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

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

Contextual vs. Behavioral Advertising

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

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

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

Why It Is Gaining Ground

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

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

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

How It Works

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

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

Getting Started: A Practical Checklist

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

The following steps provide a working framework.

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

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

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

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

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

The Broader Opportunity

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

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

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

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

Refund and replacement claims, from “this serum gave me a rash” to “the palette arrived completely shattered,” are on the rise, each accompanied by photos and screenshots as proof. The influx is driven by a new returns fraud tactic, one where abusers are doctoring images using AI to make false claims seem more convincing.

Image manipulation with AI is now contributing to the $850 billion returns problem companies experience each year, with almost $77 billion of those returns being fraudulent. AI is helping bad actors polish their stories while revealing a new blind spot for many retailers who are unable to detect when AI is used in claims or to assess its impact on their post‑purchase margins.

Many brands are responding by tightening their policies, adding return fees, and asking customers to jump through more hoops to verify their claims. But this approach creates an even greater liability: losing loyal customers who become frustrated as they’re treated the same as abusers.

Also Read: The Death of Batch-and-Blast Email Marketing

AI Is Stress-Testing Retail Blind Spots

Fake proof is cheap and easy to produce with AI, allowing the same offenders to repeatedly send slightly different, altered photos and stories that look like real customer photos. 

With AI, post-purchase abuse can look like: 

• Generate or edit damage photos to add cracks, leaks, or broken packaging.
• Creating staged “stolen package” images that show an empty doorstep or an opened box with items missing.
• Producing fake drop‑off receipts as proof that an item was sent back when it never left the customer’s home
• Writing policy-aware emails and chat messages that align with current refund and replacement policies that make it hard to say “no.”

Meanwhile, customer service teams are now serving as fraud investigators. When a bad actor opens a ticket, attaches a photo, and explains what went wrong, reps can no longer simply follow the policy, which often states that a refund, replacement, or credit should be issued when the claim appears to meet the rules. Those rules were written for a world where proof was harder to fake, and few brands have a reliable way to tell when an image or story has been manipulated with AI.

Most retailers don’t have a consistent, cross‑team way to flag suspicious content and compare it to past behavior. Support teams see individual cases, often from what appear to be different customers. But in reality, the same person may be creating new profiles and repeating the same AI‑assisted claims, which is impossible to spot in a single ticket.

Rethink How You Respond to AI-Driven Return Fraud

The knee-jerk reaction for brands is to tighten return policies and train agents to say “no” more often. That feels like taking control, but it punishes loyal customers while determined abusers simply upgrade their AI tools and create more accounts to keep going.

Fraudsters will only find new and creative ways to use AI. Today, they’re generating fake photos, but tomorrow, they’ll use a new tactic. This is why retailers need to stop treating each claim in isolation and instead look for important patterns. How often does a customer report an issue? What kinds of issues do they report? How do their claims compare to those of other customers?

Use AI to Detect AI-Assisted Abuse

Retailers need the ability to connect orders, returns, claims, support tickets, credits, and even basic interaction patterns (like device and address history) into a single view of each customer. Then, flag behaviors that deserve a closer look, such as repeated “item not received” claims, frequent high‑value “damaged” reports, or clusters of accounts sharing the same details.

Update Your Policies Based on Behavior

Make policies more dynamic, rather than stricter across the board. Trusted customers, those with normal return behavior, can continue to enjoy fast, generous resolutions with minimal friction. Meanwhile, high‑risk customers can be routed into different flows, such as extra checks before issuing a refund, different return options, smaller credits, or, in some cases, blocked future claims.

Over time, that kind of behavioral playbook does more to protect both your margins and your best customers than another round of blanket crackdowns.

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

Respond to AI Tactics With a Strong System of Defense

AI tactics in return fraud will continue to evolve, and they’re already forcing brands to confront how little they truly understand about post-purchase behaviors. But treating every customer like a suspect won’t fix retail’s returns fraud problem.

You can keep adding restrictions and hope your best customers tolerate the extra friction, or you can invest in technology that shows customer behavior end‑to‑end. Your team needs a defense system that can keep pace with these ever-evolving AI-driven return-fraud tactics. That way, claim resolution becomes less about the believability of a one-off story or supporting imagery and more about the identified patterns in how a customer shops, returns, and engages with support.

The post AI Is Supercharging Returns Fraud. Retailers Are Behind. appeared first on MartechView.

For marketers who still rely on batch-and-blast tactics, it doesn’t matter how much effort they put into their email campaigns. Statistically, consumers will never see them. In the past, email marketing “just worked.” Volume meant revenue. Then, open rates plummeted, along with email marketing revenue.

Bulk emails end up in spam more often than not. On the rare occasion messages make it to the right tab, recipients aren’t incentivized to open them. Impersonal messages don’t get attention in inboxes flooded with dozens or hundreds of emails daily.

1 in 3 Emails Never Reaches the Inbox

The scale of the deliverability problem is massive. In the golden age of email marketing, volume was king. Larger lists meant larger paychecks. Businesses used to be able to earn $36 for every dollar invested in email marketing — more if they were in retail or e-commerce. It used to be one of the highest-return-on-investment (ROI) channels available. That era is over.  

