World Cup-fueled revenue surge is highlighting how OOH has evolved beyond broad awareness. Digital inventory, localized activation and improved measurement are making the channel more flexible, relevant and accountable.

Out-of-home advertising entered the summer with strong momentum as major events, digital-first advertisers and more flexible buying options helped drive increased spending.

U.S. OOH revenue rose 10.7% year over year during the second quarter, reaching $3.16 billion, according to the Out of Home Advertising Association of America (OAAA). The World Cup contributed to the growth, with FIFA spending more than $3 million on OOH surrounding the tournament. Live Nation increased its investment by 10% compared with the same period last year.

Digital out-of-home remained a major growth engine and now represents nearly 40% of total OOH revenue. Coca-Cola, Apple, T-Mobile and McDonald’s were among the quarter’s 10 largest advertisers, while 72% of the top 100 OOH advertisers increased their spending.

The numbers reflect more than a few large national campaigns. They point to a medium becoming increasingly useful for coordinating national visibility with market-level relevance.

Turning Cultural Moments into Local Opportunities

The World Cup demonstrated how major cultural events can concentrate consumer attention, but Anna Bager, president and CEO of OAAA, said advertisers do not need a global event or national budget to take advantage of the same dynamic.

“The biggest lesson is to plan around attention, not simply around the size of an event,” Bager told Street Fight. “The World Cup creates an extraordinary cultural moment, but every community has its own high-attention occasions … from festivals and sports tournaments to concerts, conventions, college events and seasonal traditions.”

Those moments create opportunities to adapt national campaigns around the activity occurring in individual markets. A franchise system might coordinate creative around a regional sports tournament, while a restaurant chain could align messaging with concerts, conventions or neighborhood festivals near specific locations.

That requires looking beyond event sponsorship and considering where people will gather, how they will travel and what they may need before, during and after an event. OOH placements along transportation routes or near relevant destinations can then connect the brand to the occasion while directing consumers toward a nearby store, restaurant or service location.

“Local relevance is the advantage,” Bager said. “A business does not need a World Cup-sized budget to become a visible and useful part of a moment its community already cares about.”

Digital Brands Move into the Physical World

OpenAI, Genspark, Canva and Meta were among the technology companies increasing their OOH investments during the quarter. Their spending illustrates an important shift in how digital-first businesses think about brand building.

Performance marketing may help an emerging platform acquire users, but it does not always provide the public visibility or credibility needed to establish a new category. OOH places an otherwise digital product into the physical environments where people commute, shop and gather.

“Digital-first brands are discovering that building a category and earning trust require more than digital acquisition alone,” Bager said. “OOH gives emerging technology companies scale, stature, and a presence in the real world. It helps make an unfamiliar product or brand feel tangible while reaching consumers away from the clutter of their personal screens.”

That same principle applies to companies entering new markets or introducing unfamiliar services. OOH can establish awareness and credibility, while paid search, social media and mobile campaigns capture the demand it creates. The channels become complementary rather than competing parts of the media plan.

Connecting Exposure to Business Outcomes

Measurement has historically been one of the central questions surrounding OOH, especially for advertisers accustomed to digital performance metrics. OAAA’s revenue report tracks spending rather than campaign results, but separate research suggests OOH can produce measurable online and offline outcomes.

A 2026 OAAA study conducted with Kochava analyzed hundreds of campaigns across seven verticals. It found a median incremental lift of 20% for in-person outcomes and 14% for digital outcomes, compared with approximately 10% for streaming and broadcast television. Campaigns in retail environments generated median lift as high as 25%.

Those capabilities allow marketers to evaluate OOH against outcomes including store visits, searches, website traffic, app downloads, leads and sales. Agencies can also compare performance by market, audience, placement or creative treatment rather than treating OOH solely as a broad awareness channel.

The performance research is being accompanied by a broader effort to modernize the medium’s measurement foundation. As Street Fight previously reported, OAAA and Geopath are developing a next-generation audience measurement system intended to improve attribution, forecasting and interoperability with the platforms agencies already use to plan and evaluate other media.

This is particularly relevant as brands seek to understand how upper-funnel media influences local action. An OOH impression may not produce an immediate click, but it can prompt a branded search, a map lookup or a later visit to a nearby location.

DOOH Makes Its Case for More Digital Ad Dollars

DOOH Lowers the Barrier to Local Activation

Spending in the Local Services and Amusements category increased 10.5% during the quarter. Within Local Services, computer software spending rose 162%, followed by legal services at 14%, architects, contractors and engineers at 10%, and colleges and universities at 6%.

Bager attributed some of that momentum to the ability of local and regional advertisers to concentrate campaigns around defined neighborhoods and service areas. Digital inventory also lets advertisers run shorter campaigns, change creative quickly and adjust messages according to location, time, audience or real-world conditions.

“A regional advertiser does not have to buy an entire market for an extended period,” Bager said. “It can concentrate its investment around its service area, priority locations or highest-value moments.”

That flexibility may be especially valuable to franchise organizations, where national brand standards must coexist with different market conditions and local priorities. A system can maintain consistent branding while allowing individual markets or franchisees to emphasize nearby events, seasonal demand, localized offers or specific locations.

The second-quarter growth suggests OOH is gaining relevance as both national brands and regional advertisers look for ways to stand out in crowded digital environments. Its value increasingly lies in the ability to combine public visibility, geographic precision and measurable local action within a coordinated media strategy.

AI search is quickly becoming a default research step before a customer visits a store, books a service, or calls a support line. For multi-location and enterprise brands, that often means AI-generated answers are shaping decisions across hundreds or thousands of locations, and those answers aren’t always accurate.

When AI is asked about a brand, it pulls from training data and whatever it can retrieve in real time. When that information is thin, outdated, or conflicting, the model can fill the gaps with something that sounds plausible but isn’t true. That’s AI hallucinations, and at MULO scale, the risks are much bigger than a wrong address.

What Are AI Hallucinations?

AI hallucinations occur when generative AI states false information as confidently as accurate information.

When sources are sparse or contradictory, AI tends to guess rather than say it doesn’t know, because a confident answer reads as more useful than an uncertain one.

The model has no built-in way to verify anything against current reality, only patterns from training data and whatever it retrieves at the moment of the query.

Given a choice between admitting uncertainty and producing a wrong answer that sounds right, AI frequently chooses the latter.

For a single-location business, that guess might mean a wrong phone number. For a national or global brand, the same guessing behavior can get applied to policies, procedures, and other commitments, and misinformation gets relayed to customers as if official.

