Google Ads is automatically enabling conversion-based customer lists for eligible advertisers starting, with data processing scheduled to begin on Aug. 18.

The update applies to advertisers already using both Enhanced Conversions and Customer Match but who have not yet activated conversion-based customer lists.

Why we care. As privacy changes continue to reshape digital advertising, Google is increasingly encouraging advertisers to rely on first-party data. Conversion-based customer lists provide another way to build audiences using customer data already collected through conversions.

The feature could help advertisers create more relevant audience segments and improve campaign performance without requiring additional implementation work.

The details. Eligible advertisers do not need to take any action. Beginning Aug. 18, Google will start processing data and automatically make conversion-based customer lists available within affected accounts.

Advertisers can then choose whether to attach those audiences to campaigns and ad groups as part of their targeting strategy.

The catch. Advertisers who do not want the feature enabled can opt out before Aug. 18 by disabling conversion-based customer lists within their account settings.

After that date, Google will begin processing data and generating the lists automatically.

First spotted. This update was spotted by JXT Group Founder Menachem Ani, who shared the comms he recevied about it on X.

Google Ads is changing how Smart Bidding strategies are labeled, separating target-based bidding strategies from volume-based bidding strategies.

Starting this month, “Maximize conversions with a Target CPA” will once again be called Target CPA, while “Maximize conversion value with a Target ROAS” will return to Target ROAS.

Why we care. The change is designed to make it clearer whether a campaign is optimising for maximum volume or attempting to hit a specific performance target.

The details.

• Maximize Conversions remains available for advertisers focused on driving as many conversions as possible within budget.
• Maximize Conversion Value remains available for advertisers focused on generating the highest conversion value possible within budget.

What isn’t changing. The update is purely organisational.

Google says there are:

• No changes to bidding behaviour
• No changes to campaign performance
• No changes required from advertisers

Campaigns will continue to bid exactly as they do today.

For API users. Google is also aligning the interface more closely with how bidding strategies are represented in the Google Ads API.

Developers should review integrations, reporting tools, and campaign creation workflows to ensure they correctly recognise standalone TARGET_CPA and TARGET_ROAS strategy types.

Google is encouraging API users to monitor future updates related to:

• The BiddingStrategyType enum
• Standalone TargetCpa and TargetRoas messages
• Optional target settings within MaximizeConversions and MaximizeConversionValue

Bottom line. Nothing changes in how Smart Bidding works, but advertisers may find campaign setup and reporting easier to understand as Google restores Target CPA and Target ROAS as clearly defined standalone strategy names.

Watch this video on YouTube

One of the hardest lessons in PPC has nothing to do with bidding strategies, keywords, or campaign structure. It’s knowing when to walk away from a client.

On a recent episode of PPC Live The Podcast, performance marketing strategist Laura Abreu shared how taking on the wrong client early in her career became one of her most valuable professional lessons.

When your gut is telling you something

Laura’s first client was launching an ecommerce store selling beauty products from well-known brands. On the surface, it seemed like a great opportunity, but something felt off.

The products were available elsewhere at the same price, giving customers little reason to buy from an unknown retailer. Despite her concerns, Laura ignored her instincts and accepted the project anyway.

Great marketing can’t fix a weak business model

The team tried everything. Search campaigns, Meta ads, seasonal offers, product bundles, PR activity, and customer testimonials.

After three months of testing and optimisation, they hadn’t generated a single sale. The issue wasn’t the marketing. The business simply hadn’t established a compelling reason for customers to choose them over established competitors.

The importance of market validation

Many business owners believe hiring a marketer will automatically create growth. In reality, marketing amplifies demand—it doesn’t create it.

Today, Laura asks prospective clients whether they’ve tested the market, generated sales, and gathered customer feedback before investing in advertising. If the foundations aren’t there, paid media won’t solve the problem.

Pretty creative doesn’t equal performance

One of the biggest mistakes marketers make is judging creative based on personal preference rather than data.

The team invested heavily in creating beautiful visuals, but attractive creative alone wasn’t enough to drive sales. Customers don’t buy because an ad looks good; they buy because the offer resonates with their needs.

The emotional cost of a bad client

The failed project affected Laura far beyond the campaign results. As many marketers do, she tied her self-worth to the outcome.

The experience damaged her confidence so much that she stopped taking PPC clients for a period of time. Looking back, she realised she was carrying responsibility for a business problem that advertising could never have fixed.

Why expectations matter

One lesson Laura now applies with every client is setting expectations early and clearly.

Rather than promising immediate growth, she positions advertising as a way to test assumptions, validate demand, and uncover opportunities. This creates more honest conversations and avoids unrealistic expectations from the outset.

Why Laura doesn’t work with friends or family

Perhaps the strongest lesson from the experience is a rule she follows to this day: she doesn’t work with friends or family.