Volume-based email strategies were built for an era when inboxes were less crowded, and consumers had more patience. Neither condition exists anymore. Now, each message goes through automatic filtering and dozens of discrete checks, increasing the chances of ending up in the spam folder.

Data aggregated from thousands of daily deliverability tests in 2026 shows 32% of emails go straight to the spam folder. Of the 392.5 billion emails sent and received each day, over 125 billion are junk. Spam rates vary by inbox provider, so deliverability can be even worse. While ProtonMail spams just 1%, Yahoo sends 78% to spam. Even Gmail, the provider most senders optimize for, has a 27% spam rate.

This breakdown reveals a grim picture for bulk senders. A high delivery rate is deceptive — it means nothing if the emails are effectively invisible. The inbox has become a gated community, and batch-and-blast campaigns are being turned away at the door. If emails end up in spam or the promotions tab, campaigns fail before they ever truly start.

How One Impersonal Email Derailed a Rebrand

Eurostar, a European high-speed rail service, learned the cost of impersonal bulk email the hard way. It was mere weeks into a major rebrand when it launched a blast email marketing campaign advertising fares starting at £39. Upon finding very few seats at that price, complaints began to roll in. Recipients felt misled by what appeared to be a bait-and-switch tactic.

The Advertising Standards Authority ruled the promotion was misleading, handing Eurostar a regulatory black mark just as the company was trying to reshape its public image. The damage wasn’t limited to regulatory scrutiny. The incident undermined the broader rebrand effort by creating a perception that Eurostar prioritized volume over honesty. 

The generic nature of the email meant it couldn’t target the offer to routes or times where £39 fares were actually available. The batch-and-blast approach turned what could have been a successful promotion into a brand liability. 

Also Read: The CIO Who Says Governance Can Actually Speed Up AI

What Customers Expect From Brand Interactions

The failure of bulk email campaigns reflects a deeper disconnect from modern customer expectations. Consumers don’t see impersonal emails as worth their time. The batch-and-blast era died the moment the attention economy was born.

In the golden age of email marketing, volume was more important than anything. Now, it is the death knell of a good campaign. A large portion of messages never gets seen because they end up in the junk folder. Attention is now a scarce and valuable commodity, and consumers know it. They’ve learned to ignore messages that don’t feel relevant. Bulk promotions are deleted and generic subject lines get skipped.

This shift isn’t just about preference, but business outcomes. Given that 80% of people agree that the customer experience (CX) is just as important as product quality, investing in it tends to pay off. Over 84% of businesses that improved CX saw increased revenue. Some consumers are willing to pay 18% or more. 

Since batch-and-blast campaigns deliver neither personalized experience nor product relevance, businesses don’t see revenue gains. Treating recipients as anonymous data points rather than individuals with specific needs and interests doesn’t have a high ROI. In an economy where experience drives revenue, that approach is no longer defensible.

Inbox Providers Penalize Bulk Email Marketing

The decline of batch-and-blast is no longer just about poor performance. It has become a matter of technical compliance. Email providers now enforce rules that algorithmically punish this outmoded method.

Major inbox providers, such as Google, Microsoft, and Yahoo, have placed strict restrictions on bulk emails. In 2025, Microsoft strengthened email authentication for domains sending over 5,000 emails per day. Noncompliant messages are sent to junk immediately. While these measures are meant to reinforce best practices and reduce spam activity, they penalize marketers who still rely on batch-and-blast methods.

Authentication protocols like domain-based message authentication, reporting, and conformance were designed to stop spam and phishing. In practice, they penalize any sender whose recipients frequently mark messages as spam or simply ignore them.

Those who didn’t ask for generic offers mark them as spam, while overwhelmed recipients ignore them. Either way, engagement metrics tank. Inbox providers interpret the signals as evidence of unwanted mail. The algorithm doesn’t distinguish between malicious spam and poorly targeted marketing.

These requirements mean companies can no longer rely on volume to compensate for low engagement. Sending more emails won’t increase revenue if they never reach the inbox in the first place.

Also Read: Can B2B Brands Adapt to Volatility with Long-Tail Thinking?

An Inflection Point for the Modern Marketer

The batch-and-blast era is dead. It is no longer defensible from a technical, brand-risk or customer-centric perspective. Generic campaigns undermine larger strategic initiatives because CX matters as much as content quality. Moreover, discrete checks penalize the volume-based tactics that once defined email marketing success.

The implications are significant. Companies that continue to rely on batch-and-blast are actively damaging their reputation. The question isn’t whether to abandon the old playbook. It’s how much longer businesses can afford to ignore the evidence that it no longer works. Leading marketers aren’t sending better emails. They’re using better strategies.

The post The Death of Batch-and-Blast Email Marketing appeared first on MartechView.

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

And that worked…up to a point.