Why AI Hallucinations Look Different at Enterprise Scale

Large brands typically have the opposite problem from small businesses. A national retailer or a franchise network usually has plenty of press coverage, directory listings, and structured data for AI to draw from, so basic facts like a headquarters address are less likely to get mangled, but scale introduces a different kind of risk.

Enterprises have huge numbers of policies and offers, from return windows to loyalty terms to regional promotions, and those often vary by location, change frequently, or live in inconsistent places across a corporate site, franchise pages, and support documentation.

That inconsistency is exactly the kind of gap AI fills with a guess, and because these brands are the ones customers are most likely to ask AI about, an invented policy or offer is far more likely to reach a wide audience and get treated as fact.

The Consequences Go Beyond a Lost Customer

For a small business, a hallucinated address might send a shopper to a competitor down the street. For a multi-location or enterprise brand, a hallucinated policy could create a legal and financial mess.

A famous example of this isn’t from the local search world, but it should be a warning for anyone running a large consumer-facing brand. In 2024, Air Canada’s AI-powered website chatbot told a grieving customer he could apply for a discounted bereavement fare after booking his ticket. That process didn’t exist. Air Canada’s actual policy required the request before purchase.

When the airline refused to honor what its own AI had promised, the customer took the case to court and won. The court rejected Air Canada’s argument that it wasn’t responsible for its chatbot’s output, ruling that a chatbot is simply part of the company’s website and the company is accountable for everything on it.

Air Canada isn’t a multi-location business in the traditional sense, but picture the same failure at a retail chain with thousands of locations: AI confidently describing a return policy, a warranty term, or a price-match guarantee that the company never actually offers. Instead of one customer walking away, a fabricated policy like that can spread across support tickets, social posts, and news coverage before anyone at the company notices, and it can create huge headaches for the business.

Reducing the Risk for Multi-Location Brands

The fix starts with a simple principle: limit how much guessing AI has to do. For a MULO enterprise, that means keeping policy language, hours, pricing, and location-specific details consistent across the corporate site, individual location pages, franchise partner sites, and support documentation. A policy stated one way on the homepage and another way on a regional page is an open invitation for AI to merge the two into something wrong.

It also means treating your own customer-facing AI chatbots and support tools as a hallucination risk in themselves, not just a channel for delivering answers to FAQs. Test them regularly against your actual policies and offers, especially for anything with financial or legal weight.

From there, monitor what AI is actually telling people about your brand and its locations. A tool like Local Falcon can help track how AI platforms answer customer questions across a multi-location footprint, so discrepancies get caught before they create problems.

Beyond location data, enterprises should also run regular prompt audits on core brand and policy questions across leading generative AI platforms, track AI citations, and correct the source AI appears to be pulling from whenever something is wrong.

The Bottom Line

Large brands are less likely to have their address or phone number hallucinated by AI, but the stakes when something does go wrong are considerably higher.

A fabricated policy or procedure attached to a well-known name can spread fast and can carry significant consequences beyond a lost sale.

That’s why MULOs need to know exactly what AI is saying about their brand and take steps to ensure accuracy.

Marketers have never had more data or more sophisticated technology at their disposal. AI can optimize campaigns in real time, while modern media planning tools can model thousands of budget scenarios in minutes.

Yet many brands still struggle to turn those capabilities into better marketing performance. The problem isn’t necessarily the technology. It’s that the organizations responsible for media planning, measurement and budget allocation haven’t evolved as quickly as the tools they now use.

Too many organizations continue planning media in functional silos, measuring channels independently and requiring multiple rounds of internal approval before budgets can shift. The result is organizational friction that prevents marketers from acting on the insights their technology is producing.

The brands creating a competitive advantage are removing those barriers and making modern media planning more responsive. That starts with aligning finance and marketing, modernizing measurement, and treating the customer journey as one connected system rather than a collection of individual channels.

Aligning Leadership Around Media Planning

For many organizations, the first breakdown happens before a campaign even launches.

According to a June survey of senior marketing executives, when planning tools recommend a strategy, the single most common reason marketers don’t follow it is that the recommendation conflicts with leadership expectations.

Four in ten media plans are redrawn based on executive intuition rather than empirical evidence. In fact, a quarter of all mid-campaign plan changes (25.8%) are triggered by sudden leadership budget directives.

This disconnect creates a significant efficiency tax. Nearly half (49.2%) of marketers report only surface-level alignment between marketing metrics and overall business objectives. Meetings between marketing and finance teams can then stall around preferred metrics instead of focusing on how media investment should be allocated.

Fast-moving organizations address this by establishing shared business outcomes upfront, giving media planning teams clearer parameters for allocating and shifting investment. When marketing and finance agree on target incremental revenue and profitability before launch, optimization can happen against established outcomes rather than through repeated budget reviews.

That shared language matters. Some 36.7% of marketers prefer total revenue and 25.8% prefer incremental revenue to demonstrate success, reflecting a shift away from isolated metrics such as CTR or basic ROAS.

Measurement as a Media Planning Driver

As media investments become increasingly fragmented, modern media planning increasingly depends on marketing mix modeling (MMM) to understand how channels work together to drive incremental growth. Research shows that 30% of marketers now primarily use MMM for budget allocation.

However, 40.8% cite cost and resource requirements as the biggest barrier to wider adoption, suggesting many organizations recognize the value of advanced marketing measurement but struggle to integrate it into everyday planning.

AI is beginning to close part of that gap. Nearly half (48.3%) of marketers use AI for real-time budget reallocation, while 45.8% use it for predictive modeling. But technology adoption alone won’t eliminate the structural bottleneck. Analysis and modeling remain the single most time-consuming stage of measurement for 50% of marketers, meaning teams can still spend more time interpreting past data than taking future action.

The underlying issue is perspective. Many organizations continue to view measurement primarily as a scorecard rather than an engine for continuously guiding media investment. In a constantly shifting media landscape, that approach is increasingly difficult to justify.

Organizations can instead connect modern measurement platforms with AI-driven media planning capabilities that continuously refresh models, evaluate cross-channel interactions, conduct incrementality testing, and simulate future investment scenarios. Rather than waiting for post-campaign results, marketers can identify where returns are accelerating, where problems are emerging, and where budgets should shift while campaigns are still in flight.

Matching Media Planning to Modern Customer Behavior

Internal disconnects can also obscure the true customer journey. More than half of marketers (51.7%) struggle to connect top-of-funnel brand awareness with bottom-of-funnel conversions, despite tracking both.

