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.

Generative AI is accelerating creative production, but it’s also making performance harder to control. As more teams generate more assets, consistency drifts and optimization becomes less clear. The next phase of AI in marketing isn’t about speed … it’s about structure.

For midsize marketing teams, generative AI has unlocked unprecedented speed. Campaigns that once took weeks can now be launched in days, with seemingly limitless creative variations produced on demand.

But as output increases, control often decreases. Creative production is expanding beyond dedicated teams to media buyers and generalists, making it less clear who owns the integrity of the work. And yes, teams now have more creative. But they also have less clarity. Performance becomes harder to explain. Brand consistency drifts. Optimization slows down rather than accelerates.

When efficiency becomes the primary goal, effectiveness quietly erodes. How can CMOs protect against this digression?

When Everything Becomes Editable

Most gen AI creative tools operate on an open canvas. Any element of an ad can be rewritten, redesigned, or repositioned. Headlines, images, calls to action, even overall composition are all up for modification.

For experienced creative teams, that level of flexibility can be empowering. For non-creative users now tasked with managing media, generating variations, and analyzing performance, it can be overwhelming—and risky.

Without clear guardrails, small changes compound into larger issues. A headline drifts from brand voice. A layout loses visual hierarchy. A call to action becomes inconsistent across variations. Each asset may look acceptable on its own, but taken together, the campaign begins to feel fragmented. Performance suffers as a result.

At the same time, many teams introduce a different kind of inefficiency: more review cycles, more corrections, and more time spent fixing what should have been consistent from the start. What was meant to accelerate execution ends up slowing learning.

What DCO Gets Right

 Before gen AI tools became ubiquitous, dynamic creative optimization (DCO) offered a proven approach to scaling creative without sacrificing control. It wasn’t perfect. In many cases, it was manual, rigid, and heavily reliant on predefined inputs. But it was built on a principle that remains highly relevant: structure creates control.

DCO systems relied on templates to define where elements live and what can change. That structure enabled teams to generate variations at scale while maintaining consistency. It gave creative teams confidence that their brand would be represented correctly, even as assets were adapted across formats, audiences, and contexts.

The Sweet Spot: Structure Meets AI

 Today, gen AI solves many of the challenges that made DCO feel cumbersome. It can instantly produce variations, generate copy, and create visual assets without complex workflows. But without structure, that efficiency becomes difficult to manage.

The opportunity for marketers is not to choose between these approaches, but to combine them. A more balanced model starts with templates and guardrails defined by creative teams, establishing what must remain consistent and what can flex.

Consider what happens without that structure. A midsize retail brand launches a campaign using gen AI to rapidly generate variations across audiences. Media managers produce dozens of ads with different headlines, layouts, and calls to action. Some emphasize price, others quality. Messaging stretches beyond approved tone.

At first, the campaign appears productive. But performance becomes harder to diagnose. Audiences receive inconsistent signals, and the brand feels different from one impression to the next. Teams spend more time correcting assets than optimizing them.

What looked like efficiency turns into rework, inconsistency, and wasted spend. Without structure, scale dilutes creative quality and makes it harder to understand what is actually driving performance.

Three Things Every CMO Should Do Now

Gen AI is not optional. But scaling it effectively requires more than speed. Today’s marketing leaders must:

1. Redefine efficiency. Measure success by clarity of performance, not just speed of production. If more creative leads to more confusion about what works, something is off.
2. Establish guardrails early. Define brand elements, layout structure, tone, and ownership before generating variations. Decide what can change and what cannot.
3. Clarify ownership. As creative production expands, accountability cannot become diffuse. Someone must own how creative decisions are made and how performance is evaluated.

Control Makes Efficiency Sustainable

Gen AI is a powerful tool for improving efficiency, but efficiency on its own isn’t enough. Without control, speed leads to inconsistency. Without structure, flexibility leads to fragmentation.

The goal isn’t to limit what AI can do. These tools are essential to unlocking greater productivity and performance. The key is to ensure that what AI produces aligns with the standards that make campaigns effective in the first place.

The most successful teams will be the ones that design workflows where AI operates within intentional constraints. That’s how efficiency becomes sustainable

Retail media has expanded rapidly, but for most brands and agencies, it remains fragmented. Onsite retail ads and offsite media campaigns are still planned, executed, and measured in separate systems, leaving marketers to piece together performance after the fact. A new collaboration between Dollar General, Kevel, and The Trade Desk aims to change that by introducing a unified approach to full-funnel retail media activation and measurement.