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

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

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

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

Deterministic Signals Face Real Limits

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

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

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

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

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

Identity Fragmentation Is Now the Default

Modern consumers today are, by default, fragmented. 

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

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

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

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

Privacy Expectations Are Reshaping Identity

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

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

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

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

AI Is the Engine Behind Probabilistic Advertising

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

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

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

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

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

Global Scale Increasingly Favors Probability Over Certainty

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

Probabilistic models scale differently.

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

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

Probabilistic systems provide that consistency.

The Mindset Shift Marketers Need to Make

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

What matters more today is confidence at scale.

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

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

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

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

There is no shortage of executives willing to talk about responsible AI. There is a considerably shorter list of those willing to be honest about what it actually costs — and what it demands of the people whose names are on the org chart when things go wrong.

Peter Yeung, Chief Information Officer at Optimizely, is in the second group. A former practicing attorney with 18 years at the bar before moving into technology leadership, he brings an unusual combination of legal precision and operational candor to questions that the industry too often answers with carefully worded reassurance.

In a wide-ranging conversation, Yeung addresses the governance paradox at the heart of enterprise AI — how to move quickly without moving recklessly — and argues that accountability, far from being a legal fiction, is a structure that leaders must be willing to sign their name to. He also takes on explainability under GDPR, the data minimization debate, and the accelerating fragmentation of the global data landscape.

His answers are not always comfortable. That is precisely what makes them worth reading.

Excerpts from the interview; 

Companies are rolling out AI faster than governance can keep up. Is ‘responsible AI’ just a story businesses tell to move quickly, or do you truly think governance can match the pace of deployment?

The companies actually getting value from AI aren’t treating governance as a brake; they’re building it into how they scale. Most of us started broadly: put the tools in people’s hands, see what sticks. That phase served its purpose, but what’s working now is the opposite — picking a handful of high-impact use cases and making sure the data, controls, and workflows behind them are genuinely solid, secure, and trustworthy. Done right, governance accelerates things by cutting rework, risk, inaccuracies, and fragmentation.

That said, I’d be lying if I said governance doesn’t have a cost. The fastest innovation I’ve seen on AI happens in the messy middle — small teams shipping fast, breaking things, learning in days rather than quarters. The moment you wrap that in review boards, data classifications, and approval workflows, you do slow it down. That’s just the reality. The trick isn’t pretending the trade-off doesn’t exist; it’s finding the right balance for where you are. Too little governance and you end up with a graveyard of pilots and a compliance problem. Too much and you kill the energy that made AI exciting in the first place.

Responsible AI isn’t a layer you bolt on top of performance; it’s what allows AI to graduate from experimentation into something the business can actually rely on. But you have to be honest that getting the balance right is the work.

When AI systems use flawed or unclear data and cause harm, responsibility is often spread among teams and vendors. Right now, isn’t the idea of clear accountability in AI mostly just a legal fiction? 

As CIO at Optimizely, with both the CISO and Trust organization reporting into me, I’d push back on the idea that accountability is a legal fiction — but I understand why people frame it that way. AI accountability is more complex than in traditional systems because it spans multiple teams: the people sourcing the data, the people building or selecting the models, and the people deciding how outputs are actually used in the business. Spread that across vendors, too, and yes, it can feel diffuse. If you then include my statement above, which calls for empowering individuals within the business to innovate at speed, the task becomes daunting. 

But the way I look at it, regardless of the actor — vendor, third-party model, internal team, or individual employee — we are ultimately accountable, both internally and to our customers, for the end result. That accountability can’t be outsourced. The vendor contract doesn’t absolve us. The model provider doesn’t absolve us. If something goes wrong, our customers don’t care about the seven hops in the supply chain; they care that we own it. 

What makes that real, rather than rhetorical, is structure. We treat AI like any other critical business process: explicit ownership of data inputs, clear responsibility for model deployment, and a named, accountable owner for outcomes in production. Without that, accountability genuinely does dilute across vendors and teams, and that’s where the “legal fiction” critique starts to land. With it, you create a clear line of responsibility even in a distributed system, and you give the CISO and Trust functions something concrete to govern against. 

So it’s not a fiction. It’s just harder, and it requires leaders to actually sign their names.

Rules like GDPR require that automated decisions be explainable. But big AI systems often cannot give real reasons for their choices. Are we trying to enforce laws that no longer fit the world we live in?

Having practiced as an attorney for 18 years, I’d say the question is sharper than the framing suggests — but the answer isn’t quite “the laws no longer fit.” It’s that the laws were never as clear as people assume.

GDPR’s intent is absolutely still relevant: to protect individuals and hold companies accountable for automated decisions that affect them. That hasn’t aged. But read Article 22 alongside Articles 13–15 and Recital 71, and what you find is a requirement to provide “meaningful information about the logic involved” — with genuine, ongoing debate among regulators and legal scholars about what that actually means in practice. GDPR doesn’t even explicitly grant a “right to explanation”; it’s inferred. The framework was contested before modern AI arrived. Large models didn’t break a clean framework; they stress-tested an ambiguous one.

