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Category: News

How Brave Search Rankings Boost Claude’s AI Visibility

I’ve discovered something intriguing about Claude’s reliance on Brave Search rankings. Based on insights shared by Jonathan Clark during a Profound session on Zero Click, it seems that Claude frequently taps into Brave’s search results, particularly when dealing with recency, ranking, or comparison prompts.
Clark, who is the managing partner at Moving Traffic Media, emphasized a key point from the session: Claude doesn’t rearrange search results but instead incorporates Brave’s top 10 search results directly into its answers.
Claude’s web searches are selective. In fact, I learned that Claude uses web search in only 36.6% of cases compared to about 90% for ChatGPT, as per Clark’s observation.
Claude is triggered to search most often by prompts that signal current trends, rankings, location, or comparisons. For example, queries like “best XYZ” caused a search 81% of the time. Ranking focus prompts had a search rate of 67%.
Location prompts initiated searches 55% of the time, while comparison prompts such as “X vs. Y” led to searches 51% of the time.
Brave rankings are crucial. Another interesting point is that Claude’s answers only matched ChatGPT’s citations in 8% of cases for the same queries, according to Clark.
Claude’s results showed a 64% overlap with Google rankings. This indicates that Google-focused SEO strategies might be more effective for Claude than efforts targeted at boosting visibility in ChatGPT.
The analysis also highlights the significance of tracking Brave search rankings. Clark mentioned that Claude relies on Brave, and achieving good rankings in Brave provides us with measurable insights.
Memory in prompts. I found it interesting that prompts like “how does,” “what is,” and “steps to” are less likely to prompt Claude to conduct a web search. Without searching, Claude cannot cite online sources.
According to Clark, Claude searches most often for prompts with keywords like “best,” “top,” or comparative phrases.
The pattern of years in queries. Clark noted that there are consistent patterns that might simplify testing with Claude:
One noticeable trait is Claude’s query fan-outs, which consistently produced the same results 65% of the time across users.
These fan-outs frequently involve years, suggesting that titles featuring the current year might be advantageous in Claude-initiated searches, especially for queries driven by ranking and recency.
Why this matters to us. It appears that Claude’s visibility hinges more on the rankings within the search results it utilizes. Clark suggests Claude might be one of the most amendable AI answer engines due to its consistent search patterns closely tied to measurable rankings.

Inspired by this post on Search Engine Land.

June 12, 2026
Revolutionary Walmart-Google Ad Partnership Boosts Retail Success
I’ve discovered something exciting about how Google and Walmart are teaming up to enhance our advertising experiences. They’re enabling advertisers to tap into Walmart shoppers through YouTube, using Display & Video 360 (DV360) to measure sales more effectively. It’s a game-changer for those of us who focus on retail success.
This collaboration means I can access valuable shopper data from Walmart while also tracking whether my YouTube ads are translating into sales. It’s a win-win, giving me more control over my advertising efforts and results.
What’s happening? For brands like mine, this integration is a breakthrough. I can activate Walmart Connect audiences within DV360, reaching potential shoppers through YouTube with precision.
With closed-loop measurement now possible, I can directly connect the dots between ad exposure and purchasing actions at Walmart, making my advertising dollars work harder.
Why do I care? The amalgamation of Walmart’s rich shopper data with YouTube’s vast audience reach allows me to focus on real retail behavior rather than mere inferences, optimizing my targeting strategies.
Crucially, I can move beyond just monitoring views or clicks. I now have the capability to trace if my ads are actually driving Walmart sales, which helps justify my investments and refines my video advertising strategies.
Understanding the bigger picture, retail media networks are increasingly venturing beyond their platforms, delivering shopper insights and measurement capabilities into broader digital advertising spaces where I’m channeling more of my budget.
Reading between the lines, Walmart Connect’s ambition stands out, as they’re pushing to make their audience and analytics tools compatible with more advertising platforms. The conclusion of their exclusivity with The Trade Desk last year certainly paved the way for such integrations.
What do advertisers gain? As an advertiser, I unlock access to Walmart’s audience insights, can reach 150 million weekly U.S. customers via YouTube, and gain precise sales attribution tied to Walmart transactions—all streamlined within DV360.
What’s next for us? The initial focus is on YouTube campaigns, but I’m eager to see how Google and Walmart will expand this integration to cover more inventory over time.
The bottom line? This partnership is a powerful alignment of retail data, media activation, and sales measurement, offering advertisers like me a direct way to connect our YouTube ads with consumer behaviors at Walmart, both in-store and online.
Dig deeper
- We’re bringing Walmart Connect to Display & Video 360.
- Walmart Challenges Amazon With New Google Deal
Inspired by this post on Search Engine Land.
June 11, 2026
Instagram Empowers Users with Personalized Feed Controls

