Category: SEO

AI Search Visibility: How Brands Get Used and Cited

I’m seeing traditional Google rankings deliver less predictable value than they once did. Ads, AI Overviews, and other search engine results page features are pushing organic links farther down the page, which means visibility no longer depends only on where a brand ranks in the classic blue-link results.

As search keeps shifting, I believe brands need to ask a more practical question: how do I make sure my brand is represented accurately inside AI-powered answers?

The more I understand how AI engines use brand information and when they cite it, the easier it becomes to build a real AI visibility strategy. This moves the conversation beyond whether an AI model “knows” a brand and toward how that brand can earn presence, trust, and discoverability in AI search.

The click economy is shrinking

I think most brands should start learning AI search and building an AI SEO strategy now. A full shift from organic search to AI search may still be years away, but the direction is clear enough that waiting creates risk.

Google is already leaning hard into AI search. In an April article from The Verge, CEO Sundar Pichai said that search had a strong quarter, with AI experiences driving usage, queries reaching an all-time high, and revenue growing 19%.

Users are changing their behavior too. A Pew Research study found that when people see an AI-powered summary in search results, they click a blue link only 8% of the time. When no AI summary appears, that click rate rises to 15%.

AI search traffic may still be smaller than organic traffic, but I would not dismiss it. According to Similarweb, AI traffic converted at 11.4%, compared with 5.3% for organic search traffic. That makes AI visibility worth tracking even before it becomes the dominant traffic source.

How I separate AI usage from AI citation

I think about brand presence in AI systems in two main ways: usage and citation.

Usage happens when an AI engine ingests information about a brand and draws on that information when answering a query. In some ways, this reminds me of how Google traditionally indexed pages before ranking and serving them in search results.

When an AI engine uses brand content, it may mention the brand without linking to it. Even an unlinked mention can matter because it can create discovery, influence perception, and prompt users to search for the brand directly.

Ahrefs data shows most Google AI Overview citations still come from high-ranking organic pages, with 76.10% in the top 10 and a smaller share outside the top 100.

Citation is different. A citation happens when an AI engine directly references a brand as a source of information. That reference might be a link to a web page, a social profile, or even a clickable phone link that lets someone contact the business.

Within OpenAI, usage and citation appear to depend on separate technical systems. As OpenAI’s documentation explains, OAI-SearchBot and GPTBot are deployed separately among four distinct user agents. Other AI systems have their own controls, but the same broader distinction still applies.

Why citations do not tell the whole story

I do not see citations as the full AI visibility picture. AI engines often answer questions directly without citing web sources, and this pattern is not entirely new. Before AI Overviews, Google was already moving in that direction with featured snippets.

Ahrefs found that ChatGPT retrieves almost the exact same number of cited and uncited URLs to generate an average response: about 16.57 cited URLs and 16.58 uncited URLs. But Reddit made up 67.8% of uncited URLs, which means comparing cited and uncited URLs is often really a comparison between search results and Reddit API output.

That matters because AI systems are not neutral in the uncited information they surface. Some platforms and websites are simply more influential than others. If I try to push a brand into AI answers without understanding where the model gets its information, I am working at a disadvantage.

How I would improve brand usage and citation

I would start by tracking the brand’s current AI visibility and monitoring progress over time. That means running a representative set of prompts through an AI visibility platform, reviewing the sources that get cited, and asking what those sources reveal about the model’s preferences.

There are already many AI citation tracking tools available, and established platforms like Semrush and Ahrefs have added AI tracking features as well. I would choose a tool based on the prompts, markets, and engines that matter most to the brand.

I would also scale tracking and research as much as budget allows. AI prompt tracking often depends on API calls, so it can cost more than traditional rank tracking. Still, the data is usually richer, even when the sample size is smaller.

As long as the prompt sample is broadly representative, most platforms can pull multiple responses and calculate an average. That gives me a more useful view of recurring patterns instead of relying on one-off answers.

A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

I would keep reading studies from AI platforms, SEO vendors, and data providers too. Those reports are valuable because they show which sources AI engines rely on and where brands may have the best chance to appear.

The key is continual monitoring. Over time, I can work to place a brand inside the sources AI engines already trust and use most heavily.

Why I still care about traditional rankings

Yes, I still think traditional search rankings matter, but not for the same reasons they used to. The relationship between organic position and business performance is less direct now, especially as SERP features and AI answers absorb more user attention.

At the same time, Ahrefs research suggests a relationship between AI citations and Google rankings, at least inside Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic results. If AI Overviews become a dominant AI search experience, traditional rankings will still influence visibility.

I also pay attention to content quality. Semrush found that AI engines rarely cite generic content that simply repeats what other sources already say. The content that earns citations usually contributes something distinct.

That fits closely with Google’s helpful content guidance, which rewards original information and useful perspective. In my view, content with trusted data, original insight, and a clear point of view can support both Google rankings and AI citations.

Because many classic SEO tactics can also support AI citations, I would not abandon traditional SEO. I would treat it as part of a broader visibility strategy that now includes AI usage, AI citations, and brand presence across trusted third-party sources.

Where I think AI visibility is heading

Both usage and citation need ongoing tracking and analysis. If I want AI engines to use a brand’s knowledge and content, I need to understand which sources each model relies on and help the brand appear in those places. If I want citations, I need the brand’s content to stay crawlable, rank well, and say something original.

