Opening more coffee shops may be the most visible part of the Dutch Bros growth strategy. Behind the scenes, however, the company is building something just as important: the marketing infrastructure needed to acquire customers more efficiently, personalize engagement, and increase customer lifetime value. As Dutch Bros expands nationally, that infrastructure may prove to be one of its most durable competitive advantages.

The strategy is already producing measurable results. During the first quarter of 2026, Dutch Bros increased revenue 30.8% to $464.4 million, while system same-shop sales rose 8.3%, driven by 5.1% transaction growth. Management also raised its full-year outlook for revenue, adjusted EBITDA, same-shop sales, and new shop openings, signaling confidence that its investments in customer engagement and operational execution are translating into stronger business performance.

That momentum reflects a strategy first articulated during the Dutch Bros inaugural Investor Day in 2025. Management outlined plans to accelerate unit growth, increase transactions, expand digital engagement, improve unit economics, and grow from roughly 1,000 locations toward more than 2,000 by 2029. CEO Christine Barone described the objective as creating “a long-term engine of innovation, personalization and loyalty” capable of reaching “the right customer at the right moment.” While Investor Day focused heavily on store development and financial targets, it also revealed an equally important priority: building the customer infrastructure needed to support long-term national expansion.

First-Party Data Is the Foundation

The cornerstone of that strategy is Dutch Rewards, which now accounts for 74% of all transactions. Rather than functioning simply as a loyalty program, it has become one of the company’s richest sources of authenticated first-party customer data. Every purchase, preferred beverage, visit frequency, store location, and mobile interaction helps Dutch Bros better understand customer behavior while reducing its dependence on increasingly expensive paid customer acquisition.

To activate that data, Dutch Bros combines a customer data platform (Segment) with a customer engagement platform (Braze). Together, those systems unify customer information across digital touchpoints and enable personalized communications through email, SMS, push notifications, and the mobile app.

Instead of sending identical promotions to every customer, Dutch Bros can tailor communications based on purchasing behavior, visit frequency, and previous interactions. Customers showing signs of declining engagement can automatically receive retention offers, while seasonal products and promotions can be personalized using purchase history. The result is a lifecycle marketing strategy that becomes more effective as customer relationships mature.

Customer Experience Fuels Better Marketing

Order Ahead illustrates how Dutch Bros increasingly views operations and marketing as complementary investments rather than separate initiatives. Beyond reducing wait times and improving shop throughput, every authenticated digital order generates additional customer insight while creating new opportunities for personalized engagement.

Combined with expanded food offerings and continued investments in operational efficiency, these initiatives create a virtuous cycle. Better customer experiences encourage more digital interactions, richer first-party data, more relevant marketing, and ultimately stronger customer retention. Rather than optimizing individual touchpoints, Dutch Bros is steadily connecting them into a single customer journey.

Expansion Means Integrating Customers, Not Just Stores

Dutch Bros’ acquisition of Clutch Coffee demonstrates why that infrastructure matters. The transaction provided an immediate presence in North and South Carolina, accelerating the company’s entry into attractive Southeastern markets. But converting stores is only one part of the integration process. The larger opportunity lies in converting existing Clutch customers into loyal Dutch Bros customers.

That requires much more than changing signage. Success depends on enrolling customers into Dutch Rewards, encouraging adoption of mobile ordering, personalizing communications, and building habits that lead to repeat visits. With customer data unified and engagement coordinated across its marketing platform, Dutch Bros has the infrastructure to integrate customers—not just physical locations.

Management has also indicated that acquisitions could become another avenue for accelerating expansion alongside traditional new-store development. If that strategy continues, the ability to quickly migrate customers into the Dutch Bros ecosystem may become just as valuable as the ability to open new stores.

A Blueprint for Multi-Location Growth

The investments underway today closely mirror the priorities management outlined during Investor Day. Increasing transactions requires higher visit frequency. Improving unit economics depends on more efficient customer acquisition and stronger retention. Accelerating expansion requires bringing new customers into the ecosystem quickly while building relationships that continue long after the first purchase.

Dutch Bros opened 41 new shops during the first quarter and increased its 2026 outlook to at least 185 new locations. As expansion accelerates, the systems supporting customer acquisition and retention become just as important as the pace of new store development. The companies that scale most effectively will increasingly be those that connect marketing, operations, digital engagement, and customer data into a unified growth engine.

That’s the broader lesson for multi-location brands. National expansion is no longer driven solely by real estate, advertising, or operational execution. Increasingly, competitive advantage comes from the ability to transform customer data into stronger relationships, higher visit frequency, and greater lifetime value.

Dutch Bros’ store count may define its growth, but the marketing infrastructure developing behind the scenes may ultimately determine how profitable and sustainable that growth becomes.

Vevo is bringing its premium music videos into brick-and-mortar businesses through a new partnership with Rockbot, giving MULO brands another way to create more engaging and consistent in-store experiences

For multi-location brands, the in-store experience is becoming an increasingly important competitive differentiator. While digital channels continue to dominate customer acquisition, what happens inside a physical location often determines whether customers stay longer, spend more, and return. As a result, operators are investing in technologies that help create more engaging, immersive, and consistent experiences across every location in their network.

That trend received another boost this week as Vevo announced an exclusive partnership with Rockbot to bring premium, professionally curated music video programming into commercial venues nationwide. The agreement makes Rockbot the first platform to offer Vevo’s music videos for business environments, giving restaurants, retailers, fitness centers, hospitality companies, and other multi-location operators access to centrally managed, ad-free programming designed specifically for commercial use.

In-Store Media Becomes a Brand Asset

The partnership introduces six curated Vevo channels at launch. The three channels of Rock, Hip-Hop, and Dance are organized by genre, while Day, Night, and Late Night are programmed around customer traffic patterns and the changing atmosphere businesses want to create throughout the day. The channels will also be available alongside Rockbot’s broader portfolio of news, sports, lifestyle, family, and ambient programming, allowing brands to build entertainment experiences that align with their audience, operating hours, and brand identity.

