It turns out that advice is dead wrong.

As multiple researchers have found over decades of studies, stopping your marketing efforts during tough times is a mistake. Data shows that proactive marketing “pays off” during recessions—for brands across industries, including the likes of Toyota, Amazon, Coca-Cola, etc. As two marketing professors put it in a 2020 Harvard Business Review article: “Firms that maintain their marketing spend while reallocating it to suit the context … fare better than firms that cut their marketing investment.”

When everyone is on high alert, pulling back might seem like the safe bet in the short term. But keep in mind that this moment full of challenges also presents opportunities. Companies need to keep growing to succeed, and content is one of the most effective ways to do that.

That doesn’t mean you should ignore reality and pretend everything is great. The trick, as many researchers found, is to adjust and reallocate your budget rather than gutting your chance at growth. Here’s how investing in content can help you overcome today’s biggest challenges and position your company to succeed long term.

How content marketing leads to meaningful ROI

Paid ads and content marketing have inverse reputations. Ads tend to be annoying disruptions that don’t work well but are very easy to track for ROI. Content, meanwhile, offers buyers a lot of value but takes deeper effort to tie to business results. This strange dynamic is one reason why professionals fall back on paid budgets despite the crappy user experience.

In the last few years, though, content ROI has gotten much easier to track and optimize. If you examine the two tactics side by side, it’s clear content is a better investment, especially if budgets are tight.

Let’s say you have $500. If you spend that on an ad campaign at a $10 cost per click, you get 50 clicks. When the budget runs out, you’ll have to spend more to keep seeing results. But remember, you can’t necessarily tie that click-through rate to sales. You’ve got to hook them and drive them to action.

With owned content marketing, you take that same $500 and work with a skilled creator who produces an asset that truly helps your audience. It can be optimized for search and distributed through your organic channels. That article drives 500 clicks through email and social, and continues to drive results for months to come.

You can always scale ad spend up or down over time, depending on budget constraints. But no amount of paid spend can make up for weak content. Focusing on great content puts you in a better position to succeed and gives you flexibility around distribution.

Remember: Buyers don’t stop buying during a recession. They just get really choosy about where their dollars go.

Where to increase your content spend

There’s a really interesting contrast playing out in marketing right now. According to Gartner, marketing budgets as a percentage of revenue are actually up overall this year compared to 2021. However, ad spend is down, particularly in consumer and B2B tech.

Marketers would be wise to use some of those new funds on ambitious content. And if other companies are making cuts, one advantage is you won’t have to deal with as much competition for attention.

Plus, innovation happens when budgets are tight.

We don’t even have to look that far back to see an example: How we work and interact with colleagues has changed dramatically since the global pandemic. Prior to March 2020, Zoom calls and people working remotely were rare and few and far between. Now? It’s the status quo.

With dozens of companies announcing layoffs, we’re seeing an increase in demand for freelance content creators. Gartner research also revealed that 58% of CMOs don’t think they have the in-house talent “needed to execute on their strategy.” Addressing the talent gap is a smart way to adjust your content spend.

That way, you’ll be able to keep content quality and volume up with a smaller team. You can streamline communication with both internal and external teams with platforms like Slack or even our own Contently, where you can manage deadlines, projects, and communications all in one location.

How to adapt your goals during tough times

In 2016, CEO Tim Cook told a crowded room, “We believe in investing during downturns,” as he reflected on Apple’s response during bleak times, like 2008 at the height of the Great Recession.

Similarly, now is the perfect opportunity to move miles ahead of the competition. For one, you’re already out of the starting gate by understanding the important role storytelling plays in how we connect with brands. We’ve got a few ideas to help you plan your improved path forward and win the day with content.

Focus on big ideas and big rocks

While you’re adjusting your goals to meet the current need, it’s vital you step back and look at the giant opportunity waiting on the other side of the crisis.

Need some inspiration? Procter & Gamble was already famous for their “floating soap” at the start of the Great Depression. As the Depression lagged on, stores cut back on their orders and sales stagnated. P&G could have stopped producing soap altogether to take care of the immediate need. Instead, they realized they had to engage their audience in a different way—people still needed soap—and they created the first ever direct-to-consumer marketing strategy, increasing their soap sales and changing how brands interact with consumers to this day.

For today’s content marketers, look at your current strategy and goals and adapt them to fit where we’re headed, not just where we are today. That could mean focusing on your most important owned channels like email and social rather than experimenting elsewhere. Or maybe you choose to focus on a few essential big rock content pieces that you can repurpose instead of producing a bunch of shorter articles.

Audit your content

Now is the perfect time to audit the content you currently have and review your data. What are your most popular articles and have any shifted since the beginning of this downturn? Look at individual pieces and ask: How is it performing? Are people reading it all the way through? Is it still generating traffic to your site?

Update some of that content to engage today’s audience (and it’ll do better in search). You may find you have a lot of material on a topic and can create a course out of those pieces. You’ll give customers a way to learn a skill or how to use your product more efficiently, and if they’re new, you get their email address to continue building trust with them.

Listen to your public-facing teams

Your Sales and Customer Success teams are your “ears to the ground.” They know very well what your customers are saying, because they’re on the phone or in a live chat with them on the daily. What are they hearing? What fears and concerns do current clients and prospects share with them?

Take this feedback and use it to generate content that helps address their concerns. (Where do you think we got the idea for this piece?) What content can you generate that can help them make the case for your product or services and prove bang-for-the-buck? This is another moment where you can showcase your value and reinforce why they should choose you—even in the middle of a recession.

While your internal teams are listening to the customers they’re talking with, what are people saying to you on social? Are you asking questions? You should be! This is a great opportunity to be like Slack and create channels dedicated to listening to your customers directly.

Create content for your current customers

Once someone becomes a customer, it may be easy to forget about them and move on to the next lead. But that’s short-sighted and an easy way to lose them. (Anyone fed up with their internet provider and switched just because of poor customer service? Just me?)

Customer success teams tend to be overbooked as they juggle keeping 12 clients happy at once. Retention content is such an important tool that many companies don’t invest in till it’s too late. Creating content specifically for your customers, whether it’s onboarding or beyond, helps strengthen the relationship and can increase profits exponentially.

So as you prepare to balance the need for growth with the urge to save, think about how great content can lift your organization. Now’s not the time to pull back. Those willing to stay the course and double-down with their content can reap monumental rewards that will follow them well beyond the current downturn.

The post Content Marketing During a Downturn: Here’s What the Conventional Wisdom Gets Wrong appeared first on Contently.

Polished copy is easy now with AI. You can quickly write blog posts, social campaigns, video scripts, thought leadership essays, white papers, and podcasts at scale across every imaginable format and channel. And yet, after the content is published, it’s quickly forgotten.

What now separates authentic, smart content from forgettable (and sometimes regrettable) non-strategic content is taste.

When every piece of content imaginable is easy to make, deciding what not to make becomes the real work. The brands pulling ahead of everyone else are the ones making taste a core element of their content creation process.

Taste describes the ability to consistently distinguish what fits from what doesn’t. It’s an exercise in judgment about what deserves to exist in the first place. Taste is a skill that enables content teams to determine what’s worth an audience’s time from what merely fills a content calendar.

The Judgment Call

It used to be that content teams, measured by their ability to produce faster, more efficiently, at higher volume, had the advantage. But this edge has dulled as content has become a commodity. It’s simply not good enough to have “good enough” content.

Content that can be easily produced by tools and systems is competent and fluent by default. What’s often missing is judgment.

Judgement can’t be commoditized. Judgement is thinking. It’s like when a content team takes a dozen viable ideas and chooses only the three worth pursuing. When a person instinctively reframes a piece and trims it down so that what’s being communicated is genuine and advances the message, they’re making a judgment call.

Editors have always known what’s worth making and what’s best left out. The sharpest content teams are taking their cue from editors and gaining a competitive edge.

More Content Isn’t The Same as More Impact

Most organizations default to pursuing more content. More blog posts. More thought leadership. But publishing everything without taste doesn’t necessarily lead to better results.

Brands also risk diluting their message when they overload their audience with content. According to Accenture, 74% of empowered consumers walked away from purchases simply because they felt overwhelmed. Content overload works the same way. What readers want is clarity. If they get that from the content they read, they stay and reward brands with their trust. Bore or bombard them with content, and they often leave quietly.

The trap of producing more content is seductive because the metrics lag behind the damage. Publishing more can keep the pageviews and open rates looking fine for months, even as readers slowly lose interest. By the time the decline shows up in the numbers, the problem has been compounding for a long time—because nobody was asking whether any of it was worth making.

What “Taste” Actually Means

Taste sounds inherently subjective. You either have, or you don’t. But in practice, it’s far more concrete than its reputation suggests.

Content guardrails tell you what to do or not to do. For example, brand guidelines tell brands how to sound. Taste takes on a harder question: What’s actually worth making?

Creative taste involves a clear sense of what fits and what doesn’t. Organizations that have it know their own voice well enough that they don’t need to watch what other brands are doing (though your content is also competing for a spot in AI-generated answers).

Brands using taste to their advantage accept that not every audience segment will be served by every piece. They also know that there’s a payoff to being opinionated when it serves the strategy, because the safest content is often the least memorable.

Codifying Taste Without Killing Creativity

Taste can be scalable when shared, but avoid the temptation of turning “taste” into a checklist or formula. How can you define taste in a structured way so that creativity flourishes?

First: Show, don’t tell. Nothing communicates taste faster than showing people what good looks like and what it doesn’t. Collections of the brand’s best work, annotated with notes on why it works, give teams a reference point far more useful than abstract principles alone.

Second: Set clear principles. Principles can help lock in content teams to what taste is, as long as the principles are clear. An example, “We explain, we don’t lecture,” sets a standard while allowing for interpretation. Principles point content teams in a direction. But they also need freedom to experiment and adapt messaging without going off-brand.

The balance that works is shared standards plus human discretion. The system provides the framework. The people provide the judgment.

Editors Were Right All Along

As the volume of potential content grows, the need for experienced judgment grows with it. Senior editors and creative directors are filters. They’re the members of the team who look at a week’s worth of planned output and ask whether it actually says anything new.

Senior editorial leaders don’t just catch errors or enforce style guides; they decide whether content is worth sharing with the world. They set the standard for what makes sense while serving as a bridge between strategy and creative execution.

From a business standpoint, investing in strong editorial leadership helps manage risk. Any piece of content that falls short costs the company something, such as audience attention, brand reputation, or internal resources. Leaders who prevent mediocre work from being published help protect the value that’s hard to recover once it’s lost.

Taste Offers A Real Creative Advantage

The future of content belongs to teams who can say, with confidence, this is us, this isn’t, and this is worth your time.

Content creation will get easier as tools get better. Taste remains the throughline that keeps brands coherent, credible, and distinct.

The volume of content will keep increasing. But the organizations that treat editorial judgment as a strategic asset will be the ones whose content still matters five years from now.

Building that kind of editorial capability doesn’t happen by accident. It takes experienced leadership, shared systems, and a commitment to quality over quantity. Connect with Contently to work with expert managing editors who can help your team develop the taste and judgment that turns content from output into advantage.

Frequently Asked Questions (FAQs):

How do I build “taste” into my team if we don’t have a senior editor?

You may not have a senior editor yet, but you can still take key steps to establish “taste” guidelines for your team. First, gather five to ten pieces that your team thinks are their best work and note why each one succeeded. This will be your “taste” reference set. Next, create two or three clear editorial principles to guide decisions, but flexible enough to encourage creativity. Keep updating the reference set and refining the principles over time, revisiting them every quarter.

How do I convince leadership that publishing less content is the right move?

Leadership will likely want more. So offer a new perspective—too much content can weaken the brand and reduce trust. Also, producing too much can stretch resources thin, resulting in team burnout. Then connect the idea of less content to real results, such as the pipeline, engagement, or earned media generated in the last two quarters. Compare that data to the total output. Usually, a small portion of content drives most of the results. This data helps make your case.

How long does it take to see results after shifting from volume to judgment?

Plan for one full quarter. In month one, review past work and set standards. The team uses them on new projects in month two. By month three, expect results: better engagement, fewer revisions, and clearer priorities. This information will give your team a stronger understanding of what’s worth creating. Be sure to agree on this timeline with leadership before starting.

The post The Future of Content Belongs to the Tastemakers appeared first on Contently.

That model is breaking down.

In AI-driven discovery environments, your content is no longer competing with other brands in the traditional sense. Instead of vying for attention and eyeballs, now you’re competing to show up in the language, examples, and assumptions AI systems use in their answers.

The first step is to survive the summarization process. Here are some tips on how to write for the “idea ecosystem.”

The New Model

When someone asks a system like ChatGPT, Perplexity, or Google’s AI Overviews a question, the system constructs an answer assembled from many sources at once. Your content enters that system as raw material, and exits recomposed alongside other inputs.

What matters, then, is whether any part of your brand’s messaging shapes the response the system generates. The pinnacle of success is making such an impression on one of the major LLMs that you do get cited by name. A second-best outcome is seeing your terminology or logic show up consistently in AI-generated answers, even if your brand doesn’t.

While on its face, “no attribution” sounds like a raw deal, being cited by AI, even tangentially, can make a difference in multiple stages of the sales funnel. If AI repeatedly explains a category using your logic, buyers may later:

• recognize your language on your site
• hear your pitch as familiar rather than promotional
• perceive alignment instead of persuasion

When it comes time to make a decision, this familiarity can make your product or service feel like the obvious fit.

What Actually Survives AI Compression (and What Doesn’t)

Ideas that survive compression tend to function as anchors; they give the system something stable to organize around. Examples might include a clear model for thinking about a problem, or an original benchmark that gives the system a reference point. Content that introduces structure or, better yet, new and valuable data is a boon. (This is one of the reasons we’re seeing a rise in branded benchmark reports and flagship research these days.)

Generic content rarely provides that. Familiar advice and widely repeated tips dissolve into the background because they don’t change how the system understands the topic.

A sharply argued position, on the other hand, gives the system something to work with. Instead of blending seamlessly into everything else, it helps organize other inputs. This is why original language matters—but not as ornamentation. Distinct terminology can make an idea easier for AI to find and surface.

How Marketers Need to Rethink Content Strategy

Content can no longer be treated as an asset that drives traffic; it needs to function as a source of durable ideas that persist across platforms and summarization layers. That means prioritizing clarity over cleverness. A clear definition or straightforward, compelling original data point will travel farther than a witty headline.

It also means investing in strong framing. If you can name a concept, structure it, and make it easy to restate accurately, you increase the odds it will persist.

It means using memorable language: Not buzzwords or jargon, but precise, specific phrasing that’s hard to replace with a generic equivalent.

And it means recognizing that safe, consensus-driven content is the most vulnerable to erasure. If your article says what everyone else is saying, it contributes nothing distinct to the compression process. It becomes filler.

This is uncomfortable for brands that have built content strategies around avoiding risk. But in an environment where AI systems blend dozens of voices into one, the riskiest move is to have no distinct voice at all.

The New Competitive Set: Ideas

AI doesn’t care about brand equity the way human readers do. A Reddit comment with a sharp insight can outcompete a polished whitepaper if the insight is more distinct and easier to compress; an academic study with clear findings can overshadow your thought leadership if the findings are more specific.

This levels the playing field in some ways, but it also raises the bar.

If your content strategy was built for the old model, now’s the time to audit. Here are a series of questions to ask when evaluating existing and planned content for AI search:

• If this article were compressed into a single sentence, would our core idea survive? Would our framing survive? Would our name?
• Is this content safe or generic? How can we make it stand out?
• What can we say about this topic, product, or sector that nobody else is saying? What language can we use that’s distinct, or what point of view can we “own”?
• If a buyer encountered this idea elsewhere later, would they recognize it as ours?

Idea persistence is the new metric. It’s time to start measuring for it.

Learn how Contently helps brands build content strategies designed for clarity, resilience, and long-term impact. Get in touch.

Frequently Asked Questions (FAQs):

Does this mean SEO no longer matters?

No. SEO still plays a role, especially for discovery and authority signals. But it’s no longer sufficient on its own. Ranking well doesn’t guarantee influence if your ideas disappear during summarization.

How can we tell if our ideas are influencing AI answers?

You won’t see a single metric. Signals tend to be indirect: recurring language in AI-generated responses, familiar framing appearing across tools, or prospects repeating your terminology in conversations. Influence shows up over time, not in dashboards.

Is AI attribution realistic for most brands?

It depends on the category and the role your content plays in the buying journey. Direct citation does happen, especially in product-led or comparison-driven searches, but it’s inconsistent and difficult to control. For most brands—particularly those operating in crowded or concept-driven categories—the more reliable goal is idea adoption. Attribution should be treated as an upside, not the baseline measure of success.

The post Your Content Isn’t Just Competing With Other Brands Anymore appeared first on Contently.

So when a customer asks your support chatbot a routine question and the bot confidently cites that guide as current policy, the advice is wrong. Your support team now has to explain why an official brand answer is outdated.

It’s a scenario that’s becoming more and more common as AI makes its way into customer service, e-commerce, and search. Since LLMs pull from published brand materials to answer user questions and shape buying decisions, outdated or incomplete content can carry severe consequences. According to The Conference Board’s October 2025 analysis, 72% of S&P 500 companies now identify AI as a material business risk, up from just 12% in 2023.

Content teams are feeling the pressure. Marketing collateral that used to be about engagement and reach now carries far more responsibility.

Why This Shift Is Happening Now

AI systems don’t distinguish between your latest product update and a blog post from 2019; they treat all indexed content as equally valid source material.

This creates a compounding problem. When ChatGPT, Perplexity, or Google’s AI Overviews pull from your content library, disclaimers disappear, dates vanish, and nuance evaporates.

This is what leads to scenarios like the one described at the top of this piece. Here are a few other examples of how content can go awry:

• A 2023 pricing page informs a sales conversation with a chatbot, and the customer pushes back when it becomes clear the quoted numbers no longer apply.
• A deprecated feature appears as a live offering in Google’s AI Mode, leading to confusion during customer onboarding.
• An old compliance explainer is surfaced on ChatGPT as guidance, even though the underlying regulation has changed. The company is forced into a reactive audit.

For regulated industries, the exposure carries profound risk: Financial services firms might face SEC scrutiny, and healthcare organizations that have to navigate HIPAA implications could find themselves correcting patient-facing guidance after the fact.

The New Risks Content Teams Are Absorbing

Content teams didn’t sign up to be compliance officers, but the risks have arrived anyway.

Consider what happened to Air Canada a couple of years ago: In a 2024 ruling, a British Columbia civil tribunal found the airline liable after its website chatbot cited incorrect information about bereavement fares, promising a discount that did not exist under current policy. When Air Canada refused to honor the discount, the customer pursued a claim and won. The tribunal ruled that the company was responsible for the chatbot’s statements, regardless of how or where the information was generated. What began as outdated guidance surfaced through AI ended as a legal and public accountability issue.

There are a few buckets that AI-related content risk tends to fall into. Here are some common failure modes to be wary of:

• Outdated information as “current” fact. AI systems resurface archived content without timestamps, so policies, pricing, or product details that no longer apply are delivered as if they were up to date.
• Inconsistent messaging across content types. Your blog says one thing, your help docs another, and your landing page a third. AI systems amalgamate those contradictions into confident answers that may be completely off base.
• Nuance and disclaimers stripped away. Legal caveats and contextual qualifiers rarely survive AI summarization. The careful language your legal team approved gets compressed into declarative statements.

McKinsey’s 2025 State of AI survey found that 51% of AI-using organizations have already experienced at least one negative consequence from AI deployment, with inaccuracy the most commonly cited issue. This represents structural exposure that content teams now own, whether they planned to or not.

Why Most Teams Aren’t Set Up for This Role

Content teams evolved to optimize for different metrics: speed, volume, engagement, traffic. But in many cases, the established workflows that serve those goals actively work against accuracy governance: Publishing calendars prioritize velocity, and editorial reviews tend to focus on voice and clarity. Legal approval processes that were designed for campaigns (discrete, time-bound assets) might not extend to evergreen content libraries that AI systems mine indefinitely.

And ownership gets murky fast. Who’s responsible for updating a three-year-old blog post when regulations change? Who audits help documentation when product features evolve? In most organizations, that accountability doesn’t exist.

Content teams sit at the center of this vacuum, creating the assets AI systems consume, without the mandate, tools, or headcount to manage the downstream risk.

How Teams Are Adapting Without Slowing Down

The organizations getting this right are building what we call the Content Risk Triage System — four interlocking practices that maintain velocity while managing exposure.

1. Tiered review models. Not every piece of content carries equal risk. A best practice is to classify content by exposure: high-stakes claims (pricing, compliance, capabilities) route through legal review, standard editorial content moves faster with SME sign-off, and low-risk assets publish with editorial approval alone.
2. Content risk scoring. Assign risk classifications at the brief stage. Content touching regulated topics, making quantifiable claims, or likely to be cited by AI systems should get flagged for additional verification before drafting begins.
3. Clear ownership for content lifecycle. Designate owners not just for creation but for ongoing accuracy, e.g., one person who owns the quarterly audit of evergreen content and another team member who manages the sunset process for outdated assets.
4. Treating content as living systems. Instead of “publish and forget,” treat your content libraries like software: versioned, maintained, and regularly patched. When policies change, content updates follow within defined SLAs.

What Content Leaders Should Do Next

Content leaders need practical systems that reduce risk without bringing publishing to a halt. These three steps are a reasonable jumping-off point:

1. Start with an audit. Identify your highest-exposure content: pages making specific claims, documents AI systems frequently cite, assets in regulated topic areas. These are your first candidates for accuracy review.
2. Set realistic standards. You can’t fact-check everything quarterly. But you can establish clear thresholds for what triggers review: regulatory changes, product updates, specified time intervals for high-risk content.
3. Make risk management part of content strategy, not a bolt-on. Build verification into your editorial workflow. Include accuracy checkpoints in your content calendar. Staff appropriately for the governance work that now falls to content teams.

For organizations needing additional support, Contently’s Managing Editors can serve as an embedded layer of editorial governance, helping teams maintain accuracy standards without sacrificing publishing velocity.

The cost of fixing content after it spreads is far higher than the cost of managing it upfront. Don’t spend your next quarter doing damage control; put proactive systems in place today. It’s the resolution that will give back all year long.

For more on building content operations that scale responsibly, explore Contently’s enterprise content solutions.

Frequently Asked Questions (FAQs):

How do I know if my content library has risk exposure?

Start by auditing content that makes specific claims: pricing, capabilities, compliance statements, health or financial guidance, etc. Then identify assets that AI systems frequently cite by testing queries in ChatGPT, Perplexity, and Google AI Overviews. Content appearing in AI responses carries the highest exposure and should be prioritized for accuracy verification.

What do I need if I’m on a small content team with no dedicated compliance support?