Classic SEO still earns its place because the same work that improves organic visibility can often improve AI visibility too. But returns from traditional rankings are changing, and AI SEO may eventually become the primary discipline. For now, I would keep ranking, start tracking, and build for both usage and citation.

Inspired by this post on Search Engine Land.

July 7, 2026
Google Merchant Listings Gain Powerful Sale Data Updates

I’m seeing Google expand merchant listing structured data with support for sale duration and the Product.category property. The update brings Google Search’s merchant listing structured data closer to the capabilities already available in Google Merchant Center feeds.

Sale duration. Google added a new Sale duration section to its Merchant listing structured data documentation. In that update, Google said the guidance explains how to use the validFrom, validThrough, and priceValidUntil schema.org properties to define the effective date range for sale prices.

I find this useful because Google’s guidance also covers best practices and examples for placing those properties on either Offer or PriceSpecification nodes. Google said the change aligns schema.org usage with the Merchant Center feed attribute sale_price_effective_date, giving merchants clearer instructions for handling sale price timing in structured data.

Google's sale duration guidance shows merchants how to define when a sale price starts and ends in structured data, including Offer and UnitPriceSpecification JSON-LD examples.

Here is the new sale duration section Google added:

Product category. Google also updated the same Merchant listing structured data documentation to include support for the Product.category property.

Google’s merchant listing guidance now shows how product categories can mix custom text labels with Google Product Category codes in structured data.

Google wrote that the documentation now explains how Product.category can be used with both Text and CategoryCode types. According to Google, this aligns with Google Merchant Center feed specifications for the product_type and google_product_category attributes.

From my perspective, this makes the structured data more practical for merchants because it lets them provide both merchant-defined and Google-defined category details directly in schema.org markup. Google said this can enhance product information for Google Search and Shopping.

A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

Here is what Google added for product category support:

Why I care. If I maintain merchant listing structured data for Google, these additions are worth reviewing. Product category support can help Google better understand the products being provided, which may improve how those products match relevant queries.

I also see sale duration support as a practical improvement for planning promotions. When I update merchant listing structured data, I can now define sale price timing more clearly and align that markup more closely with Google Merchant Center feed behavior.

Inspired by this post on Search Engine Land.

July 7, 2026
Conductor MCP Server: Trusted AEO and SEO Data for AI

I use Conductor’s MCP Server to ground the AI tools my team already relies on in verified AEO and SEO intelligence, instead of depending on a stale snapshot of the web.

A bold launch visual introduces an AEO and SEO Intelligence Layer, framing verified search and AI visibility data as a modern layer for marketing teams.

Inspired by this post on Conductor Blog.

July 7, 2026
Prompt-Level AI Visibility: How I Measure What Matters

I do not measure AI search the same way I measure traditional search, because the user journey is no longer built around one query, one ranking page, and one click.

A prospect might ask ChatGPT for the best CRM for manufacturing companies, compare options in Google AI Mode, refine the requirements across several follow-up questions, and build a shortlist without ever visiting a website.

If my company appears in those conversations, I have influenced the buying process. The hard part is proving that influence with a measurement system I can trust.

Prompt-level visibility has become one of the fastest-growing areas of AI search optimization. It is also one of the easiest to misunderstand. I see plenty of promises about complete visibility into AI conversations, but the reality is far more complicated.

Here is how I think about what can be measured today, what cannot be measured reliably, and how I would build useful reporting despite the current limits.

A 5-step framework I use to track AI visibility

1. I accept that AI does not have traditional rankings

The first mistake I avoid is trying to recreate an old SEO ranking report. There is no universal position one inside ChatGPT.

The same prompt can produce different responses depending on conversation history, user location, personalization, follow-up questions, model version, web retrieval availability, and timing.

That means visibility is probabilistic rather than deterministic. Instead of asking, "Do we rank?" I ask, "How often are we included across the conversations that matter?"

That shift changes the entire measurement model.

2. I build a prompt library instead of only a keyword list

Keywords still matter, but I no longer treat them as enough on their own.

Instead of tracking only individual search terms, I build a library of prompts that reflects how real buyers research, compare, validate, and challenge their options.

I usually organize those prompts by intent. Discovery prompts ask for the best platforms in a category. Comparison prompts put vendors side by side. Evaluation prompts focus on specific use cases. Validation prompts ask whether a company is worth the cost. Objection prompts explore disadvantages. Alternative prompts ask what to use instead. Implementation prompts test how difficult a product may be to adopt.

Instead of monitoring 10 keywords, I may monitor 200 to 500 prompts across the full buying journey. That gives me a much more realistic view of AI visibility.

3. I measure prompt clusters, not isolated questions

One prompt rarely tells me enough to make a decision.

For example, "best CRM software" might not mention my company, while "best CRM for manufacturing companies" might. A more specific prompt, such as "CRM for manufacturers with field sales teams," could return a different set of recommendations altogether.

That is why I group similar prompts into clusters. A category cluster might include best project management software, best PM platform, and project management tools. An industry cluster might include best CRM for healthcare, manufacturing, and finance. A feature cluster might include CRM with AI automation, forecasting, or enterprise sales support.

The patterns across those clusters are more reliable than the result from any single prompt.

4. I combine synthetic prompts with real customer questions

This is where measurement becomes more difficult.

Most organizations do not know exactly what customers are typing into AI assistants, so I often start by generating synthetic prompts. That may include expanding keyword research into conversational questions, creating AI-generated prompt variations, and building comparison, objection, and follow-up prompts.

