Between a third and a half of purchased software goes unused, with analytical tools among the worst offenders. Our industry’s fix was to hire a translator instead of opening the audience data black box.

Somewhere in your operating budget is a six- or seven-figure line for audience data. You approved it. It produces real value. And the number of people in your building who can actually operate it is probably in the low single digits.

I should be careful here, because I sold those subscriptions. I ran the commercial side of Scarborough for a decade and was president of Simmons before this. The clients who got the most out of what I sold them all did the same thing: they hired someone whose job was to run it. The data was good and the value was real. I did not read the hire as a symptom at the time.

What it was, was a ceiling. Adoption stopped at however many specialists a company could justify, and the seller on the phone with a furniture retailer got whatever the specialist had time to hand over.

The broader software problem is measurable. Zylo, which tracks more than 40 million software licenses and $75 billion in spend, reported this year that organizations leave 36 percent of their licenses unused. Nexthink, measuring actual usage across more than six million customer environments in an earlier study, put the figure closer to half. Its data also showed a revealing divide: Slack, Teams and Zoom ran above 50 percent utilization, while Tableau and Spotfire sat below 15 percent. The tools people talk through get opened. The tools people analyze with do not.

I could not find a comparable study measuring how broadly broadcasters, publishers and agencies actually use the audience research they buy. But our workflows are unusually good at creating the same problem.

Specialists were the right answer to the wrong question

Salesforce surveyed just over 4,000 sales professionals last autumn and found 40 percent of a rep’s time going to selling and 60 percent to everything else. In our business, a chunk of that 60 is waiting. The seller asks the specialist, the specialist gets to it, and the answer arrives after the advertiser has decided.

Hiring the specialist made the tool usable. It also made the tool a bottleneck because every question in the building now routes through the same two people. Multiply that across a sales floor and you are not looking at a research problem. You are looking at a revenue-per-seller problem that nobody has been assigned.

The consequences of inaccessible audience data are already visible elsewhere in media. When the Association of National Advertisers sought log-level data for its programmatic media supply-chain study, 67 member companies initially expressed interest. Only 21 ultimately participated, with legal and data-access hurdles preventing many others from providing the required information.

The lesson isn’t limited to programmatic advertising. Buyers increasingly want to know where a number came from, what assumptions sit behind it and whether it can be verified. A research specialist may be able to answer those questions eventually. The seller sitting across from the advertiser increasingly needs to answer them in the meeting.

You Can’t Call It Incremental If You Can’t Measure It

What the conversation could sound like instead

Consider a furniture retailer in Kansas City. Audience data for that market in May shows about 153,000 households planning to spend $1,000 or more on furniture in the next twelve months, out of roughly 1.05 million in the market. That is an intent-level target rather than a demographic shell.

The more useful figure is where those buyers go for information. Furniture store websites lead the market at about 252,000 households. Social media runs under a third of that. Read as a media plan, that argues the retailer’s own site is where the decision gets made, and the job the local properties are hired for is to move a qualified buyer toward it. It also tells the retailer where not to spend, which is usually the part that earns the next conversation.

That is a different sale. Not reach against a demographic, but something an advertiser did not know about their own customer and a route to that person. None of it requires new data. It requires a seller who can reach the data on a Tuesday without filing a request.

The lever is cycle time, and it is an architecture problem

I have reached for the wrong lever here myself. When the data goes unused, the instinct is to buy better data, add more of it or train people on the tool nobody opens. We tried versions of all three. None of them touched the constraint.

Audience intelligence was designed as a periodic deliverable: commissioned, fielded, delivered, shelved and pulled down when somebody needs a number. Every step assumes the question arrives on a schedule and that a trained person will be the one asking it. Neither assumption holds when a seller is sitting with an advertiser trying to answer an unexpected question.

What replaces that model has to behave less like a study and more like a system a seller can query directly, on the day the question arrives, in the market the advertiser named. There is more than one way to build that. What is not optional is reducing the distance between the question and the answer, because the alternative is a subscription whose value is capped by specialist headcount.

What to check before you renew

Take the audience data subscription with the largest dollar figure next to it and count how many of your sellers opened it last month. Not the research team. The sellers. Then determine how many of them could explain where the number came from.

Next, time one unanticipated advertiser question from arrival to pitch-ready answer. That interval, rather than the size of the license or the sophistication of the interface, is what the advertiser experiences.

Finally, read the last ten proposals your team sent and count how many told the advertiser something about their own customer that they did not already know. That is the ratio the renewal conversation should turn on.

If nobody can tell you those numbers, that is the finding. If your sellers cannot tell the buyer where the number came from, that is the more expensive one.

The hardest part of marketing hundreds or thousands of locations isn’t simply doing more work. It’s knowing where the brand is winning, where it is losing and what action makes sense in each market. Two moves from Yext this week aim to connect that location-level intelligence with the people, AI agents and media budgets capable of acting on it.

A competitor can gain ground against one location while the brand continues to dominate another market. One location may need more paid support, another may have a reputation problem, while a third may already have enough organic and AI visibility that additional media produces limited incremental value. Yet multi-location marketing systems have historically made it easier to standardize execution than to respond differently to conditions across hundreds or thousands of markets.

