New data: Google AI Overviews are hurting click-through rates

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Two new studies agree: Google’s AI Overviews steal clicks from organic search results.

While Google told us that AI Overviews citations result in higher-quality clicks, the introduction of AI Overviews correlates with a measurable decline in organic visibility and clicks, particularly for top-ranking, non-branded keywords. That’s according to two new data studies from SEO tool provider Ahrefs and performance agency Amsive.

By the numbers. Here’s how AI Overviews have decreased click-through rate (CTR) for traditional organic listings, according to the two studies:

  • Ahrefs: A 34.5% drop in position 1 CTR when AI Overviews were present, based on an analysis of 300,000 keywords.
  • Amsive: An average 15.49% CTR drop, with much larger losses in specific cases (e.g., -37.04% when combined with featured snippets), based on an analysis of 700,000 keywords.

Non-branded keywords. AI Overviews are much more likely to trigger on non-branded queries, and these terms showed the largest CTR drops:

  • Amsive: -19.98% CTR decline on non-branded keywords.
  • Ahrefs: Focused exclusively on informational intent (99.2% overlap with AI Overviews).

Lower rankings = bigger CTR hits. Google’s AI Overviews push organic results further down, minimizing visibility even for solidly ranking pages.

  • There was a -27.04% CTR drop for keywords not in the Top 3 positions, according to Amsive:

AI Overviews benefit branded queries. Branded keywords are less likely to trigger AI Overviews (only 4.79%) – but when they do, they get a +18.68% CTR boost. This is possibly due to greater user intent and brand familiarity, according to Amsive.

Why we care. These two studies (as well as data from Seer Interactive, which we covered in Google organic and paid CTRs hit new lows: Report) call into question Google’s claim that AI Overviews get more clicks than traditional listings. Google’s claim may or may not be true, but these studies show that overall clicks have gone down – and many websites ranking well in Classic Search aren’t included in AI Overviews.

About the data:

  • Ahrefs: Used Ahrefs + Google Search Console (GSC) data to analyze CTR changes before (March 2024) and after (March 2025) the U.S. rollout of AI Overviews.
  • Amsive: Pulled data from 700,000 keywords across 10 websites and 5 industries to isolate patterns by keyword type, industry, and SERP feature overlap.

The studies. You can read them here:

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Meta tags for SEO: What you need to know

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Meta tags for SEO: What you need to know

Remember when meta keywords were all the rage? 

Fast forward to 2025, and while search engines have evolved dramatically, meta tags remain crucial building blocks of your SEO foundation, just not the ones you might remember.

You’re juggling countless priorities, so it’s tempting to view meta tags as “set it and forget it” HTML snippets.

But here’s the truth: properly optimized meta tags are still conversion-driving assets that both search engines and potential customers use to understand your content.

This guide cuts through the noise to spotlight the meta tags that actually move the needle – on rankings, click-through rates, and visibility.

Before we dive deep, here’s what you need to know:

  • Title tags and meta descriptions remain your most powerful meta elements in 2025.
  • With AI Overviews now prominent in search, robots meta tags have become crucial content governance tools.
  • Mobile optimization through viewport tags directly impacts your rankings.
  • Social meta tags drive significantly higher engagement when properly implemented.

What are meta tags?

You’ve heard about meta tags, but what exactly are they? 

Think of them as your website’s elevator pitch to search engines, invisible to visitors but critical for rankings.

These HTML snippets live in the <head> section of your code, quietly working behind the scenes to tell Google, Bing, and other search engines what your page is about, who should see it, and how it should appear in search results.

Meta tags remain one of the few direct communication channels between marketers and search engines. 

Despite all the algorithm changes we’ve seen, properly implemented meta tags still provide clear ranking signals.

Unlike the early 2000s when you could stuff keywords into meta tags and call it a day, today’s meta tags work as part of a sophisticated system that impacts not just rankings but also user behavior and conversion rates. 

They’ve become even more crucial with the widespread adoption of AI-driven search features like Google’s AI Overviews.

Meta tags every site must have

Title tag

If I could only optimize one meta element, it would be the title tag every single time. 

It’s the heavyweight champion of meta tags, appearing as the clickable headline in search results and significantly influencing both rankings and click-through rates.

Here’s what actually works in 2025:

  • Optimal format: Primary Keyword | Secondary Keyword | Brand Name
  • Character limit: 50-60 characters (Google typically displays about 600 pixels worth)
  • Psychology hack: Numbers and power words can entice clicks

I recently worked with a SaaS client who changed their homepage title tag from “Cloud-Based Project Management Software” to ” #1 Project Management Software for Remote Teams | Save 5hrs/Week”

The result? 

A 27% increase in click-through rate and a jump from Position 4 to Position 2 for their primary keyword. That’s the power of a well-crafted title tag.

But here’s what most marketers miss: your title tag doesn’t exist in isolation. 

It needs to work in harmony with your meta description to tell a compelling two-part story.

Meta descriptions

Think of meta descriptions as free advertising space. 

While they don’t directly impact rankings, they’re your best opportunity to convince searchers to click your result instead of the competition.

The most effective meta descriptions follow this proven formula:

  • Open with a benefit or promise that addresses search intent.
  • Include specific details that build credibility (numbers, stats, features).
  • End with a clear call-to-action that creates urgency.

For example, compare these two meta descriptions for the same article about email marketing:

❌ “This article discusses email marketing best practices for small businesses. Learn how to improve your email marketing strategy and get better results from your campaigns.”

✅ “Boost your open rates by 37% with these 7 proven email templates designed for small businesses. See how brands like yours are driving 2X conversions with our step-by-step approach.”

The second example is specific, benefit-focused, and creates urgency. 

Tip: Google now dynamically adjusts meta descriptions based on the search query, but don’t leave this to chance! Write compelling descriptions for your key pages, or Google might pull random text from your page that doesn’t convert.

