Not long ago, I was having coffee with a fellow CMO at an industry event. Like most marketing conversations these days, we ended up talking about AI. At one point, she paused and said something disarmingly honest.

“Every time leadership talks about AI, they talk about efficiency. But all my team hears is that we’re becoming replaceable.”

It is the thing nobody says out loud in the strategy session, but everyone is thinking. According to recent Pew Research Center data, 52 percent of workers say they worry about the future impact of AI in the workplace. When leaders frame AI primarily as a productivity tool, it activates what researchers call FOBO — fear of becoming obsolete. The anxiety is real, and dismissing it does not make it go away.

But as our conversation continued, we kept arriving at the same conclusion: the leaders getting this right are not managing the fear. They are changing the frame.

The real shift in marketing is not about automation or headcount. It is about how teams turn information into insight, and insight into action.

The Real Challenge Marketing Leaders Face: Digital Complexity

At some point in our conversation, the CMO said something I have not stopped thinking about since.

“I don’t need more dashboards. I need help understanding what actually matters.”

That is where the real story begins. She was not struggling with a shortage of tools. Her team had access to more data and more platforms than ever before. What she was struggling with was complexity — the kind that accumulates silently until it becomes the job itself.

Modern marketers are navigating layered content ecosystems, constant algorithm updates, accessibility and compliance requirements, and more performance data than any team can meaningfully process. The challenge is not collection. It is comprehension — turning disparate information into decisions that actually move something. This is where the next phase of AI is beginning to reshape how marketing teams operate.

Marketing Technology Is Shifting From Tools To Intelligent Systems

From years of working in enterprise SaaS and through my work with the MACH Alliance, I have learned that the most effective digital systems rarely live inside a single platform. They are composable — different capabilities working in concert across systems, each doing what it does best.

We are seeing the same logic applied to AI. Rather than isolated tools, organizations are beginning to adopt agentic systems that work alongside teams—not replacing the workflow but removing the friction within it.

When I described this shift to the CMO I had been speaking with, she understood it immediately.

“So it’s not just about faster reports,” she said. “It’s about helping teams understand what to do next.”

Exactly. Instead of spending hours stitching together insights across platforms, marketers can focus on interpreting signals and making decisions. The system handles the assembly. The human handles the judgment.

The Winning Teams Will Elevate Human Work

Later in our conversation, she said something else that stayed with me.

“Honestly, I didn’t become a marketer to spend my days pulling reports.”

It is something I hear often. For many teams, a disproportionate share of the working day is consumed by stitching together data, navigating dashboards, and translating outputs into something a stakeholder can act on. The work is necessary. It is rarely the work that inspires anyone to build a career in marketing.

What happens when that burden begins to lift is worth paying attention to. When repetitive analysis and reporting are automated, teams recover space — space to focus on strategy, to understand audiences more deeply, to craft stronger narratives, and to take the creative risks that dashboards cannot prescribe.

AI can process information at a scale no human team can match. What it cannot do is replicate the capabilities that make great marketing possible: empathy, creative instinct, storytelling, and the kind of strategic judgment that comes from experience and context. The organizations seeing the most success with AI are not simply introducing new technology. They are rethinking how work happens — using intelligent systems to remove friction so that people can focus on the work that actually matters.

The Real Promise Of Agentic Marketing

When I think back to that conversation, what strikes me most is not the concern my colleague expressed about AI. It is the hope underneath it — that these systems might finally give her team the clarity they have been missing.

The organizations that succeed in the agentic era will not be the ones that automate the most work. They will be the ones who use automation to elevate the work that remains.

When leaders position AI as a collaborator rather than a threat, something shifts. Fear turns into curiosity. Curiosity turns into experimentation. Experimentation drives the kind of innovation that no efficiency mandate ever produced on its own.

The future of marketing will not be defined by AI replacing people. It will be defined by systems that give people the visibility, the insight, and the confidence to act — turning information into intelligence, and intelligence into decisions that move the business forward.

Clarity, in the end, is not a feature. It is the whole point.

The post Clarity Is Marketing’s Most Valuable Asset appeared first on MartechView.

For nearly two decades, SEO was the digital marketer’s most reliable tool. Entire strategies and teams were built around a stable premise: rank well on Google, get seen. Visibility followed ranking, and traffic followed visibility. The system relied on Google’s shifting algorithms, but at least it was comprehensible.

The past two years have broken that logic entirely.

Rankings are holding — sometimes improving. But website traffic is declining. Dashboards look healthy. First-page positions remain. The clicks, however, are not there. In 2025, 60 percent of Google searches ended without a single click to any website.

The cause is not a mystery. AI-powered search engines now answer queries directly, absorbing the clicks that websites used to capture. We have entered a zero-click world, where AI synthesizes information and delivers it to users without ever requiring them to visit the source.

For marketers, the implication is significant: the goalposts have not just moved — they have been replaced entirely.

Ranking No Longer Leads to Reach

The old model of search rewarded technical precision. Keywords, backlinks, site architecture, and page speed sent signals that helped search engines determine relevance and authority. In today’s AI-mediated environment, relevance is no longer discovered — it is constructed.

AI chatbots do not surface results. They synthesize them. They draw from multiple sources, weigh credibility signals, and generate responses that may never require a user to visit the referenced content. Ranking highly on a search results page does not mean your brand will appear in an AI-generated answer. And if it does not appear in that answer, visibility effectively disappears — regardless of where the blue link sits.

The new definition of visibility is not where you rank. It is whether you are recognized as a source worth citing.

The End of Silos

Because AI changes how visibility is earned, it dissolves the boundaries that once separated public relations, SEO, and paid content. Historically, these functions operated independently because they influenced different outcomes. SEO drove rankings. PR generated coverage. Paid content drove traffic. Each was measured within its own channel.