Synthetic prompts are useful because they are repeatable, but I do not treat them as perfect. Generated prompts often sound cleaner and more structured than real user behavior.

A real buyer might ask something much richer, such as: "We are a 250-person SaaS company with a small HR team. We already use Workday but need something better for payroll. Budget is not a huge issue. What would you recommend?"

That is much more useful than a short phrase like "best payroll software."

For the strongest measurement program, I use synthetic prompts for consistent benchmarking and then supplement them with real questions from sales calls, customer interviews, support conversations, community discussions, internal search logs, on-site search, and AI transcripts that customers voluntarily share.

I also assume the prompt library will need to change. Customer language evolves, and the measurement set has to evolve with it.

5. I measure multi-turn conversations

Most AI-assisted buying journeys do not happen in a single prompt. A buyer may start by asking for the best cybersecurity vendors, narrow the list to companies strong in healthcare, ask which ones integrate with CrowdStrike, and then compare pricing.

My company may not appear in the first answer, but it may become highly recommended by the third response.

If I only measure the opening prompt, I miss a large share of meaningful visibility.

That is why I want prompt tracking to evaluate full conversation paths, not just one-shot questions. Multi-turn testing often reveals patterns that single prompts hide.

The AI visibility metrics I care about most

Many traditional SEO metrics do not translate neatly to AI search. Rankings, clicks, and impressions still have value, but they no longer tell the whole story.

I focus on measurements that show whether a brand appears, how it is positioned, and how consistently it is recommended inside AI-generated responses.

Inclusion rate

If I could track only one AI visibility metric, I would start here.

Inclusion rate measures the percentage of tracked prompts where my brand appears in the AI response. If I monitor 500 prompts and my company appears in 185 of them, the inclusion rate is 37%.

That number is useful as a benchmark, but it becomes more valuable when I segment it by buying stage, product category, industry, geography, or AI model. Those slices often reveal opportunities that a single overall average would hide.

Position within the response

Being mentioned is not the same as being recommended.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

I want to know whether my brand appears as the first recommendation, one of the first few options, a late mention, or merely an alternative. If the AI response includes a comparison table, I also want to know where my company appears there.

AI answers do not have traditional rankings, but prominence still matters. A top recommendation is more likely to shape a buyer’s perception than a passing mention several paragraphs later.

Brand framing

Visibility tells me whether my brand is included. Brand framing tells me how it is described.

There is a meaningful difference between an AI system describing a company as "widely considered an enterprise leader" and describing it as "best suited for smaller teams." Both may sound positive, but they position the brand very differently.

I look for recurring themes around strengths, weaknesses, differentiators, pricing, ideal customer profile, and competitive comparisons. Over time, those patterns can expose messaging gaps in my own content or show how the broader web is shaping AI’s understanding of the brand.

Sentiment and confidence

Sentiment is more than a simple positive-or-negative label. I also want to know how confidently the AI system presents my brand.

"Company A is generally considered the strongest option" carries a very different level of conviction than "Company A may be worth considering."

Neither statement is negative, but they do not create the same buyer impression. Tracking confidence, uncertainty, caution, skepticism, and strong endorsement gives me a more nuanced view of how AI systems present the company to prospective customers.

Competitive share of voice

My own visibility is only part of the picture. I also need to know how often competitors appear alongside me or instead of me.

If my inclusion rate stays at 40% month after month, that may look disappointing. But if every major competitor dropped by 20 percentage points after a model update, the story changes.

On the other hand, if one competitor jumps from 35% inclusion to 70% while everyone else stays flat, I would want to investigate what changed.

Competitive share of voice helps me separate category-wide movement from changes that are specific to my brand.

How I view the AI visibility tool landscape

The market for AI visibility platforms has grown quickly. Each product approaches the problem differently, but most are trying to answer the same core questions: does my brand appear, how often does it appear, which AI models include it, which competitors show up, and how is the brand described?

Many platforms now include prompt libraries, competitive benchmarking, citation tracking, answer monitoring, and trend reporting. These features can reduce the manual work required to test hundreds or thousands of prompts on a recurring basis.

Still, I have to be clear about what these tools are and are not measuring.

No tool has access to every AI conversation happening in the wild. Most rely on controlled prompt libraries, repeatable testing environments, or sampled interactions to create a representative view of visibility.

That is useful, but it is not the same as observing every real user interaction.

What I still cannot reliably track

This is the part I do not want to gloss over.

Even though AI measurement is improving quickly, some data is still not observable. I cannot comprehensively track every prompt where my brand appeared, every conversation that influenced a purchase, every recommendation made inside ChatGPT, every citation shown to every individual user, or exactly how personalization changed a response.

I also cannot see every multi-turn conversation across every AI platform or know how often someone acted on an AI recommendation without clicking a link.

The underlying AI platforms do not expose that level of data. If a vendor claims it can see every AI conversation involving my brand, I would ask exactly how that information is being collected.

The practical framework I would build

Rather than chasing perfect attribution, I focus on building a repeatable measurement system that I can track consistently over time.

For visibility, I would track inclusion rate, competitive share of voice, prompt coverage, and model coverage.

For response quality, I would track position within the response, brand framing, sentiment, and message consistency.

For technical signals, I would track citation frequency, content retrieval success, entity consistency, and freshness.

For business outcomes, I would look at AI referral traffic, assisted conversions, branded search lift, direct traffic trends, and pipeline influenced by AI discovery.

No single metric tells the full story. Together, these signals give me a more complete picture of how the brand is showing up and how it is being perceived across AI-assisted research.