That matters because, in the absence of clear guidelines, the standards organizations actually have to meet are believability and traceability. Can you credibly describe how the system reached its decision? Can you trace the data, the controls, and the human checkpoints? Have you documented it clearly enough to walk a regulator, a customer, or a court through it without flinching? That’s the real test today.

So no, I don’t think we’re enforcing laws that no longer fit. We’re operating in a gap that regulators and industry need to close together. Until they do, the burden is on companies to set their own bar: traceable data, auditable decisions, guardrails on outputs, and documentation you’d be comfortable defending.

AI works best with lots of data, but privacy rules call for using as little data as possible. If companies have to choose, will they prioritize performance over principle? Are we already seeing this happen?

There’s real tension here, but the framing as a binary choice between performance and principle is a bit limiting. The premise that AI works best with “lots of data” is itself worth challenging. More data isn’t automatically better — if it’s poor quality, incomplete, or stripped of the right context, you’re just feeding the model noise. And noise-in produces worse-outcomes-out: hallucinations, bias amplification, and decisions you can’t defend. I’d rather have a smaller, well-governed, well-contextualized data set than a sprawling lake of mixed-quality inputs, while certainly following the GDPR tenet of Privacy by Design.

I think it reframes the privacy question. Privacy rules pushing companies toward data minimization aren’t necessarily working against AI performance — in many cases, they’re forcing the discipline that actually improves it. The companies getting this right are being deliberate about their data strategy: prioritizing quality, relevance, and governance over volume. That’s not a compromise position; that’s just better engineering.

Are we seeing companies cut corners on privacy for short-term performance? Yes, and it tends to come back to bite them through regulatory exposure, customer trust erosion, or models that don’t generalize the way they thought. Trust is becoming a genuine differentiator, particularly in customer-facing and enterprise use cases, and you can’t retrofit it.

The right answer is to design systems where privacy and performance are engineered in from the start, rather than treated as a trade-off you settle later. When done well, they reinforce each other rather than compete.

With decisions like Schrems II and laws like the CCPA, are we heading toward a split internet where data cannot move freely across countries? If so, what will break first: innovation or trust?

What’s interesting about the question is that it frames the split as a US–Europe divergence, when the more consequential fault line is East versus West — between western frameworks debating how to balance rights and commerce, and an eastern framework where the state’s relationship to data is structurally different. That gap isn’t closing through a successor to the Privacy Shield/US-EU Data Privacy Agreement.

So yes, we’re already in a split internet. Between Schrems II, CCPA, the EU AI Act, India’s DPDP, China’s PIPL, and a patchwork of US state laws, any global business is operating across fifteen-plus regulatory environments. My background on both the technology and legal sides of things, coupled with my ability to adjust to both business and customer needs, makes this isn’t hypothetical anymore — it’s the operating environment. We architect for it: data residency, regional processing, model deployment choices that respect where data can and can’t go.

On what breaks first — innovation and trust fail together, even if one precedes the other. If regulation becomes so prescriptive that nothing can cross borders without months of legal review, innovation slows. If companies route around the rules, trust collapses, and regulators tighten further. It’s a doom loop either way.

The companies that come through this well won’t bet on innovation at all costs over trust, or really cumbersome trust over innovation. They’ll invest in both, and accept that regulatory complexity is now part of the engineering/product/support lifecycle, not separate from it.

The post The CIO Who Says Governance Can Actually Speed Up AI appeared first on MartechView.

For most of the past decade and a half, the annual marketing technology landscape had one reliable characteristic: it grew. Sometimes by a lot. Sometimes by a merely extraordinary amount. Even in the years when analysts confidently predicted consolidation was finally upon us, the landscape found another gear. This year, it did not.

The State of Martech 2026, which debuted at MartechDay on May 5 by Scott Brinker of chiefmartec and Frans Riemersma of MartechTribe, puts the total number of marketing technology products at 15,505 — up just 121 from the 15,384 counted last year. That is growth of 0.79%, rounding to effectively zero. After a run of more than 10,000% expansion since 2011, when the landscape counted just 150 products, the market appears to have hit a ceiling — or at least a plateau.

But flat, as the report and keynote make abundantly clear, is perhaps the most misleading word one could use.

A Market Metabolizing, Not Stagnating

Beneath that near-zero headline number, the market is moving with real intensity. In the past 12 months, 1,488 new products were added to the landscape while 1,367 were removed. The volume of new entrants dropped 40% year on year — down from 2,489 in 2025 — while the removal rate climbed 13%. For the first time in the post-pandemic era, additions and removals are nearly canceling each other out.

Riemersma’s framing at MartechDay was direct: “Peak Martech is a myth. Martech is entering its Darwin phase. The martech landscape is renewing. Value is growing.” The era of accumulating tools, both argued, is giving way to an era of replacing them. At the core of that transition is a structural change in where value actually lives: SaaS platforms are no longer the primary source of differentiation. They are becoming infrastructure — systems of record, workflow engines, and integration layers. The real value is moving on top of that foundation. AI is becoming the value layer.

