Interview

The Physical Rebound: Why OOH is the ‘Secret Sauce’ to Fixing Your Broken Digital Strategy

marketing 17 Mar 2026

By Greg Wise, Co-Founder at OneScreen.ai

Let’s be honest: a majority of us, especially marketers, are currently trapped in a digital toxic relationship.

We go about our days staring at dashboards, stressing over pixels, and hoping that the latest algorithm update does not impact our reach. We’ve retreated so deeply into the “digital-only” bunker that we have lost sight of a simple truth: our customers are human beings. They live in physical spaces. And, despite what screentime often suggests, they really do look up from their phones.

That is why Out-of-Home (OOH) media, i.e., the “old school” world of billboards, bus wraps, and posters, has quietly become the best way to amplify a digital strategy. It’s a revolt against the digital grain; a “touching grass” moment in a world without the sun.

The Great De-Pixeling

For years, the “Marketer’s Gut” battled a losing fight against the “CFO’s Spreadsheet.”

Instinctively, you can feel that your brand is becoming soulless. You’re well-aware that people do not fall in love with a company after seeing some grainy banner ad on a weather app. You build a brand by occupying space in someone’s mind.

Here’s the problem, though: digital efficiency is hitting a wall. Ad costs are soaring as every brand competes to own the same three inches of glass inside a consumer’s pocket. We’ve mentally optimized our way into a corner, where ads are seen as annoyances to either swipe away or ignore.

OOH is the antidote. You can’t "AdBlock" a billboard on the highway. You can’t scroll past a wrapped bus while sitting at the station. OOH offers something that no digital ad can buy: Unmissable presence.

The Tech Behind the "Magic"

Something that we refer to as the “Guesswork Factor” has been a big reason as to why marketers have strayed away from the physical world. OOH has previously been seen as something to “spray-and-pray,” – you put up a sign and hope for the best.

But, it’s 2026, and technology has finally caught up with ambition. State-of-the-art adtech platforms now have real-world search capabilities. You don’t just “buy a billboard” anymore; we’re armed with with mobile movement data and proprietary intelligence to tell us precisely where a specific target demographic spends their time.

If you are targetting, let’s say, CFO’s of mid-market tech firms, don’t only search for “high traffic.” Focus on the exact transit lines, coffee shops, and office clusters where those particular people spend most of their time. Marketers can superimpose first-party data on physical maps to see where their customers live, work, and play. That level of “surgical precision” means that for every dollar spent in the real world, you’re backed by the same intel you would receive from a search campaign, minus the “rigged casino” of bidding wars.

Breaking the Phone Addiction

We talk a big game about “meeting the customer where they’re at,” but usually, what we’re talking about is “stalking them across the internet.” It’s not a journey, but rather a harassment campaign.

Real-world advertising breaks this cycle. If someone is waiting at a train station, they aren’t in “filter mode.” They aren’t purposely seeking to shut out the 5,000 digital messages they receive daily. By placing a brand in a physical space, you’re not just “reaching” someone; you’re proving that you are a tangible company.

OOH provides a brand with the street cred that digital ads have lost. It sends a message to the market: “We exist, we have substance, and we’re confident enough in our message that we will defend it publicly.”

The Contrarian Play: Touching Grass

As the majority of the marketing world obsesses over the latest AI tool to pump out thousands of blog posts that no one will ever read, the winning move is to go where there is no noise.

But the brands winning today are not those completely abandoning digital; they’re using the physical world to make their digital ads actually work. Think of OOH as the “hype man” for your online ads. When a consumer sees a billboard on their way to work, and then sees that ad on their phone later that evening, it’s not an intrusion; it’s a reminder.

This isn't nostalgia for the Mad Men era. It’s an epiphany that human attention is a limited resource. If you want a piece of it, you have to go where people are actually expending their energy, which is usually somewhere outside of a glowing rectangle.

Amplifying ROI (For Real This Time)

The most valuable part of this strategy is not the sign itself; it’s the amplification. But make no mistake: this is not OOH versus digital, “this or that.” Both tactics are vital to the modern marketing mix to come out victorious.

We now have the proprietary insight that allows us to close the gap from the street to the screen. When a data-backed OOH campaign is run, you can identify the “halo effect” in real-time. You’ll witness branded search volume increase in the exact zip codes where your ads are active. Your Facebook or LinkedIn CPA will plummet, because people will already know your name before they even lay eyes on your promoted post.