Rather than asking individual locations to create playlists or manage entertainment independently, the platform enables brands to deploy professionally curated programming across an entire location network while maintaining centralized oversight. That approach helps ensure customers receive a consistent brand experience regardless of which location they visit while reducing the operational burden placed on store managers and franchisees.

As customer expectations continue to evolve, entertainment is becoming more than background ambience. Alongside digital signage, lighting, and store design, media is increasingly viewed as another touchpoint that influences how customers perceive a brand, how long they remain engaged inside a location, and ultimately how memorable the overall customer experience becomes.

Location Management at Scale

Rockbot currently powers entertainment and media experiences across more than 50,000 business locations, serving national brands including Best Buy, Planet Fitness, Shake Shack, Lucky Strike, and Great Wolf Lodge. Its platform enables businesses to centrally manage music, television programming, and digital signage while allowing individual locations to adjust programming based on local audiences, regional preferences, or dayparting requirements.

That balance between centralized governance and localized flexibility has become increasingly important for franchise organizations and enterprise retailers. National marketing teams want every location to reinforce the same brand standards, while local operators need the ability to adapt experiences to reflect local audiences, seasonal demand, or regional preferences.

Under the partnership, Vevo’s music video channels become another centrally managed content option that can be deployed consistently across hundreds or thousands of locations without increasing operational complexity.

Garrett Dodge, CEO and co-founder of Rockbot, said businesses require a fundamentally different streaming experience than consumers, emphasizing the importance of licensed, commercial-free programming that reflects a brand while remaining easy to manage across large location networks. He said adding Vevo’s music video catalog allows companies to match entertainment to the energy of different times of day while controlling programming across one location or an entire enterprise from a single platform.

Physical Locations Become a New Premium Video Channel

For Vevo, the partnership represents more than another distribution agreement. It reflects the company’s belief that premium video consumption is no longer confined to connected televisions, computers, or smartphones, but is increasingly extending into the physical spaces where consumers shop, dine, exercise, and socialize.

Speaking with Street Fight, Natalie Gabathuler-Scully, Executive Vice President of Revenue, Distribution & Data Operations at Vevo, said consumers no longer think about premium content in terms of channels or devices. Instead, they expect high-quality viewing experiences wherever they are, creating new opportunities for premium publishers to reach audiences beyond the living room.

Gabathuler-Scully said bringing professionally curated music videos into brick-and-mortar businesses is part of a broader evolution in content distribution that recognizes physical spaces as an increasingly important extension of the premium video ecosystem.

That perspective aligns with broader changes taking place across retail, restaurants, hospitality, and fitness. As consumers spend more time engaging with digital content outside the home, businesses are looking for professionally managed programming that enhances the customer experience while reinforcing their own brand identity. For multi-location operators, entertainment is becoming another strategic touchpoint that can be centrally managed alongside digital signage, promotions, and other in-store media.

Experience Is Becoming a Marketing Channel

The announcement highlights how enterprise media platforms continue evolving beyond background music services. Today’s platforms increasingly combine music, digital signage, television programming, promotional messaging, and branded content into unified systems that can be managed centrally across an entire organization.

That convergence creates new opportunities for marketing and operations teams alike. Marketing leaders gain another channel for reinforcing brand identity inside the store, while operations teams benefit from standardized programming that reduces complexity and improves consistency across every location. Franchise organizations, meanwhile, can better ensure that independently operated locations continue delivering experiences aligned with overall brand standards.

The shift also reflects a broader change in how brands think about physical locations. Stores, restaurants, fitness centers, and hospitality venues are increasingly viewed as owned media environments where every customer touchpoint—from digital displays and promotional messaging to music and video programming—contributes to brand perception and customer engagement. As businesses compete to differentiate physical experiences from online shopping and digital-first competitors, enterprise-managed in-store media is becoming an increasingly important part of the marketing technology stack.

Why It Matters

The Vevo-Rockbot partnership is about more than bringing music videos to commercial televisions. It signals how premium content providers and enterprise technology platforms increasingly view brick-and-mortar businesses as strategic distribution channels rather than simply commercial venues.

The ability to centrally manage entertainment, digital media, and customer-facing content across every location reflects a broader shift in how brands view their physical footprint. Stores are increasingly becoming an extension of both the brand experience and the premium media ecosystem, shaping how customers engage with brands long after they walk through the front door.

In crowded markets, it’s tempting to follow the strategies, messaging, and tactics of fast-growing competitors. But as AI increasingly influences how brands are discovered and recommended, sustainable growth belongs to organizations with a distinctive market position, clear differentiation, and expertise that competitors can’t easily replicate.

Copycats may grow, but they rarely lead. When another brand is attracting attention and growing quickly, it’s easy to assume its strategy, messaging, or technology holds the answer. Adopting the same trends, launching similar products, or copying what appears to be working can help a business keep pace with the market. But keeping pace is not the same as creating competitive advantage.

Companies that simply imitate competitors often find themselves reacting rather than leading. Their messaging becomes interchangeable, their value proposition harder to distinguish, and their growth increasingly dependent on matching everyone else instead of delivering something customers cannot find elsewhere.

Sustainable growth comes from making deliberate choices that set a business apart, not from becoming a slightly different version of someone who is already successful. Brands that consistently outperform their peers identify underserved customer needs, solve meaningful problems, and build capabilities that competitors struggle to replicate.

Taking the Lead

Toast provides a useful example. When the restaurant technology platform entered the market, operators already had access to point-of-sale systems, payment providers, reservation software, online ordering platforms, and back-office tools. The challenge wasn’t a lack of technology—it was that most of those systems operated independently, creating friction for restaurant owners.

Rather than launching another standalone product, Toast focused on integrating service, payments, digital ordering, staff management, customer engagement, and back-office operations into a single platform built specifically for restaurants. Instead of adding another tool to the technology stack, it simplified operations.

That emphasis on solving a meaningful customer problem, rather than matching competitors feature for feature, helped Toast grow to more than 171,000 locations while reaching an annual recurring revenue run rate of $2.2 billion during the first quarter of 2026.

The lesson extends well beyond restaurant technology. In crowded markets, sustainable growth rarely comes from making more noise. It comes from identifying what customers genuinely need but cannot easily find elsewhere, then delivering it consistently enough to become the standard others eventually try to emulate.