Yext is trying to change that equation with two connected additions to its agentic marketing strategy. The company introduced a “multiplayer” agent harness within Scout, designed to put corporate marketers, field teams, partners, local managers and AI agents into a shared workspace. Hours later, Yext announced an agreement to acquire Flamel.ai, which automates localized paid campaigns across Google, Meta and ChatGPT.

From Individual Productivity to Shared Intelligence

Much of today’s generative AI workflow remains individual. A marketer works with an AI assistant, generates an analysis or asset and passes the result to someone else. Context can disappear during those handoffs, particularly across distributed organizations where corporate teams, agencies, franchisees and local operators may all participate in marketing decisions.

Yext’s new harness attempts to address that coordination problem by giving those groups access to the same Scout environment and agents. Scout monitors how a brand and its competitors appear across traditional and AI search, listings, reviews, webpages and social, from portfolio-level performance down to individual locations.

The approach also addresses an organizational problem familiar to multi-location brands. The people closest to a market may see change first, while the people controlling resources sit elsewhere.

“A store manager sees a local competitor move weeks before headquarters does, and corporate holds the budget to respond,” Yext Chief Data Officer Christian Ward told Street Fight. “Yext’s multiplayer harness puts both of them in one Scout workspace with agents that carry the brand’s full competitive context, so one market’s signal can shape decisions across the whole portfolio the same day.”

The potential value isn’t simply better collaboration. It’s shortening the distance between a local signal and a portfolio-level decision while giving both sides access to the context behind it.

Paid Media Adds an Execution Lever

The planned Flamel acquisition gives that operating model a more consequential execution channel. Flamel was built for brands with hundreds or thousands of locations, allowing corporate teams to establish campaigns and guidelines while adapting targeting, budgets and creative to individual markets across Google, Meta and ChatGPT.

Consider a 500-location brand where 100 locations already dominate relevant local and AI search results, 50 are consistently losing visibility to competitors and the remainder fall somewhere in between. Allocating paid media evenly across that network — or largely according to historical spending — doesn’t account for those differences. Once the acquisition closes, Yext plans to connect Scout’s location-level competitive intelligence with Flamel’s paid-media execution so those differences can potentially influence where incremental dollars are deployed.

“Every marketer knows the frustration of paying for a click they would have earned anyway,” Yext Chairman and CEO Michael Walrath said. “Scout already sees where each location is winning and where it’s losing to competitors. Bringing paid media execution into Yext lets marketers spend where it changes the outcome, not where they’re already ahead.”

That’s a compelling proposition, but visibility alone can’t determine where media dollars should go. Location-level margins, capacity, seasonality, customer value, promotional priorities and growth objectives also influence whether incremental spending makes sense. The more significant opportunity is using competitive visibility as another signal in those decisions rather than treating every location as though it faces the same market conditions.

Moving AI Beyond Productivity

The announcements arrive as enterprises are confronting a larger gap between AI adoption and financial impact. McKinsey’s latest global AI research found that 80% of respondents using AI said it improved their productivity, while only 37% reported a positive contribution to enterprise EBIT.

Yext’s argument is that simply adding more agents won’t necessarily close that gap. Walrath describes the distinction more pointedly: “tools always commoditize, and marketers never do.” His contention is that human judgment combined with agents carrying historical context, memory and competitive intelligence creates more value than increasing the number of agents working independently.

For MULO marketers, the more interesting test is whether that combination changes decisions rather than simply accelerating tasks. Yext has already been pushing Scout from measuring AI visibility toward taking actions across listings, reviews and social. Paid media would extend that concept into decisions involving actual marketing dollars.

From Standardization to Selective Action

The two announcements ultimately make more sense together than separately. A shared agent environment without meaningful execution risks becoming another collaboration layer. Automated paid-media execution without sufficient business and local context risks optimizing activity without necessarily improving outcomes.

Connecting the two points toward a different model for MULO marketing. Corporate teams can maintain brand standards and control resources while local operators contribute market intelligence. Agents can monitor conditions across locations and surface differences that warrant attention, while execution systems can potentially respond at the market level rather than applying the same action everywhere.

That doesn’t eliminate the need for marketers to decide which signals matter or when an automated recommendation should be overridden. It makes that judgment more important as agents gain the ability to influence customer-facing content and media spending.

For multi-location brands, that may be where agentic marketing becomes more consequential. The value goes beyond using AI to do existing work faster. It’s using location-level intelligence to determine where people, agents and marketing dollars should act and where they shouldn’t.

McDonald’s is using AI to optimize prices at individual restaurants, while Walmart is drawing a clear line around how algorithms influence what customers pay. 

McDonald’s is using AI (artificial intelligence) to help determine what a Big Mac should cost at individual restaurants. The technology may produce more precise pricing, but it also raises questions about franchisee independence, customer trust and how much local variation a national brand can support.

A Reuters article found that McDonald’s is increasingly using a machine-learning pricing engine across its nearly 14,000 U.S. restaurants. Developed partly with Tiger Analytics, the system analyzes millions of transactions, local market conditions and customers’ apparent willingness to pay. It then recommends an “optimal” price for each menu item at each location.

