demand.

Rather than replacing traditional TV advertising buys, the objective is increasingly to make linear inventory more addressable, measurable, and outcome-focused. The ability to maintain audience consistency from planning through reporting is an important step in that evolution.

What It Means for Multi-Location Brands

For multi-location brands, the implications extend well beyond TV. Many national advertisers now manage media strategies that span linear TV, connected TV, search, social, retail media, and local activation. As those channel strategies become more integrated, maintaining consistent audience definitions becomes increasingly important.

A restaurant brand, automotive group, healthcare organization, or retail chain may want to understand how a specific audience segment performs across multiple touchpoints rather than evaluating each channel independently. Consistent audience measurement creates a stronger foundation for that kind of cross-channel analysis while supporting broader efforts to connect media investments to business outcomes.

The trend also aligns with the growing shift toward outcome-based marketing. Increasingly, advertisers are less focused on simply reaching large audiences and more focused on understanding which audiences drive meaningful actions, purchases, and customer growth.

The Bigger Shift

The Nielsen-Mediaocean partnership is ultimately about more than advanced audience measurement. It reflects a larger industry movement toward interoperability and workflow integration.

For years, marketers have accumulated more audience data than ever before. The challenge has not been access to data. The challenge has been making those audiences usable across planning, buying, measurement, optimization, and reporting systems without introducing friction along the way.

The next phase of TV advertising may be defined less by the creation of new audience datasets and more by how seamlessly those audiences move through the systems agencies use to manage campaigns. That is where Nielsen and Mediaocean intersect. Nielsen provides the audience intelligence and measurement framework. Mediaocean provides the operational infrastructure that allows those audiences to travel consistently throughout the campaign lifecycle.

For brand advertisers, value is not just about better audience targeting.  It is the ability to define an audience once, measure it consistently, and make more confident investment decisions based on comparable performance data. As TV continues its evolution toward data-driven planning and outcome-based measurement, that level of consistency is becoming just as important as the audience data itself.

Walmart’s new partnership with Magnite may signal a broader shift in how retail media networks operate. Rather than requiring advertisers to work within retailer-controlled ecosystems, companies are increasingly looking to make their first-party data available across the platforms agencies already use. For multi-location brands and agencies, the move points toward a more interoperable future for retail media, connected TV, and closed-loop measurement.

Retail media has become one of the fastest-growing sectors in advertising, but it has also become one of the most fragmented.

Over the past several years, retailers have raced to build media networks around their own first-party data, audience insights, and advertising inventory. The strategy has worked. Retail media is now attracting billions in ad spend as brands seek closer connections between media investments and measurable sales outcomes.

But as retail media networks have multiplied, so has operational complexity. For agencies and multi-location brands, activating campaigns often means navigating multiple platforms, separate buying environments, disconnected measurement systems, and a growing collection of retailer-specific tools. While retailers have succeeded in monetizing their data assets, advertisers have frequently been left managing the resulting fragmentation.

That dynamic may be starting to change. Walmart’s recent decision to use Magnite’s infrastructure to expand access to its first-party audience data points to what could be the next phase of retail media: open activation, interoperable workflows, and closed-loop measurement at scale.

Retail Media Is Moving Beyond the Walled Garden

The first generation of retail media networks was built around exclusivity. Retailers wanted advertisers to buy media within their own ecosystems. If a brand wanted access to retailer audience data, it typically had to activate campaigns through retailer-controlled platforms. The approach helped retailers maintain control while building new revenue streams.

But as retail media matures, advertisers are increasingly demanding flexibility. Agencies already manage campaigns across search, social, programmatic display, connected TV, and digital video. Adding separate workflows for every retail media network creates operational inefficiencies that become increasingly difficult to justify.

Walmart’s latest move addresses that challenge directly. Rather than requiring advertisers to work exclusively within Walmart-owned buying environments, the company is enabling Walmart audience activation and measurement through platforms advertisers already use. The initial rollout includes access through Yahoo DSP, but the more significant technical development is happening on the supply side. Through Magnite, Walmart is bringing its first-party audience signals directly into the supply path alongside the inventory itself, helping preserve signal fidelity and improve audience matching while still allowing advertisers to activate through certified buying platforms. As additional DSP partnerships are added, the model can scale without requiring brands to fundamentally change how they buy media.

For advertisers, the significance extends beyond convenience. It reflects a broader recognition that retailer data becomes more valuable when it can operate across the broader advertising ecosystem rather than remaining confined within retailer-controlled environments.

Magnite Is Becoming the Infrastructure Layer

While Walmart’s first-party data attracts most of the attention, Magnite may be the more important long-term story.

Historically, supply-side platforms focused on helping publishers monetize inventory. Increasingly, however, Magnite appears to be evolving into something larger: an infrastructure layer connecting premium first-party data owners, media inventory, measurement systems, and advertiser workflows.

The Walmart announcement follows a similar partnership announced earlier this year with Expedia Group Advertising. In that initiative, advertisers gained expanded access to Expedia’s traveler data across premium programmatic inventory using a comparable activation model.

