Interview
Building a Thriving Business Without Spending on Digital Ads
marketing 15 Apr 2026
Create High-Value SEO Content
Cultivate Partnerships and Collaboration For Greater Outreach
Connect With Customers Directly, In-Person and Online
Make Your Business as Visible as Possible
Target Niches Where Customers Are Plentiful
Final Thoughts
From Fragmented Martech Stacks to Unified Data Platforms as a foundation for AI
marketing 13 Apr 2026
Q1. The industry is clearly moving away from fragmented martech stacks. What are the main limitations you've observed with traditional setups involving DMPs, CDPs, and data clean rooms?
These tools were never designed to work together; they were built to solve different problems for different segments of the media industry at different points in time. DMPs were built mainly for publishers navigating the third-party cookie era. CDPs came along to fix the
single-customer-view problem for brands internally. Data clean rooms were adopted in response to signal loss across the board by brands, publishers, and retailers alike. So you’re looking at three separate architectures, three vendor relationships, three data pipelines.
What we hear constantly from publishers and retailers is that stitching these together creates enormous operational drag. Every handoff between tools is a point of latency, a potential compliance risk, and a cost center. And because none of them were built with collaboration in mind from the start, the moment you try to do something cross-party (enrichment with a partner's data, joint measurement, audience activation beyond your own properties, etc.) you hit a wall. The stack simply wasn't designed for the collaboration era, and even less for AI.
Q2. What is driving organizations to adopt more unified and flexible data platforms today, and how urgent is this shift?
Three pressures are converging simultaneously, which is what makes this moment feel different from earlier transitions.
First, regulation has fundamentally changed what's permissible. GDPR and a growing body of case law have made clear that moving customer data freely between systems is over: organizations need technical guarantees, not just contractual ones, for hassle-free and fast collaboration. Second, the signal environment has decreased: third-party cookies are declining, and universal identity solutions have helped at the margins but haven't filled the gap. Third and most importantly the value of first-party data is now demonstrably tied to collaboration. Data sitting in one organisation's DMP is interesting. Connected to a brand's CDP or a retailer's transaction history, it becomes genuinely powerful.
The media players moving now are building structural advantages. Those waiting are watching legacy DMP contracts come up for renewal with no clear answer for what replaces them.
Q3. From your perspective, what does a truly "unified" data platform look like in practice, beyond just integrating multiple tools?
"Unified" gets used to mean fewer vendor logos on a slide. That's not what I mean in this case necessarily.
A truly unified platform is one where the architecture was designed from the start for collaboration and privacy with the goal of creating networks between data owners, not just optimising data within a single organisation. When a CDP or DMP adds a clean room module, the privacy guarantees are only as strong as the wrapper. Additionally, you don't necessarily inherit any network here either, meaning each partnership might have to be built from scratch.
At Decentriq, we started from the opposite direction. Our clean room uses confidential computing: hardware-level encryption where data remains protected during processing, even from us. Using that as a foundation, we built the Collaborative Audience Platform: a unified layer adding CDP- and DMP-style capabilities segmentation, identity resolution, activation, shared audience products. In practice, a publisher can collect data, build and enrich audiences, activate to GAM or DSPs, run closed-loop measurement, and refresh automatically all in one environment, with no seams between layers. That's what genuinely unified looks like.
Q4. Many companies still rely on stitching together multiple solutions. Where do these approaches typically fall short when it comes to scalability and efficiency?
The failures tend to only become visible at scale, which is precisely when they're most painful.
The first is the identity tax. Every time data moves between tools, you make assumptions about identity resolution. If your system can only handle one ID type, you can lose a significant portion of your audience during matching. The second is engineering overhead: stitched integrations need constant maintenance, and onboarding each new partner is its own project, meaning there is a hard ceiling on how many collaborations you can run in parallel. The third, which comes up in almost every conversation with publishers replacing their DMP, is the inability to operationalize collaboration at scale. One-off clean room projects are feasible. Repeatable, automated, always-on audience collaboration with multiple partners simultaneously is a different problem (and stitched stacks weren't designed for it).
Q5. How is this shift impacting data collaboration between brands, publishers, and retailers in real-world scenarios?
The most significant change is the move from one-to-one integrations to network-based collaboration, because this changes the economics of data entirely and provides a crucial foundation for AI.
