Iridio, RRD’s social media marketing platform, has expanded its integrations to include Reddit and LinkedIn — giving brands access to two distinct yet strategically valuable audience environments that largely sit outside the conventional Facebook, Instagram, and TikTok playbook.

The expansion is backed by internal performance data that makes a direct commercial argument for the multichannel approach: combining digital display with paid social produces a 67 percent higher average featured sales lift compared to social-only campaigns, according to an Iridio study of nearly 60 consumer packaged goods campaigns conducted between the fourth quarter of 2023 and the second quarter of 2025.

Why Reddit and LinkedIn

The case for Reddit rests on audience composition as much as scale. The platform reaches more than 190 million weekly active unique users — but its value to Iridio’s clients lies in who those users are relative to other platforms. Approximately 28 percent of Reddit users are not active on Facebook, 36 percent are not on Instagram, and 38 percent are not on TikTok. For brands that have saturated conventional social channels, Reddit represents a meaningful pool of consumers they are not currently reaching.

The platform’s subreddit structure also enables contextual targeting that goes beyond demographic profiling. Brands can align messaging with the specific topics and communities their prospective customers are actively researching — a form of relevance that standard social placements rarely achieve.

LinkedIn extends the proposition into a completely different environment. As the world’s largest professional network, it provides access to business decision-makers in a context where professional identity and purchasing authority are explicit rather than inferred. Iridio is currently in beta testing for LinkedIn campaigns, with full integration expected later in 2026.

The Technology Behind the Targeting

Both integrations are powered by RRD’s patented Consumer Graph and Household Connect technologies — proprietary infrastructure that Iridio positions as a privacy-first alternative to cookie-dependent targeting.

The Consumer Graph maps 130 million unique household personas by connecting layers of intelligence from offline and online data sources, enabling high-precision audience identification without relying on third-party data. Household Connect extends this by grouping multiple devices — phones, televisions, laptops — based on shared behavior and location, creating a unified digital profile of a household and mapping it to a physical address. The result is targeted, compliant marketing at scale across devices, channels, and platforms.

Every social campaign also includes advanced brand safety monitoring, using tiered sensitivity settings and keyword blocklists to ensure ads appear only in appropriate, high-quality environments.

Also Read: Why the Future of Advertising Is Built on Probability

The Performance Case for Combination

The headline finding from Iridio’s internal study is the 67 percent sales lift premium from combining display and social. But the supporting data makes an equally compelling argument. Campaigns that combined display and paid social produced a 47 percent higher average Brand Halo lift — a metric that captures conversion across all of a brand’s measured product lines, not just the featured item — and generated four times the incremental sales compared to single-channel social campaigns.

“We are giving our clients the ability to connect with consumers no matter what platform they are on, while quantifying the direct impact on featured sales lift and incremental revenue,” said Andy Johnson, Senior Vice President and Principal at Iridio. “By integrating Reddit and LinkedIn, we are opening new doors for brands to be part of the conversations that actually drive purchase decisions.”

Iridio operates as a managed service, combining its proprietary demand-side platform with creative, media, and data analytics capabilities. 

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Publicis Groupe has agreed to acquire LiveRamp, the global data collaboration platform, for a total enterprise value of $2.167 billion in an all-cash transaction — its most significant strategic bet since the $4.4 billion acquisition of Epsilon in 2019 and a clear signal of where the world’s third-largest advertising group believes the next competitive frontier lies.

The deal, priced at $38.50 per share and representing a 29.8 percent premium to LiveRamp’s closing price on May 15, has been unanimously approved by the boards of both companies. It is expected to close before the end of 2026, subject to regulatory approval and LiveRamp shareholder sign-off.

What LiveRamp Is

LiveRamp is not a conventional advertising technology company. It is infrastructure — a data collaboration platform that enables organizations to unify, manage, and activate data across the digital ecosystem without exposing the underlying sensitive information that makes that data valuable.

