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.

Agentic commerce, or the process of AI agents helping users research, compare, and, increasingly, complete local purchases, has moved from concept to reality.

For a single-location business, getting ready for this means cleaning up one website, one set of listings, one menu, one booking system, to make sure their customer journey works equally well for real people and autonomous agents. For multi-location brands, such as hotel groups, restaurant chains, and ticketed venues like movie theaters, the challenge is different in scale and in kind.

It’s not enough for a MULO brand’s primary webpage to be agent-friendly. Every individual location has to be optimized for agentic local commerce, because agents are now evaluating and transacting at the local level.

National optimization might get a business into the conversation and enable agentic ecommerce, but location-level optimization decides whether an agent completes a local booking, fills a cart for takeout, or surfaces the right link to buy.

Hotels: Booking Now Happens Inside the Conversation

Google recently rolled out hotel booking directly inside AI Mode in Search. Users can describe a trip, get a list of hotels with reviews and comparison details, then complete the reservation without leaving the conversation. The hotel or booking platform still acts as merchant of record, but checkout runs through Google Pay within AI Mode.

For a hotel brand, this raises the stakes on property-level data optimization. An agent comparing options is pulling live rates, availability, and amenities per property, alongside guest reviews, cancellation terms, and other characteristics it can weigh against competing listings.

A flagship location with a polished digital presence doesn’t help a regional property with outdated room descriptions or disconnected availability. That property simply won’t surface as a viable option, regardless of how strong the parent brand is elsewhere or how well its flagship property performs.

Restaurant Chains: Every Location Needs Its Own Accurate Menu

New agentic Ask Maps features on Google Maps now allow the platform’s Gemini-powered AI to autonomously find food for a user and add it directly to their cart, pulling from menu, availability, and pricing data tied to a specific location. The user still completes the checkout themselves, but the agent has already done the pre-checkout work.

For a restaurant chain, the underlying data agents rely on rarely looks the same everywhere. Menus and pricing vary by region, items go in and out of stock, delivery radius differs by store, and franchisees often manage their own local search presence independently from brand HQ.

An agent filling a user’s cart with a takeout or delivery order queries the nearest location’s available data. If that location’s menu is inaccessible or its hours are wrong, the agent fills the cart from a competitor down the street, one whose data happens to be more accurate and agent-friendly.

In other words, brand recognition doesn’t compensate for a location-level optimization problem, and a restaurant chain that’s not ready for agent transactions across its brand footprint can start losing orders to competitors via agentic commerce.

Ticketed Venues: Showtimes and Seats Are a Real-Time Feed

The same new agentic Ask Maps capabilities extend to event and ticket discovery, letting users find and compare local events and showtimes, then handing off to a direct link to buy the ticket.

For movie theater chains and similar ticket-based multi-location businesses, real-time, per-venue accuracy decides whether a location gets considered and chosen. Correct showtimes, current seat availability, and accurate pricing all matter.

If a theater’s actual seating availability isn’t exposed via an agent-friendly feed, the agent surfaces a different venue instead and the brand loses the sale.

The Common Thread: Consistency, Not Just Presence

Agentic commerce doesn’t reward brand size or recognition on its own. It rewards locations based on their individual merits and agent-readiness.

That’s why MULO brands need to ensure each location has complete, accurate, and accessible data at the moment a user asks an AI agent to help them make a reservation, order food, or buy a ticket. In other words, every location needs to be equally discoverable and transactable.

Getting there requires centralized data governance that still accounts for local variation: standardized templates for hours, menus, and inventory, feeds that update in real time, and regular audits comparing what corporate believes is live against what’s actually published locally.

It also requires using a tool like Local Falcon to monitor how each location shows up in AI-driven search results in the markets they actually serve, rather than looking at aggregate brand visibility. A single underperforming market can quietly cost bookings or orders, hurting the brand’s overall AI search performance.

