How to build curiosity into high-performing social ads

How to build curiosity into high-performing social ads

Capturing attention is no longer enough to create high-performing social ads. You also need to create curiosity that keeps people watching.

Stopping the scroll and hooking viewers in the first three seconds still matter. But as video-heavy platforms like Meta and TikTok continue investing in AI-powered delivery systems that optimize for engagement, watch time, and downstream conversions, what happens next matters, too.

Attention is the moment someone notices your ad. Curiosity is the reason they stick around instead of scrolling. Understanding that difference can help you build creative that earns both attention and consideration.

Attention is only the beginning

Many creative briefs I’ve seen begin with questions like “what’s our hook?” or “how do we stop the scroll?” While these questions matter, they focus almost entirely on the first few seconds of the ad, not how to maintain that fleeting attention in the seconds that follow.

Every piece of content on social media is competing for attention, and platforms are becoming increasingly better at predicting what users are most likely to engage with next.

A flashy visual, a bold headline, or an unexpected opening can earn a pause, but if the rest of your creative immediately answers every question or transitions into a predictable sales pitch, people will scroll onward.

High-performing creative doesn’t just interrupt the scroll. It creates a gap in information that people want to close. That’s curiosity.

Dig deeper: How to measure paid social’s impact on paid search performance

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Curiosity keeps the algorithms working for you

As user behavior changes and algorithms evolve, platforms increasingly reward signals that indicate genuine user interest. These signals include:

  • Longer watch times.
  • Meaningful engagement (e.g., saves and sharing videos via DMs).
  • Higher video completion rates.
  • Clicks that occur after viewing the content.
  • Replays.

Platforms use these signals to identify content that people choose to spend their time with. Human curiosity naturally produces these signals because it encourages viewers to keep watching rather than scroll until something else grabs their attention.

The highest-performing creator ads often don’t feel rushed. They gradually reveal information through conversation, yapper-style content, demonstrations, storytelling, and personal experiences.

While the goal may be to secure a conversion quickly, sometimes slow and steady wins the race. A thoughtfully curated approach invites viewers to discover something new rather than asking them to purchase right away.

The rise of curiosity-driven creators

One recent creative shift is the growing popularity of longer-form, conversational creator ads called “yapper ads.” These ads are longer, less polished, more authentic, often unscripted, and sometimes don’t even mention the product immediately.

They seem to violate nearly every traditional best practice, but they perform well because they stand out, feel like conversations rather than commercials, and create curiosity throughout the video. They often address what happened next, why something solved a problem, and whether something would work well for the viewer.

Creators who do this well reward the attention they’ve earned with more interesting information throughout the content, and brands that boost these ads are seeing impressive results.

A screenshot of a Meta video ad from Gratsi Wine and creator, Kayce Smith
Source: Gratsi Meta Ads

Dig deeper: Why ugly ads outperform polished creative and how to test them

Don’t improvise curiosity, build it

Curiosity should be intentionally designed because most creators can’t naturally create it on the fly. The creative strategy behind these successful ads is to delay the obvious and gradually reveal information.

To do this, create open loops with a surprising statement, an unexpected result, or a relatable problem that encourages viewers to stay for the outcome. Demonstrate something rather than just describing it, or show a transformation rather than simply listing features.

While this type of content is strategic, it can feel more human and natural by telling stories, answering questions along the way, and perhaps even jumping between thoughts. That conversational rhythm creates authenticity, trust, and ultimately, the momentum you’re aiming for.

Curiosity builds trust while clickbait breaks it

There’s an important distinction between curiosity and manipulation. Clickbait typically withholds information without delivering meaningful value, whereas curiosity rewards attention by eventually answering questions in a way that satisfies the viewer.

Every promise should be fulfilled, and every story should reach a worthwhile conclusion. Don’t trick people into watching longer and create an unpleasant experience. Make every second genuinely worth the viewer’s time because prospective customers will recognize your intent, and their behavior will send signals to the platforms.

Dig deeper: Why search ROAS depends on paid social more than you think

Optimize beyond the first three seconds

The advice has long been to obsess over the first three seconds of every video, and while that advice isn’t wrong, it may be incomplete. Instead of asking only, “Will someone stop scrolling?” creative teams should also ask, “Why would someone keep watching?”

Considering both can improve how creative is written, filmed, edited, and evaluated. Every transition should introduce another point of interest, and every scene should answer one question while creating another without overwhelming the viewer. It should also deliver on the promise to close earlier loops. Don’t just earn attention. Sustain it.

Creative is what you still control

AI has made audience targeting on social media advertising platforms smarter and easier than ever. Campaign setup continues to become more automated and streamlined, while machine learning handles more optimization.

Creative strategy and development remain in your hands. Algorithms can decide who sees an ad, but you can decide whether it’s worth watching. You have the ability to create compelling creative and develop novel ideas that tap into people’s natural curiosity.

That doesn’t mean you should stop trying to earn attention. You also need to sustain it because curiosity is what keeps the conversation going.

Dig deeper: How to structure paid social creative testing for better performance

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The new SEO rules for bloggers in 2026: Why clarity matters in AI search

The new SEO rules for bloggers in 2026: Why clarity matters in AI search

For years, I told bloggers through audits, podcasts, and live trainings to write their content for “toddlers and drunk adults.”

That was simple advice, and it worked. If a 5-year-old and someone half-paying attention could follow the page content and still find what they needed, the content was probably clear enough for users and Google alike.

But in 2026, that rule needs an update.

Bloggers are no longer writing just for readers skimming on phones, distracted parents trying to make dinner, or travelers planning from an airport terminal.

They’re also writing for large language models, AI Overviews, AI Mode, and search systems that are scanning, summarizing, and deciding whether their content is clear enough to retrieve, cite, or ignore.

That’s the real shift. And as such, my advice to bloggers has evolved to “write for toddlers, drunk adults, and LLMs.”

Google is no longer just asking, “What is the best page to rank for this query?”

Increasingly, it’s asking, “What answer can we construct, what sources support that answer, and what might the user want to do next?”

That changes the job for bloggers.

The old SEO playbook was built around keywords, titles, H2s, and rankings. Those things still matter, but they’re no longer enough by themselves.

The new SEO playbook is built around clarity.

  • Can Google understand who you are?
  • Can users understand what your site is about?
  • Can AI systems understand how your articles connect?
  • Can your content answer the question quickly without making readers dig through clutter, filler, popups, and five nearly identical photos of the same finished dish?

For food, lifestyle, and travel bloggers, this isn’t a small adjustment. It’s a business survival issue.

The bloggers who continue to win won’t be the ones chasing every new acronym, every plugin score, or every AI optimization gimmick.

They’ll be the ones building clearer sites, stronger brands, better internal connections, and more useful content experiences for real people.

In 2026, clarity isn’t just good UX. It’s your SEO strategy.

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Why search changed (and why it matters)

For years, most bloggers understood SEO through a pretty simple model.

A user searched for something. Google returned a list of blue links. The blogger’s job was to create the best page for that query and earn the click.

That model still exists. But it’s no longer the only model that matters.

Traditional search mostly asked: “What are the best documents to rank for this query?”

AI search increasingly asks: “What answer can we construct, what sources support that answer, what else might the user need, and what can we help them do next?”

That’s a very different environment.

In classic search, a food blogger trying to rank for “easy chicken enchiladas” mostly had to think about the visible query, the competing pages, the title, the headings, the recipe card, the photos, the intro, the internal links, and the overall usefulness of the page.

Again, those things still matter. But AI search adds another layer.

Google’s AI experiences can use what it calls query fan-out, where a single user query may trigger multiple related searches across subtopics and data sources to build a more complete response.

A search for “easy chicken enchiladas” may also involve questions around cook time, substitutions, whether to use corn or flour tortillas, how to avoid soggy enchiladas, what sauce works best, what to serve with them, how to make them ahead, and whether they freeze well.

In other words, Google may not just be matching one page to one keyword. It may be trying to understand an entire task. That’s the shift bloggers need to internalize.

The old SEO model looked something like this: Keyword → title → H2s → rank.

The modern SEO model looks more like this: Topic clarity → answer clarity → entity clarity → internal links → technical crawlability → structured data accuracy → firsthand experience → brand trust → direct audience.

That’s not as catchy. But it’s a lot closer to how search works now.