In the old model, a publisher ran a bespoke clean room project with one advertiser at a time. High cost, limited scale. A platform model enables something fundamentally different: standardised, repeatable collaborations across a growing network simultaneously. We've seen this with OneLog in Switzerland using our technology: five publishers unified under a single audience monetization platform, enabling advertisers to plan, activate, and measure across their combined audiences.
We're seeing the same dynamic for retailers. Decentriq's Collaborative Audience Platform lets them build audiences from online and offline signals and activate with brands and premium publishers (including CTV) without raw transactional data ever leaving their control. For brands, this means accessing publisher and retailer audience data through a standardized, privacy-safe workflow instead of negotiating lots of separate agreements.
Q6. Privacy and compliance remain key concerns. How do modern unified platforms address these challenges more effectively than legacy martech stacks?
Legacy stacks address privacy primarily through contracts — data processing agreements, retention policies. These are necessary but not sufficient. Contracts tell you what should happen; they don't technically prevent what shouldn't.
Decentriq uses confidential computing as the central technology for data collaboration: a hardware-level technology where data is processed inside a secure enclave inaccessible to any party, including us. The privacy guarantee is technical, not contractual. A significant recent CJEU ruling validated exactly this approach: clarifying that pseudonymised data processed through technology where re-identification is technically impossible carries a different compliance profile than data protected only by agreement.
For organizations navigating GDPR, this shifts the burden dramatically: instead of documenting every data flow and relying on ongoing contractual enforcement, you can demonstrate provable technical compliance. That's increasingly what regulators, legal teams, and enterprise procurement are demanding.
Q7. What role does AI and automation play in enabling more seamless and actionable data collaboration within these new ecosystems?
The critical point is where AI runs. AI operating on raw data is a privacy risk. AI operating inside a confidential computing environment on data that is never exposed is a fundamentally different proposition.
At Decentriq, AI is embedded at several levels: lookalike modelling that extends a seed audience without either party revealing their underlying data (a luxury automotive brand saw
+80% engagement and +58% conversion rate using this, for example), audience size estimation before a segment is built, and automated refresh cycles that keep audiences current across partners without manual intervention.
Further out, the more AI is integrated into these environments, the more the collaboration network itself learns from joint activations, measurement results, and partner interactions rather than resetting with each new campaign. That's the direction this is heading.
Q8. Looking ahead, what key changes do you expect in how organizations approach data infrastructure and collaboration over the next 2–3 years?
Three shifts feel clear.
First, stack consolidation. Organisations running separate DMPs, CDPs, and clean rooms will consolidate around platforms that do two, if not all three three, natively. The maintenance cost, compliance complexity, and operational drag will drive that decision.
Second, the ecosystem model becomes the norm. The value of first-party data is increasingly defined not by how much you have, but by how well it connects. Publishers contributing audiences to a collaborative network unlock revenue that's unavailable to those working in isolation. Retailers whose data can activate across a premium publisher network and close the loop with sales measurement are in a completely different competitive position. That logic will only accelerate. And as AI becomes more deeply embedded in these workflows, the network itself becomes a training asset: the more data flows through a shared collaborative infrastructure, the smarter and more precise the models that power lookalike targeting, audience estimation, and measurement become. Isolated stacks simply can't compete with that.
Third, privacy-preserving infrastructure shifts from differentiator to baseline expectation. Confidential computing and hardware-level privacy guarantees are currently seen as advanced or optional. In 2–3 years, driven by regulation, enterprise procurement standards, and demonstrated risk of alternatives, they'll be standard requirements. The organisations betting on these foundations now will be ahead of that curve rather than catching up to it.
How AI and Data Are Rewriting B2B Growth Craig Dempster, CEO of Trilliad
marketing 10 Apr 2026
Nearly all organizations have adopted some sort of AI tool (if not multiple), but many say that the ROI isn’t there. Why is this happening? What’s holding back B2B companies from seeing true success with today’s advanced AI tools?
AI doesn’t fix broken systems. It accelerates them.
That’s the core problem. Most organizations are dropping AI into environments that are already fragmented. Disconnected data. Siloed teams. Inconsistent workflows. Leaders expect AI to clean that up. It doesn’t. It amplifies whatever’s already there. If the foundation is messy, AI makes the mess bigger.
The second issue is intent. Too many companies treat AI as a tool to deploy rather than a decision to make about where it can actually move the business forward. So teams automate content, automate reporting, automate outreach, and then wonder why the numbers don’t improve. Automation without alignment isn’t progress. It’s just faster noise.