Franchised brands also need clear accountability for who owns that data day to day, corporate, franchisee, or shared, so gaps get caught before an agent runs into problems completing a transaction on a customer’s behalf.

The Takeaway

Agentic local commerce is here, even if it’s not arriving all at once. Some transactions complete end-to-end, others fill a user’s cart or provide a booking link, but regardless of what the agentic commerce transaction model looks like, treating agent-readiness as a location-level operational standard is key to ensuring every address across the portfolio is equally likely to get chosen.

The brands that leave optimizing for agentic commerce to individual locations to figure out on their own may find some of their best real estate quietly filtered out of conversational transactions they never see happening.

For years, the direction of store and restaurant technology seemed clear: move more transactions into apps, kiosks and self-checkout, and ask employees to do less of the routine work. That investment has made many visits faster. It has also made the human service interactions that remain more consequential.

Aritzia, Macy’s and McDonald’s are approaching that challenge from different directions. One has made personal styling part of its identity; another is investing in associates at a selected group of stores; the third is preparing a large-scale hospitality training effort. Their shared bet is that a good location experience cannot be delivered through speed alone.

Service as Part of the Product

At Aritzia, style advisers do more than point customers toward a rack. They suggest outfits, help shoppers navigate fitting rooms and tailor recommendations to individual preferences. The retailer describes its advisers as specialists in personalized styling. That gives a customer a reason to visit a boutique even when much of the merchandise can be browsed online.

The approach is not new for Aritzia, and it would be a mistake to credit its growth to human service alone. Product, store expansion and digital sales also matter. But Wall Street Journal reporting on its stores shows how deliberately the company has made advisers part of its “Everyday Luxury” positioning. Personalized attention is not an extra at the end of a transaction; it is one of the things the brand sells.

That comes with a trade-off. The same reporting describes high sales expectations and concerns raised by former workers about the pressures of the model. Service can feel helpful to one customer and intrusive to another. A multi-location brand has to define what a good interaction looks like, train for it and give employees enough room to read the customer rather than follow a script.

What Macy’s Can and Cannot Measure

Macy’s is testing a different proposition across its Reimagine stores: put more attention into the locations most likely to benefit from improved service, merchandising and presentation. In its September 10 second-quarter results, the company reported 1.9% comparable-sales growth at its Reimagine 200 stores, compared with 1.1% for the Macy’s banner overall.

That difference matters, but it is not proof that additional associates alone produced the lift. The targeted stores are receiving several changes at once, and their location mix may differ from the rest of the chain. The more useful question for retailers is whether customers at those stores can find what they came for, receive help when they need it and leave with enough confidence to return.

Macy’s has also reported improved customer-experience measures at its reimagined locations. To make the investment case, a retailer would need to examine those measures alongside visits, conversion, repeat purchases and the additional labor cost … ideally comparing similar stores rather than only a selected group against the chainwide average. Otherwise, “better service” risks becoming an explanation applied after a sales gain rather than something the business can actually manage.

Hospitality at Franchise Scale

McDonald’s faces a harder version of the consistency problem. A customer may order through an app or kiosk, but the handoff, the greeting and the response when something goes wrong still depend on the restaurant team. The Wall Street Journal reported that the chain plans to retrain restaurant workers on hospitality later this year and make hospitality a more important part of franchisee evaluations.

In a franchised network, that is more than a marketing message. The brand can set standards, but individual operators hire, schedule and coach the people who meet customers. A hospitality score may flag an issue; it cannot, on its own, tell a franchisee whether the problem is rushed handoffs, thin staffing at lunch, weak training or a store manager who does not have time to coach.

The emphasis is also a reminder that automation has changed the job, not eliminated it. When technology takes the order, employees have fewer chances to build a relationship and more pressure to get the remaining moments right. A warm greeting does not make up for a long wait or an incorrect order. Good human service has to fit the way that particular location actually operates.

The Location-Level Test

These examples are not evidence of a wholesale return to labor-heavy retail. Apps and self-service remain useful when customers want control or convenience. The shift is more selective: use technology for predictable tasks, and make sure a person is available where judgment, expertise or recovery can change the visit.

