3D DOOH is shifting from brand awareness to a measurable local performance channel. With faster production and built-in mobile retargeting, campaigns can now drive store visits and conversions at scale across locations.

Are we ready for 3D, “Pixar-quality” digital billboards? Big Happy thinks we are and have built a 3D DOOH platform that can scale these local brand executions in less than a month, compared to the previous time frame of up to three months and higher production costs.

Working with NHL franchise, the Washington Capitals, and brands such as Windstream and Celsius to bring this approach to market, Big Happy also uses mobile retargeting to connect viewer exposure to action.

CEO Jonathan Frohlinger discusses how Big Happy transformed itself into a creative performance lever.

What’s the best example of a 3D billboard execution from Big Happy and what were the results?

One of the strongest examples of 3D DOOH execution came from a retail campaign we ran during a peak shopping period, where the goal was to drive store visits at the local level.

The 3D creative was built to do what most billboards can’t, actually grab attention in real-world environments. We leaned into bold, high-impact visuals and paired that with QR functionality, so the experience didn’t end at awareness.

From there, we made sure that attention was showing up in the right places. We activated custom geofences around store locations and focused delivery across high-traffic environments where consumers were already primed to spend. At scale, that campaign outperformed all benchmarks, delivering a 64.59% uplift, and a 7% increase in incremental store visits.

You’re positioning creative as a performance lever. Can you provide proof points supporting that 3D billboards paired with mobile retargeting actually drive measurable local actions like store visits or conversions?

We can point to proof across each part of that system. On the creative side, Nielsen-backed testing shows our formats deliver 75% unaided recall and up to 7x higher mental engagement, which is what determines whether an ad actually gets noticed. On the DOOH side, we’ve seen high-impact creative drive measurable lift in brand and behavioral signals. In one campaign, a targeted DOOH activation outperformed established benchmarks by roughly 10% across key metrics like ad recall and purchase intent.

On the mobile side, retargeting turns that attention into action. In one recent campaign, retargeting engaged audiences drove over 2,000 ticket sales and delivered more than 10x ROAS. So while each piece is measured on its own, together they form a coordinated system. Creative captures attention, DOOH builds presence in the real world, and mobile provides a direct path to conversion.

How granular can campaigns get geographically?

To the screen outside your office. Very granular. We regularly plan campaigns at the DMA, ZIP code, and individual location level. We’ve geo-targeted specific store footprints, activated custom geofences around locations, and tailored delivery based on where action is most likely. That includes ZIP-code targeting in one campaign, DMA and control-store measurement in another, and store-level geofencing in DOOH. That level of precision allows us to move beyond broad regional reach and focus on high-intent environments.

How does a sub-15-day turnaround change how local marketers plan around tentpole moments, promotions, or seasonal demand?

It shifts OOH from something you plan months in advance to something you can actually use tactically. Local marketers can align campaigns with real business moments like promotions, product drops, or seasonal spikes instead of missing those windows. It also allows for more iteration, so you’re not locked into one creative approach for an entire campaign cycle. Another good example of this is when a client came to us in mid-December with a last minute Holiday blast idea they needed to deploy before year-end. Within 12 days, we had a 3D campaign live – helping them turn a time constraint into measurable results to drive sales over the holiday period.

Programmatic OOH is often criticized for prioritizing scale over creativity. How does Big Happy balance automation with truly high-quality, localized creative execution?

We don’t separate the two. Programmatic gives you the ability to scale and target efficiently, but creative is what determines whether the campaign actually works. We’ve built the model so creative production and media activation are tightly integrated, not treated as separate steps.

For a regional advertiser, how does the cost and ROI of a Big Happy campaign compare to more traditional local channels like paid social, search, or radio?

What we can say confidently is that traditional local channels often force a tradeoff. You either get broad reach without much measurement, or more precise targeting that gets harder to manage across multiple markets. What we offer is a more integrated approach. We combine high-impact DOOH, localized targeting, and measurable outcomes in one system, which makes campaigns easier to execute and easier to evaluate across multiple locations. Rather than treating OOH, mobile, and measurement as separate efforts, we coordinate them as part of the same campaign. That’s where the efficiency comes from.

Do you see this as a tool primarily for national brands activating locally, or is there a real opportunity for independent and SMB advertisers to adopt 3D, programmatic OOH at scale?

Historically, this kind of execution was limited to large national brands because of cost and complexity. By simplifying production, activation, and measurement, we’ve made it more accessible to smaller advertisers who want to compete at a higher level. In many cases, they benefit the most because they can drive strong local impact without needing national-scale budgets.

As 3D DOOH becomes faster to deploy and easier to measure, it’s moving from a creative outlier to a repeatable performance channel. One that lets a multi-location brand connect real-world attention to real-world outcomes at scale.

Media quality and performance optimization are moving closer to the point of execution. A new integration between Basis and Protected by Mediaocean is embedding real-time, AI-driven verification, attention, and quality signals directly into campaign activation—reflecting a broader shift toward outcome-based media decisioning.