The Cost of Looking Like Everyone Else

Most brands don’t become generic overnight. It happens gradually. One competitor refreshes its positioning, others adopt similar messaging, and before long everyone is talking about transformation, optimization, AI-powered insights, or customer-centric innovation.

The language converges, the positioning becomes interchangeable, and businesses that once felt distinctive begin to sound remarkably alike.

That creates a dangerous dynamic. When customers struggle to identify meaningful differences between providers, purchasing decisions increasingly revolve around price, speed, or convenience. In many cases, buyers simply select whichever credible option appears first.

For agencies and brands, differentiation is no longer about having the loudest campaign. It is about giving customers, and increasingly AI systems,  a clear reason to recognize, remember, and recommend your business.

AI Is Raising the Stakes

Standing apart has always mattered. AI search is making it even more important.

As Google, ChatGPT, Gemini, Claude, Perplexity, and other AI platforms reshape how people research businesses, traditional search results are increasingly giving way to synthesized recommendations. Rather than presenting long lists of websites, large language models summarize information from multiple sources, compare competing providers, and recommend organizations they determine best fit a user’s needs. That changes the economics of differentiation.

Businesses that describe themselves using nearly identical language risk being grouped together by AI systems as functionally equivalent. When every company claims to deliver innovation, optimization, or AI-powered solutions, those claims lose their ability to differentiate. The businesses most likely to stand out are those with clearly defined expertise, distinctive positioning, demonstrable customer outcomes, and a recognizable point of view.

In the AI era, standing out is no longer simply a branding exercise. It has become a discoverability strategy.

Usefulness Wins

Google’s own guidance reinforces that direction. Rather than rewarding content created primarily to manipulate rankings, its search systems increasingly prioritize information that demonstrates experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) while providing genuine value to users.

That should encourage marketing leaders to rethink how they communicate.

The strongest long-term strategy is to be more useful. That means publishing original research, demonstrating named expertise, sharing measurable customer outcomes, and offering perspectives competitors cannot easily reproduce. Those assets not only strengthen customer trust, they also create richer signals that AI systems can use when evaluating authority and recommending businesses.

In a world where generative AI can produce polished but generic marketing copy within seconds, competitive advantage increasingly comes from the things machines cannot invent and competitors cannot easily imitate: authentic experience, credibility, deep industry knowledge, and original thinking.

Differentiation Starts Inside the Brand

One of the biggest mistakes growing brands make is treating differentiation as a marketing initiative instead of an operational discipline.Refreshing a brand identity or rewriting website copy can improve perception, but it cannot compensate for an undifferentiated business model. A company cannot credibly promise highly personalized service if inconsistent processes prevent it from delivering. A technology platform cannot position itself as customer-first if product development is driven primarily by competitive feature comparisons. Agencies cannot claim strategic leadership if every recommendation mirrors what competitors are already selling.

Sustainable differentiation comes from understanding customers, identifying underserved needs, and building an organization capable of consistently delivering on the promises it makes. Positioning, operations, product development, culture, customer experience, and service delivery must reinforce one another. Otherwise, differentiation becomes little more than a marketing slogan.

Many successful businesses begin with founder intuition and entrepreneurial energy. As organizations scale, however, those instincts need to evolve into repeatable operating models that preserve what makes the business unique while allowing it to grow.

Deliberate Choices Create Sustainable Growth

None of this suggests businesses should ignore competitors. Understanding how markets evolve, where others excel, and which technologies are reshaping customer expectations remains essential. The objective is to learn from competitors without becoming a reflection of them.

That requires leadership teams to ask more demanding questions. What do customers genuinely gain by choosing us? Which capabilities are difficult for competitors to replicate? Does our operating model consistently deliver on the promises our brand makes? And if AI summarized our business alongside five competitors today, would there be an obvious reason to recommend us? These questions are becoming increasingly important as AI influences how businesses are evaluated and discovered.

The goal is not to be quirky or contrarian for the sake of it. It is to be meaningfully different in ways that customers can understand, trust and value. Because in business, as in life, the companies that create lasting growth are rarely those that follow the crowd. They are the ones with the confidence to zig when everybody else zags.

For franchise brands, generating leads is no longer the primary challenge. The bigger opportunity is understanding why some locations consistently convert demand into revenue while others struggle despite receiving similar marketing support.

That shift is reflected in Thryv’s latest integration with Breesy AI, which combines customer acquisition, engagement, and operational intelligence into a unified view of franchise performance. Rather than stopping at lead generation metrics, the integration is designed to help franchise brands understand how marketing investment translates into completed jobs and revenue across their networks. The announcement also reflects a broader trend across local marketing technology as software vendors increasingly connect marketing performance with business operations, giving multi-location organizations a more complete picture of customer acquisition economics.

Connecting Marketing Investment to Revenue

According to the companies, the integration combines Thryv’s marketing, CRM, communications, and lead management capabilities with Breesy AI’s operational analytics platform.

The combined platform is intended to help franchise brands understand:

• Which marketing channels generate the highest-value jobs
• Which locations convert leads into revenue most effectively
• How much revenue is lost because of missed calls or slow response times
• Where opportunities are being lost throughout the customer journey

Grant Freeman, President of Thryv, said the objective is to give business owners greater confidence in their marketing decisions by connecting every lead back to measurable business outcomes. Rather than simply reporting where leads originated, he said the integration helps operators understand which marketing investments are producing profitable jobs and long-term growth. His comments reflect a broader shift in local marketing, where executives are placing greater emphasis on revenue attribution than on lead volume alone.

That represents an evolution from traditional marketing dashboards, which typically stop measuring performance once a lead has been generated. By linking marketing activity to completed work and revenue, franchise organizations gain a more comprehensive view of return on investment while also identifying where operational bottlenecks are preventing growth.

A New Layer of Franchise Performance Visibility

For multi-location service brands, one of the most significant aspects of the integration is its ability to compare operational performance across locations. While franchisor brands often centralize marketing, execution ultimately depends on local operators. Differences in response times, scheduling efficiency, call handling, technician availability, and sales conversion can dramatically influence revenue outcomes even when marketing investment is consistent.