For bloggers, this means Google needs to understand more than the fact that you published a recipe, tutorial, itinerary, or guide. It needs to understand:

  • Who you are and what your site is about.
  • What topics, cuisines, destinations, or lifestyle categories you specialize in.
  • Whether your content is based on real experience.
  • How your articles connect to one another.
  • Whether the page is easy to crawl, read, summarize, and trust.
  • Why your version deserves to be surfaced instead of the dozens, hundreds, or thousands of similar options already online.

This is where many bloggers get uncomfortable. For a long time, blogging SEO rewarded a certain kind of mechanical execution.

  • Find a keyword.
  • Write a long post.
  • Put the keyword in the right places.
  • Add the recipe card.
  • Insert the photos.
  • Answer a few FAQs.
  • Hit publish.

That approach was never as foolproof as people wanted it to be, but it worked often enough that bloggers kept doing it.

AI search makes that weaker because keywords alone are a poor substitute for clarity.

A food blogger who publishes another generic chocolate chip cookie recipe with the same tips, photos, substitutions, and “why you’ll love this” section as everyone else is making Google work too hard.

The same goes for a travel article without firsthand details, original recommendations, strong structure, or clear audience focus, or broad lifestyle advice that could have been produced by any website, freelancer, or AI tool.

That kind of content was already vulnerable. Now it actively works against you for algorithmic consideration.

Google’s own guidance around AI search continues to point bloggers back to the fundamentals: helpful content, crawlability, internal links, page experience, visible textual content, high-quality images, and structured data that matches what users can actually see on the page.

There’s no secret AI SEO template, magic schema type, or plugin setting that suddenly makes unclear content useful.

AI search doesn’t remove the need for good SEO. It raises the cost of bad SEO.

If your content is confusing, your site structure is messy, your internal links are generic, or your experience and author signals are weak, you’re giving Google fewer reasons to understand and trust your content.

And if the only reason a page exists is because a keyword tool said it had search volume, that’s not a strategy. That’s content roulette.

For food, lifestyle, and travel bloggers, the opportunity in 2026 is to become easier to understand – for users, Google, and AI systems.

In an AI-shaped search environment, the winning content isn’t necessarily the longest, loudest, or most aggressively optimized. It’s the content that makes the answer obvious, the expertise clear, and the next step easy.

That’s the foundation for the new SEO rules that actually move the needle.

Dig deeper: AI isn’t the enemy: How bloggers can thrive in a generative search world

The new SEO rules that actually move the needle

When search changes quickly, bad advice gets louder.

Every week, bloggers are being told they need a new AI plugin, a new content template, a new schema trick, a new prompt strategy, a new content score, or a new way to “optimize for AI.” Most of it is noise.

Just last week, a blogger told me, “I was told not to add notes to a recipe card because it’s duplicate content.” And I literally fell out of my chair laughing.

First, that’s not how duplicate content works. Second, “notes” are incredibly important in recipe cards because users often print the card and never return to the full article.

Removing “notes” removes a valuable user-first feature that ensures your users can actually make your recipe perfectly the first time by implementing your “best” advice.

Instead of giving nonsense like this validation, bloggers should focus on making real progress by embracing the boring, obvious, high-impact things better than everyone else.

That starts with three priorities.

1. Improve high-value existing content

Most bloggers don’t need to publish more random content. They need to improve the content that already has signals.

This is one of the biggest differences between mature SEO strategy and “I saw someone say this in a Facebook group” SEO strategy. A mature strategy starts with data.

Go into Google Search Console and identify articles with traffic history, strong impressions, revenue potential, seasonal upside, rankings sitting just outside meaningful visibility, clear signs of decay, or content that hasn’t been meaningfully improved in 12 to 24 months.

That’s where you start.

Not with a random list of articles you personally feel like updating. Not with every recipe that has an old date. Not with whatever your SEO tool says is missing 14 semantically related phrases.

Use Search Console. Look for opportunity. Then make the page better.

Tip: Sign up for an account with SEO Gets. It has a fantastic Content Decay Reporting option, which makes finding and updating your weakest content within GSC a snap.

A real update should improve the experience for the user. It should make the recipe, guide, tutorial, or article clearer, more accurate, more complete, or more useful.

For a food blogger, that may mean adding better testing notes, clearer process shots, more useful substitution guidance, storage information, make-ahead tips, or troubleshooting details.

For a travel blogger, it may mean updating prices, hours, logistics, neighborhood recommendations, transportation details, seasonal considerations, or firsthand observations.

For a lifestyle blogger, it may mean tightening the structure, removing generic filler, adding examples, clarifying steps, or improving the connection between related articles.

What it shouldn’t mean is changing the date and pretending the content is fresh.

A good content update answers one question: “Is this page now more useful than it was before?”

If the answer is no, you didn’t improve the content. You just touched it.

2. Build direct audience channels

Google traffic is rented land, and bloggers need to start treating it that way.

That doesn’t mean Google traffic is bad. For many publishers, organic search is still the largest traffic source, the biggest revenue driver, and the reason their businesses exist in the first place.

But you don’t own that traffic. A ranking can disappear. A featured snippet can vanish. An AI Overview can satisfy more of the query directly in the search results. A SERP layout can shift. A core update can rewrite the competitive landscape overnight.

None of that means bloggers should abandon SEO, but it does mean they need to stop pretending Google is a stable business model by itself. It’s not.

The better approach is to use Google as one discovery channel among many, not the entire foundation of the business.

Search should introduce new people to your brand, but once those people arrive, the goal should be to give them a reason to come back without needing Google as the middleman every time.

That means bloggers need to get more serious about email, branded search, repeat visitors, Pinterest, YouTube, short-form video, Facebook groups, communities, direct partnerships, products, courses, cookbooks, memberships, or whatever audience channel actually makes sense for their niche and bandwidth.

Not every blogger needs to do all of those things. In fact, most shouldn’t.

But every blogger should be asking the same basic question: “If Google sent me fewer clicks tomorrow, how would my audience still find me?”

That question is uncomfortable, but it’s also necessary.

An email list isn’t perfect. Deliverability is annoying. Open rates fluctuate. People unsubscribe. Platforms change. But an email subscriber is still closer to an owned audience than a ranking you rent from Google.

The best question for bloggers in 2026 isn’t just, “How do I get more Google traffic?”

It’s, “How do I make sure the people who find me once can find me again?”

That mindset changes how you write, how you structure your site, how you promote content, and how you think about brand.

A blogger with a recognizable point of view, repeat readers, and a direct audience is in a much stronger position than a blogger whose entire business depends on Google continuing to send traffic at the same rate it did three years ago.

SEO still matters. But it should feed the business. It shouldn’t be the entire business.

3. Make content easier for humans and machines to understand

This is where clarity becomes the strategy.

Google’s guidance around AI search continues to emphasize many of the same fundamentals it has emphasized for years: helpful content, crawlability, internal links, page experience, visible text, images, video, and structured data that matches what users can actually see on the page.

That should tell bloggers something important. There’s no separate magic version of SEO for AI search.

There’s no special “AI search” schema that suddenly makes a mediocre article useful. There’s no plugin setting that turns unclear content into trusted content. There’s just less tolerance for bloated, confusing, poorly structured pages.

For bloggers, making content easier to understand usually means improving the basics that should have mattered all along.

A recipe article should tell readers what they’re making, why the recipe works, what ingredients matter, what substitutions are realistic, what can go wrong, how to store it, and why they should trust your version.

A travel article should make it clear who the itinerary is for, when the advice applies, what has been personally tested, what to book in advance, and what mistakes readers can avoid.

A lifestyle article should be organized enough that a reader can quickly understand the problem, the recommendation, and the next step.

This isn’t about dumbing down content. It’s about removing friction.

A tired parent trying to make dinner doesn’t want to scroll through clutter to find the answer. A traveler standing in an airport doesn’t want to dig through vague storytelling to find the train information. A reader looking for a home project doesn’t want 1,500 words of generic filler before the actual instructions.

And an AI system trying to understand your content doesn’t benefit from bloated intros, weak headings, generic anchor text, mismatched schema, or buried answers either.

This is why I keep telling bloggers to write for toddlers, drunk adults, and LLMs.

The toddler needs the page to be simple enough to follow. The drunk adult needs it to be forgiving enough to skim. The LLM needs structure, context, entities, and relationships that are clear enough to interpret accurately.

If your content works for all three, you’re probably moving in the right direction.

