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. 

OOH advertising has long benefited from its physical presence in the real world. But as marketers increasingly demand the same level of attribution, interoperability, and performance accountability they expect from digital channels, the industry is racing to modernize how the medium is measured.

That effort is now accelerating through a new initiative led by the Out of Home Advertising Association of America (OAAA) and Geopath, which are developing a next-generation audience measurement platform designed to bring greater precision, interoperability, and advertiser confidence to out-of-home (OOH) media. The initiative arrives during a period of sustained momentum for the channel. According to OAAA, OOH advertising revenue reached $9.46 billion in 2025, growing 3.6% year over year. Digital OOH formats continued driving expansion, while transit advertising grew 9.2%, making it the fastest-growing segment for the second consecutive year. Street furniture and place-based media also posted gains.

“OOH is the human medium,” said Anna Bager, President and CEO of the OAAA. “It draws its power from the real world—streets, transit systems, and daily movement patterns.”

To help modernize how those movement patterns are measured, OAAA and Geopath are developing what they call the Advance Measurement System (AMS), with Ipsos selected to support an industry pilot scheduled to launch in the second half of 2026. The ambition behind the initiative is substantial: bringing OOH measurement closer to parity with digital and television channels in areas such as attribution, targeting, forecasting, and real-time analytics.

According to Bager, the AMS framework is designed to provide “decision-grade outputs” that allow advertisers to evaluate OOH investments with the same rigor increasingly applied to other media channels. “By aligning our metrics to cross-media planning standards, we ensure OOH performance withstands the same CFO and CMO scrutiny as any digital or TV investment,” Bager told StreetFight. “We are closing the confidence gap with real-time speed and results that are natively structured for attribution and Marketing Mix Modeling.”

Bringing OOH Into Omnichannel Workflows

For agencies and multi-location brands, interoperability may be one of the most consequential aspects of the initiative. Historically, OOH planning often operated independently from broader omnichannel buying workflows, limiting its ability to integrate cleanly into performance-driven media strategies. AMS aims to change that through standardized APIs and data structures that allow OOH performance data to flow directly into the planning, activation, and reconciliation systems already used by media buyers. “OOH is no longer a standalone planning exercise,” Bager said. “It is workflow-ready.”

That evolution could have particular significance for regional advertisers, franchises, and SMB-focused agencies that increasingly expect local media channels to deliver measurable business outcomes rather than broad awareness alone. Historically, many local OOH campaigns were evaluated primarily through location logic — choosing the right board, neighborhood, or trade area. While those factors still matter, advertisers increasingly want digital-style clarity around audience reach, exposure frequency, and downstream business impact. “Local and SMB advertisers now expect the same kind of clarity they get from digital,” Bager said. “Who they are reaching, how many people they are reaching, where exposure is happening, and how the media is contributing to business outcomes.”

Improved measurement could also lower the barrier to entry for smaller advertisers by making OOH easier to justify within performance-oriented marketing budgets. Instead of being treated primarily as a top-of-funnel branding tactic, OOH could increasingly become part of measurable local growth strategies tied to store traffic, retail promotions, market launches, and community-level engagement.

Building a Standardized Yet Localized Measurement System

The modernization effort also reflects broader industry shifts around privacy and data governance. Rather than relying solely on raw mobile location data, AMS combines privacy-compliant mobile SDK signals with validated “ground truth” inputs and advanced viewshed modeling designed to estimate real-world exposure more accurately while maintaining anonymity. “Accuracy and privacy are not competing goals,” Bager said. “They are the twin pillars of a calibration and validation framework designed for long-term data resiliency.”

The upcoming Ipsos-led pilot will effectively serve as a large-scale operational stress test ahead of a planned national rollout in 2027. The pilot will evaluate whether the system can accurately onboard and audit inventory, validate visibility conditions across multiple environments, normalize mobility data, and maintain stable reach and frequency metrics across formats. Industry participation has also been central to the initiative’s development. Bager said the discovery process included 95 stakeholder discussions across agencies, advertisers, and media owners ranging from national operators to regional and local companies.

“Local and regional feedback is critical,” Bager said, noting that the system must account for the nuances of varying market conditions, formats, traffic patterns, and physical viewing environments.