The move positions Dollar General at the center of a broader shift in retail media: from siloed channels to connected commerce ecosystems that more closely mirror the real customer journey.

Breaking Down Onsite and Offsite Silos

At the core of the new solution is a long-standing industry challenge. Onsite retail media offers high-intent audiences close to purchase, while offsite channels provide scale across the open internet. But the two have historically operated in parallel, limiting both coordination and measurement.

The new framework connects Dollar General’s onsite inventory with offsite activation through The Trade Desk, with Kevel powering the underlying infrastructure. Campaigns can now be planned, executed, and measured across both environments with consistent reporting.

Austin Leonard, VP and GM of Dollar General Media Network, spoke with Street Fight and framed the move as a necessary evolution in how retail media is delivered. “This collaboration is about delivering stronger outcomes for our advertisers by unifying onsite and offsite campaigns within a single platform,” said Leonard. “Retail media is media and as specialized publishers, creating a more connected ecosystem has long been a priority. Opening our inventory to The Trade Desk, alongside the progress we’ve made in our retail audience marketplace, is an important step in helping brands win at Dollar General.”

For agencies and brand marketers, the implication is clear: fewer silos, clearer attribution, and a more coherent view of performance across the full path to purchase.

From In-Store Signals to Full-Funnel Activation

The announcement builds on Dollar General’s recent push into commerce media. Earlier this year, the retailer introduced an AI-enabled in-store audio network across thousands of locations, signaling a broader effort to digitize and monetize the physical shopping experience.

With more than 21,000 stores and expanding digital capabilities, including myDG delivery across the majority of its footprint, Dollar General has been steadily building a first-party data ecosystem. The integration of onsite and offsite media extends that ecosystem beyond owned channels, enabling advertisers to engage consumers earlier in the funnel and follow them through to transaction.

Leonard further noted that the strength of Dollar General’s onsite inventory also plays a role in making this integration viable. “Our onsite display inventory, powered through Kevel, consistently delivers 70–80% viewability, making this a natural time to integrate it further into our managed service campaigns as our digital commerce business continues to grow,” he told Street Fight. “It also reflects a broader, more strategic approach to scaling the DG Media Network, creating a unified solution that supports the full customer journey, with the flexibility to expand into self-service capabilities over time.”

Kevel’s Role: Enabling Interoperability

While Dollar General is the focal point, Kevel plays a critical role in enabling the integration. The company has positioned itself as a provider of retail media infrastructure, allowing retailers to build and control their own ecosystems while connecting to broader activation channels.

This approach aligns with a growing industry trend toward interoperability, where systems can work together without requiring full consolidation.

Jaclyn Nix, COO of Kevel, emphasized that standardization is central to scaling retail media effectively. “Standardization is one of the most critical levers for scaling retail media today,” said Nix in conversation with Street Fight. “Kevel’s onsite infrastructure provides the foundational connectivity that makes a unified ecosystem possible.”

She added that the collaboration addresses one of the industry’s most persistent gaps. “We are excited to partner with Dollar General and The Trade Desk on this first-to-market solution, which bridges the gap between offsite demand and onsite activation. By enabling seamless measurement and interoperability across the full consumer journey, we are helping retailers streamline their programs and unlock new levels of growth for their brand partners.”

The announcement builds on Kevel’s recent integration with Adobe, which aimed to enable real-time retail media activation within broader marketing workflows—further positioning the company as a connective layer within the evolving commerce media stack.

Extending Reach With The Trade Desk

The Trade Desk provides the offsite scale that completes the model. Through its platform, advertisers can extend campaigns across the open internet, including channels such as connected TV, digital audio, and display. By linking those channels with onsite retail placements, the collaboration enables a more coordinated approach to campaign execution. One that aligns upper-funnel awareness with lower-funnel conversion efforts.

For agencies, this creates a more streamlined planning process and a clearer connection between media investment and outcomes.

A Shift Toward Full-Funnel Accountability

The broader significance of the collaboration lies in what it signals about the direction of retail media. As the space matures, the focus is shifting from isolated channels to integrated ecosystems that support full-funnel strategies.

Unified measurement is a key part of that evolution. By connecting onsite and offsite performance, the new solution enables advertisers to better understand what impact media is actually having beyond baseline behavior.

For brands, this means greater visibility into what drives sales. For agencies, it offers a stronger foundation for optimization and performance storytelling.

What Comes Next

Testing for the solution is expected to begin in June 2026, with broader availability anticipated in the third quarter of this year.