Jacob Cleveland, CEO of Breesy AI, said every incoming lead represents both a growth opportunity and an operational challenge. By combining Thryv’s customer acquisition capabilities with Breesy AI’s visibility into jobs, teams, calls, and revenue, he said franchise organizations gain a clearer understanding of what is actually driving business performance. Those insights can help franchise systems identify which locations need additional marketing investment and which would benefit more from operational improvements.

As franchise systems continue expanding, that distinction becomes increasingly valuable. Instead of automatically increasing advertising budgets, operators can identify whether staffing shortages, scheduling inefficiencies, or inconsistent customer response are preventing existing demand from converting into revenue. The ability to benchmark these metrics across locations also gives franchisor brands another tool for identifying best practices that can be replicated throughout the network.

AI Is Moving Beyond Marketing Automation

The announcement also illustrates how AI adoption among local service businesses continues to mature. Much of the industry’s first wave of AI focused on content creation, automated messaging, and customer communications. Increasingly, however, AI platforms are extending deeper into business operations, helping organizations identify revenue leakage, optimize workflows, and improve execution after a lead enters the sales pipeline.

Earlier this year, Thryv introduced AI Lead Flow to automate lead engagement and follow-up. The integration with Breesy AI extends that strategy further by adding operational intelligence that helps businesses understand what happens after customer inquiries are received. The move also aligns with Thryv’s broader AI strategy as the company continues expanding AI-powered capabilities across its business management platform for more than 200,000 businesses.

Why It Matters

For franchise brands and multi-location service companies, the announcement underscores a broader shift in local marketing technology. Marketing success is becoming less about generating more leads and more about understanding the operational systems that determine whether those leads become profitable customers.

As AI platforms increasingly connect advertising, customer communications, scheduling, workforce management, and revenue reporting, franchise organizations gain a more complete view of performance across every location. That visibility creates opportunities to improve marketing ROI while identifying coaching opportunities, standardizing operational best practices, and directing investment toward the factors that have the greatest impact on profitable growth. This convergence of marketing intelligence and operational insight should become a more meaningful competitive advantage than simply generating the next lead.

The line separating broadcast TV from connected TV is beginning to blur. As advertisers demand the same audience targeting, measurement, and accountability across every premium video channel, local broadcasters are responding with capabilities once considered exclusive to digital platforms. The latest example comes from Viamedia.ai and Hearst Television, which have introduced household-level addressability for local broadcast TV, marking another step in broadcast’s evolution toward performance media.

Broadcast Is Closing the Digital Gap

The partnership follows a multi-month market test involving Viamedia, Hearst-owned WLWT-TV in Cincinnati, and MediaKind, whose technology enabled dynamic household-level ad insertion over existing broadcast infrastructure. Powered by Viamedia’s Parrot Ad Decisioning System, the solution allows participating stations to make portions of their inventory available for data-driven activation while potentially increasing monetizable impressions.

The significance extends well beyond improving targeting. For years, broadcasters have watched advertisers migrate toward digital and CTV platforms that offered audience segmentation, attribution, campaign optimization, and increasingly sophisticated measurement. Local television continued to deliver premium content, trusted news environments, and market-wide reach, but increasingly lacked the precision marketers wanted for performance-oriented campaigns.

According to Evan Rutchik, President and Chief Strategy Officer of Viamedia.ai, the breakthrough was finding a way to deliver household-level addressability without waiting for nationwide deployment of ATSC 3.0, the next-generation broadcast standard. Rutchik told Street Fight, “We’ve found a way to achieve the same result now, on today’s broadcast infrastructure and even on less sophisticated, already-deployed devices” 

Delivering household-level addressability over today’s broadcast infrastructure could significantly accelerate television’s evolution toward performance media rather than forcing broadcasters to wait years for next-generation standards to achieve meaningful scale. For broadcasters competing against increasingly sophisticated digital channels, shortening that timeline could prove just as important as the technology itself.

Part of a Broader Transformation

The announcement also represents the latest step in Viamedia.ai’s broader evolution. Since acquiring location-based advertising platform LocalFactor and rebranding as Viamedia.ai, the company has steadily expanded beyond traditional television sales into AI-powered, omnichannel advertising. Recent investments in geographic intelligence, unified ad decisioning, and audience activation point to a strategy focused on making local broadcast operate more like modern digital media while preserving the broadcast TV scale and trusted viewing environment.

Hearst Television’s participation also gives the initiative added weight. Hearst operates one of the nation’s largest portfolios of local television stations and has consistently been an early adopter of advertising innovation. Its decision to commercialize household-level addressability suggests the technology is moving beyond proof-of-concept and toward broader deployment across premium local television environments.

Turning Existing Reach Into New Inventory

The technology may be just as significant for broadcasters’ economics as it is for advertisers’ targeting capabilities. Viamedia estimates participating broadcasters could increase monetizable impressions by as much as 25%. That doesn’t mean creating new viewers. Instead, it reflects a more accurate ability to recognize and monetize audiences that traditional measurement systems have historically undercounted.

As Rutchik explained, those households “always existed,” but legacy measurement methodologies failed to fully capture their value. That represents more than an operational improvement. It creates additional inventory that can be sold using audience criteria rather than broad market demographics, potentially increasing the value of local broadcast advertising without requiring additional programming or commercial breaks.

Precision Without Sacrificing Reach

The development also challenges the longstanding assumption that advertisers must choose between broadcast reach and digital precision. Rather than replacing traditional television buying, household-level addressability appears positioned to complement it. Agencies could continue using broadcast television to build broad awareness while simultaneously deploying audience-specific messaging within the same campaign. High-value prospects could receive customized creative, existing customers could be suppressed from acquisition campaigns, and different households could receive different offers without changing the underlying programming.

Rutchik believes that balance will become increasingly common rather than forcing advertisers to choose one approach over the other, stating:

“Broadcast television continues to be unmatched for reach and awareness, while addressable advertising adds precision where it matters most”

That hybrid model increasingly resembles how marketers already approach omnichannel planning. Broad-reach media establishes awareness while data-driven activation improves efficiency and conversion, allowing advertisers to unify branding and performance objectives within a single television campaign rather than treating broadcast and digital video as separate strategies.