That means using clear top-of-article summaries when they help, headings that guide the reader instead of stuffing keywords, internal links with descriptive anchor text, schema that accurately reflects visible content, and FAQs that answer real questions.

Add to that stronger about me and author signals, original images or process shots where they add value, and a page experience that doesn’t actively fight the reader. None of that is flashy. But it’s effective.

The bloggers who win in 2026 won’t be the ones who publish the most content, install the most AI tools, or chase the newest acronym. They’ll be the ones who make their content easier to understand, easier to trust, and easier to remember.

That’s what moves the needle now.

Dig deeper: Blogging, AI, and the SEO road ahead: Why clarity now decides who survives

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The outdated advice bloggers should ignore

Part of succeeding in 2026 is also knowing what to stop doing.

That may be harder than it sounds because a lot of outdated SEO advice did work at some point. Or at least, it appeared to work well enough that bloggers kept repeating it.

The problem is that many of those tactics were built for an older version of search. They were built for a world where ranking often felt like a checklist: find the keyword, put it in the title, repeat it in the headings, write more words than the competitor, add a few FAQs, hit publish, and hope Google approved.

That kind of thinking isn’t completely useless, but it’s painfully incomplete. And in an AI-shaped search environment, incomplete strategies become expensive.

The first thing bloggers need to stop doing is chasing word count.

A 3,000-word recipe article isn’t automatically better than a 900-word recipe article. A 5,000-word travel guide isn’t automatically more helpful than a tighter, better-organized 2,000-word guide. Length isn’t the goal. Usefulness is the goal.

Sometimes a topic needs depth. Sometimes a reader needs context, troubleshooting, photos, variations, and a complete explanation. But sometimes the reader just needs to know whether they can use Greek yogurt instead of sour cream or how long to bake chicken thighs at 400 degrees.

Adding more words doesn’t make a page more helpful if those words don’t solve the user’s problem.

The same goes for stuffing awkward keyword variations into H2s. Bloggers don’t need headings like “Best Easy Moist Homemade Banana Bread Recipe With Ripe Bananas” because a tool suggested combining every phrase into one linguistic crime scene.

Headings should guide the reader. They should make the page easier to scan, easier to understand, and easier to navigate. If a heading sounds like it was written for a spreadsheet instead of a human being, it probably needs to be rewritten.

Bloggers also need to be more skeptical of publishing generic content just because a keyword tool says there is search volume. This is especially important in crowded niches like recipes, travel, and lifestyle, where the web already has more than enough generic versions of almost everything.

Another “chocolate chip cookie recipe” isn’t a strategy unless there is a reason your version deserves to exist.

Another “best things to do in Paris” article isn’t a strategy unless it reflects real experience, clear audience fit, original insight, or a better way of helping the reader make decisions.

Another “how to organize your pantry” article isn’t a strategy if it says the same thing every other article says with different stock photos.

Search volume tells you there is demand. It doesn’t tell you that you deserve to rank. That part is still on you.

Food bloggers especially need to reconsider the way they structure recipe articles visually. Somewhere along the way, many articles turned into an obstacle course of nearly identical finished-dish photos before the reader gets to the actual recipe.

One hero shot is great. A useful process shot is great. A labeled ingredients photo, awesome. Photos that show texture, doneness, technique, or troubleshooting can all absolutely improve the content.

But five beauty shots of the same casserole from slightly different angles before the recipe card aren’t helping a tired person make dinner.

This doesn’t mean bloggers should strip personality, visuals, or storytelling from their sites. On the contrary, it means every element on the page should earn its place.

A photo should help, inspire, or clarify. A paragraph should answer a question, build trust, or move the reader forward. A section should exist because it improves the experience, not because everyone else’s template includes it.

Bloggers also need to stop hiding the answer.

This is one of the simplest and most common problems I see in daily audits. The user arrives with a specific need, and the page makes them work too hard to satisfy that need.

The answer is buried under a long intro, vague personal story, oversized ad units, popups, duplicate photos, or a “jump to recipe” button doing the emotional labor for the entire page.

If the page is about whether you can freeze banana pudding, say so clearly.

If the page is about the best time to visit Sedona, answer it clearly.

If the page is about how to keep meatballs from falling apart, don’t make the reader scroll through 11 paragraphs before you explain the binder.

This isn’t just an SEO issue. It’s a respect-the-reader issue.

FAQs are another place where bloggers have been led astray. A helpful FAQ can be great when it answers real questions users actually have. FAQs also allow the average blogger to optimize for People Also Ask and Things to Know search accordions.

But too many FAQ sections are still being treated as a dumping ground for keyword variations, thin answers, or filler content added because a tool, course, or template said every article needs them.

Every FAQ should pass a basic test: Would a real reader ask this, and does the answer help them do something? If not, cut it.

Updating dates without meaningful updates is another tactic that needs to die.

Changing “2024” to “2026” in the title, or adjusting the publish date and calling it refreshed, doesn’t make the content better. It just makes it newer-looking.

A real update improves accuracy, usefulness, structure, media, internal links, testing notes, examples, recommendations, or user experience.

A fake update changes the timestamp and hopes nobody notices. Readers notice. Google notices.

Bloggers also need to stop writing for SEO scores instead of users.

I understand why this happens. Tools give clear feedback. Green lights feel good. Scores feel objective. It’s comforting to believe that if a tool says the page is optimized, the page must be good.

But tools aren’t users.

Tools don’t make dinner. Tools don’t plan vacations. Tools don’t troubleshoot a broken frosting, choose a hotel near a train station, or decide whether a slow cooker recipe is safe to leave on all day.

Tools can support judgment. They can’t replace it. The same goes for panic-based SEO.

Every Google announcement doesn’t require a sitewide overhaul. Every AI update doesn’t require installing a new plugin. Every ranking drop doesn’t mean your site is broken. Every Facebook thread isn’t an emergency.

One of the worst things bloggers can do right now is make extreme technical or content changes based on fear, screenshots, rumors, or one person’s traffic chart.

The answer to uncertainty isn’t panic. It’s better analysis.

It’s Search Console. It’s content audits. It’s user behavior. It’s competitive review. It’s better internal linking. It’s clearer content. It’s understanding what actually changed before deciding what to change next.

The blunt version is this: Stop producing content that looks like it was made to satisfy a plugin, a keyword tool, or an ad network.

Start producing content that makes a tired person at 5:30 p.m. say, “Thank God, this actually helps.” That’s the standard. 

Not word count. Not tool scores. Not awkward keyword repetition. Not fake freshness. Not more filler.

The standard is whether the content helps a real person solve a real problem faster, better, or with more confidence than the alternatives.

That’s the advice bloggers should carry into 2026. Ignore anything that pulls you further away from that.

Dig deeper: The implosion of the blogging-for-dollars revenue model

Why clarity beats complexity in an AI world

When search gets more complicated, the natural reaction is to make the strategy more complicated as well.

That’s how bloggers end up chasing every new newsletter strategy, every new tool, every new AI feature, every new schema recommendation, and every new “future of SEO” thread that shows up in a Facebook group.

I understand the impulse. When traffic is volatile and Google keeps changing the interface, doing something feels better than doing nothing. The problem is that a lot of the “something” being recommended right now is either unnecessary, unproven, or a distraction from the work that would actually help.

If a blogger asked me what to focus on in 2026, my answer wouldn’t be to make their site more complicated. It would be to make their site easier to understand. That’s the through-line.

AI search doesn’t reward confusion. Users don’t reward confusion. Google doesn’t need more clutter to crawl, interpret, and summarize.

What everyone needs is clarity. That starts with the page itself.

When someone lands on a recipe article, they should quickly understand what the recipe is, why it works, what makes it different, how long it takes, what ingredients matter, and whether it solves the problem they came with.

When someone lands on a travel article, they should quickly understand who the advice is for, when the information applies, whether the writer has firsthand experience, and what decisions the reader needs to make next.

When someone lands on a lifestyle article, they should quickly understand the problem, the recommendation, the steps involved, and why the advice is trustworthy.

That doesn’t mean every article has to be short. It means every article has to be organized.

There’s a big difference between depth and bloat.

  • Depth helps the reader make a better decision. Bloat makes them scroll.
  • Depth answers follow-up questions. Bloat repeats the same point three different ways.
  • Depth shows experience, testing, judgment, and specificity. Bloat fills space because a tool, template, or competitor word count made the blogger nervous.

In an AI world, that difference matters even more.