The organizations seeing real ROI started differently. They got clear on how marketing, sales, and customer data could work together first. Then they applied AI where it could sharpen a process or improve an outcome. That sequence matters.
Your 2026 Growth Imperatives suggest that the issues are not with the AI tools themselves, but the underlying data infrastructure. How can teams make their data as actionable as possible to keep up with modern businesses today?
Stop treating data like a reporting function. Start treating it like a decision engine.
Most B2B organizations aren’t short on data. They’re short on shared data. CRM, campaign platforms, sales tools, customer platforms are all collecting signals, but nobody’s looking at the same dashboard. Marketing interprets results one way. Sales interprets them differently. Customer Success is working from something else entirely. That’s not a data problem. That’s an alignment problem.
Making data actionable starts with one question across every team: which signals actually matter? Which accounts are engaging? What topics are moving buyers? Where is momentum building, and where are deals stalling? When teams agree on those answers and pull from the same source, data starts driving real decisions.
That’s the shift. Shared signals. Shared accountability. One view of what’s working.
How should B2B marketers be rethinking their brand-to-demand strategy to stay relevant and visible, given AI’s role in search?
Here’s the reality: many buyers are encountering a brand inside an AI-generated answer before they ever visit their website. That changes everything about how visibility works.
AI answer engines are now shaping first impressions. So the question marketers need to ask isn’t just “where do we rank?” It’s “is our content structured in a way that AI systems can actually interpret and surface?” If the answer is no, you’re invisible at the most important moment in the buyer’s journey.
Content needs to be clear, authoritative, and built around the exact questions buyers are asking.
And brand and demand can’t keep operating as separate functions. The brands that stay visible are the ones creating consistent, memorable signals over time, so that when a buyer is ready to move, there’s no question about who they think of first.
Your guide emphasizes that sales enablement should move away from sporadic sales training sessions. What should effective sales enablement training look like today?
One-time training creates short-lived energy and very little lasting change. Most organizations know this, and they keep doing it anyway.
The problem is that a standalone event can’t keep pace with how buyers actually behave. Sellers need continuous reinforcement tied to real conversations and real customer signals, not a slide deck from last quarter. Managers need to coach against what’s happening right now in the field, not what was happening six months ago when the last training was scheduled.
Effective sales enablement today is embedded in the operating system of the business. It’s tied to measurable outcomes. It shows up in how deals are reviewed, how feedback is delivered, and how teams are developed week over week. The strongest organizations don’t treat sales development as an event. They treat it as part of how the business runs.
Traditional lead generation models often prioritize volume over engagement. How is the rise of AI and interactive content changing what effective lead generation looks like in 2026?
Lead generation is no longer about volume. It’s about intent.
For years, teams measured success by how many contacts they captured, even when most of those contacts showed no real buying signals. That model is breaking down fast. Buyers expect immediate value and more control over how they engage. Handing over an email address in exchange for a gated PDF isn’t the exchange it used to be.
Interactive content and AI are changing the pattern. When a buyer engages with a conversational tool that gives them something useful right away, that’s a different signal than a form fill. AI helps teams read those signals and separate casual interest from real momentum, so the response can be personalized and timed correctly.
What does it actually mean to “operationalize AI” inside a revenue organization? What are some areas where many companies go wrong?
Operationalizing AI means it’s no longer a side project. It’s how the business actually runs.
In a revenue organization, that means AI is embedded across marketing, sales, and customer success, improving decisions, timing, and execution in real workflows, not just in pilot programs or innovation labs.
Most efforts stall for three reasons: poor data quality, fragmented systems, and a lack of operational enablement. Companies implement AI before the foundation is ready. And without a solid foundation, AI has nothing meaningful to build on.
The other common mistake is letting teams operate against different goals with different measurements. If Marketing, Sales, and Customer Success aren’t aligned on what success looks like, AI can’t bridge that gap. It will just automate the misalignment. Get the foundation right first. Shared goals, shared data, shared accountability. Then AI becomes a real accelerator.
Keeping Humans in the Loop: How Adora Is Turning AI Into a True Performance Engine for Marketers
marketing 7 Apr 2026
You’re stepping into the VP of Revenue role at Adora at a time when AI is rapidly reshaping marketing. What about Adora’s approach convinced you this was the right moment to make the move?