Its network spans more than 25,000 publisher domains and over 500 technology and data partners across 14 markets. Thousands of brands, retailers, media platforms, and data providers use its clean room technology to collaborate on data they could not share through conventional means. With 1,300 employees and a business anchored in highly recurring revenue, LiveRamp has delivered a compound annual revenue growth rate of 13 percent over the past five years.

Why Publicis Is Buying It

The acquisition is a direct response to what Publicis identifies as the defining constraint on enterprise AI adoption: most companies lack the right data to make their AI systems genuinely effective. According to figures cited by Publicis, 93 percent of companies do not currently have the data infrastructure required for AI success.

LiveRamp addresses that gap through what Publicis calls data co-creation — the process by which companies combine multiple high-value data sources across partners in secure, governed environments to generate new proprietary data assets that no single organization could build alone.

Combined with Epsilon’s identity resolution capabilities, LiveRamp’s collaborative infrastructure is designed to enable clients to build AI agents that are more capable, more differentiated, and more commercially effective than anything a single organization’s data could support independently.

“After acquiring Epsilon in the name of personalization at scale and enabling our clients to take back control of their data from the walled gardens,” said Arthur Sadoun, Chairman and Chief Executive of Publicis Groupe, “once again we are looking ahead to what’s next. By building the future of data co-creation, we’re empowering our clients to generate new, exclusive and proprietary data — to build the smartest, most differentiated AI agents on top of the leading large language models.”

Also Read: Peak Martech? The Landscape Has Plateaued, but the Real Story Lies Beneath

The Use Cases

Publicis has been explicit about what data co-creation enables in practice, offering three illustrative examples.

A bank could build a wealth management agent that draws on tokenized customer data from retail banking, credit cards, and wealth management, combined with partner data from merchants, payment networks, and travel providers — without exposing underlying customer records. The result is an agent capable of cross-selling across multiple business lines with far greater precision than any single dataset would allow.

A retailer could connect loyalty, in-store, and retail media data with partner signals to measure the incremental value of each customer touchpoint and build new, proprietary shopper journeys — turning retail media from a cost center into a measurable growth driver.

A pharmaceutical company could build a therapeutic area optimization agent that draws on clinical, commercial, supply chain, and de-identified patient data simultaneously — enabling more efficient field-force deployment and better product lifecycle management across an entire portfolio.

The Strategic Architecture

The LiveRamp acquisition completes what Publicis describes as an end-to-end capability stack for agentic business transformation. Publicis Sapient provides the technology modernization layer that makes enterprise infrastructure AI-ready. Epsilon’s identity resolution connects agents to real people, behaviors, and deterministic transactions. LiveRamp enables secure data collaboration across partners to generate the co-created data that fuels smarter agents. Marcel, Publicis’s internal agentic platform, activates that data across enterprise functions.

Each component addresses a different layer of the same problem. Together, Publicis is arguing, they constitute a capability set that no competitor can currently match end to end.

Financial Implications

The transaction is expected to be earnings-accretive from the first year of consolidation, excluding transaction-related costs. Publicis is funding the acquisition with cash on hand and debt, maintaining financial leverage within existing investment-grade rating parameters, with full deleveraging expected within two years of closing.

The deal also allows Publicis to raise its medium-term financial guidance. The Groupe now targets net revenue growth of 7 to 8 percent and headline earnings per share growth of 8 to 10 percent at constant currency in 2027 and 2028 — up from prior targets of 6 to 7 percent and 7 to 9 percent respectively.

Also Read: Push Notifications Are Broken. Here Is What Comes After Them

What Changes — and What Does Not

LiveRamp will continue to operate as a neutral, interoperable platform following the acquisition. Chief Executive Scott Howe will remain in his role, reporting directly to Sadoun. The company’s data will continue to be protected in accordance with existing contractual commitments. Pricing and commercial practices will remain unchanged outside the normal course of business.

The commitment to neutrality is not incidental. LiveRamp’s value depends on the trust of the 500-plus technology and data partners and 25,000-plus publishers in its network — a trust that would erode quickly if the platform were perceived to operate in the exclusive interest of its new parent company.