By eliminating the friction between “seeing an ad in the wild” and “clicking on an ad from your couch,” you’re not just spending money on brand – you’re turbo-charging the entire performance marketing engine. The top brands in the attention economy are not those with the biggest digital budgets; rather, they’re the ones who understand that even though the transaction takes place on a pixel, trust is build by what we do outside of our screens.

The Bottom Line: Spend the Brand Dollars

It’s time to stop apologizing for spending on brand.

If your entire strategy comes down to the number of "clicks," you don’t have a brand; you have a digital coupon book. Eventually, that well will dry up, because you haven't bothered to expose yourself to new people.

OOH is the most effective way to introduce yourself to your target audience. It forces you to be brief, witty, and compelling. It forces you to become a marketer again, instead of someone who spends their day tweaking settings in an ad manager.

So, here is my advice: Stop trying to find a sneakier way to track people around the internet. They hate it. Instead, let’s meet them outside. Give them something worth looking at and being intrigued by. Let them "touch grass," and while they are, make sure your brand is the most interesting thing they see.

"The secret sauce is not in the code. It’s on the street."

Chief Technology Officer Driving CX Assurance and AI Innovation

marketing 17 Mar 2026

From the survey results, the number one reason customers ghost brands is not being able to reach a human agent. Why do you think it still matters so much even as AI gets more advanced?

Customers want confidence that their problem will actually get solved. A human agent represents that safety net. The data shows that 71% of consumers still prefer to begin customer services with a live person, and the number one dealbreaker is being unable to reach one at all.

Even as agentic AI improves, customers know there are complex situations where nuance, judgement, or empathy matter. Billing deputes, travel disruptions, or anything tied to money or personal data often carries emotional weight. People want the option to escalate to someone who can take ownership of the issue.

What kind of hybrid models between AI and human service do you think work best, and why?

The most effective hybrid models treat AI as the first layer of assistance rather than the final authority. Agentic AI is excellent at handling high volume tasks like account lookups, status checks, and other structured requests. When automation handles them well, human agents gain more time to focus on complex cases that require judgement and empathy.

Where hybrid models fail is when the escalation paths are unclear. The survey shows that many customers want to escalate immediately or after a single failed bot interaction.

A strong hybrid model depends on CX assurance. Organizations must validate that the journey works from the customer’s perspective and that the handoff from AI to human support is smooth and reliable.

The survey suggests younger generations are more open to AI handling issues if it’s seamless. Do you see this shaping long-term AI strategy?

Yes, but mainly in how companies design their customer experiences. Younger customers tend to prioritize speed and convenience. More than half (56%) of Gen Z say they would choose an AI over a human interaction if it resolved their issue seamlessly.

However, generational expectations vary widely. Older customers often prefer speaking with a human, especially when the situation involves sensitive information or financial decisions.

The long term strategy should focus on flexibility. Brands should provide multiple ways to resolve an issue and allow customers to choose the path that works for them.

Nearly half of consumers quit a brand after just a couple of bad experiences. How do you measure and improve AI’s effectiveness to avoid those moments?

Many organizations measure technical success rather than customer success. If the bot responds and the system records a completed interaction, the dashboard may show everything working correctly. Meanwhile the customer might have repeated themselves, been routed incorrectly, or received an answer that did not solve the problem.

Improving AI effectiveness requires measuring the full journey. Teams need visibility into whether the system understood the request, provided the correct information, and resolved the issue without unnecessary friction.

CX assurance plays a role here. Continuous validation across channels allows organizations to identify where journeys break and correct those issues before customers experience them.

There is a perception gap around AI capabilities versus reality, like consumers thinking humans resolve issues faster. How can tech teams help close that perception gap?

The perception gap comes from experience. Many customers have interacted with automation that failed to understand their question or trapped them in a loop. Those early experiences shape expectations long after the technology improves.

The only way to change perception is through reliability. When AI consistently resolves issues quickly and accurately, customers start to trust the channel. By continuously validating customer journeys, teams can detect and correct breakdowns before they damage customer confidence.

What’s your approach to building trust in AI-driven experiences rather than just rolling them out quickly?

Trust comes from discipline in how systems are deployed and monitored. Many organizations feel pressure to launch AI quickly. They introduce automation into customer journeys without fully validating how those systems behave under real conditions.