Today, all of it is evaluated together — by the AI model deciding whose voice to include in an answer.

AI systems do not draw from a single source. They scan and combine articles, websites, forums, comments, and sponsored content across the entire web to construct a response. The implicit question these systems are asking is: Does this brand appear consistently, clearly, and credibly across multiple sources?

A PR placement is no longer just visibility in one publication — it becomes part of the dataset AI systems are trained on and referenced going forward. SEO content is no longer just about ranking — it is a structured repository of information for AI to extract. Paid placements do not just drive clicks — they shape how frequently and favorably a brand appears across the web.

The problem is consistency. A company might describe itself one way on its website, another way in media coverage, and appear differently still in paid placements. To a human reader, that is messy but navigable. To an AI model, it is noise, and noise weakens the signal. A brand that is difficult for AI to categorize is a brand more likely to be left out of the answer entirely.

Redefining Strategy for the AI Era

If the goal is no longer to rank but to be referenced, the question becomes: how do you build a presence that AI systems will not ignore?

In 2026, an effective search strategy rests on three foundations.

The first is audience-driven visibility. AI systems weigh what real people say and think — customer reviews, forum discussions, and community conversations — more heavily than brand-produced marketing copy. The frequency with which AI Overviews and ChatGPT reference Reddit is not coincidental. If people are discussing your brand in credible, relevant spaces, those discussions become signals AI systems recognize and draw from. Marketers should actively encourage user-generated content and participate in the communities — forums, social channels, industry platforms — where their customers already gather. The goal is contribution, not promotion.

The second is strategic amplification. Search engines have long deprioritized paid content, but AI systems often make no distinction between organic and sponsored material. When placed thoughtfully on high-authority platforms, sponsored content can reinforce a brand’s presence in exactly the sources AI is most likely to reference. Paid and organic content are increasingly parts of the same ecosystem — and should be planned as such.

The third is clarity. AI models prioritize specificity. Users querying AI tools provide far more detail than they would in a traditional search — use cases, specifications, context. For content to be referenced, it must match that level of detail. Brands need to be explicit about what they do, how their products work, and what differentiates them — even when those details feel self-evident. If it is not clearly stated, it is less likely to be surfaced.

The New Definition of Success

The language of SEO has not yet caught up to the shift it is navigating. Rankings, keywords, and traffic remain the default metrics for measuring reach and success. But those metrics are growing increasingly detached from actual visibility in an AI-first environment.

The questions that matter now are different: Are you being cited? Are you being synthesized into answers? Are you present in the conversations and sources that shape those answers?

If the answer is no, it does not matter where you rank.

SEO, as it was understood for two decades, is less useful than it once was. But search is not disappearing — it is being redefined. The brands that understand that distinction and build for it deliberately will not simply adapt to the zero-click era. They will set the terms.

The post SEO Is Not Dead. But It No Longer Works Alone. appeared first on MartechView.

An e-commerce merchandiser’s day is a constant negotiation between forces that rarely agree: what customers are clicking and buying, what inventory exists and at what depth, what marketing is pushing this week, what the brand wants front and center, and what revenue and margin targets demand. Aesthetics. Creativity. Inspiration. Each pulls in a different direction. Merchandisers reconcile all of them — every day, across a vast catalog of products and collections.

Experienced merchandisers often have sharp instincts. But the forces shaping 24/7 ecommerce are dynamic, complex, and unrelenting. Under that pressure, there is rarely any confirmation that a merchandising decision outperformed what it replaced — or what else was possible. Teams won’t know for days or weeks, and by then, shoppers’ preferences, brand preferences, and market signals will have already shifted.

Every merchandising decision is a trade-off. Most are made without ever knowing what the alternative would have cost. That gap between decision and consequence is where revenue quietly slips away.

New Arrivals, Best Sellers, and Hidden Winners

Ask any senior merchandiser how they decide what goes where, and they’ll walk you through the logic: new arrivals get prominent placement to build velocity, best sellers stay visible because they convert, slow movers get deprioritized to protect real estate. It sounds rational. For many years, it worked well enough.

But in the fast-moving reality of modern e-commerce, “rational given my experience” and “optimized” are two very different things. Both matter. The gap between them, however, costs real revenue.

Consider new arrivals. Most teams give them a fixed window at the top — a week, two weeks, sometimes a month — regardless of how they are actually performing. The logic is understandable: new products need exposure to get a fair shot. But this approach treats every new arrival the same. A dud sits in a premium position just as long as a breakout hit. That is not a strategy. That is a schedule — and it burns valuable catalog real estate on products that have already signaled they won’t earn it.

Now consider best sellers. A product that ranks in your top ten overall is not necessarily a top performer in every collection. A bestseller in “Evening Wear” might underperform in “Casual,” where it occupies a prime position while stronger contextual fits remain buried. What works across a full catalog does not always work within a collection — and promoting it heavily in the wrong context wastes resources that could be driving real conversion elsewhere.

Then there are the hidden winners: products with strong conversion signals that never get the traffic to prove themselves because they’re sitting on page four, crowded out by slower-moving items consuming the placement and promotion they deserve. Without a system to surface them, they stay buried. Teams never know what they missed — or realize it only in hindsight, too late to act. And customers never find what they would have loved.

Short on Time, Not Signals

Most e-commerce teams measure what they did or conduct retrospectives on what happened days, weeks, or months ago — clicks, conversions, revenue per session, return rate. These are reasonable metrics. But very few teams systematically measure what they didn’t do: the product that could have been promoted but wasn’t, the placement that could have driven conversion but went to something weaker, the new arrival that earned its spot on day three but stayed there until day fourteen because the calendar said so.