Dig deeper: SEO for page titles and meta descriptions: How to win more clicks

Robots meta tag

The robots meta tag has evolved from a simple indexing control to a sophisticated governance tool for how your content appears in search, particularly in AI-generated results.

The most important directives you need to know:

  • index/noindex: Controls whether a page appears in search results at all.
  • follow/nofollow: Determines if Google should follow links on your page.
  • nosnippet: Prevents your content from appearing in featured snippets and from being used as input for AI Overviews.
  • max-snippet:[number]: Limits how much text can be used in snippets and AI Overviews.

This last point deserves special attention. 

With Google’s AI Overviews now answering many queries directly at the top of search results, you face a strategic decision: 

  • Do you want your content to be cited (potentially gaining visibility)?
  • Or do you want to drive direct traffic to your site?

For high-value content that answers specific questions, using max-snippet:50 can be a smart compromise.

You provide enough information to be cited in AI Overviews, but not enough for the AI to give a complete answer without the user clicking through.

Viewport meta tag 

With mobile-first indexing now the standard, the viewport meta tag is non-negotiable. 

This simple line of code ensures your site displays correctly on all devices:

<meta name="viewport" content="width=device-width, initial-scale=1.0">

This tag is so important because mobile usability is a direct ranking factor. 

Sites that force users to pinch and zoom on mobile can be impacted in search rankings, regardless of how valuable their content might be.

The strategy behind effective meta tags

Meta tags as the first impression

Your meta tags create the first impression in search results, before users reach your website. 

This first impression needs to accomplish three things:

  • Signal relevance: Clearly show that you’re answering the user’s query.
  • Build trust: Demonstrate expertise and credibility.
  • Create urgency: Give users a compelling reason to click now.

The most successful meta tags address all three of these elements simultaneously. 

Aligning meta tags with search intent

One of the biggest shifts in meta tag optimization is focusing on search intent rather than just keywords. 

Today’s successful meta tags specifically address one of these four intent types:

Intent type What users want Meta tag approach Example
Informational Learn something Educational tone, promise of insights “What is Growth Marketing: 7 Essential Strategies Explained”
Navigational Find a specific site Brand-forward, direct “Netflix Official Site – Stream Movies & TV Shows”
Commercial Research before buying Comparison terms, benefits “Best Running Shoes 2025: Compare Top Brands & Features”
Transactional Make a purchase Action terms, urgency “Shop iPhone 16 – Free Shipping & Returns Until Friday”

The key is matching your meta tags to what users actually want at this moment in their journey. 

This alignment signals to both Google and users that your content is precisely what they’re looking for.

Advanced meta tag techniques for 2025

Social meta tags

Social meta tags (Open Graph and X card tags) control how your content appears when shared on social platforms. 

With social platforms driving significant traffic, these tags are essential for comprehensive visibility.

The minimum social tags you should implement on every page:

Canonical tags

The canonical tag might not be visible to users, but it’s crucial for preventing duplicate content issues and consolidating ranking signals:

<link rel="canonical" href="https://yourdomain.com/definitive-url">

This tag is particularly important for:

  • Ecommerce sites with product pages accessible through multiple category paths.
  • News sites that publish similar content across different sections.
  • Sites with both www and non-www versions (or HTTP and HTTPS variants).

Data-nosnippet

One of the newest and most valuable tools in your meta tag arsenal is the data-nosnippet attribute. 

This HTML attribute lets you mark specific sections of content that you don’t want included in either traditional snippets or AI Overviews:

<div data-nosnippet>This content won't appear in snippets or AI Overviews</div>

This offers control, allowing you to protect your most valuable content, like executive summaries, key conclusions, or proprietary data, while still allowing other parts of your page to appear in search results.

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Measuring meta tag performance

How do you know if your meta tags are actually working? 

Here’s my three-step process for measuring and optimizing meta tag performance:

  • Track click-through rate (CTR): Use Google Search Console to identify pages with lower-than-expected CTR for their position. These are prime candidates for meta tag optimization.
  • A/B test critical pages: For high-value pages, create variations of your title and description tags to see which combinations drive the highest CTR. Even small wording changes can yield significant improvements.
  • Monitor impressions in AI Overviews: Track when your content is cited in AI Overviews and measure the impact on both direct traffic and brand awareness. This helps inform your robots tag strategy.

One test for a retail client of ours discovered that adding product prices directly in their title tags (“Men’s Leather Wallet – $49.99”) increased their CTR by 23% compared to titles without pricing information.

Common meta tag mistakes

Even seasoned marketers make these meta tag mistakes that can hurt visibility:

1. Duplicate meta descriptions across multiple pages

I recently audited a site where 62% of their product pages shared the same generic meta description. 

Google was forced to create its own snippets, resulting in inconsistent messaging and poor CTR.

The fix? Create unique, specific meta descriptions for each page, focusing on the unique value proposition of that particular content.

2. Keyword stuffing in title tags

It’s 2025, but I still see sites trying to cram every possible keyword variation into their title tags:

❌ “Best SEO Services, SEO Agency, SEO Company, Search Engine Optimization Services”

This approach looks spammy to users and triggers Google’s title rewriting algorithm, giving you even less control over your SERP appearance.

3. Missing or improper robots directives

With AI Overviews now prevalent, misconfigured robots directives can lead to either:

  • Valuable content being completely excluded from AI citations.
  • Proprietary information being fully exposed in AI summaries.

Review your robots directives quarterly to ensure they align with your current content strategy and business goals.

4. Ignoring mobile meta tag optimization

Title tags and meta descriptions appear differently on mobile devices, with even tighter character limits. 

Yet many marketers still optimize exclusively for desktop display.

Mobile optimization means:

  • Front-loading the most important information in titles and descriptions.
  • Keeping mobile meta descriptions under 120 characters.
  • Ensuring your viewport meta tag is properly implemented.