That reflects an increasingly outdated view of the shopper journey. A customer might encounter a brand through CTV, social media, retail media, search, or a website before eventually making a purchase. Each touchpoint can contribute to the outcome, but measuring and planning those channels independently makes it difficult to understand how they work together.

Modern media planning requires optimizing across that entire customer journey, aligning investment, messaging, and measurement around how consumers actually buy.

According to Nielsen data, CPG brands adopting a full-funnel approach see up to 45% higher ROI and a 7% increase in offline sales compared with single-channel campaigns. Connected planning can help break down structural barriers by giving teams a clearer view of how channels such as retail media and CTV work together to compound growth.

That also changes the role of media measurement. Instead of determining which individual channel deserves credit after a conversion, measurement can help marketers understand how channels interact and use those insights to inform the next investment decision.

Technology Isn’t the Differentiator

The next era of marketing won’t be defined simply by who has access to the latest AI, measurement, or media planning technology. Those capabilities are becoming increasingly accessible.

The differentiator will be how quickly organizations can turn their insights into action. Brands that connect finance, marketing measurement, and media planning can move budgets faster, optimize around shared business outcomes, and respond more effectively as customer behavior changes.

Winning market share increasingly requires tearing down the walls between finance, measurement, and media planning to create a more fluid, growth-driven organization. The technology can identify where the opportunity exists. The competitive advantage comes from being organized to act on it.

Digital out-of-home advertising (DOOH) has spent years adding screens, data and programmatic buying capabilities. A series of recent developments suggests the next phase will be less about adding inventory and more about making that inventory easier to buy, measure and verify.

Two announcements this week illustrate that shift. VIOOH has partnered with Screenverse to make more than 38,000 U.S. digital screens available through its programmatic DOOH platform. Separately, Big Happy and Veridooh are introducing a verified-CPM buying model designed to tie advertiser payments more directly to independently validated DOOH delivery.

Taken alongside broader industry efforts around audience measurement and the continued expansion of programmatic screen networks, the developments point toward a larger transition. DOOH is adding more of the capabilities agencies and brands expect from mainstream digital media.

Programmatic Scale Is Expanding

The VIOOH-Screenverse partnership adds significant scale to the programmatic DOOH marketplace. Screenverse says its network generates more than 7.7 billion monthly impressions across more than 38,000 screens, with inventory in major markets including New York, Los Angeles, Chicago, Washington D.C., Dallas-Fort Worth and Philadelphia.

More important than the raw screen count may be where those screens are located. Screenverse aggregates inventory across retail, residential, office, transit, entertainment and healthcare environments. That diversity demonstrates how programmatic buying is beginning to connect what historically have been highly fragmented media environments.

Instead of separately negotiating access to different place-based networks, buyers can increasingly reach them through established programmatic workflows.

“Our goal is to make our premium inventory available through all the pathways our clients prefer to buy,” said Montana Accavallo, SVP of Programmatic and Client Strategy at Screenverse. For agencies, that reduces some of the operational friction that has historically separated OOH from digital media planning. VIOOH says the integration provides buyers with greater flexibility, precision targeting and efficiency across Screenverse’s U.S. network.

More Screens Make Verification More Important

Scale, however, creates another challenge. As DOOH inventory becomes easier to aggregate and purchase programmatically, advertisers need greater confidence in what was actually delivered. That is where the Big Happy-Veridooh partnership potentially represents a meaningful next step.

The companies announced August 25 that U.S. advertisers will be able to purchase DOOH campaigns on a verified-CPM basis, with advertisers paying for plays independently validated by Veridooh. The companies describe it as the first buying model of its kind for DOOH.

DOOH has traditionally relied on audience estimates and proof-of-play data to establish campaign delivery. Independent verification has increasingly added another layer of accountability. Attaching that verification directly to the commercial transaction goes further by connecting validated delivery with what the advertiser pays.

That begins to make DOOH look more like the performance-oriented digital channels against which it increasingly competes for budget.

Measurement Is Becoming Part of the Buy

The development also arrives as the broader OOH industry works to modernize its measurement foundation. OAAA and Geopath have been developing next-generation audience measurement standards intended to provide advertisers with more consistent and granular ways of understanding OOH exposure.

Those efforts are important because programmatic access alone does not make DOOH equivalent to other digital channels. Advertisers also need comparable measurement, attribution and accountability.

Independent verification at the transaction level adds another component. Audience measurement can help establish who was likely exposed. Attribution can connect exposure with outcomes such as website visits, store visits or sales. Verification can establish whether the media itself was delivered as intended. Together, those capabilities address different parts of the same advertiser question: What did my media investment actually deliver?

There are already signs that attribution is moving in this direction as well, with location-based measurement increasingly connecting media exposure to real-world visitation rather than relying solely on impressions or clicks.

Supply Is Becoming Easier to Aggregate

The timing is notable because DOOH supply itself is expanding quickly. Samsung recently announced a partnership with Smartify that brings Smartify’s advertising platform and hardware management capabilities into Samsung’s commercial display ecosystem. The arrangement potentially opens a much broader pool of national and local screens to advertising while providing centralized systems for managing them.

That development and the VIOOH-Screenverse partnership attack the opportunity from different directions. Samsung and Smartify are helping turn more commercial displays into manageable advertising inventory, while VIOOH and Screenverse are making a large collection of existing place-based inventory easier for programmatic buyers to access. Both contribute to the same outcome: more physical screens becoming addressable through digital advertising platforms.

But the larger the supply pool becomes, the more important standardization and verification become. An advertiser buying across thousands of screens and multiple venue categories cannot practically evaluate delivery screen by screen or network by network. The platforms and measurement systems increasingly have to do that work.

Where CTV Fits in the Media Mix

DOOH’s Next Competition Is for Budgets

That may ultimately be the significance of these developments for brands and agencies. DOOH no longer needs to prove that it can generate scale. Increasingly, it needs to demonstrate that its scale can be purchased, measured and verified with the same operational discipline advertisers expect elsewhere in their media plans.

The pieces are beginning to line up: expanding digital screen supply, aggregated programmatic access, more sophisticated audience measurement, attribution tied to business outcomes and independent delivery verification. None of those capabilities alone transforms DOOH into a performance channel, but together they make the distinction between DOOH and other digitally traded media considerably smaller.