Basis and Mediaocean have announced a direct integration that brings Protected by Mediaocean’s media quality and verification capabilities into the Basis platform. The move gives agencies and brand marketers greater control over where ads run—and how effectively they perform—without relying on separate tools or post-campaign analysis.

The integration introduces a more unified approach to media execution, combining activation, verification, and optimization within a single workflow. For marketers, this represents a step toward reducing the fragmentation that has long defined digital media buying.

A Shift From Static Verification to Performance-Led Quality

At the center of the integration is a shift in how media quality is evaluated.

Traditional verification tools have relied on static rules or blocking mechanisms to prevent ads from appearing in unsafe or low-quality environments. While effective at limiting risk, those approaches often operate independently from performance outcomes—creating tradeoffs between scale, efficiency, and control.

Protected by Mediaocean introduces a more adaptive model. Its system uses AI-driven pre-bid intelligence informed by continuous post-bid data, creating a feedback loop that adjusts targeting decisions based on real-world performance signals. The result is a more dynamic approach that connects verification directly to outcomes.

Embedding Quality Signals Directly Into Media Buying

For agencies and brands, the practical implication is greater precision in media buying. Through the integration, marketers can apply customizable brand safety, fraud detection, and quality controls directly within the Basis platform.

At the same time, attention metrics, viewability data, and supply-side intelligence are used to prioritize higher-quality inventory and optimize campaigns in real time.This moves quality and performance from separate considerations into a single decisioning layer.

Reducing Waste Earlier in the Buying Process

The integration also targets one of the industry’s most persistent challenges: wasted spend.

Invalid traffic, made-for-advertising (MFA) sites, and low-quality placements continue to dilute campaign performance. By incorporating real-time quality signals into activation workflows, marketers can reduce inefficiencies earlier—before impressions are purchased—rather than correcting them after the fact.

“Advertisers need control, clarity, and simplicity across an increasingly complex media landscape,” said Mike Hoyle, Chief Product Officer at Basis. He noted that unifying quality and performance signals helps marketers activate campaigns with greater confidence while maximizing return on investment.

Real-Time Optimization Becomes Table Stakes

The emphasis on real-time optimization reflects broader changes in how media is planned and executed. As campaigns become more dynamic and data-driven, the ability to adjust targeting and investment decisions mid-flight is increasingly critical. Embedding verification and performance signals directly into activation environments enables a continuous optimization cycle, rather than relying on retrospective reporting.

“Media quality shouldn’t come at the expense of performance, or vice versa,” said Sara Maskivish, SVP of Market Enablement for Verification at Protected by Mediaocean. “By connecting verification directly to outcomes, we’re helping marketers make smarter decisions and get more from every impression.”

Toward a Unified, AI-Driven Media Lifecycle

The partnership signals a broader push to unify the media lifecycle. Both companies have positioned the integration as part of a longer-term effort to connect planning, activation, ordering, execution, measurement, and financial reconciliation into a single system. Future phases are expected to expand connectivity across additional Mediaocean platforms, including Prisma and Innovid.

The Bigger Shift: Intelligence Moves Into Execution

The larger shift is toward embedding intelligence directly into execution. Rather than treating verification, measurement, and optimization as separate steps, integrations like this point to a more unified model—where data signals continuously inform how campaigns are activated and refined.

As that model evolves, the distinction between media quality and media performance continues to narrow—bringing both into a single, real-time decisioning layer.

Brand advertising is shifting beyond the open web—and mobile apps are emerging as a key destination. LoopMe’s launch of Chartboost Direct reflects that transition, introducing infrastructure designed to connect brand demand directly with in-app inventory at scale.

LoopMe announced Chartboost Direct as a solution to help mobile app publishers access brand advertising demand more directly, positioning the company as a bridge between supply-side platforms, ad tech ecosystems, and in-app environments.

The move comes as user behavior continues to evolve. LoopMe research indicates that nearly one in five generative AI users are spending less time browsing the web, while time spent in mobile gaming apps is increasing among that same group.

For advertisers, the implication is clear: attention is fragmenting—and increasingly shifting into app-based environments.

Closing the Gap Between Brand Demand and In-App Supply

Despite growing engagement, in-app advertising has historically been optimized for performance, not brand campaigns. Limitations in measurement, data signals, and buying infrastructure have made it difficult for brand advertisers to scale within apps.

Chartboost Direct is designed to address that gap.

The platform enables publishers to connect directly with brand demand partners without overhauling existing ad tech stacks. Features include direct payment capabilities, private marketplace deal creation, and the ability to package inventory dynamically within mediation auctions.

The goal is to make brand demand work inside mobile apps—more directly, more cleanly, and at scale.

Cleaner Supply Paths and Stronger Signals

A central theme of the launch is supply path optimization (SPO), as brands continue to push for more transparent and efficient buying.

Chartboost Direct introduces a more direct route between publishers and demand sources, reducing reliance on intermediaries and improving signal quality. The platform also integrates LoopMe’s AI-driven optimization, embedding performance and brand signals directly into the bid stream.