Meta tags and AI search: Preparing for what’s next

The rise of AI in search has fundamentally changed how we approach meta tags. 

Here’s how to position your content for success in this evolving landscape:

Strategic decisions about AI content usage

Every site now faces a critical decision: Do you want your content to appear in AI-generated summaries? 

There are valid arguments on both sides:

Allowing AI usage:

  • Gains visibility as a cited source in AI Overviews.
  • Positions your brand as an authority.
  • Creates multiple entry points to your content.

Restricting AI usage

  • Preserves direct traffic to your site.
  • Protects proprietary or premium content.
  • Maintains control over how your information is presented.

There’s no one-size-fits-all answer. Every brand should decide for themselves which aligns or take a hybrid approach.

Enhanced structured data integration

While not technically meta tags, structured data (schema.org markup) works alongside your meta tags to provide context to search engines. 

In 2025, implementing relevant schema markup is essential for:

  • Qualifying for rich results (ratings, FAQs, how-tos).
  • Providing clear entity signals to AI systems.
  • Enhancing the appearance of your content in both traditional and AI search results.

The sites seeing the most success in AI-driven search are those that provide both strong meta tag signals and comprehensive structured data.

Your 15-minute meta tag audit

Ready to put these insights into action? Here’s a quick audit process you can run right now:

  • Check your top 5 landing pages in Google Search Console for CTR outliers.
  • Verify that each page has a unique, compelling title and meta description.
  • Ensure your robots meta directives align with your AI content strategy.
  • Confirm proper canonical tags are in place, especially for similar content.
  • Validate that viewport and social meta tags are correctly implemented.

This simple process can help you identify quick wins to increase organic traffic within weeks, not months.

Smart meta tags power search performance

In 2025, meta tags are no longer just technical SEO elements; they’re strategic marketing assets that require thoughtful optimization.

The most successful marketers approach meta tags with three principles in mind:

  • User-first thinking: Write for humans first, algorithms second.
  • Strategic control: Make deliberate choices about how and where your content appears.
  • Continuous testing: Regularly measure performance and refine your approach.

As search continues to evolve with AI at the forefront, your meta tags will remain one of your most powerful tools for visibility, engagement, and control. 

The time you invest in optimizing them today will pay dividends in traffic and conversions tomorrow.

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Google sends personalized growth plans to advertisers, pushing AI-driven solutions

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Google Ads logo on smartphone

Advertisers are receiving step-by-step guidance emails from Google Ads aimed at improving campaign performance over a three-month period.

The details. Google Ads is sending emails with the subject line “Personalised action plan for growth” to business advertisers, according to an X post from Govind Singh Panwar.

The email contains:

  • A three-month structured improvement plan delivered through weekly emails.
  • A progress tracker showing completed and pending actions.
  • Clear calls to action focused on ad strength improvements.
  • Claims that improving ad strength from “Poor” to “Excellent” results in an average 12% increase in conversions.

AI suggestions. The guidance pushes advertisers toward Google’s preferred strategies, including:

  • Enabling “personalized recommendations” (Google’s AI suggestions).
  • Adding broad-match keywords (which typically increase ad spend).
  • Creating Performance Max campaigns (Google’s black-box AI campaign type).

Why we care. The email campaign essentially represents Google’s effort to standardize advertiser behavior while framing it as personalized guidance. These “personalized” plans appear somewhat templated, potentially leading to more homogenized advertising approaches across competitors.

However, as more advertisers follow these guidelines, those who don’t may see performance impacts as Google’s algorithms increasingly favor accounts aligned with their recommended practices.

Bottom line. While positioned as personalized guidance, the recommendations follow Google’s standard playbook for increasing advertiser adoption of its automated solutions and broader targeting options, which typically require larger budgets.

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Google Search to redirect its country level TLDs to Google.com

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Google will begin redirecting its country code top-level domain names (ccTLD) versions of its Google domain to Google.com. That means if you frequent google.fr (in France), google.ng (in Nigeria) and so on, you will be redirected to Google.com.

Why the change. Google said, “Over the years, our ability to provide a local experience has improved. In 2017, we began providing the same experience with local results for everyone using Search, whether they were using google.com or their country’s ccTLD.” “Because of this improvement, country-level domains are no longer necessary,” Google added.

Google said, “we’ll begin redirecting traffic from these ccTLDs to google.com to streamline people’s experience on Search.”

The impact. For the most part, most searchers should not notice any difference. When you are redirected, there is a chance you may have to login to Google again and also reconfigure some of your search settings.

But overall, there won’t be any significant changes. Google wrote, “It’s important to note that while this update will change what people see in their browser address bar, it won’t affect the way Search works, nor will it change how we handle obligations under national laws.”

Timing. This change will begin today but “will be rolled out gradually over the coming months,” the company said.

Why we care. You may notice slightly different referral traffic from Google Search, related to this change.

This may also impact your signed in experience with Google.com in the short term.

But outside of that, there should be no other large changes with these ccTLD changes for Google Search.

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AI agents in SEO: What you need to know

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AI agents in SEO: What you need to know

You’ve probably been hearing a lot about AI agents lately – whether in your workplace conversations or scrolling through your social feeds (hopefully both). 

While there’s no shortage of articles discussing their general benefits, there’s surprisingly little coverage on what they mean specifically for SEO – where their impact is not just significant, but amplified.

Before we dive into the two key reasons AI agents are so important for SEOs to understand (and yes, you’re probably already using them – even if you don’t realize it), let’s first get clear on what AI agents actually are.

What are AI agents?

At their core, AI agents are autonomous systems equipped with access to external tools, data, functions, and more. 

They operate with a clear understanding of an end goal and are provided with the resources needed to achieve it.

In some cases, they’re also given instructions on how to use those tools. In others, they’re left to figure it out on their own.