Almost every CMO today is being asked by their CEO how they're leveraging AI both now and in the future. What drew me to Adora is that we give marketers a genuine answer to that question without asking them to abandon what's already working. Adora preserves the marketer's control over performance measurement and brand integrity, while enhancing creative generation and execution to help brands sell more products, more efficiently. It's a tangible, low-disruption path to realizing the benefits of AI and that's a compelling story in a market full of noise.
Many platforms claim to use AI for performance marketing, how does Adora’s model actually give brands more direct control over outcomes, rather than abstracting decision-making away from them?
The key distinction is that Adora keeps the human in the loop by design. Brands retain the ability to approve or reject anything our AI produces, and they define the rules of what success looks like. That combination human intent guiding machine execution is still enormously powerful. AI doesn't replace the marketer's judgment; it amplifies it.
Creative is increasingly being talked about as the primary driver of performance, how is Adora operationalizing creative as a true performance lever rather than just a brand asset?
It starts with being precise about what you want the creative to do. If the goal is a click, the creative needs a reason to compel action an offer, a product launch, a limited-time moment. If the goal is brand awareness, the brief looks completely different. Adora helps brands connect creative decisions directly to business objectives, so every asset is built with a measurable outcome in mind, not just aesthetics.
As AI-driven automation increases, where do you see the balance between human strategy and machine-led execution evolving, particularly for brands focused on growth?
AI performs best when humans provide it with clear objectives and context. The world changes constantly, and AI can't yet anticipate the future on its own. When a brand knows a market shift is coming, or wants to get ahead of a change in consumer behavior, the ability to feed that strategic context into the machine is where the real competitive advantage lies. The brands that win will be the ones who treat AI as a highly capable partner not an autopilot.
From a revenue and go-to-market perspective, what are you prioritizing in your first 6–12 months to scale adoption and demonstrate real business impact for advertisers?
Getting the fundamentals right. We have a clear vision at Adora for where we can genuinely move the needle for the industry, and staying disciplined about that focus is essential. AI is a broad category, and it's easy to get pulled toward solving problems that aren't core to what you're built for especially when early revenue is on the table. But we're building relationships with brands that we intend to last for decades, not months. That means setting them up for real, sustained success from day one, which ultimately sets Adora up for the same.
The Brands Winning Digital Growth Aren't Chasing Attention They're Building Habits
marketing 7 Apr 2026
Digital 100 U.S., Similarweb’s annual ranking of the fastest-growing websites and apps, points to something more structural happening beneath the surface. Across consumer categories, the brands with the most sustained momentum aren't necessarily the loudest ones. They're the ones that have quietly become indispensable.
Dependability Is the New Differentiator
Fashion & Apparel tells the story clearly. The category's standout performers, Comfrt (+330.2%), Editorialist (+246.3%), Quince (+138.5%), aren't built around trend cycles or flash sales. What they share is clarity: clear positioning on value, fit, and everyday relevance. They reduce the friction around the decision itself.
In a market where consumers are more intentional and more cost-conscious than they were three years ago, that kind of dependability is turning into a genuine growth engine. The brands that make it easy to say yes, and even easier to come back, are pulling away from those still chasing the next campaign spike.
Shopping Is Becoming a Tool, Not an Event
On mobile, the story gets more interesting. Overall app downloads in Fashion & Apparel actually fell year over year - yet Whering: Your Digital Closet, Depop, and LTK all posted significant gains. The through-line isn't category or aesthetic, it's function. Whering helps people get more out of what they already own. Depop turns the closet into a marketplace. LTK wraps commerce inside creator recommendations people already trust. They're not destinations you visit when you're in shopping mode. They're tools you reach for as part of an existing routine.
That pattern repeats across the Digital 100. In Beauty & Health, the fastest-growing apps are step counters, running trackers, and nutrition logs, habit-reinforcing by design. In Food, it's quick-service apps that make reordering feel like muscle memory. The platforms pulling ahead aren't interrupting daily life. They're threading themselves into it.
What Loyalty Actually Looks Like Now
For brand leaders, this reframes the question. The real growth metrics aren't just acquisition numbers. They're behavioral: How often do customers come back? How quickly? How much effort does it take them?
Traditional loyalty mechanics, points, discounts, limited-time offers, still have a role. But they're accelerants, not anchors. The deeper drivers are structural: frictionless checkout, preferences that persist, personalization that compounds over time, pricing transparency that builds trust before the first purchase ever happens.