Opportunity cost — the revenue left on the table by not surfacing the right product in the right place at the right time — is the most consequential metric almost no one is tracking. The reason is straightforward: you cannot measure what you cannot see. And most merchandising systems are not built to make the alternative visible.

This is not a data problem. Most brands have plenty of data, and AI is producing more of it every day. It is a decision intelligence problem. The data needed to make better calls already exists — it is simply not being assembled in a way that supports the actual decisions merchandisers need to make, in the moment they need to make them.

What AI Merchandising Support Needs to Do

As AI becomes embedded across e-commerce, the goal should not be piling on more tools and dashboards. It should be deploying a genuinely intelligent merchandising approach — not “here are your top ten products” reports, not weekly performance summaries, but real-time decision support that reconciles competing constraints, surfaces what even the most experienced merchandiser cannot possibly see, and quickly tells you whether the arrangement you just published is performing better or worse than the one it replaced.

In practice, this means an AI model that knows when a new arrival has accumulated enough signal to be called a winner or a loser — and alerts a merchandiser, or automatically adjusts placement, rather than waiting on a fixed schedule. It means understanding that a product’s rank in one collection says almost nothing about how it should be positioned in another. It means flagging overexposed products that crowd out items with stronger contextual fit and surfacing quiet performers before the window to act on them closes.

The goal is not to replace the merchandiser. It is to handle what the merchandiser cannot: processing more variables simultaneously than any human can track, across more collections than any team can actively monitor.

The teams that will win in the next few years are not the ones with the most data or the most sophisticated tech stack. They are the ones that close the loop between decision and outcome fastest. E-commerce waits for no one. The catalog changes. The customer moves on. The question is whether your merchandising intelligence moves with it — or whether you’re still finding out what happened last week.

The post Merchandisers Are Drowning in Data and Still Flying Blind appeared first on MartechView.

There was a time when brands could simply list a product online and expect it to sell. Today, success requires a coordinated strategy across retail media, the digital shelf, pricing, and inventory. That strategy should be informed by data, but with thousands of SKUs across numerous marketplaces, brands not only have to interpret endless insights but also act on them. At scale, consistent execution becomes nearly impossible to manage manually.

In a recent survey, nearly half of business leaders said their data isn’t actionable, and more than 40% cited a lack of data accessibility or decision-making time as a core operational challenge. 

Teams don’t need more visibility — they need the ability to keep up with and act on the data they already have. It’s impossible for e-commerce teams to adjust every bid, fix every product description, and catch every stock issue themselves. To close that gap, brands need to rethink how the work gets done.

Why Today’s Execution Model Can’t Keep Up

Teams aren’t lacking data; they have too much of it. Dashboards have become more sophisticated, but seeing more doesn’t necessarily help teams do more.

Every optimization still requires someone to pull the report, identify the issue, decide what to do, and then execute. That delay between spotting a problem and fixing it is where performance starts to degrade. By the time a team corrects a content issue, the marketplace algorithms have already shifted.

What retailers are experiencing is a gap between insight and execution. The old approach was to do more: more reports, more meetings, more manual tweaks, more tools. But today, teams can spend most of their time looking for what’s broken and patching the same issues over and over.

The way the work used to get done won’t keep up with how fast modern e-commerce moves. With AI-powered discovery through Amazon’s Rufus and Walmart’s Sparky shaping how shoppers see and buy, the pace will only continue to accelerate.

Many brands have tried to address this by turning to agencies for support. In a recent survey, 67% of commerce teams said they rely on agencies, and about half said they spend 15% to 30% of their budget on agency fees alone. Yet 55% said those costs are too high for the results, and 40% said response times can’t keep up with the speed of algorithms.

While agencies can provide expertise, they don’t operate 24/7 — and the marketplaces retailers sell on never pause. Retailers can’t match that speed by adding more headcount or additional agency hours. 

Brands today need an algorithm to keep pace with the algorithm. They need a new execution model that can handle the volume and velocity of decisions in modern ecommerce.

What an Agentic Retail Strategy Looks Like

The shift happening now is toward agentic AI execution, where human teams are no longer tasked with doing the heavy lifting of weeding through data, reports, and manual workflows. Instead, they drive strategy and make final decisions while their agentic AI counterparts analyze large volumes of data and identify growth opportunities. 

This approach lets brands continuously extend execution across their entire catalog around the clock, rather than focusing only on top-performing SKUs. In practice, this looks like specialized agents that operate across the digital shelf:

• A content agent identifies and resolves product page issues across thousands of SKUs simultaneously, reducing the time required to update product detail pages from 35 minutes to 35 seconds.
• A media agent optimizes campaigns using dozens of signals at a scale no human team could match. 
• A sales agent monitors pricing and promo performance to flag gaps or opportunities.

To keep pace with the speed of modern ecommerce, retailers don’t have to add more tools on top of the same manual process; instead, they need to fundamentally change how the work is done. With agents handling the execution layer, the brand manager’s role changes. They spend less time pulling reports and making fixes and more time setting guardrails and deciding priorities.

The Real Shift Starts in Execution

Teams already know the old model isn’t working. The speed required to compete today has outgrown what humans can manage manually. There’s too much data to interpret and too many actions required to keep up with how quickly the digital shelf changes. 

Agent‑driven execution removes that bottleneck. According to a recent report, 82% of commerce leaders expect AI investment to increase in the next 12 to 18 months, and 71% are already familiar with or actively using AI agents. 

With the industry moving in this direction, retailers can’t afford to be left behind. The next era of e-commerce will belong to the companies that can execute at the same speed as the market.

The post Dashboards Created Visibility, but They Didn’t Solve Commerce Execution appeared first on MartechView.

For years, B2B marketers have been stuck ranking binary options: walled gardens or the open internet. LinkedIn or programmatic. Professional environments or lifestyle placements. Brand or performance.