At a minimum, assign clear ownership for content accuracy reviews on a quarterly cadence. Create a simple risk classification system that routes high-stakes content through additional review before publishing. Document your verification process so you can demonstrate due diligence if questions arise. These basics don’t require additional headcount, just intentional workflow design.

How do I get legal and compliance teams to participate without slowing everything down?

Build tiered review into your process from the start. Define what content types require legal sign-off versus what moves with editorial approval only. Create templates and pre-approved language for recurring claim types so legal reviews become faster over time. The goal is appropriate oversight, not universal bottlenecks.

The post Why Content Teams Are Quietly Becoming Risk Managers appeared first on Contently.

Behind those millions of searches, clicks, saves, and sign-ups are real signals that show exactly what audiences care about and how they engage. For example, retail sites saw a 693% surge in traffic tied to AI-powered shopping assistants and chatbots this year. That kind of growth suggests shoppers are becoming more comfortable letting AI do the comparison shopping for them. Buy now, pay later also became an even more popular option, implying that shoppers were looking for ways to make bigger purchases feel manageable.

January is your opportunity to harness those insights or let them languish. This guide looks back at what worked during the holidays and how to use those insights to plan more effectively for 2026 and beyond.

What People Searched for in December (and Why It Matters Now)

December queries are shaped by the year people just lived through. Sometimes, they signal indulgence; other times, restraint.

Google’s Holiday 100 trends, for instance, made a few patterns clear. In 2025, search interest clustered around practical gift categories: things like movie projectors, weighted vests, kids’ scooters, and backpacks. At scale, that mix suggests steady demand for items that solve everyday needs and feel worth the spend.

In addition to category interest shifts, broader consumer behavior illuminated how people actually made decisions across the season:

• AI-driven sales in the U.S. topped $3 billion on Black Friday alone
• Conversion rates from AI referrals outpaced traditional social channels by a wide margin
• Value-seeking and sharply price-conscious shoppers favored smaller-ticket gifts
• A majority of consumers said rising prices would influence their holiday spending, especially among younger generations facing tighter budgets

Taken together, these signals point to shoppers who were deliberate, price-aware, and increasingly influenced by tools that helped them feel confident about their choices.

In terms of actionable insights here that can carry over to 2026, focus on what reduced friction for people when decisions got complicated. Look at Google Trends and see how searches like “budget gifts” stack up against “luxury gifts” in your market. Then pull last Q4’s Search Console data to see what actually brought people in, not just what you assumed would. Saves on social and interactions with short-form or AI-generated clips tend to spike when people are narrowing choices.

The throughline: Context beats cleverness. When money feels tight, “under $25 gifts” will outperform premium roundups almost every time. For 2026 content planning purposes, marketers should prioritize formats that answer real questions and make next steps obvious.

Where Specificity Wins

Holiday SEO moved fast. January is when you can finally see what held up in search and what didn’t. Rankings have settled, traffic has normalized, and it’s clearer which pages earned their visibility versus which ones were buried.

Looking back, many Q4 search wins came from specificity. Gift-giving phrases, problem-driven queries, and local intent tended to outperform broad holiday terms. Pages that spoke directly to last-minute or highly specific needs earned traction, while generic “Christmas” pages faced steeper competition and more mixed intent.

Long-tail targeting is likely to become even more useful as more discovery happens through conversational queries, whether people type them, speak them, or ask an assistant. In many categories, those behaviors create whitespace brands can capture with clearer, more specific pages.

There’s a particular opportunity with voice search that most businesses are still missing, as we can see below:

Before deciding what to update or reuse next year, check how competitive your keywords were and whether your site was realistically positioned to rank. SEO checkers are useful for validating where effort paid off and where it probably never had a chance.

A post-holiday SEO review usually surfaces takeaways like:

• Holiday URLs that performed well are worth keeping live and updating each season
• Structured data helped certain pages stand out in crowded results
• Updated pages outperformed brand-new ones
• Page speed and simple layouts mattered during high-intent searches
• Basic accessibility improvements supported engagement

Use what December showed you to make cleaner, more realistic SEO decisions going forward.

Building a Content Calendar That Works in January

If December reveals which content holds up under pressure, January is the time to translate those signals into structure. Use the month to reset your publishing rhythm around the pieces that consistently supported real decisions.

A few best practices:

• Publish anchor content early so it can build momentum over time (guides, evergreen explainers, core resources).
• Create decision-support content that aligns with key moments when people are choosing quickly.
• Craft audience-specific pieces tailored to distinct segments instead of broad, one-size-fits-all topics.
• Make space for short-cycle content that moves from idea to publish quickly during spikes.
• Focus on low-friction formats that reduce cognitive load and help people progress without extra steps.

Leaving roughly 20% of the schedule open creates space to respond to demand as it appears, while keeping the rest of the plan stable.

Turning Holiday Insights Into Your Next Plan

The holiday season has passed, and what remains is the record: what people clicked, saved, returned to, and ignored when their attention was stretched thin.

Start with what you already know. Pull the last two years of Q4 data and identify five things that consistently worked. Build around those wins. Add one new experiment to keep learning and to give yourself room to improve.

Momentum comes from simple steps taken in order. Choose one tactic from this guide and implement it today. Tomorrow, choose another. Progress stacks quickly when the next step is always clear.

Audiences respond to clarity. Content that helps them decide, solve something practical, or move forward with less friction earns trust over time. Keep doing that consistently, and your strategy keeps paying dividends, season after season.

Ready to see which stories actually move people through the funnel? Contently’s platform surfaces performance signals across search, social, and conversions — all in one place. See how it works.

Frequently Asked Questions (FAQs):

What’s the biggest lesson marketers should take from the 2025 holiday season?

That audiences reward clarity. Content that helps people compare options, feel confident, and move forward tends to outperform splashy, generic pieces — especially when budgets feel tight.

What metrics matter most when analyzing post-holiday performance?

Look beyond traffic. Prioritize assisted conversions, time on key decision pages, return visits, saves, and email sign-ups. These signals reveal which pieces reduced friction and moved people closer to a decision.

What should I prioritize in January when planning my calendar?

Build around what worked. Anchor evergreen guides early, schedule decision-support content around key moments, leave ~20% of your calendar open for flexibility, and use short-cycle formats when urgency spikes.

The post What’s Working in Content for 2026? What the Holiday Season Taught Us appeared first on Contently.

That’s because there’s still a gap between AI’s promise and its practical value — you know, all those “AI best practices” that no one can quite trace back to real outcomes. Meanwhile, clicks and organic traffic are in freefall.

Of course, at Contently we firmly believe in the value of AI as a force multiplier for great teams. Used thoughtfully, it can streamline research, tighten workflows, and help people ship higher-quality content faster.

But we also recognize that there are some persistent “marketing myths” about what AI can realistically do for content programs and how to use it effectively. These myths tend to take root because AI marketing advice swings between extremes: Hype merchants promise transformation without effort, while skeptics dismiss everything as a fad. Neither helps the marketing director trying to figure out what actually works on Monday morning.

This is the year to get that clarity. Here are five myths that deserve to stay in 2025.

Myth 1: More AI Tools Automatically Mean More Efficiency

On paper, it sounds logical: Add more AI, get more done. In practice, it often works the other way around: Instead of replacing manual steps, many teams end up layering tools on top of one another.

The takeaway isn’t “use fewer tools,” but rather that true efficiency comes from connected workflows. When AI lives inside the places work already happens — your briefs, your CMS, your editorial calendars — the gains start to show up. Good training and clear guidelines can also do more for productivity than chasing the newest feature set.

What works: Before adding anything new, map your current process end to end. Look for bottlenecks AI can realistically remove, consolidate where possible, and invest in helping your team use the tools they already have with confidence. Some basic guardrails also keep everyone from experimenting in five different directions at once.

Myth 2: AI Content Performs Just as Well on Its Own

Thanks to AI, we’re no longer short on content. Most teams can publish more than ever. The real challenge is creating work that actually sounds like you — and earns more trust than the nearly identical post your audience saw five minutes earlier.

Performance now hinges on expertise and perspective, not volume. Search engines and readers both look for signals that someone who knows the topic is actually behind the keyboard, but generic AI text often lacks the lived experience and perspective that makes content persuasive. In other words, grammatically correct copy isn’t the same thing as a compelling narrative.

What’s more, left to its own devices, AI tends to default to the safest version of an idea, which is rarely memorable (and probably won’t drive conversions).

The teams seeing results are treating the AI content creation process as a collaboration. They layer in examples from real customers, clarify claims, tighten arguments, fact-check (!!!), and make sure every piece serves a clear business goal.

What works: Use AI to speed research, outlines, and first passes. Then layer in human editing for accuracy, voice, story, and differentiation.

Myth 3: AI Will Solve Bad Strategy

AI optimizes execution. But it cannot fix fuzzy positioning or off-base business goals. Speed amplifies direction, including the wrong direction.

We see this play out all the time. Teams use AI to publish more, faster… and the metrics that matter don’t budge. Traffic goes up, but conversions stall. The content ranks for keywords, but it doesn’t speak to real buyer pain. Without clear positioning or a path to conversion, all that new visibility simply evaporates before it reaches pipeline.

What works: Get crisp on messaging and conversion paths before you scale production. Then let AI help you execute a strategy that’s already pointed in the right direction.

Myth 4: Everyone Needs to Adopt AI for Everything Immediately

FOMO drives bad technology decisions. Teams adopt tools because competitors are using them, not because they actually solve identified problems. Those wrong-fit tools then create cost, confusion, and cynicism that makes future adoption harder.

The teams that make AI work may not move the fastest, but they do make those moves deliberately. They start by identifying a problem worth solving, define what success should look like, and only then pick the technology.

Readiness also matters. A team still ironing out basic content workflows won’t get much leverage from advanced optimization features. A team without clear governance can accidentally multiply brand, legal, and data-privacy risks as soon as AI scales production.

What works: Look for a single, high-impact use case where AI can remove friction or cost. Run a contained pilot. Document what improved (and what didn’t). Expand from there.

Myth 5: AI Search Is Basically the Same as SEO

Marketers understand visibility through rankings. So it’s easy to assume AI-powered answers are just another extension of Google’s algorithm. They aren’t.

Traditional SEO metrics like site structure and performance remain foundational. But AI Search works differently. Instead of ranking pages, language models compress and rewrite information across multiple sources. According to Ahrefs’ 2025 research, AI Overviews reduce clicks to top-ranking pages by 34.5%. In short, ranking well no longer guarantees visibility.

Visibility in AI Search depends on whether your content is structured clearly and rich with credible context. Two articles might rank identically on page one. The one with clear structure, schema markup, and direct answers gets cited repeatedly by AI assistants. The other rarely appears in AI-generated responses.

What works: Maintain traditional SEO foundations while adding practices designed for AI visibility — clear entity definitions, structured data, and question-driven content formats.

If the last few years were about experimentation, the next one should be about discipline. Use AI where it helps, skip it where it doesn’t, and focus on outcomes instead of promises.

Here’s to a 2026 with fewer breathless predictions and more proof that the work is actually working.

Ready to build AI workflows that actually help your team accomplish real work? Contently’s AI-assisted content platform combines generative AI efficiency with editorial oversight — so your team accelerates without sacrificing quality or brand safety.

Frequently Asked Questions (FAQs):

How do I know if my team is ready for AI adoption?

Assess your current content operations first. If your team has documented workflows, clear brand guidelines, and consistent publishing processes, you’re ready to pilot AI tools. If basic operations still feel chaotic, strengthen those foundations before adding AI complexity.

What’s the minimum investment needed to see results from AI?

Most teams can start with existing tools. Many content platforms now include AI features at no additional cost. The real investment is time: Expect to spend two to four weeks training your team on effective prompting and editing workflows before seeing consistent productivity gains. Budget for those learning curves.

How should I balance traditional SEO with AI Search optimization?

Treat them as complementary. Continue building topical authority, improving site performance, and earning quality backlinks — these fundamentals still matter. Layer AI-specific practices on top: structured data markup, clear entity definitions, and content formats that answer questions directly.

The post 5 AI Marketing Myths to Leave Behind in 2025 appeared first on Contently.

For marketers, this means the old SEO playbook won’t cut it anymore. We’re in a whole new ballgame.

Here are a few predictions on how marketing teams will need to operate in 2026, as this shift in discovery becomes more deeply embedded in everyday search behavior.

Prediction 1: AI Answer Engines Will Become the Default Search Experience

In 2026, traditional search (the “ten blue links”) will still exist, but it’ll play a secondary role as tools like ChatGPT, Gemini, Perplexity, and Google’s AI Overviews increasingly handle the first pass at information discovery. We’ll be dealing with more of a search ecosystem than a single gateway controlled by one dominant engine, even as Google continues to set the tone.

The real shift here is the fact that answers are now assembled from a bunch of different, disparate sources. AI systems pull from publisher content, brand-owned assets, and third-party reference material; weigh their credibility; and synthesize responses. This means that content across all these channels can influence outcomes without ever earning a click.

That fundamentally redefines what both SEO and content marketing entail. Visibility is no longer about ranking first on a results page. It’s about being retrievable and trusted enough to be used as input. Structured data, clear sourcing, and explicit signals of expertise move from best practice to table stakes. Breadth, i.e., how many places you’re published consistently and recognized as an authority, matters.

By 2026, content that isn’t designed to be cited simply won’t show up where decisions are being made.

Prediction 2: Search and Recommendation Will Collapse Into a Single Discovery System

By 2026, the distinction between “search” and “recommendation” will be mostly academic.

This convergence is already visible across platforms. AI systems routinely infer what users want before they articulate it: YouTube queues up explainers you didn’t explicitly search for, LinkedIn surfaces posts aligned to your role and interests, TikTok predicts what will hold your attention within seconds, and Amazon anticipates needs before they become queries.

For marketers, that changes both the opportunity and the risk. Content can now reach the right audience without a single keyword ever being typed. A sharp industry analysis or a well-designed explainer can travel far beyond traditional search results. But content that isn’t legible to these systems—or doesn’t fit the platform’s native signals—won’t travel at all.

In 2026, marketers will need to start designing for moments of “inferred need,” not just explicit demand. That means understanding how different platforms evaluate relevance, creating content that fits their native formats, and accepting that discovery is increasingly driven by systems deciding for users.

Prediction 3: Personalization Will Get a Memory

Persistent conversational history and user-level memory are becoming standard features across major AI platforms. ChatGPT, Gemini, and Perplexity now remember past interactions, saved preferences, and accumulated context. More and more, this memory shapes what content gets recommended to users.

The consequences for discovery are profound. Somebody who has previously explored a topic at an advanced level will receive different results than someone encountering it for the first time. Past clicks and conversational patterns all influence what AI presents in its outputs.

This creates audience fragmentation at an unprecedented scale. The same query from two different users may surface entirely different content based on their individual memory profiles. Repeat searchers see increasingly tailored results that reflect their established preferences and expertise levels.

Marketers must respond with more modular content strategies. They’ll need to create content that serves different knowledge levels (e.g., beginner, intermediate, expert). That means designing content as a progression with clear entry points, deeper follow-ons, and signals that help systems understand who each piece is for.

Prediction 4: Attribution Models Will Break, but New KPIs Will Emerge

With the rise of AI search, brands are losing insight into the traditional click-based path from search to conversion. It’s getting harder to determine how content influences decisions.

This breakdown forces a rethinking of measurement. Clickthrough rates (CTRs), long the bedrock of search performance analysis, become less reliable as primary KPIs as more conversions happen through pathways that bypass traditional tracking.

New metrics will emerge to fill the gap. Citation frequency—how often AI systems reference your content—is becoming a meaningful signal. Model recall rates, excerpt usage patterns, structured data adoption, and dwell time within AI-generated summaries all offer insight into content performance in the new environment.

Perhaps most significantly, “share of answers” will emerge as a competitive benchmark. Just as share of voice became a standard PR metric, share of answers will measure how often your brand appears in AI-generated responses relative to competitors. Performance teams and forecasting models will need to incorporate these new signals, developing frameworks that capture influence even when direct attribution proves impossible.

Prediction 5: Authority Signals Will Become the New Ranking Factors

As LLMs grow more cautious about sourcing and citation quality, authority signals are displacing traditional SEO factors as the primary determinants of visibility. Trust, accuracy, and demonstrable expertise have become the currency that determines whether a brand’s content gets surfaced at all.

This shift reflects how AI systems evaluate content. They increasingly emphasize verifiable claims, named experts, publication transparency, and clear information provenance. High-signal pages—those rich in facts, specificity, structure, and consensus alignment—receive preference over high-volume content that lacks depth or originality.

Model training updates, retrieval layers, and safety guardrails all push the system toward what might be called “safe precision.” AI systems reward brands that back up their claims with evidence and penalize those that don’t. The era of thin aggregation and SEO filler content is ending.

For marketers, this means substance will beat scale more often than not. Original research, subject matter expert quotes, and first-party insights are already gaining substantial value. Brands must invest in credentials like detailed author bios, proper citations, disclosure statements, and expert review processes.

In other words: Human expertise is becoming a competitive advantage again. (There’s a reason the recent Wall Street Journal article on brands hiring “storytellers” went so viral.)

Preparing for the Search Landscape Ahead

The transformation of search represents both a challenge and an opportunity. Marketers who cling to legacy approaches will find their strategies increasingly ineffective as AI reshapes discovery, but those who adapt will position their brands for sustained organic growth.

The time to prepare is now. Audit your content for answer-readiness. Invest in structured data and expertise signals. Build measurement frameworks that capture influence beyond clicks. The search landscape of 2026 is taking shape today, and the foundations you lay now will determine your visibility in the AI-driven discovery era ahead.

Frequently Asked Questions (FAQs):

If clicks are declining, how do we prove content is working?

Measurement is shifting from traffic to influence. Metrics like citation frequency, excerpt reuse, and “share of answers” are becoming more meaningful indicators of performance than CTR alone. While these signals aren’t as clean as last-click attribution, they offer a clearer picture of how content shapes decisions upstream — even when traditional analytics can’t see it.

What kinds of content perform best in AI-driven discovery?

Content that is clear, specific, and defensible tends to travel farther than broad or generic material. AI systems favor structured explanations, verifiable claims, named experts, and well-defined scopes. Original research, expert commentary, and tightly framed explainers consistently outperform thin aggregation or keyword-driven filler.

How should teams adapt their content strategy for personalization and memory?

Teams should think in terms of progression rather than one-size-fits-all assets. That means creating modular content that serves different knowledge levels and clearly signals who each piece is for. Entry-level explainers, deeper technical breakdowns, and advanced perspectives should connect logically, allowing systems to surface the right material based on a user’s history and expertise.

The post What’s in Store for the Future of Search in 2026? 5 Predictions appeared first on Contently.

Take, for instance, a fintech team that publishes a Know Your Customer (KYC) explainer, only to learn later that a single phrasing choice misaligned with Anti-Money Laundering (AML) requirements. What seemed like a harmless line can trigger formal compliance reviews, takedown notices, or even fines.

When producing content for regulated industries, the question needs to shift from “How do we create high-performing content?” to “How do we create content that performs without crossing compliance lines?” That’s where a compliance-guided content strategy comes in.

Let’s discuss how you can create one.

1. Know the Rules

Start with identifying the legalities surrounding sensitive topics and mapping out regulatory boundaries in your industry. Consider areas like:

• Data protection and privacy
• AI use
• Advertisements
• Sociocultural nuances
• Influencer and sponsored content disclosures

Data protection laws differ by region, even for the same industry. For example, California has its own data privacy law called the California Consumer Privacy Act. Likewise, the United Kingdom enforces the Data Protection Act.

If your service reaches Europe, Canada, Asia, or African markets, expect to deal with additional policies like:

• GDPR
• PIPEDA
• POPIA
• DPDP

Each region has different rules governing the types of ad content your marketing team can produce. For instance, a health tech platform can’t market a symptom checker as a diagnostic tool in the U.S. without treating it as a regulated medical device and backing its claims with clinical validation. And if it collects or uses patient data, it also has to comply with HIPAA‘s privacy and security rules. In Europe, regulators such as France’s CNIL require clear, explicit consent and transparent disclosures before using personal data for targeted or AI-powered personalized marketing.

Study and compile the laws that apply to your operations, and use them to streamline your content framework for compliance. To stay up to date, you can track relevant regulations using legal monitoring tools such as Securiti, OneTrust, or DataGuidance.

2. Build a Compliance-First Framework

Wang Dong, founder at Vanswe Fitness, notes that a common mistake many content teams make is treating compliance as an afterthought. “You need to do the opposite. Compliance has to be built into your content skeleton, also known as the framework. That means your team needs to shift from the typical idea first, draft next, and legal review last to something more structured.”

To operationalize compliance, begin by:

• Formulating an idea and outline review process that includes compliance guardrails
• Creating compliant content briefs complete with bulleted “do’s and don’ts”
• Drafting within approved terminology
• Setting up processes for legal and expert verification before publishing
• Ensuring final content is aligned with both brand and regulatory language

Besides including legal or compliance review in each stage, you also need to:

• Use approval workflows and audit trails to track what was reviewed, by whom, and when
• Keep documentation for every claim, quote, statistic, or regulatory reference so you can defend your messaging if questioned

Anna Zhang, head of marketing at U7BUY, offers a piece of advice: “Create an internal reference sheet for your content team that summarizes what they can say, what they must avoid, what requires legal review, and what needs source citations or disclaimers,” she says. “This includes word choices permitted in your industry; pronouns in DEI-inclined regions; cultural intonations that can trigger public outrage; and overly assertive, non-permissible terms.”

3. Define Clear Messaging Boundaries for Health and Financial Claims

The health and financial sectors can be more sensitive and reactive to non-compliance due to the high stakes involved. When you’re dealing with people’s lives and financial well-being, the margin for error narrows quickly. So, it’s essential to establish crystal clear boundaries around gray areas like health promises or investment guarantees.

For instance, you shouldn’t use ambiguous phrases or absolute language like:

• “Guaranteed returns on investment”
• “Zero returns on investment”
• “Stops fraud completely”
• “Eliminates stroke risk”

Instead, consider more transparent and compliant framing like:

• “Returns tied to market performance with defined risk controls”
• “Designed to limit downside exposure under specific conditions”
• “Helps reduce fraud incidents through multi-layer verification and monitoring”
• “Supports cardiovascular health outcomes when combined with licensed medical care”

In a nutshell, assertiveness and overtly promotional phrases might violate marketing regulations in your industry. A more suggestive approach backed by verifiable data can save you legal trouble down the line.

4. Use Technology Wisely

“Manually reviewing each piece of content for compliance can be a tough nut to crack, especially if you churn out [dozens of pieces of content each week],” says Paul McKee, founder of ReadingDuck.com. He suggests that AI-powered writing tools like Grammarly and editing assistants such as Hemingway can help surface unclear phrasing, overly bold claims, or ambiguous language for compliance review and editing.