For Dollar General, the initiative represents another step in its transformation from a traditional retailer into a scaled media platform. For the industry, it underscores a clear trend: the future of retail media will be defined not just by reach, but by how effectively that reach is connected, measured, and optimized across the full customer journey.

As identity signals fade, brands and agencies are being forced to rethink how they target and convert audiences at scale. Seedtag’s NeuroX introduces a new approach, using AI to understand mindset not just location, potentially reshaping how local campaigns drive measurable outcomes.

If you are a multi-location brand looking to interact with customers while they’re in a buying frame of mind, Seedtag wants you to know about NeuroX. Its full name is Neuro-Contextual Exchange power by Liz AI, and it basically gives brands the right mindset along with targeting the right zip code. The promise is that NeuroX does not just zero in on people who happen to be nearby to get precision. It can do so at scale simultaneously across markets.

“It’s not category-level guessing, not zip-code spray,” said Joseph Meehan, Chief Exchange & Supply Officer at Seedtag. There is a connection between Neuro-Contextual targeting and store visits, regional demand signals, and local conversion behavior. “This is where NeuroX earns its keep,” he added.  “Our intent models are built to support mid- and lower-funnel objectives: store visits, time on site and local conversions. Neuro- Contextual is the foundational planning and buying layer across the exchange, and buyers or curators layer their addressable signals on top for targeting, measurement, and optimization.

“You don’t replace what works — you give it a stronger base to stand on. When the intent signal is there, we see up to a 35% lift in CTR and conversions versus baseline — and that’s the part local advertisers feel.”

Meehan sat down with StreetFight to explain more in-depth how it works.

With identity signals fading, many local marketers still rely on geo and behavioral targeting—how does NeuroX complement or replace those strategies?

Local marketers aren’t wrong to lean on geo- and behavioral-targeting — those are the tools they have. The problem is that signal loss has cut them off from more than half the audience, so behavioral has gotten quietly less reliable while everyone keeps pretending it hasn’t. NeuroX makes previously invisible inventory addressable by reading interest, emotion, and intent at the exchange layer — so a local plan keeps its scale in premium environments where the behavioral data isn’t there anymore. In the short term, that’s a complement: layer NeuroX over your existing geo and behavioral approach, and you stop losing the half-audience. Longer term, Neuro-Contextual becomes the foundation of planning, and the addressable signals layer on top of it, not the other way around.

How does NeuroX perform in local or regional news environments, where context can be highly nuanced or sensitive?

News is where the industry has struggled the longest, and it’s exactly where context matters most. For years the default response was broad blocklists — entire categories of news inventory pulled out of buys because keyword tools couldn’t tell the difference between coverage of a tragedy and coverage of a community recovering from one. That cost local publishers real revenue, and it cost local advertisers real reach.

What’s different with NeuroX is that Liz reads the actual nuance of what’s on the page — emotional tone, framing, intent — not just keywords. So, a regional brand can scale confidently in local news without layering on “safety” filters that don’t know the difference between sensitive content and content that simply shares keywords with something risky.

Underneath that, our Brand Safety and Brand Suitability models give each advertiser their own line on where they’re comfortable. It’s not one blunt category list applied to everyone — it’s tuned to what the brand actually wants. That’s how we keep buyers in local news at scale without sacrificing relevance or control.

Where does contextual intelligence outperform location-based targeting?

Honestly, location is fine for what location does — it tells you where a person is physically. What it doesn’t tell you is where they are mentally, and that’s where every interesting decision actually happens. In my 20+ years working in media, I’ve watched location get treated as a stand-in for intent, and it just isn’t one. Two people can sit at the same intersection and be in completely different mindsets — one researching luxury travel, the other reading about home fitness. Geo lumps them together. A media plan built on it misses both.

Can you give examples of how NeuroX unlocks previously “invisible” inventory for local publishers?

Local publishers have been telling me a version of this for years: their most engaging inventory often isn’t the most obvious page on the site. A small community sports recap, a recipe column, a how-to article — these don’t fit cleanly into standard advertiser taxonomies, but they can reach a reader in exactly the right frame of mind to act on a related offer.

CTV is increasingly important for local reach so how does NeuroX ensure relevance at the household or community level without identity-based targeting?

What we do in CTV is shift the focus from who is watching to what is being watched. Using Liz, we analyze the content itself, decoding Neuro-Contextual signals within the video stream. That lets us understand not just what a program is, but how it feels in the moment — tone, framing, emotional context.