The goal is not perfect measurement

Prompt-level visibility is not as mature as keyword tracking became over the past two decades.

Some signals are still emerging. Others remain inaccessible because AI platforms do not expose the underlying data. At the same time, user behavior is changing almost as quickly as the technology itself.

That does not mean measurement is impossible. It means the objective has changed.

Instead of trying to reconstruct every AI conversation, I focus on building a representative prompt library, tracking visibility consistently, benchmarking against competitors, and understanding how my brand is being framed.

Those trends are far more actionable than chasing a level of precision the current ecosystem cannot support.

The organizations making the most progress in AI search are not waiting for perfect attribution. They are establishing baselines, watching for meaningful movement, and adapting as both AI models and user behavior continue to evolve.

Inspired by this post on Search Engine Land.

July 6, 2026
6 SEO Priorities I’m Rethinking for Stronger AI Visibility

I see plenty of overlap between SEO and AEO, but I do not treat them as the same discipline. The SEO playbook that worked reliably in traditional search will not take me as far when the goal is visibility inside AI-generated answers.

So I keep coming back to one practical question: what should I change first?

Instead of revisiting content structure for AI search, I focus on three priorities I believe deserve more attention now and three SEO habits I would intentionally emphasize less.

3 SEO priorities I would emphasize more

Establish brand authority and strong entities

Before an AI system is likely to cite my brand, it needs to understand that the brand exists, what it represents, and why it is credible. Entity recognition has become foundational to AI visibility in a way that traditional search did not always require, even though Google’s Knowledge Graph has been moving in this direction for years. Large language model training data tends to reward brands that show up consistently across trusted platforms.

When I work on this for clients, I pay closer attention to whether brand information is consistent across Wikipedia, LinkedIn, Crunchbase, industry directories, and any other source an LLM might use to understand an entity.

I also think PR and SEO or AEO teams need to work much more closely together. Earned media mentions are no longer just awareness plays; they are entity-building signals.

E-E-A-T was already pushing SEO in this direction, but author entities matter even more in AI search. When bylined experts have their own credible web presence, they strengthen the authority of the content they create.

When I can invest in entity building before scaling content, I usually see stronger AI citation potential because the credibility infrastructure is already in place.

Build topical depth with content clusters

AI systems tend to favor sources that show comprehensive authority on a subject, not just pages that happen to rank for isolated keywords. A thin content footprint is much more vulnerable in AI search than it was in traditional search.

That means I need to move beyond keyword-by-keyword planning and think more seriously about topic ownership. Instead of only asking, “What do we rank for?” I ask, “What topics do I want AI systems to associate this brand with?”

Internal linking becomes more valuable in this environment because it helps signal relationships between related pieces of content. I also treat content audits as a way to find gaps in topical coverage, not just as a way to identify pages with declining traffic.

When I can go deep in a specific niche, I often see content cited across multiple related queries. One well-built content cluster can create visibility far beyond a single keyword target.

Owning the topic cluster around the problem a client’s product solves can position that brand as a trusted resource before a sales conversation even begins. I also hear more often that buyers are finding those brands in LLMs during their research process.

Earn unlinked brand mentions and community presence

LLMs learn from the broader web, not only from pages with backlinks. A mention on Reddit, Quora, a niche forum, or an industry community can matter even when there is no link attached.

I think this is one of the bigger mindset shifts for SEO teams. AI systems look for patterns in what the web says about a brand across many sources, not only what ranks in Google. Owned content alone cannot manufacture that signal.

Trusted third-party communities such as Reddit can carry particular weight because LLMs have been heavily trained on them and often treat that content as a form of authentic user sentiment.

That makes community participation and digital PR increasingly important SEO-adjacent work. I care about whether a brand is being mentioned in the right places, even when the mention does not come with a backlink.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

Monitoring unlinked brand mentions is becoming just as important to me as tracking backlinks. Tools such as Brandwatch and Mention, along with manual Reddit and Quora monitoring, can show where a brand is appearing organically and where it is absent.

I would rather talk with the team about where the brand is being discussed, whether those conversations are accurate, and whether the sentiment is positive than focus only on who is linking to the site.

Brands with an active presence in relevant communities are more likely to surface naturally in conversational, recommendation-style AI answers, including queries such as “What does Reddit think about X?” or “What’s the best Y according to users?”

For challenger brands trying to enter a category, earned community mentions can build AI-visible authority faster than traditional link building, which usually takes longer to accumulate.

B2C brands can benefit especially from genuine community presence because consumer AI queries often lean toward social proof and peer recommendations rather than formal editorial sources.

3 SEO priorities I would emphasize less

Chasing high-volume keywords with thin content

AI Overviews can absorb the click for broad informational queries. Ranking No. 1 for a head term increasingly means I may have invested a lot of effort into winning traffic that never actually reaches the site.

Search volume alone is no longer a reliable proxy for opportunity. A query with 50,000 monthly searches that triggers an AI Overview may send less traffic than a query with 2,000 searches that still requires a click.

I would rather create specific, authoritative content that answers a narrower question better than anything else available. I focus more on queries where the searcher needs to act, compare options, or access something only the site can provide. Those needs are harder for AI to fully resolve.

Keyword traffic potential is no longer the first metric I trust. I first ask whether someone will still need to click after AI answers the query. If the answer is no, the opportunity is not what it used to be.