For agencies and brands, this means greater precision in targeting and improved campaign outcomes—particularly in environments that have historically lacked consistent signals.

Reframing Mobile Apps as a Brand Channel

For agencies and brand marketers, the bigger shift is strategic.

As web traffic becomes less predictable, advertisers are looking for alternative environments that can support both scale and brand-safe execution. Mobile apps—especially gaming—offer high engagement but have lacked the infrastructure to support brand demand effectively.

Chartboost Direct signals a shift toward making in-app environments viable for brand advertising.

LoopMe CEO Stephen Upstone pointed to broader behavioral changes driven by AI adoption, noting that more consumers are relying on AI tools over traditional search. That shift is accelerating the need for new inventory sources beyond the web.

At the same time, publishers are looking to diversify revenue beyond performance advertising, with brand demand offering higher CPMs and more sustainable monetization.

The Bigger Shift: Direct Access to Demand

The launch reflects a broader industry move toward more direct, transparent connections between supply and demand.

As advertisers prioritize efficiency and data quality, platforms that reduce intermediary friction and deliver stronger signals are gaining importance. For mobile app publishers, this creates an opportunity to reposition in-app inventory as a brand channel—not just a performance one.

For agencies and multi-location brands, the challenge will be operational: integrating these new supply paths into existing workflows while maintaining consistency across channels.

As attention shifts, mobile apps are becoming less of a supplement—and more of a core part of the media mix.

Once again, the advertising industry is in the midst of a technological gold rush. The current hype around AI reflects a familiar pattern: sweeping claims, breathless predictions and a rush to declare that it will finally “fix” everything. It won’t.

While AI has transformative potential, it isn’t a cure-all to some of advertising’s biggest problems. Because problems like wasted spend, burned-out teams and opaque results are not primarily technological, they’re structural.

For decades, the industry has operated on a patchwork of disconnected platforms and fragmented processes never designed for interoperability. A six-week, cross-channel campaign seems straightforward, but in reality it means using a media planning tool that’s often disconnected from buying platforms. Audience IDs are created on one platform then transferred to a DSP. Walled gardens like Google Ads or a retail media network direct advertisers to use their proprietary data.

At the same time, the nature of discovery is shifting. As generative AI overviews and zero-click environments change how consumers discover products, the top of the funnel is becoming increasingly opaque. As a result, advertisers are left trying to piece together these new, complex user behaviors across platforms that were never built to communicate.

On top of this are platform requirements for creative assets and measurement, with each platform providing a dashboard for performance. None of these systems are designed to talk to one another natively, so coordinated orchestration becomes a sequence of translations.

Adding AI into the mix doesn’t magically create alignment. The future of effective advertising should be less about adopting AI tools and more about fixing the fragmented systems these tools are being layered onto.

Intelligence layered onto disorder doesn’t create efficiency–it scales chaos.

AI is Exposing the Industry 

The industry’s most pressing challenges stem from a lack of coordination, not a lack of smart tools. Advertisers are deploying massive budgets across siloed platforms with disconnected workflows and measurement standards that vary by channel. Half of industry professionals say they use eight or more tools to manage campaigns. Lack of shared infrastructure creates inefficiencies that no amount of automation alone can solve.

AI is a force multiplier, but it is neutral. It optimizes toward the data and objectives it is given, even if those inputs are flawed.

When measurement frameworks are inconsistent, AI scales that inconsistency. If objectives are misaligned, AI reinforces those misalignments.

If a consumer sees a CTV ad, interacts with an AI-generated search overview, and eventually purchases via mobile, identity resolution becomes a logistical nightmare. Every participant may be using a different identity graph, attribution logic, definitions of conversion, and lookback windows. Instead of a cohesive customer journey, the brand is left with multiple, often conflicting narratives with each claiming credit with the same outcome. Without a single source of truth, measurement turns into interpretation and incrementality becomes a constant source of debate.

In the manual era, human friction acted as a buffer, masking structural weaknesses. AI removes that friction. Errors and duplications that used to take weeks to materialize now happen instantly and at scale. AI doesn’t eliminate the cracks in your foundation; it makes them impossible and expensive to ignore.

What AI Can Do (With The Right Foundation)

None of this diminishes the value of AI. It clarifies where the value actually lies. AI can only work at scale when the system it operates within is structured, reconciled, and controlled.

AI delivers the highest ROI not as a replacement for operational discipline, but as an extension of it. To transition from “AI-hype” to “AI-enabled,” the industry must treat governance, transparency and reconciliation as prerequisites for scale. Operational readiness means shifting the focus from platform-specific vanity metrics to true business outcomes. Advertisers must first establish a single source of truth for their data, where media spend, campaign performance, and financial data are unified under a consistent taxonomy. It also requires reworking underlying operations, like auditing existing workflows to remove manual data entry and breaking down the silos between media buying and financial reconciliation. Only when a business can definitively map a marketing dollar to an actual business result is it positioned to scale effectively with AI.