Viewed together, the announcements reveal a pattern. Large first-party data owners increasingly want their audience assets available wherever advertisers buy media. Rather than forcing brands into proprietary platforms, the emerging model allows retailers, travel companies, and other media owners to maintain ownership of their data while expanding accessibility through existing agency workflows.

That positions Magnite as more than a technology vendor supporting individual partnerships. Sean Buckley, Magnite’s President of Revenue and Market Strategy, framed the opportunity as one of activation rather than simply data ownership. As retailers accumulate increasingly valuable first-party audience signals, the competitive advantage may come from how efficiently those signals can be deployed across premium inventory and multiple buying environments. In that model, infrastructure becomes just as important as the data itself. For agencies, that means fewer workflow barriers. For retailers, it means broader monetization opportunities without relinquishing control over audience assets or measurement.

The CTV Opportunity Is Accelerating the Shift

The Walmart partnership also highlights the growing convergence of retail media and connected television.

CTV has become one of the fastest-growing channels in advertising, but measurement has often lagged behind advertiser expectations. While marketers can easily measure ecommerce conversions or search performance, proving business outcomes from television exposure has historically been more difficult.

Retail media is helping close that gap. The initial deployment combines Walmart’s shopper data with VIZIO inventory and closed-loop measurement capabilities, giving advertisers a more direct connection between audience targeting, media exposure, and purchase outcomes. However, VIZIO is perhaps better viewed as a launch point rather than the endpoint. As the partnership expands, the broader opportunity lies in extending Walmart’s audience and measurement capabilities across additional premium CTV environments.

This is where Magnite’s role as an infrastructure layer becomes particularly important. As retailers, streaming platforms, and advertisers look to connect audience data with premium video inventory, infrastructure providers that can facilitate those connections across multiple buying environments become increasingly valuable.

For agencies, this transforms CTV from a primarily awareness-driven channel into one that increasingly supports performance objectives. For multi-location brands, the implications are equally significant. As marketers look for stronger ways to connect upper-funnel media investments to store visits, local sales, and customer acquisition, retailer-powered audience and measurement capabilities create new opportunities for accountability across premium video environments.

What It Means for Agencies and Multi-Location Brands

The broader takeaway is that retail media is entering a new stage of maturity. The first phase focused on building retail media networks. The second phase focused on attracting advertiser budgets. The next phase appears focused on interoperability.

Winning retail media networks may no longer be the ones with the most inventory or the largest audience datasets. Instead, success may increasingly depend on how easily advertisers can activate data, measure outcomes, and integrate retail media into broader omnichannel strategies.

For agencies, that means less operational friction and greater flexibility. For multi-location brands, it means the potential to connect retail media, connected TV, search, social, and local marketing efforts through increasingly unified audience and measurement frameworks.

The strategic significance of Walmart’s partnership extends beyond a single retailer’s media ambitions. Combined with Magnite’s growing role across retail media, travel media, and connected TV, it points toward a future where first-party audience intelligence becomes portable, measurement becomes more connected, and media activation becomes increasingly platform-agnostic. If that future materializes, agencies and brands may spend less time navigating fragmented media environments and more time using data to drive measurable business outcomes.

The real story is not that Walmart expanded access to its data. It’s that companies like Walmart are beginning to treat interoperability as a competitive advantage.

Citation management for multi-location businesses has long been a structured affair, consisting of claiming listings for each location, ensuring NAP data is up to date and consistent across those listings, and repeating for every location in the portfolio. These types of structured citations still matter for local SEO, but with the advent of AI-powered search, unstructured citations have become more important than ever for MULO brands.

What Are Unstructured Citations?

Unstructured citations are the brand mentions that live outside directories and listing platforms. Think: a local news feature on a newly opened location, a neighborhood subreddit thread recommending your service, a regional magazine’s roundup of the best spots in town, or a blogger’s city guide.

Unlike structured citations, there’s no standardized format for these mentions, no submission form, and no guaranteed placement. These are largely earned mentions, scattered across the open web, and they’ve quickly become one of the most significant factors in how AI-powered search surfaces and describes businesses.

Why MULOs Should Pay Attention

For a single-location business, unstructured citation gaps are a small-scale local problem. For multi-location brands, they’re a large-scale, portfolio-wide one.

When someone performs a local query on Google that triggers AI Overviews or AI Mode results, or asks a standalone AI platform like ChatGPT or Gemini for local business recommendations, the AI evaluates the nearest relevant locations on their own terms, drawing on whatever third-party coverage exists for that specific market.

This means a flagship location in a major metro with years of press mentions and community coverage may perform exceptionally well in AI-generated results, but a newer location in a secondary market may be nearly invisible or described in neutral, generic terms that don’t reflect any of the brand’s actual differentiators.

This is the challenge that MULOs face with unstructured citations. Coverage is almost always uneven across a portfolio of locations, and that unevenness directly translates into inconsistent AI visibility and inconsistent brand narratives, or AI sentiment, at the local level.

AI models don’t just determine whether a business appears in an AI-generated response to a local query. The language they use actively persuades potential customers, and the language AI uses to describe a particular location is frequently drawn from the third-party content that exists about that location.