A better approach starts with governance and testing before launch, followed by continuous monitoring once the system is live. AI systems evolve as data changes and models learn, so reliability must be validated continuously.

CX assurance provides that layer of oversight. It helps organizations confirm that AI interactions remain accurate, compliant, and aligned with the intended customer experience.

A major takeaway is that poorly validated AI can harm reputation and loyalty. How do you ensure your AI systems are tested and governed before they go live?

Testing AI requires going beyond a limited set of scripted scenarios. Traditional quality assurance might validate a small number of expected interactions. Real customers behave very differently. They interrupt conversations, change topics, and ask questions in unexpected ways.

Effective CX assurance intentionally pushes AI systems through these use cases. Teams test unusual phrasing, multi-step conversations, and cross channel interactions to see how the system responds.

It’s important to identify weaknesses before customers encounter them. This approach reduces risk and ensures the experience behaves safely under real world conditions.

Looking ahead, what do you think needs to change in AI-powered CX to delight customers rather than frustrate them?

The next stage of AI powered CX will depend on reliability across the entire customer journey. Many organizations focus primarily on the intelligence of the agentic AI model. In practice, the most common failures occur in the surrounding workflow such as knowledge accuracy and/or escalation paths.

Customer journeys also move across multiple systems and channels. Each transition introduces potential friction if context is lost or the experience resets.

To deliver experiences that truly delight customers, companies need to design those journeys end to end and validate them continuously. CX assurance helps ensure that every step works as intended, even as systems evolve and customer behavior changes.

The Marketing Stack Just Got Simpler: Prompt-Driven Campaigns With getpixel.ai

marketing 17 Mar 2026

Marketing teams are being asked to drive pipeline with smaller budgets and leaner teams. Where does getpixel.ai.ai create the most immediate impact for operators who are stretched thin?

getpixel.ai delivers immediate impact by removing the manual, fragmented work that slows small marketing teams. Instead of spending hours building assets, coordinating channels, and tracking performance, marketers simply describe their product, audience, and campaign goals in natural language. getpixel.ai then generates brand-aligned creative and launches campaigns across LinkedIn, Google, Meta, Reddit, Bing, and X from a single interface. Lean teams can achieve enterprise-level output without adding headcount or hours.

Getpixel is for SMBs who look for a prompt ->campaign execution without much interference with AI - they are not marketing experts but rather business owners who want to run campaigns in minutes, not days, and pay a fraction of the cost.

A recent customer said this - 'I launched my campaign on Pixel and within 12 hrs already had 2 meetings booked.' David Vainer, Managing Partner & CEO, Alliance Risk

MetadataONE gives the power of LLM but within enterprise requirements, including security, compliance, budget control, brand guidelines, and other necessary requirements for b2b enterprises.

getpixel.ai.ai turns a single natural-language prompt into a fully live, multi-channel campaign. What’s happening behind the scenes to ensure that level of automation actually drives qualified pipeline and revenue not just traffic and vanity metrics?

getpixel.ai reads your website to understand your brand, tone, and positioning, then autonomously creates copy, visuals, and channel-native campaigns. AI/ML models continuously optimize in real time, shifting budgets toward combinations that drive qualified leads and revenue, not just clicks or impressions. It’s a full-funnel system where the input is intent and the output is measurable growth and brand presence.

For in-house performance marketers and demand gen leaders, what changes day-to-day when they adopt getpixel.ai? What parts of their workflow disappear and what becomes more strategic?

The repetitive, low-leverage work disappears: building assets manually, resizing visuals, duplicating campaigns, monitoring budgets, and stitching reports. What becomes strategic is guiding the system: defining the vibe, positioning, messaging, offers, and audience intent. Marketers spend their energy shaping strategy and analyzing results, while getpixel.ai handles production, execution, and continuous optimization.

As automation takes on more of campaign execution and optimization, how does getpixel.ai ensure marketers maintain strategic control, brand integrity, and clear visibility into performance across channels?

Control starts with the human prompt: marketers define goals, audience, and desired brand vibe in natural language, and getpixel.ai translates that into campaigns and creative. Every decision is visible in a unified interface, with performance tied to real outcomes. You can adjust the prompt anytime to shift strategy or tone. Automation accelerates execution and optimization, but humans remain the architects, ensuring brand, strategy, and creative vision are always intact.