I’ve got exciting news for all Instagram enthusiasts! Instagram has now rolled out an update that allows us to tailor the Your Algorithm controls directly into our main feed experience. This means we have more power to manage the topics influencing our recommendations across Feed, Reels, and Explore.

About Your Algorithm. This feature is designed to allow me to view the topics Instagram thinks I’m interested in. It gives me the option to remove topics I’m not keen on and add those I want to see more frequently. Although Instagram first introduced Your Algorithm for Reels last December, it has since broadened these controls across more recommendation surfaces.

Feed joins Reels and Explore. Now, with this update, I can manage topic-level controls on my main feed. This change means the recommended posts I see—often from accounts I don’t follow—can be more aligned with my true interests.

Instagram generates a list of topics based on my activity, and any tweaks I make to this list help the system fine-tune future recommendations.

More user control. Adam Mosseri, the head of Instagram, mentions that this update addresses how we often feel out of control in recommendation-driven feeds.

“Our system learns from what I tap, watch, and share, but there hasn’t been a clear way for me to tell it what I truly want,” Mosseri explained. With the help of large language models, Instagram can now describe content clusters in simple language, offering me a clearer way to shape the system’s understanding of my preferences.

Interest media. As Gary Vaynerchuk brilliantly put it, there’s a shift happening from follower-based feeds, which he called social media, to interest-based discovery, or interest media. Insights show that platforms like Instagram are focusing on engagement-driven content rather than purely the accounts I follow. With this update, Instagram is transparent about the interests behind my recommendations.

Why we care. Matching user interests has become a priority in Instagram’s discovery process. If you’re creating content, it’s crucial to signal specific topics and audience intent to increase visibility in recommendations.

More controls are planned. Topics are just the beginning! Mosseri assured us that Instagram is also working on controls for people, moods, content types, and other signals.

Inspired by this post on Search Engine Land.

June 11, 2026
Google Enhances Local Real Estate Ads Nationwide

I’ve got some exciting news to share! Google is expanding its enhanced Local Services Ads (LSAs) for Home Listings all across the U.S., and it’s set to revolutionize the home-buying process.

As someone who frequently turns to Google at the start of my own home-searching journey, I see this as a fantastic opportunity for connecting homebuyers like me with local agents earlier in the process.

What’s New: With the updated LSA experience, I’m thrilled to see that ads now include detailed property information, such as pricing, photos, and key home features, right within the ad itself.

This new functionality is made possible through a collaboration with HouseCanary, which provides the property data showcased in the ads.

Why It’s Important: For me, having access to actual property listings, including visuals, pricing, and details directly through Google’s Local Services Ads, means I can better evaluate homes and reach out to agents without ever leaving the search page. This could very well boost lead quality and conversion rates.

How It Works: If I’m in the market for a new home, I can contact agents directly from these ads, whether through a call, message, or by booking an appointment.

Who Benefits: Existing LSA advertisers are automatically included in this enriched experience. Real estate professionals not yet using Local Services Ads have the chance to sign up and start receiving high-quality leads. Additionally, portal partners can sign up agents through Google’s managed partner program.

The Bottom Line: Google’s strategy, combining rich listing information with direct agent connections, seems designed to make Search a more beneficial starting point for homebuyers like myself. It’s poised to become a valuable resource for agents looking for high-intent leads.

Inspired by this post on Search Engine Land.

June 11, 2026
Discover OpenAI’s New Product Feed Ads for Retailers

When I think about how much time I used to spend manually creating ads for each product, the introduction of OpenAI’s latest feature feels like a game-changer. OpenAI’s Ads Manager beta now allows retail advertisers like us to upload our product feeds, automatically generating ads from individual catalog items.