Behavioral loyalty is a product of the experience itself. The second visit should feel easier than the first, and the third easier than the second. And the brands seeing durable growth are the ones engineering exactly that compression - quietly, systematically, visit by visit.
The Shift Worth Acting On
Retail strategy has long been organized around peaks: seasonal launches, promotional events, product drops. These still matter. But the Digital 100 makes clear that the brands accumulating the most durable digital advantage are doing something different in parallel. They're not optimizing for spikes. They're designing for patterns.
That requires a genuinely different kind of investment. Features that improve with use. Value made visible at every touchpoint, not just at the moment of sale. Digital experiences built not as funnels to be managed but as products to be continuously refined.
And perhaps most importantly: an honest reckoning with what friction costs. Every abandoned cart, every confusing size guide, every extra step in checkout is a reason not to return. In a market defined by choice overload and decision fatigue, convenience isn't a feature, it's a retention strategy.
The New Growth Edge
The brands growing fastest in the Digital 100 aren't doing it through hype cycles. They're doing it by making the next visit feel inevitable rather than effortful. Comfrt didn't grow 330% on the back of a single campaign. Depop didn't reach 5 million monthly active users by going viral once. These outcomes are the product of systems – digital experiences that make returning easier than starting over.
In an era of economic scrutiny and attention scarcity, that's the real competitive advantage. Not who captures the moment. Who becomes part of the routine.
Corporate Event Engagement Has a New Baseline
marketing 6 Apr 2026
Two signals make the shift hard to ignore. Freeman found that 64% of attendees say immersive experiences are the most important element of the event experience. Meanwhile, research firm GitNux reports that 62% say personalized communication around events increases their loyalty. Immersion and personalization are not “nice to have” line items anymore. They are the standard people compare every event against, whether they are in healthcare, finance, manufacturing, or SaaS.
That’s why event technology hasn’t just evolved; it’s transformed. Today, event technology supports more than logistics. It links the attendee experience directly to revenue, retention, and growth.
Connected Experiences Need Connected Data
The biggest engagement gap today is not what happens on site. It’s the disconnect between the event and everything around it.
A single customer might register from an ad, attend two sessions, book a meeting, and spend fifteen minutes in a product workshop, only to receive a follow-up email that treats them like a total stranger. This erodes customer trust and wastes the richest signal available to most marketing teams, which is what actions a person took in real-time, when marketers had the chance to lean in. Ignoring a customer's real-time engagement is a significant oversight.
This is why event technology now sits at the center of the marketing and sales ecosystem. When event data lives on an island, it stays stuck as a recap deck and a lead list. When it is integrated with the rest of the martech stack, it becomes usable. It can trigger the right next message, route the right lead to the right team, and shape the next experience based on what worked.
Measurement changes too. Instead of debating whether an event “felt successful,” teams can see what content drove meetings, what sessions influenced the pipeline, which segments were engaged with most, and where drop-off happened. That clarity is what leaders want when budgets tighten. It is also what event teams need to defend investment and improve with each cycle.
Personalization is the bridge between engagement and outcomes. If 62% say personalized communication increases loyalty, the takeaway is simple. Follow-up should reflect what people actually engaged with. And the more connected the data, the easier that becomes.
Building Engaging Experiences
When attendees say they want immersion, they are not asking for more gimmicks. They are asking to feel pulled into something that makes sense for them. It might be a hands-on lab, a demo that solves a real problem, a roundtable with peers who share their challenges, or a guided personalized path through content that matches their role. The common thread is intention.
The problem is that intention is hard to scale when every event is built from scratch. Teams end up spending energy on rebuilding pages, emails, registration flows, session frameworks, and reporting, instead of spending that energy on improving the moments that matter.
This is where repeatability becomes a strategic advantage. When teams templatize what should stay consistent, like branding, key data capture, standard journeys, and core workflows, they buy time back. They can focus on the parts that should be dynamic, like audience-specific messaging, regional preferences, and the immersive elements that make each experience feel alive.
Having the foundation of a data strategy connected to the martech stack helps drive templatization into a reliable and standardized framework. This type of consistency and intentionality makes it easier to tie back to business goals and objectives.
Templates also protect the attendee experience. When registration and communications are familiar and frictionless, people spend less time figuring things out and more time engaging. Consistency does not have to mean sameness. Done right, it creates reliability that makes personalization easier, not harder.