Inside the AiVANTA–SlangIT Collaboration: Delivering Hyper-Personalized CX for Arabic-Speaking Audiences

marketing 16 Mar 2026

1. How will the combined solution transform customer engagement for businesses in Arabic-speaking markets?

The AiVANTA–SlangIT partnership creates an end-to-end engagement ecosystem that blends intelligent conversations with hyper-personalized communication — both tailored for Arabic-speaking audiences. SlangIT brings voice, chat, and IVR capabilities enriched with dialect-specific NLP, while AiVANTA layers personalized, localized video messaging triggered by user actions or behavior.

This combined platform transforms CX by allowing businesses to move from generic, one-size-fits-all interactions to contextual, emotionally resonant experiences — in the customer’s native dialect, at the right time, and through the right channel. Whether it's onboarding, upselling, or service resolution, enterprises can now automate the entire interaction flow — conversation to communication and back — in a way that feels natural, local, and human.

2. What does ‘hyper-personalized communication’ mean in the context of video messaging for multilingual and multicultural audiences?

In this context of video messaging, hyper-personalized communication means delivering video content that is not only tailored to an individual’s data such as their name, preferences, transaction history, or plan details, but also culturally and linguistically adapted to their specific context.

For multilingual and multicultural audiences, especially in regions like the Middle East, this goes beyond language translation. It includes:

● Dialect-specific narration (e.g., Emirati vs. Egyptian Arabic),

● Culturally relevant references in visuals and tone,

● And personalized content logic based on behavioral triggers or segment attributes.

The result is video messaging that resonates on a personal, emotional, and cultural level — making users feel seen, understood, and valued — while enabling businesses to scale this experience across millions of customers, channels, and journeys.

3. Can you elaborate on how your platform will integrate with Slangit’s Knowledge Base as a Service and conversational tools?

The AiVANTA platform integrates seamlessly with Slangit’s Knowledge Base as a Service (KBaaS) and conversational AI tools to create fluid, end-to-end engagement journeys. This means a customer’s interaction doesn't stop at a chatbot or a video — instead, both experiences talk to each other and adapt dynamically based on user actions.

Here are a few illustrative workflows:

1. Communication → Conversation

A user receives a personalized video from AiVANTA — say, a product recommendation or a service reminder. Embedded within the video is a CTA that opens a SlangIT-powered chat interface. The chatbot continues the conversation, answers queries in local dialect, and even guides the user to take action (like policy upgrades or offer redemption).

2. Conversation → Communication

A customer initiates a conversation on a website or WhatsApp using SlangIT’s assistant. Once they express interest in a product or service, AiVANTA triggers a follow-up personalized video — explaining the selected plan, summarizing their choices, or confirming next steps — all in the user’s preferred dialect and tone.

3. Multi-Stage Loop (Telecom or Retail Use Case)

Customer receives a loyalty offer video → engages with chatbot to understand terms or redeem → receives a confirmation video post-action. Each touchpoint is contextual, localized, and automated end-to-end.

This integration ensures that every engagement — whether inbound or outbound — is intelligent, personalized, and complete, turning static communication into a living, evolving customer experience loop.

4. What measurable outcomes (e.g. Reduced support load, engagement uplift) have you observed or anticipate from early pilots or deployments?

From early pilots and live deployments, we’re seeing strong signals that the AiVANTA–SlangIT integration drives tangible impact across multiple KPIs:

1. Reduction in Support Load

By combining AiVANTA’s proactive, video-based education with SlangIT’s real-time conversational interfaces, enterprises report:

● Up to 30–40% reduction in repetitive support queries, particularly in onboarding, policy explanation, and benefit clarifications.

● Lower call center volumes, as many user actions shift to self-service chat and voice flows.

2. Engagement & Conversion Uplift

● Video open and completion rates as high as 60–70%, especially when the content is delivered in the user’s native dialect.

● In telecom and BFSI pilots, personalized video + chatbot flows have shown 20–25% uplift in campaign response rates compared to static SMS or email.

● Cross-sell/upsell conversions improved where conversational follow-ups were offered post-video.

3. Operational Efficiency

● Automation of entire interaction workflows (e.g., onboarding → confirmation → support) reduces dependency on manual teams, improving scalability and consistency.