For a MULO brand, the practical test is local. Do shoppers come back more often after receiving advice? Do human service complaints cluster around particular shifts or stores? Does an added associate improve conversion enough to pay for the hours? Those answers will vary by format and trade area. The strongest case for human service will come from locations that can show what customers gained and what it cost to deliver.

By every headline number, franchising is having a fine year. The International Franchise Association projects industry output will top $920 billion in 2026, with franchise GDP climbing 1.8% and nearly 13,000 new units opening. On a boardroom slide, that’s a growth story.

Walk the sidewalk in front of any given location, and the story looks different. Fast food traffic fell 4.4% year-over-year in May 2026, according to Placer.ai data reported by Restaurant Dive. Quick visits, the under-10-minute trips that drive QSR volume dropped even further, down 6.8% over the same period. Of the first five months of the year, exactly one showed a year-over-year traffic gain across quick-service brands. The IFA’s own outlook calls it “cautious growth,” which is a polite way of describing flat guest counts and tightening margins.

That’s the paradox every franchise CMO is quietly living with right now: the category is expanding, and the customer is disappearing. Worse, the customers who are still walking in cost more to reach than they used to, hospitality customer acquisition costs have climbed roughly 10% since 2022, per Benchmarkit’s most recent data,  so brands are spending more to chase fewer visits, right as guest counts soften. And the growth that does exist isn’t distributed evenly. Even in the IFA’s own optimistic 2026 outlook, the Southwest is projected to grow franchise output at 2.5%, against a national unit-growth rate of 1.5%. Zoom in past the regional level, down to the individual trade area, and that spread only widens. Every location under a given brand banner has access to the same logo, the same menu, the same national media buy. What they don’t share is what happens inside the five-mile radius around each door, and that’s where the real divergence lives.

After 25 years of building marketing functions inside several food-service franchise systems, I’ve come to believe the franchising industry keeps misdiagnosing this problem. We treat soft traffic as a brand problem and respond with a better campaign, a refreshed logo, a bigger national media buy. Those things matter. They also aren’t what’s breaking.

Scooter’s Coffee Growth and the Future of Franchise Marketing

Where Franchise Marketing Execution Breaks Down

What’s actually broken is structural, and it shows up in three places.

First, the franchisee execution gap. Franchisees are operators. They’re excellent at running a kitchen, managing labor, and hitting food cost targets. Very few were hired or trained to be marketers, yet local store marketing has always assumed they’d act like one. When traffic softens, the franchisor’s answer is usually “spend your local marketing dollars.” The dollars often go unspent, or worse, spent on whatever a local vendor pitched last, because no one gave the franchisee a real system for deciding what to do with a Tuesday afternoon and a marketing budget.

Second, the disconnect between brand and neighborhood. National franchising campaigns are built to work everywhere. A promotion tuned for a national demo can miss entirely in a trade area shaped by a nearby college or a new competitor. Corporate franchising teams have gotten very good at brand consistency, and not so great at giving local operators room to be relevant without breaking it.

Third, and this is the one most executives get wrong, even a great playbook doesn’t solve it.  I’ve sat in rooms where we have built a genuinely excellent local marketing playbook: clear tactics, seasonal calendars, ready-made creative. Traffic didn’t move. A playbook tells a franchisee what to do. It doesn’t give them the time, the confidence, or the feedback loop to actually do it well, and it almost never tells anyone at corporate whether it worked. Franchise marketing keeps investing in better instructions for a problem that was never about the instructions.

Local Store Marketing Is an Infrastructure Problem

This is why I’ve stopped thinking about local store marketing as a content or campaign challenge and started thinking about it as an infrastructure problem. The franchising brands pulling ahead of their own system right now are the ones that turned local marketing from an unfunded mandate into muscle memory: a franchisee can act on a local opportunity in minutes instead of days, and corporate can see exactly what’s being activated and what it’s producing across hundreds of locations at once. That’s the shift one platform builder in this space describes as going “from damage control to mission control.”