Grammarly screenshot by Author

Compliance platforms like Vanta can also automate evidence collection and help teams achieve and maintain compliance with frameworks such as SOC 2, HIPAA, and ISO 27001. Others, such as Riskonnect, centralize policies, compliance requirements, audit tracking, and risk reporting in a single system.

Such platforms often include features like:

• Content tagging for regulatory risk, such that each content piece is flagged for required disclosures, claims, and region-specific rules
• Audit trails to track who reviewed what, when, and what decision was made
• Alerts and changes to monitor regulation updates and notify you so you can revisit content that may now be non-compliant
• Compliance status to help you know which content pieces are safe, which need review, and which are blocked

5. Focus on Transparency and Credibility When Using AI

Morgan Taylor, co-founder of Jolly SEO, notes that regulated industries may also require more transparency when it comes to the use of AI. “Each content piece should also disclose AI involvement, and to what extent, as required by the regional and global regulations,” he says.

Stipulations may include:

• Mandating citation of verified data sources
• Disclosing sponsorships and affiliations
• Keeping messaging consistent across regions with varying regulations

AI disclosure isn’t just necessary due to industry regulations, but also because your audience demands it. According to Dentsu, about 75% of consumers said brands should disclose if branded content was created with AI.

6. Train and Align Your Internal Teams

Studying compliance laws, building a framework, and defining messaging boundaries is just half the job of developing an effective content strategy. The other half is equipping your team with this strategy. You can do that by:

• Organizing regular workshops that focus on current operational laws and your brand’s ethics
• Sharing a short compliance checklist that your writers and editors can seamlessly reference

Emily Ruby, owner of Abogada De Lesiones, suggests building a feedback loop between your legal and marketing teams to streamline compliance review. “This involves integrating your legal team into the final content review process just before any piece goes live and helping them communicate directly with your editors,” she says.

7. Measure What Matters

After implementing the above best practices, start tracking metrics that show whether your compliance process is actually working. This includes:

• Turnaround time for compliance review
• Approval rate across content themes and formats
• Number of revisions caused by regulatory language
• Content trust KPIs, such as authority signals, citation depth, and expert references

Additionally, conduct quarterly audits on published content to identify outdated claims, expired data sources, and language that no longer aligns with current industry regulations.

In highly regulated industries, credibility is fragile and the repercussions for compliance missteps can be severe. The more your team embeds compliance into everyday workflows, the safer and more effective your content becomes.

Contently’s team of expert Managing Editors and professional creators can help strengthen your workflows and ensure every piece meets your industry’s standards. Reach out to get started.

Frequently Asked Questions (FAQs):

What counts as a “regulated industry” for content teams?

Industries like healthcare, finance, insurance, cybersecurity, legal services, and fintech operate under strict compliance frameworks. If your content touches personal data, medical advice, financial guidance, or AI-driven personalization, you’re likely subject to additional oversight.

How often should content be reviewed for compliance?

A good baseline is quarterly, but high-risk industries may require monthly reviews, especially for evergreen pages making health, financial, or legal claims. Any regulatory update or product change should also trigger a review cycle.

How can small teams manage compliance without slowing down production?

Start with guardrails: approved terminology lists, claim libraries, and compliance-aligned content briefs. Then use workflows, audit trails, and documentation templates to reduce back-and-forth and make reviews predictable.

The post Developing a Content Strategy for Regulated Industries in 2026 appeared first on Contently.

Schema markup sounds like something that belongs in a developer’s basement lab, right next to the blinking server rack and a stack of vintage Linux manuals. Most marketers treat it that way too: vaguely intimidating and probably dangerous to poke without supervision.

But you don’t need to write code or summon an engineer to make sense of it. And if your content is getting outranked or out-cited by inferior articles in AI Search, this is likely the one of the missing links.

Schema isn’t magic. It’s simply the structured vocabulary that tells search engines and AI tools exactly what your page is about and whether it’s trustworthy enough to cite.

Here’s a marketer-friendly overview of this increasingly important component of your content.

The Problem: Your Content Is Invisible to AI

Schema markup is structured data you add to your website’s HTML that tells machines exactly what your content is about. Think of it as labels on a filing cabinet: Without identification, someone rifling through your files has to guess what’s inside; with clear labels, they know instantly.

Search engines and AI models face the same ambiguity problem. Your page might include a product name, price, author bio, and publication date — but without schema, machines have to infer what each piece represents. Schema removes the guesswork by marking up entities: “This is a product. This is its price. This is the author. This is when it was published.”

The payoff is twofold: in traditional search, schema powers rich results like star ratings, FAQ dropdowns, or recipe cards. In AI Search, schema helps language models identify entities, reduce ambiguity, verify facts, and cite sources. Whether someone searches on Google or asks ChatGPT, schema makes your content easier to parse and surface.

But implementation mistakes are costly. Sites that mark up content invisible to users or use schema that doesn’t match visible page content risk manual penalties from Google, which can remove rich-snippet eligibility entirely. In other words, schema only works when it accurately reflects what’s on the page.

The Three Schema Types Marketers Need First

Most marketers don’t need every schema type under the sun. These three schema types cover 80% of content marketing use cases and deliver the fastest visibility wins:

Article schema

This schema type marks up blog posts, news articles, and longform content. It tells search engines the headline, author, publication date, and featured image. LLMs rely on Article schema to disambiguate entities and verify publication dates when fact-checking claims — and without it, your “Apple” could be a fruit, a tech company, or a record label.

Use Article schema on every piece of editorial content you publish; it’s the baseline for getting your articles indexed properly and cited in AI answers.

Organization schema

This establishes your company as a verified entity; without it, AI tools may cite your content without attributing it to your company. Organization schema includes your business name, logo, contact info, and social profiles. Add this schema type to your homepage and About page to help search engines and AI models connect your brand to your content across the web.

Person schema

This marks up author bios, executive profiles, and contributor pages. It connects individuals to their credentials and organizational affiliations, and it’s critical for building expert authority. When AI tools cite content, they often cite people by name, and Person schema makes those connections explicit. This becomes particularly important as AI systems prioritize content from verified experts over anonymous sources.

According to Backlinko research, 72.6% of first-page Google results already use schema markup, meaning the majority of companies who do well with traditional SEO have implemented it, whether intentionally or through CMS defaults. With schema rapidly becoming even more important for landing in AI Search results, the window for competitive advantage is closing.

How to Implement Schema This Week

You don’t need to write JSON-LD by hand or understand HTML to implement schema. Multiple no-code pathways exist, including:

• CMS plugins. WordPress users can install Yoast SEO or Rank Math, both of which add schema automatically to posts and pages and let you fine-tune the type per template. On platforms like Shopify, Squarespace, and Webflow, many modern themes and built-in features (or apps) output structured data for products and articles. If your CMS offers any schema or “structured data” functionality, enable and configure that first. It’ll be the fastest path to broad coverage.
• Schema generators. If your CMS doesn’t do enough out of the box, use a visual generator (like Google’s older Structured Data Markup Helper or a third-party tool) to tag elements on your page and export JSON-LD. Just highlight the headline and click “headline” (or highlight the author name and click “author”), and the tool creates the markup. Paste it into your page’sand you’re done.
• Pro tip: Validation is non-negotiable. After adding schema, validate it. Google’s official tools (e.g., the Rich Results Test and Google Search Console) check highlight missing fields and flag incorrect formats. Fix what’s broken, re-test, and then publish.

To get traction fast, start with quick wins: Add Article schema to your top 10 blog posts this week, Organization schema to your homepage, and Person schema to author bio pages. Track which pages show up in AI-generated answers over the next quarter. Measure the shift.

The Bottom Line

Schema markup is a quiet layer of infrastructure that grows alongside your content. And while everyone is arguing about whether it’s “too technical,” the brands shipping it are quietly becoming the sources machines trust first.

You don’t need to overhaul your entire site this week. Start with the pages that drive the most value and build outward from there. Momentum is what matters, and the longer you wait, the more entrenched everyone else’s signals become.

Ready to level up your content operations? Explore how Contently helps brands turn strategy into measurable results.

Frequently Asked Questions (FAQs)

Do I need schema if my content already ranks well on Google?

Traditional rankings don’t guarantee visibility in AI-generated answers. Schema helps AI models understand and cite your content even when users never click through to your site. If you want to show up in ChatGPT, Perplexity, or Google AI Overviews, schema provides the structured context those systems rely on.

How long does it take to see results from schema implementation?

Google typically recrawls and reindexes pages within a few weeks of adding schema. Rich results can appear as soon as your updated markup is indexed. For AI Search visibility, expect a longer timeline (months, not weeks), but the benefits compound over time. Most brands see initial rich results within 2-4 weeks, while AI citation improvements take 2-3 months as models refresh their retrieval systems.

Can schema hurt my SEO if I implement it incorrectly?

Incorrect schema won’t tank your rankings, but it won’t help either. Google ignores malformed markup or schema that doesn’t match your page content. The bigger risk is missing out on rich results and AI citations. Use validation tools to catch errors before they go live.

The post WTF Is Schema? A Primer for Marketers appeared first on Contently.

That’s changing quickly. AI-driven video indexing, powered by large language models (LLMs), computer vision, and automatic speech recognition, now treats video content like readable text. Search engines and recommendation systems can now see everything from your captions to the text on your slides.

As a result, video is becoming SEO 2.0, a fully discoverable format that can rank and surface answers just like a blog post.

For content teams, this demands a new approach. If video is now as indexable as written content, you need a “video retrievability” strategy that ensures your clips show up when people search for the problems your product or service solves.

Why Video Is Now SEO-Relevant

The mechanics of search are evolving quickly. AI-powered systems like Google’s AI Overviews, Perplexity, and ChatGPT can now parse the actual content inside your videos, not just the title or description. With advances in automatic speech recognition, computer vision, and language modeling, search engines can extract meaning from multiple layers at once:

• Spoken dialogue transcribed and analyzed word by word
• Auto-captions and SRT files providing structured, timestamped text
• On-screen text detected through computer vision, from slide titles to product labels

This is a major shift from the old world of video SEO, where discoverability hinged on thumbnails, tags, and a few surface-level signals. Now, every meaningful moment, from your initial overview of a framework to your example at minute 3:42 to the term typed on a screen, can be read and indexed.

That’s the foundation of retrievability: a search engine’s ability to find, understand, and surface specific insights from within your video content.

Beyond SEO: How Generative Search Engines Use Video

Retrievability is only the starting point. Generative search engines go a step further by blending insights from text, video, audio, and images into a single synthesized answer. In these environments, video isn’t treated as a standalone format. It’s just one source among many that an LLM uses to construct the most authoritative response.

That’s why video citations are showing up in AI-driven answers. A YouTube clip may appear inside a Google AI Overview as supporting material, or TikTok’s “Search Highlights” might pair a trending query with a short, highly relevant clip. ChatGPT and Perplexity increasingly pull structured insights from videos that are properly indexed and easy to parse.

For brands, visibility now depends on multi-format coverage. If your expertise exists only in blog posts, you have a gap. If your videos aren’t optimized for retrieval, they won’t appear in the generative answers shaping consumer decisions.

How to Optimize Video for AI Search

If video is now discoverable at the dialogue level, your optimization strategy needs to go deeper than metadata. Here’s how to make your videos work like high-performing content.

Think of your script as both narrative and index.

Write your video scripts the way you’d compose an optimized blog post. That means clear phrasing, natural long-tail questions, and front-loading key terms in a way that feels conversational.

That “conversational” element is important because LLM-powered search engines prioritize natural language. Instead of saying “Today we’ll discuss customer acquisition strategies,” try, “How do you acquire customers without spending a fortune on ads?” The second phrasing mirrors how people actually search, and gives AI systems a clearer signal about the problem you’re solving.

If you’re explaining a concept, state it plainly early in the video. Ambiguity might work for storytelling, but it doesn’t work for retrievability.

Get serious about metadata hygiene.

Your title, description, and tags should accurately reflect the problem your video solves, not just the topic it covers. Avoid keyword dumping. Instead, prioritize clarity and user intent.

For example, in lieu of a title like “Content Marketing Tips | SEO | Video Strategy | 2025,” go with something like “How to Make Your Marketing Videos Discoverable in AI Search.” The latter is more specific and clearly describes the content’s value.

This approach applies to platforms ranging from YouTube to TikTok to LinkedIn.

Make your transcript the most accurate version of your video.

Always upload full transcripts or SRT files, which are now critical ranking signals. Well-formatted transcripts help AI systems disambiguate topics and identify key takeaways, as well as match your content to nuanced or niche queries.

Transcripts also capture long-tail queries that don’t fit neatly into titles or descriptions. Someone searching “how to handle objections in sales calls with technical buyers” might find your video because that exact phrase appears at minute 12 in your transcript, even if your title is more general.

Keep your transcripts clean. Remove filler words if they obscure meaning, but don’t over-edit. Natural phrasing is what LLMs are trained on.

Think of on-screen text as a secondary layer of indexable content that reinforces spoken points.

Everything you put on screen — callouts, lower thirds, slide text, product labels — is now crawlable. That’s a huge opportunity, but it also means you need to be intentional. If you’re introducing a framework, make sure the name of that framework appears visually. If you’re citing a stat, put it on screen in readable text.

Avoid “text spam,” i.e., cluttering your video with keywords just for the sake of crawlability. But do ensure that key terms, takeaways, and concepts appear both verbally and visually when relevant.

Practical Checklist: Your Video Retrievability Toolkit

Here’s a quick implementation guide to make your video content discoverable in AI-powered search:

• Write scripts with clear takeaways and natural phrasing that mirror how people search
• Add clean titles, accurate descriptions, and high-quality tags that reflect user intent
• Include full transcripts or SRT files with proper formatting and minimal filler
• Use intentional on-screen text for key concepts, stats, and frameworks
• Maintain consistent naming conventions across platforms to build topical authority
• Repurpose transcripts into blog posts to reinforce your expertise and capture text-based search traffic

Treat this as an evolving practice. As AI Search tools become more sophisticated, the ways they index and cite video will continue to shift. The core principle, though, remains making your content easy to find, understand, and reference.

Search engines are learning to see, hear, and cite everything. The black box is open. What you do with that power is up to you.

Learn how Contently can help you turn video into discoverable, high-performing content.

Frequently Asked Questions (FAQs)

How long should my video be for optimal discoverability?

There’s no universal “best length,” but clarity and structure matter more than duration. Shorter videos work well for intent-matching on TikTok and YouTube Shorts, while longer explainers provide deeper material for generative answers to pull from.

Do I need special tools to make my videos indexable by AI Search?

No. Most of what matters — clean scripting, accurate transcripts, readable on-screen text, and clear metadata — can be handled during production and upload. AI search engines handle the indexing automatically if the signals are there.

How quickly will I see results from video retrievability efforts?

Indexing timelines vary by platform, but many brands see improvements within weeks. The bigger gains come from consistency: using unified naming conventions, publishing across multiple formats, and reinforcing your expertise with supporting written content.

The post Your Brand Needs a Searchable Video Strategy appeared first on Contently.

The main driver behind this, of course, is the rise of Large Language Models (LLMs) like ChatGPT, Gemini, and Claude, which have changed the way people find and consume information online. Instead of typing keywords into a search engine, users are now asking their favorite AI chatbot for an answer.

For marketers, this means just making sure your brand is at the top of SERPs is no longer enough. You also want your content represented in AI Overviews or cited in AI-generated answers when users ask related questions.

On its own, good old-fashioned SEO is no longer sufficient for discoverability. You also need to mix in some generative engine optimization (GEO) and answer engine optimization (AEO).

Read on for a breakdown of what these terms mean and how they differ from basic SEO.

Back to Basics: What Is SEO?

At its core, SEO is the process of improving a website so it ranks higher in organic search engine results pages (SERPs). Everyone who’s had a brush with digital marketing knows SEO is not a set-it-and-forget-it kind of situation.

Search algorithms evolve constantly, but the core components of SEO have remained fairly consistent. Here are a few of them:

• Technical SEO: Makes sure your site is crawlable, fast, mobile-friendly, and secure.
• Content optimization: Aligns on-page content with user intent and relevant keywords.
• On-page SEO: Improves readability, structure, and overall relevance.
• Backlinks: Earns high-quality inbound links from reputable sources to strengthen your site’s credibility and domain authority.

You can confidently say that most successful websites dominating SERPs right now have been built using smart SEO strategies.

Let’s take NerdWallet as an example. The website made masterful use of SEO techniques to become an authority in financial content. As a result, many relevant inquiries will lead users to one of its pages.

Years of evolving strategies and tools have helped many brands and publishers climb the ranks in similar ways, from simple keyword research tools that help uncover audience intent to more complex platforms like Seobility that offer comprehensive site audits and optimization checks.

Is SEO Dead?

Now that LLMs are wreaking havoc on search habits, does this mean SEO doesn’t matter anymore?

In reality, things started to change a while back. Younger generations, starting with Gen Z, are not big fans of Google Search. Years ago, they started using social media like TikTok or voice assistants like Alexa to find information. LLMs are just the next best thing.

Despite all of this, most brands are not ditching SEO entirely, and for good reasons.

Sure, search is evolving, but search engines are still very much relevant. In fact, Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) are simply new forms of content optimization for different methods of content distribution.

Emerging Terms: What Is GEO & AEO?

Generative Engine Optimization (GEO) focuses on improving your chances of being referenced, cited, or paraphrased by AI-powered platforms like ChatGPT, Gemini, or Claude. These models generate responses based on trained and retrieved information from many sources, so content that’s well-structured, credible, and semantically rich is more likely to appear in AI-generated answers.

For example, when users ask ChatGPT about the best debt relief companies in California, brands such as Freedom Debt Relief may surface in the response. It’s an illustration of how visibility in generative engines can reflect effective AI-era optimization.

Answer Engine Optimization (AEO), by contrast, focuses on earning visibility in direct-answer formats within traditional search engines, such as Google’s featured snippets, People Also Ask boxes, or voice assistant results. This requires clear, concise, and structured content that aligns with user questions.

In short, GEO targets AI systems designed to generate, while AEO targets search engines designed to answer. Both rely on trust, clarity, and contextual depth, but GEO extends those principles to the new world of generative search.

How GEO & AEO Work

While AEO has been around for several years, GEO is newer and still rapidly evolving. We don’t yet have defined methods to measure impact or a playbook, but the foundational techniques are rooted in tried-and-true SEO principles.

Early adopters focus on a few key techniques that show promising results, such as:

1. Structured data & schema markup. Marking up your content with schema (like FAQPage, HowTo, or Article) helps machines understand your content contextually. It provides a strong signal for AEO, and may indirectly help GEO by improving how AI systems interpret your content.
2. Semantic clarity & factual precision. AI models and answer engines prefer clear, accurate language. Write your content in a way that’s direct, well-researched, and easy to parse.
3. Authority signals (E-E-A-T). Expertise, Experience, Authoritativeness, and Trustworthiness are still essential for high-quality content, whether you’re writing for search engines or AI. The best way to signal authority is by citing credible sources, showing author credentials, and maintaining internal consistency.
4. Structured FAQs. Frequently Asked Questions (FAQs) are a powerful format for AEO. A well-designed FAQ section increases the chances that your content will be picked up by AI overviews or voice search answers.Your FAQ section should first serve the user, and only then try to cater to AI crawlers. For instance, the FAQ section on the Event Ticket Center website is divided into nine categories so that anyone can find their answer within one minute of landing on the page.

GEO & AEO in Action

Here are a few examples that show you GEO and AEO are working for your content and what you should aspire to achieve:

• Your blog post is referenced or summarized in a ChatGPT or Copilot response
• Bing Copilot’s AI is using your product pages to formulate replies
• An informational article you wrote is used as a source in Perplexity’s multi-source summary
• A detailed how-to guide, such as ETC’s “How to Get Super Bowl Tickets in 2025” blog post, is picked up by Google’s AI Overview and cited as a source.

• Your brand is recommended in “near me” or “how to” queries
• Your FAQ page is pulled directly into a Google Assistant response when someone asks a question
• A snippet from your glossary page is used by Alexa to answer a terminology question

SEO vs. GEO vs. AEO: Three Facets of Online Visibility

Many of the techniques used in GEO and AEO, like schema markup, clean structure, and relevant language, depend on well-optimized SEO practices. Without crawlable pages, fast load times, and keyword relevance, there’s nothing for AI or voice assistants to pull from in the first place.

That’s because AI systems don’t invent information out of thin air. Many use Retrieval-Augmented Generation (RAG) or similar methods to draw on existing content from trusted sources and turn it into natural-sounding answers.

In short, if your content is already ranking well, clearly structured, and properly marked up, you’re giving both AI and answer engines exactly what they need to surface your brand.

SEO, GEO, and AEO work together to make your content more visible online. Search is changing, but these methods can help you stay on top of things and in front of your target audience.

To keep pace with AI-driven discovery, embrace SEO as your foundation, then layer on GEO and AEO to future-proof your strategy and keep your brand discoverable. Start blending all three now for smarter, more sustainable growth.

Ben Kruger is the Chief Marketing Officer at Event Tickets Center, where he oversees all marketing efforts, including paid search, social media, affiliate marketing, and email campaigns. He builds and scales paid media campaigns for growth and retention, leveraging machine learning models and predictive analytics for marketing activations and analysis. Ben has spoken at industry events such as the Coalition for Ticket Fairness Annual Conference.

Frequently Asked Questions (FAQs):

Do I need to overhaul my SEO strategy to add GEO and AEO?

Not at all. Think of SEO as your foundation for site speed, crawlability, and keyword relevance, and GEO/AEO as layers that build on top. If your SEO is strong, you’re already halfway there.

What types of content perform best in generative search?

Informational, evergreen, and expert-authored content tends to perform best, particularly pages that explain how or why something works, rather than simply selling a product.

How can I tell if my content is appearing in AI results?

You can manually check by prompting ChatGPT, Bing Copilot, or Perplexity with relevant queries to see if your brand or content is mentioned. Tools for tracking GEO visibility are still emerging, but expect more analytics options soon.

The post A Marketer’s Guide to the New Alphabet Soup of Search: SEO vs. AEO vs. GEO appeared first on Contently.

Not KPIs, not personas—entities. We know it sounds vaguely like the plot of a sci-fi film about sentient databases. But entities are real, and if AI models don’t recognize you (or your brand) as one, you may as well not exist to the millions of users currently asking AI tools for answers instead of typing searches into Google.

Somewhere between “thought leader” and “structured data,” entities are how AI search engines recognize and categorize information sources. That means your brand needs to show up as an entity and your products as their own connected entities. Beyond making your brand and flagship content machine-readable, you can tap the people within your organization who already embody that expertise—and elevate them as recognized entities, too.