But in 2026, the most sophisticated B2B marketers have stopped choosing.

B2B media strategies have shifted as marketers rethink the balance between closed and open environments. There seems to be a greater understanding these days of the option to orchestrate environments intentionally based on what marketers are actually trying to accomplish.

The End of Either/Or Thinking

Buyer journeys don’t happen in one ecosystem. Decision-makers don’t segment their attention the way marketers segment their budgets. A CFO researching enterprise software solutions isn’t confining that research to LinkedIn during business hours. They’re reading industry news on their commute, streaming business podcasts while exercising, and scrolling through professional networks late at night.

Yet historically, marketers have opted for rigid choices. Go all-in on walled gardens for precision targeting, or commit to open internet tactics for scale and attribution. Pick professional environments where you know decision-makers are “in work mode,” or risk wasting spend on lifestyle placements that feel too B2C.

This rigidity has been limiting. And frankly, it hasn’t reflected how modern professionals actually live and work. The lines between personal and professional identity have blurred across platforms. B2B decision-makers now make up a broader, more diverse buying committee than ever before. And that demographic is changing, where and how they consume content.

The opportunity now lies in strategically orchestrating both environments, leveraging the unique strengths of each while working around their limitations. When done intentionally with the right partners, this approach unlocks outcomes neither strategy could deliver on its own.

First-Party Data Changes the Equation

Part of what makes this orchestration possible is the evolution of first-party data activation. 

For years, B2B marketers used first-party data primarily for insights and audience segmentation. The actual activation of those audiences remained fragmented across different environments.

That’s changed dramatically. Today, marketers can leverage advertiser first-party data across both walled gardens and open internet tactics in ways that weren’t possible even a few years ago. 

Onboarding first-party customer data into walled gardens for deterministic targeting is certainly valuable. But it’s only one layer of the strategy. Again, the either/or dichotomy doesn’t apply anymore. The same data can be activated programmatically across the open web, extending reach to niche audiences and related segments while benefiting from the scale and flexibility of the broader ecosystem. 

In practice, together, these environments complement each other. Walled gardens provide deterministic precision, while the open internet expands reach, adds contextual signals, and often strengthens visibility into cross-channel engagement and attribution.

This flexibility means you’re no longer forced to choose between a hyper-niche target using only known accounts or an old-school “spray and pray” approach with massive contextual targeting. There’s a strategic middle ground that delivers efficiency without waste and maintains the measurement accountability that B2B is known for.

Defining Channel Roles, Not Demanding Everything from Every Channel

Here’s where orchestration gets interesting: attribution actually improves when channels are assigned clearly defined roles rather than asked to do everything.

Take, for example, advertising on LinkedIn versus using broader programmatic targeting. 

LinkedIn remains essential for many B2B marketers. Depending on the industry, it’s where you know buyers will spend some time. 

But your buyer isn’t spending all their time on that professional social network. They’re also consuming business news on sites and apps like the Wall Street Journal. They’re streaming CTV content on YouTube at home. And they’re engaging with lifestyle environments across Instagram and other platforms.

The strategic approach isn’t to pit these channels against each other. I’m not suggesting using only LinkedIn to drive both awareness and conversions while also serving as your primary attribution source. Instead, I’m talking about coordinating between all those venues. Define the purpose and role of each channel in moving buyers down the funnel, then orchestrate their efforts to move toward your broader goal.

When I work with partners who approach this collaboratively, I see them acknowledge: “I know I’m going to run on LinkedIn, but that’s not where my buyer is spending all of their time. So I want to be in lifestyle spaces. I want to activate the same audience segments—or more niche versions, or broader versions—across Instagram, across CTV, across programmatic exchanges.”

This layered approach overcomes the limitations of asking every channel to deliver everything. It acknowledges that buyer journeys are long, often fragmented, and won’t attribute cleanly to individual tactics along the way. You’re not chasing vanity metrics or falling into the instant-gratification pitfalls that plague media planning. You’re playing to the strengths of different environments to achieve an ultimate goal.

Partnership Transparency Makes It Possible

This level of orchestration only works when there’s genuine transparency between partners. When everyone is open about what they’re trying to accomplish — not just the surface-level goals, but the real business objectives and constraints — it becomes much easier to see where each channel and each partner can play a meaningful role.

That shared visibility strengthens the work. It improves outcomes. And it creates partnerships that can evolve beyond the rigid playbooks that made sense three years ago, adapting as both companies and markets change.

The Softening of B2B Hesitation

For years, the B2B industry lagged behind B2C in comfort with emerging channels. Partners would dismiss CTV or lifestyle placements as “not right for B2B.” That reluctance is softening.

As buyers blend their personal and professional identities across platforms, brands are approaching these channels with genuine curiosity and creativity. The conversations are still intentional and measured—as they should be. But what once felt experimental now feels possible. In many cases, it feels necessary.

B2B marketers in 2026 are rethinking where their audiences actually spend time and how to meet them there without sacrificing the accountability and measurability that define effective B2B marketing. They’re building presence with business decision-makers not just in traditional professional environments, but across the full spectrum of where those decision-makers live their lives.

The post Where B2B Media Strategy Is Headed Next appeared first on MartechView.

Rebrands rarely fail because of bad designs. Most modern brand agencies can deliver beautiful visuals: a sharper logo, a modernized palette, and a clean website. 

The problem isn’t Polish; it’s direction.

Rebrands Fail When Marketers Stop Listening

I learned this lesson the hard way at WiseStamp. When I joined, the company was financially strong and the product had matured in ways that matter to serious buyers: security, compliance, and scale. Yet in the market, we were still perceived as an SMB tool. That perception created hesitation with enterprise buyers and friction in deals.