With these in place, AI becomes a force multiplier rather than a force magnifier. Once that strong foundation is there, AI can improve optimization with data that is unified and trustworthy, automate repetitive workflows that drain human capacity, enhance forecasting and planning and provide real-time performance insights that are directly tied to financial outcomes.

Roadmap to Success

Rather than rushing to adopt the newest AI tools, brands and agencies need to first focus on creating an environment where those tools can operate coherently. Here are a few foundational steps to get on the right track:

1. Workflow: The entire advertising workflow process from start to finish should be evaluated. Business leaders should identify where data is being entered, adjusted, and disconnected across systems, ensuring that as generative AI is introduced into creative or planning workflows, they are feeding into a centralized, accessible data layer, not creating yet another silo.
2. Metrics: Businesses should agree on the definition of success for data connectivity, grounded in key metrics and how those metrics will be measured, so that all channels are working toward the same outcomes.
3. Financial and Media Data: Establish a unified system that connects financial and media data to ensure decisions reflect actual spend and business results, not siloed performance metrics.

Once these foundations are in place, organizations are better positioned to introduce AI in a way that drives efficiency and measurable impact.

The competitive advantage of the next decade won’t belong to the companies with the most AI tools; it will belong to the companies with the most coherent infrastructure.

Structural reform–aligning systems, standardizing data, and integrating financial visibility–is the only way to unlock the technology’s full potential. The future of advertising is not AI-first. It is structure-first, then AI-enabled.

Simply put, AI will not fix a broken ecosystem, but it will reward those who take the time to build one that works.

Advertising market intelligence is shifting toward real-time visibility. Guideline has launched Market Monitor, a new weekly insights subscription built entirely on verified, transaction-level advertising spend data—giving agencies, brands, and investors a faster, clearer view of global media investment trends. As media buying becomes more dynamic, the move reflects a broader shift toward data-driven decisions grounded in actual market activity, not modeled projections.

Guideline is introducing a new approach to ad market intelligence at a time when speed and accuracy are becoming critical to media planning.

The company’s Market Monitor product delivers weekly advertising market insights based exclusively on verified, transaction-level ad spend data. Unlike traditional advertising market reports that rely on surveys, projections, or platform-reported figures, Market Monitor is built on observed media investment activity—offering a more precise view of how budgets are actually shifting across channels and geographies. That distinction matters as the complexity of media investment continues to increase.

Advertising spend is now distributed across a growing mix of channels, formats, and platforms, making it harder for agencies and brands to rely on delayed or modeled insights. Monthly and quarterly reports often fail to reflect real-time changes in media buying behavior, creating a gap between market conditions and decision-making.

Market Monitor is designed to close that gap. Drawing from approximately $200 billion in annual media investment across 65 countries, the platform translates raw advertising spend data into weekly insights on media investment trends, category shifts, and emerging growth areas. Each report is structured for fast consumption, allowing teams to quickly apply insights to media planning, client strategy, and budget allocation decisions.

The weekly cadence is a core feature. By delivering 48 reports per year, Market Monitor introduces a near-real-time layer of advertising market intelligence—moving beyond static benchmarks toward a continuous signal of market movement. For agencies and brand marketers, this creates an opportunity to align strategy more closely with current conditions rather than historical averages.

The implications extend across the advertising ecosystem. For agencies, real-time ad spend insights can strengthen client conversations and support more responsive campaign planning. For multi-location brands and franchise systems, access to current media investment trends can help benchmark performance across markets and channels. For SaaS platforms and marketing technology providers, the data provides additional context for product positioning and customer strategy.

Guideline has positioning the product as a shift away from modeled data toward verified reality. “The advertising industry deserves market intelligence built on what actually happened—not what a model predicts might have happened,” said Sean Wright, Chief Insights and Analytics Officer at Guideline. He noted that consistent, high-frequency reporting can help organizations move from insight to execution with greater confidence.

The launch also expands access to data that has historically been limited to large enterprise clients. Guideline’s transaction-level ad spend data has long been used by major agencies, publishers, and institutional investors. With Market Monitor, that intelligence is being packaged into a subscription format designed for broader adoption across the advertising and media landscape.

CEO Vincent Mifsud framed the move as part of a broader push toward transparency in media investment. “Our clients have long relied on our data to make critical investment decisions,” he said. “Now, we’re extending that advantage to the broader market.”

The timing aligns with increased demand for more reliable advertising data. As pressure grows around measurement, ROI, and media efficiency, marketers are placing greater emphasis on verified data sources that reflect actual spend behavior. In that context, transaction-level advertising data is becoming more valuable than modeled forecasts, particularly for organizations looking to make faster, more informed decisions.

Rather than relying on periodic reports, the product delivers continuous visibility into global media investment trends—helping agencies, brands, and investors track where advertising spend is moving in near real time.

As the pace of media buying continues to accelerate, tools that combine verified data with high-frequency insights are likely to play a larger role in how organizations plan, invest, and compete.