“Our customers and partners have always been our North Star,” said Howe. “By joining forces with Publicis, we will have greater resources and flexibility to scale our business, continue innovating our platform, and help them unlock even greater value from their data.”

The transaction is expected to close before year-end 2026.

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Contextual advertising is not a new coinage in search of a use case. It is a response to a structural shift in how digital advertising works — and, increasingly, how it is permitted to work.

At its core, contextual advertising is the practice of placing ads based on the content of the page a user is currently viewing, rather than on a profile built from their browsing history. It does not require cookies, does not rely on third-party data, and does not track a user across the internet to build a behavioral profile. Instead, it attempts to reach the right person at the right moment by understanding the context in which they are already engaged — and placing a relevant message there.

Contextual vs. Behavioral Advertising

The distinction between contextual and behavioral advertising is worth understanding precisely, because the two are frequently conflated.

Behavioral advertising serves ads based on what a user has already done — their search history, the pages they have visited, and the purchases they have made. It is retrospective, drawing on accumulated data to infer likely future interest. Contextual advertising works differently. It does not wait for a user to display identifiable behavior. It attempts to anticipate relevance before that behavior occurs, matching the message to the moment rather than to the person’s recorded past.

A user reading a review of running shoes is, in that moment, a more receptive audience for athletic gear than any browsing history alone could confirm. Contextual advertising acts on that signal directly — without a cookie, without a data broker, and without the user having searched for anything at all.

Why It Is Gaining Ground

Today’s consumers are more sophisticated about advertising than previous generations. Continuous exposure to marketing across multiple platforms has produced a kind of selective attention — most people have developed an instinct for filtering out messages that do not feel immediately relevant. Contextual advertising is, in part, a response to that dynamic. By placing messages where they are genuinely pertinent to what a user is already thinking about, it improves the odds of cutting through.

It is also gaining ground for structural reasons. The deprecation of third-party cookies — a process that has been uneven but directionally consistent — has eroded the data infrastructure on which behavioral advertising depends. Privacy regulations in Europe, the United States, and a growing number of other markets have raised the compliance costs of tracking-based approaches. Contextual advertising sidesteps most of those constraints by design, making it an increasingly attractive option for publishers and advertisers navigating a more restrictive data environment.

Also Read: AI Is Supercharging Returns Fraud. Retailers Are Behind.

How It Works

Contextual advertising uses machine learning to analyze the content of a webpage — keywords, page type, topic category, and media format — and identifies the most relevant advertising placement without referencing user data. The system reads the page, not the person.

For publishers using platforms such as Google AdSense, contextual targeting is built in. Google’s system analyzes the content of each page in its display network and attempts to match the ad to the most relevant available content. For those using Google Ad Manager, ensuring that foundational targeting values are correctly configured is the essential first step.

Getting Started: A Practical Checklist

For advertisers and campaign managers approaching contextual targeting, preparation is less technical than strategic—and begins with a thorough understanding of the product being advertised and the content environments where it is most likely to resonate.

The following steps provide a working framework.

Build a robust keyword repository. Select target keywords, key topics, and commonly used phrases with care. These will determine which pages your ads appear on and, by extension, which moments of user attention you are buying.

Configure reach settings deliberately. Display network settings can be set to a broad or specific reach. Broad reach places ads based on topic targeting; specific reach restricts placement to pages that match both specified keywords and at least one targeted topic. The right choice depends on campaign objectives and the degree of contextual precision required.

Verify the ad order. Before a campaign goes live, confirm that placements have been identified that contextually match the content of the intended web pages. This step closes the loop between targeting intent and actual placement.

Monitor and refine continuously. Contextual advertising is not a set-and-forget discipline. The culture, language, and content landscape in which ads appear shift over time, and targeting parameters should be reviewed and updated accordingly.