For agencies deciding where the next incremental media dollar should go, that matters. DOOH’s opportunity isn’t simply to capture a larger portion of traditional OOH spending. It is to compete for budgets that previously would have gone to digital video, display, CTV or other measurable channels.

As programmatic inventory continues to expand, the question may therefore shift from whether advertisers can buy DOOH at digital scale to whether the industry can prove what that scale delivered. The emergence of verified CPM suggests that accountability is moving beyond measurement methodology and becoming part of the media transaction itself.

The digital screens already operating inside restaurants, retailers, hotels, gyms, and other commercial properties are increasingly becoming more than signage. They are becoming media inventory. Samsung Electronics America and Smartify Media are accelerating that transition through a partnership connecting Samsung commercial displays to Smartify’s digital out-of-home (DOOH) advertising network.

Samsung VXT Ads, Powered by Smartify, allows participating display owners to make screen inventory available to local, direct, and national advertisers without installing additional hardware or changing their existing display infrastructure. The implications extend beyond creating another revenue stream for screen owners.

By making existing commercial displays easier to activate programmatically, Samsung and Smartify could significantly expand the supply of place-based DOOH inventory available to advertisers at a time when spending on the channel is accelerating.

Turning Signage Into Media

Samsung VXT is the company’s cloud-based platform for managing commercial displays across individual locations or large portfolios. Smartify’s advertising infrastructure is being integrated directly into that environment, giving businesses the option to monetize portions of their screen time while maintaining control over their own content and scheduling.

The model removes much of the friction traditionally associated with turning signage into advertising inventory. Smartify manages the advertising marketplace, campaign delivery, monitoring, and operational support, while Samsung supplies the display and content-management infrastructure already operating inside commercial locations. A screen purchased for menus, promotions, wayfinding, or other business content can potentially become a revenue-producing media asset during available portions of its schedule.

DOOH Is Already Gaining Advertising Dollars

The expansion arrives as advertisers are putting significantly more money into out-of-home media. U.S. OOH revenue increased 10.7% year over year during the second quarter of 2026 to $3.16 billion, according to the Out of Home Advertising Association of America (OAAA), marking the first quarter in which industry revenue surpassed $3 billion. Digital formats are growing considerably faster, with DOOH revenue increasing 18.5% and representing 38.4% of total OOH revenue during the quarter.

The growth is occurring alongside an effort to make OOH more accountable to advertisers. Earlier this year, Street Fight examined how OAAA and Geopath are pushing OOH deeper into performance media through next-generation audience measurement designed to improve attribution, targeting, forecasting, interoperability, and advertiser confidence. OAAA President and CEO Anna Bager told Street Fight that the objective is measurement capable of withstanding the same CMO and CFO scrutiny applied to digital and television investments.

That combination matters for Samsung and Smartify. More physical screens are becoming programmatically available while the industry is simultaneously making those impressions easier to measure and compare with other media. Expanding inventory that can also meet increasingly rigorous performance standards is considerably more valuable than simply adding more screens.

Retail Media Is Becoming More Interoperable

The development also illustrates how the boundaries between digital signage, DOOH, and retail media continue to blur. Retail media has expanded beyond sponsored search and ecommerce placements into connected TV, off-site advertising, and physical stores. Commercial screens are becoming another layer of that infrastructure as technology makes them easier to connect to national advertising demand.

The direction resembles another trend Street Fight has been tracking. Walmart’s partnership with Magnite expanded access to Walmart Connect audience data and measurement capabilities through external advertising platforms. The significance was less about another integration than reducing friction between valuable media assets and the platforms agencies already use to buy advertising.

Samsung and Smartify approach that challenge from the inventory side. Rather than requiring commercial properties to construct their own media networks or sales infrastructure, existing displays can connect into a broader marketplace. In both cases, interoperability can increase the value of media assets by making them easier to access through established buying and measurement workflows.

Local Screens Can Create National Scale

Smartify already operates across more than 40 major U.S. markets, spanning storefronts, shopping centers, lifestyle destinations, and other commercial environments. Integrating its marketplace into Samsung VXT reduces the technical hurdle for additional properties to participate, potentially creating a much larger pool of national and local inventory.

Smartify CEO Joe Kunigonis told Street Fight that connecting Smartify’s advertising platform and infrastructure with Samsung’s commercial display ecosystem gives brands access to “a much broader pool of national and local inventory” while managed services can help standardize advertising operations across participating locations.

“That expanded scale creates new performance opportunities for advertisers while helping accelerate DOOH’s evolution into a high-reach media channel that can deliver audiences at a scale few other platforms can match,” Kunigonis said.

That scale is particularly important. DOOH has often been positioned around location and context, but connecting large numbers of existing commercial displays to programmatic demand creates the potential to combine local precision with aggregate national reach. For agencies, fragmented local inventory becomes easier to assemble into campaigns spanning multiple markets and commercial environments.

Physical Locations Are Becoming Media Infrastructure

For multi-location operators, screens can continue promoting their own products and services while unused inventory potentially generates advertising revenue. Samsung and Smartify expect adoption across retail, commercial real estate, automotive services, hospitality, fitness, and restaurants, categories where large numbers of commercial displays are already deployed.

The larger shift is that the distinction between operating a display and operating a media asset is becoming less clear. Programmatic technology is reaching deeper into physical locations, OOH measurement is becoming more performance-oriented, and retail media is becoming more interoperable with the broader advertising ecosystem.

Samsung and Smartify bring those trends together. The physical spaces where consumers shop, eat, work, exercise, and receive services are not simply places where advertising can appear. Increasingly, the technology already installed inside those locations is becoming part of the media infrastructure itself.

CTV is becoming more measurable, but the bigger shift may be how that measurement influences media allocation. New Lifesight research suggests CTV can affect search, social and second-screen behavior.

Connected TV has steadily moved beyond its traditional role as an upper-funnel awareness channel. As marketers gain more ways to connect CTV exposure with digital activity and business outcomes, the question is increasingly not simply whether CTV works, but where it belongs within the broader media mix.

That question is particularly relevant for multi-location brands. National and regional CTV campaigns can create demand across a brand’s footprint, while search, social and local media capture consumers closer to individual locations. Measuring those channels separately can make it difficult to understand which media actually influenced the customer and, ultimately, where the next advertising dollar should go.

New research from unified measurement platform Lifesight explores that issue in The State of CTV Measurement 2026: Navigating the Full-Funnel of CTV. Among its findings, the report says CTV exposure was associated with a 22.3% lift in paid-search conversion rates and an 8.5% increase in paid-social conversions, suggesting television’s impact can surface downstream in channels that ultimately receive the conversion credit.