Pursuing exact-match and manipulative link building

Low-quality link volume does not do much for AI citation likelihood. LLMs care more about the authority and relevance of the sources mentioning or citing a brand than raw link counts. The publications that matter for AI visibility usually have real editorial standards, and those are much harder to game.

I would focus on earning coverage and links from the kinds of sources AI systems are more likely to draw from, including trade publications, respected industry blogs, and academic-adjacent resources. The better long-term move is to build content worth referencing, not outreach that exists only to extract a link.

A hundred low-quality links will not necessarily get a brand cited in ChatGPT. Five links from publications the target audience actually reads might matter much more. Source authority is the metric I would watch more closely than link volume.

Optimizing for CTR on standard blue links

A growing share of informational queries are resolved without a click. That makes title tag and meta description optimization for CTR less valuable on queries dominated by AI Overviews. I would rather spend that time trying to become the cited source inside the AI answer.

For queries where clicks still happen, I put more weight on transactional and navigational intent because those searches are more resistant to full AI resolution.

CTR optimization assumes a searcher is choosing between blue links. For more queries now, that choice is shaped before the traditional results even become the focus. The opportunity has moved higher on the page.

The payoff is not always more traffic

There are more shifts I could make, but these are the first ones I would prioritize. I may lose some volume in traditional SEO metrics such as impressions and clicks, but that should matter less if the downstream business metrics remain strong. In AI search, I care more about conversions, pipeline, and revenue than vanity traffic. That is the tradeoff I believe this new search environment increasingly rewards.

Inspired by this post on Search Engine Land.

July 6, 2026
ChatGPT Owns AI Referrals: What 6.77M Sessions Show

A year ago, I watched the industry place its bets on which AI platform would own discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. In the data I’m seeing now, neither bet has really paid off.

Previsible (disclosure: I’m its CPO and co-founder) just published its third AI Traffic Study, based on 6.77 million LLM-driven sessions. What stands out to me is the level of consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026, and 92.4% of that traffic came from one platform.

The plateau was a pause

In mid-2025, it looked like AI traffic might be topping out in some sectors. I don’t think that was the real story.

Sessions climbed from 65,249 in November 2024 to 396,278 by August 2025. Then they dropped sharply in November 2025 before reaching new highs of 428,203 in February 2026 and 644,478 in May.

That November dip deserves context.

Sessions fell 50% in a single month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms were mostly steady. To me, that points to a model-related change. We’ve already seen small product shifts create major swings in referral traffic, including last fall, when many sites lost half their ChatGPT traffic because the model began favoring Wikipedia and Reddit. By December, sessions had recovered to 442,609.

The lesson I take from this is simple: one vendor’s product decision can cut your AI traffic in half overnight. I would plan for that volatility instead of treating AI referrals as a stable channel.

Consolidation, not competition

When we last published in December 2025, ChatGPT held about 84% share. Perplexity followed at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field had moved even more decisively toward the leader.

Across the full dataset, ChatGPT now commands 92.4% of trackable LLM referral traffic. It grew 12.8x over 19 months, with no clear sign of slowing. It is the only LLM sending meaningful referral volume at scale, which means I would not talk about “AI visibility” without putting ChatGPT first.

There is one important caveat. This study measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined. But that operates under a different measurement model, so it is not included here.

The challengers flipped

The surprise is not that ChatGPT is on top. What I find more interesting is the movement beneath it.

Claude

Claude grew 64x, moving from 133 sessions in November 2024 to 8,528 in May 2026. It overtook Perplexity in March 2026 for the first time, and it stayed ahead.

Claude was mostly flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage many people expected Copilot to win may be materializing for Claude instead.

If your audience includes technical buyers, developers, or professional services, I would treat Claude visibility as material now. The early positioning window is still open, but it may not stay that way for long.

Gemini

Gemini is the quiet number two in this dataset. It delivered 3.2x growth with very little volatility. Because Gemini is tied into Workspace and Android, I suspect referral numbers undercount its real discovery footprint.

Perplexity & Copilot

Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot fell even harder, dropping 96% from its August 2025 peak, from 8,651 sessions to 339.

I no longer see either platform as a strong traffic-acquisition growth bet. Both are shifting toward experiences that keep users inside their own environments, including browsers, agents, and modes where they do not need to send traffic out at all.

Where LLMs send users, and why it should change your roadmap

The most actionable finding in the study is not market share. It is where LLMs send people after they decide a site is worth visiting.

ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

My read is that the model trusts the domain but cannot always identify the exact right page. So it sends users to the site’s search box and lets them navigate from there. Because this pattern holds across verticals and time periods, I see it as structural to retrieval-augmented generation rather than a temporary quirk.

That changes the role of internal search. The model already did the hard work of choosing your domain. Now your internal search experience decides whether that high-intent visit converts or bounces.

For most sites, internal search is still treated like a neglected navigation feature. I think it needs to be treated as an acquisition surface.

The vertical-level data tells several different stories. SaaS traffic lands on search pages 34.6% of the time. Publisher traffic lands on news pages 54% of the time, but against 120+ million organic sessions, publisher penetration is only 0.11%. Publishers create the content LLMs cite, yet they capture almost none of the resulting traffic.

Ecommerce traffic tends to land on product pages, often with purchase intent already formed. Education traffic lands directly on course pages 52% of the time, bypassing marketing content. Health traffic lands on About pages 42.1% of the time, suggesting users are evaluating the source before trusting the content. Legal traffic spreads across blog, about, contact, and location pages, which reflects the full evaluation arc.