Measurement Expectations Continue to Rise

The announcement also reflects a larger shift in advertiser expectations. Media buyers increasingly evaluate every channel against the standards established by digital advertising. Impressions alone are no longer sufficient. Brands want audience verification, frequency management, incremental reach analysis, and attribution tied to measurable business outcomes such as website visits, store traffic, lead generation, or sales.

Those expectations have helped fuel the growth of CTV over the past several years. Broadcast TV now appears to be moving in the same direction. According to Rutchik, advertisers can increasingly expect household-level impression reporting, audience verification, frequency controls, and attribution capabilities that begin to resemble those available across premium digital video. While broadcast TV continues to differentiate itself through trusted local programming and premium inventory, the planning and measurement experience is becoming much more familiar to modern media buyers.

The Convergence of Premium Video

The launch also raises an important competitive question. For years, CTV differentiated itself through audience targeting and measurement while broadcast maintained advantages in reach, live programming, and trusted local content. If broadcasters can now combine household-level targeting with premium programming, the distinction between the two channels begins to narrow.

Rutchik believes broader adoption will require continued collaboration among broadcasters, MVPDs, device manufacturers, technology providers, and advertisers. The technology itself may be ready, but expanding deployments, standardizing workflows, and consistently demonstrating measurable business outcomes will determine how quickly addressable broadcast becomes a mainstream buying option. That observation extends well beyond broadcast television. Across advertising, premium media channels are increasingly converging around the same objective: combining trusted environments with digital-style targeting, measurement, and accountability. Local broadcast television may have been one of the last major channels to make that transition, but it could soon compete for performance budgets on far more equal footing with CTV and other premium video platforms.

For much of the past decade, digital marketing has been dominated by customer acquisition. Brands invested heavily in paid search, social media, retail media, influencers, and performance advertising, all with the goal of finding the next customer more efficiently than their competitors.

That equation is beginning to shift. As consumers discover brands through an expanding mix of AI assistants, retail media, search, and social platforms, customer acquisition has become more fragmented and expensive. That changing landscape provides the backdrop for Fluence Collective’s acquisition of U.S.-based email marketing agency Class A Media. Beyond expanding the group’s North American presence, the deal reflects a broader industry trend: customer retention is once again becoming a strategic growth priority.

The Economics of Customer Acquisition Are Changing

Marketers are navigating one of the biggest shifts in digital marketing since the rise of social advertising. Consumers no longer follow a predictable path to purchase. They may discover a brand through TikTok, receive a recommendation from ChatGPT, encounter a product through a retail media network, or ask an AI assistant to recommend a local business. Traditional search remains important, but it increasingly shares that role with conversational AI and other discovery platforms.

For brands, every new discovery channel creates both opportunity and complexity. Each platform requires its own investment, creative assets, optimization, and measurement, making customer acquisition more diversified, and often more costly. Against that backdrop, maximizing the value of existing customers has become increasingly attractive because it relies on relationships brands already own rather than audiences they must continually rent.

The acquisition also aligns with broader trends Street Fight has been tracking. As AI reshapes customer discovery and first-party data becomes more valuable, owned channels such as email and SMS are evolving beyond promotional tools into activation channels that strengthen customer relationships, increase lifetime value, and create more durable competitive advantages.

Owned Channels Are Regaining Strategic Importance

Email marketing has long been one of digital marketing’s highest-return channels, yet it has often been overshadowed by newer advertising platforms. That may be changing as marketers place greater emphasis on customer lifetime value, first-party data, and long-term retention. Unlike paid media, email and SMS operate through channels brands largely control themselves, allowing them to communicate directly with customers while continuously building richer customer profiles.

That helps explain why Fluence Collective identified Class A Media as an attractive acquisition target. The New York-based agency has supported more than 300 ecommerce brands and generated over $250 million in email-attributed revenue. According to Louie Cameron, Founder of Fluence Collective, the acquisition strengthens the group’s specialist capabilities in email and SMS while advancing its strategy of building focused agencies around disciplines that deliver measurable business outcomes rather than broad marketing services.

Retention Is Becoming More Local

Email itself is evolving beyond its traditional role as a promotional channel. Increasingly, it serves as the delivery mechanism for personalized customer journeys informed by behavioral data, purchase history, loyalty activity, and predictive analytics. SMS has followed a similar path, giving brands another owned channel for timely, highly targeted engagement.

Cameron believes one of the largest untapped opportunities lies in making those experiences locally relevant rather than treating every customer the same across an entire network.

“Too many multi-location brands still treat email as a national marketing channel, when customers actually expect local experiences,” Cameron told Street Fight.

“The brands seeing the strongest retention today are using customer data to personalize communications by location, store behaviour and local relevance, rather than sending the same campaign to everyone.”

That observation extends well beyond email. As brands optimize for AI discovery, retail media, and local search, generic national campaigns are becoming less effective than communications tailored to individual markets and customer behaviors. The same customer data that helps AI recommend businesses can also help brands create more relevant post-purchase experiences that strengthen long-term customer relationships.

Specialization Is Reshaping Agency Growth

The transaction also reflects another trend reshaping the agency industry: specialization. As marketing channels become increasingly complex, many brands are looking beyond traditional full-service agencies in favor of partners with deep expertise in areas such as retail media, lifecycle marketing, CRM, commerce, creator marketing, and performance measurement.

Fluence Collective appears to be following that model by building a portfolio of specialist agencies supported by shared operational infrastructure while preserving the expertise that differentiates each business. The strategy reflects a growing belief that depth of capability is becoming more valuable than breadth of services as brands seek partners capable of solving increasingly specialized marketing challenges.

Growth May Depend More on Existing Customers

The acquisition ultimately says as much about the direction of marketing as it does about Fluence Collective itself. As AI reshapes customer discovery and marketers continue investing across an expanding mix of advertising channels, customer acquisition is becoming more complicated than ever. That reality is pushing brands to place greater emphasis on customer lifetime value, loyalty, and direct relationships through channels they own.