Also Read: Peak Martech? The Landscape Has Plateaued, but the Real Story Lies Beneath

The Broader Opportunity

For advertisers, contextual advertising represents a path toward relevance that does not depend on surveillance. It is a model that aligns with where privacy regulation is heading, with what consumers say they prefer, and with what the data suggests actually works — messages placed in context perform better than messages placed by default.

Personalization has long been the stated goal of digital advertising. Contextual advertising offers a version of it that does not require knowing who someone is — only what they are paying attention to right now.

In an attention economy, that distinction turns out to matter quite a lot. 

The post Contextual Advertising: What It Is and Why It Matters appeared first on MartechView.

Jellyfish has appointed Stephanie Parry as Managing Director for its UK and Ireland business, succeeding Nick Fettiplace, who is leaving the agency after fourteen years.

Parry joined Jellyfish in 2023 as Executive Vice President of Client Management, leading accounts for Boots, Deckers, Apple, and Workday. Before joining the company, she served as Global Managing Director and Managing Partner for BlackRock at Mindshare New York, and held the role of Global Media Director at WPP’s Red Fuse Communications, where she led the dedicated Colgate-Palmolive team.

In her new role, Parry will be responsible for the performance and growth of the UK and Ireland operation, with a focus on client impact, AI capability development, and collaboration across the wider Brandtech Group.

“Jellyfish has built a powerful model at the intersection of talent, technology, and creativity,” Parry said. “My focus will be on translating our integrated capabilities — from AI-driven media and creative to emerging areas like generative engine optimization — into tangible business outcomes for our clients, while continuing to invest in the people and culture that make us unique.”

Also Read: Why the Future of Advertising Is Built on Probability

Nick Emery, Global Chief Executive of Jellyfish, said Parry brings “a rare combination of sharp commercial instinct and deep client understanding” and has been instrumental in strengthening both client relationships and the company’s business presence since her arrival. “She knows what it takes to drive growth in a complex, global environment,” he added, “and consistently builds teams that people want to be part of.”

Fettiplace’s departure after fourteen years marks the end of a significant chapter for the UK operation. No details of his next move were disclosed.

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For years, advertising has been built around precision. The goal was simple: identify the right person, on the right device, at the right time, and deliver the right message.

And that worked…up to a point.

Today’s ecosystem is more fragmented, more regulated, and more complex than ever. Consumers move fluidly between smartphones, laptops, connected TVs, and platforms like YouTube, TikTok, and streaming services. At the same time, privacy expectations are rising, and traditional identifiers are becoming less reliable.

As a result, the industry is beginning to recognize that marketing at scale doesn’t always require pinpointing specific customers with 100 percent accuracy based on their data. Instead, we’re moving toward a new model: probabilistic advertising.

The best way to think about it is this. Instead of aiming for a perfect bullseye, marketers are learning to operate more like meteorologists, using patterns, signals, and probabilities to make informed decisions at scale.

Here are five reasons why the shift toward probabilistic advertising is happening right now.

Deterministic Signals Face Real Limits

Deterministic identity, or knowing exactly who someone is because their data aligns perfectly, still exists, but it’s increasingly limited.

Take a platform like Netflix. When a user logs in on a TV, laptop, and phone with the same email address, Netflix can confidently link those devices to the same person. Hard identifiers (which can also include customer ID and more) are individual-specific; we know it’s the right person because the data matches exactly. 

But most of the internet doesn’t work that way. When someone watches content from a broadcaster like the BBC without logging in, or browses across the open web, there is no single, definitive identifier tying those interactions together. That’s where probabilistic methods come in.

Instead of relying on certainty, advertisers will analyze patterns (device behavior, timing, location, and context) to estimate whether different signals belong to the same user.

At scale, the question shifts from “do we know exactly who this is?” to “do we know enough to act?” 

Identity Fragmentation Is Now the Default

Modern consumers today are, by default, fragmented. 

A single person might stream on a connected TV, browse products on a mobile device, scroll social platforms, and interact with apps, all within a single day. Each of these environments generates its own identifier, often incompatible with the others.

