New research from GrowthLoop and Ascend2 shows how slow audience activation and delayed reporting put retail media budgets at risk

A retail media campaign can reach the right shoppers and still arrive too late. For a brand coordinating a weekend promotion across hundreds of locations, several days spent waiting for an audience segment can consume much of the opportunity to influence a purchase.

New research from GrowthLoop and Ascend2 suggests that execution speed is becoming a factor in where advertisers place their budgets. Networks competing on shopper data and inventory also need to deliver audiences and usable results quickly enough to support the decisions marketers make every day.

Audience Access Has a Deadline

The companies’ 2026 Commerce Media Investment Index surveyed 291 marketing professionals in the United States and Canada during August. Respondents were directly involved in retail or commerce media strategy and used at least one network. Eighty-six percent said slow audience fulfillment would cause them to reduce spending or leave a partner, while 85% said they would pay a premium for same-day fulfillment.

Those responses describe purchasing intentions, rather than documented departures. GrowthLoop also sells audience-activation technology, giving it a commercial interest in the problem. Still, the findings identify a practical source of friction: access to valuable customer data has limited usefulness when activating it requires a lengthy request process.

For multi-location campaigns, timing becomes more complicated as execution moves across markets. A promotion may depend on local inventory, weather, regional demand or an offer available at participating locations. Each additional audience request or approval creates another dependency between the campaign plan and its launch.

Marketers need to know how long a network takes to build, approve and activate an audience—and whether that timeline accommodates changes once a campaign is underway. Those expectations belong in partner selection alongside reach, targeting capabilities and price.

Reporting Must Inform the Following Campaign

Execution also depends on what happens after media runs. As MarTech reported, nearly half of surveyed advertisers wait more than a week for post-campaign performance data. Only 32% said they can consistently use results to improve future audience targeting, while difficulty proving ROI has caused a similar share to reduce, pause or reconsider investment.

For marketers running consecutive promotions, delayed reporting can mean committing the next budget before understanding the previous campaign. Teams may repeat audience choices or creative approaches because the evidence needed to change them has not arrived.

Faster reporting needs useful detail. A national sales total may help evaluate overall performance but offer little guidance about which markets deserve additional investment. Where campaign design and data allow, regional or location-level analysis can help guide media investment around the availability, demand and operating conditions affecting individual stores.

Speed also needs to match the measurement method. Sales reporting can support near-term adjustments, while credible incrementality analysis may require longer observation periods. Networks should explain what advertisers can learn during a campaign, what requires further analysis and when each result will become available.

Make Execution Part of the Buying Decision

Retail media buyers increasingly need direct audience-building capabilities, integration with their own customer data, and consistent campaign management across channels and markets. Reporting must also arrive in time to inform the next spending decision.

Self-service tools can reduce handoffs, but they still require clear permissions, consistent audience definitions and reliable data. As retailer audiences extend into connected TV and other external channels, agencies need workflows that avoid rebuilding the same campaign logic inside every partner’s system.

For retailers, improving those processes can strengthen advertiser relationships without adding another placement or format. Clear turnaround commitments, reusable audience structures and predictable reporting make a network easier to incorporate into recurring media operations.

Retail media’s commercial value depends on turning shopper insight into timely action. Networks that help marketers launch, learn and adjust within the rhythm of their business will be better positioned to retain the budgets they attract.

Albertsons is trying to become a more efficient national retailer without weakening the regional banners and market knowledge that differentiate its stores. Its turnaround will test whether greater centralization can coexist with local execution while preserving the relevance customers expect from Safeway, Jewel-Osco, ACME and its other grocery brands.

Why Albertsons Is Restructuring

Albertsons has withdrawn its annual outlook and launched a comprehensive business review after identical sales declined 0.8% and quarterly net income fell from $168.5 million to $51.5 million. The company is losing ground to Walmart, discount retailers and private-label products as higher food and fuel costs make shoppers more price-conscious. Executive Chair Meg Whitman said Albertsons needs to move faster to lower costs, improve prices and the store experience, and increase traffic in its strongest markets.