For years, franchise and multi-location brands have faced a persistent breakdown between strategy and execution. Corporate teams build campaigns designed to scale nationally, but at the local level, those franchise marketing campaigns often fragment—deployed inconsistently, delayed, or not executed at all. This week, Flamel.ai introduced a platform designed to close that gap.

The company announced a full expansion of its AI-powered marketing system, alongside the launch of “Luna,” an embedded AI agent built specifically to execute, analyze, and optimize franchise marketing across every location in a franchise network.

From Fragmentation to Execution Infrastructure

At the core of Flamel.ai’s expansion is consolidation. The platform brings together paid media (Meta and Google), Google Business Profile management, review management, organic social, local SEO blogging, and asset storage into a single system.

For agency and brand operators, the significance isn’t just channel coverage, it’s coordination. Historically, these functions have been managed across separate tools, often requiring manual handoffs between corporate teams, franchisees, and external partners.

That fragmentation has created a structural bottleneck: even when strategy is sound, execution at the local level becomes inconsistent. Flamel.ai’s model aims to remove that friction. Campaigns can be built once and automatically localized across every location, with AI adapting messaging, targeting, and deployment in real time.

Luna: From Reporting to Action

Central to the platform is Luna, Flamel.ai’s proprietary AI agent. Unlike traditional analytics or reporting layers, Luna is designed to operate directly within the workflow, interpreting performance data, generating content, and executing actions without requiring separate tools or specialist intervention.

“What drove the full platform expansion , and Luna specifically, was watching our clients hit the ceiling of what any single-channel tool could do,” said CEO Paul Ehlinger in comments to StreetFight. “Luna is the next step: an AI agent that doesn’t just execute the campaign but understands the entire network, surfaces the intelligence and helps every franchisee act on it in real time.”

The distinction is material. Rather than requiring marketers to move between dashboards, extract insights, and manually apply changes, Luna is positioned as an operational layer capable of responding to prompts, generating localized content, and surfacing performance insights instantly.

Solving a Structural Franchise Problem

The product direction reflects a broader insight about franchise systems: they are, fundamentally, networks of local businesses operating under centralized brand standards.

“This started with my wife,” Ehlinger said. “She owns a local therapy practice, and watching her struggle to stay visible in local search, keep up with social content and respond to reviews, while actually running a business, made the problem impossible to ignore. She didn’t need more tools. She needed someone to handle it for her.”

That dynamic becomes more complex at franchise scale. Corporate teams often solve for strategy and brand consistency, but the burden of execution falls on franchisees … operators who typically lack the time, expertise, or resources to manage multi-channel franchise marketing.

“Every franchisee was fighting a battle their franchisor had already solved at the corporate level but never pushed down to the location,” Ehlinger said. “So we went upstream.”

One-Click Deployment, Network-Wide Impact

A key element of Flamel.ai’s approach is its “one-click” deployment model. Franchisors define campaign strategy and assets centrally, while franchisees activate localized execution with minimal involvement.

Behind that simplicity is a coordinated system that handles localization, targeting, compliance, scheduling, and publishing automatically.

Early results cited by the company suggest the model is resonating. MassageLuXe, a national franchise brand, used the platform to launch a Black Friday campaign across more than 100 locations in minutes, generating 68 million impressions, a reported 512% ROI, and nearly $3 million in revenue.

“Turning our advertising investment into projected millions in profit at this scale is extraordinary,” said , MassageLuXe President & CEO, Kristen Pechacek. “Flamel.ai gave us the infrastructure to launch hyper-local campaigns across our entire system with total brand control. Our franchisees felt confident, our team stayed agile and the results exceeded anything we’ve seen from holiday campaigns before.”

While vendor-reported metrics warrant scrutiny, the inclusion of operator validation signals how removing execution barriers can materially change local marketing performance at scale.

Implications for Agencies and Multi-Location Brands

The emergence of agent-driven platforms like Flamel.ai signals a shift in where value is created. Historically, agencies have played a central role in orchestrating multi-channel execution—bridging gaps between strategy, tools, and local operators. As platforms begin to unify those layers and embed execution directly into workflows, the operational burden shifts.

What remains, and arguably increases in importance, is strategic oversight: campaign design, brand positioning, performance frameworks, and cross-channel optimization. At the same time, the rise of AI agents introduces a new expectation from franchise marketing systems: that marketing should not only be measurable, but executable at scale without adding headcount.

“A franchise is just a collection of local businesses under one roof,” Ehlinger said. “What AI makes possible, for the first time, is solving the local marketing problem for all of them simultaneously, automatically and without adding headcount.”

A Broader Shift Toward Agentic Marketing Systems

Flamel.ai’s launch aligns with a wider movement toward agentic franchise marketing systems, platforms that don’t just assist marketers, but act on their behalf.

In franchise environments, where scale and consistency are persistent challenges, that shift could be particularly impactful. “We’re still in the early innings,” Ehlinger said. “But the franchise brands that rebuild around this model in the next two years aren’t just going to market better. They’re going to operate differently than everyone else.”

When weather shifts, consumer intent spikes and brands that adapt messaging in real time are capturing the most valuable moments to drive action.