LOMA, built around exactly this problem, lets multi-unit brands centrally plan, activate, and measure local marketing spend in one system, turning a task franchisees dread into something closer to adding items to a cart, while finally giving corporate visibility into which local activity is actually moving traffic.

Making Local Execution the Path of Least Resistance

The fix was never going to be a better campaign, it’s making good local execution the path of least resistance for every operator, regardless of marketing background. That’s not a someday priority. With acquisition costs climbing and guest counts flat at best, every local marketing dollar that doesn’t convert into a visit is a dollar the P&L can no longer absorb quietly.

Brands that build that system will spend this year watching their strongest locations pull further ahead of their weakest ones,  the same divergence already visible at the regional level, just closer to home. The rest will keep mistaking a healthy industry forecast for a healthy franchisee, and wondering why the sidewalk doesn’t match the slide deck.

Yelp and Hatch are among the first platforms integrating OpenAI’s GPT-Live-1, giving restaurant and service-business callers more natural conversations with AI. The resulting voice AI is connecting conversational models with the location data, business rules and operating systems required to turn a call into a booking.

Voice AI is getting better at sounding human. OpenAI’s new GPT-Live-1 can listen and respond simultaneously, accommodate interruptions and background conversations, and better maintain the natural rhythm of a call. Yelp has integrated the model into Yelp Host, its restaurant voice AI product, and Hatch, its AI communications platform for service businesses.

Those improvements could remove some of the friction consumers have traditionally encountered with automated phone systems, but they don’t necessarily solve the harder problem for restaurants, franchises and multi-location service brands: knowing what is happening at an individual location and being able to act on that information. Yelp’s architecture provides an example of how that distinction is emerging. GPT-Live-1 serves as the front-end voice layer interacting with the caller, while Yelp Host and Hatch retain their business data, operational intelligence and connections to the systems needed to do something with the customer’s request. The increasingly natural voice may be only one part of the value proposition; the more difficult layer could be everything behind it.

A Better Conversation Is Only the Beginning

GPT-Live-1 uses a full-duplex voice architecture, allowing the model to listen and speak simultaneously rather than relying on the more rigid back-and-forth exchanges associated with many automated voice systems. A caller can interrupt, pause, change direction or speak with someone in the background without necessarily disrupting the interaction.

That matters because actual customer calls rarely follow a clean question-and-answer sequence. Someone making a restaurant reservation might change the time after checking with another person, ask about outdoor seating midway through the conversation or pause while confirming the size of the party. A homeowner calling about a repair may describe a problem, correct the address and then ask about availability.

Yelp says early production testing of the new model has improved call handling and reduced transfers. The company has also seen callers using fuller, more natural sentences instead of the short commands people often use when they know they are interacting with an automated system. Those are early company-reported findings rather than independent performance benchmarks, but they provide one indication of how improved voice technology could change consumer behavior.

Yelp Host already has meaningful call volume behind it. Since launching in October 2025, the product has handled more than one million restaurant calls, according to Yelp, the equivalent of nearly two years of phone conversations. The company reported in July that call volume had grown an average of 38% month over month from launch through June 2026.

The Intelligence Behind the Voice

More natural conversation may make voice AI easier for consumers to use, but the technology still needs to know what to say and what it is allowed to do. “Great voice AI requires more than a great voice model,” Hatch CEO and co-founder Chris Bache said in announcing the integration. In a supporting post, Bache expanded on that distinction, arguing that service businesses require deterministic business logic alongside conversational AI.

A customer calling about an urgent repair, for example, may require the system to determine whether an address falls within the company’s service area, check real-time availability and apply the appropriate scheduling or emergency-routing rules. Hatch’s underlying intelligence is designed around those types of service-business requirements. Yelp Host applies the same general principle to restaurants, drawing on restaurant-specific information including business details, reservation availability, food ordering, menu specials and seating areas.