For marketers running campaigns across platforms like Meta, Google, Amazon, and The Trade Desk, this fragmentation creates a fundamental challenge: how do you build a consistent view of your audience?

Probabilistic identity helps unify that picture. It connects disparate signals into a cohesive, privacy-conscious understanding of who is likely behind them.

Just as importantly, it simplifies execution. Instead of stitching together dozens of identifiers across regions and channels, advertisers can operate with a more unified, scalable framework that reflects how consumers actually behave.

Privacy Expectations Are Reshaping Identity

Regulation is tightening and globalizing simultaneously. What began with GDPR in Europe is now influencing frameworks across North America, Latin America, and APAC. The direction sets stricter rules on personal data and higher expectations for transparency and consent, making it increasingly difficult to rely on personally identifiable information (PII) at scale.

Probabilistic approaches offer a path forward. Operating on anonymized signals and statistical inference, they reduce the need to know exactly who someone is while still enabling timely, relevant advertising.

For consumers, this creates a more balanced, less invasive experience. Instead of being tracked individually across dozens of platforms, they can remain effectively anonymous while still receiving useful content.

For marketers, it creates a more durable model that aligns with both regulation and user expectations.

AI Is the Engine Behind Probabilistic Advertising

The math behind probabilistic advertising has always existed. What’s changed is the innovation that can run it. 

Modern AI/ML models can process vast amounts of data, far beyond what traditional systems can handle. They analyze behavioral patterns, device characteristics, and contextual signals, continuously improving their predictions as new data becomes available.

This is what enables probabilistic identity to operate at internet-scale. But AI alone isn’t enough. Without a data infrastructure capable of supporting AI workflows, the models remain only as good as the data they can access.

To unify signals across billions of interactions, run complex models, and activate audiences in near real time, companies need data foundations that can handle massive workloads as they scale. Platforms like Ocient, for example, help companies process and analyze massive datasets efficiently, so probabilistic models can run where the data lives, rather than across fragmented systems.

The combination of AI and scalable infrastructure is what makes probabilistic advertising viable today.

Global Scale Increasingly Favors Probability Over Certainty

Deterministic identity works well in closed ecosystems or specific markets where strong login data exists. But expanding that approach globally requires building and maintaining countless integrations, partnerships, and datasets, often country by country.

Probabilistic models scale differently.

If the underlying infrastructure is in place, expanding into new markets simply means ingesting more data and applying the same modeling approach. There is no need to rebuild identity frameworks from scratch in every region.

For global brands, whether it’s a multinational retailer, an airline, or a company like Microsoft operating across multiple business units, this matters. They need consistent, cross-channel visibility across geographies, not a patchwork of disconnected solutions.

Probabilistic systems provide that consistency.

The Mindset Shift Marketers Need to Make

The biggest change marketers face isn’t technical – it’s conceptual. For years, the industry has been trained to value certainty above all else. But in a fragmented, privacy-first world, certainty is limited and often misleading.

What matters more today is confidence at scale.

That means accepting that you don’t need to know with 100 percent certainty that a specific device belongs to a specific individual. You need to know, with high confidence, that a set of signals represents a real person with likely behaviors, preferences, and intent.

In practice, that shift enables better, measurable outcomes. It allows marketers to reach broader audiences, operate across more channels, and do so in a way that respects privacy while still delivering performance.

In a few years, “probabilistic advertising” won’t feel like a new approach. It will simply be advertising. And for an industry built on understanding people at scale, that’s a long overdue evolution.

The post Why the Future of Advertising Is Built on Probability appeared first on MartechView.

KERV.ai, an Austin-based advertising technology company, has launched Moment Match Engine, an AI-powered platform that identifies high-attention moments within video content and aligns them in real time with relevant brand and commerce signals — without interrupting the viewing experience.

The product represents a direct challenge to the dominant logic of video advertising, which has historically treated content as a vehicle for message delivery. KERV.ai’s approach inverts that model, using proprietary image recognition and contextual AI to find the moments within content when consumer attention and purchase intent are already elevated, and activating brand experiences at precisely those points.