Spring is trying to spring in the Northeast but so far, the vibes have been reluctant. But that doesn’t hinder weather-reactive marketing; it presents new opportunities for warm-weather retail activities and products.

Nonetheless, Luba Giglia, COO of AdOmni, sat down with StreetFight to discuss how brands can and do capitalize on cold-weather seasons, and this past one was a whopper. AdOmni is a programmatic advertising platform that simplifies buying digital out-of-home (DOOH) ads. And DOOH is especially suited to daily weather conditions.

What does a truly “weather-reactive” campaign look like at the local level?

A true weather-reactive campaign starts with predefined triggers tied to specific weather conditions – think snowfall, temperature drops, or storm timing. From there, programmatic activation can adjust creative, timing, and delivery based on market conditions. The result is a coordinated response that mirrors how people actually navigate a weather event, while ensuring the campaign still works alongside an always-on baseline.

Can you walk through how messaging, channels, and timing shift hour-by-hour during a snow event?

Early in a snow event, messaging leans into urgency, like last-minute store runs, essentials, and timing, all delivered through DOOH while people are still out in the world. As conditions worsen and people head indoors, the focus shifts to comfort, convenience, and at-home consumption, with channels like CTV reinforcing this behavioral shift. The cadence isn’t static; it has to adapt hour by hour, using day-parting and message variants in response to shifting consumer behavior and environmental conditions.

How granular can these triggers get? Are we talking citywide activation, or can campaigns respond to hyperlocal conditions like neighborhood-level snowfall or traffic disruptions?

Triggers can operate at a highly localized level, down to specific neighborhoods, traffic patterns, or micro-weather conditions. That level of precision matters because 50 degrees does not mean the same thing in every market, and consumer behavior will not shift uniformly across a city. One area may still be commuting while another is already homebound. Effective campaigns respond to those nuances through geotargeting and localized creative rather than treating markets as monolithic.

For brick-and-mortar businesses, how can weather-triggered DOOH and CTV actually drive immediate actions like store visits during narrow shopping windows?

Weather compresses decision-making into narrow windows, which makes capturing high-intent moments more valuable than maximizing reach. DOOH captures attention when consumers are actively out and making decisions, while follow-up messaging reinforces urgency or convenience as they plan their next move. When sequenced correctly, this approach converts peak intent more efficiently and drives measurable lift in store visits.

What’s the barrier to entry for SMBs to adopt this kind of automated, trigger-based buying?

Historically, the barrier has been operational complexity, adjusting campaigns mid-flight required time, expertise, and risk tolerance. Automation changes that by allowing marketers to set weather conditions, forecast windows, creative variants, and location rules in advance without manual intervention. That opens the door for smaller businesses to participate in sophisticated, real-time media strategies without needing large teams.

You mention sequencing from DOOH to CTV. How are you connecting those touchpoints in a privacy-safe way while still maintaining meaningful attribution?

Sequencing is enabled by privacy-safe identity and measurement frameworks that connect exposure across screens without relying on individual-level tracking. Marketers can understand how DOOH exposure influences downstream behaviors like streaming engagement, site visits, or store traffic. That closed-loop view enables optimization while staying compliant with evolving data standards.

Which verticals benefit most from weather-reactive strategies?

Any category where demand is influenced by timing and proximity stands to benefit, including retail, QSR, grocery, and services tied to immediate needs. These are moments where consumer intent spikes quickly and fades just as fast. Weather-reactive strategies ensure media spend aligns with demand as it materializes, reducing wasted impressions.

Are there specific categories (grocery, QSR, retail, healthcare) where you’re seeing outsized performance?

Grocery and QSR tend to see strong performance because weather directly impacts trip planning and urgency, like stock-up runs, delivery decisions, and comfort-driven purchases. Retail also benefits when campaigns align with those compressed shopping windows. The common thread is categories in which behavior shifts instantly and predictably with changing conditions.

If extreme weather becomes more frequent, do you see this evolving from a tactical play into a core always-on strategy for local media planning?

As weather volatility increases, this shifts from a tactical capability to a core component of modern media planning. Marketers will treat it as a standing signal alongside audience and location data, with campaigns designed to flex continuously. Over time, always-on planning will be defined less by fixed calendars and more by a combination of audience, location, timing, and real-world conditions that shape demand in the moment. Organizations that fail to adopt this model will operate with structurally less efficient media strategies.

Media planning is entering its fastest transformation in decades. What once took days (or weeks) of assembling briefs, analyzing data, and building presentations is now being compressed into minutes as agentic AI in advertising automates the core of AI media planning. That shift is now operational. With Compass, Basis has introduced an AI advertising platform that converts campaign briefs into omnichannel media strategies and activation-ready campaigns, collapsing the gap between planning and execution for agencies and brands.

Basis is positioning Compass as a solution to a long-standing bottleneck in digital advertising workflows: turning fragmented inputs into structured, executable strategy. As channel complexity grows, that burden has only intensified.