By combining those CTV insights with what we already understand from the open web, we can enrich otherwise limited data signals and make smarter decisions about when and where ads should appear. The result is a consistent, privacy-first targeting layer that doesn’t lean on IP addresses or device IDs — which matters more every quarter, because traditional identifiers will keep fragmenting. For local advertisers planning across screens, that means precision and scale and stay on the table even as the device-ID side gets harder to count on.

TikTok is no longer just a discovery engine—it’s becoming a measurable traffic driver for multi-location brands. A new Camp Bow Wow case study shows how native creative and intent-based targeting can turn reach into cost-efficient site visits at scale, signaling a broader shift in how agencies should be thinking about social performance.

A growing number of multi-location brands are rethinking how social platforms contribute to performance, not just awareness. A recent campaign from Camp Bow Wow, executed with Scorpion through TikTok’s Channel Sales Partner ecosystem, offers a clear example of how native content, when paired with precision targeting, can bridge that gap.

The campaign set out with a dual mandate that increasingly defines modern social strategy: build brand resonance while driving measurable site traffic. For agencies and multi-location brands navigating the shift from impression-based KPIs to outcome-driven media, the results highlight how TikTok is evolving from a top-of-funnel channel into a viable traffic and engagement engine.

The Strategy: Native Storytelling Meets Performance Intent

At its core, the campaign leaned into a principle that has become foundational on TikTok: creative must feel native to the platform. Rather than producing polished, brand-forward ads, Camp Bow Wow focused on real dogs in real daycare environments, embracing the lo-fi, authentic aesthetic that aligns with user expectations.

This approach was not simply a creative decision—it was a distribution strategy. TikTok’s algorithm prioritizes content that mirrors organic engagement patterns, and brands that replicate those signals tend to achieve stronger reach and efficiency.

To ensure relevance, Scorpion layered demographic and interest-based targeting on top of this creative, focusing on users already engaging with pet-related content. Critically, the campaign optimized not for views alone, but for landing page visits—reflecting a broader shift in how agencies are structuring TikTok campaigns toward mid-funnel actions that signal intent.

The Infrastructure Behind the Campaign

The campaign also reflects a broader infrastructure shift behind the scenes, specifically the growing role of TikTok’s partner ecosystem. In early 2026, Scorpion became a certified partner within TikTok’s Marketing Partner Program, gaining deeper access to platform integrations, automation tools, and advanced analytics.

That status goes beyond a credential. It enables tighter campaign execution through streamlined workflows, real-time optimization, and integrated reporting—capabilities that are particularly critical for scaling campaigns across distributed SMB and franchise networks.

More broadly, the partnership aligns with TikTok’s push to formalize a Channel Sales Partner layer, connecting SMB-focused platforms like Scorpion directly to its advertising infrastructure. The goal is to reduce friction for smaller advertisers while accelerating adoption, as more businesses look to TikTok not just for discovery, but for measurable outcomes like traffic, leads, and bookings.

For Scorpion, the move also reflects a larger strategic bet on video. As acquisition costs rise across search and other digital channels, video—particularly short-form—has become a central lever for driving both engagement and performance.

The Results: Scale Without Cost Tradeoffs

The performance metrics underscore the effectiveness of aligning creative, targeting, and optimization around traffic outcomes:

• 23,000 landing page views at a $2.01 cost per landing page view
• 50,000+ clicks at $0.92 CPC
• 7.3 million video views at just $0.006 per view
• 65% follower growth over the campaign period

When managing multi-location brands, the standout metric is not reach, it’s the cost efficiency of traffic. A roughly $2 landing page view at national scale positions TikTok competitively against more mature performance channels, particularly when paired with high engagement rates.

Why This Matters: TikTok’s Expanding Role in the Funnel

Historically, platforms like TikTok were positioned as awareness drivers, with limited expectations for downstream conversion. That framing is rapidly changing.

This case illustrates three structural shifts agencies and brands should note:

Creative is now a performance lever, not just a branding tool.
The campaign’s success was driven by creative that blended into the feed rather than interrupting it, reinforcing that on TikTok, creative quality directly impacts media efficiency.

Interest-based targeting is replacing keyword intent in early discovery.
By reaching users already engaging with pet content, the campaign captured intent signals upstream of search, reflecting a broader shift where discovery happens within feeds rather than queries.

Traffic optimization is closing the gap between social and search.
By optimizing for landing page views, the campaign ensured that reach translated into measurable site activity, positioning TikTok as a viable complement—or alternative—to paid search in certain verticals.