So we did what many companies do. We hired a top-tier branding agency. 

We invested heavily, and the results were impressive: beautiful concepts, refined typography, a strong visual system…and yet something felt off. The work focused on how the brand looked, not what customers actually needed to hear.

It’s a moment every marketing leader should recognize: when the brand begins to become an internal project instead of a market truth. Design can amplify truth, but it cannot replace it. 

If your message does not align with customer reality, the rebrand will not fix the problem. It simply makes you confidently wrong.

AI Is Most Valuable When It Helps You Hear, Not When It Helps You Speak

When people talk about AI in branding, most assume the use case is generation: taglines, messaging, content, and creative variations. It’s the most obvious way to use AI, and it’s also the easiest place to get distracted.

The more valuable use case is listening.

If you’re rebranding, your biggest risk isn’t writing too slowly. Your biggest risk is building a story that customers don’t recognize and can’t relate to. AI can reduce that risk by helping you process what customers are already telling you, at a scale and speed humans cannot match.

This is why I use AI as a listening engine. It’s a force multiplier for customer truth.

Customer truth isn’t found in a dashboard. It lives in real conversations and interactions: sales calls, support tickets, onboarding feedback, renewal conversations, churn notes, and unfiltered reactions. It shows up in repetition, frustration, and the words customers use when they try to explain your value to someone else.

The challenge is that this material is messy and high volume. Most companies have it, but few can operationalize it. AI changes that. Used well, AI can summarize, cluster, and highlight patterns across hundreds of conversations. It can surface the phrases customers repeat verbatim. It can identify where trust is built, where confusion spikes, and what ultimately triggers buying decisions.

Used correctly, AI doesn’t replace a brand strategy. It makes the strategy more honest.

The Listening Workflow That Makes Rebranding Work

If I were advising a brand leader starting a rebrand today, I would recommend a simple workflow. Not complicated. Not tool-heavy. Just disciplined.

• Build a voice-of-customer corpus.
  
  Start by collecting customer language across their journey, not just one phase. Sales calls tend to capture aspirations and outcomes. Support conversations capture friction and gaps. Churn feedback captures unmet expectations. Renewal conversations capture what customers value once the novelty fades.

  The goal is balance. If you only listen to buyers, you build a brand that sells. If you only listen to support, you build a brand that apologizes. You need both.

• Use AI to detect patterns, not to produce slogans.

Point AI at the corpus and ask questions that lead to insight, not output:

• What themes show up repeatedly?
• Which words appear across customer segments?
• What promises create trust?
• What objections block momentum?
• What comparisons do customers make to alternatives?

This is where AI earns its keep. Humans are good at deep context. AI is good at breadth. Together, they reveal signals.

• Translate patterns into message pillars tied to proof.

A theme is not positioning until it becomes a claim + evidence.

If customers repeatedly talk about control and consistency, make that a pillar, then connect it to specific product capabilities and stories. If they talk about risk reduction, connect it to concrete guardrails, such as compliance controls and governance. If they talk about measurable impact, define the metrics and reporting that make it real.

This step is strategic. AI can suggest themes. Humans must choose what the brand stands for and what it refuses to be.

• Pressure-test with humans before you ship anything.

AI can summarize customer language, but it cannot own the consequences of a narrative. 

Strategy requires tradeoffs. It requires judgment about what’s true, what’s differentiated, and what’s defensible.

Before you lock in the new brand, validate it with customers and frontline teams. Show the messaging to sales. Put it in front of customer success. Test it with a small set of customers who will tell you the truth. Then iterate.

Listening is not a phase. It’s the operating system.

The Shift: From Internal Storytelling to Market Confirmation

When rebranding is grounded in customer truth, your marketing becomes easier. And that’s not just a feel-good statement. It is operational.

Your copy becomes clearer because it uses the customer’s language, not your internal vocabulary. Your positioning becomes more legible because it reflects what buyers already recognize as valuable. And your differentiation becomes sharper because you stop guessing and start confirming.

In our case, once we anchored the rebrand in real customer conversations, the positioning shift was instantly recognized, and the messaging became so clear that competitors started copying it. Copycats are annoying, but they’re also a signal. They suggest you’ve found language that maps to the market’s real buying brain.

Guardrails That Keep AI From Flattening Nuance

AI can accelerate listening, but it can also create false confidence. To avoid that, you need guardrails:

• Keep humans in charge of strategy and judgment.

AI should surface patterns. Leadership should interpret them. The final narrative has to be owned by someone who understands customers, category dynamics, and internal reality.

• Do not skip real customer contact.

AI is an amplifier, not a substitute. Visit customers. Sit down and watch them use the product. Listen to their frustrations in their words, not yours.

• Be clear on source integrity and bias.

Every data source has a bias. Support data over-indexes on problems. Sales data over-indexes on best-case outcomes. Executive feedback often reflects internal politics. The listening engine only works if you diversify your inputs and label each source.

• Turn insights into action, not more content.

The goal is not a larger messaging doc. The goal is a brand that shows up consistently in the touchpoints that matter, with clarity and credibility.

The One Rule I Would Put on Every Rebrand Brief

If you’re rebranding, don’t move another pixel until you have listened.

There is no greater gold mine in marketing than listening to your customers.

AI makes that listening faster and more scalable, but the real unlock here is the mindset. Use AI to hear what’s true, and use humans to decide what it means. Then build a brand that customers recognize instantly, because it sounds like them, not like you.

That is how AI becomes a listening engine. And that is how rebranding becomes a growth lever, not a design exercise.

The post Your Rebrand Is Failing. Have You Tried Listening? appeared first on MartechView.

Before a big sales event goes live, you put your focus on setting competitive pricing and maintaining healthy inventory to protect the buy box. But midway through, the lift you expected isn’t there. Traffic is strong, and the buy box is intact, but shoppers aren’t converting. The issue isn’t with price or availability; it’s with content.

