Retail execution has never suffered from a lack of data. What it has lacked—until now—is confidence. GOcxm is betting that artificial intelligence, applied at the right layer, can close that gap.

The retail execution and consumer engagement platform announced it has acquired Emotive Technologies, the company behind Apex, an AI-powered consumer insights and decision intelligence platform. The move adds predictive modeling, simulation, and behavioral-science-trained AI directly into GOcxm’s Shopper Engagement and Execution Platform, signaling a clear push beyond reporting toward real-time decisioning.

In short: GOcxm wants brands to stop reacting to data after the fact—and start acting on it before execution even begins.

From Data-Rich to Decision-Ready

For global CPG brands, the challenge is no longer collecting data. Between loyalty programs, digital activations, promotions, and retail touchpoints, brands are awash in zero- and first-party signals. The harder problem is translating that information into clear, confident actions that drive performance at shelf and across channels.

“Brands have more data than ever but often struggle to translate it into confident action and workflows,” said Gary Kalk, CEO of GOcxm. “Apex adds a powerful intelligence layer that allows teams to model outcomes, validate strategies, and optimize performance using predictive intelligence and AI-driven decisioning.”

That framing reflects a broader industry shift. Retail is increasingly viewed not just as a point of sale, but as a continuous feedback loop—one that can inform strategy if insights are surfaced quickly enough and tied directly to execution.

What Apex Adds to the GOcxm Platform

Before the acquisition, GOcxm’s platform already covered a wide span of the retail lifecycle. Brands use it to:

• Activate consumers and shopper campaigns

• Capture zero- and first-party data

• Validate initiatives in-market

• Gather shopper sentiment

• Verify proof of purchase

Apex extends this foundation upstream.

The AI platform combines proprietary datasets with applied data science to simulate outcomes and generate predictive recommendations. Rather than telling teams what happened, Apex focuses on what is likely to happen—and what actions are most likely to work.

Embedded into GOcxm’s execution layer, that intelligence allows brands to design, test, measure, and continuously optimize initiatives inside a single system, rather than bouncing between analytics tools, dashboards, and agencies.

Predictive Intelligence Meets Retail Reality

What makes the acquisition notable is how tightly Apex is being integrated into execution, not just analytics.

In many CPG organizations, insights live in one place while activation happens somewhere else—often weeks later. That lag undermines the value of even the best data.

GOcxm’s strategy is to collapse that distance. By pairing predictive modeling with real-world retail execution, teams can:

• Validate strategies before rollout

• Model different scenarios and trade-offs

• Adjust programs during execution, not after

• Tie outcomes directly back to consumer behavior

That shift reflects a growing expectation among enterprise marketers: intelligence should be operational, not observational.

A Behavioral Science Angle

Another differentiator in Apex’s approach is its emphasis on behavioral-science-trained AI. Rather than relying solely on historical correlations, the platform is designed to account for how consumers actually make decisions—particularly in retail environments where context, incentives, and timing matter as much as messaging.

This is increasingly important as CPG brands face tighter margins and higher execution costs. Incremental gains now depend on making fewer, better decisions, not just more campaigns.

“Apex was built to help organizations make better decisions with greater confidence,” said Jordan Van Schyndel, Founder of Emotive Technologies. “By joining GOcxm, we’re able to extend our mission at a greater scale and help brands learn faster, prioritize actions, and turn insight into confident, measurable decisions.”

Consolidation in Retail Tech Continues

The acquisition also fits into a broader consolidation trend across retail and shopper marketing technology.

Brands are pushing back against fragmented stacks that separate insights, activation, and measurement. The demand is shifting toward unified platforms that can close the loop—from data capture to execution to performance validation.

By absorbing Apex, GOcxm is positioning itself less as a point solution and more as an intelligence-driven operating system for retail performance. That’s a competitive stance in a market where differentiation increasingly comes from how well platforms drive action, not how many metrics they expose.

What This Means for CPG Brands

For CPG teams under pressure to justify spend and prove ROI, the implications are significant.

Instead of relying on static reports or post-mortem analysis, brands gain the ability to:

• Predict outcomes before committing budget

• Optimize execution while campaigns are live

• Treat retail touchpoints as a continuous insight engine

• Align teams around a single source of truth

That alignment is especially valuable as retailers demand more precision and accountability from brand partners.

Looking Ahead: Intelligence as the Core of Execution

GOcxm says Apex’s technology and team will be fully integrated into its product roadmap, with continued investment in AI and data science. The goal is to ensure that insight, activation, and measurement operate as one connected system, rather than as loosely coupled functions.

The larger signal is clear: retail execution platforms are evolving beyond task management and reporting into decision intelligence systems.

As data volumes grow and execution windows shrink, brands that can simulate, predict, and adapt in real time will hold the advantage. With this acquisition, GOcxm is making a decisive move to compete in that future.

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Performance advertising is entering a decisively algorithmic phase—and Cognitiv is riding the center of that wave. The advanced performance partner reported strong business momentum in 2025, led by 388% growth in ContextGPT™ and accelerating adoption of its deep learning advertising platform across brands and agencies.