If the local coverage for a given unit emphasizes speed and friendliness, AI-generated search results are likely to reflect that. If there’s no meaningful local coverage at all, the AI may either skip the location entirely or fall back on thin, generic descriptions that won’t win customers.

Building Unstructured Citations at Scale Without Losing Local Authenticity

This is where multi-location brands face an important strategic decision. The instinct in a MULO organization is to centralize and standardize citation building, but it’s harder to do this for unstructured citations. They’re inherently local, and the sources that carry the most weight with AI, sources such as regional news outlets and neighborhood publications, often require local networking to earn mentions.

What MULOs can do is build localized outreach infrastructure. That means equipping individual location managers or regional teams with the tools and relationships to pursue local coverage, while providing centralized support in the form of things like press release templates, pre-approved story angles, and a roster of local PR contacts in each market.

Start With a Location-by-Location Audit

Before creating a localized unstructured citation building strategy, multi-location brands need to understand exactly where the gaps are across their portfolio. That requires knowing exactly what AI is currently saying about each location, and more importantly, what sources it’s drawing from.

Tools like Local Falcon’s AI visibility reports allow you to see which locations are appearing in AI-generated recommendations, which ones are being overlooked, and critically, what citations are shaping the narratives that AI is generating for both the brand and its competitors. That last piece is especially valuable for MULOs.

When you can see that a competitor’s downtown location is consistently surfaced in AI results because of recurring local news coverage, or that your own outlying location is underperforming sentiment wise because its only unstructured citations are a handful of outdated blog posts, you have a clear brief for where to focus your local PR energy.

Running this analysis across every location in the portfolio, and benchmarking AI search performance against local competitors market by market, gives multi-location brands the kind of granular visibility gap data that’s nearly impossible to surface any other way. It also makes the business case internally for investing in localized unstructured citation building, which can be a hard sell in organizations that have historically measured citation performance through structured listing audits alone.

The Bottom Line for Multi-Location Brands

AI-generated local search results are directly influencing customer decisions at the market level, making it crucial for multi-location organizations to start paying more attention to their unstructured citations.

Strong unstructured citations are not only determining whether you show up in AI-generated results at all, but also whether what AI says about you is actually compelling enough to drive a customer through the door.

For MULOs managing dozens or hundreds of locations, figuring out how to build local unstructured citation equity at scale, location by location, market by market, is now key to winning business through AI-powered local search experiences.

Digital out-of-home (DOOH) advertising has traditionally been viewed as a channel for building brand awareness, driving store visits, and influencing purchase decisions. But one of its longest-running use cases highlights a different strength: its ability to generate real-world action quickly and at scale.

The Out of Home Advertising Association of America (OAAA) recently announced the renewal of its partnership with the FBI, extending a nationwide initiative that uses digital billboards and other DOOH inventory to support missing persons cases, fugitive investigations, and urgent public safety communications. While the program may appear far removed from commercial marketing, it demonstrates many of the same capabilities that are making DOOH increasingly valuable to multi-location brands and agencies.

At its core, the initiative showcases how location intelligence, real-time messaging, and broad geographic reach can be combined to influence behavior. Those same characteristics are driving renewed interest in DOOH as marketers look for channels capable of connecting digital targeting with physical-world outcomes.

Geographic Precision Is Becoming a Competitive Advantage

The effectiveness of the FBI program relies heavily on one of DOOH’s defining advantages: geographic precision. Messages can be deployed in specific markets, neighborhoods, transit corridors, or locations connected to an active investigation, allowing law enforcement to reach audiences most likely to recognize a face, recall information, or provide a useful tip.

The objective is not simply awareness. It is relevance. By placing messages where they have the highest likelihood of generating a response, the program transforms digital screens from passive media assets into active communication tools.

For marketers, the implications are significant. The same location intelligence that helps law enforcement determine where an alert should appear can help brands engage consumers near stores, restaurants, dealerships, healthcare facilities, or event venues. As marketers continue shifting investment toward channels that combine scale with local relevance, geographic targeting has become one of DOOH’s most valuable differentiators.

Why Real-Time Messaging Matters

Another lesson from the FBI partnership is the growing importance of speed and flexibility. Digital networks allow public safety messages to be activated, modified, and distributed across thousands of screens in near real time. Information can be updated as circumstances change and concentrated in areas where visibility matters most.

That capability has direct applications for commercial advertisers. Retailers can promote limited-time offers, restaurants can highlight seasonal menu items, and brands can align messaging with local events, weather conditions, or inventory availability. Rather than relying on static creative that remains unchanged for weeks, marketers increasingly have the ability to make campaigns responsive to real-world conditions.

As consumer attention becomes more fragmented, the ability to deliver relevant messages at precisely the right moment is becoming more valuable than simply increasing media volume. Timing and context are often the factors that determine whether a message is noticed, remembered, or acted upon.

Hyperlocal Relevance Drives Consumer Action

The public safety initiative also underscores the growing importance of hyperlocal relevance. Missing persons alerts and investigative messages are effective because they are often tied directly to the communities where people live, work, and travel. Viewers understand that the information may have immediate relevance to their daily routines and surroundings.