“As digital advertising swells in complexity, media teams continue to be buried under an avalanche of tasks—synthesizing inputs, assembling frameworks, and building presentations,”, Shawn Riegsecker, CEO of Basis, told Street Fight. Compass, he added, is designed to “ease the burden on marketers and accelerate business growth through truly omnichannel advertising.”

For agencies and multi-location brands, the value is not just speed, but the ability to scale AI-driven media strategy without adding operational overhead.

From Brief to Activation

Media planning remains one of the most labor-intensive functions in advertising. Teams must synthesize briefs, performance data, and audience insights into strategies across programmatic, search, social, and direct channels – often requiring days of work.

Compass compresses that process into a continuous system. Users upload inputs, and the platform generates a complete cross-channel media plan (including audience targeting, budget allocation, and channel mix) then moves directly into campaign activation automation. By connecting brief-to-activation workflows, Basis reduces the fragmentation between strategy and execution teams.

Agentic AI Enters the Workflow

Compass reflects a broader shift toward agentic workflows in marketing, with AI systems that orchestrate multi-step processes rather than assist with isolated tasks. Basis’ LLM agents are trained on internal media expertise to replicate how planners build AI-driven media strategies.

The system also provides transparency. Users can review rationale, refine outputs, and apply constraints that are critical for producing client-ready media plans.

Basis says its framework also monitors bias and demographic sensitivity, an increasing priority as AI campaign planning shapes targeting decisions.

Efficiency and Scale

Early adopters are using Compass to improve agency media planning efficiency and expand capacity. Agencies report faster turnaround times and greater confidence entering new verticals or channels.

This reflects a broader shift in advertising operations automation: AI is reducing time spent on assembly work—research, media mix modeling, and presentations—so teams can focus on strategy and performance.

Riegsecker describes Compass as “a connected intelligence system” that helps advertisers “accelerate expansion and profitability, control costs, win and retain new business, and improve employee satisfaction and engagement.”

The immediate impact is capacity. Faster AI media planning cycles allow agencies to manage more campaigns without increasing headcount.

Closing the Loop

Compass is integrated across planning, activation, and financial workflows, creating a closed-loop media planning system.

By linking strategy directly to execution and outcomes, teams can refine omnichannel media strategies based on real-time performance data rather than delayed reporting.

For multi-location brands, this enables more granular optimization—critical as performance varies by market.

This model reflects the shift toward AI-powered campaign orchestration, where strategy is continuously informed by performance.

What It Means for Agencies and Brands

Compass signals a broader structural change. As AI-driven omnichannel execution becomes standard, the line between planning and activation is disappearing.

For agencies, media planning automation increases scalability but raises expectations around speed and cost. For brands, unified AI campaign planning offers better alignment—but requires clearer inputs and stronger coordination.

In both cases, human roles are shifting toward strategy, interpretation, and accountability.

A Signal to the Market

Compass also represents a competitive signal within the broader AI advertising platform and DSP landscape. While many platforms offer AI-assisted features, Basis is positioning Compass as a fully embedded AI media operating system that spans the campaign lifecycle.

“Compass is a major leap forward,” Riegsecker said, emphasizing its role in reducing operational burden while enabling scale. Whether that positioning holds will depend on adoption and performance. But the direction is clear: AI media planning, agentic workflows, and brief-to-activation automation are rapidly becoming foundational to modern advertising operations.

In that context, Compass is less a feature release and more an early indicator of how agencies and brands will execute omnichannel media strategy in an AI-driven environment.

Meta is automating ad execution, but the bigger problem for brands remains unresolved: deciding where to invest across channels to actually drive growth.

The era of needing specialist teams to manage campaign execution is ending. Platforms are taking over more of that work, handling campaign construction, delivery optimization, and in-flight management within their own environments. Meta’s announcement accelerates that shift and extends it to the 250 million businesses already on its platforms.

That is the right direction. And it raises a harder question.

The Shift Already Underway

This is not a sudden change. Advertising infrastructure has been reorganizing for years around a consistent pattern: give a platform your objectives, budget, and creative, and it increasingly handles the rest.

According to Keen’s 2026 survey, 47% of marketers are already using AI for media planning and campaign creation. Another 26% are actively testing or spending in generative AI interfaces like ChatGPT and Gemini. The platform layer is catching up to behavior that marketers have already adopted.

What is also shifting is where competitive advantage comes from. As platforms handle more of the execution work, the definition of advantage changes. It moves away from who has the best operators running campaigns inside each platform, and instead moves toward who owns the most effective feedback loop across all of them.

The Structural Gap That Remains

Platform execution improving doesn’t solve a specific set of problems that sit above the platform level.

No single platform can tell you how spend in one channel changes performance in another. No single platform can provide neutral accountability across the full portfolio as each reports success in its own terms, against its own objectives. And no single platform is designed to tell you where the next dollar belongs across all the channels you operate in simultaneously. Each has different incentives, different measurement windows, and no visibility into what is happening elsewhere.