This update opens the door for brands to scale their advertising efforts within ChatGPT, seamlessly serving up products that truly matter to users during purchase-focused conversations. It’s an exciting development, as it aims to enhance ad relevance and impact.

What’s happening? Now, we can upload our entire product catalogs and generate dynamic ads using feed data, bypassing the need to create campaigns item by item. It’s a major efficiency boost, and so far, feed-based ads have demonstrated strong performance in the Ads Beta phase.

Why does this matter to us? With OpenAI’s product feed ads, retailers gain a scalable method to align catalog inventory with high-intent conversations, promising improved ad performance. This new functionality mirrors tried-and-true strategies from giants like Google and Meta.

Getting started. For those of us participating in the beta, it’s time to review feed requirements and start creating campaigns directly from our uploaded product catalogs. This could be the beginning of a new era in how we manage ad setup.

The bottom line. By expanding its advertising capabilities, OpenAI is offering a more scalable and automated advertising solution in ChatGPT, specifically tailored for retailers like us aiming to enhance ad performance.

Inside scoop. The announcement of this update was shared by Menachem Ani, the Founder of JXT Group. He posted about it on X, sharing the email he received from OpenAI.

Inspired by this post on Search Engine Land.

June 11, 2026
Google Held Accountable for False AI Claims in Germany

Recently, a German court ruling caught my attention because it asserts that Google can be directly liable for false claims made in their AI Overviews. The Regional Court of Munich’s decision highlights a significant shift, considering AI-generated summaries as Google’s own content rather than just protected search results.

This ruling emerged from a case where AI Overviews mistakenly linked two Munich publishers to scams and dubious practices, despite the linked pages containing no such evidence, as reported by The Decoder.

AI Overviews are not just search tools. According to the court, these Overviews go beyond merely assisting users in finding third-party content. They actually process and present information in their own distinctive manner.

What struck me was the court’s findings that the AI Overview allegedly made standalone accusations regarding questionable business practices, which were not substantiated by the linked sources. Because Google crafts and controls these features and their algorithms, the court ruled these statements to be Google’s own content.

Traditional search protections didn’t apply here. Google argued that they should be protected by German case law, which generally shields search engines as indirect infringers. However, the court disagreed, emphasizing that AI Overviews are distinct as they generate new statements from multiple sources.

The court also dismissed Google’s argument that users could verify claims by reviewing linked content. They highlighted that AI Overviews offer claims that stand as complete answers without needing verification.

Why does this matter to me? The court’s stance implies that AI Overviews aren’t neutral links. If they issue incorrect claims about a company, Google may bear direct responsibility for these words.

Mismatched connections and misinformation. The court determined that misinformation resulted from AI conflating data about other entities with that concerning the publishers.

Given that the contested claims weren’t present on the linked sites, the publishers lacked a clear third party to target legally, should Google be considered only as an intermediary.

Interestingly, the court insisted that Google could compare AI-generated content against primary sources, at least in analogous situations.

Action required from Google. The injunction demands that Google refrains from repeating the disputed claims, which include allegations of scams and nonexistent business practices.

Furthermore, Google is instructed to bear 80% of the legal costs, while each publisher covers 10%. Despite Google’s lack of a cease-and-desist declaration with a penalty clause, the potential for repeat violations was noted, emphasizing the importance of this ruling for future similar claims.

Inspired by this post on Search Engine Land.

June 10, 2026
Publishers Demand Halt in AI Data Collection by Common Crawl
Could AI be losing a crucial source of its training data? As a major shift looms, significant publishers are urging Common Crawl to pause its collection and distribution of their content for AI training.

Digital Content Next (DCN) has sent a cease-and-desist letter to the Common Crawl Foundation, asking them to stop scraping and sharing protected publisher content.

Representing leading digital publishers like the AP, the New York Times, NBC Universal, Bloomberg, NPR, and Fox, DCN is also insisting that Common Crawl remove its members’ content, including paywalled and subscriber-only news articles, from its datasets.