“Yelp Host’s value to restaurants is not only how it sounds, but how deeply it understands each restaurant’s operations,” Akhil Kuduvalli Ramesh, chief product officer at Yelp, said. For multi-location brands, that distinction becomes increasingly important because a sophisticated voice model can be available to many companies through the same API. Connecting that model with accurate location-level data, business rules and operating systems is a different challenge.

A natural conversation isn’t particularly valuable if the AI gives the wrong hours, doesn’t understand what a location offers, schedules outside a service territory or promises an appointment that can’t actually be fulfilled. As voice agents become customer-facing representatives of the brand, the operational context behind them becomes part of the customer experience.

From Call Handling to Conversion

Yelp Host isn’t limited to answering restaurant questions. It can handle reservations and takeout orders, giving the system the ability to move from customer inquiry toward a transaction. Hatch can qualify leads, check service areas and availability, schedule jobs and route calls that require human intervention. Together, those capabilities illustrate how voice AI can move from primarily being a labor-efficiency tool toward becoming part of a brand’s conversion infrastructure.

For multi-location marketers, the distinction is important. A paid search campaign, local listing, social post or AI recommendation may successfully generate consumer demand, but the customer journey can still break down when someone calls a location and nobody answers. Restaurants are particularly susceptible during busy service periods, while service businesses can miss high-intent leads after hours or when employees are already handling other customers.

Voice AI potentially closes some of that gap. Instead of measuring success primarily through calls answered or staff time saved, brands can begin evaluating whether those conversations produce reservations, orders, qualified leads and booked appointments. If a media investment generates an inbound call and an AI agent ultimately converts that caller into a customer, call handling is no longer simply a back-office operating function; it becomes part of the path from acquisition to revenue.

Scale Changes the Voice AI Equation

The opportunity becomes more complicated across a distributed organization. A single restaurant can train employees on how to answer common questions and manage reservations, while a restaurant chain operating hundreds of locations has to maintain that experience while accounting for differences in hours, menus, seating, availability and other local conditions. Service brands face similar variation in technician schedules, service territories and operating rules.

Voice AI offers a way to centralize parts of that experience without necessarily making every interaction generic, but that only works when the AI has reliable access to the underlying location data and operating systems. The challenge increasingly resembles the one brands already face elsewhere in AI-powered local discovery: centralized governance has to coexist with accurate local context.

Brands will need to determine which actions voice agents can complete autonomously, which require approval or escalation, how quickly location-level changes reach the AI and how performance is monitored across the network. Those questions become more consequential as voice agents take on tasks that directly affect revenue and customer experience. An incorrect answer from an automated FAQ is frustrating; an AI agent incorrectly booking a reservation or dispatching a service call creates a different level of operational risk.

Conversation and Conversion Begin to Converge

The announcement also fits into a broader change underway at Yelp. Street Fight recently examined Yelp’s integration of Reservations and Waitlist capabilities into ChatGPT, which allows consumers to move from an AI-driven restaurant recommendation toward taking action within the conversational experience. That development illustrated how recommendation and conversion are beginning to converge as AI takes on more of the traditional discovery journey.

Voice AI approaches the same shift from another direction. Instead of beginning with an AI recommendation and moving toward a transaction, Yelp Host and Hatch begin with an inbound customer conversation and attempt to turn it directly into an action. For a restaurant, “Do you have a table tonight?” can potentially end with a reservation; for a service brand, describing a broken air conditioner can end with a scheduled technician visit. Each removes steps between expressed customer intent and conversion.

As frontier voice models become available to more developers and platforms, access to the conversational technology itself may become less of a differentiator. For restaurant chains, franchises and multi-location service brands, the harder work sits behind the voice: maintaining accurate location data, availability, customer context and operating rules, then connecting those inputs to systems capable of completing an action. The voice may be what the customer experiences, but the intelligence behind it will determine whether the interaction actually works.