Rather than diving into a chart or technical diagram of a sample agenting system, I think a simpler – and surprisingly accurate – illustration can be found in one of nature’s most complex yet overlooked lifeforms: the humble ant.

Ant colony and AI agents

Imagine an ant colony: the queen, much like a master AI algorithm, sets the overarching goal. The worker ants – each equipped with their own specialized tools – are the individual agents tasked with specific functions.

Consider the parallels:

  • Queen = Agent operator: Directs and adjusts the overall strategy.
  • Worker ants = Sub-agents: Each has a specialized tool or function, whether it’s gathering data, analyzing content, or communicating findings.
  • Colony efficiency = System optimization: As ants work together, the system optimizes resources and information flow, mirroring how AI agents coordinate to achieve complex tasks.

The queen communicates the goal to each “tool,” which each ant then tries to accomplish. 

They return with their requested resource, communicate and assess their status, share information to accomplish their macro goal faster and report back. 

An overall status is reported to the queen, who communicates adjusted commands to her tools.

This is not all that different from an AI agent, other than being generally more sophisticated (though not as impressive to us, as it only sustains a species and doesn’t automatically make a stock trade 56 nanoseconds faster after catching a new trend and applying the sentiment as positive).

I’ll poorly parallel this to AI agents below.

But before I do that, let me answer why one of my assertions above is true. 

Why the impact of AI agents in SEO is multiplied many times over most other professions

I can’t think of an industry that won’t be touched by agents, at least indirectly. 

  • Lawyers will use agents to look up and summarize judgments and analyze loopholes used for their clients.
  • Software engineers will use them to assist in developing code and systems, referencing their internal docs, repos, and external knowledge.
  • Bakers will receive their ingredients through shippers coordinated using agents.
  • SEOs will use them as tools to do their jobs faster and better – as I’ll illustrate below.
A cartoon ant holding a microphone

On top of that, we also need to learn and adapt to marketing into agentic systems.

Generative engine optimization (GEO) entered the scene not that long ago. 

But what it is evolving into is something different — something far more powerful. 

Something that takes us past optimizing for an algorithm, even one driven by an LLM like AI Overviews or ChatGPT, and into optimizing for agents, their functions, and their tools.

We’re seeing this evolution in its toddler years right now, and if you’re on the ground floor, that’s a great place to be. 

While there are exceptions, for the most part, generative engines are performing a lot like search engines in their presentation of solutions.

  • The user enters a query.
  • The user receives a reply.
  • That reply might have a few links in it.

Sure, the system might check on the web for additional references outside of its current knowledge base, but nothing revolutionary. 

Again, it functions a lot like traditional search with a better user experience. 

I expect the next steps in this evolution will be gradual, as tools like Google and ChatGPT add new capabilities – such as the recently announced feature where an AI-driven system can call a store to gather additional information for you.

However, new pieces will gradually fall into place until we reach a point where providing your agent with insights into your goals or needs will trigger actions in ways we likely can’t fully understand yet.

Here’s a simple example.

You give the Google agent (for example) your goal, want, or need. 

Let’s say you need new shoes for a wedding. The agent can then:

  • Check your calendar for the wedding date.
  • Check the weather in that city on that date, or likely weather based on the time of year if specifics are unavailable.
  • Ask what you’ll be wearing.
  • Knowing your size, general style, and preferred brands and stores – source options that will arrive in time for the wedding.
  • Source and store a local backup, in case something goes wrong with the delivery or fit, to have that information ready in case it detects a problem.
  • Ask if you would like to see the options:
    • If yes, send them to a display of your choosing.
    • If not, move on to the next step.
  • Once the shoe is selected, complete the order.
  • Check what other common items might be needed for weddings, based on your status at it (guest, best person, bride or groom, etc.), and optionally send an email list of these to you if it doesn’t have evidence these are completed.

Imagining this world, I have a couple of questions for you:

  • How do you attribute that to Google?
  • Was it their crawler that surfaced the information to them? What kind of optimization does that take with LLMs?
  • Was it a product feed through Google Merchant Center?
  • Did they use an operator to navigate your site to get to it? Is there optimization you need to apply to filters to simplify that?
  • If you sell umbrellas, how do you ensure you’re part of those emailed suggestions from earlier in the event that it’s going to rain.
  • Oh, and how do you even get attribution for that?

This simple example highlights the immense complexity of what lies ahead. 

New technologies will emerge that companies and teams will need to adopt and optimize. 

Additionally, with the development of new protocols like Anthropic’s Model Context Protocol (MCP), adding your store’s feed to a marketplace – or even creating your own tools for other agents to use – will become much easier. 

This opens the door to greater distribution, though it may come with challenges like difficult attribution and untested effectiveness. 

The question is: 

  • Do you really want to wait and see if your competitors dive in first, or will you seize the opportunity now?

While I can’t predict the exact shape of the marketing world in the next two weeks, let alone a year from now, I can confidently say that we’ve already entered the agentic era. 

The rate of adoption and development in this space is unlike anything I’ve seen in over two decades of online marketing.

It’s even more disruptive than the changes brought on Google’s Panda and Penguin updates.

A red ant plus small pandas and penguins

Dig deeper: From search to AI agents – The future of digital experiences

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SEOs and GEOs use agentic AI, too

And on the other side of the coin, we also have SEOs using their own agentic systems.

As an example, I’ll share an agenting system I created to help generate article outlines for authors at Weights & Biases. 

What started as a simple replacement for a script I had previously written for the same task has since evolved. 

I’ll also highlight a few upcoming expansions to better illustrate the potential of AI agents.

This agentic system begins by asking the user for five things:

  • The primary phrase they are hoping to rank for with an article.
  • Any secondary terms.
  • The type of article they were writing.
  • The title (if they have one in mind).
  • The author.

It uses this information to inform the other agents within the system what to do and what data to access.

I’ve created several agents and data sources for the agent to access. 