Maintaining professional distance allows her to stay objective, make decisions based on data, and avoid the emotional complications that can arise when personal relationships and business become intertwined.

Reputation is more valuable than revenue

When campaigns don’t go as planned, Laura believes honesty is non-negotiable.

Whether that means admitting mistakes, offering additional support, or refunding fees where appropriate, protecting your reputation is more important than protecting your ego. In an industry built on referrals, trust is everything.

Common mistakes Laura sees in PPC accounts

Having audited accounts across multiple markets, Laura says one of the biggest mistakes marketers make is treating campaigns as “set and forget” assets. She often finds underperforming creatives left running for months, ad copy that hasn’t been refreshed, and winning ads that aren’t being scaled effectively.

She also sees businesses creating unnecessary friction in lead generation campaigns. Long-form copy, overly complex forms, and sending users to external landing pages instead of testing native lead forms can all reduce conversion rates. In her experience, simpler journeys often deliver better results.

How Laura thinks marketers should use AI

Laura sees AI as a powerful tool for automating repetitive tasks rather than replacing marketers. She recommends using it to monitor performance, automate alerts, and streamline workflows so practitioners can spend more time on strategy and client communication.

At the same time, she warns against relying blindly on AI-generated outputs. Poor-quality ad descriptions and generic messaging can hurt performance, so human oversight remains essential. The marketers who succeed will be those who combine AI efficiency with strong strategic thinking.

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OpenAI is quietly expanding its advertising infrastructure, giving UK businesses early access to a self-serve Ads Manager for ChatGPT — a signal that the company is building out the tools needed to scale advertising on its rapidly growing AI platform.

What’s happening. OpenAI has begun rolling out Ads Manager Beta to businesses in the UK, according to an email sent to advertisers announcing availability of the platform.

The self-serve interface allows businesses to create advertising accounts and begin exploring campaign management tools with minimal setup friction.

How it works. The Ads Manager dashboard is organized into four core areas: campaigns, tools, billing and settings.

The interface is designed to be familiar to digital marketers, with user management and campaign controls accessible through a simplified navigation structure.

For agencies. OpenAI is advising agencies and freelancers not to create accounts on behalf of clients.

Instead, clients should:

1. Create their own Ads Manager account.
2. Navigate to Settings → Users → Invites.
3. Invite agency partners with appropriate permission levels.

Once invited, users receive an email prompting them to accept access and can then switch between client accounts within the platform.

The catch. Unlike Google Ads’ Manager Account (MCC) structure, advertisers cannot currently view or manage multiple accounts simultaneously from a centralized interface. Users can switch between accounts, but each account must be accessed individually.

Why we care. Access to Ads Manager gives UK brands and agencies the opportunity to understand the interface, workflows, and account structure before broader adoption begins.

By removing requirements such as upfront billing information and simplifying account creation, OpenAI is lowering barriers for marketers who want to test and familiarize themselves with ChatGPT’s emerging advertising ecosystem.

What to watch. The UK rollout offers one of the clearest indications yet that OpenAI is moving beyond experimentation and toward a scalable advertising platform.

The next questions for advertisers will be less about account setup and more about inventory, targeting capabilities, measurement tools, and how ads ultimately appear inside ChatGPT conversations.

For now, marketers are getting their first hands-on look at the infrastructure that could underpin OpenAI’s future advertising business.

First spotted. The update was spotted by Head of Paid Media at Evoluted, when he shared the comms he received on LinkedIn.

Google is bringing generative AI directly into Google Ad Manager with the launch of Ask Ad Manager, a new Gemini-powered assistant designed to help publishers analyze performance, troubleshoot issues and navigate the platform using natural language.

The beta launches this month as Google pushes deeper into AI-powered ad operations.

What’s happening. Ask Ad Manager is a conversational AI agent built specifically for publishers using Google Ad Manager.

Unlike traditional reporting tools, publishers can ask questions in plain language and receive personalized answers, recommendations and reports based on their own Ad Manager data.

Google says the tool is designed to help users move from analysis to action faster by reducing the time spent generating reports, diagnosing problems and navigating the platform.

What it can do:

Troubleshoot delivery issues.

Instead of manually pulling reports to investigate underperforming line items, publishers can ask the AI agent questions and receive guidance on potential causes and next steps.

Generate reports on demand.

Users can request custom metrics, benchmarks and performance reports through a simple prompt rather than building multiple reports manually.

Navigate Ad Manager faster.

Ask Ad Manager can direct users to relevant pages within the platform and automatically apply the appropriate filters and settings based on the conversation.

Why we care. For publishers managing large inventories and complex campaigns, the ability to quickly surface insights and diagnose issues could reduce operational workload and accelerate decision-making.

The feature also reflects a growing shift across ad tech toward AI agents that can perform tasks and streamline workflows instead of simply generating information.