The Multi-Location Implication

For franchise and multi-location brands, the implications are particularly relevant. The campaign demonstrates that national-scale reach can be achieved without sacrificing cost efficiency, while still driving meaningful engagement tied to local intent.

This aligns with broader trends in local marketing, where brands are working to unify top-of-funnel awareness with localized conversion paths. TikTok’s ability to deliver both, through a combination of algorithmic distribution and partner-enabled execution, makes it an increasingly important component of the local growth stack.

The Bottom Line

The Camp Bow Wow case is less about a single campaign and more about a playbook shift. It shows that when creative aligns with platform behavior and campaigns are optimized for intent, not just exposure. TikTok can function as both a branding and performance channel.

Platforms that once sat firmly at the top of the funnel are now driving measurable outcomes and those that adapt their strategies accordingly are likely to capture the advantage.

Picture two locations of the same quick-service restaurant brand, both in the same metro area, both with solid review scores, both with optimized Google Business Profiles. Yet when you ask ChatGPT to recommend a spot in each of their respective neighborhoods, one location gets highlighted at the beginning of the AI-generated response with specific dish recommendations and language that signals genuine consumer enthusiasm. The other gets a short mention halfway through the response stating little more than its category, address, and hours.

Both locations are from the same brand, serving the same market, but they have completely different AI representation, or AI brand sentiment. This is what happens when multi-location brands treat AI search performance as a binary metric — you either show up or you don’t — and stop measuring there.

AI visibility is crucial, but what actually drives customers through the door is how AI describes you once you appear.

Why Location-Level AI Sentiment Diverges (And Why It Matters)

Each AI platform is assembling its impression of a business from a different mix of sources. Gemini, for example, draws heavily from Google’s search ecosystem, including Google Business Profile. ChatGPT, on the other hand, gives significant weight to Bing Places for Business data and Bing’s broader web index. Each AI platform also pulls from a range of third-party directories, data aggregators, blogs, and other publications across the web.

For a single-location business, this can lead to annoying inconsistencies in representation across AI platforms. But for a brand with dozens or hundreds of locations, it can result in a sprawling patchwork of AI sentiment, each shaped by a specific location’s individual data quality, citation footprint, review profile, and how well its information has been optimized across the sources each platform trusts most.

A location that has been meticulously optimized for Google but neglected on Bing could appear strongly on Gemini and weakly on ChatGPT. A location in a market with strong local press coverage will get richer, more textured AI descriptions than an identical location in a quieter market where there’s simply less editorial content for the model to draw from. These differences are often direct reflections of uneven brand data across the sources AI draws from.

Sentiment Is Where Revenue Leaks

A weak AI description doesn’t mean a location is invisible. It means it’s present but unconvincing. There’s a real difference between an AI response that describes a restaurant location as a neighborhood staple with a standout happy hour and enthusiastic recent reviews, versus one that outputs its name, cuisine type, and cross streets. Both responses technically surfaced the business, giving it AI visibility, yet only one of them really makes a customer want to go.

For multi-location enterprise organizations and franchise networks, this creates a brand consistency problem on top of the performance problem. For instance, the entire premise of a franchise brand is that customers know what they’re getting. If AI is painting wildly different pictures of different franchisee locations — some compelling, some forgettable — that inconsistency is undermining brand equity in ways that are currently invisible to many operators.

Agencies managing multi-location clients are vulnerable to the same blind spot. A client’s aggregate AI visibility can look healthy while specific markets quietly underperform because the AI descriptions in those markets are flat and undifferentiated.

AI visibility reporting alone won’t surface this. To truly optimize AI search performance for MULOs, you need to be measuring both AI visibility and brand sentiment simultaneously.

The Audit Comes First

Before you can fix location-level AI sentiment, you need to know what each platform is actually saying about each location — and right now, most brands don’t. They might spot-check a flagship market occasionally, but systematic sentiment tracking across AI platforms and locations isn’t yet standard practice for most MULOs.

It needs to be. Tools like Local Falcon now make it possible to audit AI brand sentiment at scale — surfacing how ChatGPT, Gemini, Google AI Overviews, Google’s AI Mode, and other platforms are characterizing each of your locations, where the descriptions are strong, and where they’re failing to differentiate.

When you find a location with weak sentiment on a specific platform, the citations within that AI response tell you what to fix. If the platform is drawing from thin directory data, that’s a listings problem. If it’s missing the specific attributes that make that location worth visiting, that’s a structured data and GBP optimization problem. If it’s not surfacing positive review language, that’s a review management problem. The path to better AI sentiment runs through the source inputs, and the audit shows you which inputs to prioritize per location.