The review could also lead the company to exit underperforming areas or sell parts of its portfolio. Albertsons currently operates approximately 2,200 stores under 22 banners. That scale creates purchasing and operational advantages, but it also makes the company more complex to manage.

The Risk of Over-Centralization

Albertsons is consolidating 11 operating divisions into four regions and centralizing merchandising. CEO Susan Morris said the company had accumulated too many initiatives, processes and organizational layers, increasing expenses and slowing decisions.

Simplification could eliminate duplicated functions, improve purchasing power and make it easier to deploy pricing, technology and marketing programs across the enterprise. Those savings could help Albertsons compete with retailers that use grocery to generate traffic while earning higher margins in other categories. But grocery remains highly local. Jewel-Osco’s competitive position in Chicago differs from Safeway’s in Northern California or ACME’s in Philadelphia. Product preferences, promotional patterns and pricing pressure can vary considerably among those markets.

Albertsons therefore needs to distinguish between complexity that adds cost and local authority that creates value. Centralized merchandising may improve efficiency, but assortments and promotions that do not reflect local demand could further weaken traffic and loyalty.

Digital Growth Must Work by Market

Digital sales increased 11% during the quarter, making ecommerce one of Albertsons’ stronger growth areas. Higher delivery and handling costs, however, contributed to pressure on gross margin. The results show why digital growth is not automatically profitable. Grocery-delivery economics depend on order size, picking efficiency, delivery density and the distance between stores and customers. A fulfillment model that works in a dense urban market may perform very differently across a more dispersed footprint.

Albertsons must evaluate digital performance at the trade-area level rather than treating ecommerce as one national channel. That includes deciding where store-based fulfillment is sustainable and where delivery costs are consuming too much of the value created by higher digital sales.

Those decisions also affect Albertsons Media Collective. Ecommerce activity produces purchase data and advertising inventory, but store closures or market exits could reduce the retail-media network’s local reach. As other retailers have demonstrated, physical context, store proximity and local inventory can strengthen retail media in ways that national audience scale alone cannot.

Market Exits Create Local Customer Problems

If Albertsons closes stores, sells a regional banner or withdraws from a market, execution will matter as much as the portfolio decision. Customers must understand whether nearby locations are closing, changing ownership or continuing under another name.

Location data must remain accurate across every discovery surface, including search engines, maps, business profiles, apps and AI-generated recommendations. Local pages, digital ordering and customer communications must also be updated together. Otherwise, shoppers may encounter incorrect hours, unavailable fulfillment options or promotions connected to stores that no longer operate.

Loyalty and pharmacy relationships add another layer. Customers who have accumulated rewards or manage prescriptions through a local banner will expect continuity and clear communication. Mishandling those relationships could damage confidence in nearby stores that remain open.

Albertsons does not need every regional banner to operate as a separate company. But it cannot manage 2,200 stores as if local competition, shopping habits and customer relationships were uniform. The turnaround will succeed only if Albertsons centralizes the capabilities that benefit from scale while leaving market-level teams enough authority to compete locally.

Digital screens already operating inside commercial buildings are creating new opportunities for DOOH advertising. Vertical City’s expansion to 10,000 elevator screens shows how existing infrastructure can become a scalable media network and why accessibility and measurement will determine its value to advertisers.

Digital screens installed for building operations are becoming a new source of advertising inventory. From elevators to commercial properties, displays originally intended for communications and information services are creating opportunities for media companies to expand their networks without installing dedicated advertising hardware.

Vertical City’s expansion to 10,000 elevator screens across North America illustrates how the model is taking shape. By integrating advertising into displays used for building communications and elevator operations, the company is turning existing infrastructure into a scalable DOOH network.

For property owners, the approach creates a potential new revenue stream. For advertisers, it opens additional place-based inventory that can be purchased as part of broader media campaigns.