The same principle increasingly applies to commercial marketing. Consumers care less about generic national messaging and more about what is happening at a nearby location. Whether they are searching for a restaurant, retailer, fitness center, or healthcare provider, proximity and relevance often influence decisions more than brand awareness alone.

This shift is pushing agencies and multi-location brands to think differently about audience engagement. Instead of focusing exclusively on demographics, marketers are increasingly using location, context, and intent signals to create messaging that feels locally relevant while still maintaining brand consistency at scale.

Measurement Is Bringing DOOH Closer to Performance Media

Historically, one of the biggest challenges facing out-of-home advertising has been attribution. While marketers understood the branding value of the medium, proving direct business impact was often more difficult than with digital channels.

That dynamic is changing rapidly. Advances in mobility data, exposure modeling, audience measurement, and cross-channel attribution are helping marketers better understand how DOOH contributes to outcomes such as store visits, app downloads, event attendance, website engagement, and retail sales.

The FBI initiative provides an interesting parallel. Success is often measured through increases in tip volume, accelerated investigations, or public response in areas where messaging has been deployed. While the outcome differs from a commercial campaign, the underlying objective is similar: understanding whether exposure to a message generated a measurable action.

As attribution capabilities continue improving, DOOH is increasingly being evaluated alongside search, social, connected TV, and retail media rather than being viewed solely as an awareness channel. For marketers focused on measurable business outcomes, that evolution is helping elevate the medium’s strategic value.

Automation Is Expanding What DOOH Can Do

The operational requirements of the FBI partnership also highlight another important industry trend: automation. Coordinating public safety messaging across thousands of screens requires systems capable of rapidly deploying, updating, and managing campaigns across diverse markets and media owners.

Those same capabilities are becoming increasingly important for advertisers. Programmatic buying, dynamic creative optimization, and centralized campaign management are allowing brands to activate localized campaigns at a scale that would have been difficult to manage manually just a few years ago.

Artificial intelligence may further accelerate this trend. As AI-driven planning and optimization tools mature, marketers may be able to automatically adjust creative, identify optimal locations, and activate campaigns based on real-time conditions. The result is a more responsive media environment that can adapt to both consumer behavior and changing market conditions.

The Bigger Opportunity for Multi-Location Brands

The renewed FBI partnership serves as a reminder that digital out-of-home is more than a branding channel. Its ability to deliver relevant information in specific locations at the moments when people are most likely to act is what makes the medium uniquely effective.

For multi-location brands, that capability is becoming increasingly important as local marketing strategies evolve. Consumers expect information that is timely, contextual, and relevant to their immediate surroundings. Channels that can bridge digital intelligence with physical-world behavior are therefore becoming more valuable.

The same qualities that help communities locate missing persons—precision targeting, localized relevance, rapid deployment, and measurable action—are also helping marketers drive store visits, event attendance, and customer engagement. As DOOH becomes more dynamic, automated, and data-driven, the lessons from this long-running public safety initiative may offer a useful glimpse into the future of location-based marketing.

As multi-location brands push deeper into expansion mode, one of the biggest operational challenges is no longer simply opening new locations. It is maintaining consistent customer engagement, local visibility, and brand standards across hundreds of markets simultaneously. Scooter’s Coffee is increasingly confronting that reality firsthand.

The rapidly growing drive-thru coffee chain, Scooter’s Coffee,  has expanded to more than 900 locations across 32 states, continuing a franchise-driven growth strategy that is pushing the company closer to the 1,000-store milestone. But with that growth comes increasing complexity around local marketing execution.

When Franchise Growth Creates Operational Complexity

For franchise systems operating at this scale, activities such as responding to reviews, managing local search visibility, updating business information, and maintaining customer engagement across hundreds of locations can quickly become operationally overwhelming. This is particularly the case when local operators are already managing day-to-day store operations.

That challenge is helping drive a broader shift across franchise and multi-location marketing: the rise of AI-powered operational systems designed to automate localized marketing execution while preserving brand consistency.

Scooter’s Coffee recently implemented  SOCi’s AI-driven local marketing automation across its network to help streamline review responses, improve local engagement, and reduce repetitive manual workflows for franchisees. The broader goal was not simply efficiency, but scalability enabling the company to maintain localized digital engagement as its physical footprint continues expanding rapidly.

The strategy reflects a growing realization among multi-location brands that local marketing increasingly functions as an operational discipline rather than simply a promotional one.

Balancing Brand Consistency With Local Authenticity

Historically, franchise organizations have had to balance centralized brand governance with localized authenticity. Corporate teams need consistency in messaging and customer experience, while franchisees need flexibility to communicate naturally within their communities. At scale, however, maintaining that balance manually becomes increasingly difficult. Tasks that once appeared manageable at 50 or 100 locations become exponentially harder at 900.

Review management offers a clear example. Consumers increasingly expect timely responses to reviews and questions, while search platforms increasingly reward active engagement, responsiveness, and accurate local business information. At the same time, operators often lack the bandwidth to consistently manage those interactions manually across every location.

Scooter’s Coffee’s automation initiative was designed to reduce that operational friction while improving consistency across its expanding footprint.