Franchisees retain the final decision, but some operators reportedly feel pressure to accept the recommendations. That creates tension between corporate pricing intelligence and the autonomy of the local businesses expected to implement it.

AI Makes Local Pricing More Precise

McDonald’s restaurants have never necessarily charged identical prices. Franchisees face different labor, occupancy and food costs, while serving markets with different income levels, competitors and demand. Local operators need room to adjust prices to their restaurant economics.

AI changes the scale and precision of that process. Instead of relying primarily on local experience or broad corporate guidance, the system can estimate how customers near a specific restaurant may respond to a price change. Pricing can reflect local conditions while drawing on the data and analytical capabilities of the national system.

The risk is that optimization gradually replaces judgment. When a corporate system presents one price as mathematically optimal, rejecting it may become difficult, even with the franchisee technically remaining in control.

Local Flexibility Can Create a Brand Problem

Customers do not necessarily think of individual McDonald’s restaurants as independent businesses. They see one brand and generally expect comparable value wherever they encounter it.

AI-generated recommendations have reportedly contributed to significant differences between nearby restaurants. The most visible example came from a Connecticut location that charged approximately $18 for a Big Mac meal. Although the price generated national criticism, the restaurant’s transaction data reportedly showed little damage to sales.

That may look successful inside a pricing model, but the brand calculation is more complicated. One location’s price can become evidence that the entire chain is no longer affordable, particularly once a receipt or menu-board image circulates online.

McDonald’s therefore needs to measure more than whether a higher price reduces transactions at the restaurant adopting it. The company must also consider sentiment, value perceptions and the effect one highly visible price can have across the system.

The Franchise Governance Challenge

Franchisees are independent businesses and, for legal purposes, potential competitors. Corporate recommendations must be structured carefully to avoid becoming centralized coordination among operators that are supposed to set prices independently.

McDonald’s reportedly warns franchisees about this issue while continuing to provide more sophisticated recommendations. The company wants the benefits of systemwide data without appearing to dictate what separately owned restaurants charge, a familiar version of the tension between corporate control and local franchisee execution.

Operators need to understand which inputs affect a recommendation, how expected transaction losses are calculated and whether corporate priorities differ from restaurant-level profitability. They must also retain a meaningful ability to override the system when local knowledge points toward a different decision. AI should inform the operator without quietly becoming the operator.pro

Walmart Draws a Different Boundary

Walmart offers a useful comparison. As the retailer expands digital shelf labels and its AI shopping assistant, CEO John Furner has committed that Walmart will not use a customer’s income, shopping history, urgency or perceived willingness to pay to determine an individualized price.

“We price the product, not the person,” Furner wrote in a letter to customers. Walmart says its digital labels improve accuracy and reduce the work required to replace paper tags—not enable personalized or time-sensitive price increases.

McDonald’s system recommends prices for locations rather than individual customers. That differs from surveillance pricing, in which personal information produces different prices for different people. However, both companies recognize that consumers are becoming more sensitive to how algorithms influence what they pay.

Walmart’s position is easier to communicate in the current environment. Shoppers already feel pressure from higher everyday costs, while pricing consistency has become an important part of customer trust. McDonald’s may gain greater location-level precision, but it assumes more reputational risk when one restaurant’s optimized price becomes a national symbol of declining value. AI can recommend what a market will tolerate; the brand must decide what customers will consider fair.

Incrementality is becoming one of advertising’s most overused claims. Without a true holdout and a disciplined test, reported performance can’t tell brands what would have happened anyway.

Choosing a new DSP partner should really come down to one question: Does it bring genuinely different intelligence, or is it just another place to spend the same budget? That question only has a real answer if incrementality is measured, not just claimed. 

“Incrementality” should become the standard advertisers reach for when judging a new platform, a new algorithm, or a new audience strategy. It’s the right standard, but it is also becoming a word people use without a method behind it. A platform review that concludes a partner is “driving incremental results” based on its own reported conversions hasn’t tested incrementality at all. It has repeated the platform’s homework.

Before incrementality can become the standard, it needs a measurement framework. That requires a different kind of test, and most media organizations are not yet set up to run it.

Reported performance isn’t proof of incrementality

Every platform can report CPA or ROAS alongside a conversion count. None of those numbers say what would have happened without the campaign. A platform that reports strong performance may simply be claiming credit for people who were going to convert anyway, or who another partner in the stack was already reaching. Two DSPs can both report attractive results while splitting the same pool of likely buyers, and the reporting from each will look identical to genuine growth.

This is a structural issue rather than a data-quality problem. The reporting may be accurate on its own terms while remaining unable to establish causality.

The holdout is the only honest comparison

A real incrementality test compares exposed and unexposed groups that are otherwise identical. It then measures the gap in outcomes between them. 

In digital media, that typically means using a public service ad or segment test, with a portion of the eligible audience withheld from the campaign entirely. Advertisers can also use a geo-based holdout, with matched markets serving as test and control.

Give the test enough time to mean something

A holdout that runs for a week captures noise more than it captures signal, particularly for products with a longer consideration cycle. Advertisers should plan for enough flight time to accumulate a meaningful sample in both groups, plus additional time after spend ends for delayed conversions to appear.