That balance between standardization and local complexity remains one of the industry’s biggest challenges. While AMS seeks to establish a standardized measurement currency that enables cross-channel comparison, the platform also incorporates advanced visibility logic designed to reflect real-world variables such as obstructions, differing formats, and localized movement behaviors. “We are standardizing the currency while addressing the real-world requirements of OOH measurement,” Bager said.

Ultimately, OAAA sees improved measurement as more than a technical upgrade. The organization views it as a foundational requirement for OOH to compete for larger portions of modern media budgets increasingly governed by accountability, interoperability, and measurable outcomes.

“Measurement confidence equals market momentum,” Bager said. “AMS provides the credibility and transparency required to compete for a larger share of the total media pie.”

For multi-location brands and the agencies that serve them, maintaining consistent AI and local search visibility at scale is a resource problem, but AI agents are beginning to solve it.

Managing local SEO across hundreds of locations means doing the same things you’d do for one location, including optimizing listings, responding to reviews, publishing posts, and monitoring performance, multiplied by however many locations are in the portfolio. The work is straightforward, but the volume isn’t.

Without a large in-house or agency team executing local SEO, inconsistencies across locations are often inevitable. That’s why the emergence of AI agents is such a significant development for multi-location local SEO. They can help improve coverage across every location in a portfolio, ensuring no listing goes neglected, no review goes unanswered, and no performance issue goes unnoticed.

The Cost of Inconsistent Local SEO at MULO Scale

Most multi-location brands face similar problems when it comes to managing their local search performance at scale. Some locations have well-optimized GBP profiles; others were set up years ago and never revisited. Some have healthy review response rates; others have pages of unanswered reviews sitting in plain sight. Some markets are performing well in local search; others are quietly underperforming with no one flagging it.

That inconsistency has real business consequences. Visibility gaps in underperforming markets mean lost foot traffic and lost revenue. Unanswered reviews, especially negative ones, shape customer perception and send unfavorable brand signals to Google and AI search engines. Outdated listings erode trust and rankings.

MULO brands and their local SEO teams often know there are consistency and performance issues, but for teams managing local SEO across large portfolios of locations, closing those gaps without adding headcount has traditionally been a major challenge.

AI Agents Offer a New Kind of Efficiency

AI agents for local SEO address this problem because they don’t have time or other resource constraints. A single agent can monitor performance and manage execution across every location simultaneously, resulting in huge efficiency gains.

Consider what consistent execution actually looks like across a large portfolio. Every GBP listing is kept current and optimized. Every incoming review receives a timely, contextually appropriate response. GBP posts go out regularly across all locations. Performance data is continuously monitored, with meaningful changes surfaced before they become entrenched problems. Getting all of that done manually across hundreds of locations requires a dedicated team and significant manual work. AI agents make it an automated workflow.

For agencies, this reshapes the conversation with clients. Rather than explaining how resource-intensive local SEO is at scale and why there are performance gaps, agencies can deliver more consistent outcomes across client portfolios at attractive costs. The value delivered per location increases; the marginal cost of adding locations decreases.

What Agents Can Execute for Multi-Location Businesses

The clearest ROI opportunities for MULO operators fall into a few specific areas: review response, GBP optimization, and post publishing.

Review response is one of the highest-volume, highest-visibility tasks in local SEO — and one of the most commonly neglected at multi-location scale. An agent that monitors incoming reviews and publishes responses automatically, calibrating tone for sentiment and context across every location, eliminates one of the most persistent execution gaps for MULOs.

Local Falcon’s Falcon Agent, for example, lets multi-location brands configure per-location review response rules so that every incoming review gets an appropriately customized, on-brand reply rather than a generic one (or no reply at all).

GBP optimization is another area where agents deliver compounding returns. At the individual location level, monitoring GBP performance and continuously optimizing is relatively easy to manage. At MULO scale, systematically optimizing underperforming locations (updating categories, refining descriptions, ensuring attributes are accurate and current) is far more complex and time consuming, yet key to increasing visibility across an entire brand footprint.

GBP post publishing represents perhaps the most straightforward efficiency gain. Maintaining a consistent posting cadence requires ongoing effort, which means it’s frequently deprioritized for MULO brands without dedicated resources. An agent that generates and publishes posts across all locations according to brand guidelines and local context keeps listings active and engaging without the manual overhead.