Looking ahead. Google says Ask Ad Manager is just the beginning of a broader move toward what it calls a more “agentic” future for advertising operations.

The company plans to introduce additional AI capabilities throughout the year, including developer tools such as REST APIs and an MCP server to support workflow automation and integrations.

Google is also developing specialized agents that could help publishers and agencies discover inventory, negotiate deals and execute campaigns more efficiently.

Bottom line. Ask Ad Manager brings Gemini-powered assistance directly into Google Ad Manager, giving publishers a new way to access insights, resolve issues and manage advertising operations through natural language prompts.

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USA Today Co. is using AI-assisted shell files to publish breaking sports coverage faster. The strategy is designed to capture search traffic before Google’s AI Overviews summarize the news.

The publisher tested the approach during the 2026 Winter Olympics and is now using it for coverage of the 2026 FIFA World Cup, Digiday reported.

USA Today pre-writes breaking stories. The USA Today network, which includes the flagship site and more than 200 local publications, creates automated shell files for likely breaking news events. AI pulls subheads, photos, and links from the publisher’s archive. Editors turn that material into ready-to-publish files, allowing reporters to add new details, update the headline, and publish quickly.

• “We’re trying not to be as reliant on SEO strategy. Pre-writes are huge,” Alicia DelGallo, USA Today Sports editorial director, told Digiday.

The search window is shrinking. Publishers have long pre-written stories to move faster in Google Search. AI Overviews have increased the pressure.

• DelGallo said USA Today wants to publish while search interest is still rising, before Google has enough information to generate an AI Overview.
• Barry Adams, founder of Polemic Digital, told Digiday he has seen AI Overviews appear for news events within about four hours and no later than half a day, though he said there is no firm data yet.

Olympics coverage drove 116 million views. USA Today Co. said its national and local network generated 116 million page views from Winter Olympics coverage between Jan. 1 and Feb. 28. The flagship USA Today site drew 91 million page views, up 82% from the 2022 Winter Olympics.

• DelGallo said the shell-file system helped the publisher move quickly on breaking Olympics coverage, including Lindsey Vonn’s crash.

Why we care. AI Overviews can compress breaking news into answers within hours. Publishing first improves your chances of capturing search demand before Google answers the query itself.

World Cup gets the playbook. USA Today Co. is now using the system for World Cup coverage, with five shell files ready each day. The publisher is also investing in original reporting. It has reporters in all 16 host cities and a dedicated World Cup hub.

• DelGallo said the newsroom wants stories that don’t read like generic search content. That means stronger byline authority, more on-the-ground reporting, and angles readers can’t find elsewhere.

Traffic may still fall short. USA Today Co. has 40 million monthly unique visitors to its sports content and expects a World Cup traffic boost, especially with the U.S. co-hosting the tournament. DelGallo said USA Today still expects “massive audience” spikes from the World Cup. But she said AI Overviews have likely lowered the traffic ceiling compared with a year ago.

The report. How USA Today Co. is trying to beat AI Overviews on World Cup news

Google Ads is rolling out a beta that allows advertisers to connect additional data sources directly to website conversion actions, giving marketers a new way to supplement tag-based measurement with backend conversion data.

The feature enables advertisers to combine conversion signals collected through Google tags with transaction data from systems such as CRMs, order databases and ecommerce platforms.

What’s new. Advertisers can now attach an additional data source to an existing website conversion action through Google Ads Data Manager or the Data Manager API.

The beta is designed to supplement — not replace — website tagging by allowing advertisers to send conversion data from backend systems into the same conversion action used for campaign measurement and optimization.

Why we care. The new beta helps fill conversion measurement gaps by combining Google tag data with first-party data from backend systems like CRMs and order databases. This can recover conversions that may be missed due to browser restrictions, privacy settings, or ad blockers, giving advertisers a more complete view of campaign performance.

Why Google launched it. According to Google, combining tag-based measurement with backend conversion data can help advertisers create a more complete picture of conversions and improve campaign performance.

The company says the feature can help:

• Recover conversions that may not be captured by website tags.
• Improve measurement resilience.
• Provide more comprehensive data for automated bidding.
• Simplify data integration through Data Manager.

How it works. The system combines website conversion data collected through Google tags with conversion records uploaded from an advertiser’s backend systems.

To prevent duplicate reporting, Google uses transaction IDs to identify and deduplicate conversions between the tag and the additional data source within the same conversion action.

What advertisers need to know. The beta is currently limited to website conversion actions that use Google tag or Google Tag Manager implementations.

It is not available for:

• Google Analytics imported conversions.
• URL-based conversion actions.

Google recommends adding an additional data source to an existing conversion action rather than creating a new one to avoid potential double-counting across campaign goals.

Data requirements. Every upload must include:

• Transaction ID.
• Conversion date and time.