The companies exiting the market tell their own story. More than half of this year’s removals — 51.7% — came from the 2010–2019 wave of software-as-a-service startups, the first great generation of martech builders. The exits are concentrated among smaller firms: 41.2% had between one and ten employees; 38.7% had between 11 and 50. By revenue, the $1 million to $10 million band accounts for 45.5% of removed products — companies that found enough early traction to survive past zero revenue, but not enough to build a truly defensible position, caught between incumbents bundling AI features from above and AI-native startups attacking from below.

The Content Marketing Bust

Perhaps the single most striking data point concerns content marketing tools. When generative AI went mainstream in 2023, content marketing was one of the first categories to feel the full force of the wave, nearly doubling in two years from 575 tools to 1,102. In 2026, it leads all subcategories in a less coveted ranking: the highest net product removal of any category, at minus-37, with 176 removed and only 139 added.

Three forces converged. The major AI laboratories absorbed the core functionality; incumbent platforms such as Adobe, HubSpot, and Salesforce rapidly embedded generative AI into existing workflows; and many first-wave tools solved the problem of generating content fast without solving the harder problem of generating content that actually works. The report describes this as a natural selection event: not the end of AI-powered content technology, but the clearing out of an undifferentiated first generation in favor of a more mature second.

The Stack Is Stratifying, Not Consolidating

One of the most significant conclusions from the MartechDay keynote — drawing on a survey of 208 marketing and marketing operations leaders across 70 specific AI use cases — is that the long-running debate between platform consolidation and best-of-breed diversification has a 2026 answer: neither. Instead, the stack is stratifying into layers with different competitive physics. 

AI-native tools are largely winning creation — copy ideation, pitch decks, visual production, competitive intelligence — tasks where the primary input is a prompt and model quality is the product. Incumbent SaaS platforms such as HubSpot and Salesforce are largely holding on to orchestration: lead scoring and routing, pipeline management, and channel delivery. These systems increasingly serve as infrastructure for other commercial and custom AI agents.

The survey also revealed a striking divergence between B2B and B2C adoption patterns. Conventional wisdom holds that B2C leads technology adoption. On AI, the data inverts that pattern: B2B shows broader adoption across more use cases, with consistently lower non-adoption rates — likely because B2B teams are chronically understaffed relative to their content and operational demands, and a decade of CRM, MAP, CDP, and revenue intelligence investment had already built natural docking stations for AI capabilities. When B2C does adopt, it builds deeper: the customer-facing AI output is the brand experience, and the differentiation lives in the final 20% — brand voice calibration, proprietary guardrails, custom data integration — that off-the-shelf tools cannot provide.

The AI Agent Paradox

A central tension running through the MartechDay findings is the gap between AI enthusiasm and AI deployment, as researchers described it. Some 90.3% of marketing organizations now use AI agents in some capacity, yet only 23.3% have deployed them in full production. The rest are piloting, experimenting, or running agents in narrow workflows with a human approving every output. The report identifies this as the “Trust Wall”: currently, 80.6% of marketing organizations refuse to let AI agents operate autonomously, requiring a human in the loop for every final decision.

Governance is moving in the right direction — 73% of respondents now report having a formal generative AI policy, up from 52% in 2024 — but the gap between having a policy and having the infrastructure to enforce it remains wide.

Where Growth Is Actually Happening

If content marketing is the cautionary tale, content management systems and e-commerce platforms are the 2026 growth story. CMS and web experience management grew 21.4%, jumping from 504 to 612 products. E-commerce platforms grew 19.9%, from 547 to 656. These are not new categories. They are being reshaped. CMS is evolving into a machine-readable infrastructure for AI agents. E-commerce is adapting to AI-driven discovery. iPaaS is becoming the orchestration layer that connects everything. Growth is happening where AI changes the job to be done.

The explanation lies in a fundamental shift in who—or what—digital properties are built for. For two decades, marketing teams designed experiences primarily for human visitors and search engine crawlers. That audience now includes AI search assistants, agentic browsers, shopping agents, and procurement systems that arrive not to browse but to extract, evaluate, and act. Other fast-growing subcategories follow the same logic: mobile and web analytics grew 11.3%, call analytics 8.9%, data integration 8.0%, and marketing automation 5.9% — the last a sign that AI is reinventing what campaign orchestration can look like, attracting builders who see agentic marketing automation as a meaningful step beyond rule-based systems.

SEO Becomes AEO — but Visibility Is Shrinking

Search engine optimization, widely eulogized as AI assistants swallowed the top of the funnel, is in fact metamorphosing rather than dying. The SEO and answer engine optimization subcategory posted a net positive result this year — 44 added, 38 removed — and has grown for three consecutive years. The market is reflecting a shift in the underlying discipline: from making brands findable by search crawlers to making them findable, credible, and actionable across AI search assistants, answer engines, and agentic browsers. The challenge, the report notes, is that the tools are improving while the marketer’s visibility is shrinking — when a customer consults an AI assistant about which product to buy, that conversation is entirely invisible to conventional tracking.