Local Visibility Is Becoming an Operations Issue

The company reported measurable gains following implementation. Those improvements highlight how local marketing increasingly intersects with operational performance. Review responsiveness, listings accuracy, and local engagement became more than just reputation-management functions; they directly influenced customer discovery, local search visibility, and conversion behavior in AI-influenced search environments.

The timing is notable because Scooter’s Coffee’s digital scaling efforts mirror broader operational investments happening across the organization. As the company continues expanding geographically, it has also invested in larger distribution and logistics infrastructure intended to support long-term franchise growth and operational consistency. In many ways, the company’s local marketing automation efforts represent a digital extension of that same operational scaling mindset.

The Future of Franchise Marketing Infrastructure

Rather than relying entirely on local operators to manually execute every marketing task, franchise systems are increasingly looking to build centralized operational frameworks that can scale customer engagement more efficiently across distributed networks of locations. That trend is becoming particularly important as AI-driven search and discovery systems place greater weight on signals such as review activity, business responsiveness, customer sentiment, and local relevance.

For franchise organizations and agencies, the implications are significant. The next competitive advantage may not simply come from producing more marketing content, but from building scalable systems capable of maintaining localized engagement and operational consistency across hundreds or thousands of locations simultaneously.

Scooter’s Coffee offers an early example of how rapidly expanding franchise brands are beginning to approach local marketing less as a standalone campaign function and more as operational infrastructure necessary to support growth at scale.

For too long, the conversation around AI has centered on capability. What it can do. What it might unlock. But capability without underlying quality will only scale the wrong things. AI amplifies the marketing data that’s already there. Done right, it accelerates insight, boosts creativity, and enables rewarding collaboration. Done wrong, it amplifies risk, bias, and loss of control.

These effects can be seen at both the enterprise and individual level. AI makes a strong engineer more productive, but can expose the weaknesses of judgement in others. In the same way, AI can help a solid business achieve better results, but it will make poorly prepared organizations fail faster. 

McKinsey reports that 92% of businesses plan to increase spend on AI over the next three years. But without the right marketing data foundations, that investment won’t just underperform; it will compound existing weaknesses at scale.

Don’t get your AI strategy back-to-front

According to research from PwC, businesses in industries that are most exposed to AI, such as financial services and software production, are seeing a fourfold increase in productivity. But it can’t be assumed that AI projects will deliver positive results.

The truth is that if organizations get their approach to AI wrong, then they won’t get the best out of this transformative technology. And if AI initiatives begin with models and tools rather than with data strategy and infrastructure, then they’re doomed to fail. Businesses must walk before they run, so focusing on fundamentals first is the fastest way to AI maturity.

As AI becomes more deeply embedded into everyday processes and decision-making, the cost of weak marketing data foundations becomes apparent. AI doesn’t just expose cracks in weak systems, but widens them. Research from the OECD shows that AI layered on top of fragile infrastructure can lead to problems such as bias and poor data governance.

AI agents add another layer of complexity. Increasingly, these agents interact directly with APIs and datasets across the organization. If the APIs are well-constructed and have strict permissions, role-based access controls, and monitoring, then they will operate within their intended boundaries. But if the APIs lack these guardrails, agents may be able to access or combine data in unintended ways. Even a proof-of-concept implementation could create significant governance problems requiring corrective action if built on brittle foundations.

Building strong data foundations begins with privacy

Organizations that want to succeed with AI must build strong data foundations with privacy as the central pillar. No single company holds all the data required to train high-performing AI models. Each has to determine how they can connect their data to the datasets of partners such as suppliers, resellers, retailers, publishers, and market intelligence providers. And it’s vital they do this in a way that protects customer privacy and preserves the integrity and value of their own data, as well as that of their collaborators.

Traditional approaches to marketing data management were never designed for modern regulatory environments, where 82% of citizens now have their data protection rights enshrined in law. Centralizing data and sharing it across an ecosystem of third parties is not compatible with today’s privacy legislation and also limits the quality of intelligence AI can produce.

Privacy-first, decentralized architecture must be a priority

Decentralized and privacy-first architectures enable AI to become a competitive advantage by safely enabling richer signals and faster activation. When partners collaborate, marketing data isn’t pooled into central repositories; instead, intelligence is generated through secure computation across environments, without moving any data. With strong governance protocols encoded into infrastructure, collaboration between businesses becomes more effective. Data owners retain control, and regulators and customers gain transparency into how information is used.

AI performs best when it can learn from diverse, permissioned signals across organizations. Static identity records and isolated data pools restrict model performance, but federated collaboration expands the range of signals available for training and analysis while preserving sovereignty. The shift toward privacy-first collaboration also gives organizations another way to realize the value of their data. Rather than the volume of data they control, businesses can differentiate themselves through the quality of their connections and the intelligence they can generate together.

Interoperability is another key to AI success. With different cloud providers and technology stacks in play, an infrastructure-agnostic approach enables secure collaboration across all environments, without reconfiguring for every new partnership. AI initiatives can then be scaled up without needing to rebuild data pipelines for each new partnership.

AI maturity is dictated by support structures, not models

AI isn’t a silver bullet; it’s a multiplier. It magnifies the quality of the existing marketing data practices and governance policies of enterprises. If these foundations are strong, AI can streamline processes, supercharge insight, and provide a platform for enhanced creativity. But if the foundations are weak, AI amplifies those weaknesses, leading to higher risk and poor performance. 