These metrics reinforce the core value proposition: smarter conversations + emotionally resonant communication = better CX, lower costs, and higher lifetime value.

5. What upcoming innovations or features are planned in the roadmap for your AI video personalization technology?

We’re actively investing in three key innovation areas to expand the value of our personalization engine and make customer engagement truly seamless:

1. Journey-Oriented Video Automation

We’re moving beyond one-off personalized videos toward automated, multi-touch video journeys. This includes smart orchestration where videos adapt based on customer actions — e.g., onboarding → reminder → upsell — all triggered through CRM or SlangIT conversational flows.

2. Plug-and-Play Integrations

We’re building out-of-the-box connectors for platforms like WhatsApp, Salesforce, and SlangIT’s KBaaS layer to allow videos to be auto-triggered and embedded across any customer touchpoint — web, app, chat, or email — without manual configuration.

3. New Language & Dialect Expansion

To serve diverse markets, we’re deepening our multilingual stack — including expansion into new Arabic dialects, Urdu, and Farsi. We’re also training AI avatars and voice models that mirror regional tones, emotional delivery styles, and cultural references.

Together, these innovations will allow brands to not only personalize what they say, but how, when, and where they say it — delivering truly end-to-end, emotionally intelligent engagement at scale.

6. Are there plans to replicate this co-development model for other regional markets or languages?

While we currently have no immediate plans to replicate this model, we absolutely see the value in strategic co-development with regional specialists — especially where language, dialect, or cultural nuance plays a pivotal role in customer engagement.

The partnership with SlangIT is a strong proof point: when deep local intelligence is combined with scalable AI infrastructure, the result is far more impactful than a generic solution.

We’ll continue to explore similar partnerships in other linguistically complex or under-served markets — where combining our personalization engine with local conversational AI or content intelligence can unlock meaningful, region-specific experiences.

Why Soft Declines Are Still Costing Subscription Businesses Millions

marketing 13 Mar 2026

By Dan Nadeau, founder and CEO, Payway

For subscription-based businesses, payment declines remain one of the most persistent and misunderstood sources of revenue loss. While fraud and chargebacks tend to grab headlines, it’s the quieter category of “soft declines” that quietly erodes customer lifetime value month after month.

As we head into 2026, the subscription economy has become more mature, more competitive, and less forgiving. Margins are tighter, customer acquisition costs are higher, and merchants can’t afford to treat payment recovery as an afterthought. Understanding why declines happen has become crucial and important to operations.

Not All Soft Declines Are Created Equal

Soft declines are often grouped together, but in they actually fall into two very different categories.

The first includes non-recoverable declines. These are situations where the payment simply can’t be saved. This includes lost or expired cards, closed accounts, or outdated credentials. In these cases, no amount of retry logic will fix the problem, and, the customer must take action.

The second category includes recoverable declines, often triggered by temporary issues such as suspected fraud, payment limits, or network interruptions. These transactions may succeed minutes, hours, or days later if handled correctly.

The challenge for merchants is that these two categories often look identical on the surface. Without deeper insight into decline codes and issuer responses, systems treat them the same and that’s where revenue leaks begin.

Is Recovery Always Worth It?

One of the more common debates I with others in the industry is whether it’s worth pursuing recovery for low-dollar transactions. If a retry costs time, processing fees, or additional infrastructure, does it make sense to chase a $1 charge?

The answer depends less on the transaction amount and more on customer lifetime value.

A small recovery today may save a high-value subscriber tomorrow. But indiscriminate retries can also backfire and trigger issuer fatigue, increasing costs, or frustrating customers.

Smart recovery strategies don’t treat every decline the same. They prioritize recoverable transactions tied to long-term relationships and avoid wasting effort on declines that are unlikely to convert.

Why One Recovery Method Isn’t Enough

Many merchants have adopted payment recovery tools, but they often rely on a single approach and that is usually basic retry logic. That is no longer sufficient.

Subscription environments today require multiple, complementary recovery strategies, not just a lazy one and done. These might include:

Retry logic for transient issues
Customer notifications for credential updates
Network and issuer-aware timing to avoid repeated failures
Fallback routing or payment flexibility when appropriate

Each method addresses a different failure mode. Used together, they can significantly improve outcomes. Used alone, they leave gaps.