The results reflect more than a good year. They signal a structural shift in how performance marketing is executed as marketers contend with signal loss, media fragmentation, and rising expectations for measurable outcomes. In that environment, Cognitiv’s pitch—custom deep learning algorithms operating in real time—has found a receptive market.

Industry forecasts help explain why. Algorithm-driven spending is expected to account for 71.6% of global ad spend in 2026, rising to 76% by 2028, placing Cognitiv squarely in the fastest-growing segment of digital advertising.

From Self-Serve Optimization to Deep Learning Systems

For years, performance marketing has relied on incremental optimization inside self-serve platforms. That model is starting to show strain.

“Our growth reflects a clear shift in how brands and agencies are approaching performance marketing,” said Jeremy Fain, CEO of Cognitiv. “Advertisers are moving beyond self-serve optimization toward automated deep learning systems that can understand context, interpret intent, and drive outcomes at scale.”

Cognitiv’s differentiation lies in how those systems are deployed. Rather than running containerized models that process data after the fact, Cognitiv co-locates its infrastructure with leading SSPs, allowing algorithms to make bidding decisions in milliseconds.

According to the company, that architecture delivers up to 10 times the computing power of many competing solutions, enabling real-time optimization of metrics like viewability, video completion rate (VCR), and clicks—while impressions are still being auctioned.

In an environment where milliseconds increasingly determine outcomes, that speed advantage matters.

The Curated Contextual Era Takes Shape

At the heart of Cognitiv’s growth is what it calls the Curated Contextual Era—a move away from static audience segments toward real-time, algorithmic decisioning driven by content and context.

This strategy centers on ContextGPT, which Cognitiv positions as the industry’s first real-time contextual targeting intelligence platform. Unlike legacy contextual tools that rely on keyword matching or rigid taxonomies, ContextGPT uses deep learning and advanced language models to interpret content with human-like nuance.

The platform allows advertisers to activate performance without cookies, pixels, or user IDs, a capability that has become table stakes as privacy restrictions and signal loss reshape digital advertising.

In 2025, ContextGPT customer adoption grew more than 44%, fueled by continued product innovation. New features such as Interactive Audience Exploration and an upgraded Relevance Engine delivered up to 40% greater accuracy in connecting brands with custom-defined audiences.

The result is advertising that is not only more privacy-resilient, but often more relevant—because it’s grounded in real-time intent rather than inferred identity.

Performance Gains Drive Customer Expansion

Cognitiv’s momentum isn’t limited to adoption metrics. Customer performance improved meaningfully over the course of the year.

In 2025, the company saw:

• A 67% increase in new clients year over year

• A 29% improvement in average ROAS from the first half to the second half of the year

Those gains translated into deeper customer relationships. Existing clients expanded spend and launched more campaigns as results improved, particularly across verticals like CPG, pharma, and travel, where contextual relevance and brand suitability are critical.

This performance-driven expansion highlights a broader trend: advertisers are increasingly willing to consolidate spend with partners that can prove incremental value, not just incremental reach.

Talent and Leadership Signal a Scaling Phase

Cognitiv’s growth in 2025 wasn’t purely technical. The company also invested heavily in organizational readiness as demand increased.

Over the year, Cognitiv added new talent and promoted dozens of team members across engineering, data science, product, customer success, and go-to-market functions—a sign the company is building for scale rather than chasing short-term growth.

A notable milestone was the promotion of Justine Frostad to Chief Marketing Officer, marking the company’s first CMO appointment. The move signals a more deliberate investment in brand, narrative, and executive visibility as Cognitiv enters a new phase of maturity.

The company also added four new VP and SVP leaders, alongside multiple senior-level promotions, reinforcing a stated commitment to developing talent from within.

At the board level, Cognitiv strengthened its strategic perspective with the addition of Michael Kassan, a longtime marketing and media industry veteran—an appointment that underscores the company’s long-term ambitions.

Why This Matters for the Ad Industry

Cognitiv’s results arrive at a pivotal moment for digital advertising.

As identifiers disappear and platforms fragment, performance advantage is shifting toward infrastructure, algorithms, and execution speed, not audience ownership. Contextual intelligence—once viewed as a fallback—has become a primary strategy, especially when paired with deep learning that can operate in real time.

Cognitiv’s growth suggests that advertisers are no longer experimenting at the edges. They are committing budget to systems that can autonomously interpret context, optimize bids, and adapt continuously.

If forecasts around algorithm-driven spend hold, companies built natively around deep learning—not retrofitted onto legacy stacks—will shape the next phase of performance marketing.

The Takeaway

Cognitiv’s 2025 momentum tells a clear story: performance advertising is becoming a machine-led discipline, and contextual intelligence is no longer optional.

With ContextGPT growing nearly fourfold, improving ROAS, and a platform designed for speed at scale, Cognitiv is positioning itself not just as a tool provider, but as an infrastructure partner for the algorithmic future of advertising.