So if you’ve got a CTO who wows the crowd on stage with her cutting analysis of AI ethics, or a chief economist whose byline shows up in every industry trade mag, you’re halfway there. But you still need to figure out how to turn these living, breathing experts into machine-legible profiles complete with context, connections, and citations that LLMs can actually read.

Why Internal Experts Matter in AI Search

As AI-driven search tools evolve, they’re often rewarding recognizable human expertise over anonymous brand content. Research from BrightEdge identifies author expertise as one of the key quality signals AI algorithms use to evaluate trustworthiness and relevance. In other words, an article bylined “Marketing Team” carries less authority than one attributed to a real person with verifiable experience and a digital footprint to match.

This ties into a larger shift in how credibility is gauged online. Search Engine Land notes that “verifiable authorship makes your content stand out as trustworthy in a sea of generic AI material,” recommending brands use structured data to help AI systems understand who is behind the content (more on this in a sec). When search engines and AI models can connect a name to reputable publications and other professional activity, they’re more likely to surface that expert as a reliable source.

This matters because buyers trust people more than logos. The 2024 Edelman-LinkedIn B2B Thought Leadership Impact Report found that nearly three-quarters (73%) of decision-makers say an organization’s thought‐leadership content is a more trustworthy basis for assessing its capabilities than its marketing materials.

Put simply: Both algorithms and audiences are looking for the same thing, and that’s credibility. When brands elevate internal experts with visible, verifiable identities, they improve their odds of being cited in AI-generated answers and influencing real-world buying decisions.

Three Implementation Layers

Turning experts into search entities requires three systems working together.

1. Optimizing authorship metadata

Think of your expert pages as digital passports for your people. If AI systems can’t read the name or credentials on that passport, your content risks rejection.

This first layer is about definition, i.e., making sure every expert within your organization has a clear, consistent identity that algorithms can recognize. Maybe your head of compliance appears as “J.R. Martinez” on your blog, “John Martinez, JD” on LinkedIn, and “John Martinez” on a conference agenda. To a human, it’s obviously the same person; to an algorithm, it may be three separate entities (there’s that fun term again).

Likewise, specificity matters. The same rules that make a resume effective apply here: A vague bio like “20 years in B2B SaaS” tells a weaker story than “former VP of Product at Salesforce, led three launches generating $50M ARR, published in Harvard Business Review.” This layer is about getting the foundational data right so AI systems know who your experts are.

Action items for marketers:

• Add structured data: Use Schema.org/Person markup on every author bio to make expertise machine-readable, and link to LinkedIn and external publications.
• Standardize bylines: Keep author names, titles, and bios consistent across all platforms, and maintain a canonical author page as the single source of truth. Update on a consistent basis (quarterly or every six months) to reflect new achievements or expertise.
• Show concrete credentials: Use specific, verifiable achievements (e.g., awards, results, publications) instead of vague experience statements.

2. Building cross-platform credibility

If your experts only exist on your blog, they might as well be whispering into the void. Once identity is defined, visibility is the next layer. AI engines (and human audiences alike… those still matter too) take cues from signals across the web. A CTO who posts on LinkedIn, appears on a podcast, receives invites to CES and SXSW every year, and gets quoted in TechCrunch looks a lot more “real” to both humans and machines than one who lives exclusively on a company site.

This layer is about amplification: showing up in trusted spaces where expertise carries weight. Each verified appearance helps algorithms cross-reference your experts and build confidence in their authority.

Action items for marketers:

• Show up beyond your own domain: Encourage experts to share insights on LinkedIn, contribute guest articles, join panels, or appear on podcasts. Each mention reinforces their authority signal.
• Keep bios consistent: Use the same headshot, job title, and expertise descriptors across platforms so AI sees one cohesive identity.
• Prioritize trusted venues: Focus your experts’ visibility in the channels and publications your audience already trusts. Quality beats quantity.

3. Connecting human voices to structured data

Your VP of Product might publish a brilliant post on API security, but unless that article links her name to the subject in structured data, those insights will disappear into the algorithmic abyss. This third layer closes the loop and linking who your experts are and where they appear to what they know.

This is where human knowledge becomes data that machines can understand and reuse. By embedding structured tags and capturing expert insights in standard formats, you make it easy for AI systems to retrieve and cite that expertise again and again.

Action items for marketers:

• Connect people to topics: Use internal knowledge graphs or structured tagging to link each expert to their focus areas within your content taxonomy.
• Use Q&A formats strategically: Create FAQs or explainers where experts answer common questions, then mark up with FAQPage schema to give AI clean, citable quotes.
• Close the feedback loop: When experts share new insights or answer customer questions, capture that information in structured formats so AI systems can find and surface it.

Common Barriers to Expert Participation

Getting insights out of busy SMEs or execs is messy, political, and often lands low on their priority list. Here are the five roadblocks that show up again and again:

1. Time (and attention) scarcity. Experts are underwater. Billable work and internal projects always come first, leaving “content” to fight for scraps.
2. The curse of knowledge. The more experienced someone is, the harder it is for them to explain what they know. SMEs often skip context or assume everyone understands their shorthand, which makes it tough to extract content that’s clear and usable.
3. Legal and brand risk aversion. Some organizations hesitate to spotlight individuals, fearing off-brand messaging or intellectual property leaks.
4. Internal competition. In fields where credibility equals career capital, multiple people may want to “own” the same topic. Without guidelines for who speaks on what, thought leadership can turn into a turf war.
5. No infrastructure for knowledge capture. Most teams lack the systems to document, tag, and reuse insights efficiently. Without templates, structured interviews, or AI-assisted content extraction, valuable expertise slips through the cracks.

Extraction Tactics That Work

Most content programs stall not because experts lack ideas, but because teams lack infrastructure. When you fix the process, expert participation scales naturally.

1. Make participation low-friction. Stop asking experts to write. Instead, schedule 30-minute interviews where content teams extract insights. One conversation can fuel three blog posts, five LinkedIn updates, and a dozen quotable soundbites. Layer in micro-content opportunities, e.g., quick takes on breaking news or short Slack replies that can be repurposed later. Even better, host “office hours” where content teams drop in with questions.
2. Level up your content team. Train writers to think like interviewers. Teach them to draw out “atomic insights”—the smallest, most original nuggets of expertise that make content stand out. Close the loop by showing experts how their words evolve into polished stories.
3. Partner early with legal and comms. Bring them into the process instead of treating them as gatekeepers. Create simple review workflows and clear guardrails, e.g., what experts can and can’t comment on, how approvals work, and where quotes will appear.
4. Frame it as career growth. Recast participation as professional development. Show how visible experts land conference invites or grow their LinkedIn following. The more your people see real outcomes, the easier it is to get them on board.
5. Create repeatable extraction systems. Build interview templates by content type, e.g., thought-leadership sessions, tactical how-tos, or case-study debriefs. Run monthly roundtables where three to five SMEs discuss one topic; use AI transcription to surface quotes instantly (but have a human double-check for accuracy, of course).

The Long Game

Building expert authority takes time; you probably won’t see results in 30 days. AI systems need consistent, credible signals across platforms before they cite your experts by name in generated answers.

But bit by bit, those signals create a map of expertise that algorithms rely on. Over time, AI builds its own understanding of who knows what. The organizations that keep contributing credible information will shape how their fields are defined in the years ahead.

We can’t change the jargon, but we can make it useful. If “entities” are what the algorithms respect, your experts deserve to be recognized as some of the best.

Learn more about how Contently can help your brand build lasting visibility through expert-driven content.

Frequently Asked Questions (FAQs):

Why should marketers care about entities?

If your experts aren’t recognized as entities, their insights are harder for AI to associate with your brand. Your competitors’ names might even show up in generated answers, even if they’re referencing ideas you originated.

How can I tell if my experts are already “recognized” by AI?

Search for their names alongside key topics on Google and emerging AI search tools like Perplexity or ChatGPT’s search mode. If their profiles or quotes appear consistently, they’re already surfacing as credible entities. If not, you’ve got an opportunity to strengthen their visibility through structured data, authorship pages, and off-site presence.

What’s the fastest way to start building entity recognition, and how long does it take for results to show up?

Start small. Add Schema.org/Person markup to your expert bio pages, link those bios to LinkedIn and other verified sources, and make sure bylines and job titles are consistent across platforms. Then, publish or syndicate content where the algorithms and your audience already look for expertise.

As for how long it takes, this depends. In most cases, consistent, well-structured authorship data starts showing traction in a few months. Over time, as AI models absorb more signals, that visibility compounds.

The post How to Turn Your Internal Experts Into Search Entities appeared first on Contently.

If your brand is lucky enough to be mentioned, it’s usually one line, stripped of style. But even then, the story isn’t yours anymore. The headline your managing editor spent hours crafting has been rewritten, all nuance flattened. Your once-differentiated point of view now reads like it came from a committee.

This is the new reality for marketers: Humans still read content, but increasingly, machines decide what they read first. The job now entails speaking to both audiences — human customers with distinct motivations and mercurial emotions, and robotic algorithms that extract, rewrite, and rank your ideas — without turning your content into boring slop.

The marketers who win in this new era will be the ones whose ideas survive translation. Here’s how to pair standout storytelling with extraction-ready structure.

The Two Audiences Problem

AI Overviews, ChatGPT Search, Perplexity, and voice assistants now read, compress, and represent your content, often before a human sees your page. They snip, condense, and reword, meaning fewer clicks, more paraphrase risk, and new rules for how credit and context travel.

This distribution shift has two practical implications for brands: serve humans with memorable narrative and serve machines with cleanly extractable facts.

Creating content for humans

We may be marketing in the age of machines, but people are still the ones who share (and ultimately buy from) your brand. Ipsos finds that even in marketing content, audiences have a strong preference for human-created content. So even if you’re using AI in your content marketing (which, let’s face it, in 2025 you should be), your message shouldn’t sound mechanical.

What moves people:

• Story arcs with specificity: real scenes, anecdotes, tension, stakes, resolution, and a clear POV that says something new (or says the obvious better).
• Line-level craft: vivid verbs, concrete examples, first-person takes, judicious humor, and sensory detail that earns attention.
• Useful originality: facts or information they can use today, e.g., frameworks, checklists, decision trees, before/after examples.
• Social proof with texture: quotes, screenshots, data points, and customer language that sounds like a person, not a press release.

The challenge:

• Attention math: As attention spans shrink, especially for text-based content, if the first 150 words don’t land, you’ve lost the scroll.
• Audience sophistication: Readers have seen a thousand AI-polished articles; they can spot recycled or over-optimized copy instantly. Every line needs a reason to exist.
• Trust deficit: Audiences crave authenticity; their spidey senses tingle when everything you publish sounds the same. Marketers must balance polish with personality and stay clear without sounding canned.

The takeaway for marketers: Algorithms can summarize information, but only humans can be moved by it. The best human-centric content earns attention by saying something that feels both familiar and fresh, useful and relatable. It draws readers in because it sounds like it was written by someone who understands them.

Even as generative AI reshapes how content is discovered and distributed, you can’t afford to forget these fundamentals.

Creating content for machines

AI engines and LLMs tokenize, extract, and rank. They don’t care how lyrical your prose is or how many hours your writing team struggled to find the exact right turn of phrase for that tagline. They want the claim, evidence, and context mapped to recognizable entities so they can answer a question confidently.

Machines tend to prioritize:

• Clarity & consistency: canonical terms, stable naming, unambiguous definitions, scannable H2s phrased like questions people ask.
• Structure & metadata: JSON-LD schema (Article, FAQ, Product, HowTo), bulleted summaries, glossaries, datelines, author credentials, organization details, canonical URLs.
• Credible citations: first-party data published on your site, outbound links to authoritative sources, methods sections for studies, and consistent cross-site alignment (docs, product pages, partner listings, and PR all using the same names and numbers).

The challenge:

• Zero-click reality: Assistants render answers inline; influence depends on how they summarize and cite you.
• Voice flattening: Witty lines or flowery language get lost in translation; only unambiguous phrasing gets reused.
• Attribution drift: The most parsable source often wins credit, even if they learned it from you.

The takeaway for marketers: Write with the model in mind. Label your answers, standardize your terms, and publish receipts. When writing for AI, clarity — not cleverness — is what earns citations. It’s also becoming clear that freshness is another factor that counts.

How Do Brands Create Content that Speaks to Both Humans and Machines?

To succeed in today’s search-and-summary landscape, you need a dual-pronged content strategy designed for both people and AI parsers. The art is in creating something that reads beautifully to humans while feeding machines the clean signals they need to understand and amplify your story.

Here are five moves to master both:

1. Lead with a scene; label with structure.

Start every piece with a hook that drops readers into a moment by opening with a question, conflict, or vivid visual. Then make sure your subheads, schema, and summaries clearly outline the main takeaways so machines can interpret them. Humans remember stories; machines remember scaffolding.

2. Make every claim quotable and parsable.

When you state an insight, back it with data, name sources explicitly, and phrase it cleanly enough for AI to lift. Think of it as writing for citation: a line that resonates with readers and a sentence that can stand on its own in an AI Overview.

3. Design visuals that speak in two languages.

For humans, visuals should tell a story complete with emotion and context. Machines need text alternatives, descriptive filenames, and clear captions. Whether it’s a chart or a product demo video, metadata is your friend.

4. Use video to teach twice — once to viewers, once to models.

In video or short-form content, open strong; the first three seconds are your headline. Speak keywords naturally in voiceovers, add captions with consistent terminology, and include a structured description when uploading. That helps algorithms surface you, and gives humans a reason to stick with your video until the end.

5. Keep your message stable across every touchpoint.

Machines learn from repetition and alignment. Humans learn from consistency and tone. Use the same product names, taglines, and phrasing everywhere, from blog copy to YouTube titles, so both audiences recognize and recall you.

Measuring Success in a Zero-Click Era

As AI summaries become the new first impressions, traditional traffic metrics no longer tell the whole story. A spike in visibility may not show up as a click, but it can still shape perception, recall, and buying behavior.

The new KPIs live at the intersection of influence and alignment:

• Share of summary: What percentage of AI answers use your phrasing, cite your brand, or reference your data?
• Assisted influence: Does AI visibility correlate with downstream impact, i.e., more branded searches, higher demo requests, stronger sales enablement conversations?
• Funnel impact: Measure the halo; influenced opportunities, demo-to-trial conversions, or ABM coverage lift tied to AI answer visibility.
• Recall tests: Prompt ChatGPT, Gemini, or Perplexity with category questions. Do they echo your terminology, your frameworks, your stats? That’s narrative imprint, not chance.
• Update velocity: How quickly and consistently can you update facts, numbers, and names across every owned channel? Alignment beats speed in a world of retrained models.

We’ve spent years optimizing for people and platforms. Now we’re optimizing for people and parsers. That doesn’t mean stripping the soul from your stories, but it does involve teaching machines how to carry them forward.

The marketers who can do both will own the next era of visibility.

Your stories deserve to be seen and cited. Discover how Contently’s platform helps brands build AI-ready content.

Frequently Asked Questions (FAQs):

What does it mean to create “machine-readable” content?

Machine-readable content is structured in a way that AI systems, search engines, and voice assistants can easily interpret and summarize. That means clear headers, consistent terminology, schema markup, and unambiguous claims so your ideas are easy to extract without losing their meaning.

Should marketers still care about SEO if AI Overviews and chatbots dominate search?

Yes, but SEO now means structuring for understanding, not just ranking for keywords. Schema, entity alignment, and first-party credibility matter more than ever. Traditional keyword tactics may fade, but semantic clarity and topical authority remain critical.

Does this shift change how we approach video and visual content?

Definitely. Treat every visual as both a story and a signal. Use descriptive titles, captions, and metadata so algorithms can understand the context, but still lead with human emotion and pacing that hooks a viewer in seconds.

The post Are We Still Marketing to Humans? appeared first on Contently.

Wait there! Wait right there!

If you sniffed out AI’s trademark tone in the first paragraph, you deserve a gold star. If you didn’t, you need to read through this entire piece.

AI has become central to marketing, and most brands, including their writing teams, have jumped on the bandwagon. Good for productivity? Definitely.

But there’s a catch. AI-generated content is like branded content itself: When it tries too hard to sound human, it’s obvious. AI detection tools aren’t bulletproof, either, as they may flag content inaccurately in either direction, insisting a fully human-written article is AI or vice versa. (Studies have found these tools are nearly useless.)

While AI-generated content may not hurt your SEO or GEO rankings, there’s clear evidence that human audiences find it off-putting. According to research by Data Access Management (DAM) company Bynder, 50% of readers can detect when copy is AI-generated, and 52% find such content less engaging. Hootsuite’s 2024 Social Media Consumer study also found that nearly two-thirds (62%) of users say they’re less likely to trust or engage with social posts if they know AI was involved in their creation.

Source

So if you want to reach real humans with your content, your copy should sound like it came from one.

Here are a few ways to edit your AI content so it sounds less like a corporate-jargon robot and more like an actual person with a point of view.

Common Signs Your Content Sounds Too Much Like AI

AI-generated content is sometimes so obvious you only need a glance to identify it, like when a single article contains 55 em-dashes.

Other times, you might need to read through the lines to identify telltale signs like:

Generic or empty introductions

Nicolas Breedlove, CEO at PlaygroundEquipment.com, shares that one of the easiest giveaways is a lack of real perspective or context. “Earlier versions of AI models, like ChatGPT 3.5, often provide content intros that sound overly rigid and repetitive. Their outputs also lack substance and do not possess the right hook to onboard your readers.”

Compare this generic intro for a travel guide:

To this human-written version:

Your flight is delayed, your bag is overweight, and the hotel just sent an email saying check-in isn’t until 4 pm. Anyone who has ever stepped foot in an airport, bleary-eyed and running on bad coffee and optimism, knows that travel is full of these little headaches. But it doesn’t have to be. This blog will show you how to pack like a pro, skip tourist traps, and save money while still enjoying every stop of your journey.

The second is much more personalized and relatable.

Hedging language

Hedging language includes terms or phrases that weaken the assertiveness of a statement and make it less absolute. While this is great for academic writing, where researchers want to avoid overstating conclusions or implying absolute certainty, using it in your marketing copy can undermine your authority and make your brand sound unsure of itself.

Examples include:

• It is important to note…
• It can be argued…
• Perhaps…
• Maybe…

You might also come across vague phrases like “a number of…” instead of a definite figure.

Claims without evidence

Recent data suggests that even the latest version of ChatGPT (5.0) hallucinates about 1 in 10 times in its outputs. Even domain-specific AI can’t be fully trusted: In one Stanford benchmark, legal models got things wrong in up to one-third of their responses.

In marketing writing, hallucinations might include fabricated quotes (yikes), bogus statistics (oof), or made-up studies (just ask Deloitte why this is problematic.)

So, once you see sentences starting with:

• “Some research shows that…”
• “Studies suggest that…”
• “Experts agree that…”
• “It is widely known that…”

Stop and check whether there’s a real source or data point behind any claims AI churns out. Getting caught in an error is embarrassing, and it can quickly erode audience trust.

Monotonous sentence structures

“You might see tons of formulaic transitions or connectors in a single or consecutive paragraphs, which ends up making the whole block feel flat. This occurs [especially often] when using older LLM versions,” says Jeffrey Zhou, CEO and Founder of Fig Loans.

See this:

AI also loves lists of three (innovate, iterate, and inspire! Create, connect, and convert!). These are fine in moderation, but if you start seeing them in every section or paragraph, use your editorial judgment to gauge if they can be streamlined or phrased differently.

When every sentence length looks just the same or with as slight a variation as possible, raise your whiskers too.

Excessive use of contrastive parallelism

AI content often uses statements like, “While X is true, Y is also important,” or, “It’s not just about sales; it’s about…”

You’ll sometimes see this back-to-back in a piece and it automatically creates a rhythm that feels forced.

Other examples:

• Social media is more than a broadcast tool; it’s a place to build relationships.
• It’s not about working harder; it’s about working smarter.
• It’s less storytelling and more story-selling.
• The question isn’t what can AI do for us — it’s what should we do with AI.

Specific “tells”/phrases AI loves

You can spot AI’s favorite phrases from a mile away — the linguistic equivalent of clip art. Here are a few dead giveaways you’ve probably seen a hundred times:

• Delve
• Fast-paced world
• More than ever
• Rapidly changing landscape
• Picture/imagine this
• The implications are clear
• The takeaway? or, The result?

Spot similar patterns in this blog’s intro:

Source

AI also has an affinity for colons in headlines and subheadlines, so edit excessive instances out to be few and far between. Random bolded text and oddly capitalized phrases are other formatting quirks that make a piece feel more like a machine trying to emphasize importance than a human making a stylistic choice.

Must-Haves on Every Editor’s Anti-AI Checklist

Editing AI-isms out of your content isn’t as simple as recognizing catchphrases and pulling out parallelisms. Here are six things you must look out for before you hit publish.

1. Replace weak verbs and cut filler

AI often uses padded language and limp verbs — phrases like “helps with,” “is aimed at,” “can be used to,” and “serves as.” Beyond being an AI tell, these flabby constructions are simply lazy writing. Savvy editors have been striking these empty phrases from drafts for decades.

Replace these with stronger alternatives and cut out excesses. For instance:

• Helps with → powers
• Is aimed at → delivers
• Works to → improves / strengthens / supports (depending on context)
• Is focused on → prioritizes / advances
• Plays a role in → shapes / influences

It’s also a good idea to cut filler like “in order to,” “as well as,” or “in terms of” that bloats sentences without adding meaning.

2. Add names, numbers, examples

AI-generated text loves to speak in broad claims and sweeping generalities. The fastest way to humanize these eye-glazing paragraphs is to get concrete. Seek out generalisms and personalize them by adding names, proper pronouns, or specific examples where applicable.

Add statistical numerical evidence, case studies, reports, infographics — anything that confirms your claims.

3. Fact-check, fact-check, fact-check

If you’re using tools like Perplexity or ChatGPT, they can be incredibly useful for surfacing quick references. Sometimes, those links will point you in the right direction — but not always. Generative systems are trained to sound confident, not to be correct.

That’s why every fact deserves a manual check. Click through every source to make sure it leads to a real, credible page, ideally from a primary source or recognized publication. Verify numbers by cross-referencing them with the original study or dataset, not just the AI summary. And if you see suspiciously perfect phrasing (like a too-good-to-be-true stat or a quote that sounds like marketing copy), trace it back to its origin.

4. Increase use of expert quotes or original input

Weave expert quotes or insights into your content to make it feel less automated, especially when addressing sensitive topics or offering practical tips that require more than generic advice.

To find sources, you can utilize platforms like HARO (which relaunched earlier in 2025), and social media platforms, such as LinkedIn, as well as professional communities. You can also reference publicly available quotes from credible individuals on reputable third-party publications in your piece, so long as you cite the original source with a link or by name.