Why This Is the Next Local SEO Battleground

Agencies that get ahead of AI sentiment tracking will have a differentiated service offering and a clearer performance story to tell clients. “We improved your AI visibility” is a reasonable claim. “We improved how AI describes your locations in the markets that drive your highest revenue” is a much more compelling one — especially when you can tie it to foot traffic and conversion data.

For brand-side teams, it’s a new operational discipline that belongs in the same category as listing and review management: something that requires ongoing monitoring, not a one-time fix.

Ultimately, the brands that come out ahead in AI-driven local search won’t necessarily be the biggest or the most visible. They’ll be the ones that figured out, location by location, what AI is saying about their business and optimized for sentiment alongside visibility.

For years, many multi-location brands treated reviews as a reputation issue. Something to monitor. Something to clean up. Something to escalate when a location had a public problem. That framing is outdated. 

Reviews now sit much closer to performance than many brands still acknowledge. They influence whether a customer trusts a location enough to call, book, visit, or keep searching. They shape local conversion. They expose operational inconsistency. And they often reveal location-level weakness before it shows up in broader reporting. 

For multi-location marketers, that changes the role reviews play in the business. Reviews are no longer just a brand perception signal. They are part of the local growth system. 

Trust Breaks Down at the Location Level 

Customers do not experience a multi-location brand as a national average. They experience one location, in one moment, with one set of signals that help them decide whether that business feels credible.  That is why review performance matters more than many organizations still admit. 

A paid impression, local listing, map result, or branded search may create the opportunity. Reviews often help determine whether that opportunity turns into action. If a location has weak review volume, stale feedback, unresolved complaints, or inconsistent ratings, trust starts to erode before the customer ever reaches out. 

That is not a reputation issue alone. It is a performance issue. When trust weakens at the market level, conversion conditions weaken at the market level too. 

Brand Averages Can Hide the Real Problem 

One of the biggest reasons review strategy remains under-managed is that brand-level averages make performance look healthier than it really is. 

A blended rating can conceal a lot. It can hide locations with declining sentiment, inconsistent review flow, poor response behavior, or service problems that have not yet surfaced in enterprise reporting. From the corporate view, the brand may look stable. In individual markets, trust may already be slipping. 

That matters because multi-location growth is built location by location. Enterprise performance is the sum of local experiences, local decisions, and local trust signals. If customers are hesitating in specific markets, the national average does not protect growth where it actually happens. 

Review Health Is More Than a Star Rating 

Many organizations still reduce review performance to one question: what is our rating?  That question is too narrow. A better question is whether each location is producing a credible, current, and consistent trust signal. 

That means looking beyond stars alone. Review recency matters because stale feedback can make the experience feel uncertain. Review volume matters because a steady flow of feedback gives customers more confidence that what they see reflects the business today, not six months ago. Consistency matters because sudden swings often point to underlying operational issues. Response behavior matters because customers are not only reading what reviewers say. They are also evaluating how the business shows up in public when something goes wrong. 

When brands only monitor the average rating, they miss the broader picture that shapes local choice. 

Review Strategy Needs Governance 

This is where many multi-location brands still fall short.  Everyone agrees reviews matter, but the system behind them is often fragmented. Marketing may partially own the program. Operations may influence the customer experience behind the feedback. Local teams may be expected to respond. Customer care may step in when issues escalate. But in many organizations, no one owns the full operating model. 

That usually leads to predictable outcomes: inconsistent review generation, delayed responses, uneven field participation, and review conversations that only happen when performance becomes a problem. 

A stronger approach treats reviews as a governed discipline with location-level accountability. It gives the organization a way to identify where trust is strengthening, where it is slipping, and where operational support is needed before issues spread. 

Reviews Belong Inside the Performance System 

Multi-location brands already apply governance to paid media, listings, reporting, and brand standards. Reviews should be managed with the same discipline. If reviews influence whether a customer trusts a location enough to choose it, then reviews influence growth. If review strength varies by market, then growth conditions vary by market too. 

That means review strategy does not belong off to the side under reputation management. It belongs inside the operating model for local performance. 

The important question is no longer whether reviews matter. Most leaders already believe they do. The more important question is whether the organization manages reviews with the same seriousness it applies to the rest of its growth system. Because at this point, reviews are not just telling the market what customers think. They are helping determine which locations grow.