Creating Demand Versus Capturing It

For Rajeev Nair, Co-Founder and CPO at Lifesight, that distinction exposes a weakness in how marketers frequently evaluate media. “Marketers often overvalue the channels that capture demand while undervaluing the channels that create it,” Nair told Street Fight. As a result, he said, last-click conversion rates and direct-response ROAS can become the definitive measures of a channel’s value, particularly for paid search.

For a multi-location brand, the distinction is important. A CTV campaign might introduce a consumer to a restaurant chain, retailer, fitness brand or home-services company, but the eventual conversion could begin days later with a branded search, social interaction or search for a nearby location.

If measurement gives the final interaction all the credit, the channel that created the initial demand can appear less productive than the channel that captured it. That doesn’t mean search deserves less investment. It means evaluating search, social, CTV and local media independently can provide an incomplete picture of how the channels work together.

CTV’s Place in the Multi-Location Media Mix

The shift increasingly positions CTV somewhere between traditional brand advertising and performance marketing. For multi-location brands, the customer journey might move from CTV exposure to search or social activity, then to local discovery and eventually an individual store, restaurant or service location.

That has implications for agencies making media-allocation decisions. If CTV is evaluated primarily against direct-response conversions while paid search receives credit for last-click activity, budget can naturally migrate toward the channel closest to the transaction even when another channel helped create the demand.

The opposite assumption is equally problematic. Marketers shouldn’t automatically credit CTV with every downstream increase in branded search or social conversion simply because someone was exposed to an ad. The more useful question is whether adding CTV produced business outcomes that otherwise would not have occurred.

That shifts CTV measurement from an attribution exercise toward a media-allocation tool, helping brands and agencies determine not just what happened, but where additional investment is most likely to produce incremental results.

Scorpion Pushes MULO Advertising Earlier in the Journey

Second Screens Create New Signals

Changing consumer behavior is also narrowing the distance between television exposure and measurable digital activity. Lifesight’s report found that 31% of viewers have used a second screen to shop through a QR code or shopping link during a show.

Nair said those interactions currently provide some of CTV’s clearest attribution signals because they establish a relatively direct connection between television exposure and subsequent mobile action. Visual search could eventually strengthen that connection further as technologies such as Google Lens make content appearing on a screen another starting point for product discovery, search and commerce.

For multi-location brands, the opportunity isn’t limited to ecommerce. A restaurant offer, retail product, fitness promotion or home-service message encountered on CTV can initiate a digital interaction that eventually ends at a physical location. The television screen becomes less isolated from the rest of the customer journey as viewing, mobile activity, search and local conversion increasingly intersect.

Multi-Location Brands Have a Testing Advantage

Proving that influence doesn’t necessarily require a sophisticated measurement operation. In fact, the distributed footprints that make multi-location marketing complicated can provide brands and agencies with a useful testing advantage.

Nair recommends geo-testing as a practical starting point for regional retailers, franchises and other distributed brands. Marketers can identify comparable markets, expose one group to CTV while maintaining another as a holdout, and compare the resulting business outcomes.

“The minimum is a reliable view of business outcomes, basic attribution to understand the data you already have and a way to run simple incrementality tests,” Nair said.

For a restaurant chain, those outcomes might include transactions, sales or visits across comparable markets. A retailer could examine store revenue or new customers, while a home-services franchise could compare leads, appointments or revenue across similar service areas. That moves the analysis beyond whether people exposed to CTV converted andpresents a more consequential question about CTV and incremental business activity.

Incrementality Matters More Than Someone Else’s ROAS

Lifesight’s report includes case studies showing incremental returns from CTV, but Nair cautions marketers against turning an individual advertiser’s results into an industry benchmark.

“I wouldn’t treat a 3x incremental ROAS as a benchmark that another advertiser should expect to reproduce,” he told Street Fight. “The goal isn’t to replicate someone else’s ROAS. It’s to understand the role CTV plays in your own media mix and where it delivers incremental value.”

For multi-location brands, that means identifying markets and audiences where CTV reaches consumers other channels aren’t reaching and determining when additional investment begins to lose efficiency.

That information is ultimately more useful for budget allocation than a generalized ROAS benchmark. A brand doesn’t need to prove that CTV produces the same return as another advertiser. It needs to determine whether moving another dollar into, or out of, CTV improves the performance of its own media mix.

Don’t Replace One Black Box With Another

As measurement becomes more sophisticated, marketers face another risk: replacing opaque platform attribution with equally opaque measurement models.

“No marketer should replace one black box with another,” Nair told Street Fight. He argues that marketers should apply the same scrutiny to measurement providers that they apply to advertising platforms, including understanding assumptions, methodologies and limitations.

That includes Lifesight’s own findings. Nair said the question marketers should ask about figures such as the report’s 22.3% paid-search and 8.5% paid-social lift is whether CTV actually caused those increases. Independent validation and controlled testing remain important before translating correlation into budget decisions.

For agencies, that creates an important role. Measurement isn’t simply about producing more sophisticated dashboards; it is about giving clients enough evidence to make better investment decisions across channels and markets.

Measurement Should Decide What Happens Next

CTV’s evolution creates a larger challenge for multi-location media planning. Brand, performance and local advertising can no longer be evaluated as completely separate activities when consumers move among television, search, social, mobile and physical locations.

Multi-location brands don’t necessarily need to begin with a complex unified measurement platform. They need reliable business outcomes, enough attribution to understand existing activity, and controlled tests capable of determining whether changes in media investment actually change those outcomes. More sophisticated modeling can follow as media volume and complexity increase.

“We’d rather see three well-designed geo-tests produce actionable learning than a dashboard full of metrics that no one can validate,” Nair said. “The value of measurement isn’t in how much it tells you, but in whether it helps you decide what to do next.”

That may be where CTV measurement increasingly fits within the advertising ecosystem. The objective isn’t simply to prove that CTV deserves credit. It’s to determine what incremental demand CTV creates, how that demand moves through other channels and locations, and whether the next media dollar should go there.

CKE Restaurants is building a more centralized local marketing infrastructure across Carl’s Jr. and Hardee’s while moving parts of its marketing closer to individual regions and locations. For multi-location brands, the strategy shows how automation can make enterprise scale and local relevance increasingly complementary.