5. Insert local or unique details

AI content can feel like it’s addressing everyone and everything. Make it sound more unique by referencing local angles or examples, like those in your industry, geographical location, or relevant events in your niche.

Bring in examples that your audience can easily relate to. For instance, if you’re writing about the state of the SaaS ecosystem, reference a real milestone (e.g., Figma’s acquisition by Adobe) instead of a vague “major tech deal.”

These small touches make your piece feel like it was written by someone who actually has expertise and knows the audience, versus scraped or regurgitated advice from the internet.

6. Adjust transitions to sound natural

Excessive transitions give your piece a pre-scripted, bland tone. Ensure they’re only used when necessary and cut out others. Try to limit formulaic transitions (like “moreover,” “therefore,” or “in addition”) to no more than two every three paragraphs.

You can introduce a mix of pauses, conjunctions, and short sentences to make your transitions sound natural and smooth.

Improving Drafts at the Source

Before using AI to populate your content or refine your paragraphs, you need to condition it to give you drafts that are as human as possible. Here are some tips to make your job easier later on in the process.

Prompt for specific sources, not general summaries

“AI-assisted drafts are only as human-like as the prompts you provide. If you ask ChatGPT to write an overview of ‘Generative Engine Optimization,’ it will likely stitch together generic summaries from its training data,” says Anna Zhang, Head of Marketing at U7BUY. “Instead, prompt it to look at a specific source, reference an example, or cite a particular fact.”

The same principle applies to statistics and case studies used to support claims. Instruct your model to retrieve data only from direct sources, rather than general statistical summaries.

Supply voice samples for tone

Once your prompts are grounded in real sources, the next step is to make sure the voice feels equally grounded in your brand.

“Help AI understand your brand tone both in text and by voice by recording comprehensive samples. You can simply lift your previous podcasts, blog samples, and vlogs (if available) and paste them as training data, provided it doesn’t breach data privacy laws,” advises Leigh McKenzie, community advocate at Traffic Think Tank.

Other representative branded details, such as case studies, positive public reviews, and event highlights, will also help AI create content that adopts your tone, whether playful, like Duolingo, or formal, like Forbes.

Add style rules: concise verbs, active voice, varied rhythm

Create a style guide that details words to avoid, which verbs to use as alternatives, preferred voice patterns, ideal rhythms, and recommended sentence lengths. Then feed it to your AI model and run tests to see how closely it matches your brand style.

The more detailed your style guide is, the better your drafts will align with your brand’s voice from the start. You should also share the guide with your content team so they can better prompt AI models and ensure consistency in their own writing.

Use negative rules: no clichés, no hedging, no generic intros

AI tools like ChatGPT allow you to set guardrails around its output. Head to the personalization box and set negative rules protecting against clichés, hedging phrases, generic introductions, and repetitive phrases.

These settings are not absolute, and the AI model might still bypass them. You may need to paste the rules into the chatbox each time before generating new drafts, but the “personalization” feature acts as reinforcement.

Build a Workflow That Supports Human-First Editing

Good content is a result of a well-functioning content workflow. You can build one by defining clear editorial processes across the outlining, drafting, reviewing, and publishing stages, including specifying who handles editing and compliance.

Your outlining and reviewing stages should emphasize human-first editing, with a particular focus on implementing your checklists and style guides.

You can also adopt AI content platforms offering dedicated human editors, such as Contently, to ensure your drafts are accurate, on-brand, and polished before publication.

AI isn’t going anywhere — but neither is your reader’s instinct for authenticity. Keep that your north star.

Content that clicks with algorithms and audiences. See how Contently can help.

Frequently Asked Questions (FAQs):

What’s the best way to make AI-generated content sound human?

Edit for specificity. Add real examples, quotes, and numbers. Vary sentence rhythm, swap out filler verbs, and trim hedging phrases. Be on the lookout for common AI tells and phrases, like “delve” and excessive use of contrastive parallelism or em-dashes.

Should I disclose when AI was used to create content?

Transparency builds trust. If AI helped with research or outlining, it’s fine to say so, especially if a human editor verified and refined the final draft.

How can Contently help with AI-edited content?

Contently pairs AI-assisted tools with a global network of professional editors who ensure every piece sounds human, aligns with your brand, and meets editorial standards before it’s published.

The post How to Edit the AI-isms Out of Your Content (No Detectors Needed) appeared first on Contently.

Okay, so maybe that’s an overstatement. But if you’re a human who scrolls or swipes on the semi-regular, you’ve surely noticed the TikTokification of just about everything. And as a B2B brand, you can’t ignore this shift in how people consume and share ideas.

Scroll through any feed and you’ll see the power of this now-ubiquitous format. A sharp, 20-second video clip can extend the half-life of your best ideas; it can pull a key takeaway out of your latest report, give it visual and emotional context, and send it rippling through executive feeds within hours. It can turn depth into reach, and thought leadership into momentum. And in 2025, the brands mastering this balance between insight and immediacy are the ones shaping the conversation.

This playbook lays out a practical framework for scaling short-form production without sacrificing your sanity (or your brand voice).

Why Invest in Short-Form Video Now?

In recent years, three converging forces have made the format indispensable.

1. Platform algorithms reward native video content. LinkedIn’s algorithm favors native uploads and visible engagement (likes, comments, and reshares) over external links. That means a short video posted directly to the feed will almost always travel farther than a link to your blog or YouTube page. YouTube itself is doubling down on Shorts as a discovery engine, logging over 70 billion daily views and driving new traffic to longer videos on the same channels.
2. Buyer behavior has fundamentally shifted. Short-form videos work because they fit into micro-moments: the scrolls between calls, inbox breaks, or quick research before a pitch. A single, well-edited clip can become both an external thought-leadership post and an internal enablement asset.
3. The ROI proof is in. HubSpot’s annual State of Marketing report notes that short-form leads in ROI, engagement, and lead generation compared to other video formats.

Here’s an example of why this format is so critical in 2025: Imagine your team hosts an insightful webinar that draws a few hundred live attendees. The response is positive, but small scale and contained. But a day later, your marketing team clips a 30-second highlight from the event, and suddenly, the insight is everywhere on LinkedIn — it’s even picking up traction on TikTok. Same idea. Same audience. Different velocity.

Formats That Work in B2B in 2025

Successful B2B video strategies rely on repeatable formats that teams can batch-produce efficiently.

These might include:

• Expert snippets and micro-takes (30–45 seconds) can work well for sharing perspectives on industry statistics/trends/reports or highlighting customer insights. Tap into your organization’s own subject-matter experts or internal data storytellers to surface fresh insights that customers or peers actually care about (e.g., a surprising trend from your latest benchmark report or a question your sales team keeps hearing).
• Explainer videos cut into digestible nuggets (30–60 seconds) break down complex frameworks, demonstrate before-and-after scenarios, or define emerging trends in three clear beats. The winning structure follows a simple pattern: Hook (identify the problem) → Core insight → Actionable step → Clear CTA.
• Behind-the-scenes content humanizes expertise while strengthening employer branding. For instance, show how customer success managers solve real client issues or how research teams uncover insights. Clips like these remind audiences that your company is made up of real people solving tangible problems.
• Series formats create viewing habits through familiar cadences like “60-Second Whiteboard,” “One Metric Monday,” or “3 Slides in 30 Seconds.” Consistent naming and timing can lower the cognitive load for viewers while simplifying planning and batch production for content teams.
• Strategic thought starters grab and maintain attention through provocative openings: “hot take” cold opens, “We were wrong about…” admissions, or direct challenges like, “If you only change one thing this quarter, make it this.”

Think of these formats as your highlight reel templates — they make it easier to share what your brand already knows, one clip at a time.

Production Techniques to Prioritize

In social feeds, clarity and pacing matter far more than cinematic production value. The most effective short-form clips hook viewers within the first second or two.

Smart editors also build in “pattern interrupts” every few seconds, swapping angles, adding B-roll, or flashing quick on-screen stats to keep attention from drifting. Because most platforms autoplay videos without sound, captions are critical. Burn them in, highlight key words for emphasis, and use visual cues like progress bars to nudge viewers toward completion.

Remember that you’re not striving for perfection; rather, you should aim to keep up momentum. An “80%-there” version published within 72 hours of a webinar or interview will outperform the flawless cut that ships a month late.

Finally, keep in mind that authenticity almost always beats polish. A quick, well-lit phone recording that feels human will connect better than a high-production shoot that feels staged.

To keep your process sustainable, treat short-form production like a feedback loop: Publish quickly, learn from watch-through data and comments, and adjust pacing or framing as you go. With accessible tools like Descript, CapCut, Adobe Premiere Rush, or VEED for editing — and Riverside, Zoom, or Loom for capture — teams no longer need full studio setups. Even AI-assisted repurposing tools such as OpusClip can help jump-start edits (though a human pass for quality and tone is still essential before anything goes live).

Platform-Specific Distribution and Optimization

Each platform has distinct engagement patterns and optimization requirements. To get the most out of every clip, tailor how you publish and frame it to match where your audience actually consumes content.

For instance:

• LinkedIn optimization centers on native uploads with strong opening lines and specific questions that encourage comments. Pin top comments with resource links and encourage authentic internal engagement within the first hour of posting to boost algorithmic distribution.
• YouTube Shorts require keyword-rich titles, series naming conventions, and dedicated Shorts playlists that encourage binge-watching while connecting to relevant long-form content on the same channel.
• Website integration through dedicated “Video Briefings” archives improves SEO through schema markup and interlinking with related guides and resources.
• Sales enablement packages should compile the top five performing clips monthly with specific use case guidance for prospecting, objection handling, and deal progression conversations.

No matter the platform, consistency beats complexity; the brands that show up regularly stay more visible.

From Long-Form to Shareable Short-Form: A Step-by-Step Guide

The most efficient B2B teams start with a single, insight-dense “anchor” asset, then break it into smaller, platform-ready pieces that keep the conversation going long after the original launch.

Here’s an example of what this process looks like step by step:

1. Choose the right anchor.

Start with something that already carries weight: a webinar, research report, executive interview, or customer roundtable. The best anchor content offers a clear point of view and connects directly to your broader marketing themes. Think: “What’s our take on this trend?” not “What can we summarize?”

2. Map out the moments worth sharing.

Before you ever hit record, list 8–15 potential short-form clips (“video atoms”) you could create from the anchor. These might include:

• A single strong stat or takeaway
• A myth your expert can debunk in 30 seconds
• A customer soundbite that illustrates impact
• A quick “how-we-did-it” tip from your team
• A question your audience asks again and again

Each one should have a rough script skeleton: a hook, a core insight (two or three lines max), a visual cue, and a clear call-to-action (CTA).

3. Batch record and assign clear roles.

Get everyone involved on the same page early. Strategists should identify anchor assets and tie them to upcoming campaigns. Subject-matter experts can block a short monthly recording session to capture multiple takes at once. Producers will handle editing, captioning, and versioning by platform. Social leads can write titles, schedule uploads, and engage in the first-hour comment window.

4. Build guardrails that let you move fast.

Nothing kills momentum faster than a 17-step approval chain. To avoid the death-by-approvals spiral, set up pre-approved brand templates for all the components you can. Maintain a short “greenlight list” of safe, recurring topics that can skip full legal review, and agree internally on a 48-hour turnaround standard from clip completion to publish.

5. Distribute and track smartly.

From one anchor asset, aim to create 10–15 video clips, a handful of static visuals, one short newsletter embed, and a quick sales-enablement reel. Assign each piece to a specific channel and goal (awareness, engagement, lead generation, or internal enablement) and monitor how each performs to refine the next round.

Turn Big Ideas into Bite-Sized Impact

The next time you publish a major report or host a webinar, keep the momentum going. Find the 30 seconds that say the most, put it in motion, and give your audience a reason to stop scrolling.

Attention may be fleeting, but influence compounds. Each short-form clip is a small opportunity to reinforce what your brand stands for — in your voice, on your timeline, and in front of the audiences that matter. When those moments stack up, they start to shape perception long after the video ends.

Learn how Contently helps B2B marketers turn depth into reach, and reach into measurable ROI.

Frequently Asked Questions (FAQs):

Q: What if my subject-matter experts hate being on camera?

Remind them that realness often performs better anyway. Try audio-over-PPT, screen recordings with voiceover, or micro-shorts where the expert speaks one idea directly. Over time, confidence follows repetition.

Q: Do I have to publish across all platforms at once?

Nope. It’s smarter to start where your audience already is (LinkedIn, Slack communities, internal channels) and scale gradually. Use your top-performing formats there before branching into Shorts, newsletters, or website archives.

Q: How do I make sure short-form video doesn’t become a siloed half-effort?

Embed it into the bigger content strategy. Map each clip to themes, campaigns or buyer stages. Use the same language, link back to related content, and integrate clips into newsletters, sales decks, or blog posts so they reinforce—not distract from—your core narrative.

The post The B2B Brand’s Guide to Short-Form Video in 2025 appeared first on Contently.

And the stakes are real. Webflow reports that 8% of their signups now come from LLM traffic, and that traffic converts at 6x the rate of traditional Google Search. For B2B companies, this isn’t a future trend. It’s happening now.

But here’s what most brands get wrong: they think GEO is just “SEO but for ChatGPT.” It’s not. The agencies that actually deliver results understand that LLMs don’t rank pages the way Google does. They select, synthesize, and attribute from multiple sources. Owning the #1 Google ranking for a query is no longer enough. If no one else mentions you, you won’t appear in the AI answer.

That’s why the agencies below stand out. They understand the technical foundations (RAG retrieval, fan-out queries, citation overlap), they run controlled experiments with actual measurement discipline, and they build both on-site and off-site strategies.

One thing I’ve learned watching visibility data across enterprise brands: the companies winning in AI search aren’t the ones with the most content. They’re the ones getting mentioned by other sources. Third-party validation is the new backlink.

Here’s what to look for, and who’s doing it right.

Methodology

How we evaluated: Each agency was assessed on published methodology, client case studies, original research, technical depth, and full-funnel capability.

Quick Comparison

Questions to Ask Any GEO Agency

Before signing, ask these:

1. “Do you run controlled experiments with baselines?” If they can’t explain their measurement methodology, they can’t prove results.
2. “What percentage of your strategy is on-site vs off-site?” For top-funnel queries, ~85% of citations come from third-party sources. If they only talk about your website, they’re missing the bigger picture.
3. “How do you track visibility across different LLMs?” ChatGPT, Perplexity, Gemini, and Google AI Overviews all behave differently. A good agency monitors all of them.
4. “Can you show me a before/after case study with actual numbers?” Vague claims like “visibility improved” mean nothing without baselines and controls.
5. “What’s your approach to YouTube and video?” YouTube is heavily cited by Perplexity and Google AI Overviews. If they’re not talking about video, they’re leaving opportunity on the table.

What to Look for in a GEO Agency

Before we get to the list, here’s how to evaluate whether an agency actually understands GEO or is just rebranding their SEO services:

1. Technical Understanding

Do they know the difference between core model optimization (nearly impossible to influence) and RAG optimization (where the real opportunity is)? Do they understand fan-out queries—the multiple additional searches that AI platforms run at runtime to retrieve fresh information?

2. Measurement Discipline

Do they run experiments with control groups? If an agency tells you “we optimized and visibility went up,” ask whether they controlled for the baseline growth in LLM usage. Without a control group, you can’t attribute results to their intervention.

3. Full-Funnel Approach

GEO isn’t just on-site optimization. For top-of-funnel queries, ~85% of citations come from off-site sources. A good agency builds YouTube presence, earns third-party mentions, and thinks about citation mix by funnel stage.

4. YouTube Capability

YouTube is heavily cited by Perplexity and Google AI Overviews (though less so by ChatGPT), and there’s almost no competition for “boring” B2B topics. If an agency isn’t talking about video, they’re missing the lowest-hanging fruit.

5. Honesty About What Works

Question stuffing is the new keyword stuffing. Agencies that promise 100 FAQs per page or guarantee specific visibility percentages are selling snake oil. The best agencies admit what they don’t know and focus on reproducible experiments.

The 10 Agencies

1. Graphite

Graphite’s CEO Ethan Smith has done more to define GEO methodology than anyone else in the space. His frameworks (the 7-step methodology, the 1/20 rule, the citation overlap research) have become the foundation for how serious practitioners approach this work. If you’ve read anything credible about GEO in the last year, it probably cited his research.

What they do: Full-service GEO for high-growth SaaS and technology companies. They work with brands like MasterClass, Robinhood, Calm, and BetterUp. Their approach combines editorial strategy with programmatic SEO and Answer Engine Optimization.

What sets them apart: Original research. Graphite’s data on citation overlap shows that ChatGPT citations differ significantly from Google rankings, while Perplexity is much closer to traditional SERPs. They also developed the Webflow case study showing 8% LLM signups with 6x conversion.

Best for: Mid-market to enterprise SaaS companies that want a methodology-driven approach.

2. Contently

Why they’re here: Full disclosure: I work with Contently, so take this with appropriate context. But the reason Contently ranks this high is simple: very few companies on this list cover the entire GEO value chain. From AI visibility tracking to content strategy to creation to measurement, it’s all under one roof.

What they do: Contently is one of the few platforms that combines a Content Marketing Platform (CMP) for workflow and governance, a Creative Marketplace with 160,000+ vetted freelancers including domain experts across industries, Managing Editors for quality control, and Radarly for AI visibility tracking across major LLM platforms. They’ve worked with brands like American Express, Spotify, JPMorgan Chase, Barclays, and Marriott

What sets them apart: The full stack. Most GEO agencies can audit your content and tell you what to optimize. Some can help you create content. Very few can measure whether it’s working in AI answers. Contently does all three, and has been doing enterprise content at scale since 2010. That’s over a decade as G2’s top-rated Enterprise Content Creation platform for a reason.

I’ve watched brands spend six months on a GEO audit only to realize they don’t have the content engine to act on the recommendations. Strategy without execution is expensive shelf-ware.

The other differentiator: format coverage. GEO requires presence across surfaces, not just blog posts, but video, audio, infographics, interactive content. Contently’s freelancer network includes writers, designers, videographers, and audio producers. If YouTube is a top-cited domain in LLM responses (and the data suggests it is), you need multimedia capability, not just writers.

Best for: Enterprise teams that need the full value chain: AI visibility measurement, content audit, strategy, creation at scale with domain experts, and governance. Not just consulting.

3. iPullRank

Mike King has been a technical SEO thought leader for years, and his team’s “AI Search Manual” is one of the most comprehensive resources on how LLMs actually retrieve and cite content.

What they do: Enterprise-level technical SEO with deep expertise in JavaScript rendering, log file analysis, and large-scale site architecture. If your site is complex (and most enterprise sites are), they can diagnose why generative platforms aren’t seeing your content properly.

What sets them apart: Their research on query fan-out behavior and agentic commerce protocols shows up in most serious GEO discussions.

Best for: Enterprise brands with JavaScript-heavy websites that need technical precision.

4. Go Fish Digital

Go Fish Digital takes a data-science approach that most agencies can’t match. They’ve built custom technology to analyze Google patents and backtest results, which means their recommendations come from reverse-engineering how these systems actually work, not guessing.

What they do: Data-driven SEO and GEO consulting with proprietary analysis tools. They work with brands like Jelly Belly, HP, and Joybird

What sets them apart: Their team can pinpoint specific content gaps and structural issues that limit AI visibility using backtested methodologies instead of hunches.

Best for: Brands that want data-science rigor and patent-based insights.

5. Siege Media

Siege Media built their reputation on content quality, and they’ve successfully adapted that approach for GEO. Their research division studies how content formats affect AI citation rates. One finding that stuck with me: listicles and content with embedded data tables earn significantly more citations than plain text. Format matters.

What they do: High-quality content creation with a focus on “information gain”—saying something that someone else hasn’t said. This principle matters more in GEO than traditional SEO because LLMs reward original insights.

What sets them apart: If two sources say the same thing, the model picks one. If you say something new, you get cited. Siege understands this better than most.

Best for: Enterprise brands that need high-quality content performing in both traditional search and AI.

6. Omniscient Digital

Omniscient Digital understands that “source-worthiness” is the primary GEO signal. LLMs cite content that is crawlable, topically relevant, clearly structured, and backed by authority signals.

What they do: Premium B2B content strategy and creation. They work with brands like SAP, Adobe, Loom, and Jasper. Their team includes former journalists from NYT, New Yorker, and WSJ.

What sets them apart: They build the kind of content that earns citations, not just traffic. Executive-level content that builds actual authority, not just keyword coverage.

Best for: B2B SaaS companies that want real thought leadership.

7. First Page Sage

First Page Sage has been building long-term SEO programs for B2B companies for over a decade. They’ve pivoted to GEO with structured publishing calendars designed for AI visibility.

What they do: Long-term content programs for regulated industries, including enterprise clients in medtech and financial services.

What sets them apart: Vertical expertise. They understand compliance requirements that generalist agencies stumble on.

Best for: Fintech, medtech, or legal companies where regulatory knowledge matters.

8. Intero Digital

Intero Digital brings nearly three decades of digital marketing experience to GEO. Their proprietary crawler simulation, InteroBOT, mimics how generative engines evaluate content.

What they do: Technical optimization for large content libraries at scale.

What sets them apart: If you have thousands of pages that need GEO optimization, Intero Digital has the systems to handle that complexity. Most agencies break down at scale. These don’t.

Best for: Enterprise brands with massive content libraries.

9. Omnius

Omnius is European-based and has carved out a niche in GEO for SaaS, fintech, and AI companies. They work with TextCortex AI, Zencoder, and AuthoredUp.

What they do: GEO across multiple languages and regions with cross-cultural content relevance.

What sets them apart: International expertise. Most US-based agencies treat international as an afterthought. These don’t.

Best for: Companies that need GEO across multiple languages and regions.

10. NoGood

NoGood treats GEO as part of a larger growth engine rather than a siloed function. They work with enterprise brands on B2B content strategies built for testing and iteration.

What they do: Integrated growth marketing with GEO as a component. They combine AI visibility with paid acquisition, CRO, and full-funnel analytics.

What sets them apart: If you want AI visibility to tie directly to pipeline metrics, NoGood speaks that language.

Best for: High-growth companies that want GEO integrated with broader demand generation.

How to Choose

If you need methodology and original research: Graphite (#1)

If you need the full value chain (strategy + creation + measurement): Contently (#2)

If you have a complex technical site: iPullRank (#3)

If you want data-science rigor: Go Fish Digital (#4)

If content quality is paramount: Siege Media (#5) or Omniscient Digital (#6)

If you’re in a regulated industry: First Page Sage (#7)

If you have a massive content library: Intero Digital (#8)

If you’re international or European: Omnius (#9)

If you want GEO integrated with growth: NoGood (#10)

Frequently Asked Questions

How long does it take to see results from GEO?