Large language models, AI Overviews, and AI-assisted search experiences are trying to understand relationships. They’re looking for entities, context, steps, attributes, comparisons, caveats, and source quality signals.

If your content is buried under vague headings, generic filler, weak internal links, and unclear structure, you’re making that job harder.

And making Google work harder is rarely a winning SEO strategy.

This is why “clarity beats complexity” isn’t just a nice phrase. It’s a practical publishing standard.

  • Before adding another section, ask whether it makes the page easier to use.
  • Before adding another FAQ, ask whether a real reader would ask that question.
  • Before adding another image, ask whether it clarifies a step, shows texture, demonstrates technique, or builds confidence.
  • Before adding another internal link, ask whether it helps the reader move naturally to a related answer.
  • Before adding another AI tool, ask whether it improves the user experience or just makes the blogger feel like they’re keeping up.

That last one is important. AI tools aren’t automatically bad. AI summaries, AI buttons, chat features, personalization tools, and recipe modification features can all be worth testing in the right context.

But they should be treated like UX experiments, not magic ranking levers.

If an AI button helps a reader scale a recipe, find a substitution, adjust serving sizes, or understand a process faster, great. Test it. Measure it. See how users respond.

But don’t install something because everyone else is panicking. Don’t assume “AI” in the feature name means it improves SEO. And definitely don’t let the tool become more important than the content.

For most bloggers, the highest-impact AI strategy is still not very sexy: make the page easier to read, easier to crawl, easier to summarize, and easier to trust.

That means clear introductions, useful summaries, descriptive headings, original experience, accurate schema, strong internal links, and visible text that actually says something.

It also means removing the things that get in the way.

  • Over-aggressive ads get in the way.
  • Intrusive popups get in the way.
  • Repetitive photos get in the way.
  • Generic “why you’ll love this” sections get in the way when they’re not personalized to the article.
  • Filler FAQs get in the way.
  • Weak author signals get in the way.
  • Internal links with anchor text like “click here” get in the way.
  • Redundant equipment lists added purely for affiliate reasons get in the way.

A blogger doesn’t need a 47-point AI optimization checklist to understand this.

They need to look at the page and ask a simpler question: “Is this easier to understand than what already ranks?”

Does your page help the reader faster, better, and with more confidence than the alternatives?

That’s what clarity does. It removes friction, makes expertise easier to see, helps Google and AI systems understand your content, builds trust, and makes your brand more memorable.

For food, lifestyle, and travel bloggers, clarity works across traditional organic search, AI Overviews, AI Mode, direct traffic, email, social, and conversion.

That’s why I keep coming back to the same point: clarity isn’t dumbing down your content. It’s respecting the reader’s time.

The bloggers who win in 2026 won’t be the ones who make their content more complicated to look sophisticated. They’ll be the ones who make complicated decisions feel easier for the reader.

  • What should I cook tonight?
  • Can I make this ahead?
  • Is this destination worth three days?
  • Will this work for my family?
  • Can I trust this recommendation?
  • What do I do next?

Answer those questions clearly, and you’re already doing more than most of the web.

That’s why clarity beats complexity. And that’s why clarity is no longer just a writing principle. It’s an SEO strategy.

Your 2026 blogging SEO action plan

The good news is that bloggers don’t need to rebuild their entire sites overnight.

They don’t need to chase every AI feature, rewrite every article, install every new tool, or turn their content strategy into a science experiment.

In most cases, the best next step is much simpler: 

  • Identify the pages that matter most.
  • Make them clearer.
  • Strengthen the signals around them.
  • Stop doing the things that create friction for users.

Start with your highest-value content

These are the articles that already have traffic history, strong impressions, seasonal value, affiliate or ad revenue potential, or rankings that are close enough to improve.

Don’t begin with the random article that annoys you personally or the recipe you happen to have new photos for. Start where the opportunity is visible.

Use Search Console. Look at what Google is already showing. Find the articles where better clarity, stronger internal links, improved structure, fresher details, or more useful supporting content could reasonably move the needle.

Then work through the page like a real user.

Add a clear top-of-article summary

For most bloggers, the first improvement I’d recommend is a clear top-of-article summary. This doesn’t need to be long, and it definitely shouldn’t be a generic AI-written blob pasted across the site. It should be specific to the article and useful for the reader.

  • For a recipe, that summary might explain what the dish is, why it works, how long it takes, what makes it different, which ingredients matter most, and any major make-ahead, storage, or substitution notes.
  • For a travel article, it might clarify who the guide is for, when the information applies, how much time the reader needs, and what decisions they should make first.
  • For a lifestyle article, it might define the problem, preview the solution, and make the next step obvious.

The goal isn’t to repeat the whole article at the top. It’s to reassure the reader that they’re in the right place while giving Google and AI systems a clearer understanding of the page.

Again, this isn’t magic. It’s just good communication.

Strengthen internal links

Improve internal linking with descriptive anchor text.

This is still one of the most underused SEO opportunities in blogging, especially on older sites with hundreds or thousands of articles. Too many bloggers rely on related article widgets, sidebar links, or generic anchor text and assume that’s enough.

In-content links are stronger when they’re natural, useful, and descriptive.

A reader making a slow cooker chicken dinner may also need a related side dish, a sauce, a storage guide, or another easy weeknight meal. A reader planning a weekend in Charleston may need a hotel guide, a restaurant list, a packing guide, or a nearby day trip. A reader working through a furniture troubleshooting article may need links to paint, sanding, and woodworking guides and tutorials.

The anchor text should tell the reader where they’re going.

“Click here” doesn’t help much. “Easy slow cooker chicken dinners for busy weeknights” does.

“Read more” doesn’t help much. “Beginner furniture restoration guide” does.

Clear internal links make topical relationships easier for users, Google, and AI systems to understand. They also keep readers moving through the site instead of sending them back to Google.

Make headings useful

Headings shouldn’t be keyword dumpsters. They should be signposts that help readers and search systems understand the structure of the page.

For recipes, that usually means headings like “Why this recipe works,” “Key ingredients,” “How to make X recipe,” “Recipe testing notes,” “Substitutions and variations,” “Storage and reheating,” “What to serve with X recipe,” and “X recipe FAQs.”

For travel content, it might mean “Best time to visit,” “How many days you need,” “Where to stay in X,” “How to get around X,” “What to book in advance,” “Mistakes to avoid,” and “Sample itinerary in X.”

For lifestyle content, it might mean “What you’ll need,” “How to get started with X,” “Common mistakes with X,” “What worked for me,” “When to avoid this,” and “Next steps.”

The clearer the structure, the easier the page is to use and interpret.

Review your schema

Bloggers should also review schema, but they should do so with restraint.

Structured data isn’t a place to invent things, exaggerate claims, or mark up content that users can’t see. It should accurately reflect what’s visible on the page.

If the recipe says the total time is 45 minutes, the schema shouldn’t say 30 minutes because that looks better in search results. If the author, date, images, ratings, or recipe details are inaccurate, fix them.

Schema should clarify. It shouldn’t cosplay as content quality.

Reduce content friction

Focus on reducing content friction. Ads, popups, sticky units, email captures, video players, and interstitials may all have business value, but they also create user experience costs.

If a user can’t read the recipe because the screen is covered in interruptions, or a travel guide is buried under popups and sticky elements before the reader can find the itinerary, that’s a problem.

This doesn’t mean bloggers should remove every ad or stop monetizing their work. But the page still has to be usable.

If monetization makes the content harder to trust, read, or act on, the short-term revenue win may be creating a long-term visibility problem.

Test AI features carefully

AI features, such as AI buttons, can be useful in the right context. A button that helps users scale a recipe, find substitutions, modify ingredients, summarize key steps, or troubleshoot a process may improve the user experience.

But bloggers should measure before assuming.

  • Do users click the buttons?
  • Do they stay longer?
  • Do they convert better?
  • Do they subscribe?
  • Do they view more pages?
  • Do they actually use the feature, or does it just make the site look more “AI-ready”?

AI tools should support the reader. They shouldn’t distract from the content, replace firsthand experience, or become the strategy by themselves.

Dig deeper: How to design content that AI systems prefer and promote

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

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Clarity is what wins in 2026

Improve the articles that already have opportunity, add clearer summaries, strengthen internal links, clean up headings, verify schema, reduce friction, build direct audience channels, and test AI features only when they clearly help users.

That’s not flashy. But most durable SEO isn’t flashy. It’s a consistent, thoughtful improvement applied to the pages that matter most.