Retailer readiness in 2026 goes beyond defending the buy box before a big launch. It requires keeping content aligned across every marketplace and SKU at all times. Because even small gaps can erode sales and weaken retail media performance. 

Google reports that 85% of shoppers say product information and pictures are important when deciding which brand or retailer to buy from. High-quality, detailed visual content builds trust, influences purchasing decisions, and reduces return rates.

This makes one thing clear: Brands can’t afford to have content gaps. Product detail pages that aren’t aligned with the PIM or retailer guidelines force teams to make copy changes based on intuition rather than hard data, which hurts SEO, AEO, and conversion rates. 

In 2026, the retailers that outperform their competition won’t be the ones searching for answers in a postmortem. They’ll be the ones leveraging content agents to keep every PDP compliant, optimized, and up to date at the speed at which the marketplaces they sell on shift. 

Why Content Is a Critical Retailer Readiness Gap

For most brands, every content change means bouncing between the PIM, retailer product detail pages, retailer guidelines, analytics tools, and even possibly an LLM for copy suggestions. That’s far too much application switching for any one person to keep in working memory, so most optimizations end up being educated guesses rather than decisions grounded in a complete, up‑to‑date view of the data.

When a hero SKU has copy that isn’t synced with the PIM, ignores retailer guidelines, or misses critical keywords, it quietly falls down the page while a competitor with richer, better‑optimized content takes the top spots. 

These content gaps are expensive. Almost 53% of U.S. shoppers abandon their carts when met with conflicting, missing, or confusing details. By the time teams notice, the damage is already done. Media budgets have continued to push shoppers to weak product pages. Shoppers who were ready to buy shifted toward competitors with better content. 

As retailer platforms, performance tools, and SKU catalogs expand, retailer readiness becomes far more complex. Even the most nimble teams can’t keep every title, image, bullet, and description up to date and compliant across all marketplaces. When relying on manual checks and traditional SaaS dashboards, gaps quickly become major problems, such as lost sales, wasted media, and weaker category positions.

Retailer Readiness Improves When AI Content Agents Lead

The gap between retailers that use AI and those that don’t is only going to continue to widen. Retailer readiness today requires technology that optimizes the full SKU catalog for content across every marketplace. 

Instead of juggling PIM data, PDPs, analytics tools, and ad platforms, teams need to see what’s working and what’s not all in one place. Content agents are built for exactly this. They connect to back‑end systems, check against retailer content guidelines, and consult search and consumer behavior data to understand which words, structures, and assets actually drive visibility and conversion. Then they present the content lead with specific recommendations and the reasoning behind them, so they can accept it as is or make quick modifications. 

This allows teams to maintain content compliance with the PIM, deliver retailer‑optimized PDPs, and continuously improve SEO and AEO at scale, which is now a core factor in retailer readiness. Working with some of our largest customers, our content agent has reduced the time it takes to update a PDP with conversion-ready content from 35 minutes to 35 seconds. 

These AI agents go beyond simply reporting what’s happening. They diagnose why a product detail page is underperforming, highlight the changes most likely to move search rank and conversion, and can even implement approved updates across marketplaces. The result is execution speed and consistency that no manual process can match. 

This is what happens when AI leads retailer readiness. Brands don’t just get ready once and hope for the best; they stay ready until the last customer checks out.

What the Next Era of Retailer Readiness Looks Like

Retailer readiness today means moving from postgame analysis to real-time adjustment. With content agents, you can make updates as conditions shift on the field, instead of reviewing the tape after the final whistle to figure out how weak content cost you the win. 

In 2026, it will be the brands that redesign retailer readiness to leverage AI content agents who will own the digital shelf in the years ahead.

The post The Real Retailer Readiness Gap Isn’t Price. It’s Content. appeared first on MartechView.

I once spoke with a marketer at a B2C brand who told me that if they can make a consumer feel something, they’ve done their job.

As a B2B CMO, my job is done when I’ve hit my numbers. That difference isn’t about one side being right or wrong — it’s a difference in perspective. Tech and CPG marketers don’t think alike.

This year, that theory played out clearly on the Super Bowl LX stage.

At Zappi, we tested every ad that aired during the game — more than 65 spots in total. Tech brands made up just under a third of the lineup, spanning AI companies, devices, platforms, and apps. When we looked at overall performance using a sales-impact score that captures distinctiveness, brand recall, and purchase impact, tech ads underperformed non-tech ads by an average of 47 percent.

That’s a jarring gap. And it points to a simple conclusion: tech just doesn’t get the Super Bowl. Let’s look at the good, the bad, and the ugly of tech at Super Bowl LX.

Why Tech Ads Miss the Mark

Expectations matter

Super Bowl viewers show up primed for humor, spectacle, and the occasional emotional ad that pulls at the heartstrings. Categories like beer, snacks, and entertainment are “native” to the game because they naturally tap into these shared cultural feelings. Tech often leads with features instead of feeling, asking viewers to think at the exact moment they’re ready to feel.

Where technology can excel is in removing pain points for consumers. It’s less emotionally charged and more functional. Even within that constraint, Grubhub stood out by solving a familiar frustration: excessive fees. At a time when many consumers are feeling pinched, Grubhub used its Clue-styled spot featuring George Clooney to position the brand as the hero — the one willing to “eat the fees” on orders over $50. The message was clear, memorable, and accessible, and it showed up in performance: above-norm distinctiveness and more than double the benchmark for laughter.