Ultimately, AI readiness is not determined by how advanced a model is, but by how well a business is prepared to support it. The next phase of AI adoption will reward organizations that prioritize privacy-first infrastructure and interoperability, and the key competitive difference will be the quality of these foundations.

As marketers search for incremental reach across an increasingly fragmented media landscape, audio advertising is emerging as a powerful complement to CTV, social, and digital video. A new partnership between Basis and DAX highlights how programmatic audio, audience targeting, and omnichannel campaign management are reshaping media planning.

While connected TV, retail media, digital video, and social platforms continue to dominate advertising budgets and industry headlines, audio remains one of the largest sources of consumer attention in the media ecosystem.

According to Nielsen data cited by Basis and DAX US, radio and podcasts account for more than 80% of daily ad-supported audio listening, with consumers spending roughly four hours per day listening across broadcast radio, podcasts, streaming music services, and satellite radio. For advertisers navigating audience fragmentation and rising media costs, that level of engagement represents a significant opportunity for incremental reach.

The growing importance of programmatic audio is reflected in a new partnership between Basis and DAX US, which gives political advertisers exclusive access to DAX’s premium digital audio inventory through the Basis platform. While designed for the 2026 election cycle, the announcement highlights broader shifts in media planning, audience targeting, and omnichannel advertising strategies.

Consumer Attention Remains Concentrated in Audio

The advertising industry often focuses on emerging channels and technologies, from connected TV and retail media to AI-powered marketing platforms. Yet audio continues to occupy a unique position in consumers’ daily routines.

Unlike video channels that require active screen engagement, audio accompanies listeners throughout the day. Consumers listen while commuting, exercising, working, shopping, and completing everyday tasks, creating opportunities for advertisers to engage audiences during moments that many visual formats cannot reach.

This persistent presence helps explain why audio continues to deliver substantial scale despite increased competition from digital channels. Podcasts, streaming audio platforms, and digital radio have become essential components of consumers’ media diets, offering advertisers access to engaged audiences across a wide range of content categories.

For agencies and brands seeking media efficiency, audio can also provide valuable incremental reach beyond audiences already exposed to campaigns through social media, display advertising, or connected TV.

Programmatic Audio Moves Into the Omnichannel Media Mix

Historically, audio advertising has been fragmented across local radio buys, direct podcast sponsorships, streaming platforms, and individual publisher relationships. That complexity often made the channel more difficult to manage compared with other forms of digital advertising.

Programmatic audio is helping change that dynamic.

The Basis-DAX integration enables political advertisers to access premium audio inventory programmatically through a single platform that also supports planning, activation, optimization, reporting, and measurement. The approach mirrors broader industry trends toward media orchestration and centralized campaign management across channels.

DAX reports reaching more than 100 million U.S. listeners through its network of ad-supported audio properties. The company says its inventory reaches approximately 80% of all digital audio listeners aged 18 and older in the United States, including more than 41 million unique listeners unavailable through other streaming networks.

Its footprint includes more than 60,000 podcasts across news, sports, music, and culture categories, along with hundreds of terrestrial radio stations and streaming audio services.

For advertisers increasingly focused on omnichannel advertising, the ability to activate audio alongside other digital channels within a unified workflow can simplify campaign execution while improving visibility into overall advertising performance.

“Political advertising demands precision, speed, and accountability — and audio is one of the most powerful, and underutilized channels for reaching engaged voters,” said Grace Briscoe, EVP of Client Development at Basis.

Audience Targeting and Data Drive Performance

As with most digital advertising channels, the value of audio increasingly depends on audience targeting capabilities.

The partnership combines DAX’s inventory with more than 60 political audience datasets from L2 as well as Experian audience segments and predictive modeling capabilities. The integration enables advertisers to target audiences using voter registration information, demographic characteristics, behavioral signals, geographic data, and modeled audience insights.

Geo-targeting capabilities are particularly important in political advertising, where campaigns often focus spending on specific districts, counties, or battleground states. Advertisers can also access multicultural inventory designed to reach Spanish-speaking audiences, a key voter segment in many markets.

For political campaigns operating in compressed election cycles, the combination of premium audio inventory, audience intelligence, and campaign automation may help improve execution speed while maintaining targeting precision.

More broadly, the partnership reflects how audio advertising is becoming increasingly data-driven and measurable. The same advances that transformed display, video, and social advertising are now making audio easier to buy, optimize, and evaluate within larger cross-channel campaigns.

What the Partnership Signals for Agencies and Brands

Although the agreement focuses on political advertisers, the larger implications extend beyond election campaigns.

Across the advertising industry, agencies and brands are searching for ways to maintain reach and engagement as audiences continue to fragment across platforms and devices. Consumer attention has become one of the most valuable commodities in media planning, and audio continues to command a substantial share of it.

At the same time, improvements in programmatic buying, audience targeting, campaign automation, and cross-channel measurement are making audio easier to integrate into broader media strategies. What was once viewed primarily as a branding channel is increasingly becoming a measurable performance channel capable of delivering targeted reach at scale.