Performance monitoring with proactive alerting rounds out the picture. Rather than passive reporting that gets reviewed periodically, agents can surface visibility declines, competitor movements, unauthorized GBP edits, or unusual review patterns to act on right away.

Keeping the Right Things Human

Effective implementation of AI agents for local SEO doesn’t remove human judgment from workflows. Instead, it redirects effort. Anything brand-sensitive warrants oversight: escalated reviews, significant listing changes, strategic decisions about how specific markets are positioned. The agent handles execution at volume; the practitioner or account manager handles the calls that require context and experience.

For agencies especially, that reallocation of capacity is where the longer-term value lies. Time that previously went towards repetitive execution tasks can go into strategy, client relationships, and growth. The work that requires skilled people gets their attention; the work that doesn’t gets handled automatically.

For MULO brands and the agencies serving them, the core question is no longer whether AI agents can handle local SEO execution at scale. The operational capability exists. The more relevant question now is how to implement it in a way that produces consistent, brand-appropriate outcomes across every location.

In short, AI agents make consistent, efficient local SEO execution across large multi-location portfolios more achievable than it’s ever been.

Local search visibility has been one of the most stable areas of SEO for years, relying on conventional techniques like keyword matching and search positioning. However, search behavior is now changing due to the implementation of AI-generated answers. Users are expecting synthesized suggestions for local businesses rather than sifting through a list of options.

The popularity of services like ChatGPT, Perplexity, and Google Gemini is also growing, and users are turning to these platforms more often for local recommendations. For multi-location brands, this shift is important because it fundamentally changes the approach to local SEO. This article explores how local visibility is changing and how to protect multi-location brands from AI visibility gaps in 2026 and beyond.

How AI Is Changing Local Search

AI integration has entirely reshaped local search results and behavior. Historically, search results were presented as a list of links, providing web pages from local businesses for users to vet themselves. Localized queries involved simple searches such as “pizza shops near me” or “dentists in Pittsburgh.” These relied primarily on geographical information to provide the closest results within the scope of the search.

With the growing popularity and inclusion of AI, location alone isn’t enough to compete in local search results anymore. According to SOCi’s 2026 Local Visibility Index, only 1.2% of locations were recommended by ChatGPT, 7.4% by Perplexity, and 11% by Gemini. This makes it up to 30 times more difficult to maintain local visibility in AI-generated content than in traditional search.

AI algorithms are capable of interpreting complex queries and providing tailored suggestions. Users have started to expect more specific recommendations, such as “best affordable plumbers with same-day service” or “Italian restaurants with outdoor seating,” rather than simply which options are “near me.” For multi-location brands, this means that SEO techniques will require a different approach to be effective, placing less emphasis on clicks and more focus on inclusion in AI-generated responses.

What Drives Local Search Visibility?

Local AI search visibility is primarily driven by the following factors:

• Accuracy and consistency: The consistency of your brand across online platforms play a major role in AI visibility. Your business name, address, phone number, and other relevant information should be accurate and consistent across platforms. Locations with inconsistent information send mixed signals to AI algorithms, warranting exclusion from responses.
• Online reviews and ratings: Locations recommended by AI are scrutinized more strictly than in traditional search engines. Results are typically reserved for businesses with positive reviews and strong reputations. A higher volume of 5-star ratings and user-generated positive reviews sends stronger confidence signals to AI.
• Location-specific content: Content specific to your location helps differentiate your business and provide context that AI can use to deliver recommendations. Posting content about local staff, specials, or upcoming events shows AI systems that your business is relevant to local customers and their queries.
• Authority signals: External authority signals are becoming increasingly important for local recommendations. Cross-platform engagement from other local businesses through brand mentions, citations, and backlinks signals to AI that your business is credible.
• Structured data: Structured data makes it easier for AI to synthesize information about your business. It’s important that your site has a strong technical foundation and provides consistent metadata for AI to understand.

Why Traditional SEO Tactics Aren’t Working Anymore

Visibility is no longer determined by traditional criteria such as proximity, keywords, and search ranking. Instead, modern SEO focuses on context, authority, and optimizing content for inclusion in AI-generated responses. This reduces the focus on clicks for local businesses and shifts the approach towards visibility through AI-generated content. Ranking #1 no longer guarantees visibility, and many users get the answer they’re looking for without ever clicking to another website.