The main ones (including a few still being finished after some testing) are:

A search agent

This agent has access to Google search and removes social platforms, which tend to block our web scrapers.

An analysis agent

This agent does a few things:

  • Extracts the entities from the pages using Google’s Natural Language API.
  • Summarizes content.
  • Extracts questions from the content.

I’ll likely separate these into their own agents as I expand the capabilities, but combining them works well in the current iteration.

A data store of examples

For each author, I created a folder with 10 markdown files that include:

  • The inputs they provided (primary phrase, secondary terms, title, etc.).
  • The outlines generated by the system.
  • The final outlines I handed off after manual editing.
  • The first paragraphs from the published articles, based on my criteria for how section intros should read.

This collection trains the agentic system to understand each author’s preferred structure and tone. It also helps suggest first paragraphs that align with their writing style.

I log all of this – inputs, extracted entities, questions, and outlines – to W&B Weave to monitor performance and guide improvements.

An outline agent

This agent takes in the information from the user, the search results, entities, questions, and summaries and generates an article outline.

Coming soon

Some agents I’m adding in presently are:

  • A keyword agent that will have access to the Google Ads API to get additional keyword ideas and search volumes.
  • A social listening agent that will monitor social channels for trending topics and auto-generate and outline when one crosses a threshold of likely importance.
  • A Slack/email agent: When an article outline is generated automatically, the agentic system will inform me – including a list of notable people talking about the topic and a summary.
  • A competitor agent that will check to see if known competitors are ranking for the content and send them to me with the outline.

I’m sure there’s more to come. (I considered waiting until everything was finished before writing this, but new ideas keep popping up, and this article would never get written.)

You should (and can) build agents too

I’m not alone in developing agents, and while some SEO tools claim to be agentic, I haven’t found any worth paying for yet. 

The real benefit of building agents is that they help me understand the environment I’m marketing in. 

If you want to try developing one, I’ve used obot.ai, which is simple and great for creating basic, useful agents for various tasks.

Big thanks to Marc Sirkin, CEO of Third Door Media, for introducing me to it. 

At the very least, it’ll give you a feel for how agents work, which is a big advantage over competitors who don’t understand what’s happening behind the scenes.

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How and why to ‘be the primary source’ for organic search

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How – and why – to ‘be the primary source’ for organic search

“Just Google it” – ah, so 2021. 

These days, organic search and discovery – although still largely conducted on Google – have fanned out to many sources, with user behavior more multi-layered and dynamic than ever.

SEO professionals these days need to follow course.

Consider a user who: 

  • Starts by watching a TikTok video of a runner boasting about hitting a new PR with the help of a coach.
  • Then does a top-of-funnel search on Perplexity (“what does a running coach help with”).
  • Then hits Google for a search of online running coaches.
  • Then browses a list of sources from AI Overviews.
  • Then hits up a running community on Reddit to ask about peoples’ experiences with one coaching organization or another.

Doesn’t sound like it’s all about keywords anymore, does it?

Instead, we’ve been helping clients establish themselves as the primary source on a topic. 

That means showing up wherever users are looking for relevant information – while also building brand awareness as the subject matter expert.

The picture is changing quickly, so rather than chasing channels and keywords, we’re focusing on understanding and adapting to user behavior (with some healthy analysis of emerging platform trends thrown in). 

Here’s my take on what SEOs need to do to thrive in the age of diversified organic search.

Broaden your channel focus

Expand your focus beyond traditional SEO.

Understand how community-driven platforms (like Reddit and TikTok) and other emerging AI tools are impacting consumer search behavior. 

This means tracking search trends across various channels, not just focusing on Google.

These channels will vary by vertical. (If you’re not completely up to speed on what’s feeding your site traffic, make sure you’re setting up and referencing referral reports in Google Analytics.)

The stakes are high here.

Failing to adapt to these new search behaviors could lead to missed opportunities and a disconnect with target audiences, especially younger consumers.

Dig deeper: Beyond Google – How to put a total search strategy together

Know where your users are going for info – and what kind of info they’re looking for

Where are your users going, and what are they trying to find? 

That’s a much more complicated question than it was a few years ago. 

What your users are looking to learn on Reddit is very different from TikTok (whose algorithm is much more top-of-funnel/discovery-focused). 

And even LLMs and Google are used for fairly discrete behaviors.

The broad “how to do x” and “what is” questions might not be as effective on Google. 

Still, that’s probably what gets cited the most in AI search or large language models (LLMs). 

Consider creating a matrix of funnel intent by channel and crafting content accordingly. 

Track how your strategy works and adjust as you go.

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Stay in touch with the algorithms

Staying on top of shifting user behavior is the biggest priority right now in organic search.

However, that doesn’t mean you can afford to ignore how newer platforms are ingesting content.

The question I get most (by orders of magnitude) these days is about AI search and LLMs (which operate by predicting the next few words or phrases that connect to a topic). 

One way to boost your chances of citation is to position your brand name as close to your industry or solution as frequently as possible – whether that’s in earned, owned, or even strategic paid content.

For instance, if you have a great piece of content that’s getting organic traction, consider syndicating it.

It’s also a good idea to reverse-engineer this by:

  • Analyzing which sources/citations are being used in AI search responses.
  • Angling to get your brand covered there.
  • And/or creating similar kinds of content.

Last, scour those trades (including this one) to find AI search guidance from experts and tidbits provided by the AI search models themselves on influential ranking factors – like this one from Microsoft on Copilot.  

Dig deeper: Your 2025 playbook for AI-powered cross-channel brand visibility

Provide (even more) value

What could your company produce for thought leadership that might get picked up by the top outlet in your vertical? 

Proprietary research, a well-informed perspective from a company leader, or data that introduces a fresh narrative – any one of these can outperform hundreds of formulaic content pieces that flood your vertical.

Publishing content that supplements E-E-A-T principles with effort, originality, and value (my favorite content descriptors these days) does more than catch media attention.