Honest answer: it depends on your starting point and competitive landscape. Some brands see visibility improvements within 30-60 days for tail queries. For competitive head terms, expect 3-6 months of sustained effort. Be skeptical of any agency that guarantees specific timelines. LLM behavior changes frequently, and what works today may shift tomorrow.

Can smaller companies compete with enterprise brands in AI search?

Yes, especially on specific, long-tail queries. LLMs often cite the most relevant answer, not the biggest brand. If you can be the only source that answers a specific question comprehensively, you’ll get cited regardless of company size. The Webflow approach (YouTube videos for “boring” topics nobody else covers) works at any scale.

What metrics should I track?

Start with visibility rate (percentage of responses mentioning your brand) and share of voice versus competitors. But don’t stop there. Implement “How did you hear about us?” post-conversion. LLM-referred traffic often shows up as direct or branded search because users open a new tab. Without self-reported attribution, you’ll undercount the channel.

Ready to build a GEO strategy? Contact Contently to see how our Content Marketing Platform and Radarly AI visibility tracking can help your team create content that gets cited.

The post The 10 Agencies Leading the LLM SEO Revolution in 2026 appeared first on Contently.

For brands accustomed to agonizing over campaign messaging for weeks, this speed is punishing. The challenge associated with keeping pace is twofold: Brands must learn to implement faster, lighter approval cycles, while ensuring their messaging still meets brand safety and compliance standards.

Luckily, social listening tools and strategies can help brands hone their radar and ride cultural momentum without becoming the corporate version of the Steve Buscemi “fellow kids” meme.

Here’s a guide on how to move from passive monitoring to active cultural intelligence.

Turning Sentiment into Strategy

Sentiment analysis has its place in reputation management, but on its own it misses social listening’s broader strategic potential. The most effective content teams go beyond reacting to brand mentions and use listening to surface cultural signals that can shape their next move.

Consider how this shift plays out in practice: A spike in negative comments may look like a reputational crisis at first glance, but viewed through a wider lens, the chatter could point to an emerging consumer need or even momentum around a competitor. What looks like a fire to put out may actually be an early market signal — and a chance to lead the conversation rather than chase it.

To leap from sentiment analysis to true strategic advantage, brands need to reframe listening as an input to planning, not just reporting. The teams that excel track core indicators like:

• Conversation velocity (how quickly discussions accelerate)
• Community reach (which groups drive the narrative)
• Emotional resonance (the intensity of engagement)

When these signals pinpoint a cultural insight, marketing teams should try to respond while the discussion is still gaining traction, ideally within a 48- to 72-hour window. Later than that and they risk looking reactive or irrelevant.

Spotting Micro-Virality Early

“Micro-virality” is a recent shift in how cultural trends emerge and spread. Unlike traditional viral content that explodes across demographics simultaneously, micro-viral moments ignite within specific communities before potentially crossing into mainstream consciousness. What’s more, the earliest ripples often start where brands aren’t looking: A meme circulating on a 20,000-member Discord server or a LinkedIn post gaining unusual traction among B2B marketers can signal tomorrow’s broader trend.

Detecting such early sparks is a challenge for even the most culturally clued-in brands. Standard analytics dashboards prioritize volume over velocity, missing these signals entirely.

Effective micro-virality detection demands monitoring beyond obvious channels. Brands should consider branching out beyond big, public platforms to digital subcultures like:

• Reddit threads
• Discord conversations
• Slack community reactions
• Twitch chat patterns

Such spaces can provide invaluable early warnings, but breaking in can be tricky if you show up heavy-handed. To earn trust, brands need to listen first and contribute in ways that feel native to the community rather than bolted on from the outside. They might also consider partnering with credible voices inside those spaces to amplify their presence organically and avoid looking like outsiders trying to hijack the conversation.

Trendjacking with Finesse

For every successful trendjacking moment, there are dozens of tone-deaf attempts that ultimately breed mockery and backlash instead of engagement and authentic connection. Brand missteps share common DNA: rushed execution, misunderstood tone, and forced brand insertion.

For instance, when the “#GirlDinner” trend exploded on TikTok, Popeyes tried to capitalize by launching a “Girl Dinner” menu made up of its side dishes. But instead of eliciting delight, the move was widely panned as lazy and off-base. What could have been an opportunity to align with Gen Z humor ended up highlighting the risks of jumping in without adding genuine value.

Brands that take the time to understand nuances are far more likely to show up in ways that feel relevant rather than opportunistic. That’s the difference social listening makes. HelloFresh, for example, actively tracks not just brand mentions but larger conversations around cooking habits, recipe trends, and packaging feedback. By analyzing these signals, the company adapts its product offerings and content strategy in real time.

Shaping the Content Machine from the Inside Out

Social listening’s greatest impact emerges when insights flow directly into content operations. Leading organizations are moving beyond surface metrics to let real-time audience intelligence inform four critical functions:

Editorial Calendar Evolution

Streaming platforms like Netflix have shown how closely tracking audience chatter can shape promotional priorities. Conversations around genres, moods, or cultural touchpoints often guide what gets emphasized in marketing campaigns — think highlighting “comfort viewing” during moments of collective stress.

Language and Tone Optimization

Ryanair has become a case study in how brands can use listening to inform voice and tone. Their cheeky, self-deprecating voice (“yes, our legroom is terrible, but our fares are cheap”) is a direct reflection of what they know people are already saying. Posts that mirror the humor of its audience consistently drive higher engagement, showing how listening can shape not just what a brand says, but how it says it.

Executive Positioning

Enterprise brands like Salesforce lean on trend monitoring to inform thought leadership. By paying attention to emerging business discussions (whether about AI, customer data, or sustainability), they position their executives to weigh in early and credibly.

Message Testing

Technology companies regularly validate their positioning by tracking how potential narratives land in the market. Slack’s evolution from “be less busy” to “digital HQ” reflects this kind of feedback loop, where conversation analysis helps sharpen the language before a campaign scales.

Practical Takeaways and Best Practices

Turning social listening from a passive tool into a performance driver requires discipline and integration. Here are three best practices to follow:

• Choose tools you’ll actually use. Start simple with native analytics (Twitter/X, TikTok, LinkedIn dashboards) to get comfortable tracking mentions and trends. As your needs grow, layer in dedicated tools like Brandwatch or Talkwalker for sentiment and community analysis. Larger enterprises may graduate to suites like Sprinklr or Sprout Social, but only when the scope and scale of insights demand it. The best tool is the one your team can use consistently, not the flashiest platform.
• Embed listening into existing workflows. Instead of creating extra steps, fold listening into routines you already run. Add a 10-minute trend scan to daily standups. Set up a Slack or Teams channel for real-time cultural alerts. Summarize key insights in weekly performance reviews so listening is always linked back to outcomes. Teams that systematize these habits are the ones that actually act on what they hear.
• Measure what matters. Don’t stop at tracking “mentions.” Tie listening to business outcomes. Measure speed-to-publish on trend-informed content (is your team able to turn ideas around within 24 hours?). Compare engagement lift between listening-driven posts and pre-planned content. Track how early your brand enters cultural conversations — and whether that timing translates into more relevance, share of voice, or even conversion.

The brands thriving in today’s compressed attention economy anticipate conversations, shape them, and build durable competitive advantage through cultural intelligence.

That shift requires reframing budgets and mindsets. A social listening line item is an investment in performance. In a landscape where cultural moments flare and fade faster than you can say “Barbenheimer,” the ability to detect, interpret, and respond in hours — not weeks — is a differentiator.

Don’t be the brand that shows up after the party’s already over. Social listening helps you arrive on time, and join the conversation in a way that feels welcome.

Social listening is only as powerful as the stories it informs. Learn how Contently can help your team build a content engine around real-time insights.

Frequently Asked Questions (FAQs)

What’s the biggest mistake brands make when they start?

Treating listening as a reporting function rather than an action driver. It’s easy to produce dashboards that look impressive but never inform a decision. The real value comes when insights directly change how you plan, create, or publish content.

Can social listening replace customer research?

Not entirely. Social listening shows you how people talk in public, often in real time. It complements surveys, focus groups, and user testing by surfacing unfiltered opinions and emerging behaviors — but it shouldn’t replace those methods.

How do I balance speed with brand safety?

Build lightweight guardrails: a pre-approved “do/don’t” list for language, topics, and tone; a short approval chain for rapid responses; and clear escalation paths for sensitive issues. This way, you can move quickly without exposing the brand to unnecessary risk.

The post Social Listening as Your Brand’s Secret Performance Tool appeared first on Contently.

If it feels like every vendor suddenly has a “State of Something” report, you’re not imagining it. Benchmark studies and branded data have become the new calling cards of content marketing.

While blog posts and brand manifestos still have their place in the ecosystem, they rarely break through on their own anymore. Content fatigue has reached critical mass, and audiences simply scroll past “5 tips for better marketing” articles. At the same time, AI search has changed the SEO game and raised the bar for credibility; in order to rank and get cited by large language models, marketers need original insights no one else can offer.

One solution emerging across industries has been to double down on proprietary data. From HubSpot’s State of Marketing to LinkedIn’s Workplace Learning Report, companies are mining their unique data assets to create content that commands attention, drives citations, and builds lasting authority.

Here’s why the trend is on the rise — and why it works.

Why Now? The AI Search Effect

When ChatGPT answers a question about average email open rates or Perplexity summarizes industry trends, these AI systems pull from sources with transparent, compelling data and authoritative positioning. Branded benchmarking reports check these boxes by providing structured, factual content with clear methodologies and context.

Even in a “zero click” scenario, your brand still benefits from being cited as the source of record. There can be a compounding effect to such attributions: When your report becomes the default data point for “average B2B sales cycle length” or “content marketing budget allocation,” you gain visibility across thousands of AI-generated responses, journalist articles, analyst reports, and competitor presentations. Each mention amplifies your brand’s authority, and the qualified traffic that does flow back to your domain is more likely to convert than traffic from traditional SEO.

How Benchmarking Reports Drive Value

Smart marketing leaders recognize benchmarking reports deliver measurable value across three critical dimensions:

Public Relations Impact

Proprietary data transforms your brand into a media magnet. Journalists constantly hunt for fresh statistics to anchor their stories. When you publish exclusive insights about industry trends, you hand them ready-made hooks — and the result is earned media coverage that would cost six figures through traditional PR campaigns.

Pipeline Generation

Whether gated or ungated, benchmarking reports tend to attract high-intent prospects. Gated reports identify serious buyers willing to exchange contact information for valuable insights, and ungated versions can maximize reach by getting your data in front of analysts and influencers.

Trust and Authority

Publishing rigorous, methodology-driven research signals deep expertise. You shift from vendor to trusted advisor. Transparent methodology matters here — you’ll want to ensure you’re clearly explaining data sources, sample sizes, and analysis methods to give readers confidence in the validity of your findings.

What It Takes to Create a Report That Sticks

Building a benchmarking report that achieves these outcomes requires strategic planning across a few key areas:

Data Sourcing Strategy

Start with data only you possess, like first-party usage data from your platform that provides unmatched insights competitors cannot replicate. Combine this with customer surveys or supplement with subject matter expert quotes for qualitative depth. Aggregate and anonymize to protect individual customer data while revealing category-wide patterns.

Design and Format Excellence

The most successful reports balance comprehensive analysis with scannable highlights. Transform raw data into compelling visual stories by partnering with designers who understand data visualization. Create charts that reveal insights at a glance. Write copy that explains why the data matters, not just what it shows. Package statistics as “snackable” social media content, and include downloadable one-pagers for easy sharing.

Multi-Channel Distribution

Great data dies without strong distribution. To maximize impact, launch your report with coordinated campaigns across PR, social media, email, webinars, and sales enablement. Create tiered assets: executive summary for time-pressed leaders, full report for practitioners, slide decks for internal sharing, etc. And don’t forget to train sales teams to reference key statistics during their conversations with prospective clients.

Optimizing for Citations

You’ll also want to structure your content for maximum quotability and citability by AI engines. To boost discoverability, use descriptive subheadings that work as standalone facts, and be sure to create FAQ sections addressing common questions. Build infographics for visual learners, and implement schema markup to help search engines understand your data. Include methodology sections that establish credibility and, finally, make statistics easy to cite with clear sourcing guidelines.

Consistent Refresh Cadence

A report is only as valuable as it is current. To keep your data fresh, commit to regular updates, e.g. annually for comprehensive reports, quarterly for trend data. Mark your calendar now: If you launch in January, start data collection in October. Teasing an ongoing data initiative creates anticipation and provides reasons for re-engagement.

Pitfalls to Avoid

Even with the best intentions and compelling data, a few critical mistakes can undermine even well-executed benchmarking reports. Here are three to avoid:

Weak Methodology

Small sample sizes and cherry-picked data can destroy your credibility. Invest in rigorous methodology, even if it means less favorable results. Consider partnering with research professionals if your own team doesn’t have the resources necessary to produce a truly top-notch report. And always disclose limitations or margins of error.

Sales-First Content

Readers — both humans and machines — detect and reject reports that exist primarily to promote products. Focus on category-wide insights, include competitor data where relevant, and save product mentions for subtle footer CTAs.

Underinvesting in Distribution

It doesn’t matter how brilliant your report is if nobody reads it. Budget 40% of project resources for distribution and amplification. That number may sound daunting, but without aggressive distribution, even the most groundbreaking data won’t move the needle.

The Future of Authority Marketing

The window of opportunity is open now, but it won’t be forever. Categories without established benchmark reports offer first-mover advantages. So, it’s a good idea to start now: Begin by auditing your data assets, surveying your customers, or analyzing your platform metrics. Then, transform these insights into the authoritative report your industry needs but doesn’t yet have.

As AI search changes how information is surfaced and cited, the brands supplying reliable benchmarks will own the reference points that everyone else leans on. Those who wait risk competing in categories already defined by others’ data.

Need help turning raw data into a report that drives citations and pipeline? Talk to Contently about building your next benchmark study.

Frequently Asked Questions (FAQs):

What makes a benchmarking report different from a white paper?

A white paper typically presents a company’s perspective or solution, while a benchmarking report is rooted in original data and industry-wide trends. The latter is designed to be cited, compared against, and referenced as an objective standard.

How much data do I need to publish a credible benchmarking report?

There’s no magic number, but larger sample sizes improve credibility. What matters most is transparency: Clearly explain your methodology, sample size, and any limitations so readers trust your findings.

What resources are required to create a strong benchmarking report?

Successful reports usually require collaboration across data, design, and distribution. This might mean partnering with research specialists, investing in design for clarity and impact, and budgeting a sizable share for promotion.

The post Why Branded Benchmarking Reports Are Everywhere Right Now appeared first on Contently.

Remember when the “I’m Feeling Lucky” button was Google’s biggest gamble? Now, it’s their entire business model, and your traffic is the casino’s take.

Your best-performing article still ranks #1, but traffic’s down 30%. Search your primary keyword and there it is: a Google AI Overview perfectly summarizing your content. No click required.

Welcome to the age of zero-click search, a blunt term that means exactly what it sounds like: searches where users get their answers without ever visiting your site. Industry veterans are calling the phenomenon “Google Zero” (less self-explanatory but just as ominous-sounding).

This new era means rankings alone no longer guarantee engagement. Your audience absorbs AI-generated answers directly on Google’s platform, bypassing your site entirely. This is the defining challenge of content marketing today. In an era dominated by AI-powered SERP previews, winning means creating digital destinations worth visiting, not just pages that pull rank.

Here’s how brands can adapt.

Understanding the Zero-Click Search Landscape

Google’s AI Mode represents a fundamental shift in how users source information online, compressing entire articles into punchy, AI-powered summaries. Users love the instant gratification. Brands and media companies, on the other hand, are in panic mode.SparkToro analysis from 2024 found that for every 1,000 Google searches in the US, only 360 clicks went to the open web.

Let that implication set in: Out of every 1,000 queries, 640 now lead to no clicked results.

Some publishers are impacted more than others

This trend has only ballooned in 2025, and click-through rates (CTRs) are plummeting. Publishers in reference verticals that have historically relied on search visibility report devastating traffic losses; some witness double-digit drops in referral visits, even as their keyword rankings hold steady. Semrush data finds that science, health, people & society, and law & government are the industries seeing the largest share of AI Overview growth.

The erosion is surgical, with Google’s AI scalpel removing the meat and leaving only bones:

• Recipe sites see their content distilled into lists and cook times.
• Long product reviews shrink to bite-sized bullets.
• Communities and dev sites have detailed Q&As sliced into decontextualized code snippets.

Zero-click search also strips away narrative, perspective, and experience, leaving only commoditized fragments that serve Google’s ecosystem.

The upside for marketers

But here’s what many panicked marketers miss: This isn’t a content apocalypse, but a process of natural selection. Commodity content is the dinosaur.

Most of the formats endangered by zero-click search were already oversaturated (all those “10 Best Tools for X” listicles you’ve been banging your head against a wall writing for the past decade). Many were competing on efficiency, completeness, and SEO tricks rather than on real innovation or brand distinction. Google Zero simply accelerates a reckoning that was always coming.

And here’s the twist: AI Search often surfaces sources that live well beyond the first page of Google’s traditional rankings. In other words, content that was once invisible in the old SEO hierarchy can suddenly become citable and top-of-mind in AI summaries. For brands willing to invest in distinctive, authoritative insights, the playing field may actually be more open than before.

Building a Destination Content Strategy

There are a few tactics for thriving in the Google Zero era. “Destination content,” for instance, inverts the classic SEO playbook. Forget adjusting for every last query and optimizing around keyword density; instead, focus on building branded content experiences that users actively seek out. These are digital destinations that drive interaction, build habit, and deliver value AI cannot compress.

Here are a few examples of what these strategies look like in practice:

1. Utility and interactivity

Example: Tools, assessments, and calculators. Google’s AI Overviews can summarize general best practices, frameworks, or even steps to use a tool, but they can’t generate dynamic, personalized outcomes tied to an individual user’s inputs, data, or context. ChatGPT can mimic personalization if you paste in content or data, but without integrations it can’t apply the proprietary scoring logic, benchmarks, or datasets that make branded tools defensible.

That unique value — rooted in owned IP and interactivity — is what keeps tools like HubSpot’s Website Grader a step ahead of zero-click answers. Users enter their site to get their specific recommendations, a direct exchange of effort for individualized insight that no AI summary can replicate.

2. Memorable Narrative and Voice

Example: Serialized storytelling and editorial franchises. Readers return for evolving narratives, strong opinions, and a distinct voice beyond just facts. (Think of the difference between Wikipedia and a respected analyst’s ongoing columns.) AI can summarize the facts, but not the evolving insight, context, or strategic nuance. For instance, Rare Beauty’s Substack leans into longform, behind-the-scenes storytelling that blends personal anecdotes, mental-health reflections, and candid product development updates. It stands out by offering authenticity tied deeply to the brand, giving readers a reason to subscribe rather than passively consume.

3. Deep, Engaging Experience

Example: Interactive flipbooks, quizzes, and content hubs. Build content networks that reward deeper exploration. Think of an immersive guide that walks a user through a complex topic using clickable flows, rich visuals, and progressive disclosure, instead of flattening content into a one-and-done summary. According to industry guides, formats like flipbooks, quizzes, polls, and interactive infographics are trending as tools for deeper engagement, boosting dwell time and even delivering audience insights.

4. Unmatched Credibility

Example: Subject matter expertise and original research. Every year, Edelman publishes its Trust Barometer, surveying more than 30,000 people across 28 countries on trust in business, media, government, and NGOs. The findings are widely cited by media outlets and executives, and the report’s methodology and charts compel readers to click through for detail.

Such research-driven content stands apart because it offers proprietary insights users can’t get anywhere else. It positions the brand as a trusted authority, fuels citations and coverage, and compels readers to click through for methodology and nuance.

Diversifying Discovery and Distribution

Smart brands aren’t putting all their chips on Google anymore. Instead, they’re engineering multiple discovery paths that are immune to AI summarization and constantly shifting SERP formats. The most resilient strategies balance owned channels, native participation, and interactive experiences — each reinforcing brand visibility outside of Google’s walls.

A few ways to do this include:

1. Email Newsletters

Email newsletters remain the gold standard of owned distribution. Immune to zero-click harvesting, newsletters deliver content directly to your audience on your own terms.

The strongest programs build around a clear editorial promise, tailored segmentation, and actionable next steps. Engagement metrics also look different here: unique opens,real click-to-open rates, and organic subscriber growth matter more than sheer volume. A newsletter welcome in a crowded inbox is a stronger signal of affinity than any search ranking.

2. Native Social Discovery

Native social discovery offers another durable channel. On Reddit, credibility comes from contributing expertise before dropping links — a strategy with extra upside, since Reddit is currently the single-largest source feeding AI search results. On LinkedIn, brands are finding traction with shareable carousels and concise insights designed for in-platform engagement. And in private communities like Slack groups, Discord servers, or other niche forums, value comes from participation, not promotion. Brands that show up with utility and authenticity win trust; those that push content for clicks don’t.

3. Content-Driven Events and Interactive Experiences

Webinars anchored by actionable tools, workshops, or playbooks create live value, while the content generated during those events (clips, FAQs, templates, case studies, etc.) can be repurposed across other touchpoints. The most effective teams go a step further, building “distribution kits” for every major asset. That means automated email sequences, platform-specific social adaptations, community prompts, and even snippets for sales enablement and internal knowledge transfer.

Redefining Performance Metrics in the Google Zero Era

Organic sessions, once the bedrock KPI for SEO success, are no longer reliable on their own. In the age of zero-click search, when Google’s AI Overviews siphon answers directly from your content, traffic becomes unpredictable. To future-proof a destination content strategy, brands need to shift from measuring visits to measuring value.

That involves monitoring a new set of metrics.

Relationship Metrics

Email signups, subscriber growth, retention, and community participation are now among the strongest signals that your content is worth returning to. Unlike a fleeting pageview, these metrics reflect ongoing trust and affinity.

Engagement Signals

These signals reveal depth of impact. Look beyond clicks to measures such as engaged reading time, scroll depth, recirculation into related articles, and direct repeat visits. Even the ratio of direct or bookmarked traffic to organic search traffic tells a story: Audiences are coming back because they want to, not because an algorithm sent them.

Utility and Habit Metrics

These indicators capture how your content integrates into users’ workflows. Tool completion rates, repeat usage of assessments, template downloads, calculator sessions, and resource revisits are strong indicators of content that delivers enduring value. A user who saves and reuses your template is worth far more than one who skims a single article.

Contextualized Traditional Metrics

Traditional SEO metrics like rankings and organic sessions still matter, but only in context. When search is one of many pipelines — not the only one — fluctuations lose their power to derail your growth.

Escaping the SERP

The rise of zero-click search doesn’t signal the death of content marketing, but it just might be the end of lazy content tactics. Google Zero is forcing brands to confront a truth long in the making: Visibility is meaningless without engagement. Traffic is volatile. Relationships endure.