Ending a test early because the topline numbers look promising is one of the most common ways an incrementality read gets corrupted. The lift that shows up in week two doesn’t always hold in week four, and the only way to know is to let the test run its full course.

Isolate what’s actually being tested

A platform test answers a different question from an audience or creative test. Collapsing them into one read produces a clear answer to none of them. If a new DSP is being evaluated, the audience strategy and creative should stay constant across test and control so that any lift can be attributed to the platform’s decisioning, not to a different targeting approach running underneath it.

The same logic applies to evaluating a new audience or a new algorithm. Whatever variable is under review should be the only one that changes. Everything else needs to be held still long enough to isolate its effect.

What a credible incrementality test requires

Before launching a test that will inform a real budget decision, advertisers should be able to answer the following questions:

• Is there a true holdout, not just a reporting comparison? A control group that isn’t exposed to the campaign is the only way to see the counterfactual.
• Is the sample size large enough to detect a meaningful lift? Small holdouts produce results that look directional but aren’t statistically reliable.
• Is the flight length matched to the conversion cycle? Tests ended before lag time has played out will understate or misstate the result.
• Is every other variable held constant? All test conditions beyond the variable under review should stay fixed for the duration of the test.
• Is there a predetermined threshold for what counts as a win? Deciding what “enough lift” looks like after seeing the results invites the test to confirm whatever the organization already wanted to believe.

Incrementality is a discipline

Incrementality testing takes work, and directional reads may be sufficient for lower-stakes decisions. For decisions that materially affect the media stack or budget allocation, however, a claim of incrementality is only as strong as the test behind it.

Advertisers get real value from their media stack by looking beyond the highest reported ROAS. They build the discipline to ask what would have happened anyway and trust a platform’s contribution only once a proper test has answered that question.

For most multi-location brands, and the agencies that manage their local SEO, Apple Maps has historically lived somewhere near the bottom of the priority list. It’s the platform you claim your listing on, fill in with basic information, and occasionally revisit if something needs fixing.

Google Maps listings get the comprehensive audits, the dedicated review response workflows, and the regular rank tracking and reporting, while Apple Maps largely gets checked off a list and forgotten.

Regardless of whether a MULO brand has been actively managing their presence and tracking rankings on Apple Maps, it’s something that’s worth taking a closer look at now.

Why? Well, Apple has spent the last few years building a real local business management and local search platform, and it’s now feeding that platform’s data directly into the AI the company is starting to build out across its entire ecosystem.

Apple Maps Is Finally Looking Like a True Local Business Platform

Apple Business Connect launched in January 2023 as Apple’s answer to Google Business Profile, giving businesses control over their listing, called a Place Card, and allowing them to upload photos, edit hours, select categories, post promotions, and create CTA links for things like reservations and ordering.

Earlier this year, Apple folded that tool, together with Apple Business Essentials and Apple Business Manager, into a single platform called Apple Business, unifying brand and location management under one login alongside device and account administration.

Then in August, Apple began rolling out ads on Maps, giving businesses a paid placement option in Suggested Places lists and in-app search results for the first time.

It’s important to note that none of this closes the usage gap with Google. Apple Maps still accounts for no more than a quarter of the US mapping market, compared to Google’s at least two-thirds.

But for a portfolio of locations, a quarter of a large and growing market is not something to write off, especially once you factor in what that data now feeds into.

Siri AI, Another Big Reason to Start Paying Attention to Apple Maps

Apple’s local business data has always powered more than the Maps app itself. When someone asks Siri to find a restaurant nearby or the closest hardware store, Siri has long pulled that answer from Apple Maps listings. That dependency isn’t going away as Apple’s AI ambitions grow. If anything, it’s becoming more central.

Siri AI, Apple’s ground-up rebuild of its Siri assistant powered by Apple Intelligence, began rolling out in beta alongside iOS 27. It’s a far more conversational, context-aware assistant, and Apple has been explicit that it’s meant to work systemwide, answering questions and taking action on a user’s behalf based on conversational prompts. It’s integrated into Spotlight on iPad and Mac and into onscreen context menus across the OS.

That matters for multi-location brands because as Apple’s AI continues to evolve, Apple Maps will play a central role in influencing which locations get discovered via conversational search.

In other words, a complete, accurate, optimized listing won’t just help a location show up when someone opens Apple Maps and searches. Apple Maps data will also determine whether that location gets surfaced when a customer asks Siri a conversational, local-intent question from their mobile device’s lock screen, their CarPlay display, or their Mac.

What This Means for MULOs and Agencies

For agencies and in-house marketing and SEO teams managing dozens or hundreds of locations for a MULO brand, this is a case for making Apple Business optimization and rank tracking as disciplined a process as that which they already apply to Google Business Profile, rather than treating it as a separate, lower-tier task.

That starts with treating Apple Maps as a channel that gets tracked and reported on, with a tool like Local Falcon, the same way Google Maps does for existing clients and locations. Apple Maps rank tracking across a portfolio of locations needs a consistent cadence, not a one-off audit when the platform happens to come up as an afterthought.