(This is more important now than it was pre-LLMs.)

This type of content has the potential to transcend platforms by associating your brand with leadership within your vertical.

You may begin to see it cited in communities, forums, and social channels as users (not just algorithms) reference it organically.

Define your lane

The topic clustering strategy is still extremely relevant in this search era, and with that comes the frequent question of just how far you should expand that cluster. 

My take: owning your sphere and updating it as needed is better than expanding to less relevant subjects. 

Here’s an example of what that might look like:

Owning your sphere

Stay nimble

We’ve never seen the organic scene change this rapidly. 

  • Do your best to keep your finger on the pulse of newer algorithms, emerging platforms and communities, and shifting user behaviors.
  • Update and track your KPIs accordingly.
  • Make sure you’re including an action-oriented “so what” step that follows this regular analysis.

Whether you’re in-house or at an agency, remember that educating your colleagues about what’s changing is more than just providing value in your role.

It’s being proactive about aligning on strategic shifts you’ll need to make down the road. 

Dig deeper: 6 easy ways to adapt your SEO strategy for stronger AI visibility

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Google’s anti-privacy bill push sparks outrage among advertisers

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Google is being criticized for sending emails to small business owners urging them to oppose California Assembly Bill 566, legislation that would strengthen consumer privacy protections in digital advertising.

The outreach campaign, which asks recipients to sign a Connected Commerce Council letter opposing the bill, has prompted marketing professionals to publicly rebuke the tech giant’s tactics on LinkedIn.

Why we care. The dispute highlights growing tensions between digital advertising platforms and privacy advocates as California lawmakers consider new regulations on data collection practices.

AB 566 would require browsers and mobile operating systems to offer a built-in setting allowing users to easily opt out of data collection

Political misinformation. Google’s request was met with rejection by Navah Hopkins, brand evangelist of Optmyzr. In a LinkedIn post, she encouraged support for AB 566, arguing that businesses should build “consent-driven conversations” with customers rather than assuming entitlement to user data.

“We deserve the right to opt out of sharing our information and as marketers, we can absolutely ‘make do’ without perfect data,” she wrote, expressing disappointment in what she called “political misinformation” from Google.

Other advertisers speak up. Hopkins wasn’t the only one with concerns about this request.

Performance marketer Louis Halton Davies said that Google keeps stacking the chips in its favor when it comes to consent rules:

  • “Another sad thing is that having consented data is incredibly valuable to Google and not having it is just annoying for SMBs. Appreciate Google is a commercial business but they really take the mick stacking the chips so far in their favor.”

Lead generation specialist Julie Friedman Bacchini said that companies should get express agreement for what will be done with user data. If more people knew exactly what was being done, they would reject having their data collected, she said:

  • “Google is pretty notorious for astroturfing issues like this. I have long said that if you cannot get people to actively agree to what you might/want to do with their data then you should not be doing it. The argument that people don’t object is not a fair one as most people have no idea that companies they buy from or provide information to might upload that information to an ad platform like Google Ads. If they did, most would say no thank you, just like they have with Apple’s ATT prompts.”

The other side. In its email campaign, Google claims:

  • California Governor Gavin Newsom vetoed similar legislation last year.
  • AB 566 would mandate “new and untested technology” that might confuse consumers.
  • The bill would force businesses to “waste money showing ads to people who live far away or aren’t in the market” for their products.

What to watch. How Google responds to this push back could signal its approach to similar privacy legislation in other states, as the company navigates growing public concern over data collection practices while protecting its core advertising business.

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Temu pulls its U.S. Google Shopping ads

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Google shopping ads

Temu completely shut off Google Shopping ads in the U.S. on April 9, with its App Store ranking subsequently plummeting from a typical third or fourth position to 58th in just three days.

The company’s impression share, which measures how often their ads appear compared to eligibility, dropped sharply before disappearing completely from advertiser auction data by April 12.

The timing coincided with the Trump administration’s hardened stance on Chinese imports, raising tariffs to 125% while maintaining a more moderate approach to other trading partners.

First seen. Mike Ryan, head of ecommerce insights at Smarter Ecommerce, shared this news on LinkedIn:

Between the lines. Temu’s business model relied on heavily subsidized orders from parent company PDD to drive market share growth, despite operating at a loss on individual sales.

  • New tariffs, combined with crackdowns on “de minimis” import loopholes, have severely undermined Temu’s direct-from-manufacturer approach.
  • The company’s inability to maintain app performance without advertising for even a single day demonstrates the fragility of its market position.

Why we care. Ecommerce advertisers may experience temporary relief in digital advertising costs as Temu’s aggressive spending vanishes from auction platforms. Similar rapid market exits (e.g., Amazon during early pandemic lockdowns) led to drops in cost-per-click metrics. Some reduction in CPM rates is expected, potentially lowering both CPC and cost-per-conversion for remaining advertisers.

Tariffs. The underlying causes of Temu’s retreat (tariffs and import restrictions) could ultimately prove more damaging to the ecommerce landscape, particularly for small and medium-sized businesses.

Bottom line. Unlike failed competitor Wish.com, Temu’s parent company remains fundamentally sound. With U.S. trade policy still in flux and facing internal opposition even within the administration, Temu’s retreat may not be permanent.

Read more at Read More

Google AI Overview-organic ranking overlap drops after core update

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AI Overviews are now less likely to cite pages that rank in Google’s top 10 organic positions, according to new BrightEdge data. This change was observed following Google’s March 2025 core update.

By the numbers. The overlap between AI Overview citations and Google’s top 10 organic positions dropped from 16% to 15% following the March 2025 core update. Shift by industry:

  • Travel industry: 6.6 percentage point increase in regular result citations (from 12.9% to 19.5%).
  • Entertainment: 4.9 percentage point increase (from 8.8% to 13.7%) for movie queries.
  • Restaurants: 4.6 percentage point increase (from 9.5% to 14.1%) for dining content.