Turning Building Infrastructure Into Advertising Inventory

Vertical City operates an elevator media network spanning residential buildings, commercial properties, hotels and other locations across Canada and the United States. Its growth is supported by partnerships with elevator manufacturers, including KONE and MAD Elevator, that allow advertising software to be integrated into displays serving existing building functions.

Those screens are increasingly becoming part of modern elevator systems. They provide emergency communications, floor information and building announcements, creating an opportunity to deliver advertising alongside operational content.

This changes the economics of network expansion. As commercial displays become advertising inventory, media revenue can provide an additional return on existing technology investments rather than serving as the sole justification for installing hardware.

A Different Model for Scaling DOOH

Vertical City’s approach reduces some of the barriers to network expansion by working with elevator manufacturers and property operators. As connected elevator systems are installed or upgraded, additional screens can potentially become part of the advertising network without requiring a separate media-specific hardware deployment.

The company has reached 10,000 screens across North America and expects further expansion through its manufacturer relationships. Its advertising division, Vertical Impression, works with Screenverse to make the inventory available to advertisers planning local and multi-market campaigns.

For DOOH operators, the ability to expand through existing infrastructure could become an alternative to building and owning every screen in a network.

More Places Become Advertising Channels

Elevators are one example of a larger opportunity developing across commercial environments. Retail stores, hotels, office buildings, transportation facilities and entertainment venues already operate digital displays for customer information, navigation, operational communications and other purposes.

As those screens become connected to advertising platforms, they can support commercial messages without abandoning their original functions.

Retail media networks are pursuing a related strategy by turning in-store displays into advertising channels. The difference is that elevator and commercial-building networks extend the model beyond retail environments, creating inventory tied to where people live, work and travel.

For multi-location brands, these environments offer another way to reach consumers in specific geographic markets. Restaurants, retailers, financial services companies and other businesses with physical locations could use building-level advertising to support campaigns around nearby stores or service areas.

Elevator screens may also provide repeated exposure as residents, employees and visitors move through the same buildings. Whether that frequency produces stronger advertising outcomes, however, depends on audience composition, campaign execution and measurement.

Scale Alone Will Not Attract Larger Budgets

Expanding the number of available screens creates inventory, but advertisers need more than a physical footprint to justify investment.

Agencies evaluating place-based media require information about geographic coverage, audience size, exposure frequency and campaign performance. As programmatic DOOH becomes more widely available, consistent inventory definitions and reporting become essential for incorporating screens from different operators into broader media campaigns.

Vertical Impression has taken steps toward more standardized measurement, including a previously announced partnership with Adelaide to bring attention-based metrics into its advertising offering.

Those capabilities could help advertisers assess the quality of exposure within elevator environments. However, the company’s latest expansion announcement does not disclose standardized audience figures, geographic distribution or campaign performance results for the 10,000-screen network.

For multi-location advertisers, the priority is connecting screen exposure to store visits, customer acquisition and sales. That evidence will determine how effectively these networks compete for advertising budgets.

Commercial Displays Become Media Assets

The expansion of advertising into existing commercial displays could change who participates in the DOOH market. Building owners, equipment manufacturers and software providers can generate media revenue from infrastructure originally deployed for other purposes, creating new competition and partnership opportunities for established advertising networks.

Vertical City’s expansion demonstrates how existing commercial displays can support scalable advertising networks. The competitive advantage will come from making that inventory accessible to advertisers, integrating it into established buying platforms and demonstrating measurable business outcomes.

Best Buy is extending its advertising business into connected television through a new partnership with LG Ad Solutions, bringing its shopper audiences and sales measurement capabilities to television advertising.

The agreement makes LG’s premium connected-TV inventory, including its Home Screen, available to U.S. advertisers through Best Buy Ads. The resulting combination gives brands a way to combine Best Buy’s first-party shopping data with LG’s television inventory, reaching consumers beyond the retailer’s stores and ecommerce properties.