The Transformation Beneath the Numbers

What ties these shifts together is a structural transformation of marketing itself. As Brinker argued in the lead-up to MartechDay: “AI doesn’t eliminate constraints. It moves them. When content becomes abundant, the bottleneck shifts to relevance. When integrations get easier, the bottleneck shifts to orchestration.” The organizations pulling ahead are those that have recognized where the new bottleneck sits and invested in context engineering, governance, and strategic coherence — rather than continuing to optimize against constraints that AI has already dissolved.

The best stacks are not the most feature-rich. They are the most aligned — focused on a small number of high-impact use cases where SaaS enables, and AI amplifies. Integration is no longer just technical. It is a strategic asset.

Whether 2026 marks the peak of martech or simply a pause before the next expansion remains genuinely uncertain. Brinker and Riemersma’s own position is the latter. The cost to build keeps falling, AI keeps opening new niches, and the minimum viable scale for a sustainable martech business keeps shrinking. The landscape is metabolizing — not dying. But the shape of whatever emerges from the chrysalis will bear little resemblance to what went in.

The State of Martech 2026 was debuted by Scott Brinker and Frans Riemersma at MartechDay on May 5, 2026, and is available free at chiefmartec.com.

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

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

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

The key takeaway, more work for fewer journalists. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Market volatility often exposes gaps in how businesses operate. Whether it’s sudden demand shifts during the pandemic or ongoing uncertainty around tariffs and global trade, these moments not only disrupt supply chains and pricing strategies but also reveal how companies are prepared to support their customers through change.

For B2B organizations, one of the most overlooked areas of adaptation sits at the intersection of commerce, payments, and customer experience. Historically treated as back-office functions, payments and invoicing now play a far more visible role in how companies retain customers, sustain growth, and respond to uncertainty.

What’s emerging is a more durable way of thinking about resilience. Instead of reacting to disruption, leading organizations are building systems and experiences designed to flex with it. One useful way to frame this is through what I call “long TAIL” thinking: a focus on Trust, Adaptability, Intelligence, and Localization as core drivers of both the customer experience and commercial performance.

Why Volatility Is Accelerating Change

When dealing with tough situations, one thing is clear: buyers don’t adjust expectations, even when things get tough. B2B buyers are quick to adapt. But no matter how much change they endure, they still expect transactions to be predictable, easy, and aligned with how their organizations operate.

Many traditional systems weren’t built for this level of variability. Manual processes, rigid payment terms, and disconnected systems create friction just when responsiveness matters most. The result isn’t inefficiency, it’s a breakdown in the customer experience at a critical moment.

Trust as a Commercial Advantage

Trust is built through consistency. Buyers want to know that orders will be fulfilled, invoices will be accurate, and payment processes will work the way they expect. When those fundamentals break down, even strong commercial relationships can weaken. In uncertain markets, trust becomes even more valuable as it becomes a deciding factor in where buyers spend their budgets.

Much of this experience is shaped during payment and invoicing. If buyers can’t pay in a way that fits their processes, friction builds. When companies offer flexibility and integrate smoothly into procurement workflows, they make it easier to keep business moving.

Trust, in this context, moves from the abstract to the operational.

Adaptability Across the Order-to-Cash Journey

Volatility changes how buyers purchase, how quickly decisions are made, and how risk is evaluated. This puts pressure on the entire order-to-cash process. Companies that rely on static workflows often struggle to keep up, while those with more adaptive systems can adjust more quickly.

From a marketing and customer experience perspective, this is where the lines between front-end and back-end systems start to blur. The checkout experience, payment options, and invoicing workflows are no longer isolated steps. They are part of a continuous journey that influences whether a deal moves forward or stalls.

Adaptability here means more than adding new payment methods. It’s about designing processes that can accommodate different buyer types, transaction sizes, and regional requirements without introducing friction.

It also means recognizing that flexibility is part of a growth strategy. When buyers can purchase on terms that align with their business, they are more likely to complete transactions and return for future ones.

Intelligence That Supports Better Decisions

Another shift accelerated by market volatility is the need for better, faster decision-making. In stable environments, delayed insights may be manageable. In volatile ones, they become a liability. 

Automation and data-driven insights help organizations move faster by reducing manual steps across invoicing, reconciliation, and credit processes. More importantly, they provide clearer visibility into customer behavior, payment trends, and potential risks.

For marketers, this provides a more comprehensive view of the customer. Payment data, when combined with broader customer insights, can reveal purchasing habits, account health, and early signs of churn.

The goal isn’t just better reporting. It’s using intelligence to respond in real time.

Localization as a Growth Lever

As companies expand across regions, another challenge is understanding that no two markets operate the same way.

Payment preferences, regulatory requirements, and invoicing standards can vary significantly. What works in one region may create resistance in another.

When things are uncertain, it’s even more crucial to understand these differences. Changes in currency values, new regulations, and shifts in trade patterns all make things more complicated.

Localization is more than compliance. It’s about relevance. Companies that customize their payment and invoicing experiences to meet local expectations make it easier for buyers to transact, regardless of market conditions.