For the average blogger, that’s the plan I’d recommend. Do fewer random things. Do more useful things. Make the best content easier to understand, easier to trust, and easier to revisit.

That’s how you build a site that can compete in traditional search, AI search, and whatever version of AI Mode Google throws at us next.

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Schema for AI search: How to identify and prioritize entity gaps

After presenting a custom schema my team built to evaluate knowledge graphs for university programs, I saw firsthand how differently SEOs view the role of schema markup. Our approach used 23 existing Schema.org entities and more than 60 additional entities to assess gaps in entity coverage.

Schema connects entities (objects, people, concepts, and ideas) to build knowledge graphs that provide deeper semantic understanding. It can also serve as a framework for evaluating a site’s vector embeddings and identifying gaps in entity coverage.

Let’s look at how you can use schema and knowledge graphs to identify and prioritize those gaps.

How knowledge graphs turn entities into context

A knowledge graph stores entities as nodes and relationships as edges, so machines can understand context and meaning rather than matching keywords. It’s how a system knows that the Tulane Freeman School of Business is an organization that offers business courses taught by a person rather than a string of text.

Enterprise-level businesses use knowledge graphs to remove data silos. In the book “The Knowledge Graph Cookbook: Recipes That Work” by Andreas Blumauer and Helmut Nagy, knowledge graphs are used as the “ultimate linking engine” to create a semantic data fabric for business intelligence.

In this context, your website uses the same recipe that enterprise-level businesses use for internal data. Your website is essentially functioning as a public API through which your brand’s entities, such as organization, location, products, positioning, values, features, and key benefits, are connected to search engines and LLMs.

Dig deeper: When and how to use knowledge graphs and entities for SEO

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Treat schema as the on-ramp to the graph

Schema markup declares entities in a language that search engines and LLMs already speak. JSON-LD explicitly asserts both entities and the relationships between them rather than relying on bots to infer them from website copy.

For example, an executive Master of Business Administration (MBA) degree from New York University should present a coherent body of information rather than a loose collection of unrelated facts.

The organization is NYU, which offers a Brand and Digital Strategy course taught by professor and author Scott Galloway, a faculty member in the MBA program at the NYU Stern School of Business.

Stop padding FAQ schema and optimizing for rich results. Website pages are vehicles for nodes and edges that feed the graph. This approach provides critical context about your business, helping search engines and LLMs better connect your products and services with relevant audiences.

Build the graph with markup, vectors, and agents

Our custom schema is a framework for explicitly asserting all the entities and relationships a prospective student engages with on their enrollment journey. The schema prioritizes these entities by importance. 

That declared truth is what we use to analyze the vectorized version of a partner’s website. Vector embeddings of .edu website content are measured against the schema to capture semantic proximity and context that the markup doesn’t state outright.

We use agents to compare the static custom schema markup, semantic proximity, and vector embeddings to ensure comprehensive coverage of all the entities surrounding a university program. 

The results surface entity gaps, which can inform content strategy across websites, social media properties, and earned media. The final output is a knowledge graph that highlights both covered and missing entities related to a university program.

We aim to provide robust context that helps shape and frame the value of our programs through a search or conversation. This approach is directly aligned with Google’s patents on understanding entities. 

Google’s need to understand context is driven by a desire to deliver a more relevant answer to its end user. Building your knowledge graph adds context that can help search engines and AI systems better understand your business.

Dig deeper: Google’s LLM patent suggests a new goal for SEO: Teaching AI who you are

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Get honest about schema and AI visibility

In March 2025 at SMX Munich, Fabrice Canel, principal product manager at Microsoft Bing, confirmed that Copilot uses schema markup to understand content.

When we consider how other LLMs use search indexes for grounding, schema markup becomes an important factor in connecting entities back to your site. Semrush has also published guides showing correlations among cited pages.

Other research points in a different direction. A 2025 study by Search Atlas found that schema doesn’t affect LLM visibility, while a study by Mark William-Cook showed that LLMs don’t read on-page schema. However, those studies look at performance and citations rather than entity connections.

These findings may seem conflicting, but they point to a distinction between using JSON-LD as infrastructure for machine understanding and treating it as a GEO hack for increasing citations. JSON-LD provides explicit information about entities and relationships that can help machines understand your site. Visibility is a byproduct of being understood.

Use schema and vector embeddings to find entity gaps

The custom schema I discussed above is specifically designed for higher education, but you can build your own. Use Schema.org as your library to build a robust collection of entities that matter to your product, service, or business. 

Does Schema.org map to all your important entities? In our case, Schema.org covered only 23 higher education-specific entities, leaving significant gaps. We filled the gaps by creating our own entities.

Start with the ideal entity model. In a perfect world, what does that complete set of information look like? This exercise will help you determine what can be built using custom JSON-LD in addition to established entities. 

With that custom schema, you can compare it against vector embeddings to understand entity coverage. Prioritize your entity gaps by focusing on what drives the most value.

Dig deeper: How schema markup fits into AI search — without the hype

Measure entity visibility, not just rich results

As you develop a content strategy for entity coverage, it’s important to use prompt-tracking and brand sentiment tools to monitor AI visibility. Monitor your priority entities to understand how you appear across different models.

Brand sentiment tracking is especially important because it allows you to see shifts among your covered entities. Is your content matching how people are actually talking about the entity?

AI visibility and brand perception aren’t complete metrics on their own. As you see shifts in visibility and perception, compare them with conversions and performance. As citations grow, does the volume or quality of leads rise?

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

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Schema as infrastructure for AI understanding

Schema markup isn’t an overnight solution for driving AI citations. It’s infrastructure for helping search engines and LLMs understand your site by declaring entities and relationships, building your knowledge graph, and uncovering gaps in entity coverage.

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How category framing changes which brands AI recommends

Your customers' query wording determines your AI visibility, not your brand strength

Most brands approaching AI visibility ask the wrong question: How do we get stronger as an entity so that LLMs recommend us more?

In entity SEO, we tend to say, “Build the Knowledge Graph, add schema, and get more press.” But that logic assumes the LLM is evaluating the brand and deciding whether it’s good enough to recommend for any query related to what the brand sells. The LLM evaluates the query and matches it against whatever category associations it has built for the brand from third-party content.

The difference matters enormously in practice.

As we’ve seen in multiple scenarios, recognition isn’t the same as recommendation. So being a recognized brand isn’t synonymous with being a strong brand.

What matters is whether the category your customers are using to search for you matches the category the LLM has coded you into.

What the data showed

João da Silva and I conducted a study of 12 athletic apparel brands in the U.K. over seven days, with 14,140 API runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. We tested the same brands using two different category framings: athleisure and athletic footwear.

After looking at the results derived from co-mentions and putting numbers on the impact of framing on category recognition for LLMs, we took the test one step further and changed the category register in the prompt.

The results were symmetric to a degree that rules out noise:

Brand Knowledge Graph (KG) score Athleisure rate Footwear rate Δ Verdict
New Balance 64,235 1% 90% +89 Jumped (footwear-coded)
Nike 25,996 77% 90% +13 Small shift (footwear-coded with strong athleisure co-mentions)
Alo Yoga 3,062 63% 0% -63 Dropped (athleisure-coded)
lululemon 810 90% 0% -90 Dropped (athleisure-coded)
Sweaty Betty 751 9% 0% -9 Stable
Reebok 665 1% 20% +19 Small shift
Outdoor Voices 455 26% 0% -26 Small shift
Rhone Apparel 400 5% 0% -5 Stable
Varley 381 6% 0% -6 Stable
TALA 356 5% 0% -5 Stable
Gymshark 277 37% 0% -37 Dropped (athleisure-coded)
LNDR 2 0% 0% 0 Stable

Notes:

  • New Balance goes from 1% to 90%.
  • lululemon goes from 90% to 0%.

The variation is approximately 0.9 points in both directions simultaneously.

We’re not talking about correlation here, but a controlled observation: what happens when we change only one variable — the category word in the prompt.

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Why this happens: Category coding

Nike, New Balance, and Reebok share the exact same Google Knowledge Graph (KG) description: “Footwear company,” so all three are recognized perfectly by every LLM we tested. From an entity standpoint (recognition), they start from an identical position. However, their behavior under different category framings isn’t identical at all.

The reason is what the paper formalizes as category coding: the combination of the KG description field and the third-party content corpus that has accumulated around a brand in a given category.