“By bringing exclusive inventory together with best-in-class data and execution, we’re giving political parties a powerful way to reach voters with precision and impact,” said Brian Conlan, President of DAX US.

For agencies, brands, and media planners, the lesson may be broader than politics. Despite the industry’s ongoing focus on video, social media, retail media, and emerging AI-driven advertising tools, consumers continue to spend hours every day listening to audio content.

As advertisers look for new ways to capture consumer attention, improve media efficiency, and extend campaign reach across fragmented audiences, programmatic audio is becoming harder to ignore. The Basis-DAX partnership is the latest indication that audio’s role in the omnichannel media mix may be growing faster than many marketers realize.

The advertising industry has spent a decade solving for targeting. Most of the major platforms have gotten quite good at it. That is not where broadcast is losing. Broadcast is losing on proof.

The buyers moving budgets to Netflix, Amazon, and programmatic platforms aren’t doing it because those platforms have bigger audiences. In most markets, they don’t. They’re doing it because those platforms can answer a straightforward question with current data: who saw this, what did they do next, and how does that compare to last week?

Most broadcasters cannot answer that question at the speed buyers now expect. The data exists, but in fragments across systems and on quarterly reporting cycles. It doesn’t arrive in time to change a selling conversation.

Reach Is Not the Problem

That gap has a name. I call it the intelligence infrastructure gap. And in 2026, a midterm election year and a World Cup year, when FAST monetization pressure is peaking across every major media group, it is going to cost broadcasters real money.

The evidence is already in the earnings releases.

TelevisaUnivision’s CEO acknowledged publicly that his company missed the 2024 political cycle. Committed spend had already been allocated before TelevisaUnivision engaged directly with campaigns. The Hispanic audience in U.S. battleground markets is among the most undervalued in broadcast advertising. The opportunity was there. The data to prove it, at the moment campaigns were making decisions, was not.

Nexstar’s Q4 2025 revenue fell 13.4%, with political advertising down $233 million year over year. The instinct is to read that as a cycle problem, an off-year with no election. But non-political core advertising grew only 4.5%, nowhere near enough to offset the swing. A sales team with current category-level intelligence — meaning which advertisers are actively spending in this market right now — sells differently than one working from last quarter’s data.

Scripps launched an enterprise-wide transformation plan in February 2026 targeting $125 million to $150 million in annualized EBITDA growth by 2028 through AI and automation. CEO Adam Symson framed it plainly: the goal is to operate “with the fast and agile infrastructure and technology that our economic environment demands.” That is a serious commitment. The question is whether the intelligence layer can move as fast as the commercial ambition requires.

These are not isolated failures. They are the same failure at different companies, in different segments, in the same year.

The Intelligence Infrastructure Gap

The industry knows it. iHeartMedia CEO Bob Pittman has said publicly that his goal is to make broadcast inventory “transact like digital.” That is precisely the right ambition. But transacting like digital requires proving performance like digital. That starts with having the audience intelligence that programmatic buyers require before they will bid at scale. The DSP connection is the last mile. The intelligence layer is what makes buyers confident enough to use it.

Broadcast still possesses enormous advantages. Local broadcast news continues to command some of the highest trust scores in media. In many markets, broadcasters reach audiences at a scale streaming platforms cannot match. The challenge is not audience quality or audience size.

The challenge is transforming audience data into actionable intelligence quickly enough to influence buying decisions.

Competing With Digital on Proof

Think about what actually happens in a sales conversation when a buyer asks why they should shift budget from a programmatic platform to a local broadcast package.

The platform representative responds with data from last Tuesday: impression delivery, audience composition, content performance, and category share. The broadcaster responds with last quarter’s ratings book, a reach estimate, and a promise to follow up with more information.

That is not a content problem. It is not a reach problem. It is an answer-speed problem.

The buyer is making a decision now. The information capable of changing that decision often arrives too late to matter.

This reality increasingly shapes how media budgets are allocated. Marketers are under pressure to justify spend with measurable outcomes, and channels that can provide timely intelligence naturally gain an advantage. Even when broadcast delivers superior reach, delayed proof weakens its position in the planning process.

The Window to Act Is Closing

Closing the intelligence infrastructure gap doesn’t require rebuilding a broadcaster’s entire data stack from scratch. Most of the underlying signals already exist: audience data, content performance metrics, competitive category movement, and market benchmarks.

What’s missing is the synthesis layer that converts those signals into actionable intelligence that sales teams can use in real time rather than days or weeks later. That layer is not merely a technology project. It is an operating decision.

And the broadcasters that make that decision in 2026 — before midterm election spending accelerates and before World Cup advertising inventory tightens — will enter critical sales conversations with a fundamentally different value proposition than those that do not.

Broadcast audiences are not the problem. In most markets, the reach remains extraordinary. The problem is that extraordinary reach, without the data to prove its value in the currencies modern buyers require, loses to smaller audiences that can.

That is the gap 2026 will expose. And it is a gap that can be closed.

AI agents are quickly moving beyond experimentation and becoming part of the operating infrastructure behind enterprise marketing.