The platforms have distinct behaviors, too. ChatGPT and Gemini act more like search-pattern models: they show domain trust but page-level uncertainty. Perplexity and Claude behave more like content-selection models, picking specific pages and over-indexing on long-form content.

If your strategy depends on editorial content driving qualified traffic, I would give Perplexity and Claude more attention than their raw share suggests.

What I would do now

First, I would optimize for ChatGPT before anything else and expand to other platforms only when the volume justifies the work. ChatGPT is where the measurable standalone LLM referral traffic is concentrated.

Second, I would monitor Claude closely. It overtook Perplexity in March 2026, and early visibility advantages can compound quickly when a platform is still forming its citation and recommendation patterns.

Third, I would treat product pages as AI entry points. Product pages capture 43% of ecommerce LLM traffic, which makes structured, comparable product data a discoverability requirement rather than a nice-to-have.

Fourth, I would make pricing machine-readable wherever possible. “Contact us for pricing” gives AI systems very little to summarize, compare, or recommend.

Fifth, I would prioritize internal search. It is not just a navigation feature anymore. For AI-referred users, it may be the first real conversion point.

Finally, I would track AI traffic by page type instead of relying only on site-wide averages. Your overall AI traffic number can hide where the real concentration is. A pricing page, for example, might run 3x your site-wide penetration.

The next question I want answered is conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce?

We built this dataset to answer that. If the last 19 months are any guide, I expect the answers to change faster than most teams are prepared for.

About the data

This analysis includes 166 GA4 properties from November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so I’m looking at behavioral change rather than sample expansion.

The report

You can find the full report at previsible.io.

Inspired by this post on Search Engine Land.

July 6, 2026
Build Smarter Site Architecture for SEO, AI, and Users

I see advanced architecture as much more than a technical framework now. It shapes whether my content can be found, understood, and surfaced by search engines and AI systems.

That is why I am paying close attention to the next SMX Now on July 15, featuring Shari Thurow, co-founder, information scientist, and search director at the Information Architecture Gateway. She will explain how advanced architecture really works and where many AI, SEO, and site development workflows tend to fall short.

In this session, I will explore a five-phase framework Thurow has tested through decades of client work with organizations including Microsoft, Google Cloud, Abbott Laboratories, CVS Pharmacy, WebMD, Sony Music, the Library of Congress, Best Buy, and Merriam-Webster. I will learn how architecture decisions influence labeling systems, wayfinding networks, taxonomy, wireframes, and AI access to valuable content.

I also expect the session to challenge some long-standing assumptions, including the three-click rule, the idea that taxonomy is only a hierarchy, and the belief that AI can create effective wireframes without a deeper architectural model behind them.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

By the end, I will have a practical framework for building sites that communicate more clearly with users, search engines, and human-centered AI systems.

I’m saving my spot

Inspired by this post on Search Engine Land.

July 6, 2026
Why I Think Meta AI Is Search’s Sleeping Giant Now

I do not think enough people are treating Meta AI as a serious AI search contender.

In SEO circles, I hear plenty about Google AI Mode, ChatGPT, Claude, Gemini, Perplexity, RAG, and every new answer engine worth testing. Those conversations matter. But I think Meta AI already has something most AI companies would spend years and billions trying to build: massive distribution.

By May 2025, Meta AI had reached one billion monthly active users across Meta’s apps, according to Mark Zuckerberg.

Zuckerberg has also made the direction clear. He wants Meta AI to become a leading personal AI, shaped around personalization, voice conversations, and entertainment, with monetization through paid recommendations or subscriptions already being considered.

That is why I think Meta AI is becoming one of the most important AI search contenders to watch.

Meta’s Advantage Is Distribution

I think the AI search debate spends too much time on model quality and channel ownership. Which tool is smarter? Which answer engine cites better? Is this just SEO with a new label?

Those questions matter, but distribution matters more than the search industry often wants to admit.

Meta reported 3.56 billion family daily active people across its apps in March. In that same quarter, revenue reached $56.31 billion, up 33% year over year.

WhatsApp passed 3 billion monthly users in 2025. Instagram reached 3 billion monthly active users in September 2025. Threads reached 500 million monthly active users in June.

I know Facebook is not the cool platform anymore. The metaverse stumbled. Threads can still feel like a corporate response to Elon Musk running, or ruining, the artist formerly known as Twitter.

But none of that changes the important point. Meta can put AI inside the apps where people already spend their time. In doing that, it can bring search-like behavior directly into the places where discovery already happens.

I think that could push public AI adoption faster than almost anything else in the market.

The First Search Is The Search That Matters

Google’s historic power has always rested on a simple habit. When people wanted to know something, compare options, buy a product, find a local business, or settle an argument, they started with Google.

That starting point became the most valuable real estate on the internet.

AI search changes where that starting point can live. If someone sees a product on Instagram, they do not have to leave the app and search Google. They can ask Meta AI whether the product is any good, what alternatives exist, whether the brand is trustworthy, or where they can buy it.

If a WhatsApp group is planning a weekend away, they do not need to switch to Google to compare hotels, restaurants, venues, or train times. Meta AI can sit inside the conversation at the exact moment intent appears.

If someone is scrolling through a Facebook thread full of local recommendations, they can ask Meta AI to summarize what people are saying across Groups, Reels, and public posts.

That is not traditional SEO. I see it as search behavior being absorbed into social platforms.

The strategic question is no longer only, “Who ranks?” I think the better question is, “Where does the question begin?”