By contrast, Genspark fell flat. On the surface, the ad checked familiar Super Bowl boxes — a celebrity cameo, a self-aware joke, a wink to the audience — but it stopped short of clearly stating what it is. In Genspark’s focus on being relatable, consumers left feeling confused about what it actually does.  On a night when brands are expected to show their best selves, Genspark used its airtime to gesture at scale and disruption without clarifying relevance. The result felt lightweight, especially compared with brands that translated innovation into tangible results.

Focus and cues misfire

Tech ads tend to veer in one of two directions: they either drift so far into abstraction that the product disappears, or they try so hard to be part of the spectacle that they miss the point entirely.

Squarespace leaned heavily into arthouse visuals. The spot was visually compelling, with real star power — but did it make anyone want to buy a domain during the game? Not really.

Salesforce went the opposite route, anchoring its ad in internet culture by centering it on MrBeast and gamifying the visuals with a million-dollar giveaway. MrBeast is instantly recognizable to my kids, but far less relevant to people who actually buy enterprise technology. Advertising to kids to influence purchase decisions is an age-old practice — just not in SaaS. The idea landed, just not with an audience that would ever convert.

Across the AI category, the issue wasn’t technical depth — it was vanity. Broad use cases layered with thin jokes, frenetic energy, and unsettling music dominated OpenAI’s ads. Others, like Anthropic, spent more time signaling scale and taking shots at competitors than actually appealing to potential users. Innovation became noise.

You can see how differently this plays out within tech itself. The Oakley and Meta collaboration landed because the feature at its core had clear, exciting implications. Viewers didn’t just see something futuristic — they understood that the product unlocked possibilities. That clarity made the brand easy to consider both now and later, placing the ad in the top tier of performance.

Emotional resonance beats prestige

Consumers remember stories, not specs. 

Too many tech brands chase prestige or vague signals of “innovation” without anchoring them in a relatable human truth. The result is often admiration without action — or indifference altogether.

One brand that stood out was Google, with its Gemini ad. Unlike other AI spots, Gemini avoided jokes and spectacle in favor of showing real applications — from dropping photos into a chatbot to visualizing life changes in concrete ways. While the ad still struggled with brand recall, it resonated emotionally: love scores exceeded benchmark levels, suggesting the message connected even if the branding lagged.

The takeaway isn’t that tech should avoid the Super Bowl. It’s that the Super Bowl demands a different kind of discipline. This is a moment built for people, not products. Even the most advanced technology needs an emotional hook to earn attention.

Tech can succeed on this stage — but only when ambition is balanced with accessibility, and amid the spectacle, you can provide clarity.

The post Tech Doesn’t “Get” the Super Bowl — and the Data Shows Why appeared first on MartechView.

The past few years have reshaped B2B marketing. As generative AI becomes a larger part of how buyers research and evaluate vendors, to explore options, compare approaches and pressure-test claims, the entry point for sales happens later in the process. Buyers can assess all those tasks before they ever contact sales.

This moves marketing teams beyond predefined, persona-driven prediction and toward real-time participation, where teams interpret signals and adapt content to meet buyers where they are without compromising trust.

If buyers are using AI to narrow options before they ever raise a hand, marketing can’t rely on a fixed journey. The job is no longer to push prepackaged messages to broad segments, but to respond to live buyer intent as it emerges across channels. That means allowing the buyer’s behavior to set the direction of the interaction, and using those signals to adjust in real time.

In practice, this can mean emphasizing integration features when a buyer is exploring technical fit, elevating trust and security validation as risk questions surface, or reducing complexity, terms, and invoicing options once procurement enters the discussion. Across functions, AI enables marketing teams to listen continuously, interpret intent and adapt content and experiences while the conversation evolves, meeting buyers where they are without forcing them into a predefined path.

The Shift from Fixed Audience Segments

One of the biggest changes AI brings to marketing is shifting from predicting what customers will do to responding to what customers do. Traditional marketing tries to guess who will buy and what they like, but AI enables merchants to monitor and respond to customers in near real time. 

Instead of asking visitors to label themselves or fill out forms, the software observes their behavior and learns from their actions. Behavioral signals might include pages visited, repeat visits, category depth, content type preference, or questions asked in chat. Over time, content and messaging evolve based on the buyer’s interests and actions. These elements adapt to the buyer’s choices.

The result is a shift from static segments to dynamic context. Rather than designing a single journey for a persona, teams can adapt messaging to what a buyer is trying to solve in the moment. The longstanding aim of achieving genuine one-to-one personalization – frequently discussed but rarely implemented at scale – is now within reach.

Scaling AI Requires More Than New Tools   

Real-time engagement raises the bar on speed, relevance and measurement. Despite rapid tech advances, successfully integrating AI into marketing teams requires discipline. It’s not enough to simply deploy tools. AI applications must align with clear business objectives. 

A more effective strategy begins with pinpointing where AI can deliver measurable benefits, such as improving engagement, reducing production cycles and rework, accelerating time-to-value, or delivering deeper insights throughout the funnel. For example, start with funnel-aligned use cases. AI can streamline content production and iteration, improve performance insight by surfacing patterns across channels, and support sales and customer teams with timely, context-aware outreach. The point is to pick a few high-impact workflows and measure them like any other marketing investment.

From that point, experimentation shifts from exploration to a goal-driven approach. Leaders assess AI performance in controlled environments, quickly analyze results and focus on expanding only what shows value. Once you know which use cases matter, the next question becomes organizational: who owns them, how they’re tested and how they’re scaled without creating risk.

The Rise of Agile, Cross-Functional Marketing Models

As AI becomes more embedded in the buying journey, marketing teams need operating models built for speed and learning. That’s why many organizations are moving from traditional functional setups to squad-based teams aligned to outcomes like demand generation, lifecycle growth, or product launches. Squads shorten the loop between signal, message and measurement so teams can adjust quickly as buyer behavior changes. To support this pace without disrupting core work, some teams create dedicated environments, such as labs or studios, where they can test new AI-powered workflows or prompts.