Events Are Becoming the Most Measurable And Memorable Form of Human Connection
Engagement expectations are rising because people are overwhelmed with digital noise and increasingly protective of their time. They want immersive experiences that feel real, and they reward brands that communicate with relevance.
Modern event technology is what makes that possible at scale. It helps teams design connected experiences across the customer journey, standardize what should be repeatable, personalize what should be personal, and prove what worked. The organizations that win will be the ones that stop treating events as isolated moments and start treating them as measurable, connected experiences that build relationships and move the business forward.
The Emergence of Vibe Marketing
marketing 6 Apr 2026
Vibe Marketing is a new era of marketing shaped by generative AI creativity, speed, and intelligence enabling marketers and executives to automate marketing campaigns from client brief to concepts, campaigns to optimization.
The concept draws inspiration from the software engineering world, where “vibe coding” describes using natural language and AI-powered tools to accelerate development, especially for users who do not know how to code. In marketing, the idea is similar: generative AI allows teams to quickly create, test, and iterate on campaigns that once took weeks or months to produce. And, vibe marketing enables professionals with no marketing experience to create brilliant campaigns. AI removes the traditional barriers of production budgets, technical language requirements, and long lead times, allowing challenger brands to act with the speed of a startup and the production value and sophisticated analytics of a global leader.
Searches for “vibe marketing ”increased nearly 700% in the past year, and Forrester found that 50% of B2B marketing decision-makers are already experimenting with or currently using generative AI.
To make Vibe Marketing work, it requires human intellect and artificial intelligence working in concert to create an accurate and comprehensive data foundation, sophisticated modeling, generative audiences and creative production, and a scalable method for measuring impact. The result, authentic, relevant experiences that deliver real ROI.
Smarter decisions about audiences, channels, and creative experiences.
Vibe marketing is not about letting AI do everything; it’s about augmented intelligence where AI accelerates insights and recommendations, while humans act as the compass. According to a recent EMARKETER survey, about 91% of marketers called human involvement "critical" or "very important" in evaluating or generating AI-driven creative.
Vibe marketing enables marketers to act on subtle shifts in consumer sentiment that occur before a trend fully breaks. What a marketer needs is the ability to see how their audience’s engagement changes before their conversion patterns change.
By analyzing unstructured data with the right queries and dashboards through tools like BigQuery, AI can surface those contextual signals and build “intent cohorts” automatically, simply prompting humans for approvals. When these insights are paired with Looker Dashboards or Adobe Customer Journey Analytics (CJA), marketers gain the ability to see the "why" behind these shifts in real-time, allowing them to instantly how their AI models have bridged the gap between raw data and a live customer experience faster and frictionlessly.
Clustering users by intent cohorts and understanding the why behind their structure then makes it easier for marketers to allow AI tools to create personalized, dynamic content. A strong data foundation ensures that their "vibe" is backed by the reality of what their audience actually wants not just today, but tomorrow, too.
Test cultural resonance before you spend
Vibe marketing can also help balance the tension between speed and effectiveness. By creating digital twins of intent cohorts, marketers can simulate how specific personas might react to new creative directions in seconds rather than weeks. This allows companies to test and optimize campaigns in a sandbox environment, ensuring that they scale with the strongest, most authentic content.
Measure what actually moves the needle
The final piece of the puzzle is measurement. Because vibe marketing often triggers rapid content shifts across multiple channels, a linear funnel view won't cut it.
For a truly holistic view, practitioners are increasingly turning to marketing mix modeling (MMM) with tools like Google Meridian. Meridian’s near-real-time calibration capability means you can use it to sense channel efficiency shifts as cultural moments occur, not six weeks later. For marketers making fast creative decisions, knowing in near-real-time that connected TV (CTV) is outperforming paid social this week changes where you put the next dollar.
Coordinate the entire stack for speed
The "possible" outcomes of vibe marketing are only achievable if martech and adtech stacks are properly integrated and flexible enough to handle real-time shifts. In 2026, the focus has shifted from simple automation to autonomous, intelligent systems that empower marketers to move from being task-doers to strategic thinkers.
A successful agentic engine requires a seamless loop:
- Ingest and aggregate: Centralize your web and app data into a warehouse like Google Cloud to get a clear view of your marketplace coverage.
- Analyze and cluster: Use machine learning models to identify high-propensity intent cohorts across BigQuery or Adobe Customer Journey Analytics.
- Synthesize and QA: Use generative AI to iterate with multiple creative variations, using humans as the final quality check for brand safety.