Up-to-date business information is still necessary for building authority, but is no longer a competitive advantage. AI algorithms prefer businesses with content that is relevant, trustworthy, and high-quality. Templated location pages are no longer sufficient for search visibility. Instead, AI will prioritize results that provide meaningful, location-specific information. The top AI SEO agencies have picked up on this trend, while others using outdated tactics are falling behind.

What Multi-Location Brands Should Do

Multi-location brands should focus on these strategies to improve local AI visibility:

1. Treat locations as separate entities

The first step in optimizing your local brand for AI visibility is strengthening your location-level content. Treat each location as a distinct entity with unique digital assets by moving beyond templated pages. Create location-specific pages that highlight your services, differentiators, and local expertise. This includes content such as staff bios, location-related FAQs, neighborhood photos, and local events.

2. Optimize for contextual relevance

Proximity alone isn’t enough to rank for location-based searches anymore. Businesses should create content that aligns with their local customers’ preferences and search habits. Optimizing content to fit contextual differences, such as services offered or competitive advantages. Question-based articles, FAQ content that provides solutions, and clear answers that AI can extract are just a few examples.

3. Build trust signals (reviews, ratings, citations)

Perceived authority and trust signals are important factors in whether a business is selected as part of AI recommendations. The primary means of establishing trust online is through real customer reviews, ratings, and external citations.

The importance of customer reviews for local brands can’t be understated. AI systems synthesize online reviews to gauge the customer experience and determine whether a business is recommendation worthy. Building relationships with other local businesses to earn mentions on their websites, in news reports, on blogs, or relevant forums is another way to establish local credibility.

Final Thoughts

Local search is no longer dictated by proximity and rankings. It’s increasingly shaped by how effectively AI tools can understand, trust, and recommend businesses. The “near me” search is being replaced by “best for me” searches, and AI is making the decisions. This is especially relevant for multi-location brands, which need to shift their approach to remain visible in local search results.

Businesses that adapt to this new model will be able to maintain and expand their visibility, while those who don’t will fall behind. The future of SEO for local businesses is becoming more complex, strategic, and tied more closely with brand authority. If you’re looking for expert advice to help your brand perform better in local searches, consider working with a top GEO agency to streamline the process.

Most local SEO agencies have a citation management offering of some kind. They might audit client listings, fix NAP inconsistencies, claim the obvious missing directories, and report back to the client. This process gets the job done as a basic optimization to check off, but it was designed for improving traditional local rankings — not improving visibility and sentiment in a local search environment that now includes AI-generated recommendations.

Clients are starting to ask hard questions about AI visibility. A client notices they’re not showing up when potential customers use ChatGPT to find businesses in their category. Their Google Business Profile is optimized. Their star ratings are solid. Their traditional local rankings are fine. So why isn’t AI recommending them?

In many cases, the problem is that nobody has looked closely enough at where the AI is pulling its data from — the specific sources it’s citing — and without that information, there’s no way to give the client a real answer or a real plan.

The Citation Layer Beneath AI Recommendations

When an AI model generates a local recommendation, it’s synthesizing information from a wide mix of sources. Some of those are familiar — the business’s own website, Google Business Profile, Yelp, Facebook, etc. Others are less obvious: regional news articles, neighborhood blogs, “best of” lists, Reddit threads, or industry-specific directories that rarely come up in traditional citation work. The AI doesn’t advertise which sources it’s going to check ahead of time; it just produces an answer that often includes a list of citations.

In a practical sense, what this means is that two businesses in the same category, in the same city, with similar traditional local search performance, can get very different treatment from AI — because one of them has a stronger footprint across the specific sources AI happens to be drawing from in that market.

That footprint isn’t always the result of deliberate strategy. Sometimes a business earned a mention in a local publication two years ago and it’s been quietly influencing AI recommendations ever since. Sometimes a competitor claimed a niche directory listing that consistently surfaces in AI results for that vertical.

The gap is real, but it’s invisible unless you’re specifically looking at the source layer. That’s what citation intelligence is — understanding which sources AI is actually citing for a given location, category, and query, rather than assuming it mirrors traditional SEO logic.