From Retail Audiences to Television Advertising

Retail media networks have traditionally focused on advertising close to the point of purchase, using sponsored products, search results and onsite display placements to reach active shoppers. Increasingly, retailers are applying their customer data to advertising across external websites, streaming services and connected television.

Through the LG partnership, consumer electronics advertisers working with Best Buy Ads can activate shopper audiences across Home Screen and CTV video inventory. The arrangement connects retail purchase intelligence with television advertising, bringing audience targeting and sales measurement into a channel traditionally evaluated through media exposure and engagement.

The Home Screen Creates a New Advertising Opportunity

LG’s Home Screen reaches consumers before they select a streaming service or begin watching content. Unlike conventional CTV video advertising, the placement occupies the television interface itself, giving advertisers visibility at the point of content discovery.

Serge Matta, president of global ad sales at LG Ad Solutions, has emphasized the Home Screen’s role in reaching consumers before content begins. The partnership also creates another source of advertising demand for LG by making that inventory accessible through Best Buy’s advertiser relationships.

For consumer electronics manufacturers, the connection is especially relevant. Brands selling products through Best Buy can use the retailer’s shopper audiences to reach consumers on LG televisions, connecting product consideration and purchase behavior with television advertising.

Magnite Connects the Buying Platforms

Magnite provides the programmatic transaction technology behind the partnership, connecting Best Buy’s advertising demand with LG’s available inventory. Its role reflects how retail media is being integrated into established programmatic buying systems, rather than requiring separate transaction processes for each advertising environment.

The arrangement gives LG access to additional advertiser budgets while expanding the television inventory available through Best Buy Ads. For agencies, it creates another way to coordinate retailer audiences and CTV campaigns within broader media strategies.

As these connections expand, the boundaries between retail media and traditional digital advertising are becoming less rigid. Audience data, buying technology and measurement capabilities increasingly determine how inventory is packaged and sold.

Connecting Television Exposure to Sales

Best Buy’s closed-loop measurement capabilities are central to the offering. The companies say advertisers will be able to connect television advertising exposure with sales outcomes through LG Ad Solutions’ Own the Outcome measurement framework.

Lisa Valentino, chief ads and media officer at Best Buy, has emphasized advertisers’ demand for premium media environments supported by measurable sales results. The partnership combines the retailer’s first-party audience insights with LG’s television inventory and attribution capabilities.

Television advertisers have long measured reach, frequency and engagement, but linking those exposures to purchases requires additional data. Retail media networks bring transaction data into that equation, allowing advertisers to assess campaigns against commercial outcomes rather than relying exclusively on traditional media metrics.

The companies have not disclosed campaign performance, sales lift or detailed attribution methodology for the new offering. Its effectiveness will ultimately depend on how reliably television exposure can be connected to actual purchasing behavior.

What It Means for Multi-Location Brands

For multi-location retailers, the Best Buy–LG partnership demonstrates how customer relationships and transaction data can support advertising businesses that extend beyond their physical and digital storefronts.

It also gives brands and agencies additional ways to connect television campaigns with consumers who have demonstrated shopping interest. As more retailers bring their audiences into CTV, advertisers will have greater opportunities to coordinate audience targeting, media investment and sales measurement across channels.

Best Buy supplies the shopper intelligence, LG provides the television inventory, and Magnite handles the programmatic transactions. The partnership brings those capabilities together in a model that could should make television a more measurable extension of retailers’ advertising businesses.

Magnite is strengthening its role in retail media’s expansion into connected TV. Recent announcements from Walmart Connect and Best Buy Ads place its technology behind two arrangements connecting retailer audiences and sales measurement with television advertising inventory.

For multi-location brands and agencies, the significance extends beyond another way to buy streaming ads. These connections raise a practical question: can purchase signals make television investment more accountable while supporting the different markets, customers and locations a national campaign needs to serve?