How It Works

Moment Match Engine ingests and analyzes both video-on-demand and live video content at the pixel level, generating contextual signals that identify scenes, objects, and emotional registers most likely to drive viewer engagement and conversion. Rather than targeting audiences based on demographic profiles or third-party behavioral data, the system targets moments — a distinction that its backers argue is more durable in an era of signal loss and privacy regulation.

For publishers, the technology introduces a new monetization model built on contextual depth rather than ad load. Brands and commerce experiences are surfaced within content in ways the company says feel additive to the viewer rather than intrusive, with compliance frameworks and metadata validation built into the platform.

For advertisers, the promise is moment-level precision within premium connected television environments — a step beyond broad contextual targeting toward what KERV.ai describes as true scene-level relevance.

Also Read: Peak Martech? The Landscape Has Plateaued, but the Real Story Lies Beneath

Early Results

The company has been working with NBCUniversal and Warner Bros. Discovery, among other major media partners. Brands and agencies including IKEA, Carat, and dentsu’s broader media practice have deployed the technology.

Early performance data offers a meaningful signal. An interactive in-stream campaign for IKEA and Carat using contextually targeted placements generated interaction rates 129 percent above those of standard third-party behavioral placements — a result that, if replicable at scale, would represent a significant commercial argument for the moment-driven approach.

“At Carat, we’re focused on helping brands show up in the moments that truly matter — where attention is highest and intent is strongest,” said Mike Law, chief executive of Carat North America. “KERV’s object-level contextual precision enables a new level of targeting, allowing brands to align with those high-value, leaned-in moments.”

Derek Gatts, Vice President of Advertising Strategy and Innovation at Warner Bros. Discovery, said the technology plays a central role in the company’s Shop HBO Max and Moments products, enabling “precise product alignment and scene-level adjacency” that ensures brands appear in the right context with the right message.

Also Read: Is Your CRM Making Your Customer Service Worse?

The Bigger Bet

“Historically, advertising has been about inserting messages into content,” said Gary Mittman, chief executive of KERV.ai. “We’re shifting that model to align with the moments that already carry meaning, so the experience feels natural and welcomed by the viewer.”

The launch extends KERV.ai’s existing Contextual Marketplace and advances what the company frames as a broader mission: transforming passive video into an interactive layer for commerce and brand discovery. Advertisers can activate across connected TV, online video, programmatic, and direct channels, with flexibility to align with their existing buying strategies.

The underlying thesis — that attention, not inventory, is the scarce resource in modern advertising — is not new. What KERV.ai is wagering is that it has built the infrastructure to act on it at scale.

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A London-based advertising technology company has launched a native ads application for Shopify, aiming to give merchants more precise control over how they reach and recapture potential customers.

Preciso announced on Tuesday the availability of Ultima Ads for Advertisers on the Shopify App Store, billing it as the first native advertising plugin for the platform built specifically around a merchant’s campaign goals. The app connects directly to a merchant’s Shopify dashboard, allowing them to manage targeted advertising campaigns without switching between platforms.

By syncing product feeds, purchase history and browsing behavior, merchants can build custom audience segments, retarget visitors who browsed without buying, and construct a lookalike audience modeled on their most valuable existing customers.

Ultima is Preciso’s flagship native advertising product. It uses machine learning to generate personalized ad creatives and place them contextually within web pages — an approach designed to reduce banner blindness, which has blunted the effectiveness of traditional display advertising.

The company says internal testing shows the product outperforms industry benchmarks across key metrics: a click-through rate of 2.1 percent against an industry average of 1.84 percent, an engagement rate of nearly 66 percent compared with 60 percent for the sector, and an average session duration of three minutes and 46 seconds, roughly a minute longer than the industry norm.