Advertisers must also provide at least one attribution identifier, such as hashed customer information or a Google click identifier.

Google recommends uploading conversion data as quickly as possible and ensuring uploaded conversion values match the same currency format used by website tags.

Bottom line. The beta marks Google’s latest effort to strengthen conversion measurement by bringing backend transaction data directly into Google Ads. As advertisers look for more complete performance data, the new capability offers a streamlined way to supplement website measurement with first-party business data.

AI is changing how Americans search for information. A new Pew Research Center report found that 60% read AI-generated summaries at the top of search results, and about 40% use chatbots to find information.

AI-generated answers now appear across both traditional search results and dedicated chatbot platforms, including tools like ChatGPT, Gemini, and Copilot, Pew found.

AI summaries reach most searchers. Six in 10 U.S. adults said they’ve read AI summaries at the top of search results, Pew found. Three in 10 said they haven’t.

• Another 10% were unsure, suggesting some users don’t clearly recognize AI summaries in search results.
• Men were slightly more likely than women to report reading them, 63% versus 57%. Adults 65 and older were the least likely age group to read them.

Chatbots are search tools. About half of U.S. adults now use AI chatbots, up from about one-third in 2024. Roughly one in four adults use them daily.

• Searching for information was the most common chatbot use Pew measured. About 40% of U.S. adults use chatbots for search, ahead of entertainment, image and video creation, medical advice, fitness information, news, emotional support, and companionship.
• Work was close behind. Among employed adults, 38% said they use chatbots for job-related tasks.

ChatGPT dominates. ChatGPT remains the most widely used chatbot by a wide margin. Pew found that 44% of U.S. adults now use ChatGPT, up from 34% last year and more than double the share measured in 2023.

• Gemini ranked second, with about a quarter of adults using it. Copilot and Meta AI followed.
• Grok, Claude, and Character.ai had much smaller reach. About one in 10 adults or fewer said they had used each tool.

Why we care. People now find information through traditional results, AI summaries, and chatbot answers. A traditional search ranking may not reflect every place people now find answers.

Dig deeper. AI search adoption rises as consumer trust declines: Study

About the data. Pew Research Center surveyed 5,119 U.S. adults from Feb. 17-23, 2026, through its nationally representative American Trends Panel. The full-sample margin of error was plus or minus 1.6 percentage points.

The report. Americans and AI 2026: Chatbots, Smart Devices and Views on Impact

Google AI Overviews cited self-promotional “best” listicles while excluding the brands behind them from recommendations in 69% of cases, according to a new analysis of B2B software queries by Lily Ray.

Brands have used self-serving listicles to influence AI search results, but Ray found Google often cited those pages while recommending competitors instead.

By the numbers. Ray analyzed 100 B2B “best [category] software” queries in Google AI Overviews across three dates: April 15, May 15, and June 8.

• Of the 80 prompts that triggered an AI Overview, self-promotional listicles were cited 323 times.
• In 224 cases, Google cited a brand’s own page but didn’t recommend that brand.

Competitors get recommended. Ray documented several cases where Google cited a brand’s “best” listicle while recommending better-known competitors.

• For “best LMS for selling courses,” Google cited Oasis LMS but did not recommend it. Instead, it recommended Kajabi, Thinkific, LearnWorlds, and Teachable — all of which are named in the Oasis LMS article.
• Similar patterns appeared in queries for help desk, task management, survey, CRM, and SEO software.

Stronger brands still appeared. Brands that already led their categories, were widely mentioned by third-party sources, and had stronger link profiles were more likely to appear in AI Overview recommendations, according to Ray.

• The data showed a consistent split between citations and recommendations. A brand’s page could appear as a source while competitors received the recommendation.

Organic visibility fell. Ray also reported organic search declines for many sites that relied heavily on self-promotional listicles.

• The declines began around Jan. 20, across dozens of sites she analyzed. Many also scaled other SEO- and GEO-focused content formats, including AI-generated articles, comparison pages, and large volumes of “best” pages ranking their own brand first.
• Those declines continued and accelerated during Google’s May 2026 core update, according to Ray.

Review sites gained citations. Ray found Google relied heavily on third-party and user-generated-content sites for “best” queries, with Reddit citations increasing sharply in recent months.

• Forbes, Reddit, and YouTube were among the most-cited domains in AI Overview responses containing “best.”

Why we care. A citation is not a recommendation. Your content can appear in an AI answer while helping competitors capture the visibility that matters most.

Catch up quick. Search Engine Land previously reported that some SaaS and B2B brands lost 30% to 50% of their visibility after relying heavily on self-ranked “best” pages, based on earlier research from Ray.

• Search Engine Land also reported that the tactic may create legal risk under the FTC’s Consumer Review Rule when company-controlled content is presented as independent reviews, reviews are not based on real use, or material relationships are not clearly disclosed.