As the industry moves toward 2026 and beyond, the question may not be whether deep learning drives performance marketing—but which platforms are fast enough, smart enough, and close enough to the exchange to matter.

Get in touch with our MarTech Experts.

As tracking weakens and retargeting becomes less reliable, opt-in has quietly become one of the most valuable moments in marketing. Intuit is making that case forcefully with a new global report that suggests list building isn’t the finish line—it’s where the real work begins.

Intuit Inc. (Nasdaq: INTU) has released The Art of the Opt-In: Why List Building is Only the Beginning, a research report developed by Intuit Mailchimp in partnership with Ascend2. Drawing on insights from thousands of marketers and consumers across the U.S., Canada, the U.K., and Australia/New Zealand, the report maps a widening gap between what brands ask for at opt-in—and what consumers are actually willing to give.

The conclusion is blunt: brands are still optimizing for volume, while consumers are optimizing for trust.

Opt-In Is the Last Clean Signal

In a marketing ecosystem shaped by privacy regulation, signal loss, and platform fragmentation, opt-in has become something rare—a moment of explicit permission.

“As tracking and re-targeting become more complex, the opt-in stands out as one of the few moments when a brand can earn a direct relationship,” said Matt Cimino, product manager at Intuit Mailchimp. “Sign-up is the first signal that someone is willing to engage, and what a brand does in that moment sets expectations for everything that comes next.”

That framing reflects a broader shift underway in MarTech. As third-party data erodes, first-party relationships are no longer just valuable—they’re fragile. The inbox and SMS channel are increasingly guarded spaces, and consumers are far more selective about who they let in.

Marketers Have Lists—But Not Confidence

The data reveals a paradox at the heart of modern marketing execution.

Nearly all marketers maintain email and SMS lists. Yet:

• Fewer than one-third consider their lists “very high quality”

• Only 8% report conversion rates above 20%

• Just 21% have fully automated email and SMS campaigns

• Only one-third feel very confident about which channels drive opt-ins

In other words, most brands are collecting contacts—but few are convinced those contacts will actually convert.

That lack of confidence has consequences. Without clarity on source, intent, or preference, marketers struggle to personalize responsibly. The result is more messaging, not better messaging—fueling consumer fatigue.

Consumers Are Paying Less Attention, Not More

On the other side of the relationship, consumers are noticing the increase in marketing messages—but that doesn’t mean it’s working.

According to the report:

• Only 40% of consumers say they’re paying more attention to marketing emails and texts

• About 25% say they’re tuning these channels out more than a year ago

For those who remain engaged, the expectations are clear:

• 56% want content that genuinely adds value

• 40% want messaging frequency that doesn’t feel like spam

The problem isn’t the channel. It’s relevance.

Asking Too Much, Too Soon

One of the report’s most telling findings highlights how early missteps at opt-in can erode trust before a relationship even starts.

For example:

• 65% of brands ask for a phone number in popup forms

• Only 28% of consumers are willing to provide it

That gap signals a trust mismatch. Brands are optimizing for downstream value, while consumers are still deciding whether the relationship is worth starting.

“Most opt-ins come up short because they’re created only thinking about what the business needs, not what the customer actually wants,” Cimino explained.

The data suggests brands perform better when they focus on high-intent moments rather than intrusive interruptions:

• 50% of consumers are more likely to opt in after browsing

• 39% are more likely during checkout

Timing, context, and restraint matter more than aggressive data capture.

Trust Is Generational—and Visual

The report also reveals sharp generational differences in how trust is formed.

• 39% of Gen Z assume brands will follow privacy laws

• That number drops to just 19% among Baby Boomers

For younger consumers, trust is often immediate and design-driven:

• 43% of Gen Z say clean, simple design makes them more comfortable completing opt-in forms

• Only 29% of Boomers+ say the same

This suggests that for Gen Z, trust is communicated visually and experientially, not through fine print or lengthy explanations. For older audiences, skepticism remains higher—and harder to overcome.

Automation Separates List Builders From Leaders

One of the report’s clearest signals is the role of automation in closing the relevance gap.

Brands that consider their contact lists best-in-class—dubbed “List Quality Leaders”—are:

• 3x more likely to have fully automated email and SMS programs

• More likely to run structured welcome series (64% vs. 53%)

• More likely to deploy cross-sell and upsell flows (45% vs. 36%)

Automation, in this context, isn’t about sending more messages. It’s about responding appropriately, consistently, and at scale.

Omnichannel Orchestration Beats Channel Optimization

The research also challenges channel-by-channel thinking.

Brands with highly aligned omnichannel messaging and timing report significantly higher value across nearly every channel:

• Organic social: 62% vs. 43%

• Paid social: 56% vs. 40%

• Emerging channels like generative engines: 10% vs. 5%

The implication is clear: when channels reinforce each other, each one performs better. When they operate in silos, even strong channels underperform.

This is especially relevant as AI-driven discovery reshapes how consumers find and evaluate brands. Opt-in no longer sits downstream of awareness—it’s part of a continuous, cross-channel conversation.