Also Read: Your PR Strategy Was Built for a Newsroom That No Longer Exists

“Ultima Ads for Advertisers has been designed from the ground up to enable Shopify merchants to use premium native placements that target users in a non-intrusive manner,” said Piero Pavone, Chief Executive of Preciso. “Thanks to a very simple Shopify integration, merchants can get campaigns live in a few hours.”

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A growing number of television news producers start the day the way most of us do now. Not by opening their inbox, but by opening an AI tool.

Before they read a single pitch, they’re already asking questions. What stories are trending? What experts are credible? What angles are audiences searching for right now? Within seconds, the AI surfaces answers pulled from recent coverage, past interviews, and digital content tied to those topics.

The AI revolution has come to the TV newsroom. It’s the latest shock to the system, already under siege. I’m sure you are familiar with the story after story of job cuts hitting the newsroom. You hear of giant mergers that threaten even more jobs. What you’re probably less familiar with is that local news content has increased dramatically during the recent 10-year period tracked by Pew. They noted a 30-40% increase in English-language local broadcasts and a doubling of Spanish-language local news. 

The key takeaway, more work for fewer journalists. 

It’s an unprecedented opportunity for the PR community. In fact, according to our “AI and the Newsroom” report, 94% of TV producers are now open to being pitched by PR people. That’s the highest it’s ever been. They need PR people and AI.

The data support it. We found 37% of TV producers now use AI to identify stories to cover. Doesn’t sound like much? It’s up from 0% in two years. If a producer knew a story they were pitched was optimized for AI search on Large Language Models (LLMs) like ChatGPT, 68% would be more interested in covering it. They are using AI for research, fact-checking, writing digital stories, and even the graphics they are creating.

The pitches that align with what the AI has surfaced feel relevant, timely, and easy to execute. The rest, even when well written, often get ignored. Not because they are bad stories, but because they were built for a newsroom staff size and workflow that no longer exists.

The good news is that brands are spending hundreds of millions of dollars to figure out how they can be discovered in the AI Search/Generative Engine Optimization (GEO) economy. However, they may be missing out on a huge opportunity by failing to recognize how the newsroom has changed. 

A television broadcast campaign is no longer just about the millions of people who might see it in the moment. Earned media has become the leading contributor to discoverability. When a brand appears in a broadcast segment, producers also create more content and feed it to multiple platforms. More than 90% of stations post their content on both their websites and social media. 86% are posting content to YouTube. According to MuckRack, YouTube content is now the leading driver of discovery for AI search across financial services, travel, entertainment, energy, technology, and healthcare on Google’s Gemini platform. Broadcast hits have become a force multiplier.

For years, we have operated under the assumption that if a story is strong enough, it will find its audience. In an AI-driven environment, the opposite is often true. If a story cannot be found in the way producers and platforms surface information, its quality becomes irrelevant.

Discoverability has become just as important as the story itself. That is a difficult adjustment because it forces us to rethink how we think about earned media. Brands that align their PR strategy with this reality now are effectively building a future-proof growth plan. Every piece of earned media strengthens its position. Every interview, every segment, every piece of content increases the likelihood that they will be discovered again.

Competitors who are slower to adapt are not just missing out on individual placements. They are falling behind in a system that compounds over time. Closing that gap is not a matter of running a better campaign next quarter. It can take years. That is why this moment matters.

The shift is not theoretical. It is already reflected in how newsrooms operate, how producers make decisions, and how audiences find information. Adapting to this reality does not require abandoning the fundamentals of PR; it requires reframing them. It starts with how stories are developed. The most effective campaigns now begin with understanding what people are searching for. That insight shapes the narrative, the spokesperson’s role, and the way the story is positioned.

It continues with a discussion of how content is created. Every interview is an opportunity to produce material that is not only compelling for an audience but also structured in a way that makes it discoverable. The language, the framing, and even the questions themselves all play a role.

And it extends to how success is measured. Reach still matters, but it is no longer the full picture. Visibility over time, frequency of appearance in relevant contexts, and the ability to be surfaced by AI systems are becoming more important.