That trend was on display this week as SOCi announced that it has surpassed 300,000 deployed Genius Agents, a milestone the company says establishes the largest deployed agentic workforce in marketing. The announcement underscores how rapidly enterprise brands are embracing autonomous systems to manage localized marketing activities across hundreds or thousands of locations. SOCi says its AI agents are on pace to execute more than 20 million localized marketing tasks annually across search, social media, reputation management, customer engagement, and emerging AI-driven discovery channels.

The growth has been substantial. SOCi reports that its deployed agent base increased more than 350% over the past year, growing from approximately 66,400 agents to more than 300,000 today. The expansion comes as marketers face mounting pressure to maintain visibility across an increasingly fragmented digital landscape that now spans traditional search, AI-generated answers, maps, social platforms, review sites, and messaging channels.

“The agentic workforce isn’t a pilot anymore…it’s operational infrastructure,” said Afif Khoury, CEO of SOCi. “Enterprises aren’t asking if they should deploy agents. They’re asking how fast they can scale them.”

SOCi by the Numbers

According to the company, its agentic workforce has delivered measurable operational and marketing gains:

• 300,000+ Genius Agents deployed
• 20 million+ localized marketing tasks projected annually
• 3.5 million+ hours saved
• $2.5 billion+ in annualized marketing value recaptured
• 98.7% publish-ready acceptance rate

While those figures are company-reported, they highlight the scale at which brands are beginning to deploy AI-driven execution systems rather than relying solely on traditional marketing teams and workflows.

From AI Copilots to Operational Infrastructure

The announcement also reflects a broader evolution in how enterprises are thinking about artificial intelligence. Early generative AI tools primarily assisted marketers with content creation and analysis. Agentic AI goes further, executing marketing tasks autonomously while operating within predefined brand and compliance guardrails. SOCi launched Genius Agents in 2024, when many organizations were still trying to define what agentic AI would mean in practice. Two years later, the conversation has shifted from experimentation toward deployment.

Each Genius Agent is trained on a brand’s voice, standards, and compliance requirements before being deployed across locations to manage review engagement, local search visibility, content creation, listings management, and reputation monitoring. For large multi-location organizations, these tasks can quickly multiply across hundreds or thousands of locations. The challenge is no longer creating a local marketing strategy. It is executing that strategy consistently and continuously at scale.

Why Local Marketing Is Becoming an Agent Problem

The timing of SOCi’s milestone is significant because local discovery itself is undergoing a major transformation. Consumers increasingly discover businesses through AI-generated recommendations, maps, social platforms, reviews, and conversational search. As a result, brands must continuously maintain accurate information, fresh content, customer engagement, and reputation signals across multiple channels.

That level of execution has traditionally required substantial human effort. For MULO brands managing hundreds of storefronts, franchise locations, service territories, or properties, maintaining consistency across every local touchpoint can strain even well-resourced marketing teams. Agentic systems are increasingly being positioned as a way to close that gap, operating continuously while remaining aligned with corporate governance standards and approval processes.

The shift is particularly relevant as AI-powered discovery experiences continue to expand. Visibility is increasingly influenced by the freshness of business information, the consistency of customer engagement, and the volume of localized content available across the web. Those are precisely the types of activities that can be difficult to scale manually but are well suited to autonomous systems.

Enterprise Brands Report Early Results

Several SOCi customers highlighted in the announcement point to measurable business outcomes from AI agent deployment.

Nékter Juice Bar uses SOCi’s reputation and local search agents to automate review management and support local visibility efforts. According to the company, locations with stronger review engagement have experienced stronger revenue growth alongside gains in search visibility and social audience growth.

“It was effective right out of the box,” said Jon Asher, chief technology officer at Nékter Juice Bar. “The agents quickly learned our brand voice and now run our reputation management in the background…consistently, accurately, and at scale.”

Meanwhile, Presidium deployed SOCi’s full Genius Agent suite across its digital marketing operations and reported a 102% year-over-year increase in local search engagements. With SOCi Genius Agents, we have more time to focus on what matters most…our customers,” said CG Millier, Director of Marketing and Training.

SOCi also cited deployments at Liberty Tax, Rita’s Italian Ice, and The Goddard School, where AI agents are being used to automate reputation management, standardize brand presence, and centralize marketing operations across large location networks.

Expanding the Scope of Autonomous Execution

The next phase of agentic marketing may be defined less by the number of AI agents deployed and more by what those AI agents are capable of doing. SOCi says it continues to expand an evolving library of Skills that increase the volume and complexity of work agents can perform autonomously. Current Skills include optimizing listings and local pages for traditional and AI search, generating location-specific content, responding to customer interactions, managing messaging channels, collecting feedback, and surfacing voice-of-customer insights.

The approach mirrors a broader movement across enterprise software, where vendors are racing to develop AI systems capable of orchestrating increasingly sophisticated workflows with minimal human intervention. For local marketers, success may depend less on periodic optimization campaigns and more on maintaining continuous visibility, engagement, and accuracy across every local digital touchpoint.

SOCi’s 300,000-agent milestone suggests many MULO brands are already moving in that direction. What was once an experimental AI initiative is increasingly becoming operational infrastructure.