The KG description anchors a brand to a category in the model’s representation (impacts recognition).

The third-party corpus — articles, reviews, editorial comparisons, and roundups — fills in the detail of what that category association actually looks like (impacts recommendation).

Looking at the example from New Balance

New Balance’s KG description says “Footwear company,” and the third-party corpus that has accumulated around it corroborates the category by focusing on topics related to running shoes, performance footwear, and athletic training. 

When a user asks about athleisure brands, the model doesn’t find New Balance in that corpus because there is no third-party association. But it does find lululemon, Alo Yoga, and Gymshark: all brands whose corpus is built from fashion publications, lifestyle editorial, and activewear roundups.

When we changed the query to athletic footwear, the retrieval flipped: New Balance is suddenly in the right corpus, and lululemon is not.

The model itself can’t and isn’t making a judgment about brand quality or belonging. What an LLM does is pattern-match a query category against a content category. If those two things align, the brand surfaces. If they don’t, it doesn’t, regardless of how established the brand is.

So, can you just recode your KG description? 

Some brands reading this will consider the obvious shortcut: Change the KG description. If “Footwear company” is anchoring you to the wrong category, recode it to “Apparel company,” and the problem is solved.

However, the KG description is only half of what determines category coding. The other half is the third-party content corpus that has accumulated around your brand, and that doesn’t change because you updated a field in the Knowledge Graph. If your entire external content history is performance footwear, running, and athletic training, changing the description gives the model a new anchor with nothing attached to it. The corpus still says what it always said.

The corrective lever is third-party content investment in the specific category framing your customers are using: in the publications the model retrieves from, alongside the brands that already define that space. The KG description can support that work once the corpus exists.

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What this means for your GEO strategy

The standard GEO advice is to strengthen your entity: a consistent name, clean schema, a strong About page, and more press coverage. That advice is correct for getting recognized and even recommended within the brand’s coded category, but it isn’t sufficient for getting recommended in adjacent category queries.

What determines recommendation in adjacent categories is whether the third-party content corpus around your brand matches the category framing your customers are actually using.

The questions worth asking about any brand are:

  • Are we visible in AI?
  • What category has the LLM coded us into?
  • Is that the category our customers are querying?

If a brand is strong in one category, but its customers are increasingly using adjacent category language to search (for example, athleisure instead of sportswear, or performance wellness instead of fitness), and the brand’s third-party corpus hasn’t kept pace with that language shift, the brand will be invisible in exactly the queries customers are using.

Nike is the study’s clearest positive case, surfacing in both athleisure (77%) and athletic footwear (90%) queries, despite being KG-coded as a footwear brand.

The reason is that Nike has accumulated enough athleisure-coded third-party content, including editorial coverage in fashion publications, inclusion in activewear roundups, and co-mentions with other athleisure brands, to register as category-eligible in both framings. It built a sub-stream in the adjacent category that New Balance didn’t.

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

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What is the audit question everyone should be asking?

Before investing further in entity optimization, it’s worth running a simple diagnostic: Take the five or six different ways your customers might phrase a category query for what you do, and test each one across two or three LLMs. Note which formulations surface your brand and which don’t.

For the ones that don’t, the questions to ask are:

  • Does third-party content about your brand actually use that language?
  • Are you being written about in publications that cover that category?
  • Are you appearing in editorial roundups that use that phrasing?

If the answer is no, you know where to start: getting into the external conversations that speak the language of that query.

Closing that gap means becoming a participant in the category comparison content that defines who belongs in that space.

This article is based on findings from “The recognition-recommendation gap: Empirical evidence that category coding, not knowledge-graph strength, determines brand visibility in generative AI output,” co-authored with João da Silva and published open access on Zenodo.

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Why your match rate is the most important number you’re not tracking by Rokt mParticle

Ask a performance marketer which numbers they check every morning and you’ll get the same list: CPM, CTR, CVR, ROAS. Ask them their match rate on Meta or Google, the share of an audience they uploaded that the platform could actually recognize and target, and you’ll usually get a pause. Most teams don’t track it. Plenty don’t know it’s a number at all.

That pause is expensive. Build an audience of 100,000 customers, upload it to a platform that matches 55% of them, and the campaign is running against 55,000 people. The other 45,000 are invisible to it, no matter how good the targeting or the creative is. And match rate sits upstream of every metric that teams do track. 

When a platform recognizes only part of the audience you built, every number that follows (reach, frequency, conversions, return on spend) is quietly computed against that smaller matched audience. You can rework creative, retune bids, and rebuild your conversion model forever, and none of it touches the slice of your audience the platform never saw. The part worth examining is where matching breaks, why it’s getting harder, and how much reach teams lose without ever seeing it on a dashboard.

The gap between the audience you build and the audience you reach

Here’s the mechanic most teams never examine. When you push a first-party audience to a paid platform, the platform doesn’t target “your customers.” It targets the subset of your list it can resolve to its own logged-in users, usually by matching hashed emails and phone numbers against the identifiers on its accounts. Every record it can’t resolve simply falls out, with no error and no warning. The campaign runs against whoever survived.

Every privacy shift of the past few years has widened that gap. Third-party cookie deprecation removed the connective tissue that bridged identities across sites. Apple’s App Tracking Transparency cut off device identifiers. The walled gardens keep tightening their matching logic. And the mundane failure modes never went away: the customer who signs up with a work email but uses a personal one on social, the phone number formatted differently on each side, the record that’s three years stale. Identifiers are splintering faster than most CRMs and CDPs can consolidate them, so the distance between the audience you build and the audience you can reach is growing, not shrinking, no matter how clean your data is.

The insidious part: platforms report performance against the matched portion. So the campaign looks fine. You’re measuring the efficiency of the audience the platform found, not the audience you built, and the difference between the two never shows up in any report you open.

Four places it’s costing you right now

Most marketers who have thought about match rate file it under “retargeting problem.” It’s far broader than that.

  • Acquisition. Seed and exclusion lists that only partially match make prospecting less precise, and a platform training on partial signal has to guess more. That usually surfaces as inflated CAC, and never gets traced back to matching.
  • Retargeting. The obvious one, but state the math plainly: if your CRM list matches at 45%, more than half the customers you meant to re-engage never see the campaign. The program runs at less than half capacity, and its reported numbers say nothing about the people it never reached.
  • Suppression. The sneakiest. Suppression lists only suppress the customers a platform recognizes. Every existing customer who doesn’t match is invisible to your exclusions, so you pay acquisition prices to re-buy people you already have, and some of them get served the new-customer discount your loyal buyers never see. Low match rates don’t just waste budget; they fund your own margin erosion.
  • Lookalike seeding. Lookalike models expand from the matched portion of your seed, not the seed you uploaded. A weak match rate means the model learns from a skewed sub-sample of your best customers, and that error compounds as the platform extrapolates across millions of impressions.

Add it up, and match rate isn’t a data team curiosity. It’s a tax on every dollar of paid spend, and almost nobody has measured how big it is.

What happens when you close the gap

This isn’t just theoretical. CKE Restaurants, the company behind Carl’s Jr. and Hardee’s, ran their audiences through Rokt mParticle’s Match Boost to enrich identifiers for ad platforms. Match rates rose up to 117% on Google Ads and 29% on Meta.

Note what didn’t change: the budget, the creative, the campaign structure. The same spend simply reached more of the audience the brands had already built, and ROAS improved on that same spend. That’s the signature of a match-rate problem. When recognition goes up, efficiency follows, because the waste you’re removing was never visible to begin with.

This used to be a procurement project. Now it’s a setting.

If match rate has been ignored, part of the reason is that fixing it used to be genuinely painful. Improving recognition meant licensing third-party data: vendor evaluations, procurement cycles, legal review, an integration build, and months before you could measure anything. The cost of the fix outweighed an upside most teams weren’t even quantifying.

That’s no longer the shape of the problem. Enrichment increasingly happens at the point where audiences leave your customer-data infrastructure for the ad platform, a setting on the connection rather than a system you build. 

Done well, it inherits the governance you already have: identifiers you’ve deliberately excluded for privacy or compliance stay excluded, and the enriched data is used only to sharpen the match in flight, never written back into your profiles or stored in the destination platform. Closing the gap has become a configuration decision, not a data-strategy overhaul. That doesn’t mean it’s solved for everyone. It means the excuse for not looking is gone.

How to check your own match rate

Measure the gap. It takes about thirty minutes.