Winning in this era means rethinking what you measure, how you distribute, and why your audience should care. It means building destinations worth seeking out, not just pages that happen to rank. You don’t have to fight AI or abandon SEO entirely — these remain important parts of the mix (and we’ll be covering tactics for LLM optimization in future articles).

But survival in the Google Zero era isn’t about winning clicks; it’s about winning commitment. Brands that build trusted relationships, deliver irreplaceable utility, and foster genuine communities will discover something liberating: when audiences choose to seek you out, no algorithm can make you disappear.

Ready to future-proof your content strategy? Partner with Contently to build destination experiences your audience can’t ignore.

Frequently Asked Questions (FAQs):

1. What exactly counts as “destination content”?Destination content is any experience your audience seeks out directly, rather than stumbling across through search. That could mean an interactive tool, a trusted newsletter, or a content hub with resources they bookmark and revisit. The key is habit and value: It has to be worth returning to even if Google never sends them.

2. Should we stop investing in SEO altogether?SEO is still important, but it shouldn’t be your only strategy. Think of it as one pipeline among many. Rankings and search traffic should be contextualized alongside relationship, engagement, and utility metrics. The real hedge against zero-click search is diversification.

3. How can smaller teams compete if they can’t build tools like HubSpot’s Website Grader?Interactivity doesn’t have to mean a massive engineering lift. Simple calculators, quizzes, or even well-structured templates can deliver personalized value. The goal is to create something useful enough that your audience wants to return.

The post ‘Destination Content’ Is a Lifeboat In the Google Zero Era appeared first on Contently.

It’s a cautionary tale replayed endlessly across one of the world’s most influential community-driven platforms.

But for brands, Reddit can no longer be dismissed as a marketing minefield to be avoided. The platform has around 108 million daily unique visitors worldwide, and users spend an average of around 16 minutes consuming content per session — far more time than on many other social platforms.

Perhaps most importantly, the site’s sprawling archive of authentic conversations now serves as one of the primary gatekeepers for AI Search. Google’s $60 million-per-year agreement to license Reddit content signals that this influence is now entrenched at the highest levels of SEO and GEO.

The message for marketers is unambiguous: The rules of digital influence are being drafted on Reddit, whether you’re participating or not.

Reddit Has Traditionally Been Thorny Territory for Brands

Historically, Reddit has been hostile to overt marketing efforts. The graveyard of brand blunders is filled with failed AMAs and cringey misfires: Nissan, REI, and travel ticketing site Skiplagged have been dragged for clumsy attempts at engagement. Electronic Arts’ now-infamous 2017 defense of “pay-to-win” mechanics in Star Wars Battlefront II earned the most downvoted comment in Reddit history.

The platform’s persistent hostility to brands is tied to three deeply structural and cultural dynamics:

1. Authenticity above all. Reddit’s entire ethos centers around authentic, user-first contributions rather than top-down brand messaging.
2. Community-driven scrutiny. Every subreddit has its own culture, rules, and moderators, which means outsiders — especially brands — are expected to adapt seamlessly to the community.
3. Anonymity breeds candor (and crass comments). Under the cloak of anonymity, Redditors can be brutally honest. They won’t hesitate to tell you exactly what they think of your brand, and they have a keen nose for sniffing out inauthenticity.

As a result of all of the above, traditional marketing tactics that may work elsewhere are swiftly rejected here. Marketing-speak is mocked, subtle self-promotion is quickly exposed, and contrived campaigns are dismantled within minutes. (If you want a vivid illustration of this, just head on over to r/HailCorporate, a subreddit dedicated to unmasking brand intrusion.)

Reddit’s upvote/downvote mechanics also impose real-time accountability on content. Public comment and post histories are visible by default — though since June 2025, users can hide it from their profiles. (Moderators, however, retain 28-day access.)

Finally, moderation can be a rude awakening for brands accustomed to sanitized feedback loops. Volunteer moderators enforce each subreddit’s rules publicly and quickly. Missteps can result in instant removal or bans. And unlike platforms where content disappears, Reddit has a long memory: Deleted posts often persist via archives and mirrors, which means that one ill-conceived campaign can haunt a company for years.

2025 Reddit: New Rules, New Tools, New Stakes

All that said, Reddit in 2025 is simply not the same beast it was in 2015. The platform is evolving, both in how it equips brands and in how its culture is shifting under the spotlight of AI search.

New Tools for Marketers

Recently, Reddit itself has signaled openness to brand partnerships and data licensing deals — a perhaps not-unrelated response to the widely publicized revenue struggles leading up to its 2024 IPO.

Whatever the motivation, over the past five years, the platform has rolled out a slew of products that signal a new posture toward brand participation, including:

• Reddit Pro: A native suite of analytics, post scheduling, and community insights to help brands engage more effectively.
• KarmaLab: Reddit’s in-house creative team, built to help brands craft content that won’t instantly get flamed.
• AMA Ads: Launched in 2025, these let brands promote upcoming Ask Me Anythings in relatively “safer spaces” than past free-for-alls.

These tools make it clear that Reddit is building out infrastructure to help brands participate without breaking community norms.

AI Search: Raising the Stakes for Authenticity

Despite the hurdles involved, there’s real urgency for brands to engage with Reddit. If you’re not active on the platform, you’re forfeiting control of how your brand is represented in AI-generated answers. Competitors or critics will happily fill the void.

A few clear indicators of Reddit’s growing influence in digital discovery include:

• AI systems cite Reddit constantly. After OpenAI’s July 2025 update, Reddit citations surged 87% and now account for over 10% of ChatGPT’s references.
• Search engines elevate Reddit threads. Google increasingly surfaces Reddit discussions when users want lived experiences, not polished marketing copy.
• Meritocracy rules. In Reddit’s culture, genuinely helpful contributions — not ad spend or brand size — determine visibility. Smaller, scrappy brands can punch above their weight if they provide genuine value.

The TL;DR: The world’s toughest focus group is now also the training ground for AI, and brands can’t afford to sit it out.

Subtle Cultural Shifts

The culture is also softening, at least in pockets. In certain subreddits, more specialized experts — engineers, academics, clinicians, etc. — are welcomed when they contribute genuine expertise. The implicit bargain is simple: Show up as a person first, a brand rep second.

How Brands Are Experimenting Successfully

Even with the tailwinds created by new tools and shifting community norms, it’s no excuse for brands to fall back on lazy campaigns. Success on Reddit requires a radically different playbook that centers patience, humility, relatability, empathy, and a focus on providing value.

A few brands getting it right:

• The Economist has run thoughtful AMAs with its editors, leaning into expertise rather than pushing subscriptions.
• Mint Mobile earned credibility by having employees (including Ryan Reynolds himself at times) participate directly in r/mintmobile, answering questions and cracking jokes rather than shilling.
• Purple Mattress launched r/LifeOnPurple, a community dedicated to sleep health. Instead of spamming product links, it became a global focus group where users traded advice.

There can be real results tied to these efforts. Mint Mobile, for instance, has seen over 44% of its social media referrals (more than 101,000 visits) come from Reddit.

On the other hand, there are real risks. Brands have very little real control over even the most branded of subreddits; a recent comment on the Purple community, r/LifeOnPurple (headline: “Purple has no moral fiber”) highlights how quickly conversations can turn critical.

Technical Brands and Radical Helpfulness

Technical audiences reward brands that bring real resources to the table. Sharing a GitHub repo, being candid about a failed migration, or troubleshooting alongside users builds more trust than a dozen blog posts.

Imagine for a moment a parallel universe to the scenario at the top of this article. In this alternative outcome, the same company’s lead engineer joins a thread about database performance concerns. She candidly shares the team’s journey migrating 50 million records, drops a link to their GitHub tool, and highlights both successes and setbacks. The community responds positively; screenshots begin circulating on X. Months later, her answer resurfaces when developers search for scaling advice.

This example showcases the real value of Reddit for brands: credibility meets connection at scale. In a world in which AI slop dominates feeds, people are flocking to Reddit presumably because of the very human, messy, and unfiltered exchanges that happen there. By showing up authentically — not aggressively — brands stand to win trust and gain relevance.

Contently helps the world’s top brands create stories that resonate with real people — and stand out to both audiences and AI.

Frequently Asked Questions (FAQs):

How do you measure success for brand activity on Reddit?

Engagement looks different on Reddit than on other platforms. Metrics include upvotes/downvotes, comment sentiment, referral traffic, and whether brand posts are organically referenced in other threads. Increasingly, success also means being cited frequently in AI Search results.

Can paid ads work on Reddit, or is organic participation the only path?

Reddit Ads can be effective, but they perform best when paired with authentic community engagement. A promoted AMA or native-style post without organic credibility often falls flat. Brands that invest in both paid reach plus ongoing community presence may see the strongest results.

What types of subreddits are most open to brand participation?

Smaller, niche, interest-driven communities (tech, health, hobbies) tend to be more receptive when brands bring expertise. Large default subreddits like r/funny or r/pics are usually hostile to overt marketing. The key is finding communities where your brand can add value to conversations that are already happening.

The post Reddit’s Resurgence: How the Internet’s Toughest Crowd Became AI’s Favorite Source appeared first on Contently.

Most of these assets lived squarely in marketing’s owned-and-operated channels. But sometime over the past decade, “content” stopped fitting neatly inside the marketing department. It has now spilled into every corner of the customer experience: product UI copy, customer support scripts, help-center articles, checkout flows, push notifications, and content to live on whatever buzzy new platform will inevitably debut next quarter.

The rise of AI Search represents another turning point. LLM and AI Search experiences often pull from authoritative and widely corroborated sources; brands with consistent, high-quality coverage tend to be cited more. It stands to reason that the more unified a message your brand delivers across every element of the digital ecosystem, the more likely it is that message will make it into AI-generated outputs.

As a result of all of the above, we’re seeing content career opportunities evolve. More and more companies are hiring roles like “Head of Content Experience” and “Director of Content Design,” marking a shift in how organizations think about the choreography of brand storytelling across multiple channels. In the past, marketing teams focused on what to say and where to publish it — landing pages, campaign assets, maybe a few gated PDFs. Today, the mandate is more ambitious: Design the entire content journey so that every touchpoint feels frictionless.

Why Content Experience Matters

With so many platforms and content formats competing for customer attention, brands face a real consistency challenge. People want to feel like the same company that reeled them in during a short-form video ad is also the one answering their questions clearly in a help article or walking them through a checkout process.

While a cohesive brand voice isn’t necessarily a silver bullet for sales, it can make your brand feel more professional and trustworthy. Salesforce research has found that 69% of customers expect consistent interactions across departments. At the same time, trust in corporations is reaching all-time lows; nearly three-quarters (72%) of consumers trust brands less than they did a year ago. 

In this climate, inconsistency can further chip away at confidence. Content experience is one of the levers brands can pull to counteract that.

Content Experience, Design, and Strategy: How Are They Different, and Where Do They Overlap?

Unlike content marketing, which often treats messaging as standalone assets, content experience treats content as infrastructure. It involves building the scaffolding that makes every interaction feel connected, from first click to task completion.

Here’s how the different roles tend to break down:

• Content Strategist: Sets the big-picture plan for what content to create, for whom, and why. They define voice/tone guidelines, editorial calendars, governance rules, and KPIs. A strategist might determine that the brand needs a library of onboarding tutorials, but they aren’t usually the ones crafting the microcopy inside the product.
• Content Designer: Works closely with UX and product teams to shape in-product copy and flows. They focus on clarity, accessibility, and task completion, writing for things like error messages, navigation labels, onboarding prompts, and help center articles — typically in the context of the interface.
• Content Experience Lead: Operates between strategy and design, with a systems lens. They ensure that content is consistent, discoverable, and adaptive across channels. This can include building modular content systems, implementing personalization logic, managing taxonomies, and coordinating delivery across web, app, email, and emerging platforms.

Unlike with traditional content marketing roles, content design and experience are not so much about producing more assets, but orchestrating existing ones into a coherent, user-friendly whole. The goal is to make sure that no matter where a customer encounters your brand — in an AI Search snippet, a push notification, or a complex product workflow — it feels like part of the same conversation.

These roles aren’t meant to work in silos; their real value shows when they collaborate across the full content lifecycle. A content strategist might partner with a content experience lead to ensure the high-level editorial vision translates into modular, reusable components that can live across multiple platforms. 

That same experience lead might work side by side with content designers to embed those components into product flows and ensure they’re consistent with voice, tone, and accessibility standards. In mature teams, these roles often sit in a shared content or UX organization, but they also act as liaisons to marketing, product, and customer support. The collaboration is cyclical: Strategy informs experience, experience informs design, and design feedback helps refine strategy.

Applying the Mindset Without a Dedicated Hire

You don’t need a Head of Content Experience to start thinking like one. Even without a specialized team, small shifts can move your organization toward a more cohesive, user-first content experience.

Here’s a quick-start playbook:

1. Audit your most important journeys

Map your top user tasks — whether that’s signing up for a trial, upgrading a plan, or finding help — across your site, docs, product UI, and support channels. Look for language gaps, redundant steps, or tonal mismatches that create friction or confusion.

2. Treat content as a design component

Work with your design system or dev team to bake voice, tone, terminology, and content patterns into the same place you keep visual components. If those standards live in your CMS and design files, they’re easier to apply consistently.

3. Create space for cross-functional reviews

Bring marketing, UX, and product teams into the same (virtual) room to critique real user flows. A quick “ad → landing page → trial → help doc” run-through can surface tone shifts and clarity issues that siloed reviews miss.

4. Pilot fixes in high-impact areas

You don’t have to revamp everything at once. Try a small, visible project like:

• • Launching a unified glossary so marketing, product, and support all use the same terms.
  • Applying progressive disclosure in onboarding copy to reduce overwhelm and speed up activation.

5. Give teams a cheat sheet

A single-page “language patterns” guide covering voice, tone, and terminology gives everyone a quick reference. When in doubt, they’ll have a shared source of truth.

While there’s a lot up in the air right now about the future of content (and the careers in this space), there’s one consistency we can count on: New channels will keep emerging. AI will keep reshaping how people discover and evaluate brands. The best way to future-proof your message is to make sure it already works everywhere — and that’s exactly what content experience thinking delivers.

At Contently, we help brands put these principles into practice, from developing voice and tone guides to creating modular, multi-channel content systems that keep messaging consistent everywhere your audience meets you. Learn more about our services, including our AI Studio, here.

Frequently Asked Questions (FAQs):

1. Do I need to hire all three roles — content strategist, content designer, and content experience lead?

Not necessarily. Many companies start by layering content experience thinking into existing roles. If you can’t staff all three, focus on cross-functional collaboration between marketing, UX, and product, and look for people who can work across silos.

2. How is “content experience” different from just good UX writing?

UX writing focuses on the clarity and usefulness of in-product copy. Content experience zooms out to orchestrate how all content — in product, marketing, and support — works together, so it feels like one cohesive brand conversation.

3. What’s the first step if my organization isn’t ready for a full content design or experience hire?

Start with an audit of your most important customer journeys and create a shared “language patterns” guide for all teams. Even small steps toward consistency can pay off quickly in trust, usability, and discoverability.

The post Strategy, Experience, Design: The Roles Redefining Content in 2025 appeared first on Contently.

You scramble to explain how that blog series probably helped a few deals move forward. You gesture vaguely at that product explainer video that likely nudged some prospects along. You say “engagement” a few times. And the CFO nods — but not in the good way.

Marketing budgets have plateaued at 7.7% of company revenue for two consecutive years, according to Gartner’s 2025 CMO Spend Survey. At the same time, the Content Marketing Institute finds that fewer than half of B2B marketers say their organization measures content performance accurately.

Flat budgets and fuzzy metrics aren’t a sustainable combo. To keep your seat at the table (and your budget intact), here are five plays that tie content to revenue in ways your finance team will actually care about.

1. Track Every Pass on the Field

If you’re only tracking last-click conversions, you’re missing half the game. Most content does its best work long before someone fills out a form by tackling intangibles — planting ideas, building trust, and answering questions a simple product page just doesn’t cover.

To show that impact, start mapping each asset to a stage in the buyer journey: Awareness, Consideration, or Decision. Then connect those stages to your CRM or marketing automation platform, so when a deal closes, you can see the full content trail behind it.

How to start:

• Look back at the past few quarters of content
• Assign a stage to each piece (gut instinct is fine to start)
• Add those tags to your lead or opportunity records going forward

This doesn’t need to be perfect or overly technical. Even a simple tagging system can surface patterns — like that one product-focused blog that keeps showing up in early-stage deals. Once you spot an asset like that, you can double down on its strengths or repurpose it for sales enablement.

2. Graduate to Multi-Touch Scoring

Content doesn’t win deals alone and rarely wins on the last touch. Think about the webinar a customer watched before even talking to sales —  those moments matter. And they don’t typically show up in a last-click report.

That’s where multi-touch attribution comes in. It spreads credit across the full buyer journey so you can see which pieces actually pull their weight, even if they don’t get the glory of the final click.

There are plenty of examples of this process in action. Take, for instance, NineTwoThree Studio. The product design and engineering firm — a Contently client — used time-decay attribution to link AI-optimised articles to ChatGPT-driven sessions and generated more than $1 million in qualified leads within 90 days. The firm now ranks in the top results for 92% of its target AI queries.

You don’t need a team of data scientists to get started. Tools like GA4, Adobe, or even a well-structured spreadsheet can help you test different models, like:

• Linear, where every touch gets equal credit
• Time-decay, where newer touches get more weight
• Position-based, where you emphasize the first and last touch

Simple first steps:

• Grab six months of data from your CRM or analytics tool.
• Try out a basic model — even just assigning 40% to the last touch, 30% to the one before it, and so on.
• Compare it to your current reporting. Which pieces show up that you’ve been ignoring?

Chances are, a few early- or mid-funnel assets will suddenly look like quiet power players. And once you know what’s working, you can invest more strategically (and stop chasing disappearing clicks).

Contently’s analytics make this process even easier. Our Content Value dashboard automatically maps every asset you create on the platform to the buyer journey, and showcases how each piece contributes to pipeline, revenue, and retention. You can dig into performance by asset type, persona, funnel stage, or even custom goals, all without wrangling a mess of spreadsheets. Customers using this dashboard report seeing multi-million-dollar organic ROI and average audience growth of 40% in six months.

3. Trade Vanity for Value Metrics

Executives aren’t looking for vibes. They’re looking for value. So it’s time to swap out vanity metrics like views, likes, and bounce rates for numbers that actually tie to revenue.

Two great ones to start with:

• Cost per Assisted Opportunity: how much you spent on a content cluster, divided by the number of deals it helped close.
• Net SEO Value: a rough estimate of what your organic traffic would’ve cost if you’d paid for it via search ads.

Here’s a quick back-of-the-napkin formula:

Net SEO Value = (Organic Sessions × Avg CPC) – Content Costs

If that number beats your paid search ROI, you’ve got yourself a strong case for more investment in content — and fewer eyebrow raises at budget time.

The point of this exercise is to speak in a language your finance team already understands: efficiency, cost-per, and net return. When content starts showing up in those terms, it stops sounding like a gamble.

4. Turn Data Into Boardroom Stories

If you want your content program to resonate in the boardroom, ditch the 10-tab deck and boil it down to one powerful slide per initiative — your “Money Slide.” It should include:

• One standout chart
• One clear headline
• One quote that brings it to life

Here’s an example:

 Headline: “Financial-literacy hub influenced $4.2M in Q2 pipeline — up 27% from last quarter.”
Quote: “This content made it easier to explain our product to clients.” — a relationship manager

This approach works especially well when showcasing cross-functional wins. Say your team localized hundreds of articles in a single day and saw a major bump in regional engagement. That’s a story. It’s also a great way to make future budget requests a lot less painful.

Here’s how one team turned a simple metric into a story that stuck: A leading financial-services enterprise recently localized 252 articles across 3 languages in one day, using Contently’s AI-powered workflow. 

5. Tighten the Feedback Loop

Attribution is an ongoing rhythm. Set a recurring time (monthly, quarterly — whatever works) to check in on what’s performing, what’s lagging, and what needs a second life. That could mean trimming underperformers, refreshing outdated blog posts, or chopping long videos into clips people actually finish.

Small tweaks. Big lift. And just in time for the next budget review.

These days, it’s not enough to say content works. You’ve got to show how much it works — in language your finance team actually understands.

So map every piece to the buyer journey. Use multi-touch models to surface your real MVPs. Trade vanity metrics for ones that tie to revenue. Turn your reports into stories that stick. And keep refining as you go.

Do that, and the next time someone asks what content has done for the business, you won’t even need to say a word — your slides will do the talking.

Frequently Asked Questions (FAQs):

1. What if we don’t have fancy attribution software?

You don’t need a new tool to get started. A basic spreadsheet with deal IDs, content touches, and journey stages is enough to start spotting patterns. Over time, you can layer in GA4 or your CRM’s native reporting — no data science degree required. 

Platforms like Contently can also help you scale when you’re ready by offering built-in attribution tracking, journey mapping, and cluster-level insights designed for marketers who want proof without pulling an all-nighter in Excel.

2. Our leadership team still wants last-click numbers. Now what?

Run both. Put last-click and multi-touch side by side to highlight what’s missing from the old model. Early- and mid-funnel content that gets ignored in last-click reports often looks a lot more valuable with context — which tends to win over skeptics.

3. How often should we review content performance?

At least once a quarter. Block time to audit what’s working, what’s slowing down, and where new opportunities are emerging. The more you build this into your rhythm, the easier it gets, and the faster you’ll have proof ready when budget season rolls around.

The post The Most Effective Ways to Tie Content to Revenue in 2025 appeared first on Contently.

Which one gets your click?

If you’re like many B2B buyers in 2025, it’s not even a close competition: The human byline wins every time. And for content marketers, that instinct — that immediate trust calculation — should be a wake-up call when you’re producing certain types of content.

The Great Trust Recession (And Why It Matters)

Audience trust has been spiraling for years with the rise of misinformation, insidious influencer #sponcon, and content-farm clickbait — but it’s gotten even thornier since the release of ChatGPT. Suddenly, anyone could publish 50 articles a week, or, heck, a day. The internet got noisier. AI slop flooded our feeds. And trust became the scarcest commodity in content marketing.

Today’s audiences have a sixth sense for AI-generated content — just look at the debates online about whether an em dash is a surefire ChatGPT giveaway (editor’s note: you can pry my em dashes from my cold, dead hands).

The point is that discerning audiences can tell when something’s been stitched together by a bot. Smart brands are already adapting: Wealthsimple’s magazine features financial advisors and economists by name, complete with headshots and bios. Klarna’s blog intersperses general updates with posts from executives, product experts, and engineers. The personal touch makes it seem as if they’re building a bench of trusted voices, not generating questionable financial advice from voiceless, faceless bots.