Data and governance make this all sustainable. AI-driven marketing depends on clean, connected data so signals can move across channels and performance can be measured consistently. Without it, even strong tools end up operating in silos. Governance matters equally as much. As generative AI influences content and engagement, leaders need clear brand and compliance guardrails – such as prompt libraries or disclosure rules – for how LLMs are used. Human-led judgment should be built in from the start so teams can move fast while staying consistent, accurate and trusted.

AI as a Partner, Not a Replacement

AI raises the bar on execution, but it doesn’t set direction. Strategy still comes from humans: choosing which markets to pursue, which narratives to lead with and which trade-offs you’re willing to make.

This means AI should be used as a partner and treated as a tool for drafting, synthesizing, testing variations and accelerating analysis. This transition can improve both speed and quality, but it demands investment in skills development, change management and fostering a supportive culture.

As buyers are already using AI to do their homework, we expect the advantage to shift to marketing teams that can respond with relevance and consistency at that speed, without sacrificing accuracy or trust. Start by choosing two or three use cases, assign ownership, connect the data and set the rules before you scale.

The post The End of the Predictable B2B Buyer Journey appeared first on MartechView.

Predictive consumer intelligence. Performance through precision. Powered by AI. What does it all mean? To some, these are buzzy concepts that might not mean much, but at their heart, they describe our belief that there’s a better way to achieve your marketing goals: If you can predict the future, you can drive better results today with confidence, not hope.

You don’t get that by describing what people look like or knowing where they live. You get it by understanding their values, motivations, and preferences at the individual level, to know what they’ll do next. We’ve spent 17 years leveraging our massive AI and data science infrastructure to predict consumer behavior as it changes – and they’re changing faster than ever. Performance requires that we keep up. 

This article outlines five ways Resonate’s data is moving marketing into a new era with proven performance. 

Our Predictive Data Breaks the Reactive Data Cycle

Most marketing stacks are stuck looking backward, tuning strategies to what audiences did last quarter instead of what they’ll do next. Resonate flips that script. Our data is built to predict and project behavior, so you can act ahead of shifts in demand, sentiment, and intent. Marketing leaders use it to reduce guesswork, compress time-to-profit, and move dollars to what’s working now, not what used to. 

Our Predictive AI Captures the “Why”

One of the biggest data gaps is understanding buyer decision-making. Demographics and last-purchase signals explain “who” and “what,” but they miss why people choose, switch, or stop. Resonate fills that gap with a proprietary consumer study that surfaces values, beliefs, preferences, and motivation and then scales those insights to the full U.S. population via our predictive AI. When a consumer’s motivations change (say, prioritizing health and shifting away from alcohol), our data catches it quickly, so your targeting and messaging pivot before the market does. 

Our Predictive Consumer Intelligence Gives You Precision at Scale

Quality data starts with confirmed facts, not guesses. Resonate’s data combines proprietary offline and online data sources to create predictive insights, scaled across the U.S. adult population

• Proprietary Ground Truth: Our U.S. Consumer Study contributes deep psychographics and intent: what actual people value, believe, and plan to do.
• Massive, consented behavioral fabric: 30B+ consented, deterministic daily observations across hundreds of thousands of sites reveal what people are actually doing online – right now. 
• Robust identity & coverage: A multi-sourced graph connects behaviors to people in a privacy-safe way, wherever and however they consume media. 
• Predictive scale: rAI—the proprietary Resonate predictive AI—fuses these inputs to produce 15,000+ attributes on ~250M U.S. consumer profiles, refreshed continuously. The result is individual-level truth you can activate anywhere.

This is why our data doesn’t just describe audiences; it forecasts who’s likely to buy, switch, or churn, and why.

Our Data Is Built to Be Acted On (Not Just Analyzed)

Resonate data is discoverable in our Ignite platform for immediate, deep understanding; it’s quickly actionable in the ad and martech ecosystem (no proxies or guesswork trying to recreate the perfect theoretical audience); and it’s portable into your models, CDP, and activation tools. Whether you need off-the-shelf segments, custom attributes, or predictive models, we meet you where your team works, without months of lift.

Quality is only real if it performs. Resonate data repeatedly lifts outcomes across channels and industries:

• A leading media agency client cut its Facebook CPA by 23% using Resonate audiences and activation.
• Another client saw ROAS climb by 50% by aligning creative and media to our dynamic insights.
• Our agency client Swellshark used our insights to fuel 3–4× category growth for its client Waterloo Sparkling Water.

These aren’t isolated wins, they’re the consistent result of real-time signals, individual-level insights, and intent data.

Resonate Data Is Safe by Design

Resonate uses best-in-class data capture and de-identification methods, and we exclude original study respondents from activation populations. Health- and sensitive-data categories are handled with privacy-safe, context-aware techniques. You get confidence, compliance, and coverage.

Why This Matters to Growth Leaders

CMOs and data leaders don’t buy data; they buy outcomes. Resonate’s advantage shows up as:

• Speed: Rapid time-to-profit and faster pivots when consumer behavior changes. 
• Accuracy: Individual-level profiles grounded in verified truth, not stitched-together household proxies.
• Relevance: Daily refreshed psychographics and intent that keep targeting and creative in lockstep with the market.
• Activation: Direct connections into your channels to turn intelligence into ROI, immediately.

The bottom line: If your current data is tuned to yesterday’s signals, you’re optimizing the past.  means seeing what’s next—and moving first.

Resonate’s data outperforms traditional data because it is predictive, continuously refreshed, and built for action. It replaces lagging indicators with forward-looking intelligence you can trust, so you can plan with confidence, not hope.

The post Marketing That Predicts, Not Reacts appeared first on MartechView.