About the data. Using Ahrefs Brand Radar, Ray collected AI Overview answer text and cited sources for 100 B2B “best [category] software” queries at three checkpoints between April and June. The analysis measured two outcomes: whether a self-promotional listicle was cited and whether the brand behind it was recommended.

The report. Why Calling Yourself the Best Could Be Helping Your Competitors Win in AI Search

Adobe announced a new solution to help businesses ensure their brands are visible, trusted, and chosen across AI surfaces.

Called Adobe Brand Visibility, the product is part of Adobe CX Enterprise, an agentic AI system designed to simplify customer lifecycle management — from acquisition and prospect engagement to conversion and long-term loyalty.

AI traffic is exploding. The use of LLMs to identify and research products and services marks a significant shift for both marketers and consumers. Alongside the announcement, Adobe released data showing substantial growth in LLM usage. AI traffic to U.S. retail sites surged 1,324% between October 2024 and May 2026. In the travel sector, AI traffic increased 2,215% over the same period.

• “We used to get back the same thing (a SERP page with links on it). Now, the answers appear to be random, but they aren’t at scale. But companies don’t have tools to do it,” Loni Stark, vice president of strategy and product, Adobe, told MarTech.

Measuring brand visibility in AI search. Adobe Brand Visibility is Adobe’s first generative engine optimization (GEO) product since its acquisition of Semrush in May. It combines Adobe LLM Optimizer with Semrush’s AI Optimization tool.

Adobe Brand Visibility draws on nearly 300 million real-world AI search prompts, which Adobe says is the largest global database of its kind, helping teams identify which prompts they’re winning or losing.

Combined with Adobe’s first-party signals from owned channels, the platform gives marketers a view of how their brands appear across ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics include mention frequency, audience reach, competitive share of voice, and content gaps. AI agents then surface prioritized recommendations, enabling teams to deploy updates quickly and measure their impact directly in the platform.

Competitive intelligence. Adobe Brand Visibility includes competitive brand comparison tools that let marketers benchmark against competitors, identify where their brands are cited, track brand mentions, and analyze historical trends.

The platform also includes SEO intelligence, reflecting the continued importance of SEO fundamentals in AI search visibility. Powered by Semrush data spanning 28.5 billion keywords and 43 trillion backlinks collected over 17 years, it shows where existing search authority should be generating AI citations and where content investments can close gaps across both channels.

There’s still much to learn about how LLMs work and how brands can improve visibility, but Star is confident Adobe is well positioned to lead in this space.

• “Adobe had owned data. Semrush had data and trends. We don’t have all of the answers, but we have the best data,” Stark said.

Google updated its site move documentation to say that you should enter in all the domain variants in the Google change of address tool when doing a site move.

What Google wrote. Google posted the following new note in the document:

“For domain migrations: If you’re moving your site from one domain to another, make sure to submit Change of Address requests for all subdomains and the www and non-www variants of the old domain name (for example, from en.example.com, www.example.com, and example.com to new-example.net), even if you’re not actively using these variants. Ensure that you have all of these variants verified in Search Console.”

Domain variants. Domain variants are all the variations of your domains, including sub-domains, different TLDs, www versus non-www and so forth. These include en.example.com, www.example.com, and example.com to new-example.net, as an example.

Why we care. Google wrote that “domain migrations work best when all variants of a site are migrated properly,” which is why you should follow Google’s guidelines carefully when making site moves and domain migrations.

Site moves and domain migrations are scary tasks for SEOs and site owners, so having very specific steps and guidelines make the process a little less painful.

The change of address tool is there to help you speed up these migrations, so make sure to use it properly.

Meta is introducing new commerce features across Facebook and Instagram as it looks to turn more AI-driven product discovery into completed purchases.

What’s happening. Meta is expanding Live Video Ads globally on Facebook and bringing them to Instagram, allowing businesses to promote livestreams to larger audiences and drive sales directly from live shopping experiences.

In the U.S., Meta is working with live commerce providers including CommentSold, Firework, LiveMeUp, Sprii and TalkShopLive, enabling sellers to convert eligible livestreams into ads that can reach potential customers beyond their existing audiences.

To support live commerce, Facebook’s Live Shopping Tools let viewers browse products, view pricing and discover items to buy without leaving the livestream.

New checkout experience. Starting this summer, Meta will roll out a virtual card payment option on Facebook and Instagram in partnership with Mastercard and Visa.

The feature generates temporary, one-time card numbers linked to a shopper’s existing payment card, allowing users to complete purchases without sharing their actual card details with merchants. Meta says the move is designed to increase consumer confidence and improve transaction security.

For advertisers. Meta is also making product data a foundational element of all Sales campaigns. Instead of selecting from separate catalog and creative ad formats, advertisers will be able to provide both product feeds and creative assets, with Meta’s AI automatically assembling the most effective ad for each individual user.