What Agencies Can Do With This

For agencies managing multiple clients, particularly multi-location and enterprise brands, this creates a genuinely new service opportunity. Not a rebranding of existing citation work, but rather an expansion of it.

The starting point is source-level AI visibility data, available from a tool like Local Falcon. Every Local Falcon AI visibility report includes a Source Information section that identifies the specific pages AI cited when constructing responses for a given location and query. That data tells you not just whether a client is appearing in AI results, but also what sources are driving the results both they and their competitors are getting. This granular level of citation intelligence makes optimizing for local AI-driven search specific and actionable, rather than theoretical.

For instance, maybe the data shows a regional business journal consistently getting cited for competitors in a client’s category — and the client has never been featured there. That’s a PR pitch worth making. Maybe a vertical-specific directory appears in AI source data repeatedly across multiple markets, and the client isn’t listed there. That’s a quick fix with a measurable impact. Maybe a client’s own website service pages are thin and generic, and AI is recommending competitors and citing their business sites instead. That points to a content optimization problem.

For multi-location brands, this AI citation analysis also reveals something crucial: variability across markets. A brand with 50 locations might have strong AI visibility in some cities and almost none in others, not necessarily because of anything they’ve done differently, but because the local citation ecosystems in those markets look different. AI may be drawing from different regional publications, different community sites, or different local directories. Identifying that pattern — and building location-specific strategies to address it — is exactly the kind of high-value work agencies are positioned to deliver using AI citation intelligence.

How To Package It

The deliverable itself doesn’t need to be complex. A citation intelligence report for AI search that maps the source landscape for a client’s market, shows where competitors are earning citations the client isn’t, and lays out a prioritized action plan is something most clients have genuinely never seen. More importantly, it answers a question they care about — why isn’t AI recommending us — with actual data instead of generalizations.

It also creates a natural ongoing engagement. Citation sources shift over time. New publications earn authority. AI models update their behavior. Monitoring those changes and adjusting strategy accordingly is recurring work, and clients who understand the value of citation intelligence will recognize why ongoing monitoring matters.

The businesses consistently showing up in AI recommendations aren’t always the biggest names or the highest-rated — they’re the ones with the most credible, well-distributed brand footprint across the sources AI reads and cites. Helping clients build that footprint, with data driving every decision, is an agency service worth selling in the era of AI-driven local search.

New data suggests America’s deepest music engagement is happening far from the country’s largest media markets, creating new implications for brands, agencies, and local media companies trying to understand consumer intent and hyperlocal audience engagement.

If asked to name America’s most music-obsessed cities, most marketers would probably start with New York, Nashville, Los Angeles, or New Orleans. But new research from Wiingy tells a very different story.

The tutoring marketplace, which connects students with instructors across subjects including music, analyzed Google search behavior across 25 U.S. cities to determine where residents are most actively engaging with music learning and discovery. Rather than measuring streaming consumption or ticket sales, the report focused on music-related search intent. Those are searches tied to learning instruments, taking lessons, improving skills, and participating in music culture directly.

The findings point to a growing hyperlocal music engagement trend that may reshape how brands and media companies think about local audience engagement. According to Wiingy’s Music Pulse Score, Asheville, North Carolina ranked as the most music-engaged city in America on a per-capita basis, followed by Minneapolis, St. Louis, Atlanta, and Cleveland.

Meanwhile, traditional music capitals ranked surprisingly low. New York finished last among the 25 cities studied. Los Angeles ranked 23rd. Memphis placed 24th, while Nashville landed in the middle of the pack at 13th.

The report tracked 290 Google search keywords across guitar, piano, violin, singing, music theory, and music lessons, including searches like “guitar lessons near me,” “best online piano teacher,” and “how to read sheet music.” Importantly, the research focused on participation behavior rather than passive listening.

“This is participation behavior, not passive consumption,” said Shifa Ali, Lead Researcher at Wiingy. “Search behavior captures intent at the moment someone wants to engage actively, whether that means learning an instrument, finding a local teacher, or understanding music theory.”