Local SEO agencies should also be building Apple Maps optimization and rank tracking into the standard onboarding process for new multi-location clients.

The bigger change here for marketers is one of mindset more than process. Apple Maps has gone from a platform where “good enough” was an acceptable standard to one where managing visibility with the same rigor applied to Google can help drive measurable business results.

Local SEO teams that make that adjustment sooner rather than later, while much of the industry still treats Apple Maps as a low-priority channel, stand to build a meaningful edge for the multi-location brands they represent when it comes to visibility across Apple’s local search ecosystem.

This doesn’t mean Google Business Profile should lose its place at the top of the local SEO priority list. Visibility across Google Maps, the local pack, and Google’s generative AI search experiences still drives most local search traffic.

What’s important is recognizing that Apple has quietly built a local search platform comparable to Google Maps, and it’s worth taking seriously for MULO brands looking to attract more business through Apple’s local and AI-driven search experiences.

Home services marketing has traditionally been built around capturing consumers when a local need turns into a search. Scorpion brought together OpenAI, TikTok, ServiceTitan and others to reflect on how that acquisition model is expanding as AI discovery, short-form video and operational data reshape the path from awareness to booked job.

For home services brands, search has long occupied a privileged position in the customer journey. A homeowner needs an HVAC repair, plumber or pest-control provider, searches for one nearby and creates an opportunity for marketers to capture high-intent demand.

That model isn’t disappearing, but it is getting more complicated. Consumers can now encounter a service brand through short-form video, ask an AI assistant which provider to hire and move between discovery channels before ever conducting a conventional Google search. That changing journey was the underlying theme of Scorpion’s Home Services Growth Summit, which brought together executives from OpenAI, TikTok, ServiceTitan, Thumbtack and others. The event itself isn’t particularly significant for multi-location marketers; the mix of companies participating is.

Discovery Is Spreading Across More Channels

Scorpion’s 2026 State of Home Services Marketing Report found that 83% of homeowners begin their search for a provider online, but 22% already use AI tools such as ChatGPT to research vendors or gather recommendations. At the same time, 80% of home-services business leaders surveyed said they were unsure how to appear in AI-driven search environments.

That creates a widening challenge for brands operating across multiple markets. Traditional search visibility remains important, but marketers increasingly need to consider whether their locations are represented accurately when an AI system synthesizes recommendations rather than displaying a familiar list of search results.

Advertising is following that behavior. Scorpion became a technology partner supporting advertising in ChatGPT earlier this year, and OpenAI participated in the summit with a session addressing what ChatGPT Ads could mean for home services businesses. For multi-location marketers, conversational advertising potentially adds another layer to an already fragmented media mix. Paid search captures explicit demand, while AI advertising could reach consumers inside the environment where they are researching options and narrowing their choices.

The Funnel Is Expanding in the Other Direction

AI isn’t the only force changing the acquisition model. TikTok’s presence at the event reflects increasing attention on reaching homeowners before they declare immediate purchase intent. That mirrors Scorpion’s recent expansion into Google Demand Gen, which Street Fight examined in August.

Google data cited by Scorpion showed that 68% of Demand Gen conversions came from consumers who hadn’t seen the advertiser’s Search ads during the previous 30 days. For distributed brands, the implication is that local customer acquisition increasingly begins before the “near me” search.

Video can establish familiarity, demonstrate expertise and address common homeowner problems before an HVAC system fails or a plumbing emergency occurs. Search can then capture demand when that need becomes explicit. The challenge is coordinating those stages across markets, with national or regional creative establishing the brand while local media and location-level information connect consumers with the appropriate provider when intent develops.

Marketing Data Meets Operating Data

The presence of ServiceTitan points to another piece of the changing equation: connecting marketing activity to what happens after a lead arrives. Scorpion’s research found that 67% of home-services businesses couldn’t directly connect marketing spend to revenue, a limitation that becomes increasingly problematic as customer acquisition spreads across more channels.

Scorpion and ServiceTitan have been integrating marketing and operational data since establishing their strategic partnership in 2024. That connection can extend measurement beyond clicks and leads into appointments, jobs and revenue while also providing visibility into operational factors such as capacity. We previously examined how Scorpion is connecting marketing with technician availability, booked jobs and revenue as local discovery becomes more fragmented.

For multi-location organizations, those signals can influence media allocation. Generating more demand in a market has limited value if technicians are already at capacity, while another location with available crews may be able to absorb additional marketing investment. Connecting marketing and operating data therefore creates the potential to allocate spending based not only on where demand exists, but where the organization can actually convert that demand into revenue.

The Local Acquisition Stack Gets More Connected

The emerging model doesn’t replace paid search with ChatGPT or TikTok. Instead, it adds more places where demand can be created, influenced and captured. AI assistants can increasingly participate in discovery and recommendation, short-form video can build awareness before immediate intent exists, and search can capture consumers actively looking for a provider.

Operational platforms complete another part of that picture by connecting resulting leads to booked jobs and revenue. That makes the central challenge for multi-location home services brands less about choosing a single winning channel and more about connecting the system. As discovery fragments across AI, search, social and other surfaces, brands need a clearer view from initial exposure through local conversion and revenue to understand where their marketing investment is actually working.