The goal isn’t to recover every transaction, but it is to recover the right ones efficiently.

Decline Codes

One of the biggest missed opportunities in subscription recovery is the underutilization of decline data itself. Decline codes are signals to tell you whether a failure is permanent or temporary, issuer-driven or merchant-driven, customer-related or system-related.

Organizations that take the time to analyze decline patterns gain a powerful advantage. By understanding why transactions fail, they can adjust retry timing, messaging, and routing to match real-world conditions.

In practice, this means fewer blind retries, lower costs, and higher recovery rates.

Recovery Should Start Before the Decline Happens

Perhaps the most important shift that the subscription business needs is that the best recovery strategy is prevention.

Many declines are avoidable with better system design. Expired cards, outdated credentials, and unnecessary authorization attempts often stem from a fragmented payment infrastructure.

By setting up systems that proactively manage credentials, monitor account status, and reduce unnecessary friction, merchants can prevent declines before they happen.

Partnerships and the Rise of Recovery Ecosystems

As the payments landscape becomes more complex, merchants are increasingly relying on specialized partners to help manage recovery. This has led to the emergence of recovery ecosystems where gateways, processors, and recovery providers work together rather than in silos.

The most effective solutions don’t force merchants into a single path. They provide flexibility, insight, and control and allow businesses to tailor recovery strategies to their customers, geographies, and business models.

In 2026, we should be asking ourselves how to integrate recovery intelligently into the broader payments stack, because soft declines will always be a part of subscription commerce and merchants’ need the ability to respond.

We need to treat declines as data, not just failures. Then companies can start to distinguish between recoverable and non-recoverable transactions, invest in multiple recovery paths, and design systems that reduce friction before it turns into churn.

Payment recovery about protecting long-term relationships, and in today that distinction makes all the difference.

Supply Shaping Delivers Performance Lift for Advertisers

marketing 13 Mar 2026

What was Butler/Till looking to achieve for their health and wellness client?

Butler/Till’s client was a leading health and wellness brand with more than 1,000 locations across the US. The client had 18 high-priority markets where they wanted to increase efficiency. Their key measurement metric was CPA — so the challenge for Butler/Till was to reduce CPA across their 18 target markets.

The agency knew they still needed local precision targeting at a scale that would deliver true CPA reduction across the entire campaign, but had to find a way to increase efficiency and performance of every impression.

Butler/Till wanted to reduce the inherent waste in the programmatic ecosystem and access more good impressions. Managing campaigns across dozens of markets typically introduces supply fragmentation, duplicated inventory, and inflated prices. Butler/Till needed a way to simplify the supply path automatically at scale while still maintaining the flexibility and intelligence required to tailor media delivery market by market.

Why did Butler/Till select OpenX and SWYM to help?

Butler/Till already worked closely with OpenX and SWYM on other campaigns and knew that they could achieve significant efficiency by applying SWYM’s unique supply shaping model to OpenX’s high-quality inventory to target their intended local audiences.

SWYM’s upstream intelligence paired seamlessly with OpenX’s platform and identity-driven infrastructure. Together, they enabled Butler/Till to activate locally optimized supply paths across multiple U.S. markets through a single, scalable workflow. OpenX provided premium, transparent inventory access, while SWYM ensured that only the most relevant impressions were surfaced for bidding across target markets.

What was SWYM’s role in the partnership?

SWYM acted as the intelligent decisioning layer, giving access to decisioning before the bid on the supply side. Its AI-native platform evaluated bid requests in real time, filtering and prioritizing supply based on performance signals, geography, and relevance before bids were placed. Instead of relying on DSP optimization alone, SWYM reshaped the supply landscape itself — giving Butler/Till cleaner, more efficient access to inventory aligned with campaign goals.

What is supply shaping and how did it work in this campaign?

SWYM improves performance by providing an intelligence layer before the bid on the supply side — this gives buyers like Butler/Till the ability to access more relevant impressions, filter our irrelevant impressions and simplify their media buying. Working with SWYM increased the quality of every bid opportunity. By eliminating low-performing impressions upstream, the platform reduced competition on irrelevant inventory and concentrated spend on supply proven to drive results.

Most media buying happens only after the bid occurs. Due to a number of complicating factors including bid throttling, duplication and overly complicated supply paths, buyers don’t always get access to the best impressions. Getting access to impressions before the bid is like evaluating and filtering at the source. This gives buyers a much higher quality flow of impressions while eliminating the waste in the system.