Email and SMS may not generate the same attention as conversational AI or retail media, but they remain among the few marketing channels where brands maintain direct control over both the customer relationship and the underlying data. As acquisition costs continue rising and discovery becomes increasingly fragmented, that control may prove to be one of the industry’s most valuable competitive advantages.

National AI visibility metrics can hide the markets where multi-location brands are winning, and the ones where they’re quietly losing customers. As AI search becomes more local, brands need market-level intelligence to prioritize GEO and improve AI recommendations.

Multi-location businesses have always known that local search performance can’t be managed from a national-level dashboard. Each location operates in its own market, with its own competitors, its own reviews, and its own baseline of local authority. That’s why multi-location local SEO exists as a discipline: because ranking signals and local relevance vary by market, and strategy has to follow.

AI search works the same way. When someone asks an AI assistant for a local recommendation, geography helps shape the answer. The businesses that get mentioned, how they’re described, and which sources the AI draws on all vary based on where the question is being asked. For MULOs, that means AI visibility is playing out differently across every market in their brand footprint, and a high-level view of that visibility isn’t sufficient to do anything meaningful with it.

Location-Level AI Visibility Data Is Essential for Prioritizing GEO Strategies

When a MULO has AI visibility data broken down by market, it stops being a reporting exercise and starts informing real decisions: where to allocate generative engine optimization (GEO) resources, which locations need attention, and where competitive gaps exist that are costing the brand customers. That kind of specificity isn’t available in aggregated national data, and without it, optimization efforts tend to be applied broadly rather than where they’d actually have the most impact.

When you can see AI visibility broken down by market, you can prioritize resources. If three of your fifteen locations are significantly underperforming in AI search, you know where to focus GEO efforts first. If a competitor is dominating AI recommendations in a specific region, you can investigate why and build a plan to close the gap. If a newly opened location is already earning strong AI visibility, you can study what’s working and replicate it.

None of that is possible with aggregated national data. You might know your overall AI visibility is decent, but you have no idea which markets are carrying the brand and which ones are quietly dragging it down with a lower Share of AI Voice (SAIV).

Citation patterns are another area where local granularity matters. AI models draw on sources when forming their answers, and those sources vary by location. Understanding which assets, whether review platforms, local directories, regional press coverage, or local landing pages, are driving AI citations in specific markets tells you exactly where to invest in building authority. That’s a far more efficient use of optimization resources than taking broad swings based on national-level citation data.

Sentiment Varies by Market. Treating It as Uniform Is a Strategic Mistake.

AI doesn’t just surface brands. It describes them, building a narrative that helps customers make a purchase decision. The language a model uses when recommending, or cautioning against, a business shapes customer perception before any direct interaction with that brand takes place.

For MULOs, that characterization isn’t always consistent across markets. A brand that AI describes as a trusted, highly-rated option in one city might be described more cautiously in another, or positioned behind a competitor, or mentioned with qualifications that undercut the recommendation. These differences emerge from the underlying signals AI draws on: local review sentiment, regional citation sources, how well local content establishes relevance for that market, and more.

If your AI sentiment tracking is operating at the national level, you’ll see an averaged picture that might look acceptable even when specific markets have real problems. You won’t know whether mixed sentiment is concentrated in one or two locations or spread evenly across your footprint. You won’t be able to connect sentiment issues to specific markets where review profiles are weak or local content is thin.

Local AI sentiment data gives MULOs the granular, location-specific insights to act. When sentiment tracking is tied to geography, you can identify exactly which markets need attention, diagnose the underlying causes, and build location-tailored plans to improve how AI characterizes your brand where it matters most.

The Competitive Dimension

MULOs also face a competitive dynamic that single-location businesses don’t. At the national level, your AI visibility competitors might look like other large chains or regional players in your category. But at the local level, you’re often competing against smaller independents that have built strong local authority in specific markets.

AI search reflects that reality. A well-established local competitor with deep community ties, strong local reviews, and robust regional citations can outperform a national chain in AI recommendations for that specific market, regardless of how the national brand performs elsewhere.

Understanding MULO AI visibility market by market means understanding the actual competitive landscape in each one. That’s the only intelligence that leads to strategies capable of shaping results where your customers actually are.

The Key Takeaway

AI search isn’t a single channel with a single outcome for your brand. It’s playing out location by location, market by market. That’s why for MULOs, local AI visibility intelligence is the foundation any serious AI search strategy has to be built on.

Many AI visibility tracking and generative engine optimization tools only provide high-level insights, lacking the granular, hyperlocal insights multi-location brands need to succeed in AI-powered local search. You need a partner that tells you how AI talks about your brand from one market to the next, whether it’s from country to country, region to region, city to city, or block by block.

Based on a survey of 1,000 U.S. consumer aged 18-65 conducted with Pollfish in May of this year, the findings of Amperity’s recent report about customer loyalty in the age of AI, reveals just how much services such as ChatGPT, Claude, and Gemini are impacting the customer journey.

These LLM’s are not just answering questions, they are influencing where and how consumers spend their money. For example, just as with search engines, AI is used to research products, compare services, and plan travel.

So what’s new?

Amperity’s report found that a majority of consumers have discovered a brand through AI that they hadn’t considered before, while only a small fraction say they bypass AI entirely to go straight to brands online that they already know. But customer loyalty stops there. Most consumers said they’d switch brands for a better offer, even as they continue to value personalization.

To unpack what this shift means for marketers, especially those competing for attention in local and regional markets, Street Fight spoke with Derek Slager, co-founder and co-CEO of Amperity, a customer data cloud, about how AI is reshaping brand discovery and why relevance may carry more weight than personalization.

How should local retailers and multi-location brands rethink their marketing strategies if consumers are now starting their shopping journeys with AI instead of a search engine or brand web site?

Consumers want AI to narrow their options before they ever visit a website or walk into a store. That changes what brands need to optimize for. Instead of competing for clicks, they’re competing to become the recommendation.

AI systems prioritize relevance, consistency, and confidence. That means brands need accurate product information, authentic customer feedback, and experiences that consistently deliver on customer expectations. The objective isn’t to outsmart another algorithm. It’s to create enough value that AI can confidently recommend your brand.