Why we care. Tens of millions of searches per day now feature AI-generated summaries that don’t cite the highest-ranked results from organic search. The good news? Pages ranking outside Google’s top 10 positions now have a better shot at being cited in AI Overviews.

But. This appears to be a major shift in how Google is synthesizing information via its AI-generated answers. The change could pose new visibility and attribution (and even more rank tracking) challenges. Other ongoing challenges:

The big picture. Google’s John Mueller confirmed that Google AI Overviews are impacted by core updates. BrightEdge’s latest finding seems to be further confirmation of that. Mueller said last August:

  • “These are a part of search, and core updates affect search, so yes.”

What to do? Here are two areas to focus on, according to Jim Yu, founder and executive chair of BrightEdge:

  • Create complementary content that answers the next logical question(s). Your content addressing follow-up questions now has better chances of being cited even if it doesn’t rank in the top 10 regular results.
  • Don’t choose between ranking well or appearing in AI Overviews – aim for both. Track your presence in both areas to get a complete picture of your search visibility.

What’s next. We will continue to watch how Google’s AI Overviews and core updates impact organic traffic (that “necessary evil” which makes it possible for websites to exist and Google to have all the fresh, helpful content it needs).

Dig deeper. Google AI Overviews spiked during March 2025 core update

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How to track visibility across AI platforms

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How to track visibility across AI platforms

AI has changed how people search – and what it means to be “visible” in results. 

Links and rankings still matter, but they’re no longer the full picture.

Now, it’s about mentions, citations, and whether your brand even shows up in the conversation.

Most SEO tools haven’t caught up. This makes tracking that kind of visibility hard – but not impossible. 

Here’s how to rethink visibility in the age of AI.

Why tracking AI visibility is so tricky

Remember when SEO was (relatively) simple? 

People typed in short phrases like:

  • “Best project management tool or SEO tips 2020.” 

You knew how they searched, what they were probably looking for, and how to optimize for it.

Fast-forward to today, and that same user might type: 

  • “Act as a SaaS expert and give me the top 3 project management tools for remote teams with a $50/month budget.”

Welcome to the era of conversational search – where queries sound more like DMs to a colleague than keyword strings. 

Tools like ChatGPT, Gemini, Perplexity, and Claude have normalized full-sentence prompts and pushed search behavior into a new territory. 

We’ve seen glimpses of this shift before with voice search, but AI has made it feel seamless, fast, and dangerously convenient.

That’s great for users – until it’s not. 

AI-generated answers don’t always cite their sources. 

Even when they do, the links might be missing, vague, or tossed in like an afterthought. 

As a result, people often end up back on Google to double-check facts, dig deeper, or figure out if the AI just hallucinated an entire case study. 

Still, many users are happy to take the shortcut – even if it means missing context or nuance – because who wants to read 20 blog posts when ChatGPT gives you an instant TL;DR?

This has created a hybrid search habit: start with AI, fact-check with traditional search, and hope the truth lives somewhere in between. 

Or at least, this is the current situation. There is no guarantee it will be the same in six months. 

But even now, for SEOs, it’s chaos. Visibility is no longer just about ranking in Google’s top 10.

Your brand might be mentioned in a Perplexity answer or your website cited in Google’s AI Overviews. 

AI visibility Tools per Perplexity

And the tools we’ve relied on? 

They’re still stuck in the exact-match keyword era, blissfully unaware of how users are actually searching in these new environments.

The result: SEO teams are flying blind. 

You can’t optimize for what you can’t see – and right now, most of what’s happening in AI-driven search is happening in the dark.

It doesn’t sound great, right?

So, what’s the next move?

We can’t just sit and hope for the best. 

We should start from somewhere. The first step is to understand what matters in the AI era.

Dig deeper: Answer engine optimization: 6 AI models you should optimize for

Capabilities that matter in the AI era

When it comes to tracking AI visibility, your needs will depend on your business size, market focus, and available resources. 

A small team may get by with basic tracking or even manual checks (something we have tried and I won’t recommend). 

But if you’re operating at mid-size or enterprise level – especially in a competitive niche – you’ll need more advanced features to get real value.

Here’s a checklist of potential capabilities to look for when evaluating tools or building a solution in-house.

Custom prompt tracking

You should be able to import your prompts, not just rely on a default list. 

Without this, you’re measuring performance on queries your customers may never actually use. 

AI tools are smart, but your team knows the audience better. 

Multi-country and language support

AI answers can vary widely by region and language. 

If you work on a website with multiple languages without localization, your visibility data might be incomplete or even wrong. 

For example, when you search in English, results in the U.S. and the UK might be completely different.

Cross-platform tracking

Your audience doesn’t live on one AI tool. A proper solution should cover ChatGPT, Gemini, Perplexity, and others. 

Otherwise, you’re only seeing part of the picture. 

Especially if you are a B2B business, some of your potential customers might be already “married” to Microsoft’s or Google’s ecosystem and unwilling to pay for another platform.

Competitor identification

You need the ability to set your known competitors and discover others based on how often they’re mentioned in the answers to the prompts. 

If you miss this, you might not realize who’s gaining ground.

Historical data access

AI results change fast. 

You’re not the only one optimizing your website – your competition is not sleeping. 

Tracking historical performance is essential for spotting trends. No history means no real benchmarking.

Topic and platform breakdowns

Not all mentions are equal. 

You should be able to slice your visibility data by topic, category, or platform. Without this, your reporting stays surface-level.

Exportable answer sets

Make sure you can export the full AI responses tied to your prompts. 

This is critical for internal analysis, validation, and documentation. If you can’t export it, you don’t own it.

Visual dashboards

To make sense of your data and communicate it effectively, you’ll need clear visualization by time, prompt, platform, or topic. 

Otherwise, you’re stuck sifting through raw tables and spreadsheets.