What was OpenX’s role in the partnership?

OpenX, a leading SSPs in programmatic advertising, provides direct access to high-quality supply, enabling Butler/Till to activate a highly local targeting campaign at a massive scale.

OpenX acted as the supply-side partner for the Butler/Till campaign, providing streamlined access to transparent, quality inventory. The solution was deployed as intelligent deals via OpenXSelect™, enabling Butler/Till to tap into curated inventory with strong identity signals and scalable reach across the 18 targeted U.S. markets.

OpenX’s infrastructure ensured that the campaign could be activated against inventory that met brand safety, transparency, and performance standards, and that those impressions could be scaled consistently across local geographies. In this way, OpenX translated strategic media decisions into measurable delivery and outcomes.

What role did AI and feedback loops play in ongoing optimization?

SWYM’s platform continuously ingested performance signals at the local level and fed those learnings back into its decisioning engine. These feedback loops allowed supply paths to evolve in near real time, adapting to changing market dynamics and audience behavior. The result was sustained performance improvement throughout the campaign, not just early wins.

The campaign delivered meaningful efficiency and performance gains across multiple markets, including a 57% reduction in cost per action (CPA). By focusing spend on higher-quality, better-performing supply, Butler/Till was able to achieve stronger results while operating within a simpler, more transparent supply framework — validating the impact of upstream supply shaping at scale.

What does this partnership signal about the future of programmatic buying?

This collaboration highlights a broader shift in programmatic toward intelligent supply control, not just bid optimization. As advertisers demand both precision and scale, platforms like SWYM demonstrate that the future lies in making smarter decisions earlier in the process. Supply shaping, powered by AI and activated through premium partners like OpenX, is quickly becoming a cornerstone of high-performance programmatic strategies.

Inside the Programmatic Audio Boom with Jen Oon, SVP of DAX US

marketing 12 Mar 2026

Q1. Give us a short introduction about who you are and how you got into programmatic advertising

I’m Jennifer Louie Oon, Senior Vice President of Sales at DAX United States, where I focus on helping brands and publishers unlock the value of programmatic audio. My career didn’t follow a straight line. I’ve always prioritized learning and being curious about how media connects with people, which naturally led me into programmatic and the evolving opportunities in audio advertising.

Q2. Why is programmatic audio finally operating like a scaled, reliable channel?

Programmatic audio has matured because the ecosystem is finally building the infrastructure and standards that make scale and reliability possible. Better targeting, improved measurement, and more sophisticated tools have helped reduce friction for buyers and sellers alike. As audio’s measurement capabilities improve, the channel is starting to deliver at a level brands can confidently invest in. The kind of consistency that used to exist only in video and display is now becoming real in audio.

Q3. How do you think buyers and sellers are navigating fragmentation across audio environments?

Fragmentation, from podcasts to ad-supported streaming and local audio, is both a challenge and an opportunity. Buyers are recognizing that focusing only on a handful of major platforms leaves gaps in reach and audience coverage. Sellers are looking to technology to standardize access and measurement across those environments. By embracing programmatic solutions and expanding how audio inventory is packaged and activated, the industry can actually turn fragmentation into reach and precision rather than confusion.

Q4. What do you believe advertisers are getting wrong about audio performance and measurement?

A lot of advertisers still think about audio the way they did years ago, as a support channel that’s hard to measure. That perception comes from outdated measurement models that haven’t kept up with how people actually consume audio. Audio delivers attention and engagement, but without reliable measurement, its performance hasn’t been properly credited. Once measurement systems catch up and show audio’s true impact, especially outside the biggest platforms, spend will shift to better reflect how audiences are actually listening.

Q5. How can DAX help brands navigate a fragmented audio market and drive measurable results through programmatic audio?

DAX helps brands and publishers by reducing the complexity of a fragmented market. We bring premium ad-supported audio inventory into a programmatic environment where buyers can efficiently reach audiences at scale and with transparency. We also focus on driving better measurement and insights so advertisers can understand what’s working and optimize in real time. By combining reach, reliability, and clearer performance data, DAX enables brands to invest confidently in audio as a core part of their media mix.