More than half of respondents said AI has led them to discover brands they otherwise wouldn’t have considered. How is this an opportunity for smaller local businesses to compete with national brands?

This is one of the biggest structural shifts digital commerce has experienced. Historically, large brands benefited from marketing budgets and dominance in traditional search. AI changes that dynamic because large language models optimize for relevance and confidence, not brand size.

Our research found that more than half of consumers have already chosen a brand they hadn’t previously considered because of an AI recommendation. That creates a meaningful opportunity for regional retailers, franchises, and local businesses that deliver exceptional products and customer experiences.

Success depends on making it easy for AI to understand and trust your business. That means maintaining accurate product information, consistent business details, strong customer reviews, and demonstrating expertise in your category. AI is increasingly surfacing the brands most likely to solve a customer’s problem, regardless of whether they’re the biggest name in the market.

How should loyalty programs evolve to remain effective in an environment if AI is surfacing competing options?

AI makes transactional loyalty easier to replace. If every assistant can instantly compare prices, promotions, and rewards across competing brands, points alone become much less of a differentiator.

The brands that build customer loyalty will be the ones that consistently make people feel recognized and understood. That requires a trusted, real-time understanding of the customer across every interaction, whether they’re shopping online, visiting a store, opening an email, or using a mobile app.

When brands combine meaningful rewards with relevant experiences that save customers time or reduce friction, they build customer loyalty that’s much harder for competitors to disrupt.

Which channel should marketers invest first, particularly those with limited budgets?

The research reinforces that the fundamentals haven’t changed. Consumers still value brands that recognize them consistently, respect their data, and deliver reliable experiences across every channel.

Marketers shouldn’t rush to adopt every new AI capability before addressing the underlying data. If customer information is fragmented, experiences are inconsistent, or trust is missing, AI simply scales those problems.

The first investment should be building trusted customer context. Once brands have accurate, connected customer data, AI becomes significantly more valuable because it has the context needed to make better decisions and deliver genuinely relevant experiences.

Nearly 58% of respondents said “creepy” personalization makes them less likely to choose a brand. Where should marketers draw the line between relevance and intrusion, and what are the biggest mistakes you see brands making today?

The difference between helpful and creepy isn’t how much data a brand has. It’s whether the personalization matches what the customer reasonably expects in that moment.

Customers are generally willing to share information when it creates a better experience. What they don’t want is personalization that feels disconnected from their relationship with the brand or reveals information in unexpected ways.

The biggest mistake brands make is assuming more personalization automatically creates better experiences. It doesn’t. Effective personalization uses the right information at the right time to remove friction or help someone make a decision.

Trusted customer context allows brands to personalize with precision instead of overwhelming customers with irrelevant or repetitive messages.

How can local retailers and franchises turn privacy and transparency into part of their customer experience rather than simply a compliance exercise?

Nearly the same percentage are more willing to engage with personalized experiences when they trust how their information is being handled.

Retailers can build trust instead of treating privacy as a legal requirement. Be transparent about what data is collected, why it’s collected, and how it benefits the customer. Then honor those preferences consistently across every interaction.

As AI becomes more integrated into commerce, trust becomes a competitive advantage that can drive customer loyalty. Transparency isn’t just about compliance. It’s how brands earn permission to deliver the personalized experiences customers actually want.

Conversational AI is rapidly becoming another place where consumers discover local businesses and the advertising infrastructure is beginning to follow. As  brands prepare, the challenge is shifting from simply being recommended by AI to managing paid visibility at scale.

For years, digital advertising for multi-location brands followed a familiar playbook. Consumers searched Google, clicked through to websites, compared businesses, and brands competed for visibility through paid search and social media.

That customer journey is beginning to change. According to Yext’s 2026 Consumer Search Behaviors Report, which surveyed 3,848 consumers globally, nearly half of all U.S. adults used an AI tool to find a local business in the past month. Among households earning more than $150,000 annually, AI has already surpassed Google as the starting point for local business searches.

Those findings suggest conversational AI has moved well beyond experimentation and into mainstream consumer behavior. For marketers, the implications extend beyond local SEO and organic visibility. As consumers increasingly ask ChatGPT where to eat, which home services provider to hire, or which healthcare clinic to visit, advertising is beginning to follow consumers into conversational interfaces.

That shift provides important context for Flamel’s latest product launch. The AI-powered marketing platform has introduced ChatGPT advertising capabilities for franchise and multi-location brands, becoming the first platform focused on the franchise industry to operationalize paid campaigns for conversational AI. While the announcement centers on a new advertising capability, it also reflects a broader evolution taking place across local marketing: conversational advertising is beginning to mature into a channel that enterprise brands can manage at scale.

Consumer Discovery Is Moving Faster Than Marketing Operations

For most organizations, adapting to AI has so far meant improving visibility. Marketing teams have spent the past year optimizing websites, Google Business Profiles, location pages, reviews, and structured data so AI systems can confidently recommend their businesses. Increasingly, however, marketers are recognizing that recommendation alone may not be enough as conversational AI platforms develop their own advertising ecosystems.

Advertising introduces a fundamentally different operational challenge. Rather than simply appearing in AI-generated recommendations, brands now have the opportunity to influence discovery through paid media delivered inside conversational experiences. That requires many of the same disciplines marketers developed for Google Ads and Meta over the past decade, but applied to a very different consumer interface.

For businesses operating hundreds or thousands of locations, managing that transition manually is unrealistic. A franchise organization cannot afford to build individual conversational advertising campaigns market by market any more than it could have created thousands of independent Google Ads campaigns a decade ago.

Flamel is attempting to solve that operational challenge by applying an approach already familiar to franchise marketers. Rather than asking franchisees to build campaigns independently, the platform allows franchisors to establish brand standards for targeting, bidding, campaign structure, and localization while giving individual locations flexibility to tailor campaigns within predefined guardrails. Campaign templates automatically incorporate location-specific information, allowing brands to generate localized advertising across hundreds of markets while maintaining centralized governance.

“Our Google and Meta playbooks were built so franchise brands never had to choose between brand consistency and local relevance,” stated Paul Ehlinger, CEO anf founder of Flamel. “ChatGPT Ads follows that same philosophy. It’s a new channel, but a familiar experience for anyone who’s already running paid media on Flamel.”