Two Retailers, a Shared Connection

On October 6, Walmart Connect announced that its integration with Yahoo DSP, supported by Magnite, is moving from proof of concept into alpha. The offering brings Walmart audiences and closed-loop measurement into Yahoo’s buying platform and is expanding beyond VIZIO to additional CTV publishers. Magnite supplies the sell-side technology behind that integration.

The following day, LG Ad Solutions announced a partnership with Best Buy Ads connecting the retailer’s audiences and sales measurement with LG Home Screen and CTV video inventory. The inventory is transacted programmatically through Magnite, and the offering is available immediately.t

The developments build on the earlier Walmart–Magnite partnership, which connected retailer audience capabilities with external buying platforms. The expansion beyond VIZIO and the Best Buy–LG arrangement give Magnite a broader role in bringing commerce capabilities to television inventory.

Magnite’s role is to support the transactions on the inventory side. The retailer provides the shopper relationship and measurement capabilities; the buying platform provides campaign activation. Understanding those responsibilities matters when evaluating what each partnership actually delivers.

Magnite Connects Existing Buying Workflows

For agencies managing national brands with local priorities, adding a retail media relationship can introduce another buying process, reporting system and definition of success. An integration into an existing demand-side platform offers a way to bring retailer audiences into workflows media teams already use.

That can matter operationally. A campaign spanning hundreds of locations already requires decisions about market coverage, creative, budgets and customer availability. If audience access comes with another disconnected process, the administrative burden can grow alongside the media opportunity.

The Walmart arrangement provides an example of connecting retailer capabilities with an established buying platform. It also illustrates why buyers should examine each route carefully: Walmart’s separately announced live linear TV offering uses FreeWheel technology. Magnite’s identified role is in the Yahoo DSP CTV integration.

For brands, the useful question is how the available buying route fits their campaign requirements. That includes supported inventory, audience definitions, reporting access and the ability to coordinate with other media investments.

National Data Needs Local Relevance

Retail purchase signals can help identify consumers interested in a category. Multi-location marketing requires another layer of judgment: whether those consumers can act on the advertisement in the markets being promoted.

A manufacturer selling through a retail chain may value a connection between television exposure and retailer sales. A restaurant franchise promoting a regional offer needs evidence of visits or transactions within its own system. Both seek accountability, but their business outcomes and measurement requirements differ.

Brands should therefore examine geographic controls, eligible audiences and reporting detail before treating a retail media partnership as a solution for local growth. Store coverage, product availability and regional promotions should inform how a campaign is planned and where its results are evaluated.

Measurement Must Match the Decision

Early Walmart results show why advertisers are interested. A Hidden Valley Ranch proof of concept reported 55% incremental reach beyond linear TV and CPMs 27% below previous CTV campaigns of similar duration. Those figures describe reach and buying efficiency; they do not establish incremental sales attributable to Magnite.

Closed-loop reporting can connect exposure with purchases recorded by a retailer. Determining whether advertising caused additional purchases requires further analysis. That question sits at the center of CTV’s continuing measurement and trust challenge. Agencies should understand attribution windows, comparison groups and which sales are included before comparing results across networks.

Magnite’s role in both announcements shows how it is helping retail media expand into connected TV. By connecting retailer audiences with television inventory through established buying platforms, Magnite gives multi-location brands more ways to put shopper data to work within their existing media operations.

Fast-food chains are racing to automate one of their most important customer touchpoints. Chick-fil-A is choosing a different route.

CEO Andrew Cathy recently said the company does not intend to replace employees taking drive-thru orders with artificial intelligence. While competitors including McDonald’s and Wendy’s are testing or expanding voice-ordering systems, Chick-fil-A considers face-to-face service central to its hospitality model. The decision is especially notable because drive-thru demand represents a large share of the chain’s business and because Chick-fil-A is hardly resistant to technology.

Technology That Supports the Interaction

Instead, the company is drawing a boundary between technology that removes customer interaction and technology that helps employees deliver a better experience. Its elevated drive-thru restaurant in McDonough, Georgia, shows the difference. The restaurant places its kitchen above four drive-thru lanes and uses a conveyor system capable of delivering a meal to an employee every six seconds. Chick-fil-A says the design can accommodate two to three times the traffic of a conventional location. Customers can order digitally, but those who order at the restaurant still interact with an employee.