This is particularly important for organizations looking to diversify their customer base or reduce exposure to specific regions. A localized approach allows them to scale more confidently while maintaining a consistent customer experience.

A More Connected View of Marketing and Payments

For marketing leaders, one of the most important implications of long tail thinking is the need to broaden the definition of customer experience.

We’ve moved past focusing on awareness, engagement, and conversion in isolation. The moments that follow—checkout, payment, and invoicing—are just as critical in shaping perception and loyalty.

These are the moments that can determine whether the relationship continues at all.

This doesn’t mean marketers need to become payments experts. But it does mean collaborating more closely with finance, operations, and technology teams to ensure the end-to-end experience is aligned.

When that alignment exists, companies are better positioned to respond to volatility, retain customers, and uncover new growth opportunities.

Turning Disruption Into Opportunity

Periods of uncertainty feel like challenges that are difficult to overcome. But in reality, they are catalysts for change.

The organizations that come out stronger are typically those that use disruption as an opportunity to rethink how they operate and serve their customers.

Long tail thinking offers a practical framework for doing just that. By focusing on trust, adaptability, intelligence, and localization, companies can build systems and experiences that are resilient and responsive to buyer needs.

In a volatile market, that responsiveness turns uncertainty into advantage.

The post Can B2B Brands Adapt to Volatility with Long-Tail Thinking? appeared first on MartechView.

Most enterprises have no problem handing off customer service to an outside partner. Billing disputes, tech support, and returns are all fair game when it comes to outsourcing. Yet, the moment the conversation shifts to revenue-generating functions like sales, leaders become more cautious. Sales is often perceived to be too close to the brand, too strategic, and too important to trust to anyone outside the building.

That logic is costing companies growth.

A recent IDC InfoBrief, “From Efficiency to Excellence: Driving Enterprise Value Through Customer Experience Partnerships,” sponsored by TELUS Digital, draws on survey data from 287 enterprise decision-makers on their business priorities and CX partnership strategies. The results show a striking imbalance in modern outsourcing. 

The Trust Gap Is the Real Bottleneck

Organizations are comfortable delegating customer friction, but they remain tethered to an outdated belief that revenue-generating functions must stay strictly in-house to maintain control. That mindset is holding enterprises back. 

According to IDC’s InfoBrief, customer analytics is now the most outsourced function to CX partners, cited by 27% of respondents. Inbound B2B sales trail at 17%, inbound B2C sales at 9%, and outbound sales functions rank even lower.

The disparity is telling. Enterprises are willing to hand over the data and insight layer of the customer relationship, but they still hesitate at the transactional moment. Modern CX outsourcing has evolved from transactional labor to high-fidelity brand extension. The barrier isn’t a lack of partner capability. It’s a legacy mindset that treats sales as ‘core identity’ and service as a ‘utility.’ When enterprises mistake proximity to the office for quality of the outcome, they inadvertently limit their own scale.

Same Tools, Different Silos

Consider this: the same AI and predictive analytics that enterprises deploy to resolve support tickets are perfectly suited for identifying upsell triggers, forecasting churn, and surfacing expansion opportunities.

The IDC data shows that 34% of enterprises rank improving operational efficiency as their top priority over the next 12 to 24 months, with 31% prioritizing improved customer experience and 21% focused on revenue growth. Most companies chase these with separate budgets and separate teams. But think about what actually happens in a single customer interaction: an AI tool resolves an issue faster, and that’s efficiency. The customer walks away satisfied, and that’s experience. And because the system flagged a cross-sell opportunity during that same conversation, the agent closes an expansion, and that’s revenue. One interaction, one platform, three outcomes. The only reason companies don’t see it that way is that they’ve organized themselves not to.

Technology and talent are commercially agnostic. Only internal silos prevent a service tool from becoming a revenue engine. CX partners, by nature, sit outside organizational walls. The best among them have spent years perfecting the science of hiring, training, and scaling the specific skill sets required for consultative, high-conversion interactions.

When Procurement Undermines Growth Strategy

The most significant insight from the IDC InfoBrief concerns how enterprises buy CX partnerships. Contract pricing and flexibility are the top vendor selection factors at 28%, and 72% of enterprises expect 10% to 19% cost savings from their CX partner. Cost discipline matters, of course. But when pricing is commoditized, partners are forced into a defensive posture, focusing on baseline service level agreements (SLAs) rather than proactive growth.

In a revenue partnership, the conversation must shift from cost per head to return on investment. In a service-only model, success is often measured by how quickly you can get off the phone. In a revenue partnership, success is measured by conversion rates, pipeline contribution, and account expansion. 

Enterprise leaders need to think beyond the cost of a partner, considering how effectively they can accelerate outcomes and shorten the sales cycle. IDC’s data shows that 22% of enterprises already view revenue growth as a quantifiable outcome of their CX partnerships. That number should be much higher. You cannot expect a vendor to drive strategic growth if the contract is designed only to manage tactical costs. A true revenue partnership transforms the CX provider from a defensive cost center into an offensive growth engine that pays for itself.