Data Isn’t the Problem—Fragmentation Is

Despite access to more data than ever, marketers struggle to turn signals into relevance.

Only:

• 30% use preference or frequency data

• 29% use browsing behavior

These are among the strongest predictors of engagement, yet they remain underutilized—largely because data lives in disconnected systems.

“Relevance comes from clarity, not volume,” said Diana Williams, Vice President of Product, Intuit Mailchimp. “When data is fragmented, even the best intentions fall short.”

Mailchimp’s strategy, as outlined in the report, centers on unifying behavioral signals, automations, and omnichannel insights so marketers can act with confidence—not guesswork.

Why This Matters Now

The timing of the report is notable.

As privacy regulations tighten and AI reshapes discovery, permission-based marketing is becoming the foundation of sustainable growth. Opt-in is no longer a mechanical step—it’s a value exchange that sets the tone for the entire relationship.

Brands that treat opt-in as a checkbox will continue to see list fatigue and declining engagement. Those that treat it as a moment of trust—earned through relevance, restraint, and clarity—stand to build relationships that compound over time.

The Takeaway

The Art of the Opt-In makes a simple but uncomfortable point: most brands are still designing opt-ins for themselves, not for their customers.

In a world where attention is scarce and permission is precious, list building is no longer the goal. Earning the right to stay relevant is.

For marketers navigating signal loss, AI disruption, and rising consumer skepticism, the opt-in moment may be the most strategic lever left—and one too often mishandled.

Get in touch with our MarTech Experts.

Connected TV has officially taken the lead—and it’s rewriting the rules of media buying. As streaming surpassed broadcast and cable combined for the first time in 2025, advertisers are facing a tougher challenge than ever: how to reach audiences whose attention is fragmented across apps, screens, and formats.

Nexxen (NASDAQ: NEXN) and independent agency H/L believe they have an answer. By pairing Nexxen’s demand-side platform (DSP) and advanced data insights with H/L’s performance-driven media strategy, the partners report up to a 14x lift in conversion outcomes for clients across multiple verticals. The results highlight a broader shift underway in CTV—from reach-first buying to signal-backed, outcome-oriented execution.

Streaming Wins, Attention Fragments

The backdrop to this partnership is a historic inflection point. In 2025, streaming accounted for 44.8% of total TV usage, overtaking broadcast and cable combined for the first time. At the same time, free ad-supported streaming TV (FAST) channels are surging, with monthly viewership up roughly 12% year over year and session lengths continuing to grow.

For advertisers, that growth is a double-edged sword. CTV now offers access to highly engaged audiences—but those audiences are scattered across platforms, apps, and content types. Add in second-screen behavior, and the old playbook of broad, undifferentiated media buys starts to fall apart.

The implication is clear: attention, not impressions, is the scarce resource.

From CPMs to Cost-Per-Unique Reach

H/L’s approach with Nexxen reflects this reality. Instead of optimizing around traditional CPMs, the agency has shifted focus to cost-per-unique reach, blended frequency management by app, and optimization tied directly to down-funnel business outcomes.

“At H/L, we’ve strategically embraced this evolving CTV landscape,” said Jeremy Cobb, Vice President of Digital Platforms at H/L. “By tapping into Nexxen’s advanced insights, we’ve crafted a strategy that blends premium inventories and custom placements for initial viewer activation, with cost-effective long-tail.”

That balance matters. Premium placements help capture attention early, while long-tail inventory extends reach efficiently—without oversaturating the same viewers. The result is frequency discipline in a medium where overexposure is increasingly easy and increasingly expensive.

Measurable Results, Not Just Promising Signals

The payoff has been tangible. For H/L’s clients in sectors like automotive and insurance, these signal-backed strategies have driven up to fourteen times higher conversion outcomes compared with more traditional CTV programs.

Crucially, those gains weren’t self-attributed. They were validated by Marketing Mix Modeling (MMM) partners, giving advertisers greater confidence that CTV isn’t just delivering awareness—but measurable performance.

This kind of validation is becoming table stakes as CFOs scrutinize media spend more closely and demand proof that CTV can compete with, or complement, lower-funnel digital channels.

Nexxen’s Bet: Know Who’s Actually Watching

Nexxen’s role in this equation centers on insight density and execution speed. Its DSP combines buying capabilities with supply-side intelligence, allowing advertisers to understand not just where ads appear, but what content viewers are actively engaged with.

“Navigating the new CTV landscape requires knowing who’s truly paying attention and managing ad frequency with precision,” said Kara Puccinelli, Chief Customer Officer at Nexxen. “That’s why agencies are leaning into advanced measurement, balancing high-attention placements with cost-efficient reach.”

Rather than treating CTV inventory as interchangeable, Nexxen emphasizes contextual and engagement signals—an approach that aligns with how advertisers now evaluate performance in a multi-screen world.

Publishers Lean In With Smarter Supply

The push toward signal-backed buying isn’t limited to DSPs and agencies. Media owners are also adapting, surfacing richer data and more flexible formats to help buyers transact more intelligently.