For a restaurant company operating thousands of locations, the local marketing challenge isn’t simply getting more work done. It is maintaining accurate information, brand consistency, and market relevance across a footprint where customers, promotions, and competitive conditions can vary considerably by location.

That challenge is particularly pronounced for CKE Restaurants, parent company of Carl’s Jr. and Hardee’s. The two brands span more than 3,800 franchised and company-operated restaurants across 44 states and 43 foreign countries and U.S. territories. At that scale, even routine local marketing activities become enterprise operating problems.

CKE has been centralizing and automating more of the infrastructure supporting location data, local search, pages, social activity, and other elements of digital discovery. The larger takeaway is that technology is beginning to make location-level execution more feasible at enterprise scale.

The Scale Problem Is a Localization Problem

Multi-location marketing has traditionally involved a trade-off. Centralization creates efficiency, consistency, and brand control, but too much can produce generic marketing. Greater localization improves relevance but becomes difficult and expensive across thousands of locations.

CKE illustrates that tension particularly well because Carl’s Jr. and Hardee’s aren’t interchangeable brands operating across identical markets. Their geographic concentrations differ, and CKE has increasingly treated those differences as a marketing consideration.

That shift extends beyond local search. CKE has moved toward a more regional media strategy based partly on where each restaurant brand is concentrated, according to comments this year from CKE Director of Media Scott Sutton. That makes its local marketing efforts part of a broader direction: centralizing the infrastructure doesn’t necessarily mean centralizing every message.

Local Data Becomes Marketing Infrastructure

One foundation of that model is control over the information consumers encounter when looking for an individual restaurant.

Addresses, hours, menus, attributes, local pages, and other location-level information have traditionally been treated as components of listings management and local SEO. At CKE’s scale, they increasingly function as infrastructure connecting enterprise marketing with restaurant-level discovery.

A national or regional campaign can generate demand for Carl’s Jr. or Hardee’s, but converting that demand often depends on whether consumers can accurately find and evaluate the appropriate restaurant nearby. That becomes more consequential as discovery expands beyond traditional search into Maps, reviews, social platforms, and AI-powered recommendations.

CKE has been building toward a more connected digital operation for several years, including investments in online ordering, loyalty, data, and other digital capabilities. In 2024, the company also created a Chief Technology & Growth Officer position designed to align technology, digital strategy, operations, loyalty, and digital channels.

For a multi-location organization, location data therefore becomes more than an operational asset. It helps determine whether demand created at the brand level ultimately converts at the restaurant level.

AI Changes the Economics of Local Execution

Historically, increasing localization meant increasing labor somewhere in the system. Brands could add central staff, rely more heavily on agencies, ask franchisees and restaurant managers to do more, or accept less localization.

AI and automation introduce another option. More routine execution can be managed centrally without requiring people to initiate every action for every restaurant. For an organization operating more than 3,800 locations, that can change the economics of local marketing considerably.

The relevant metric starts shifting from how many campaigns a central team can manage to how much useful, locally relevant activity the organization can reliably execute across its footprint.

For franchise systems, that also offers a middle ground between allowing thousands of locations to market independently and forcing every restaurant into an identical national program. Data, technology, workflows, and brand standards can remain centralized while execution varies by brand, region, market, or location.

The objective isn’t unlimited localization. It is controlled localization, where enterprise infrastructure establishes the guardrails while local signals determine where variation makes sense.

Part of a Larger CKE Transformation

CKE’s local marketing strategy also sits within a broader modernization of its restaurant network. The company and its franchise community previously committed $500 million to a multi-year physical and digital transformation of Carl’s Jr. and Hardee’s, including restaurant renovations, technology upgrades, digital menu boards, and operational improvements.

That context matters because it shows that CKE’s approach to local marketing isn’t occurring in isolation. The company has been modernizing the connection between its enterprise infrastructure and what consumers experience at individual restaurants.

The same thinking is now visible in marketing. CKE’s regional media strategy, location infrastructure, and increasing automation point toward a model where more of the underlying operation is centralized while customer-facing execution becomes more relevant to individual markets.

Enterprise Scale Meets Location-Level Relevance

Customers don’t experience CKE as an organization managing more than 3,800 restaurants. They encounter a particular Carl’s Jr. or Hardee’s through search, Maps, reviews, social content, advertising, or increasingly an AI recommendation, and then decide whether that restaurant meets their needs.

That makes the individual location an increasingly important unit of marketing execution, even as the technology supporting it becomes more centralized.

For multi-location brands, CKE illustrates a potentially important shift. Scale has historically pushed large organizations toward standardization because customization was expensive to manage. AI and automation can begin changing that equation, allowing enterprise scale and location-level relevance to work together rather than against each other.

Scorpion is expanding its Google Ads offering with Demand Gen, giving multi-location brands another way to reach consumers before they actively search for a nearby service provider.

Paid search has long been a cornerstone of local customer acquisition. A consumer searches for a nearby home-services provider, attorney, pest-control company, or other service, and brands compete to put the appropriate location in front of that customer when intent becomes explicit. For multi-location brands, however, that may be too late to begin influencing the decision.

Scorpion is addressing that earlier stage by adding Google Demand Gen to its advertising offering, pairing it with tools including AI Max, Broad Match, and Smart Bidding. The combination allows brands to reach potential customers across YouTube, YouTube Shorts, Discover, and Gmail before they initiate a service-related search.

For multi-location marketers, that raises an important allocation question. Search remains essential for capturing demand around individual locations and service areas, but brands may also need to invest upstream to ensure consumers already know or trust them when local intent emerges.

Getting Ahead of the Local Search

Demand Gen uses Google’s AI to identify consumers demonstrating relevant signals of interest and reach them across Google’s visual properties rather than waiting for a specific query. Google data cited by Scorpion suggests those campaigns reach consumers beyond the audience exposed to traditional search advertising, with 68% of Demand Gen conversions coming from people who hadn’t seen the advertiser’s Search ads during the preceding 30 days.

That doesn’t establish that Demand Gen caused every conversion, but it suggests brands can reach a substantial pool of customers outside the conventional paid-search journey. Consumers may encounter a brand while watching video, researching a problem, or browsing before an immediate local need develops.

“If you’re a major brand in your space, Demand Gen is a must,” Ashlie Kim, SVP of Advertising at Scorpion, told Street Fight. “As consumer journeys become more complex and digital, brands need to be able to reach the right audiences with the right message across the entire purchasing journey. Demand Gen gives our clients another powerful way to do that.”