Agentic AI Changes the Math

The emergence of agentic AI accelerates all of this. According to IDC, 32% of enterprises have already deployed agentic AI use cases in their outsourcing operations, and only 6% report no interest in the technology. This is no longer about chatbots deflecting routine inquiries. Agentic AI analyzes behavior in real time to trigger next-best-action recommendations, ensuring expansion opportunities aren’t missed during a service interaction. It moves CX from manual task execution to autonomous revenue orchestration.

For CX partners already managing both the data layer and the customer interaction, agentic AI can be the connective tissue that turns a support conversation into a qualified pipeline event.

The Full-Lifecycle Imperative

The enterprises that will pull ahead are the ones that stop treating acquisition, retention, and expansion as separate workstreams managed by separate vendors. Deploying a single partner across the full customer lifecycle ensures data continuity, with the sales team informed by support data and the support team staying aligned with the original value proposition. IDC’s data reinforces this: 25% of enterprises say the highest satisfaction driver is alignment of service delivery with their business. A fragmented customer journey is a silent revenue killer. A unified lifecycle is a force multiplier

Time to Rethink the CX Partner Relationship

The CX outsourcing market has matured well beyond transactional staffing models. The partners, the technology, and the talent models exist today to drive measurable revenue impact. What hasn’t caught up is the enterprise mindset. The risk isn’t in outsourcing sales, but in the cost of not scaling it.

For leaders still treating their CX partner as a cost center, the IDC InfoBrief sponsored by TELUS Digital offers a clear direction: the same partnership you’re using to manage friction could be the engine that drives your next phase of growth.

The post Your CX Partner Is a Revenue Engine. Treat It Like One. appeared first on MartechView.

Companies have spent years and significant resources collecting more data than ever before. Yet the bottleneck is rarely acquisition — it’s communication. The most consequential insights routinely get buried under dashboards, spreadsheets, and competing reports, leaving decision-makers with more noise than signal. The result is a paradox: organisations drowning in data, starved of clarity.

The High Cost of Information Overload

The modern workplace is awash in dashboards and reports that were designed to improve decisions but often do the opposite. When employees must sift through increasingly complex datasets simply to identify what matters, data becomes a burden rather than an asset — adding to workload and accelerating burnout without delivering commensurate value.

The consequences of data without context can be severe. In 2018, Zillow launched Zillow Offers, an AI-powered instant homebuying service with an ambition to generate $20 billion in annual revenue within five years. The platform ingested vast amounts of seller data to make near-instant property offers — but by the third quarter of 2021, it had lost $421 million and was shut down. Chief executive Rich Barton attributed the failure to the AI’s inability to accurately predict listing prices. The volume of data was not the problem; the absence of contextual judgment was.

Also Read: Three Myths That Are Keeping Brands Away From AI

The Shift to Meaningful Data Communication

The shift required is not technological but editorial — from collecting data to communicating it. Research supports the urgency: 84% of communication specialists report that C-suite executives have asked them to provide greater clarity on corporate strategy, suggesting that even senior leadership feels the weight of information overload. The solution is not another dashboard. It is a discipline: interpreting data correctly, stripping out what is peripheral, and building a narrative that connects numbers to decisions.

3 Strategies to Improve How You Communicate Data

To shift from information overload to actionable insights, you must adopt a strategic approach that concentrates on narratives, purpose-driven visualization and a methodical system. These three strategies can help you transform your data communication from a jumbled mess to a message that resonates and drives results.

Tell a Story

Narrative structure makes data digestible and memorable in ways that raw numbers cannot. The approach is straightforward: identify the business challenge the data speaks to, walk the audience through what the findings reveal, and close with a clear recommendation and its likely impact. Data presented as a story is harder to ignore and easier to act on than a table of figures — even when the underlying numbers are identical.

Visualize With a Purpose

Visualisation is not decoration — it is an analytical tool. Every chart or graphic should answer a specific business question; if it does not, it should not be there. The most effective visuals are almost always the simplest ones: the goal is clarity, not sophistication. Well-designed dashboards surface patterns and anomalies that would remain invisible in raw data, which is where their real value lies.

Focus On Minimum Viable Metrics

Tracking too many metrics is functionally equivalent to tracking none — attention gets diluted and the signal disappears into the noise. The discipline here is ruthless prioritisation: identify the handful of KPIs that are genuinely tied to strategic outcomes, and make everything else secondary. Misaligned metrics are a well-documented failure mode. When a measure diverges from the actual goal, teams will optimise for the measure rather than the outcome — a dynamic economists call Goodhart’s Law. The call centre example illustrates it plainly: reward agents for call volume and you will get short calls, not satisfied customers.

Also Read: The Organizations That Survive Disruption Never Had to Recover From It

Closing

The competitive advantage in data no longer belongs to the organisation that collects the most — it belongs to the one that communicates it most clearly. Narrative, purposeful visualisation, and disciplined metric selection are not soft skills peripheral to analysis. They are the mechanism by which analysis becomes decision, and decision becomes outcome.

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