- Activate and attribute: Serve dynamic content and use closed-loop attribution to feed results back into the model.
Drive smarter decisions and faster execution
Vibe marketing is an incredible opportunity to connect with audiences on a human level while scaling your marketing team’s productivity. But most marketers are stuck somewhere in between understanding the “possible” and not knowing how to achieve it because their adtech and martech tools are configured for the world that existed two years ago. The technical foundation is the key to turning these "vibes" into a true marketing engine.
NRF 2026: Biggest Takeaways for B2B Marketers to Stand Out Amongst a Sea of Tradeshow Vendors
marketing 6 Apr 2026
While flashy exhibits turn heads, espresso martinis draw a crowd, and the smell of freshly popped buttery popcorn certainly helps attract booth traffic, the standouts now are those building community, personalizing engagement across the full event lifecycle, and reinforcing their activations with integrated technology that provides actionable data.
NRF creates the blueprint for how marketers and brands can connect one-time engagements directly to long-term, measurable business outcomes and deeper customer relations, backed by technology solutions.
NRF as an Industry Ecosystem
NRF illustrates how a trade show can function as a year-round industry ecosystem, rather than a one-time activation. While the show happens across only three days, its ongoing virtual sessions, in-person meetups, and other available gatherings around the world extend meaningful dialogues beyond the show floor. This sustained engagement creates a common platform where relationships deepen, and ideas evolve over time.
This year’s NRF delivered on its theme of “The Next Now.” The focus was preparing retailers for the future that is unfolding around agentic AI, human-centered experiences, unified experiences, the physical transcending to experiential hubs, and insights.
Because NRF convenes retailers alongside tech providers and other partners, conversations also naturally reflect the interconnected nature of the retail industry today. The activations that resonated most this year were not isolated product showcases, but those that acknowledged the broader landscape and positioned brand narratives and offerings as contributors to an ongoing industry conversation. By embracing this ecosystem mindset, marketers and brands can better signal relevance, credibility, and long-term commitment.
Community as Competitive Differentiator
In a crowded exhibition center stacked with immersive activations and flashy AI tools, attendees this year gravitated toward experiences that offered true substance. The most effective activations weren’t the largest, they were the most intentional.
Brands that hosted smaller group discussions, curated demos, and targeted networking sessions, created space for practical conversations around shared challenges. These interactions inspired more valuable engagement and positioned companies as true partners versus one-time vendors.
For example, brand booths this year tapped cultural touchpoints, with several exhibitors incorporating World Cup fan zones or sports activations such as SAP’s tennis and pickleball themes, and HP/Intel’s sports jersey displays, to draw attention and create immersive physical spaces. These activations worked because they tapped into passions attendees already share like sports, competition, and global events, creating natural gathering points on the show floor. Instead of simply drawing foot traffic, they gave strangers an easier entry into conversation.
The takeaway from NRF 2026 is clear: foot traffic is no longer the primary measure of success. Meaningful dialogue and sustained connection are what differentiate brands, and what can turn event moments into lasting business relationships.
From Ecosystem Engagement to Sustained Momentum with AI
As conferences and trade shows surge back to pre-pandemic scale, the pressure is on to create experiences that are more personalized than ever. But the real question remains: how can marketers implement NRF’s sustained ecosystem and community-based approaches at the same time? The answer lies in technology and data-backed strategies.
By leveraging AI, teams can look beyond isolated actions from one event like booth traffic, engagement metrics, or survey feedback, to understand how attendee experiences impact future pipeline, revenue, and relationships. Traditionally, marketers have relied on surface-level metrics such as number of booth visits, but counting vanity metrics falls short when it comes to understanding how touchpoints over time, or your brand’s ‘ecosystem,’ is influencing business impact.
AI can also boost personalization at the event itself, further inspiring the community-based approach that NRF excels in. While conventional AI relies on human input, AI agents, for example, act independently, functioning as a real-time ‘concierges’ that can tailor suggestions at an event based on user behavior and demographics. This can help to put like-minded attendees in the same group or same session, sparking conversations and insightful discussions. With Gartner projecting that 40% of enterprise applications will include task-specific AI agents by the end of 2026, personalization is becoming embedded and expected infrastructure in events.
Overall, in 2026, high-impact events will be defined by their ability to operate as connected ecosystems powered by integrated data and AI, converting engagement into sustained momentum, rather than isolated activity.
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