That approach runs against the industry’s prevailing assumption that the order-taking conversation is an obvious target for automation. Wendy’s has been deploying FreshAI, developed with Google Cloud, while McDonald’s is preparing another voice-AI test after ending its earlier trial with IBM. Other chains are turning to automated ordering to improve consistency, reduce pressure on employees and create more opportunities for algorithmic upselling. Voice AI is now used in an estimated 6% of U.S. drive-thrus, although its performance and its reception, among customers, remains mixed.

Human Service as a Brand Advantage

For Chick-fil-A, however, eliminating the employee could also eliminate part of the product. The chain has spent years developing its face-to-face ordering model, in which workers carrying tablets approach vehicles before they reach a menu board. That system gets orders to the kitchen sooner while giving customers a more personal interaction than they would receive through a speaker. Chick-fil-A moved fully to face-to-face drive-thru ordering in 2012, long before generative AI made automated conversation seem practical.

The strategy is backed by unusually strong restaurant economics. Chick-fil-A generated approximately $23.9 billion in U.S. systemwide sales in 2025, according to figures from its franchise disclosure documents. Its restaurants have also historically produced far higher average sales than most fast-food competitors. Even when its drive-thru lines appear slow, research has found that the chain performs strongly when service time is adjusted for the number of vehicles in line. Customers have also given it high marks for accuracy, food quality and employee friendliness.

What Franchise Operators Must Weigh

That performance gives Chick-fil-A more room than a struggling chain might have to reject labor-saving automation. The decision is not necessarily a model every restaurant brand can copy. Operators facing severe staffing shortages or inconsistent service may conclude that voice AI produces a better experience than an overburdened employee. Automated systems can also provide standardized upselling, multilingual ordering and round-the-clock availability. The relevant question is not whether human service is inherently superior, but which parts of the experience customers associate with the brand and whether automation strengthens or weakens them.

The franchise implications are significant. New technology is often evaluated through centralized measures such as order time, accuracy and average check. Operators must also account for training requirements, exception handling, equipment reliability and the effect on customer satisfaction at individual restaurants. A system that performs well across routine orders may still create friction when a customer has an allergy question, wants to modify several items or simply cannot make the software understand them. Human intervention remains part of the operating model even when the initial conversation is automated. Similar questions arise with AI-driven pricing at McDonald’s, where systemwide recommendations must leave room for local judgment and customer trust.

Automating Around Hospitality

Chick-fil-A’s answer is to automate around the interaction. Conveyors move meals faster, digital ordering absorbs customers who prefer self-service, and redesigned kitchens increase capacity. Employees remain visible at the point where hospitality is delivered. That is a more selective use of technology than replacing a worker because a machine can complete the same transaction.

As restaurant AI advances, brands will need to decide which forms of friction should be removed and which interactions carry value. Chick-fil-A is betting that the person greeting a customer in the drive-thru is not operational friction. It is part of what the customer came for.

Franchise brands spend enormous energy searching for new growth levers. Yet one of the most underused may already be built into the economics of the franchise system: local marketing dollars that franchisees are expected to spend but often struggle to deploy effectively.

Every franchise system operates around a Franchise Disclosure Document, or FDD, that spells out the economics, obligations and structure prospective franchisees are entering. The franchise agreement establishes the contractual relationship itself. Buried in those documents across many foodservice systems I’ve reviewed is a requirement that deserves more attention: franchisees may be expected to spend somewhere in the range of 1% to 3% of gross sales on local store marketing, or LSM, in addition to royalties and other marketing contributions.

That isn’t simply a suggestion. Where included in the franchise agreement, it’s a contractual obligation. Yet in many systems, those dollars are not being consistently activated, tracked or measured.