Most tools on the market don’t do all of this perfectly, or if they promise that they can do it – their features come with a high price. 

Unfortunately, building something in-house also takes time and technical expertise. 

The key here is to prioritize based on your team’s goals – whether that’s: 

  • Improving brand presence.
  • Monitoring competitors.
  • Understanding how AI tools are shaping the customer journey. 

Also, be mindful of your own resources

Buying a tracking tool won’t increase your capacity for optimizations, and it will just show you partly the right path.

Once you’ve defined the right capabilities, the next step is knowing what to actually measure.

Dig deeper: AI optimization – How to optimize your content for AI search and agents

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What to measure when rankings don’t matter

In AI-driven search, you’re no longer measuring rankings or CTRs – you’re measuring brand exposure. 

Traditional SEO metrics still matter, but they won’t tell you how often your brand is mentioned or cited in AI-generated answers.

Many of the metrics SEOs now need to track look more like PR KPIs: 

  • Mentions.
  • Citations.
  • Share of voice. 

Visibility is less about position and more about presence – and whether you’re being referenced as an authority.

Here’s a list of metrics that can help you understand and track your AI visibility. 

You likely won’t need (or be able) to track all of them – especially early on. 

However, knowing what’s possible can help you prioritize based on your goals and resources.

Brand mentions

The number of times your brand or the brand of your competitors is referenced in AI-generated responses, regardless of whether a link is included.

  • Why it matters: Mentions are the new impressions – a signal of awareness and authority. If your competitors are mentioned more often, you’re losing visibility at the top of the funnel.

Citations (linked references)

The number of times your website and the websites of your competitors are actually linked in AI answers.

  • Why it matters: Mentions are good, but links are better. They offer validation and can drive traffic (depending on how the platform displays links). Tracking citations helps identify which content AI models consider authoritative.

Prompt-triggered visibility

Which prompts lead to your brand being mentioned or cited? 

Which prompts trigger the same for your competitor?

  • Why it matters: It helps you understand the user intent that surfaces your brand. This is especially valuable for optimizing messaging and identifying new positioning angles.

Context of mentions

Are you listed as the top recommendation? One of 10 options? 

Are you described positively, neutrally, or vaguely?

  • Why it matters: The quality of the mention shapes user perception. Being “mentioned” isn’t always a win if you’re buried in a list or framed as a secondary option.

Share of voice (SOV)

What percentage of relevant AI answers include your brand vs. competitors?

  • Why it matters: SOV gives you a benchmark to measure your presence relative to others in your category. It’s useful for spotting gains and losses in competitive positioning.

Dig deeper: How to monitor brand visibility across AI search channels

Link destination and depth

Are the links going to your homepage, product pages, blog posts, or support content?

  • Why it matters: Shows which content is earning trust – and what type of pages you should prioritize to increase citations.

Visibility over time

Mentions and citations aren’t static. You need to track changes over time to understand trends.

  • Why it matters: It helps you measure the impact of SEO and content work, PR activity, or product updates on your AI presence.

Platform-specific performance

How does your brand visibility compare across different tools – ChatGPT, Gemini, Perplexity, etc.?

  • Why it matters: AI models pull from different data sources and respond differently to prompts. Tracking platform-specific visibility can help prioritize where to focus next.

Not every team needs to track all of these, and most tools don’t cover all of them. 

Start with the metrics that align more closely with your goals and upgrade when needed.

And now, for the fun part: finding tools that can track these metrics.

Dig deeper: Your 2025 playbook for AI-powered cross-channel brand visibility

Where to start with AI visibility tools

The good news is that the landscape of AI visibility tools is evolving rapidly. 

The bad news is that most platforms currently don’t do it all. 

Most tools are still maturing and focusing on specific aspects of the visibility puzzle, such as just one or two of the main AI platforms. 

That makes tool selection less about finding “the best” solution and more about choosing the right fit for your needs and resources.

Here are a few tools currently on the radar of SEO teams exploring AI visibility:

  • Profound: Tracks brand visibility across AI platforms like Perplexity and ChatGPT.
  • Peec AI: Designed for prompt monitoring, brand detection, benchmarking, and historical trendline.
  • Otterly: Offers prompt research, similar to the keyword research process, and tracking of selected prompts.
  • Goodie: Combines SEO data with generative AI monitoring across different models.
  • Adsmurai: Originally ad-focused, now expanding into AI visibility and performance insights.
  • RankRaven: Built for tracking brand mentions and share of voice in AI-generated answers.
  • seoClarity: The enterprise suite now offers tools to monitor visibility in AI-driven search results.

Many others are emerging – and more are launching every month. 

Some tools may eventually cover everything you need, but the price quickly becomes a factor. 

The reason is simple. For most SEO teams, this means adding yet another platform to an already crowded stack. 

Something that rarely excites stakeholders, whether you’re in-house or agency-side.

Building your own system is also an option – and it might seem cost-effective on paper. 

However, maintaining a reliable AI tracking setup requires engineering time, constant testing, and a high tolerance for platform changes. 

Depending on your scale, it may cost more in time than it saves in budget.

Some teams may end up using a combination of tools:

  • One external tool for broad coverage.
  • One internal for deeper tracking.

Whatever direction you choose, set aside time to explore, test, and watch demos. 

Most of these platforms are still evolving, and what works for your team today might need rethinking in six months. 

A flexible mindset and a willingness to experiment are just as important as the tools themselves.

Tracking AI visibility in a changing search landscape

Tracking AI visibility isn’t about figuring it all out today – it’s about laying the groundwork. 

  • Define the signals that matter.
  • Pick the tools that fit.
  • Be ready to pivot as the landscape changes.

This is an exciting time to rethink what visibility means. Take the opportunity to think outside of the box and experiment. 

Dig deeper: 6 easy ways to adapt your SEO strategy for stronger AI visibility

Read more at Read More