Product information such as price, availability and descriptions will be used across more campaign formats, helping advertisers create richer shopping experiences while maintaining performance.

Why we care. Meta is giving brands more ways to convert product discovery into sales without users leaving its apps. The expansion of Live Video Ads could help advertisers reach larger audiences with livestream shopping content, while AI-powered Sales campaigns will automatically combine product data and creative assets to serve the most relevant products to shoppers.

The addition of virtual card checkout could also reduce purchase friction and increase consumer trust, potentially improving conversion rates.

The bigger picture. Meta says AI is fundamentally changing how people discover products, with recommendations increasingly occurring within content feeds, creator videos and conversations rather than through traditional search.

The company is positioning product catalogs as a key signal powering these experiences, helping products appear across shopping surfaces such as creator content, business recommendations and Meta AI-powered shopping experiences.

Bottom line. Meta is investing in tools that reduce friction between product discovery and purchase, combining AI-powered ad delivery, live shopping formats and more secure checkout experiences to encourage consumers to buy without leaving its apps.

The UK’s Competition and Markets Authority (CMA) has ordered Google not just to give site owners a way to opt out of AI Overviews but also to explain how the search engine ranks its search results. Also, the CMA has ordered Google to allow users to port their search data to certain third-party services.

Transparency on search rankings. The CMA wants Google to “improve transparency and fairness in how search results are ranked,” and implement this within 6 months.

UK businesses told the CMA that Google’s “ranking practices are neither fair nor transparent,” adding “changes are made without sufficient notice, and when these changes impact their businesses, they do not have effective ways to raise concerns.”

Technically, yes, we cover Google search updates all the time. Google continues to adjust its ranking algorithms to (1) improve the relevancy of those search results and (2) to remove those who try to manipulate those results.

The CMA added that under this conduct requirement, Google must:

• Introduce clear processes for businesses to raise concerns about how Google ranks results and have them addressed effectively
• Rank ‘organic’ search results using objective and non-discriminatory criteria (including in AI Overviews but not sponsored results)
• Provide greater transparency to businesses about how rankings work and give advance notice of significant changes

Data portability. The CMA also wants Google to “Allow users to port their search data to authorized third parties such as rewards platforms or companies offering personalized offers or discount codes” within 3 months.

“Third-party firms are keen to offer people new products and services based on their Google search data but need to be able to access it with confidence. Using this data would allow third parties to offer people more personalized features – like tailored travel suggestions, more relevant shopping deals, and rewards (including cashback and discounts),” the CMA wrote.

Why we care. I highly doubt Google will follow these orders, as doing so would put its most prized asset – the search ranking algorithm – in its competitors’ hands. It will also show all how rankings work, thus making it easier to manipulate and spam.

The CMA is not the first to ask for this and won’t be the last, but Google will no doubt vigorously fight these orders.

ChatGPT’s product recommendations changed 80.2% when search was enabled, according to a study of 20,000 responses by Jeff Oxford, founder and CEO of Visibility Labs.

Oxford tested 1,000 product-recommendation prompts 10 times each with ChatGPT search enabled and 10 times with search disabled.

Only 19.8% of products recommended without search also appeared in recommendations generated with search enabled.

Search changed top picks. Even products ChatGPT recommended most often without search rarely carried over. Among products that appeared in 100% of search-disabled responses, only 15.8% also appeared when search was enabled.

• Oxford expected the most consistently recommended products to remain common when search was turned on. Instead, that group had the lowest overlap.

Source mentions tracked visibility. The study also examined whether products mentioned in ChatGPT’s cited sources appeared more often in its recommendations. Oxford reported a 0.4 Pearson correlation between cited-source mentions and recommendation frequency.

• The study measured recommendation frequency with a “Visibility Score,” defined as the percentage of runs in which a product appeared for a given prompt. Products mentioned more often in cited sources tended to have higher Visibility Scores.
• The analysis didn’t establish that cited-source mentions caused products to be recommended.

Search narrowed recommendations. ChatGPT responses with search enabled contained an average of 5.2 products, compared with 6.2 when search was disabled.

• Across 10 runs of each prompt, ChatGPT returned an average of 19 unique products per prompt with search enabled and 21.8 with search disabled.

Why we care. Search changed which products ChatGPT recommended, including products it named every time when web access was disabled. The findings suggest products appearing in cited sources may receive greater visibility when search is enabled, though the study does not determine whether cited-source visibility matters more than broader web visibility.

About the data. Oxford analyzed 1,000 product-recommendation prompts, running each 10 times with search enabled and 10 times with search disabled. Product names were standardized so naming variations counted as the same product. Because the study was observational, it did not establish a causal relationship between cited-source mentions and recommendation frequency.