Concerns Over Opt-Outs: Questions arise regarding Common Crawl’s adherence to publisher opt-out requests. Specifically, DCN’s lawyers are scrutinizing whether previous statements about compliance—often citing technical costs and delays—were perhaps misleading.
- The registry maintained by Common Crawl does list sites opting out, including several prominent news organizations.
Claims of Infringement: DCN firmly holds that copyright isn’t an opt-out system. They allege Common Crawl has been “flagrantly infringing” on publisher copyrights by distributing protected content without authorization or compensation.
- The group further critiques how Common Crawl shares this content with AI developers.
- DCN’s CEO, Jason Kint, signifies this legal action is a stance against the notion that online content is available for unrestricted collection, storage, and reuse.
Common Crawl’s Defense: Rich Skrenta, the Executive Director, denies allegations of bypassing paywalls and misleading publishers. He references a prompt and technical response to remove previously crawled content upon request.
- “Our removal process aligns with our dataset’s technical framework,” Skrenta explains.
Importance of This Battle: The outcome of this dispute could drastically influence the scope of publisher content that AI search engines use without explicit permission. Should there be heightened consent requirements, licensed sources may prevail, reducing reliance on openly available web content.

The High Stakes of AI Training: Established in 2008, Common Crawl has amassed billions of webpages to form a free public repository, a vital tool for training AI models. Notably, The New York Times’ lawsuit against OpenAI in 2023 cited that Common Crawl comprised 60% of GPT-3’s training data, as reported by Press Gazette.
- A 2024 Mozilla Foundation paper found generative AI would scarcely exist today without Common Crawl.
- Common Crawl’s ongoing efforts to create AI crawling standards indicate a willingness to adapt, yet DCN calls for decisive action—fully halting the scraping of protected content.
Inspired by this post on Search Engine Land.
June 10, 2026
Discover How Many Websites Use Each Schema Type with Schema.org
Have you ever been curious about how many sites use a specific type of structured data? Now, you have the chance to find out.

I recently discovered that Schema.org is now sharing aggregated usage statistics for its terms across the public web. This means you can see exactly how many domains are using a particular schema or structured data element.

According to a Schema.org announcement, they are excited to offer a new dataset providing these statistics. Updated monthly, the data is aggregated at the domain level and categorized into popularity range buckets, which helps to filter daily noise while emphasizing meaningful adoption trends for researchers and tool developers.

What’s the appearance like? Take a look at a snapshot of two Schema.org pages, featuring author schema and event schema, displaying the usage statistics prominently at the top:

Delving deeper into the data. Schema.org has further detailed the usage statistics. Here’s a brief overview:
- Schema.org term frequencies are evaluated within Google’s public web crawling infrastructure. The aggregation occurs at the domain level (e.g., example.com), not page by page. If you use the same term on 100 pages, it still only counts as one domain using it.
- Rather than displaying exact numbers, which can fluctuate daily, websites are categorized into range buckets (e.g., “10K – 100K” domains). This approach stabilizes the data and respects website privacy.
- The raw data files can be accessed on GitHub under the Google Public Stats dataset. Both JSON and CSV formats are available, alongside a JSON summary format offering aggregated bucket distributions, all updated monthly.
- Term Type: Specifies whether the term is a Type (e.g., “Person” or “Event”) or a Property (e.g., “price” or “telephone”).
- URI: Shows the official URI of the term, such as http://schema.org/Person.
- Domain Count Bucket: The range of unique domains utilizing the term, for instance, 100K - 1M domains.
$```json { "alt": "GitHub repository page showing a CSV file preview in schemaorg project.", "caption": "A glimpse into the schema.org GitHub repository, showcasing a CSV file preview detailing Schema.org statistics.", "description": "This image captures a GitHub repository page titled 'schemaorg/schemaorg'. It features a preview of a CSV file named '2026_05.csv' located within the 'data/public_stats/google' directory. The file contains several schema types such as EventVenue and TVClip, along with their domain usage statistics. The header section shows navigation tabs including Code, Issues, Pull requests, and more. The page is part of a public repository highlighted by the Schema.org Stats Bot update." } ```$

If you’re interested, here’s a peek at GitHub:

Why is this important? Well, besides my love for data, understanding the popularity of a specific schema element might just convince your development team to incorporate that schema code on your site.