This creates a durable gap. The market is developing intelligent execution machines, but still lacks a trusted, neutral system that decides where investment should go across the full portfolio to achieve business outcomes, and learns continuously from what comes back.

What Closing That Gap Requires

The translation between a strategic plan and live campaigns across Meta, Google, and commerce channels has always depended on expert operators to bridge the gap. That friction is where decisions slow down, where intelligence gets lost, and where the loop between what you learned and what you do next breaks.

Closing that gap requires a system that operates differently from a platform. It measures what is actually driving business outcomes, like incremental revenue, profit, and long-term brand equity, not platform-reported performance. Next, it translates those learnings into an optimized investment plan before dollars move and executes that plan directly into the ad platforms where customers are. Finally, it reconciles what was expected against what actually happened, feeding those learnings back into the next cycle automatically.

That is the loop that compounds. And it is where the next phase of marketing infrastructure is being built.

Meta’s announcement will make campaign execution meaningfully more accessible. That matters. The channels it is investing in, alongside Google and the commerce infrastructure where small businesses transact, are exactly where purchasing decisions happen for most growing brands.

The businesses that benefit most won’t simply be the ones with easier access to better execution tools. They’ll be the ones that pair that access with a system capable of making the investment decisions those tools can’t make. Measuring what actually drives outcomes, deciding where dollars go across the full portfolio and executing that plan directly into the platforms where customers are.

Platform execution is getting better. The feedback loop above it is where durable advantage is built.

A great PR campaign can make or break the future of a restaurant location. But in the case of multi-location restaurant brands, this requires strong collaboration between local or franchise leadership and the overall brand to ensure a consistent, coordinated effort.

The unique challenges of restaurant PR

Running a PR campaign for a restaurant is unlike doing public relations for any other type of business. A key reason for this is the stiff competition. Restaurants are not only competing with other businesses in their field but also with those in adjacent fields. A Chinese restaurant faces competition not only from other Chinese restaurants but also from Japanese, Korean, and ‌other restaurants that diners may choose.

Restaurant PR is also interesting because it combines characteristics of both niche and broad approaches. A restaurant falls into several niches, including local market and cuisine style, but at the same time, the goal is also to expand the consumer base. Still, it is important to know a target audience — and their behavior — to maximize return on investment. For example, people probably aren’t going to travel far for a new burger joint, so a highly targeted campaign is likely better, but a fine-dining establishment might have a wider geographic pull.

The final and perhaps most difficult challenge multi-location restaurant brands face with their PR campaigns is the need to balance local and national (or even international) interests. When you do PR for a specific location, the goal is, of course, to increase awareness and traffic for that location. However, messaging and initiatives must also be in line with the brand’s overall core identity. Especially for multi-location brands, customers have specific expectations that must be met.

Strategies to maximize your restaurant PR campaign

There are a few strategies that restaurants and their PR firms can use to garner publicity. The best PR campaigns combine these strategies into an integrated, holistic approach to create and drive attention. Some of these strategies include:

• Targeted local outreach: The most traditional approach to restaurant PR is to invite local news stations, newspapers, and food blogs to the restaurant for reviews and feature stories. This approach is a great way to build awareness in the local community among those who may potentially become regular customers.
• Press releases: If a restaurant is part of a national or international brand, that brand will often issue a press release announcing the opening of the new location. For brands with a large, loyal following that are just establishing a presence in a new market, this can be a great way to drum up anticipation and excitement.
• Influencer campaigns: Modern PR campaigns also extensively use influencer marketing, which might involve inviting social media content creators to come out to collaborate on a social media post or do a video review. Influencer marketing is particularly useful for restaurants in high-tourism areas, as a strong influencer-driven campaign can make a restaurant a go-to destination for people who don’t live in the city.
• Events/grand openings: New restaurant locations often put all of these strategies together by holding an event or grand opening. In addition to local media and influencers, restaurants can invite the general public to these events, giving them the opportunity to gain significant media coverage and grassroots word-of-mouth all at once.

Building both local excitement and long-term brand awareness

A great example of this can be seen in our work with the pizza franchise Mister O1 Pizza. We worked with the company to launch several new locations in the Central Florida area, which represented a massive expansion for the brand across state lines. To raise awareness, we ran an influencer marketing campaign that placed Mister O1 locations on several “best new restaurants” lists, as well as a media outreach campaign targeting both local and industry publications. Now, the brand continues to grow with 25 locations open in the Sunshine State.

For an example of a more traditional, local-oriented campaign, we worked with the coffeehouse chain Southern Grounds on its expansion throughout Florida. By combining a heavy focus on local coverage to support the new St. Petersburg location’s grand opening with a lighter focus on national media to establish Southern Grounds as a budding leader in the space, we helped increase awareness for the new location while building overall brand awareness for potential future growth.

Ultimately, the lesson to be learned is that restaurant PR is not just about one-off events, but about building brand strength and long-term awareness. The initial surge in publicity that comes from an opening is great, but it should just be a launchpad to continued work in areas like influencer marketing and media outreach to sustain interest not only in that location but the brand as a whole, so that future locations can be equally successful.