Why Search Intent Matters More Than Streaming Data

For brands and agencies increasingly focused on consumer intent signals, the distinction is significant. Streaming data and ticket sales reflect audience attention after music has already been packaged and consumed, while music-related search trends reveal intent before a transaction or engagement occurs.

“Streaming data and ticket sales capture audience attention after music has already been packaged and sold,” Ali said. “Search behavior captures intent.”

That creates an important opportunity for marketers trying to align advertising with active consumer behavior. A music school advertising on Spotify reaches people who enjoy listening, while advertising against search intent data reaches consumers actively looking for lessons, instructors, or music education services.

“Conversion rates and intent alignment are fundamentally different,” Ali explained.

The shift mirrors broader changes happening across digital marketing and AI-driven discovery, where search intent increasingly matters more than broad demographic targeting alone. Participation-focused platforms have already recognized the trend. Ali points to companies like YouTube Music, Fender Play, and Yousician as examples of businesses capitalizing on active learning behavior rather than passive media consumption.

The Rise of Hyperlocal Music Culture

One of the report’s clearest patterns is the dominance of Southern and Midwestern cities in per-capita music engagement. While New York generated the highest raw volume of music-related searches overall, its population size diluted engagement intensity on a per-person basis. Asheville, with a far smaller population, produced dramatically higher music participation rates relative to its size.

Real American Beer Supports Live Music

“The methodology holds population constant precisely to surface this distinction,” Ali said.

But the trend extends beyond simple math. Ali believes many secondary cities maintain stronger traditions of participatory music culture than larger coastal markets.

“Appalachian folk and bluegrass culture in Asheville, blues and roots music in St. Louis, the funk and soul lineage of Minneapolis, and the jazz and R&B heritage of Kansas City and Cleveland all stem from traditions where music was made by ordinary people, not exported from a professional class,” she said.

Those traditions continue to create local engagement patterns rooted in participation rather than spectatorship. Affordability may also be contributing to the trend.

“Lower costs of living in cities like Asheville, Minneapolis, and St. Louis allow residents to invest time and money in music education, instrument ownership, and participation in local music scenes,” Ali noted.

For brands seeking highly engaged local audiences, those secondary cities may offer stronger community-level engagement than larger but more fragmented markets.

The Creator Economy Is Becoming More Decentralized

The report also reflects broader shifts happening across the creator economy. Ali sees DIY music learning and creator-led distribution as evidence that music culture is becoming increasingly decentralized and hyperlocal.

“YouTube, TikTok, and Instagram have created distribution channels where a guitarist from St. Louis or a singer from Atlanta can build an audience without label infrastructure, touring machinery, or major-market presence,” she said.

That dynamic increasingly resembles trends unfolding across local commerce more broadly, where creators, small businesses, and niche communities can build influence without relying on traditional media centers.

For agencies and multi-location brands, the implications extend beyond music. The Wiingy data suggests many national advertisers may still be over-indexing toward large coastal markets while underestimating secondary cities with disproportionately strong engagement signals.

“The evidence from the Wiingy study suggests brands and media companies are underinvesting in secondary cities with high engagement but lower visibility,” Ali said.

Despite Asheville producing more than four times New York’s per-capita music engagement, advertising investment patterns remain heavily concentrated around scale and media infrastructure.

“Advertising spending has historically correlated with raw market size, demographic wealth, and media infrastructure rather than per-capita engagement,” Ali explained.

But as AI-driven discovery, local search behavior, and consumer intent data become more central to marketing strategy, hyperlocal audience engagement may become increasingly difficult for brands to ignore. In that environment, the cities generating the deepest participation behavior,  not simply the loudest cultural reputation, could become some of the most valuable local markets in America.

For years, multi-location brands built local marketing around a fairly simple model. A customer searched, clicked a result, visited the website, and made a decision from there. Visibility was measured through rankings and traffic. Persuasion was expected to happen on the site. Performance was judged largely by what could be tracked after the click. That local discovery model is no longer reliable.

Local discovery has become distributed across a wider set of surfaces that shape preference before a website visit ever happens. Maps, reviews, business profiles, AI-generated answers, local articles, social content, and third-party mentions now play a much larger role in how customers evaluate options. In many cases, the website is no longer where discovery begins. It is where people go to confirm a decision that has already been influenced elsewhere.  

That is not a small channel shift. It is a structural change in how local demand is created and captured.