Platforms like Philo and DIRECTV Advertising are working with Nexxen to expose high-value inventory—particularly in premium and live content—paired with granular audience and contextual signals.

“CTV remains one of the most impactful ways to connect with audiences, particularly while watching content they’re passionate about,” said Aulden Kaye Yi, Head of Advertising Partnerships at Philo. “Working with Nexxen allows us to surface our inventory with granular audience and contextual signaling.”

That capability becomes even more important as live sports continues its migration to CTV, a trend DIRECTV Advertising is actively capitalizing on.

“As live sports programming continues to shift to CTV, Nexxen allows us to surface and package this high-value ad inventory with contextual and audience data signals,” said Edmund Jules, Senior Director of Ad Sales Partnerships at DIRECTV Advertising.

Why This Matters for Advertisers Now

The Nexxen–H/L results point to a broader industry lesson: CTV performance hinges less on scale and more on signal quality.

As streaming dominates viewing time, advertisers can no longer afford to treat CTV as a blunt instrument. Success depends on:

• Understanding where attention is concentrated

• Managing frequency with discipline across apps

• Optimizing toward outcomes, not just exposure

In that sense, CTV is starting to look less like traditional TV and more like performance media—with all the accountability that implies.

The Bigger Takeaway

CTV’s growth story is no longer about adoption—it’s about execution. As the market matures, the winners will be those who can navigate fragmentation with precision, not those who simply buy more impressions.

The partnership between Nexxen and H/L shows what’s possible when advanced data, flexible buying, and outcome-driven strategy converge. In a world where attention is splintered and budgets are scrutinized, a reported 14x lift in conversions is more than a case study—it’s a signpost for where CTV buying is headed next.

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Artificial intelligence is now deeply embedded in everyday marketing workflows—but new research suggests accuracy hasn’t kept pace with adoption.

According to NP Digital’s AI Hallucinations and Accuracy Report, AI-generated errors are not only common, they’re increasingly slipping into live campaigns. Nearly half of marketers (47.1%) encounter AI inaccuracies several times per week, and 36.5% report that hallucinated or incorrect AI content has already gone public.

The findings underscore a growing tension in modern marketing: AI delivers speed and scale, but without sufficient oversight, that efficiency can introduce serious brand risk.

AI Errors Are Frequent—and Time-Consuming

The report combines two data sources:

• An accuracy analysis of 600 prompts tested across six major large language models (LLMs), including ChatGPT, Claude, and Gemini

• A survey of 565 U.S.-based digital marketers

Together, the data paints a picture of widespread friction between AI output and real-world accuracy.

More than 70% of marketers say they spend one to five hours each week fact-checking AI-generated content, eroding some of the productivity gains AI is supposed to deliver. Despite this effort, errors still escape into production.

“AI has become an incredible tool to accelerate efficiencies, but speed without accuracy creates real risk,” said Chad Gilbert, Vice President of Content at NP Digital. “What makes AI hallucinations especially dangerous is that many of them look believable at first glance.”

When AI Mistakes Go Live

Among marketers who reported publishing inaccurate AI-generated content, the most common issues included:

• False or fabricated facts

• Broken or nonexistent citations

• Brand-unsafe or misleading language

These errors often appear polished and confident, making them harder to detect without careful review. Once published, they can damage credibility, confuse audiences, or expose brands to compliance and reputational risks.

Yet despite these dangers, 23% of marketers say they are comfortable using AI output without human review, a gap between awareness and behavior that the report flags as particularly concerning.

No Model Is Error-Free

NP Digital’s accuracy testing also evaluated how different LLMs perform under scrutiny.

• ChatGPT delivered the highest rate of fully correct responses at 59.7%

• No model consistently avoided hallucinations

• Error rates increased sharply for:

  • Multi-part questions

  • Niche or specialized topics

  • Real-time or time-sensitive queries

The most common hallucination types across all models included:

• Omissions

• Outdated information

• Fabrication

• Misclassification

Crucially, these errors were often delivered with high confidence—making them more persuasive and more dangerous.

Where AI Breaks Down Most Often

The report found that AI struggles most with tasks requiring precision, structure, or technical rigor, including:

• HTML or schema creation

• Full long-form content development

• Reporting and data-driven summaries

These are also the areas where marketers are most likely to trust AI to “just handle it,” increasing the likelihood of mistakes slipping through.

The Real Takeaway: AI Needs Guardrails

The data points to a clear conclusion: AI works best as an assistant, not an authority.

Strong prompts, defined review processes, and human oversight consistently reduce risk. With no single LLM emerging as reliably accurate across use cases, marketers can’t solve the hallucination problem by switching tools alone.

Instead, the report reinforces a mindset shift:

• Treat AI output as a draft, not a final answer

• Match AI tasks to its strengths, not its hype

• Keep humans accountable for what goes live

As AI becomes standard infrastructure in marketing, accuracy—not speed—may be the new competitive advantage.

Get in touch with our MarTech Experts.