AI Advertising Is Beginning to Look Familiar

One of the more interesting aspects of the launch is how closely conversational advertising resembles existing paid media operations. Rather than requiring marketers to adopt an entirely new operating model, Flamel incorporates capabilities already common across enterprise advertising platforms, including centralized account management, localized campaign templates, product-feed support, location-level reporting, and campaign governance.

That mirrors a broader trend unfolding across local marketing. AI is changing how consumers discover businesses, but marketers continue looking for ways to integrate those new behaviors into established operating models rather than replacing them altogether. In many respects, conversational advertising appears to be following the same path. The technology may be new, but the operational requirements of governance, localization, automation, reporting, and performance measurement remain remarkably familiar.

The launch also reflects how quickly the conversational AI ecosystem is evolving. Only months ago, industry conversations centered on whether advertising would eventually arrive inside AI assistants. Today, the discussion has shifted toward campaign management, localization, reporting, optimization, and governance. Those are the kinds of infrastructure questions that typically emerge only after marketers begin treating a channel as part of their long-term media strategy rather than an experiment.

The progression mirrors earlier chapters in digital advertising. Google Ads evolved from individual campaigns into sophisticated enterprise platforms. Social advertising followed a similar trajectory as brands sought centralized ways to manage campaigns across thousands of locations. More recently, retail media networks have expanded beyond isolated retailer programs into integrated media ecosystems. Conversational advertising now appears to be entering that same stage of maturity.

The Next Layer of Multi-Location Marketing

For franchise organizations and multi-location brands, the larger opportunity may extend well beyond adding another advertising channel. The organizations that have spent years building scalable operating models for paid search and social media are now positioned to apply many of those same principles to conversational AI. The technology driving consumer discovery may be changing rapidly, but the disciplines required to manage campaigns across hundreds of markets remain largely unchanged.

The significance of Flamel’s launch may ultimately have less to do with introducing another advertising product than with demonstrating that conversational AI is becoming operational.

Retail promotions are evolving from marketing tactics into customer experience strategies. As pricing inconsistencies become more costly and personalized offers more important, retailers are rethinking how promotions drive not just transactions, but trust and loyalty.

Retail promotions have traditionally been viewed as a way to drive sales. But new research suggests they may be just as important for protecting customer trust.

According to a new study from retail promotions platform XC Commerce, 60% of shoppers would abandon a retailer after encountering inconsistent pricing or promotions across channels. At the same time, more than 70% say they are already using or exploring AI-powered tools to help them find deals before making a purchase. Together, those findings point to a broader shift in retail marketing: promotions are no longer simply about offering discounts. They are becoming a critical part of the customer experience, influencing everything from brand credibility to long-term loyalty.

For retailers and the agencies that support them, that raises an important operational challenge. Delivering personalized offers has become easier than ever, but delivering those offers consistently across ecommerce, mobile apps, stores, marketplaces, and loyalty programs has become significantly more complex.

Promotions Have Become an Enterprise Capability

Today’s retailers rarely operate through a single commerce channel. Customers move fluidly between websites, mobile devices, physical stores, social commerce, and loyalty programs, often interacting with several touchpoints before completing a purchase. Every one of those interactions creates another opportunity to engage the customer—but also another opportunity for pricing discrepancies or promotional errors.

Much of that complexity stems from the technology supporting those promotions. Many retailers continue to manage offers through separate promotion engines and legacy systems that were never designed to coordinate personalized discounts, loyalty rewards, coupons, and stacked promotions across every customer touchpoint.

“The effectiveness of any AI strategy is ultimately dependent on the quality, consistency, and accessibility of the underlying data,” Dan Surtees, Vice President of Strategy at XC Commerce, told Street Fight. Retailers that centralize promotional data and execution, he argues, are far better positioned to deliver the consistent experiences customers increasingly expect.

Customer Trust Is Now Part of the Promotion

The operational consequences extend well beyond a failed coupon or incorrect price.

When promotions work differently across channels, customers often interpret those inconsistencies as a sign that the retailer itself is unreliable. What might appear internally as a technology issue quickly becomes a customer experience issue—and one that can spread rapidly through online reviews and social media.

That helps explain why promotions are evolving beyond traditional merchandising tactics. Retailers increasingly evaluate them not only by redemption rates or incremental revenue, but also by their impact on customer satisfaction, loyalty, and lifetime value. Consistency has become part of the brand promise.

Value Means More Than Discounts

The research also highlights a shift in how retailers define promotional success. Rather than relying on broad percentage-off discounts designed to appeal to everyone, many brands are investing more heavily in personalization built around customer behavior, preferences, and purchase history. Surtees said:

“The most effective promotions are not necessarily the most aggressive discounts.”

Instead, retailers create stronger engagement when offers reflect what individual shoppers actually perceive as valuable. That requires a much deeper understanding of the customer journey. Browsing behavior, loyalty participation, previous purchases, channel preferences, and engagement history all become signals that help determine not only which promotion should be delivered, but when and where it should appear.

For retailers, the objective is shifting from maximizing discount volume to maximizing relevance. Well-targeted offers can strengthen customer relationships without conditioning shoppers to wait for the next sale, helping retailers protect both margins and long-term loyalty.

Promotions Are Becoming Part of the Brand Experience

As promotional strategies mature, retailers are also looking beyond discounts as their primary differentiator. Exclusive loyalty benefits, personalized rewards, bundled offers, and interactive promotional experiences are increasingly replacing one-size-fits-all campaigns.

That reflects a broader evolution in retail marketing. Promotions are becoming less about isolated pricing events and more about creating memorable customer experiences that reinforce the brand while encouraging deeper engagement.

For agencies, the shift also changes how promotional campaigns are planned and executed. Success increasingly depends on close coordination between commerce platforms, loyalty programs, customer data platforms, media activation, and operational systems rather than creative messaging alone.

As consumers continue discovering products through an expanding mix of AI assistants, search engines, retailer apps, social platforms, and digital marketplaces, promotions are becoming one of the clearest signals of how well a retailer understands its customers.