For multi-location brands, the objective is to become a familiar option before the local search happens rather than another unfamiliar result competing for attention once intent has already formed.

Creating Demand at Scale, Capturing It Locally

Scorpion isn’t positioning Demand Gen as a replacement for search. Google reports that advertisers adding Demand Gen to Search or Performance Max have seen an average 14% increase in conversions, supporting a strategy that combines earlier-stage demand creation with campaigns that capture consumers once intent becomes explicit.

For multi-location brands, those campaign types can operate at different levels of the customer journey. Video and visual media can establish familiarity across national or regional audiences, while search responds to intent around specific locations, markets, and service areas.

That can be particularly relevant to franchise systems. Corporate marketing may create awareness and consideration across the brand footprint, while individual locations ultimately need leads, appointments, calls, or customers. Connecting those investments turns brand-building and local performance marketing into different stages of the same acquisition strategy.

TikTok Is Becoming a Core Marketing Channel — Not Just a Social App

Video Gets More Local

Demand Gen also brings video more directly into the MULO performance-marketing mix. Google says 45% of U.S. YouTube Shorts users aren’t on TikTok and 65% aren’t on Instagram Reels, potentially giving brands incremental short-form video reach.

The opportunity for distributed brands goes beyond producing one national video. Home-services companies can showcase completed projects, pest-control brands can address seasonal problems, and other service businesses can demonstrate expertise before consumers begin comparing local providers.

For multi-location organizations, core creative and messaging can be developed centrally while services, seasonal needs, and calls to action reflect individual markets. Scorpion is producing the video and display assets required for its clients’ Demand Gen campaigns, addressing one of the operational challenges of extending visual advertising across a large location footprint.

Connecting Brand Demand to Local Conversion

Demand Gen also illustrates how AI is expanding the addressable audience around local markets. Search captures consumers who explicitly declare what they want; Demand Gen attempts to identify potential customers before they make that declaration.

That makes measurement more complicated. If someone encounters a brand on YouTube and ultimately converts through a location-level search ad, evaluating only the final interaction can give too much credit to the channel that finished the journey. For brands and agencies managing distributed media, understanding how national, regional, and local investments work together becomes increasingly important.

The significance of Scorpion’s expansion therefore isn’t simply another Google campaign type. For MULO brands, it points toward a more connected acquisition model built around brand-level demand creation, market-level consideration, and location-level conversion.

Search remains critical for capturing local intent. The larger question is whether enough future customers have encountered the brand before they search for a nearby provider and whether the appropriate location is ready to capture them when they do.

Digital Remedy and Arrivalist are connecting media exposure with GPS-verified visitation and consumer spending, giving travel marketers a clearer view of what happens after an ad is served.

Clicks, impressions, and website visits can tell marketers whether advertising generated digital activity, but they are considerably less useful for answering the question that matters to businesses dependent on physical locations: Did the campaign actually cause someone to show up and spend money?

A partnership between performance marketing company Digital Remedy and travel intelligence platform Arrivalist is attempting to close that gap for travel marketers by combining media activation with GPS-verified visitation and consumer spending intelligence. The companies are offering destination impact measurement alongside travel audiences built from observed travel behavior rather than inferred interests.

The immediate customers include destination marketing organizations (DMOs), hotels, resorts, and attractions, but the measurement problem extends well beyond tourism. Restaurant chains, retailers, entertainment companies, and other multi-location businesses face much the same challenge when trying to connect geographically targeted media investment with incremental store visits and revenue.

Moving Attribution Beyond the Click

The partnership combines Digital Remedy’s omnichannel media capabilities with Arrivalist’s VisitLift, SpendLift, and return-on-ad-spend measurement. Marketers can examine incremental visitation, visitor spending, origin markets, and the channels and campaigns associated with real-world travel behavior.

For Arrivalist CEO Ktimene Axetell, spending is the critical addition. “We’ve heard this resound from clients, prospects, agencies, and the greater industry: visitation lift is powerful, but marketers care most about ROAS,” Axetell told Street Fight. “They want dollar figures.”

That’s particularly important for DMOs, where Axetell said media can represent more than 70% of an organization’s budget and marketers must justify that spending to boards, hotels, local businesses, and other community stakeholders. The same logic applies to multi-location brands because foot traffic can demonstrate that consumers arrived at a restaurant, store, hotel, or attraction, but it doesn’t necessarily reveal the economic value of those visits. Connecting exposure to incremental visitation and then spending moves attribution closer to the business outcome marketers are ultimately trying to generate.

Accounting for People Who Would Have Come Anyway

One of the persistent problems with location attribution is separating campaign-driven visits from people who would have visited regardless of advertising. Arrivalist uses exposed and matched control groups to estimate incremental impact, which becomes especially important in travel, where seasonality, holidays, weather, events, and existing demand can all cause visitation to change independently of a campaign.

“Seasonality is exactly the kind of variable a matched control group is built to handle,” Axetell said. Natural arrivals can increase or decline for exposed and control populations simultaneously, she explained, allowing the remaining difference between the groups to provide a measure of campaign lift.

That principle has implications for any geographically distributed business. A restaurant experiencing increased summer traffic or a retailer benefiting from holiday demand shouldn’t necessarily credit those additional visits to advertising simply because customers were exposed to a campaign. Incrementality attempts to answer the harder and more useful question: how much activity happened because of the media investment?

Rethinking the Attribution Window

Travel creates another measurement complication with relevance beyond the category: consumers don’t necessarily convert on an advertiser’s timetable. A traveler can see an advertisement, research a destination, wait weeks before booking, and travel months later, making conventional attribution windows potentially incomplete measures of performance.

“Travel doesn’t work that way,” Axetell told Street Fight, describing Arrivalist’s measurement as longitudinal rather than ending with a fixed 30-day snapshot. “Consumers need time to make their trips, and we show those patterns over time to reflect real purchase behavior.”

The underlying lesson applies to other considered purchases and location-based experiences. Attribution methodology needs to reflect the actual customer journey rather than forcing every consumer decision into the same measurement window.

Arrivalist says that across its measurement clients it sees a 40% SpendLift over control groups associated with website visitation. That is a company-reported aggregate rather than an independently verified result, but Axetell sees an interesting implication as AI changes search behavior. Destination website traffic may be declining, yet consumers who do visit can still be influenced toward additional experiences and spending.