The Shelf, a performance-first influencer marketing agency, has earned multiple top industry honors in 2025, reinforcing its position at the intersection of creator-led storytelling and measurable growth.

The agency was named Best Activation in Support of Sustainability at the Adweek Experiential Awards for its work with Natura, and received finalist recognition at the Drum Awards in Retail & Consumer Products for its campaign with Weekday, as well as for Micro-Influencer Campaigns with Gregory Mountain Products. The Shelf also earned a Platinum MarCom Award for its work with Self Financial.

Together, these accolades reflect more than creative excellence. They point to a structural shift underway in performance marketing—one in which creative has become the primary driver of optimization, learning, and scale.

When Targeting Fades, Creative Becomes the Signal

Major platform changes over the past year have fundamentally altered how performance is generated. With automated campaign structures expanding and granular targeting controls shrinking, platforms increasingly rely on creative behavior signals—how content is watched, saved, shared, and engaged with—to determine delivery and scale.

Updates like Meta’s Andromeda have accelerated this shift. As deterministic targeting weakens, platforms can only optimize against what creative reveals in real time. In this environment, creative is no longer just a message—it is the feedback loop.

Many brands have responded by increasing content volume, assuming scale would offset the loss of control. But results have often remained inconsistent. According to The Shelf, the issue isn’t quantity—it’s structure.

Without systems designed to test, interpret, and compound creative insight quickly, content fails to generate durable performance signals.

A System Built for Signal, Not Just Output

The Shelf’s operating model was designed for this reality before platforms forced the shift.

Rather than starting with creators or deliverables, the agency begins with social listening and market intelligence, mapping interest clusters that reveal how audiences actually think, speak, and make decisions. These insights shape creator selection, briefing, and narrative design—aligning brands with creators embedded in relevant, interest-based communities.

From there, creative variations are tested rapidly to surface early engagement signals—such as saves, shares, and watch time—that indicate downstream performance potential. Winning narratives are scaled, while underperforming ones are iterated or retired.

Creative is treated as a source of intelligence, not just an output.

Repeatable Performance at Scale

That systemized approach has delivered consistent results across categories and regions.

Over the past year, The Shelf reports:

• Sustained ROAS across multiple U.S. and European markets for fashion and retail brands

• More than 40 million organic impressions driven by creator-led campaigns at retail scale

• Performance lifts through iterative optimization, including ROAS up to 23x during peak seasonal windows, with efficiency sustained in the weeks that followed

Early engagement signals proved to be reliable predictors of downstream efficiency, allowing platforms to learn faster and scale more effectively.

“These platform changes didn’t make marketing harder, they made weak systems visible,” said Atul Singh, Co-Founder and CEO of The Shelf. “When targeting control fades, the only thing platforms can optimize against is signal. If your creative can’t generate meaningful signal, the platform can’t learn from you. We built our model to turn creative into intelligence long before platforms forced that shift.”

Structure Becomes the Advantage

As performance marketing becomes creative-led by design, the competitive advantage is no longer who produces the most content—but who learns the fastest.

In an environment where platforms optimize behavior, the brands that win will be those with systems built to:

• Identify the right creators

• Pair them with insight-driven narratives

• Interpret performance signals in motion

In that future, scale follows structure—not the other way around.

Get in touch with our MarTech Experts.

Marketing AI is everywhere right now—but in healthcare and life sciences, “everywhere” often means “nowhere useful.” Between regulatory hurdles, fragmented data, and long approval cycles, most off-the-shelf AI tools struggle to move beyond surface-level productivity gains.

Supreme Group thinks it has an answer.

Today, the healthcare-focused marketing and communications agency announced Supreme Intelligence, a proprietary Artificial Intelligence Platform (AIP) purpose-built for the commercial realities of life sciences and healthcare companies. Rather than bolting AI onto existing workflows, Supreme Intelligence aims to rewire how campaigns are planned, created, approved, and optimized—without breaking compliance.

It’s a bold claim in a crowded AI market. But Supreme Group is betting that deep domain expertise—and not just better models—will be the real differentiator.

A New Kind of AI Platform, Not Another Chatbot

Supreme Intelligence is positioned as an end-to-end platform rather than a collection of AI features. The company says it unifies proprietary data, analytics, and healthcare-specific insights into a single, secure environment that agency teams and clients can use across the entire marketing lifecycle.

That lifecycle spans everything from early strategy and messaging development to medical, legal, and regulatory (MLR) review, activation, and performance measurement.

Unlike general-purpose AI tools that excel at narrow tasks—copywriting, summarization, or data analysis—Supreme Intelligence is designed to operate as a connected system. The idea is to reduce friction between steps that are traditionally siloed and slow, especially in regulated industries.

“We built Supreme Intelligence leveraging our life sciences and healthcare domain expertise to drive maximum commercial impact,” said Sheldon Zhai, Founder and Chief AI Officer at Supreme Group. According to Zhai, that domain-first approach is what makes the platform adaptable enough to be considered “a fundamentally new class of AI platforms.”