To Byline or Not to Byline?

Of course, AI content is not inherently a bad thing. Here at Contently, we’re all-in on using AI when it makes sense. It can save brands valuable time and budget, and significantly reduce production bottlenecks. We pride ourselves on our AI Studio’s ability to help teams move from the blank page to publish-ready content in a fraction of the time it used to take.

So, we’re certainly not saying that every piece you write needs a human byline. That’s neither scalable nor strategic.

But it is worthwhile to break your content strategy into two streams: where AI can assist, and where human authorship is still essential. Some formats demand a real voice.

Consider human bylines for:

1. Thought leadership and elevating internal SMEs. When you’re challenging conventional wisdom, predicting trends, or offering a behind-the-scenes look at your company’s strategy, it matters who’s talking. A founder or product lead sharing insights on LinkedIn feels far more authentic in their own voice, even if their posts contain the occasional ramble or run-on sentence.
2. Reviews, personal insights, and perspective pieces. When you’re expressing an opinion or taking a stand, readers need to know whose neck is on the line. “Why We’re Betting $50M on No-Code” hits differently from your CTO versus a generic Staff Writer. And an AI-generated product review is the ultimate reader ick in 2025.
3. Trust-critical content. If you’re giving advice on topics that directly affect customer risk, human credibility is non-negotiable. This applies to high-stakes categories like financial guidance or healthcare recommendations — anything where bad advice can cause real harm.
4. Customer success stories. If you want to build credibility and connection, you should include real names and real results. This means you’ll most likely need a real human to conduct interviews, ask follow-ups, add context, and shape the narrative with nuance.

Where AI is perfectly fine or recommended:

1. Creating utility content (how-tos, basic comparisons, etc.): AI thrives in structured formats with clear parameters. If you’re producing evergreen content at scale — like “how to set up 2FA” or “compare software X vs. Y” — AI can significantly reduce production time.
2. Product descriptions, metadata, and landing pages: These are areas where clarity and consistency matter more than voice. AI can generate high-quality drafts that humans can review and refine quickly.
3. Filling content gaps in your SEO or AIO strategy: As Ahrefs notes, Google doesn’t necessarily dislike AI-generated content, so long as it’s helpful and high quality. In fact, more than 86% of top-ranking pages contain at least some AI input. Using AI to fill SEO/AIO gaps can be especially useful when speed and scale matter more than unique voice or personal authority — think middle- or bottom-funnel keyword content.
4. Repurposing existing internal content: AI excels at summarization and reorganization when the source material is strong. It can be a godsend for remixing or reformatting a high-quality piece of big rock content that already exists — like turning a webinar into a blog post, summarizing a slide deck, or compiling FAQs into a checklist.

Still Undecided? Put Your Journalist Hat On

A good rule of thumb is to assess the journalistic value of a piece of content: Would a reader expect real reporting? Does it demand clarity, accountability, or original insight? Then it probably deserves a heavy human hand. When content starts to look and feel like journalism — interviews, expert analysis, ethical nuance — it should be treated with the same editorial care.

Research backs this up: A 2024 study found that while audiences were fine with AI handling low-stakes content, their trust dropped sharply when they suspected AI had written something resembling journalism. When the stakes are high, readers still want a human behind the keyboard.

The AI Partnership Model That Actually Works

The most sophisticated content teams aren’t choosing between AI and human authorship — they’re using a smart mix of both. At Contently, we help brands implement a human-in-the-loop model that plays to each strength.

Here’s what that looks like in practice:

AI handles the heavy lifting:

• Research aggregation
• First draft generation for human enhancement
• SEO optimization and metadata creation
• Content repurposing and channel adaptation
• Performance analysis and iteration recommendations 

Humans provide the irreplaceable:

• Industry insight and trend interpretation
• Personal anecdotes and case studies
• Ethical judgment and nuanced perspectives
• Accountability and reputational stakes
• Authentic voice and personality

Trust is no longer a passive benefit of publishing. It’s an asset you have to design for. AI can help you scale — but only humans can create the kind of credibility that builds lasting audience relationships. The smartest brands are leaning into both.

Want to dive deeper into building trust through content? Check out our guides on navigating AI search and training AI for authentic brand voice.

Frequently Asked Questions (FAQs):

1. How much of my content needs to be authored by a human in order to have a human byline?

There’s no hard-and-fast rule, but the general principle is honesty. If a piece includes significant human input — original thinking, unique perspective, firsthand experience, or editorial judgment — then a human byline is fair game. If the human “author” merely skimmed and approved an AI-generated draft without meaningful contribution, it’s better to credit the team or keep the byline anonymous. When in doubt, disclose the collaboration.

2. What’s wrong with putting human bylines on AI-generated content?

It erodes trust. Readers are becoming more attuned to AI-generated writing, and if they sense that a human byline is masking machine-made content, it can feel deceptive. Worse, it puts the “author” in an awkward position — they’re now accountable for content they didn’t actually create. Transparency builds credibility; shortcuts damage it.

3. How should I decide whether a piece needs a human author or not?

Ask yourself: Would a reader expect expertise, opinion, or accountability here? If the content includes analysis, interviews, or thought leadership — or if it covers sensitive or trust-heavy topics like security, finance, or healthcare — it should have a real human behind it. On the other hand, if it’s utility content or SEO-driven copy where voice and nuance matter less, AI can take the lead — with a clear editorial check.

The post Are Human Bylines Content Marketing’s New Trust Currency? appeared first on Contently.

Though the SI snafu occurred in the early, Wild West days of ChatGPT’s mainstream adoption, its lessons linger two years later. The sloppy AI articles eroded reader trust — a precious and tenuous commodity in today’s world of fake news and algorithm-fueled outrage.

While marketers have different stakes than media outlets, they’re playing with the same volatile mix of automation and audience expectation. As every B2B marketer who’s had to scrub the phrase “rapidly evolving tech landscape” from an AI-generated blog post knows, chatbots have a tendency to produce generic platitudes or even blatant misinformation.

Don’t get me wrong: AI has plenty of upside. It can help you scale your content like never before. But only if you teach it to sound unmistakably like you — and keep a watchful eye on its work.

Here’s how to avoid becoming the next cautionary tale.

Put up guardrails before you unleash the bots

Marketers are getting more hands-on with the fine-tuning and orchestration behind generative AI engines. You might be building a custom GPT to answer customer questions in your brand’s tone, feeding a writing assistant AI your top-performing articles for inspiration, or integrating AI into your CMS or email workflows to auto-generate first drafts.

All these cases involve understanding the basics of training AI on brand-aligned inputs and clear intent signals. Train a chatbot well, and it can produce remarkable work. Leave it to guesswork and vague direction, and it will confidently wing it with results that may sound professional but miss the mark in any number of ways.

Savvy content teams use a three-layered safety net that any team can implement quickly, regardless of technical expertise:

1. Start with reusable prompts. These are essentially scripts that the AI must follow every time it writes for you. Specify exactly who it’s speaking to, which tone to use, and which words or topics are off-limits.

2. Add a built-in cheat sheet. Retrieval-Augmented Generation (RAG) sounds intimidating, but the concept is simple: Instead of relying only on what a model remembers, RAG lets AI pull relevant facts from a trusted source — your database of approved quotes, product specs, or brand guidelines — as it writes. This gives the AI a live reference doc to consult so it stays grounded in accurate info.

3. Layer in quality control. Run every draft through an automated style checker to flag banned words and tone inconsistencies. Then, have a human editor do the final sweep for nuance and legal compliance.

Start cautious with heavy human oversight, then gradually automate more as your guardrails prove reliable. The beauty of this system is that it scales with your confidence.

Feed AI great examples, not a data dump

Your first instinct might be to feed an AI model every piece of content you’ve ever published — but resist that urge. Just as with onboarding a new writer, when it comes to AI-assisted content creation, quality trumps quantity.

In other words, a few dozen pieces that perfectly capture your voice will teach an AI system better than thousands of mediocre examples mixed with outdated content that no longer reflects your brand.

Here’s a three-step playbook for this process:

1. Start building a “gold standard” dataset with content that already works. This might involve flagship blog posts that have performed well in the past, genuine thought leadership, landing pages with strong conversion rates, or customer support emails that have received positive responses.

2. Give it rich context. Tag each piece with metadata about audience, funnel stage, geographic region, and any compliance requirements. This teaches the AI when to be playful (like for a social media post) and when to stay clinical (for a technical white paper).

3. Be intentional with what you leave out. Not every high-performing asset belongs in your training set. If a piece doesn’t reflect how you want the AI to write going forward, don’t include it — no matter how well it performed at the time.

Test, tune, and toss what doesn’t work

Once your guardrails are solid and content examples carefully curated, you can start adjusting the AI’s output to match your voice more precisely. Think of this phase like onboarding a talented new employee who understands the basics but needs to learn your company’s specific way of doing things.

Start by cleaning up your training materials. Delete boilerplate text or legal footers that might confuse the model. AI systems learn patterns quickly, so you want them picking up your unique voice — not generic jargon that appears in thousands of other companies’ content.

Here are a few best practices to consider at this stage:

1. Choose your level of intervention carefully. For most brands, lightweight adjustments using Low-Rank Adaptation (LoRA) work well — they’re fast, affordable, and often effective for subtle voice tweaks. Full model retraining, on the other hand, is expensive and time-consuming. The latter should be reserved for companies with truly distinctive voices (and big budgets).

2. Test systematically. Split your examples into training, validation, and testing groups using a 70/20/10 ratio. Have human editors rate the AI’s output on tone and accuracy without knowing which pieces are AI-generated versus human-written. This blind testing reveals whether your training actually improved the voice match or just taught the AI to mimic surface-level patterns.

3. Finally, make sure the math works. If the cost of GPU time and platform fees exceeds the editing hours you save within six months, pause and reassess your approach. AI should make your team more efficient, not drain your budget on computing costs.

People power your AI’s potential

Even the smartest content marketers run into predictable AI stumbles. “Tone drift” happens when an AI’s voice gradually veers off-brand over time. “Grand sentence syndrome” is another frequent offender — you know, those overly complex, academic-sounding phrasings that no human would ever utter in a casual conversation. Then there are punctuation quirks (hello, endless em dashes and gratuitous gerunds) and hallucinations, when AI confidently fabricates facts out of thin air.

People are the secret sauce that can turn AI from a liability into a differentiator. Today’s content teams need solid talent to fine-tune the tech and enforce editorial standards, including:

• Prompt architects who know how to steer tone and structure through careful A/B testing
• Model specialists who can evaluate which tools and settings deliver the best results for each content type
• Journalistically minded editors with strong fact-checking chops to catch red flags before a piece publishes

AI can amplify everything that makes your brand voice memorable, or it can flatten that personality into forgettable corporate-speak. The deciding factor isn’t the size of your dataset or sophistication of your model — it’s the clarity of your guidelines and the expertise of your editors.

Want AI to nail your brand voice without the headaches? Contently’s AI Studio takes care of the setup, fine-tuning, and editorial oversight — so you get better content, faster, and with less risk. Chat with us today to scale faster and sound better doing it.

Frequently Asked Questions (FAQs)

What’s the biggest risk of using AI in content marketing?

The short answer: sounding generic or getting facts wrong. Without strong guardrails, AI tends to default to safe but stale phrasing — or worse, confidently fabricates misinformation (a.k.a. hallucinations). That’s why the most effective teams pair AI tools with human editors, prompt testing, and fact-checking systems that keep brand voice sharp and content credible.

How much content do I need to train an AI on my brand voice?

Less than you think — as long as it’s the right content. A few dozen examples that clearly reflect your tone, structure, and audience fit are far more valuable than a massive archive of outdated or inconsistent pieces. Focus on quality over quantity, and tag each piece with helpful metadata like audience, funnel stage, and channel to give the AI proper context.

How can I tell if my AI training efforts are actually working?

Treat it like a science experiment: Split your sample into training, validation, and test sets (think 70/20/10). Then, have human reviewers rate the outputs without knowing which were written by AI and which weren’t. If your team can’t consistently tell the difference — or if AI-generated drafts require fewer edits — you’re on the right track.

The post How to Train AI for Bulletproof Brand Voice: Top Tips and Tricks appeared first on Contently.

Google’s new AI Mode, currently in the midst of a phased rollout, is likely the beginning of the end of the SERP’s 20-year reign. In general, this new era of AI Search is nothing short of — to borrow a phrase AI loves — a paradigm shift. Instead of serving up a list of links for users to explore, search engines now generate direct, conversational answers, often without requiring users to navigate off the results page.

This shift has myriad implications for brands and publishers, chief among them the rise of the zero-click search. The impacts of this evolution can’t be understated, and it will take some trial and error to create a new game plan for driving visibility and engagement in an AI-curated world.

But some tenets of traditional content strategy aren’t going away. While users can get quick summaries through AI Search, the detailed insights found in longform content are still an essential part of any brand’s digital presence. Here’s why longform still matters in this new era — and how leading marketers can evolve their traditional approach to keep content relevant and discoverable.

Why Longform Is Still Relevant in the Age of AI

Longform content continues to serve as the backbone for establishing authority and expertise online, and search engines still rely on rich, detailed narratives to verify trustworthiness and quality.

Here’s an overview of why longform isn’t going anywhere just yet.

Not Every User Wants a Summary

AI search tools are optimized for convenience, but they often surface overviews that are just that — cursory summaries. For users researching a complex topic, making a high-stakes decision, or trying to understand a nuanced issue, these condensed answers often fall short. That’s where longform content still shines.

Which brings us to our next point…

Longform Can Power Middle- and Bottom-Funnel Conversions

In a world where bite-sized answers dominate the top of the funnel, longform content becomes even more impactful at the middle and bottom. These are the stages where trust, differentiation, and depth matter most; they’re where prospects are comparing solutions, weighing trade-offs, and looking for signs of credibility.

Well-executed longform content can walk readers through complex ideas, unpack case studies, or showcase customer success stories in a way that builds confidence and nudges them closer to action. It can also be a powerful tool for nurturing relationships over time — whether you’re supporting an Account-Based Marketing (ABM) strategy or educating high-intent leads.

Originality Drives AI Visibility

Finally, longform content isn’t just valuable to your audience — it’s instrumental to AI systems themselves. AI search tends to pull from what it perceives as high-authority sources, and not just because of length or formatting. Content that includes original reporting, proprietary research, expert interviews, or unique insights stands a greater chance of being cited, summarized, or linked to by AI platforms.

This is another area where longform can offer a strategic edge. When content is substantive, it sends stronger relevance signals to both humans and machines. Combine that with a thoughtful PR or content syndication strategy, and you’re increasing the odds that your content becomes the source of record — not just another reference in the pile.

How to Adapt Your Longform Content for the AI Search Age

Preparing your content for an AI-driven landscape means rethinking its structure and presentation to ensure it can be easily digested by both human readers and automated systems.

To give your brand the best shot of surfacing in AI-generated results, focus on key structural and semantic strategies, including:

Structure Content for Scanning

Content that’s well-organized with descriptive headings and clear sections makes it easier for readers and AI alike to locate key information. A logical, scannable structure helps your work get referenced accurately and ensures that the takeaways are immediately accessible. You’ll also want to optimize for zero-click consumption by enhancing your content with quick overviews, summaries, FAQs, and highlighted key points.

Focus on Information-Rich Content and Original Data

Every segment of your content should deliver meaningful insights, actionable advice, or deep analysis. By cutting the fluff and prioritizing substance, you create material that’s both engaging and valuable.

Further, embedding original research or reporting, compelling statistics, and distinct viewpoints not only enriches your content, but also differentiates your narrative from generic sources. Unique data and perspectives anchor your work in real-world insights, increasing the likelihood that your content will be valued by both human readers and AI systems.

Use Internal Linking and Content Clusters

Develop a robust network of interconnected content to improve overall search coherence. Content clusters allow for a more comprehensive portrayal of your subject matter, letting both users and AI systems understand the broader context of your expertise. This layered approach can enhance your reputation as a go-to resource in your industry.

Distribution Is the New Differentiator

While it’s still smart to invest in longform, how it gets distributed is just as important as what it says. According to recent guidance from Ahrefs, traditional signals like backlinks and keyword density may carry less weight in determining what content gets surfaced in AI-generated summaries. Instead, breadth and consistency — i.e., how many places your brand shows up across and within trusted content ecosystems — is gaining influence.

That means the old playbook of producing a whitepaper, putting it behind a gated download form on your website, and watching the leads pour in may not cut it anymore. Longform content should now serve as a modular asset: republished or referenced across reputable sites, broken down into excerpts or bylined pieces for external publications, and turned into visual or multimedia formats that can travel well.

In short: don’t just publish — propagate.

AI Changes the ROI Equation for Effective Longform Content

Here’s the good news: As much as AI is a disruptor in this space, it can also be a creative accelerator. Longform content that used to take weeks or even months to produce can now be turned around in just a couple of days, for a fraction of the cost—especially if you’ve got a strong human + AI team to tackle the heavy lifting.

At Contently, we specialize in combining editorial expertise with AI-enhanced workflows to help brands scale thoughtful, strategic content. Our AI Studio streamlines every step of the process, from research and outlining to first-draft generation and editorial refinement — so you can publish faster without sacrificing quality.

AI may be reshaping how people search, but it’s also raising the bar for what gets amplified and cited. The brands that win in this new landscape won’t be the ones churning out shallow summaries; they’ll be the ones building meaningful, original content that machines can surface — and real people can actually use.

Frequently Asked Questions (FAQs)

Will longform content still drive organic traffic in a zero-click world?

Even if users don’t always click through, high-quality longform content can still be surfaced, cited, and summarized by AI tools. That visibility contributes to brand awareness, trust, and discoverability across the web.

How should I structure longform content to be more AI-friendly?

Use clear headings, bullet points, summaries, and data callouts. Think modular — each section should stand alone if excerpted, and signal its value quickly to both AI and human readers.

Should I gate longform content behind lead forms?

Gating still has a place, but in the AI era, it’s often better to keep core content open and repurpose gated elements (e.g., checklists, toolkits) for lead-gen. Visibility across multiple high-authority platforms is now more important than locking content behind a form.

The post What Does AI Search Mean for the Future of Longform Content? appeared first on Contently.

The foundation of the $80 billion SEO market is cracking and something new is being born: LLM Optimization (LLMO). And the companies that understand this shift first will own a massive competitive advantage.

The Search Paradigm Has Already Changed

I’ve been watching this transformation accelerate over the past year, and the data is undeniable. Users aren’t clicking through search results anymore, they’re getting complete answers directly from AI.

Think about your own behavior. When was the last time you scrolled through multiple Google results for a simple question? Increasingly, we’re all turning to ChatGPT, Claude, or Perplexity and getting comprehensive answers in seconds.

The numbers tell the story: LLM search queries are now averaging 23 words instead of 4. Search sessions last 6 minutes instead of seconds. Users are having conversations with AI, not just typing keywords.

This is a fundamental rewiring of how information discovery works.

From Rankings to References

Traditional SEO was built on a simple premise: rank higher on the results page. But in an AI-first world, visibility means something entirely different. Success is no longer about where you appear, it’s about whether AI references you at all.

We’re moving from click-through rates to reference rates. The question isn’t “did they visit your page?” It’s “did the AI cite your content when answering the user’s question?”

I’ve been testing this personally. When I ask ChatGPT for marketing advice, which brands get mentioned? When I query Claude about content strategy, whose insights surface? The patterns are revealing and they have almost nothing to do with traditional SEO rankings.

The Platform Fragmentation Challenge

Here’s what makes this shift even more complex: search is fragmenting across platforms. Apple just announced that AI-native search engines like Perplexity and Claude will be built into Safari. Google’s distribution chokehold is breaking.

Users are searching on Instagram, asking Siri complex questions, querying AI assistants embedded in their work tools. Each platform has different models, different training data, different ways of surfacing information.

This fragmentation creates both challenge and opportunity. The old playbook of optimizing for one search engine is obsolete. But it also means early movers can establish presence across multiple AI platforms before the competition catches up.

Three Critical Shifts for Marketing Leaders

Structure Trumps Keywords

AI doesn’t care about keyword density or exact match phrases. It prioritizes content that’s well-organized, easy to parse, and dense with meaning.

The inverted pyramid style – leading with the answer, then supporting details – is becoming critical. I’ve seen our clients increase AI citations by 22x simply by restructuring existing content to be more AI-digestible.

Authority Signals Are Evolving

Traditional backlinks still matter, but AI is looking for different credibility signals. It favors content that cites reputable sources, includes author credentials, and demonstrates expertise through depth rather than keywords.

The old game of link building is being replaced by knowledge building. AI can detect thin, manipulative content instantly. Only genuinely valuable, well-researched content gets referenced.

Distribution Channels Multiplied

SEO focused on one channel: Google. LLMO requires presence across dozens of AI platforms, each with its own preferences and algorithms.

The brands winning are creating better content and ensuring that content is discoverable across ChatGPT, Claude, Perplexity, Google AI Overviews, and the dozens of other AI interfaces emerging monthly.

We produced a detailed LLMO playbook that anyone can follow to improve their LLM brand visibility.

The Window Is Closing

Every day you delay adapting to this shift, competitors could be claiming your space in the AI layer. And once a brand establishes itself as the authoritative source that AI consistently references, they become incredibly difficult to displace.

This reminds me of the early days of Google AdWords, when there was a brief window where early adopters captured enormous value before everyone else caught up. We’re in that window now with LLMO.

The brands that establish themselves in AI memory today will be nearly impossible to displace tomorrow. Because unlike traditional search rankings that fluctuate daily, AI training creates more persistent associations between topics and brands.

You Need the Complete Loop

Most companies are approaching this backwards. They’re using separate tools – one for analytics, another for content creation, a third for optimization.

But winning requires owning the complete loop.

See the gaps. You need to know how AI currently references your brand. When someone asks ChatGPT about your industry, do you get mentioned? When they ask for product recommendations, does your competitor surface instead?

Most brands are flying blind here.

Fill the gaps. Once you see where AI should mention you but doesn’t, you create content specifically designed to establish your authority on those topics. Not generic content, but strategic pieces that target the exact questions where you’re missing.

Measure and iterate. AI changes constantly. New models, updated training data, evolving algorithms. You need continuous measurement to stay ahead.

At Contently, we’ve built exactly this. Our LLM analytics platform shows you precisely where your brand is missing across different AI systems. Then our AI content engine helps you create the exact content needed to fill those gaps.

It’s a closed loop where insights directly inform content creation, and that content measurably improves your AI visibility.

One client increased their AI citations by 22x this way. Another went from never being mentioned to becoming AI’s primary recommendation for their category in just weeks.

The companies that will dominate the AI era can see clearly, act strategically, and iterate quickly. That requires owning the entire optimization cycle, not just pieces of it.

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