Digital out-of-home advertising has traditionally benefited from proximity. Put an ad near a restaurant, retailer or dealership and the physical location itself becomes part of the media strategy. Programmatic DOOH is making that relationship more precise.

New research from Vistar Media, Omnicom Media and Annalect finds that something as simple as telling consumers where to find the nearest location can meaningfully increase their intent to visit or buy. The study tested store-locator information within programmatic DOOH creative across retail, quick-service restaurant and consumer packaged goods campaigns, comparing different placements with a control that contained no location details.

The results suggest DOOH can do more than generate awareness near a location. With location data and dynamic creative, the screen can help move a consumer toward the next physical action.

Location Information Changes the Creative

Where the store locator appeared mattered. For CPG campaigns, putting location information at the top of the creative produced a 20% lift in purchase intent compared with the control. Retail and QSR campaigns performed best when the store locator appeared at the bottom, generating a 9% lift in visit intent.

Middle placement generally performed worse because the location information competed with the primary headline and imagery. The effect was especially pronounced for impulse-oriented CPG products such as sweets and chocolate, where purchase intent increased by more than 110%.

The findings suggest proximity alone isn’t enough. How location information is incorporated into the creative can affect how consumers respond to an ad.

Small Design Decisions Matter

The research also found that how location information is presented can influence response. Using a recognizable map pin increased visit intent by 16%, while creating stronger visual contrast between the location information and the rest of the ad generated a 10% lift in visit or purchase intent, depending on the category.

Dynamic creative optimization has often focused on larger changes such as switching products, offers or messages based on weather, time of day or audience. The Vistar research indicates that relatively simple location-based elements, where the information appears and how clearly it is presented, can also influence consumer response.

Vistar already supports dynamic creative that can identify the nearest store and distance to that location across categories including retail, restaurants, automotive, financial services and telecom.

The Retailer Can Help or Compete for Attention

There was another finding with implications for brands sold through third-party retailers. When the store locator prominently included another brand, such as the supermarket, dealership or retailer where the advertised product could be purchased, attention could be divided between the advertiser and reseller.

That creates a creative tradeoff for CPG and other distributed brands. Identifying the nearby retailer can make an ad more actionable, but giving the retailer greater prominence can also dilute recognition of the product brand itself.

As retail media, shopper marketing and DOOH increasingly overlap, those decisions become more important. A brand isn’t simply deciding which audience to reach. It is also deciding how the product, retailer and physical location should work together within the same creative.

From Audience Targeting to Local Action

The research arrives as DOOH is becoming easier to buy through programmatic platforms. Street Fight recently covered how expanding programmatic inventory and new verification models are making DOOH look more like the digital channels against which it competes for advertising budgets.

Vistar’s research adds another piece to that transition by making the creative itself responsive to the physical world around the screen. A restaurant chain with 1,000 locations doesn’t necessarily need 1,000 independently managed DOOH campaigns. Dynamic creative can connect a broader media buy with location-level information.

The same model can apply to franchise locations, dealerships, bank branches and retailers. Location data becomes the bridge between national media scale and local execution, allowing a campaign to adapt based on where the consumer encounters it.

The Screen Gets Closer to the Sale

There is an important limitation to the findings. The Vistar study measures visit and purchase intent, not verified incremental visits or transactions, so intent lift shouldn’t be treated as equivalent to sales attribution.

Even with that distinction, the direction is significant. Programmatic buying determines where and when an ad appears, while dynamic creative can determine which location, offer or message a consumer sees based on that context. Measurement can increasingly connect exposure with what happens afterward.

Together, those capabilities move DOOH further down the marketing funnel and changes the role of the screen. DOOH doesn’t have to stop at telling consumers about a brand when they are out in the physical world. It can increasingly tell them where to act next.

Restaurant chains are increasingly looking beyond burgers, sandwiches and value meals to create new reasons for customers to visit. Specialty beverages have emerged as one of the industry’s most active battlegrounds, and Sonic Drive-In believes its long history of drink customization gives it an advantage.

The trend extends well beyond another seasonal menu cycle. The U.S. nonalcoholic foodservice beverage market generated $264.1 billion in 2025, representing approximately 23% of industry revenue. Circana data cited in recent reporting also shows that case volume for “dirty sodas” (soft drinks mixed with cream, syrups or other flavors) has increased 318% over the past two years. Sonic is trying to turn that demand into more visits, higher spending and stronger differentiation across its network of more than 3,400 restaurants.

Beverages Create New Reasons to Visit

Fast-food chains traditionally used drinks to complete a meal. Increasingly, the beverage can be the primary reason for the transaction, creating customer occasions that are not tied to hunger or conventional meal periods.

Specialty drinks can generate afternoon trips, after-school visits and other occasions that fall between breakfast, lunch and dinner. They can also encourage more frequent visits because customers have multiple flavors and combinations to try.

Marion Campbell, Sonic’s vice president of integrated marketing and communications, said the broader quick-service industry is still determining how to make specialty beverages a central part of its business. Sonic, by contrast, has spent years building customization into its menu and operating model.