Supreme Group claims early deployments are already delivering 10x improvements in campaign speed, quality, and performance outcomes, powered by adoption across more than 350 subject matter experts, including over 55 PhDs.

Why Healthcare Marketing Is Ripe for Specialized AI

Healthcare and life sciences marketing sits at the intersection of high stakes and high friction. Campaigns must be accurate, compliant, and evidence-based—while still competing for attention in an increasingly digital-first world.

This tension has created a gap between what modern AI can do and what regulated organizations feel safe deploying. Many companies experiment with AI in isolated pilots, but few scale it across strategy, production, and measurement.

Supreme Intelligence is clearly designed to close that gap.

By embedding regulatory considerations, approval workflows, and privacy protections directly into the platform, Supreme Group is positioning AI not as a risk—but as infrastructure.

“Our fundamental promise to our clients is to solve complex business problems,” said Tom Donnelly, CEO of Supreme Group. “We developed Supreme Intelligence to fulfill that promise more effectively.”

That framing matters. In healthcare marketing, speed alone isn’t enough; trust and auditability are equally critical.

Persona-Driven Strategy: Testing Before You Launch

One of the platform’s standout capabilities is persona-driven strategy simulation. Instead of relying solely on static personas or historical assumptions, teams can deploy trained AI personas—such as a “Director of Clinical Development” or “Head of Cardiology”—to test how messaging might land before a campaign goes live.

These personas are grounded in Supreme Group’s proprietary research and industry data, allowing marketers to simulate reactions, objections, and preferences in a controlled environment.

This approach reflects a broader industry shift toward pre-launch experimentation. As budgets tighten and timelines compress, brands are looking for ways to de-risk campaigns earlier in the process. AI-powered persona testing could offer a faster, cheaper alternative to traditional market research—especially for early-stage messaging decisions.

If it works as advertised, it could change how healthcare marketers think about validation and iteration.

From “Chat” to Production-Ready Content

Content generation is where most marketers first encounter AI—but it’s also where regulated industries hit the hardest limits. Generating compliant, brand-safe, production-ready assets is a very different challenge from drafting a clever headline.

Supreme Intelligence aims to bridge that gap.

The platform supports custom workflow applications that generate assets at scale—ranging from regionalized digital campaigns to email templates—while enforcing brand guidelines, regulatory constraints, and approval requirements.

Rather than producing raw text that still needs heavy human cleanup, Supreme Group says the system is designed to output content that’s ready for real-world deployment.

That’s a significant promise. In healthcare marketing, even small compliance errors can delay campaigns by weeks. Automating guardrails, rather than relying on post-hoc review, could materially change throughput.

Dynamic Orchestration Under the Hood

A quieter—but potentially more important—feature of Supreme Intelligence is its dynamic orchestration layer.

Instead of locking teams into a single AI model or vendor, the platform automatically curates and integrates the best-performing models for each task. That could mean one model for real-time data analysis, another for content generation, and a different one for regulatory checks.

This model-agnostic approach reflects a growing realization in enterprise AI: the fastest-moving innovation isn’t in platforms, but in models. By abstracting model selection away from end users, Supreme Intelligence aims to future-proof itself as the AI ecosystem evolves.

For clients, that means less concern about betting on the “wrong” AI stack—and more focus on outcomes.

Continuous Optimization, Not Quarterly Postmortems

Measurement is another area where Supreme Intelligence pushes beyond traditional dashboards.

Rather than presenting static reports, the platform interprets live performance data to surface actionable insights in real time. Teams can adjust messaging, targeting, or spend based on immediate feedback—rather than waiting for quarterly reviews.

This aligns with broader trends in marketing analytics, where the emphasis is shifting from retrospective analysis to continuous optimization. In fast-moving therapeutic areas, the ability to pivot quickly can translate directly into competitive advantage.

For healthcare marketers used to long feedback loops, this could be one of the platform’s most disruptive features.

Built for Compliance—and Customization

Supreme Group emphasizes that Supreme Intelligence was built with regulatory rigor from day one. The platform supports privacy protections, approval workflows, and audit trails designed for healthcare and life sciences environments.

At the same time, its agentic architecture allows for deep customization. Supreme Intelligence isn’t limited to a fixed feature set; it can be configured to address specific challenges across the marketing lifecycle.

That flexibility matters as clients’ needs evolve. Supreme Group says it is actively expanding the platform with new workflows and applications in response to customer demand.

“We are rapidly building new AI workflows and applications, expanding alongside our customer’s needs,” Donnelly said.

How It Compares to the Rest of the Market

Supreme Intelligence enters a competitive—but fragmented—AI landscape.

On one end are general-purpose AI tools that offer broad capabilities but limited industry specificity. On the other are point solutions focused on narrow use cases like content generation or analytics.

Supreme Group is aiming for the middle ground: a verticalized AI platform that combines breadth with deep domain expertise. That strategy mirrors what’s happening in other regulated sectors, where vertical AI platforms are gaining traction over horizontal tools.

If successful, Supreme Intelligence could set a template for how agencies—and not just software vendors—build and deploy AI at scale.