The newsroom has not disappeared. It has transformed. Producers are still making decisions, stories still need to resonate, and they still need to compete for eyeballs, but the process that determines which stories rise to the top has fundamentally changed.

PR strategies need to catch up. Because in a newsroom where AI has become a first filter, the brands that are easiest to find are the ones that get covered. And the ones that understand that dynamic early are not just keeping pace. They are building an advantage that others will spend years trying to close.

The post Your PR Strategy Was Built for a Newsroom That No Longer Exists appeared first on MartechView.

TriCoast Media, the content-first adtech company that grew out of global film and television distributor TriCoast Entertainment, has announced an expansion of its unified supply-side and demand-side platform, scaling both its sales team and content library to meet growing demand.

The company’s model is built around a content flywheel. Drawing on a library of more than 5,000 film and television titles, TriCoast Media offers publishers more favorable revenue shares in exchange for preferred access to backfill inventory — a structure it says preserves publisher economics while allowing the company to build direct partnerships with more than 250 connected TV publishers and 3,000-plus channels, including VIZIO, Samsung, Paramount, Xumo, Tubi and Plex.

For advertisers, the company claims the model delivers CPMs 30% to 50% lower than traditional buying approaches by reducing the number of intermediary hops between buyers and inventory. The platform integrates first-party, third-party and contextual data, with campaign verification supported by HUMAN and Mediaocean.

Also Read: Push Notifications Are Broken. Here Is What Comes After Them

“Right now, everyone is talking about the convergence of buy and sell sides, but we’ve been built that way from the start,” said Nick Risher, chief executive of TriCoast Media. “Because we began as content owners filling our own inventory, we developed a fully integrated SSP and DSP that optimizes the supply path, resulting in fewer hops, better economics, and stronger outcomes.”

The expansion positions TriCoast Media at an unusual intersection for the adtech industry — operating simultaneously as a studio, distributor and technology platform — a combination the company says gives it structural advantages that purpose-built adtech players cannot replicate.

The post TriCoast Media Wants to Cut the CTV Supply Chain Tax appeared first on MartechView.

Rakuten International and impact.com have announced a strategic alliance designed to consolidate the affiliate and performance marketing ecosystem, combining their respective technology platforms, managed services and consumer data into a single integrated offering.

The partnership brings together three distinct assets: Rakuten Advertising’s global partner relationships, managed services and performance intelligence; Rakuten Rewards’ cash-back shopping platform and direct consumer signals; and impact.com’s technology infrastructure for contracting, tracking and payments across commerce partnerships.

For advertisers, the alliance is designed to deliver expanded platform capabilities alongside Rakuten Rewards’ first-party consumer data — giving brands a clearer picture of incrementality, attribution and return on investment. Publishers and creators will gain access to a unified platform and a broader portfolio of advertisers, offers and monetization models across affiliate, creator and commerce channels.

“As a global network, publisher, and strategic operator for more than two decades, Rakuten has developed an unparalleled perspective on what drives performance,” said Amit Patel, chief executive of Rakuten International. “This alliance brings together infrastructure, service excellence, global reach, and consumer intelligence to offer a fundamentally stronger approach to performance marketing. By pairing with impact.com’s industry-leading platform, advertisers will have access to a unified technology and services solution that combines performance intelligence, managed execution, and consumer insight to deliver programmatic, data-driven programs with smarter insights, stronger execution, and measurable results at global scale.”

Also Read: AI Isn’t Killing PR. Bad Measurement Is.

“Partnership marketing has become one of the most important growth channels for modern businesses, but the systems supporting it have remained fragmented,” said David Yovanno, chief executive of impact.com. “Bringing our platform, Rakuten Advertising’s global scale, and Rakuten Rewards’ consumer platform into a unified value proposition will make it easier for advertisers to achieve their goals with better economics, greater transparency, and measurable outcomes. This is a significant step toward a more scalable and powerful alternative to traditional performance marketing channels.”

The companies said they plan to share additional product and capability details in the coming months.

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