Customers can combine sodas, slushes, lemonades, teas, flavor add-ins and toppings into a wide range of personalized drinks. That makes customization part of the customer experience rather than a temporary response to the dirty-soda trend.

It also changes the competitive frame. Sonic is not competing only with other burger chains. Its drinks put the brand into consideration alongside coffee chains, drive-thru beverage concepts and convenience stores seeking the same incremental customer occasions.

National Marketing With Regional Context

Sonic’s “Hotumn” campaign shows how beverage marketing can also become more geographically relevant. While many brands begin promoting conventional fall flavors as summer ends, much of Sonic’s core Southern footprint continues to experience hot weather.

The campaign acknowledges that disconnect by combining fall-inspired flavors with cold drinks and messaging designed for customers who may be ready for fall culturally but not meteorologically. It gives Sonic a consistent seasonal platform while making the creative more relevant to the markets where it operates.

That approach resembles the weather-triggered retail media strategy being used by Ace Hardware. National campaigns do not necessarily need different products in every market, but the message, timing and featured item can reflect local conditions.

For brand marketers, the opportunity is to move beyond a single promotional calendar applied evenly across every location. Weather, regional tastes, local events and store-level demand can all help determine which products receive media support and when that support should appear.

Customization Still Has to Work at the Local Level

The strength of Sonic’s strategy ultimately depends on execution. Every additional flavor, cream or topping creates another choice for the customer, but it can also add inventory, preparation and training requirements for individual restaurants.

That makes beverage customization both a marketing advantage and an operating challenge. As CKE’s approach to scaling local marketing also demonstrates, national strategy depends on technology, operations and local execution working together. A campaign can generate interest nationally, but customers experience the promise one location and one drink at a time. Inconsistent preparation, unavailable ingredients or slower service can quickly turn personalization into friction.

The most useful measurement will therefore go beyond impressions or sales of a featured drink. Sonic can evaluate whether beverage campaigns generate incremental visits, attract new customers, expand activity outside traditional meal periods and increase total spending without creating unacceptable operating costs.

Specialty drinks may look like a product trend, but the larger opportunity is behavioral. They give customers another reason to visit, more control over what they order and an experience that can change each time. That combination can make customization a repeatable traffic strategy for brands, provided the operators at the local level can consistently deliver it.

Programmatic DOOH (digital out-of-home) has spent years promising to make physical media easier to buy like digital advertising. New global data suggests that transition is well underway, but also shows how far DOOH still has to go before it becomes a routine component of the digital media plan.

The World Out of Home Organization’s inaugural Global pDOOH Expenditure Study puts worldwide programmatic DOOH spending at $1.339 billion in 2025, representing 7% of total DOOH spending. The study covers more than 40 markets, with confidential revenue data submitted by 12 supply-side platforms and independently aggregated by PwC.

The U.S. is the world’s largest programmatic DOOH market at $545.5 million, but only 15.9% of U.S. DOOH spending is currently transacted programmatically. Programmatic DOOH has achieved meaningful scale, but making screens programmatically available hasn’t yet made DOOH behave like other digital media channels.

Programmatic Doesn’t Necessarily Mean Open Exchange

The way DOOH is being bought helps explain the difference. Private marketplaces account for 75.7% of global programmatic DOOH spending, or just over $1 billion, according to WOO. Rather than primarily flowing through open exchanges, most pDOOH spending is taking place through structured transactions between selected buyers and sellers.

The technology used to execute those buys is similarly specialized. OOH-focused DSPs account for 65.5% of global pDOOH spending, representing $877 million, compared with 34.5% flowing through omnichannel DSPs.

WOO identifies Broadsign, Vistar Media, VIOOH and Hivestack by Perion among the specialist platforms serving the market. Their continued strength suggests buying DOOH still requires capabilities that haven’t been completely absorbed into the broader digital advertising ecosystem.

That doesn’t necessarily represent a weakness. Private marketplaces can give agencies more control over inventory quality, pricing and placement, particularly in a medium where the location and context of a screen can be as important as the audience being targeted.

Specialist Platforms Still Matter

The role of specialist platforms is notable given how aggressively DOOH inventory is expanding. These platforms have been built around the particular requirements of buying and monetizing physical-world media, even as more inventory becomes available programmatically.

VIOOH’s recent partnership with Screenverse illustrates that expansion. It opened programmatic access to more than 38,000 U.S. screens generating a reported 7.7 billion monthly impressions across retail, residential, office, transit, entertainment and healthcare environments.

These platforms have to account for characteristics unique to OOH, including location, screen environments, audience movement, scheduling and the relationship between an impression and a physical audience.

Broadsign’s own 2026 analysis of aggregated transactions across Broadsign and Place Exchange reinforces the preference for curated buying. It found custom private marketplace deals accounted for 65.8% of programmatic OOH spending in its dataset during 2025, reinforcing WOO’s finding that private marketplaces dominate globally.

More Screens Don’t Automatically Mean More Spending

The supply side of DOOH is changing quickly. Commercial displays, retail screens and other place-based inventory are increasingly being connected to advertising platforms. Samsung’s partnership with Smartify, for example, creates another pathway for commercial displays to become monetizable advertising inventory.