The New Marketing Stack: Where PR Fits in the AI-Driven Martech Ecosystem.

marketing 11 Mar 2026

By Pilar Lewis, Senior Public Relations Associate at Marketri

For most of my career, PR has been viewed as something that supports marketing. Now it plays a much more central role.

As generative tools like ChatGPT, Google AI Overviews, Perplexity, and Claude become a default layer of discovery, brand visibility is shifting from “Can you find us?” to “Will the AI cite us?” That change elevates PR from a communications function to a foundational input into how AI interprets authority, credibility, and relevance.

Discovery is moving from search results to AI answers

Traditional search rewarded the best mix of technical SEO, content depth, and domain authority. Generative discovery changes the interface and the outcome. Buyers increasingly get a synthesized answer, not a list of links, and the brands that appear in the citations become the brands that feel “known.”

This is why the martech stack conversation needs an update. It's no longer enough to orchestrate journeys and optimize conversion paths if the brand never enters the consideration set because it’s not showing up in AI-generated answers.

AI systems trust third-party validation more than brand messaging

AI answer engines don't treat brand-owned content and third-party coverage as equals. In practice, credible reporting, expert commentary, and industry publications tend to carry more weight than promotional content because they look like independent validation.

PR is the system that produces that validation at scale. Earned media creates durable signals about what a company does, what it’s known for, and who’s willing to quote it. Over time, those signals form “entity memory,” a pattern of consistent, verifiable references that increases AI confidence and makes citation more likely.

PR is becoming a core layer of the modern marketing stack

The modern stack has been built around four pillars: data, automation, content, and measurement. PR now belongs in that set because it supplies a unique input: third-party authority.

Here's an easy way to think about it:

1. SEO and content help you publish what you want to be known for.

2. PR helps others validate it publicly, in places AI systems trust.

3. Analytics and attribution help you see how visibility turns into demand.

4. Automation helps you act on that demand.

The industry is already moving toward systems that act, not just report. If marketing is becoming more autonomous, the inputs to those systems matter more. PR is one of the inputs that shapes the “truth set” AI pulls from when they speak on your behalf.

PR and GEO work better together than either does alone

Generative Engine Optimization (GEO) is the discipline of structuring and publishing content so AI systems can extract it, understand it, and cite it. PR strengthens GEO because earned media adds validation.

The practical connection is straightforward:

· PR builds authority signals through credible mentions, quotes, and bylined expertise.

· Structured content builds clarity through definitions, proof points, and consistent terminology.

· AI systems synthesize both and reward what looks well-supported across multiple sources.

This is also where the martech stack is heading. Gartner projects that by 2028, 33% of enterprise software applications will include agentic AI that can enable autonomous decisions in marketing. As those systems make more choices automatically, brands will need to engineer for how they are interpreted, not just how they rank.

Real-time PR is becoming an operating model

AI-driven discovery moves at the speed of the news cycle. The brand that shows up early and consistently in credible coverage does not just win attention in the moment, but it builds durable entity memory that compounds.

And the way AI systems cite sources makes the case for why PR has to operate continuously, not periodically:

· Earned media dominates what gets cited. According to Muck Rack, 89% of cited links are earned media, reinforcing that third-party validation is the “language” AI systems rely on when they answer questions.

· Recency matters more than most teams plan for. Half of all citations are published within the last 11 months, which means stale authority fades faster than traditional brand and SEO playbooks assume.

· The first week is the citation window that matters most. The first seven days after publication have the highest citation rate, so the speed of distribution, amplification, and follow-on placements directly influences what AI tools ingest and repeat.

Basically, PR isn’t only about placement now. It’s about managing freshness, consistency, and corroboration across trusted outlets so the brand remains present in the source layer AI systems reference. The outcome is visibility with credibility, delivered at the pace buyers and AI now expect.

The next marketing stack focuses on authority not volume

Marketing leaders already know how to invest in content and automation. Now it’s learning to invest in authority signals with the same intent because visibility depends less on how much content a brand produces and more on whether credible third-party sources consistently validate the brand.

That validation is exactly what PR produces, which is why it belongs inside the modern marketing stack alongside data, automation, content, and measurement. It creates the external signals that help AI systems interpret credibility and decide which sources are trustworthy enough to cite.

As AI is increasingly shaping how people discover and evaluate companies, then credibility across trusted sources matters more than ever, and PR is a major part of how brands earn it.