From Measuring Visitors to Building Audiences

The partnership isn’t limited to post-campaign measurement. Arrivalist is also making travel-behavior audiences available for activation through Digital Remedy. Rather than targeting people because a model predicts they might enjoy skiing, beaches, business travel, or other activities, the audiences can be built around observed visits to actual places.

That creates another potentially relevant application for multi-location brands: conquesting based on physical-world behavior. “Across private industry, where there is rich first-party data and more know-how, we see more conquesting moves,” Axetell said. Marketers may want to identify previous visitors to a particular place, category, or competitor and use those behavioral signals to build audiences for future campaigns.

Hotels and resorts could examine competitors’ visitors, while attractions could identify travelers already visiting complementary destinations. Restaurants, retailers, and other multi-location businesses can apply similar thinking around category visitation, competitive sets, and geographic markets.

Axetell said private-sector marketers are also interested in share of wallet, including how spending changes by season, origin market, category, and competitive set. That moves location intelligence beyond simply counting visits and toward understanding the economic behavior surrounding them.

Location Attribution Faces a Privacy Test

The growing sophistication of location intelligence creates a corresponding privacy challenge. Axetell said Arrivalist clients receive aggregated visitation information rather than device- or individual-level data and that upstream data is collected from users who have provided consent.

The company also adjusts its handling of geolocation information to comply with state-level restrictions on precise location data. That issue is becoming increasingly consequential as state privacy requirements around geolocation evolve, meaning the durability of a location-measurement strategy may depend not only on attribution methodology but also on whether its underlying data practices can withstand a tightening regulatory environment.

From Foot Traffic to Business Outcomes

Travel makes the attribution challenge particularly visible because the distance between ad exposure, website activity, physical visitation, and eventual spending can be substantial. But the underlying question is increasingly common across location-based businesses.

For MULO brands and their agencies, measuring a visit is becoming less of an endpoint and more of an intermediate metric. More useful measurement connects media exposure to incremental visitation, distinguishes customers who may have arrived because of advertising from those who would have come anyway, and ultimately establishes whether those visits produced enough spending to justify the investment.

That evolution brings location attribution closer to the metric marketers have wanted all along. That being ROAS grounded in what actually happened in the physical world.

Managing local SEO across dozens or hundreds of locations was already a heavy lift before generative AI entered the picture. Keeping Google Business Profiles current, review volume steady, and NAP data consistent across a large brand footprint takes real infrastructure, not to mention time. Generative AI search experiences have added more weight on top of that work.

Google AI Overviews, AI Mode, Gemini, ChatGPT, and other answer engines now generate responses to many of the same local-intent queries that traditional local SEO ranks businesses for, and they draw on overlapping but non-identical signals.

A location that ranks well in the map pack can still be nearly invisible in an AI-generated answer. For MULO brands, winning at generative engine optimization (GEO) requires another layer of tracking, optimization, and infrastructure on top of an already resource-intensive local SEO program.

A Five-Step Framework for MULO Scale

The same approach that works for improving AI visibility at a single location holds up at MULO scale, but the mechanics change when dozens or hundreds of locations are involved.

1. Establish a baseline across every location

Measure AI visibility for the queries that matter across all of your locations, not just a handful of flagship markets. Track Share of AI Voice, a measure of how often your brand appears in AI-generated answers for a given query and area, at the market level.

Keep in mind that aggregate, brand-wide numbers can hide serious performance gaps. A brand might look strong nationally while a dozen individual markets are nearly invisible in AI-generated answers.

Without a reliable baseline, it’s difficult to determine whether changes in AI visibility are the result of your optimizations, competitive activity, or shifts in how AI platforms generate responses. Establishing that starting point makes it much easier to measure progress and identify emerging problems before they affect large portions of your portfolio.

2. Watch sentiment, not just mentions

A location can appear constantly in AI answers and still be losing business if it’s mentioned in a neutral or unflattering context next to a stronger competitor. At MULO scale, sentiment drift is difficult to spot manually across hundreds of markets, making continuous monitoring essential.

3. Prioritize optimizations according to visibility and sentiment

Armed with localized AI visibility intelligence, you can see exactly which locations need attention first.

Start with locations that appear frequently in AI-generated answers but are associated with neutral or negative sentiment, as those mentions may be actively hurting conversions. Fixing negative brand sentiment here can offer high returns.

Next, identify high-value markets where AI visibility is lagging behind traditional local search performance. Focusing on the locations with the greatest combination of business importance and visibility gaps allows enterprise teams to allocate time and budget where they’ll have the biggest impact.

4. Identify the sources driving visibility, market by market

The sources AI cites for a query in one city are often different from the sources it cites for the same query in another. A directory or publication that carries weight in one region might be irrelevant in the next. Understanding those patterns, and where competitors are being cited that you aren’t, is what turns a multi-location GEO strategy into something that actually performs locally.

5. Build a feedback loop

Just like traditional local SEO, GEO is an ongoing practice. Optimizations affect visibility and sentiment, AI source preferences change, and competitors adjust their own presence. At MULO scale, AI visibility tracking needs to run on a frequent schedule, ideally weekly or bi-weekly, across all locations, rather than as a quarterly report that’s already stale by the time someone looks at it.

Because different AI models rely on different mixes of sources and ranking signals, it’s also important to remember that strong performance in one platform doesn’t guarantee high visibility or positive sentiment in another. MULO brands should monitor AI visibility across multiple platforms rather than assuming success in one translates directly to another.

How MULO Scale Creates a GEO Advantage

One of the biggest advantages MULO brands have over independent businesses is the ability to identify trends across an entire portfolio of locations. Looking at a single store in isolation can reveal what’s working in one market, but comparing hundreds of locations makes it much easier to spot the signals that consistently influence AI visibility.

For example, if locations with more complete Google Business Profiles, fresher reviews, or stronger local media coverage consistently achieve higher AI visibility, those insights can become organization-wide standards instead of isolated best practices.

The same applies to citation sources. If AI platforms repeatedly reference a particular local directory or regional publication in certain markets, other nearby locations can prioritize optimizing their presence there as well.

This kind of cross-location analysis also helps brands separate local anomalies from broader opportunities. A sudden drop in AI visibility for one location may point to a local issue, while similar declines across multiple regions could indicate a wider problem or a shift in the sources an AI platform trusts. Recognizing those patterns early allows enterprise teams to respond before visibility losses spread across the portfolio.

Instead of treating every location as a separate GEO project, successful MULO organizations use data from their entire footprint to continuously refine their strategy. Every optimization becomes another data point that helps improve the entire network, making each location stronger because of what others have already learned.