The report. ChatGPT’s Product Recommendations Change 80.2% When Search is Enabled vs Disabled (Study of 20,000 Responses)

Dig deeper. AI recommendation lists repeat less than 1% of the time: Study

AI referrals to U.S. travel sites nearly tripled in May. AI visitors spent more time on site and bounced less than visitors from traditional sources, according to Adobe.

By the numbers. Traffic from AI sources to U.S. travel sites grew 194% year over year in May 2026, Adobe said. It was up 2,215% since October 2024, when Adobe began tracking AI traffic.

• AI-assisted travel planning has expanded beyond early research. Travelers use large language models to compare destinations, evaluate hotel amenities, build itineraries, find promotions, and book trips.

AI visitors showed stronger engagement. AI-referred travel visitors still converted 28% less than non-AI traffic. That gap has actually narrowed nearly 70% since October 2024, Adobe said.

• Engagement metrics were stronger. AI-referred travelers were 21% more engaged than non-AI visitors, spent 70% longer per visit, and had bounce rates 41% lower.
• Adobe said those engagement patterns suggest more purposeful, high-intent behavior, though AI-referred travel visitors still converted at lower rates.

Travel pages and AI readability. Adobe also measured how readable travel websites are to large language models. Its AI Content Visibility Checker scores how much page content AI systems can read.

• Hotels and car rentals led the travel sector. Hotel homepages scored 63% readability, while car rental homepages scored 59%. Product pages scored higher: 73% for hotels and 71% for car rentals.
• Even so, more than one-third of content on some leading travel pages remained unreadable to AI systems, Adobe said.

Where travel sites scored best. Hotels led across several page types, including destination guides, activities, search results, customer service, and promotions pages.

• Car rentals led FAQ pages. Cruises led blog and news content. Airlines trailed the leading travel sectors across every page type Adobe measured.
• The pattern favored pages with rich, structured information. Property details, amenities, vehicle descriptions, and core offerings gave AI systems more content to parse.

Retail’s conversion advantage. AI referrals to U.S. retail sites also hit a record high in May, rising 138% year over year and 1,324% since October 2024.

• Retail AI traffic has progressed further on conversion. Adobe said AI-referred retail visitors converted 54% better than non-AI traffic, reversing last year’s pattern, when AI conversion rates were nearly half as high.
• Cosmetics and electronics led retail readability, aided by ingredient lists, tutorials, product specifications, how-to guides, and customer service content. Grocery and furniture lagged.

Why we care. Adobe’s data suggests AI referrals are becoming more commercially valuable, especially in retail, while its visibility scores indicate many sites still leave significant content inaccessible to AI systems. If key content is blocked, buried, or poorly structured, you may lose visibility before a traveler or shopper reaches your site.

About the data. Adobe’s findings were based on more than 8 million visits to U.S. travel sites, more than 1 trillion visits to U.S. retail sites, and more than 100 million SKUs. The company also surveyed more than 5,000 U.S. consumers in March about how they use AI for shopping and travel planning.

Google is removing a layer of advertiser control over Customer Match audience classification, automatically assigning customer types to conversion-based lists starting in August 2026.

Advertisers will no longer be able to leave eligible lists unclassified.

What’s changing. Beginning in August 2026, Google Ads will automatically assign conversion-based customer lists to one of several customer types, including:

• Existing customers
• New customers
• Other customer segments

Google is encouraging advertisers to review and update their audience classifications in Audience Manager before the change takes effect.

Why Google is making the change. The move appears aimed at improving audience consistency across Google’s growing suite of customer acquisition and retention tools.

By standardizing customer lifecycle classifications, Google can more accurately distinguish between prospecting and retention audiences, helping automated bidding and targeting systems make better optimization decisions.

Why we care. For advertisers using customer acquisition goals, new customer bidding, or retention-focused strategies, the accuracy of customer classifications could have a direct impact on campaign performance.

Misclassified audiences could affect how Google’s systems evaluate and optimize users throughout the customer lifecycle.

What advertisers should do. Advertisers using Customer Match lists derived from conversion data should use audience manager to audit their audiences before August.

Key questions include:

• Are customer lists currently categorized correctly?
• Which lists represent existing customers versus acquisition audiences?
• Will automatic classification align with internal customer definitions?

Reviewing audience settings now may help avoid unexpected changes once Google’s classifications become mandatory.

The bottom line. Google is taking a more active role in audience management, automatically assigning customer lifecycle labels to conversion-based customer lists and further standardizing the signals that power its automated advertising systems.

First spotted. This update was spotted by Google Ads expert Bia Camargo, who shared seeing the alert on LinkedIn.

Meta launched AI Mode in Facebook Search. AI Mode gives users AI-generated answers based on public content from Facebook Groups, Reels, and other Meta apps.

Instead of showing a standard list of search results, AI Mode uses Meta AI to answer questions directly within Facebook. Meta said responses are grounded in what people are publicly saying across its apps, including real experiences and recommendations.