But connecting more screens doesn’t automatically translate into programmatic adoption. The U.S. is the world’s largest pDOOH market by dollars, yet Germany has nearly twice the programmatic penetration at 31.8%. Belgium stands at 25.5%, while France is at 16.7%, demonstrating that market size and programmatic adoption are not necessarily moving in lockstep.

Measurement Remains Part of the Equation

Buying technology is only part of what needs to change. DOOH also has to provide the measurement and accountability agencies increasingly expect from digital media, and the industry has been moving in that direction through improved audience measurement, attribution and independent verification.

WOO has identified audience measurement, effectiveness and ad-tech connectivity as areas requiring continued work across OOH. Its latest measurement guidelines address standardized DOOH measurement, impression multipliers, more current data and increased use of third-party datasets.

An agency may be able to access thousands of screens programmatically, but accessibility alone doesn’t make those screens interchangeable with CTV, display or digital video. Buyers also need confidence in audiences, delivery and business outcomes.

The Next Step Is Making DOOH Routine

There is an important counterpoint in WOO’s numbers. Omnichannel DSPs already account for 34.5% of global programmatic DOOH spending, showing that DOOH is moving into platforms agencies use to manage broader digital campaigns.

Specialist OOH platforms have helped turn physical screens into programmatically tradable media. The opportunity now is to make that inventory easier to incorporate into the planning, buying and measurement workflows agencies already use across digital channels.

WOO’s numbers make the state of the market clear. At $1.339 billion, programmatic DOOH has reached meaningful scale, but at just 7% of global DOOH spending, it still represents a relatively small portion of the channel. The next stage is making programmatic DOOH easier to buy, measure and include as a routine part of the broader media plan.

Lowe’s has made an organizational change that says more about the future of multi-location marketing than a typical executive promotion. The home-improvement retailer has expanded CMO Jen Wilson’s role to include Lowes.com and its third-party marketplace. Wilson, now executive vice president and chief marketing and digital commerce officer, already oversaw marketing, loyalty, personalization and Lowe’s retail media business.

Putting those functions under one executive reflects how little separation remains between advertising, product discovery and the transaction itself. For Lowe’s, marketing is no longer responsible only for bringing shoppers to the door. It is increasingly responsible for helping them move through a journey that may begin with an online search, continue through local product availability and end at a nearby store.

A Customer Journey Without Channel Boundaries

The timing is significant. Lowe’s reported that online sales increased 15.7% in its second quarter, while overall comparable sales rose just 0.2%. Wilson also told Marketing Dive that more than 80% of home-improvement shopping journeys now begin online. Those shoppers do not necessarily remain online; they may research products, check local inventory and compare reviews before visiting a store, arranging delivery or collecting an order curbside.

Bringing marketing and digital commerce together creates a better chance of managing that experience around the customer rather than the channel. The same audiences used for media, loyalty and personalization can inform product discovery and conversion. Commerce data can then show whether marketing generated a sale, a store visit or a longer-term customer relationship.

That is especially important for a retailer whose physical network remains central to fulfillment. Local inventory accuracy, pickup readiness and delivery options can determine whether a digital interaction converts. A national campaign can create demand, but the promise is ultimately kept—or broken—at the location level.

Marketplace Adds a New Discovery Layer

Lowe’s marketplace extends its assortment beyond products carried in stores, helping the retailer appear in more searches while identifying demand for new categories. Wilson said the marketplace is already revealing interest in higher-end products, including premium faucets and chandeliers. That gives Lowe’s a way to test customer demand without placing every item in every store.

The marketplace can also affect the economics of discovery. A larger and more relevant online assortment may improve organic search visibility, potentially reducing reliance on paid acquisition for some queries. At the same time, marketplace sellers become potential advertisers for Lowe’s retail media network, giving the company another way to monetize traffic and first-party customer signals.

Together, marketing, commerce, loyalty and retail media give Lowe’s a more complete view of how customers are acquired, converted and retained. Under one leader, Lowe’s can theoretically make decisions based on the value of the complete customer relationship.

The Hard Part Still Happens Across the Organization

An integrated executive portfolio does not automatically create an integrated customer experience. Store operations, merchandising, supply chain and technology still control many of the moments that determine whether Lowe’s delivers on its marketing.

If an ad promotes a product that is unavailable nearby, if a pickup order is not ready or if marketplace delivery disappoints, shared audience data will not repair the experience. Lowe’s will need common goals that extend beyond marketing efficiency and ecommerce conversion. The useful measures will include how often digital discovery produces store-assisted sales, whether loyalty members shop across channels, how local availability affects conversion and whether retail media campaigns generate incremental purchases rather than shifting sales Lowe’s would have captured anyway.

There is also an inherent tension in placing retail media beside customer experience. Sponsored placements generate revenue, but they must remain useful to shoppers. A retailer that allows advertising goals to overwhelm relevance risks making product discovery harder at the moment it is trying to make the journey more personal.

Lowe’s reorganization is a recognition that the old handoff from marketing to commerce no longer matches how people shop. Time will tell if putting more of the customer journey under one executive produces a journey that actually feels unified in every market and at every location.