Inspired by this post on Search Engine Land.
June 10, 2026
Google’s Zero-Click Searches Surpass 68%: A 2026 Study Insight

In early 2026, a significant shift unfolded in the world of search engines—68.01% of Google searches ended without a click. I discovered this intriguing fact through a study by SparkToro, which utilized Similarweb clickstream data. This percentage marks a noticeable rise from 60.45% in 2024, a 7.56-point increase over two years.
Fewer searches are resulting in clicks. Between 2024 and 2026, the share of searches generating at least one click fell by 9.51 percentage points, representing a decline of 22.9%. This includes clicks to organic results, paid ads, and Google-owned platforms like Maps and YouTube, excluding follow-up searches within Google.
During this period, I noticed that the share of searches leading to another Google search increased by 7.2 percentage points. This trend demonstrates Google’s growing proficiency in providing direct answers within its search results, encouraging us to refine or continue our searches without leaving the platform.
AI Overviews and the zero-click phenomenon. SparkToro suggests that AI Overviews might be contributing to the rise in zero-click searches, though the study doesn’t pinpoint how much of the rise from 2024 to 2026 can be specifically attributed to these overviews.
According to the research, I’ve observed that AI Overviews now appear in over 20% of Google searches, causing click-through rates to plummet by nearly 60% when they do.
AI Mode and zero-click growth. While AI Mode seemed to play a minor role during the study period from January to April 2026, SparkToro noted that only 0.34% of searches transitioned into AI Mode. However, Google announced during I/O 2026 that AI Mode had attracted over 1 billion monthly users, with query volume more than doubling each quarter, indicating a future increase in influence on search behavior.
Historical perspective on zero-click searches. SparkToro’s long-standing tracking of zero-click searches reveals an upward trend, although constantly changing data sources mean that long-term comparisons might lack precision. Nonetheless, available data consistently indicates an increase in zero-click behavior over time.
Here are some historical insights: In 2019, 49% of Google searches ended without a click, based on Jumpshot clickstream data. By 2020, SimilarWeb data showed that the figure had risen to 64.82%. And in 2024, 58.5% of U.S. searches (59.7% in the EU) ended without clicks, according to Datos data.
Why this matters to us. These findings imply that Google is increasingly meeting user needs internally, which might reduce traffic to external websites. However, direct year-to-year comparisons should be approached with caution due to differing methodologies in SparkToro’s analyses.
The evolving role of SEO. SEO remains crucial, but it’s not the sole solution for regaining traditional levels of Google-referred traffic. Rand Fishkin, SparkToro’s co-founder, advised us to focus on building brand awareness and engagement on platforms where our audience is active, irrespective of the impact on direct site visits.
SEO is still valuable for certain categories, such as branded searches, local business inquiries, and high-intent transactional searches, according to Fishkin.
About the study data. The research utilized Similarweb desktop and mobile web panel data on U.S. Google searches from January through April 2026. SparkToro estimated two-thirds of searches occurred on mobile devices, with the remainder on desktops. Searches within Google’s mobile search app, where zero-click behavior might be higher, were excluded.
To explore these insights further, check out the study titled In 2026, Less than One Third of Google Searches Still Send a Click.

Inspired by this post on Search Engine Land.

June 9, 2026
Google’s July Update: Transforming Local Services Ads for Clarity

I’m intrigued by Google’s decision to update its Local Services Ads on July 6. This change isn’t just a simple update—they’re renaming policies as “requirements” and aligning everything with a recent badge system overhaul.

So, what’s going on? Google is working to refine the rules governing Local Services Ads. They’re not just updating the language; they’re also aligning advertiser requirements with their new verification standards.

One key change is the renaming of “Local Services platform policies” to “Local Services Ads requirements.” It might sound administrative, but these adjustments suggest a more straightforward way for businesses to comply and earn those coveted Google Guarantee badges.

For those of us in advertising, these updates are vital. They not only promise clarity but hint at the possibility that compliance will tie directly to badge status. Agencies and local businesses must stay vigilant and ensure their credentials and standards are spot-on.

What does this mean in the grand scheme of things? Google aims to make the advertiser requirements crystal clear, aligning them with the new badge framework while simplifying the guidance on compliance.

To be clear, Google isn’t cracking down hard on policy. Instead, they’re focused on clarity and modernization, simplifying how businesses access these requirements.

In summary, Google is refreshing its Local Services Ads policies. The shift is towards “requirements,” backed by a badge-driven approach, enhancing trust and eligibility for businesses.

Inspired by this post on Search Engine Land.

June 8, 2026

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