  • Pick your top three paid destinations by spend.
  • For each, compare the size of the list you uploaded against what the platform actually matched. Google Ads reports a match rate on Customer Match uploads (bucketed, but close enough); Meta shows the resulting audience size, which you can hold against the list you sent. Most brands land somewhere in the 40–60% range for email-only lists, well below what most teams assume.
  • Run the same check on your largest suppression list. That’s the one that will sting.

If your numbers come back north of 70%, go back to optimizing creative. If you’re like most brands, you’ll find you’ve been paying full price to reach a fraction of your audience. Every metric you already track is downstream of that one number, and most teams have never looked at it.

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New in the Yoast SEO Bulk Editor: filters, focus keyphrases, and AI drafts

The Yoast SEO Bulk Editor already gave you one place to update titles and meta descriptions across your whole site. Now it does much more. This release adds focus keyphrases to the editor, new search and status filters, a dedicated view for every content type, and AI-drafted metadata with Yoast SEO Premium. In short: you can find what’s broken, fix it in bulk, and let AI handle the heavy lifting while you keep final say.

Running an online store? Jump to what this means for your shop.

A focused workspace

The Bulk Editor now shows only the fields that matter: your titles, meta descriptions, and, new in this version, your focus keyphrases. Everything is searchable, filterable, and organized per content type. As a result, posts, pages, products, and custom post types each get their own editor, which is ideal for publishers and stores alike.

Whether you manage 50 pages or 5,000, you can still move through them quickly and systematically. Just inherited a site? The Bulk Editor remains the fastest way to get it to a solid SEO baseline without starting from scratch.

New: search and status filters

You can’t fix what you can’t see all at once. The new filters get you straight to the pages that need work. For example, you can pull up every page missing a description, every draft, or every post targeting a keyphrase in seconds instead of scrolling. From there, you review, update, and move on without opening a single page.

Easily search for the page, product or post you are looking to edit

New: AI drafts it, you approve it (selected plans*)

Even when you can see what’s missing, writing hundreds of titles and descriptions by hand is a project nobody has time for. With paid plans like Yoast SEO Premium, you can now select the content that needs work and let AI draft the metadata for you. You then review each suggestion, apply what you like, and tweak or discard the rest.

Crucially, nothing saves without your permission. Per-row and batch apply or discard controls, plus unsaved-changes warnings, give you bulk editing without bulk mistakes. That means you can work through hundreds of pages with confidence, not caution.

Working with the AI powered metadata generator

*AI drafting is available with Yoast SEO Premium. WooCommerce and Yoast SEO AI+ plans extend it to products and categories.

New for stores: catalog metadata at scale

Keeping product metadata complete and unique across a large catalog is one of the most tedious jobs in e-commerce. This release helps in two ways. First, products get their own view in the Bulk Editor, so you can filter your catalog and spot the gaps fast. Second, Yoast WooCommerce SEO and Yoast SEO AI+ plans extend AI drafting to products and categories. Your store can therefore generate, review, and approve catalog metadata at scale, right in time for the next seasonal peak.

The post New in the Yoast SEO Bulk Editor: filters, focus keyphrases, and AI drafts appeared first on Yoast.

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Google confirms Local Inventory Ads will be enabled by default

After notifying advertisers about the upcoming change last week, Google has now officially confirmed on the Google Ads Developer Blog that Local Inventory Ads (LIA) will be enabled by default for all Shopping campaigns beginning August 31st.

Why we care. Advertisers using Shopping campaigns will no longer be able to disable Local Inventory Ads through the Google Ads API. If your workflows or campaign structure rely on keeping local inventory separate from online inventory, you’ll need to update both your code and campaign configuration before the change takes effect.

What’s changing. Starting Aug. 31, Google will automatically enable the “Local products” setting for every Shopping campaign.

Previously, advertisers had to explicitly set the enable_local field to true in a campaign’s ShoppingSetting to serve products from their Local Inventory Ads feed.

After the update:

  • Shopping campaigns will always have Local Inventory Ads enabled.
  • The Campaign.ShoppingSetting.enable_local field will be ignored.
  • Google will automatically treat the setting as true, regardless of the value submitted.

The change aligns Shopping campaigns with Performance Max for Retail, where Local Inventory Ads are already enabled by default.

What developers need to do. Developers using Google Ads API v25.1 or later should stop attempting to set enable_local to false.

Doing so will return:

  • ContextError.OPERATION_NOT_PERMITTED_FOR_CONTEXT

For API versions earlier than v25.1, existing code will continue to work, but any attempts to disable Local Inventory Ads will simply be ignored, with Google treating the value as true.

How to prevent local products from serving. Advertisers that previously disabled Local Inventory Ads for specific Shopping campaigns now have two alternatives:

  • Create a listing scope using CampaignCriterionService with product_channel set to ONLINE.
  • Use the Inventory filter in the Google Ads campaign settings to exclude local inventory and separate budgets between online and local products.

In other words, Google is shifting inventory control away from a simple campaign setting and toward inventory filtering.

Not changing. The update applies only to Shopping campaigns.

The enable_local field will continue to function normally for other supported campaign types, including:

  • Performance Max
  • Demand Gen

Bottom line. Beginning Aug. 31, Local Inventory Ads will automatically be enabled for every Shopping campaign. Advertisers that currently rely on disabling local inventory should update their API integrations and inventory filtering strategy before the rollout.

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Google drops $50K ad spend requirement for Lead Form assets

Google has quietly updated its documentation for Lead Form assets, removing one of the biggest barriers that prevented smaller advertisers from using the format.

Why we care. The long-standing requirement for advertisers to spend more than $50,000 in Google Ads before becoming eligible for certain Lead Form experiences has disappeared from the Help documentation. If this reflects a product change—and not just a documentation update—it could make Google-hosted lead generation available to significantly more advertisers.

What’s changed. The $50,000 spend threshold is gone.

Previously, advertisers needed to meet one of two requirements to use Lead Forms in certain formats:

  • Spend more than $50,000 USD in Google Ads.
  • Or qualify as a reputable advertiser spending more than $1,000 per account (or $15,000 across accounts) and complete Advertiser Verification.

The updated documentation removes the $50,000 lifetime spend requirement entirely. The only eligibility path now mentioned is the advertiser reputation and verification requirement.

That is the most significant change in the update and substantially lowers the documented barrier to entry.

Campaign support has changed. Google has also simplified where Lead Form assets can be used.

Previously:

  • Search
  • Performance Max
  • Display
  • Video (beta)

Now:

  • Search
  • Performance Max

References to Video campaigns have been removed from the overview, while Display is no longer listed as a supported campaign type. Interestingly, the requirements section still references Display campaigns, suggesting the documentation may not yet be fully aligned across all Help pages.

More lead delivery options. Google expanded the documented methods for receiving leads.

Newly documented options include:

  • Email notifications
  • Zapier integration

These join existing delivery methods:

  • CSV download
  • Webhooks
  • Google Ads API

The addition of Zapier gives advertisers a no-code way to automatically send leads into thousands of CRM platforms and business applications.

Better clarification on lead retention. Google also clarified how long lead data is stored.

The updated documentation now explicitly states:

  • Manual CSV downloads are available for 30 days.
  • Google stores lead data for 60 days.
  • API exports can access up to 60 days of lead data.

Previously, the 60-day retention period wasn’t clearly explained alongside manual downloads.

New positioning. Google also refreshed how it describes Lead Form assets.

The previous documentation focused on generating leads and increasing conversions.

The updated version emphasizes:

  • Higher-quality leads
  • Higher lead volume
  • Easier implementation
  • OTP verification for improving lead quality

Expanded country availability. Google added more than two dozen additional countries where Lead Form assets are eligible, including Bahrain, Croatia, Estonia, Jordan, Kuwait, Morocco, Qatar, Serbia, Slovenia and Tunisia.

Bottom line. Google’s latest Lead Form documentation refresh includes several usability improvements, expanded integrations and broader geographic availability. But the standout update is the disappearance of the $50,000 Google Ads spend requirement—potentially opening one of Google’s most valuable lead-generation formats to far more advertisers.

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How to report PPC performance without lying to yourself (or your boss)

How to report PPC performance without lying to yourself (or your boss)

Early in my career, I was responsible for reporting metrics for a company’s homepage. A member of the usability team wanted to know how much traffic a particular widget was getting. When we pulled the numbers, the result was underwhelming: About 2.5% of visitors actually used it.