What It Means for Healthcare Marketing

The launch of Supreme Intelligence signals a broader shift in the agency-client relationship. AI is no longer just a productivity layer; it’s becoming a shared operating system for strategy, execution, and measurement.

For healthcare and life sciences companies, that could mean faster launches, more confident experimentation, and better alignment between creativity and compliance.

For agencies, it raises the bar. As proprietary AI platforms become differentiators, the value of domain expertise—and the ability to operationalize AI responsibly—will matter more than ever.

Supreme Group is betting that its investment in a purpose-built AIP will pay off as clients demand more speed, transparency, and measurable impact from their marketing partners.

Whether Supreme Intelligence becomes a model for the industry remains to be seen. But one thing is clear: in regulated marketing, AI is finally moving from novelty to infrastructure.

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Formula E may race on a closed circuit, but for the 2026 Miami E-Prix, its presence won’t be confined to the track. OUTFRONT Media, one of the largest out-of-home (OOH) companies in the U.S., has been named the Official Out-of-Home Advertising Partner of the 2026 ABB FIA Formula E Miami E-Prix, alongside serving as Associate Partner of Change. Accelerated. Live: Miami.

The one-year partnership makes OUTFRONT a core media extension of Formula E, using Miami itself as the canvas. With access to Formula E intellectual property, OUTFRONT can deploy official branding across marketing campaigns, creative executions, and client activations—starting immediately with the recently held Miami E-Prix.

For Formula E, the move reinforces a growing focus on meeting fans beyond broadcast and digital channels. For OUTFRONT, it’s another signal that IRL media is becoming central to how major sporting events scale attention in dense urban environments.

Turning a Race Weekend into a Citywide Moment

The partnership centers on amplification. OUTFRONT’s OOH network is designed to push the energy of race weekend into everyday routines—commutes, neighborhoods, and public spaces—well beyond the Miami International Autodrome.

“Our entire Formula E team is excited to welcome OUTFRONT Media as the Official Out-of-Home Advertising Partner for the 2026 Miami E-Prix,” said Lee Zohlman, Partnerships Director at Formula E. “OUTFRONT has been instrumental in capturing the spirit of Formula E… ensuring that the energy of the Miami E-Prix reaches fans exactly where they live, work, and play.”

That philosophy is reflected in a citywide campaign launched around Hard Rock Stadium and throughout South Florida. The activation includes digital signage, fan village banners, media backdrops, high-impact digital billboards, and transit placements—formats designed to reach fans and non-fans alike as they move through the city.

The creative was designed and produced by OUTFRONT STUDIOS, the company’s in-house agency, underscoring a broader industry trend: OOH providers increasingly acting not just as media owners, but as full-service creative and experiential partners.

IRL Media’s Growing Role in Sports Marketing

OUTFRONT describes itself as an “in-real-life” (IRL) media company—a framing that’s gaining traction as brands look to complement digital reach with physical presence. In a media landscape dominated by screens, live sports remain one of the few reliably mass, culturally relevant moments. OOH, by design, intersects directly with those moments.

“IRL media is a force multiplier for fan engagement,” said Chris Mallen, Senior Director of Sports Marketing & Partnerships at OUTFRONT. “This partnership with Formula E represents a meaningful evolution in how live sports and IRL come together to connect brands to fans.”

That evolution is visible across OUTFRONT’s recent dealmaking. Late last year, the company announced partnerships with the Bay Area Host Committee and the Los Angeles Sports & Entertainment Commission, adding to a portfolio that already spans tentpole events like the Super Bowl and World Cup. Formula E slots neatly into that strategy, particularly as global motorsports continue to attract younger, sustainability-conscious audiences.

Formula E, Sustainability, and Brand Alignment

Formula E’s all-electric racing series has long positioned sustainability as a core differentiator, not a side narrative. OUTFRONT is leaning into that alignment.

The company highlighted its own sustainability initiatives as part of the announcement, including converting more than 75,000 lighting fixtures to LEDs—cutting energy usage per fixture by roughly 70%—recycling or repurposing nearly all vinyl canvases, installing solar panels at major office locations, and supporting public transit systems that reduce single-occupancy vehicle use.

For marketers, that alignment matters. As brands increasingly scrutinize the environmental impact of their media investments, partnerships that combine reach, cultural relevance, and sustainability credentials are becoming easier to justify—and harder to ignore.

What This Means for Brands and Cities

The OUTFRONT–Formula E partnership highlights a broader shift in how large-scale events think about media. Instead of treating OOH as a supporting channel, it’s being used as connective tissue—linking the event, the city, sponsors, and everyday audiences.

For brands activating around Formula E, the deal opens access to official IP and high-visibility placements that operate before, during, and after race weekend. For Miami, it reinforces the city’s growing role as a global sports and entertainment hub capable of hosting—and amplifying—international events.

And for the OOH industry, it’s another reminder that physical media isn’t retreating in the digital era; it’s evolving. When paired with live sports, experiential design, and social amplification, IRL media can turn a single race into a citywide cultural moment.

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