Meta AI Is More Than Another Chatbot

I think search marketers often approach AI through too narrow a lens. We find the chatbot, test a few brand queries, check which sources get cited, and decide we understand the platform.

That is a mistake.

Meta AI is becoming a layer across feeds, chats, search, content creation, recommendations, smart glasses, and social discovery. Meta says it is available across Facebook, Instagram, WhatsApp, and Messenger, including in feeds, chats, and search, giving users real-time information without leaving the app. The use cases include restaurant recommendations, travel planning, study help, and shopping inspiration.

The standalone Meta AI app, launched in 2025, was designed around a more personal AI experience. Meta says it can use information people have chosen to share across Meta products, along with profile data and content engagement, to deliver more relevant answers in supported markets.

I can see where this is heading. Meta AI could become the free AI tool that everyday consumers use without thinking much about it.

How Meta AI Could Become Consumer AI

ChatGPT and Claude still feel like work tools to me. They are excellent tools, but they are tools people deliberately open because they have decided to do something.

Meta AI feels more like consumer AI. It is messier, more visual, more embedded, and less like launching a productivity suite. It feels more like finding an answer while doing what you were already doing.

For many people outside tech, opening ChatGPT still feels like an intentional act. Asking a question inside WhatsApp or Instagram can feel almost frictionless.

That is Meta’s advantage. It does not have to convince people to adopt AI from scratch. It can fold AI into existing habits.

This is where it gets interesting. Meta AI is also a playground, and Meta gets to watch how people actually use it.

I can imagine a 65-year-old grandmother using it to animate family photos and share them in a WhatsApp group.

I can imagine a dog groomer using it to create short videos of clients’ pets and post them on Instagram.

When AI becomes mainstream and easy to use, people will use it where they can reach other people. That gives Meta a powerful feedback loop. The more people play with Meta AI, the more Meta learns, improves, and adds features that fit real consumer behavior.

AI Becomes Social, Visual, And Shoppable

Then there is Meta AI Studio.

Users can create AI characters built around their interests, work from templates, or start from scratch. They can build assistants for advice, captions, entertainment, and creator interactions.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

Then there is Vibes. In September 2025, Meta introduced Vibes as a feed inside the Meta AI app and on Meta AI, where users can create, remix, and share short-form AI-generated videos, then distribute them through DMs, Instagram, Facebook Stories, and Reels.

I will be honest: parts of this feel strange. Generative AI video on social platforms is a messy mix of creativity, novelty, nonsense, and low-quality output. But early weirdness is not the same as strategic irrelevance.

I never expected AI to arrive as one perfect super-app that everyone understood immediately. Meta is putting new formats into users’ hands, watching what people do with them, and reshaping the product around that behavior.

The Ad Machine Makes This A Google Problem

Forecasts suggest Meta will reach $243.46 billion in net worldwide ad revenue in 2026, putting it ahead of Google at $239.54 billion. The same forecast has Meta capturing 26.8% of worldwide digital ad spend, compared with Google’s 26.4%.

I think those numbers should get Google’s attention.

If AI answers are monetized through paid recommendations, sponsored answers, shopping suggestions, or conversational ad units, the commercial value collects around the platform that owns the query. That platform does not always have to be the one with the best model.

Meta has the audience, the ad graph, creator relationships, commerce signals, and behavioral data built from years of social, messaging, and content engagement. It can promote Meta AI inside its own products to billions of existing users.

Google still has search intent, which is enormously powerful. But Meta has attention, habit, and context. Google is where people go when they have decided to search. Meta is where many people already are.

Why “It’s Just SEO” Misses The Point

The AI optimization debate keeps collapsing into the same comforting line: it is just SEO.

Sometimes, I agree. Technical hygiene, crawlable content, authoritative pages, clear entities, strong brand signals, helpful content, and consistent information still matter.

But I think the harder question is this: how exactly do you optimize for Meta AI?

Facebook AI Mode makes the challenge obvious. In June, Meta introduced AI Mode as a Facebook search tab that uses Meta AI to surface answers rooted in public culture, opinions, and recommendations shared across Meta’s apps, rather than a traditional list of links. It draws on what people are posting publicly in Groups and Reels to provide perspectives instead of standard search results.

That is a fundamentally different environment. If Meta AI pulls from public posts, Groups, Reels, creator content, user engagement, web information, social recommendations, product content, and eventually paid data, the standard SEO playbook is not enough.

Your website may still matter. Your public social content may matter, too. Your creator strategy may matter. Your product feed may matter. Your reviews may matter. I think the point is clear: visibility is getting more complicated.

Nobody can honestly say they know exactly how all of this works yet. Anyone who claims total certainty is probably selling a dashboard and a dream.

The honest answer is frustrating: I do not think we know enough yet. But that is not a reason to ignore Meta AI.

Google Is Being Attacked From Every Angle

Google is still Google. I do not want to overstate the case. It remains central to search, commerce, publishing, advertising, and the open web.

But Google is being pushed from every direction at once. ChatGPT is pressuring answers. Perplexity is pressuring research. Amazon is pressuring product search. TikTok and Instagram are pressuring discovery. Regulators are pressuring market power. Publishers are challenging AI content extraction. Meta is pressuring attention, ads, and AI-assisted discovery.

In the UK, the Competition and Markets Authority imposed new conduct requirements on Google Search in June. Publishers will be able to opt out of having their content used to power AI features in Google Search, including AI Overviews. Google is also required to properly attribute publisher content with clear links in AI-generated results.