Where the Operating Model Breaks

Before ever reaching the company’s website, a customer comparing service providers may:

• scan Google Business Profiles
• read recent reviews
• notice how a business appears in local search results
• see a summary generated by an AI assistant
• come across a local mention or recommendation  

By the time that customer clicks through, much of the evaluation is already underway. In some cases, the choice has already narrowed to one or two options.  

The website still is critical for validation, structured information, conversion, and first-party data capture. But it no longer holds the same strategic position it once did. Treating it as the center of local discovery creates blind spots that many organizations still do not fully see.

That is where the operating model starts to break.

A website-centered approach assumes that:

• visibility is won primarily through rankings
• persuasion happens mostly through on-site content
• performance can be explained through site traffic and conversion data

But when influence is happening across third-party surfaces, those assumptions start to fail. Brands can lose ground before the click without realizing it. They can underinvest in the signals shaping local trust.  

The Performance Signals Teams Miss

This shows up in the numbers, even if teams do not always interpret it correctly.

One market may maintain stable traffic while lead quality declines.  

Another may show lower traffic volume but stronger conversion because reviews are fresher, listings are more complete, or local visibility is stronger where decisions are being made.  

If reporting is centered almost entirely on the website, those differences are hard to diagnose. Teams are left trying to explain performance changes with incomplete evidence.  

That is a measurement problem, but it is also a strategy problem.

What a Distributed Discovery Model Requires

A distributed discovery model requires a different mindset.

It starts with recognizing that visibility, trust, and preference are now built across an interconnected set of surfaces.  

Owned content still matters, but so do listings accuracy, review generation and response practices, local profile completeness, editorial mentions, paid support, social preseence, and the data infrastructure needed to connect those signals.  

This is not about adding complexity for its own sake. It is about aligning the operating model to the real customer journey.  

For multi-location brands, that has practical implications.

• Location data must be consistent and accurate across platforms.  
• Reviews have to be managed as an active layer of local performance, not a passive reputation exercise.  
• Content strategy has to extend beyond the website into the places where customers are actually forming opinions.  
• External mentions matter because they influence how both people and AI systems interpret a brand.  
• Measurement has to move beyond site sessions and form fills to include the pre-click conditions that shape demand.  

This is where a lot of organizations remain underbuilt. Brands that cannot see those signals clearly will struggle to explain local performance, allocate investment accurately, or improve market-level results with confidence.  

The Competitive Advantage Has Shifted

The competitive advantage now goes to brands that stop treating the website as the center of local discovery and start treating it as one part of a broader visibility system.

The winners will not be the brands with the most polished websites alone. They will be the ones that understand how local customers actually choose, build coordinated systems around that reality, and develop better line of sight into the signals shaping preference before a click ever occurs.

Local discovery has already moved. The question is whether the operating model has moved with it.  

As multi-location brands demand clearer proof that digital media drives real-world business outcomes, attribution is moving beyond clicks and impressions toward store visits and sales impact. A new partnership between PadSquad and Cuebiq aims to give marketers a more granular, real-time view of how campaigns influence consumer behavior at the local level.

For years, location-based advertising has promised marketers a cleaner line between digital impressions and real-world business outcomes. But for many multi-location brands, the reality has often been a patchwork of regional assumptions, lagging reports, and attribution models that stop short of meaningful operational insight.

A new partnership between PadSquad and Cuebiq aims to tighten that connection by giving brands more granular visibility into how digital media exposure translates into physical store visits and actual transaction outcomes. The partnership combines PadSquad’s digital advertising capabilities with Cuebiq’s mobility intelligence and consumer insights platform, allowing advertisers to measure metrics including in-store visitation, cost per visit, visitation frequency, and geographic performance trends at the individual store level.

For agency and brand marketers managing hundreds or thousands of locations, the move reflects a broader shift underway in local media measurement: the industry is moving beyond proxy metrics like clicks and impressions toward operationally actionable signals tied to real-world consumer behavior.

“Regional averages are comfortable, but nearly useless if you’re trying to understand what’s actually working,” said Lance Wolder, Head of Strategy at PadSquad, a digital advertising company. “Multi-location brands have been making million-dollar decisions based on numbers that were never built to support them.”