But that’s not the number that ended up in the report. Instead, the widget’s usage was reframed as “a couple thousand visits per month.” That was technically true. It also told a completely different story from 2.5%.

That moment taught me something I’ve carried throughout my paid search career: Data isn’t black and white, and the person presenting it has a responsibility to tell an accurate story, not just a flattering one.

Data doesn’t lie, but PPC practitioners sometimes do. In PPC, we have more opportunities to blur that line than most people realize. Here’s where it happens and how to make sure your reporting holds up so you stay honest and ethical.

Conversions aren’t just conversions

If there’s one number that gets flattened most often in paid search reporting, it’s conversions.

A “conversion” can mean something completely different depending on what’s actually being counted. A form fill isn’t a marketing qualified lead (MQL). An MQL isn’t a sale.

For example, a phone call, a chat initiation, and a user watching 50% of a video are all conversion actions I’ve seen tracked in the same account, sometimes rolled into the same headline number and reported as “conversions.”

When you tell a client or stakeholder, “We got an excellent number of conversions,” without specifying what that conversion actually was, that’s not reporting. That’s editorializing, and it’s not a solid foundation for PPC strategy decisions.

Before you present a conversion number, ask yourself:

  • What action is actually being counted? A form fill, a call, a chat, a video view, or a purchase?
  • How far is that action from an actual business outcome? A lead and a closed sale aren’t the same type of win.
  • Would the person reading this report make a different decision if they knew what was behind the number?

If the answer to the third question is yes, you owe them that context, and you should include it when you deliver the report.

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Your CTR benchmark is probably a decade out of date

I still hear practitioners say a campaign is performing well because its click-through rate is “over 2%.” That benchmark comes from an era of PPC that doesn’t exist anymore.

Today’s bidding algorithms are far more sophisticated at finding users who resemble your existing converters. That alone pushes click-through rate (CTR) up across the board, independent of anything you did strategically.

A 2% CTR benchmark from 10 years ago tells you almost nothing about whether a modern, algorithmically targeted campaign is actually healthy and meeting its goals.

Reporting that “CTR is above benchmark” without acknowledging what’s driving that lift, whether it’s better targeting, better creative, or simply a more capable algorithm finding easier audiences, is another way data gets presented as good news without earning that designation.

I don’t think there’s a legitimate universal benchmark left to point to. The algorithm has gotten too good at finding easy clicks for a single number to mean the same thing across accounts, industries, or even campaigns within the same account.

Stakeholders will always ask the fundamental question: “Are these numbers good or bad?”

As subject matter experts, our job isn’t to hand them a legacy benchmark to check off, especially one that doesn’t really exist in any meaningful form.

True expertise means redefining success and shifting the conversation away from vanity metrics that the algorithm inflates for us. It means anchoring our reports in the business outcomes we were hired to drive.

It’s also important to explain how modern bid strategies affect the metrics you’re reporting.

Dig deeper: Why a lower CTR can be better for your PPC campaigns

Raw numbers and percentages tell different stories — use both

The widget story I opened with is really a story about raw numbers versus percentages, and that same tension shows up constantly in paid search reporting.

When you’re breaking down conversions by type, for example, showing that phone calls make up 40% of conversions versus leads at 60% tells a very different story than saying “142 calls, 213 leads.” Neither version is wrong. But presenting only one of them, especially the one that happens to look better, is a choice. It isn’t neutral reporting.

It’s something we have to be conscious of whenever we present data.

The fix isn’t complicated: Show the data in more than one way.

By presenting raw counts and percentages together, you give whoever reads the report enough context to understand what actually happened, instead of what you want them to take away from the report. Percentages add context to the data.

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What you choose to focus on in a report is itself a form of manipulation

This kind of manipulation by omission is something I think about most often because I’ve seen it cost advertisers real money and create confusion.

For example, I once took over an account from another practitioner who had been telling the business that its low cost per click (CPC) was a sign of success.

If a low CPC were actually the goal, it’d be easy to hit that number by running everything through the Display Network or another upper-funnel campaign. But that campaign type may not align with what the brand actually needs.

In my example, because the business had been told for months that a low CPC meant good performance, it had bought into the wrong metric entirely.

In reality, a higher CPC often drives better business outcomes and can even result in a lower cost per acquisition because you’re paying more to reach higher-intent, higher-value users instead of optimizing for cheap clicks that don’t convert. For my client, that turned out to be true.

Focusing a stakeholder’s attention on the metric that makes your work look best instead of the one that reflects their actual goals is one of the quietest ways data gets weaponized in this industry. We should be client-first when it comes to reporting data.

Attribution can hide whether your spend is doing anything at all

Even accurate reporting on conversions, CTR, and CPC can still mask a bigger question: Would those conversions have happened anyway?

Attribution models give credit for conversions across touchpoints, but it’s important to remember that credit isn’t causation. A branded search campaign can show a huge volume of “conversions” that would’ve happened through organic or direct traffic, regardless of whether the ad ever ran. The report looks great, but the incremental business impact may be close to zero.

This doesn’t mean we wouldn’t run a brand campaign. It means the data needs more context and nuance.

Incrementality testing — whether that’s using a holdout group, running a geo experiment, or conducting a conversion lift study — is the only real way to answer whether your media spend is creating new business or simply claiming credit for outcomes that would’ve happened anyway.

Reporting conversion volume without ever addressing incrementality is one of the most common and most defensible-sounding ways paid search data tells an incomplete story.

Dig deeper: Why your B2B PPC metrics may be lying to you

3 manipulation tactics worth naming directly

Most of the metric issues above happen without anyone intending to mislead. But a few specific patterns are worth calling out because once you see them, you can’t unsee them in your own reporting.

  • Conversion stacking: Counting multiple actions from a single user journey — say, a chat, then a call, then a form fill from the same person — as three separate wins instead of one.
  • Cherry-picked date ranges: Comparing this month to a deliberately slow month last quarter, or quietly excluding the week your account had an outage or tracking issue. A date range chosen against a weak baseline can make almost any account look like it’s improving.
  • Vanity metric substitution: Leading with a metric that looks good — perhaps impressions, clicks, or “reach” – when the metric that actually matters — qualified leads, revenue, or CPA – tells a less flattering story.

I’m not saying every practitioner who uses these patterns is acting in bad faith. Most of the time, it’s habit, not deception. But habits are exactly what need to be questioned if we’re serious about reporting data honestly.

Paid search doesn’t have a governing board. That’s exactly why this matters

Unlike many licensed professions, paid search practitioners don’t answer to a regulatory body. We have platform certifications, not an ethics board. That means the standard for how honestly we present data to clients and stakeholders is largely self-imposed.

I don’t think that gets talked about enough in our industry. It’s easy to frame a number in the best possible light, especially when your job security or a client relationship depends on the story that number tells.

But contextualizing conversions accurately, using current benchmarks, showing both raw numbers and percentages, and focusing on the metrics that actually reflect business outcomes isn’t just good practice. It’s the ethical baseline for PPC.

If we don’t hold ourselves to that standard, no one else will.

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Google Tag Manager adds guided setup for Google Ads purchase tracking

Google is making it easier to implement Google Ads purchase conversion tracking by introducing a Guided Setup experience in Google Tag Manager, reducing the manual work required to configure tags.

What’s happening. A new “Guided Setup” card is appearing in Google Tag Manager for Google Ads Purchase Conversions.

The beta feature automatically creates the required:

  • Tags
  • Triggers
  • Variables

Instead of requiring users to configure each component manually.

Why we care. Conversion tracking is essential for campaign optimisation, but implementation can be complex—particularly for advertisers new to Google Tag Manager. Guided Setup aims to simplify the process, reducing setup errors and helping advertisers start measuring purchases more quickly.

Between the lines. The update reflects Google’s broader push to lower the technical barriers to measurement as advertisers increasingly rely on automated bidding strategies that depend on accurate conversion data. By automating much of the configuration, Google could improve tracking adoption and data quality across Google Ads accounts.

What we’re watching. The feature is currently in beta, meaning it isn’t yet available to all Google Tag Manager users. Google has not announced when a wider rollout will begin.

The bottom line. Google Tag Manager’s new Guided Setup streamlines Google Ads purchase conversion tracking by automatically configuring key measurement components, making implementation easier for both new and experienced advertisers.

First spotted. The beta feature was shared by Paid Search expert,Vivek Gupta on LinkedIn.

Every click they win is a customer you lose.

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