I think this matters because AI search is not just another product feature. It changes the value exchange between users, publishers, platforms, and advertisers. While Google works through that challenge, Meta is quietly building AI into social behavior.

What I Think Brands And SEOs Should Do Now

I would not panic. Panic is rarely a strategy, even if it shows up in plenty of marketing meetings. But I would start testing now.

I would run brand, category, product, local, and comparison queries in Meta AI. I would test Facebook, Instagram, WhatsApp, and the standalone app wherever possible, then compare the results with Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude.

I would track whether my brand appears, whether answers cite or link to me, and whether public Meta content seems to shape responses. I would look closely at Facebook Groups, Reels, creator posts, Instagram content, product mentions, and recommendation language.

If discovery moves into Meta’s AI layer, I want to understand what my brand needs in order to be visible there.

That might mean stronger public social content, clearer product information across Meta surfaces, creator partnerships, better community management, more consistent entity signals, or paid social tests designed around AI visibility. It might also mean none of those things yet.

Either way, I would rather have data than keep repeating “it’s just SEO” while the market reorganizes itself.

The Sleeping Giant

I do not think Meta AI has to beat Google at Google’s own version of search. It does not need to.

It only needs to absorb enough search behavior into the places where people already spend their time.

It needs to become the casual AI layer for people who may never deliberately open ChatGPT.

It needs to make product discovery, recommendations, local advice, content creation, and shopping assistance feel native inside social apps.

That is a serious threat. Meta AI may feel clunky right now, but so did much of the early web.

I think the search industry should stop asking whether Meta AI looks like search. The better question is whether users care.

If people start asking Meta before they ask Google, the game changes. That is how sleeping giants wake up.

Inspired by this post on Search Engine Land.

July 6, 2026
Traffic Think Tank Joins Search Engine Land Community

[Boston, MA, July 6, 2026] — I am sharing that Traffic Think Tank has officially joined the Search Engine Land family, creating more opportunities for search marketers like us to connect, collaborate, and keep learning through one of the industry’s most established professional communities.

I want members to know that Traffic Think Tank will continue operating as a private Slack community. It will remain a trusted place where we can exchange ideas, validate strategies, solve real marketing challenges, and stay current on search engine optimization, paid media, artificial intelligence, and related marketing topics.

As part of this relationship, I see Search Engine Land supporting the community’s continued growth by increasing visibility across its editorial and marketing channels while preserving the collaborative environment members already value.

“For years, Search Engine Land has represented the marketing community through its contributor network in a way few other sites have,” said Kyle Morley, Head of Sales and Marketing at Third Door Media, parent to Search Engine Land. “Launching a community like Traffic Think Tank feels like a natural extension of our identity, and I’m thrilled we now have more opportunity to connect with marketers in our space.”

I am also noting that David Broderick has been appointed Lead Community Manager and will oversee the day-to-day community experience. He will be supported by Liz Dougherty, who will take an active role in encouraging member engagement and helping guide the community’s continued growth.

Beyond ongoing peer-to-peer discussions, I expect members to benefit from expanded community programming and discussions, increased visibility through Search Engine Land and Third Door Media channels, exclusive discounts on Search Marketing Expo events and training, and new opportunities to connect with search marketers across the industry.

For me, Traffic Think Tank fits naturally with Search Engine Land’s mission of helping marketers stay informed and succeed in a rapidly evolving search landscape. Together, the publication and community give us access to trusted journalism, practical education, live events, and an active peer network for ongoing professional development.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

If you are a search marketer interested in joining the community, I recommend learning more at https://searchengineland.com/trafficthinktank.

About Search Engine Land

I view Search Engine Land as a leading publication for news, insights, and education covering search engine optimization, paid media, artificial intelligence, and digital marketing. Through editorial coverage, events, training, and professional resources, Search Engine Land helps marketers stay ahead of industry change.

About Traffic Think Tank

I see Traffic Think Tank as a private community for search marketers that connects professionals through expert discussions, peer collaboration, and practical knowledge sharing. Members use the community to exchange ideas, solve challenges, validate strategies, and stay current on what’s working across search engine optimization, paid media, and artificial intelligence.

Inspired by this post on Search Engine Land.

July 6, 2026
Google Search Console Indexing Report Finally Updates

I can finally say the page indexing report inside Google Search Console has been updated after a frustrating three-week delay. Instead of showing data stuck on June 11, 2026, the report is now displaying data through June 29, 2026.

The delay. I previously noted that the page indexing report had been frozen at June 11, which made it much harder to understand what Google was seeing across a site.

Now, as of Friday, July 3, the report is showing much fresher data, with updates running through June 29.

Page indexing report. I use this report to see which pages Google can find and index on a website. It also helps surface indexing issues Google may have run into while crawling the site.

I can access the report directly in Search Console over here, or by opening the Indexing section and selecting Pages.

The report shows indexed pages in green and not indexed pages in gray. I can also overlay impressions on the chart, then review the listed reasons explaining why certain pages on a website are not being indexed.

For more details on how the page indexing report works, I would refer to Google’s help document.

Why I care. If I was trying to diagnose why Google had not indexed specific pages over the past couple of weeks, the delayed report left me with limited visibility.

Now that the data has finally been refreshed through June 29, I can dig back into the indexing report, review the latest issues, and decide what needs attention next.

Inspired by this post on Search Engine Land.

July 3, 2026