Attribution Moves Closer to the Store Level

One of the central differentiators in the partnership is the level of geographic precision available to advertisers.

According to CEO, Francesco Guglielmino, Cuebiq can connect campaign exposure directly to visitation at individual store locations, while also aggregating performance across designated market areas (DMAs), trade areas, or custom geographic groupings. “For every campaign, Cuebiq tracks when users that were served an impression show up at one of the targeted stores,” Guglielmino said. “But we only count it as a conversion if it meets a minimum dwell time threshold.” 

That dwell-time methodology is designed to filter out non-meaningful traffic such as delivery drivers, passersby, or consumers who briefly stop without engaging with the business. The result is a more refined view of physical visitation patterns that marketers can analyze by store, market, or campaign segment.

For multi-location brands increasingly pressured to justify local media investments with measurable business outcomes, that level of granularity has become more important as advertising budgets tighten and performance expectations rise.

Real-Time Optimization Becomes a Larger Priority

Historically, much of location-based attribution has functioned as a retrospective reporting tool. One useful for proving campaign impact after media dollars were already spent. But both companies argue that local marketers are now demanding something more operational: in-flight visibility that can influence decisions while campaigns are still active.

“Post-campaign reporting alone isn’t good enough anymore,” Wolder said. “If the only time insights reach the team is after the budget is spent, you’ve missed the opportunity.”  Instead, marketers increasingly want to know which markets are responding, which creative assets are driving visitation, and where budget allocation should shift while campaigns are still running.

Cuebiq’s reporting infrastructure delivers daily performance updates throughout campaign execution and during the final conversion window, according to Guglielmino. That enables brands and agencies to optimize media delivery against visitation trends in near real time.

The emphasis on in-flight optimization mirrors a broader evolution occurring across local media and retail media ecosystems, where advertisers are moving away from static campaign cycles toward continuous performance management models.

For agencies managing distributed campaigns across dozens or hundreds of markets, that operational feedback loop can become particularly valuable when performance varies significantly by geography.

The Organizational Problem May Be Bigger Than the Technical One

While attribution technology has matured considerably in recent years, Wolder argues the larger challenge today is no longer purely technical. “The technology stopped being the biggest obstacle a while ago,” he said. “The harder problem is organizational.”

That organizational gap is increasingly familiar across enterprise marketing teams. Measurement data often exists, but fails to influence planning, budgeting, or creative strategy in meaningful ways.

“I’ve seen great data sit completely unused because it never made it into the right conversation,” Wolder said. “It didn’t influence the brief, and it didn’t change the media plan. It just lived in a dashboard that nobody opened after the campaign ended.”

The implication is that attribution systems are no longer being evaluated solely on accuracy. They are increasingly being judged on whether the insights can integrate into active decision-making workflows across agencies, brand teams, and media operations.

Guglielmino similarly framed the partnership around integration and orchestration rather than standalone measurement. “Often, media players try to optimize costs and select a different partner for each step, without orchestrating a true integration with their systems,” he said. The partnership, he further added, is designed to create “a compounding effect of positive outcomes along every stage of the campaign journey.”

The Industry Pushes Toward Sales Attribution

Perhaps the most notable element of the partnership is its expansion beyond visitation measurement into transaction-level attribution.

Through Cuebiq’s partnership with Affinity Solutions, marketers can combine location intelligence with transaction data to analyze not only whether a consumer visited a store after ad exposure, but also what they spent once they arrived.

That opens the door to metrics including total spend, sales rate, sales uplift, and average basket size alongside visitation performance.

“In many ways, this is truly the gold standard of holistic attribution,” Guglielmino said. The integration reflects a broader industry trend toward omnichannel attribution models that connect digital exposure to both online and offline commerce outcomes.

For years, many local campaigns have optimized around store visits because visitation data was more accessible than transaction-level insights. But increasingly, brands want proof not just that consumers showed up — but that campaigns influenced revenue outcomes.

The ability to view both visitation and sales performance within a single reporting environment could become increasingly important as brands seek tighter alignment between media investment and business impact.The partnership also underscores how attribution itself is evolving from a reporting function into a strategic operational layer designed to continuously inform targeting, creative decisions, geographic investment, and customer acquisition strategy in real time.