The Local Marketing Requirement That Goes Unused

A franchisor can hand a new operator a 200-page manual and a fully built brand, but once that franchisee is running a location three states away, enforcing any individual requirement competes with everything else happening on the P&L.

Local store marketing has increasingly become one of the obligations that can lose that fight. Since 2020, franchisors have navigated labor shortages, supply-chain disruptions and significant input-cost inflation. Franchisees have faced those same pressures with considerably less room to maneuver.

Faced with operators already under financial pressure, many franchisors made an understandable choice: don’t push too hard on the marketing obligation that feels most negotiable. Nobody wants to chase a struggling franchisee over local marketing spend while that operator is trying to cover payroll.

But negotiable in practice doesn’t mean optional in the contract. The gap between the two is where franchise systems may be leaving growth on the table.

Why Local Marketing Matters More Now

Franchise brands are generally built and managed centrally, with one brand voice, a common set of standards and a national marketing calendar. But every franchisee operates inside a local community where corporate has limited organic presence.

Corporate isn’t sponsoring the youth league. It isn’t the familiar face at the farmers market or supporting the neighborhood fundraiser. That distance is normal and, in most franchise systems, unavoidable. It is also exactly what local store marketing is designed to close.

When that activity doesn’t happen, the brand loses more than a few local social posts or community sponsorships. It loses one of its most direct opportunities to build relevance and trust with the people who actually walk through the door.

The economics make that increasingly important. Guest counts across parts of the restaurant industry have been under pressure, while acquiring incremental customers has become more expensive. A franchise system that leaves committed local marketing dollars inactive may find itself absorbing higher acquisition costs through broader marketing channels while underusing a locally relevant traffic lever already built into its model.

The Problem Is Infrastructure, Not Intent

It’s tempting to frame this as a compliance problem. I think that misses the larger issue. The money may already be committed, and the need for local marketing is structural. Yet many systems still lack an easy way for franchisees to decide how to spend those dollars, activate campaigns and understand whether they worked.

At the same time, corporate teams often lack consistent visibility into what individual locations are doing and what those investments are producing across markets. That isn’t necessarily a franchisee failure or a franchisor failure. It’s an infrastructure gap.

Nobody built the obvious system for a franchisee to put the required local marketing budget to work without becoming a marketer themselves. And without consistent measurement, corporate has little ability to distinguish effective local investment from activity that simply checks a box.

Doing nothing can become the path of least resistance for both sides.

Turning an FDD Requirement Into a Growth System

This is the gap platforms such as LOMA were built to address. Rather than treating local store marketing primarily as a compliance requirement to police, LOMA approaches it as operating infrastructure. Franchisees can choose from vetted local marketing activities rather than starting with a spreadsheet, a percentage requirement and the question of what to do next.

The concept is essentially the local marketing equivalent of adding items to a cart: give operators defined choices they can activate without requiring them to build campaigns from scratch.

On the corporate side, LOMA says it provides continuous monitoring of performance across those local channels. That gives franchisors visibility into how local marketing dollars are being deployed and what they are producing across individual markets.

The larger idea matters beyond any one platform. If franchise systems can make local marketing easier to activate and more measurable, the conversation changes from enforcing spend to optimizing it.

From Contractual Spend to Measurable Growth

That reframes a conversation many franchisors have avoided. This isn’t necessarily a request to write a new check or create another franchisee obligation. In many systems, the commitment already exists. The question is whether the franchise system has built the infrastructure to make those dollars productive.

Franchise brands will continue investing in site selection, menu innovation, national advertising and new customer-acquisition channels. All of those can contribute to growth. But before searching for the next growth lever, franchisors should examine one they may already have.

Local marketing dollars can be committed on paper yet remain inconsistent in practice. Turning that requirement into measurable local activity could give franchise systems something increasingly difficult to find: incremental growth without first having to invent a new source of marketing spend.

The opportunity isn’t hidden because nobody thought of it. In many cases, it’s already written into the franchise model. The next step is building the system that puts it to work.

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.