AI answers in search. AI Mode supports both broad discovery and specific questions. Users can search or explore their Feed and receive responses from Meta AI within Facebook. This gives Facebook a new way to surface public social content.

• Groups and Reels could become part of how Meta answers questions about products, places, hobbies, and everyday advice.

Source selection is unclear. Meta said the feature delivers “real answers from real people.” But it didn’t explain how AI Mode selects which public posts, Groups, or Reels appear in responses. It also didn’t say whether brands, creators, or publishers will be able to see when their content is used.

Why we care. Facebook search is moving toward an AI answer experience built on public social content. That could change how people discover recommendations, local information, and brand-related conversations across Meta’s apps.

A familiar name. Obviously, Meta’s new AI Mode feature shares its name with Google’s AI Mode. Meta gets no points for creativity.

What Meta is saying. AI Mode is powered by Meta AI and Muse Spark. Meta didn’t explain how Muse Spark influences search ranking, source selection, or answer generation.

• The search update was part of a broader Facebook AI rollout that also included new creative tools for photos, videos, profile pictures, and Stories.

The announcement. New AI Tools to Help You Make Things Happen on Facebook

Microsoft is now officially releasing a preview of the new AI performance report within Bing Webmaster Tools that now includes Intents, Topics, Citation Share, and Compare. We saw Microsoft demo these features in late April but now it is actually starting to roll out to users.

As a reminder, Bing officially rolled out its AI performance report in February. Google didn’t roll out its AI reporting in Search Console until June, and it seemed forced.

What is new. “These new capabilities build on that foundation by helping publishers better understand why their content is being surfaced, which broader subject areas they are gaining visibility in, how their presence evolves relative to other cited sources, and how citation patterns change over time,” wrote Krishna Madhavan from Microsoft.

Intent: The new Intents feature in Bing Webmaster Tools now classifies the grounding queries in the AI Performance Report in broader categories, such as Informational, Commercial, Navigational, Learn and Solve, Research, Creation, Local, and more. This in a sense helps you understand the intent behind the prompt or query. “This helps publishers move beyond simply seeing which queries triggered citations and begin understanding the broader query context our systems associate with those citation appearances,” Krishna Madhavan wrote.

The example provided was that an e-commerce publisher may discover strong visibility in comparison-oriented or shopping-focused AI experiences, while an educational publisher may find that their content is frequently surfaced in research or learning-oriented interactions. These insights can help publishers better align content structure and depth with the types of experiences where AI systems are surfacing their content.

Topics: The Topics in the AI performance reports group related grounding queries into broader thematic clusters. AI systems reason across concepts and themes rather than isolated keywords, Microsoft explained. So by having topics, it will help publishers understand visibility in the same thematic structure that modern AI systems use to organize information.

So for example, queries such as “solar panels,” “solar energy efficiency,” and “residential solar installation,” for example, may all map into a broader topic cluster like Solar Energy.  “This creates a more natural way to analyze AI visibility. Content teams and publishers often think in terms of themes, editorial areas, and audience interests rather than isolated keywords. Topics help bridge that gap by turning grounding query data into a more thematic view of AI engagement,” Microsoft wrote.

One note, “during the preview phase, some labels may still be broad – especially for highly specialized or niche domains – but the system is already beginning to reveal meaningful thematic patterns,” Microsoft wrote.

Citations. Microsoft also added citation share, which shows how much of the citation space your site receives for a specific grounding query. Citation share is calculated as the percentage of citations attributed to your site out of all citations shown across all sites for that same grounding query. “This helps publishers understand not just whether they were cited, but how much visibility they received within the full set of cited sources for that query,” Microsoft explained.

Microsoft added these points:

• “This can provide a more directional view into how visibility is evolving over time. Publishers may begin to identify areas where their content has strong and growing representation in AI-generated experiences, as well as areas where visibility may be more fragmented across many sources.”
• “Importantly, Citation Share is designed as an observational metric – not a ranking system or a competitive scoreboard. It does not expose competitor domains, represent traffic share, or assign quality scores to content.”
• “AI citation ecosystems are inherently dynamic. Citation patterns can shift due to changes in user behavior, evolving models, freshness signals, partner refresh cycles, and broader changes across the web itself.”

Compare. With all of that, you can also compare the changes over time. The compare feature allows you to overlay a previous time period directly onto the current reporting view. 

“Compare is designed to help publishers observe changes over time. Citation activity can be influenced by many factors including evolving AI models, competing content, freshness signals, and shifts in user demand,” Microsoft wrote.

Here is what it looks like:

Why we care. While we still do not have click and click-through rate data, Microsoft keeps adding more and more to its AI performance reports.

I am hopful that one day we will get click data, but I am still not expecting to see that from Google or Microsoft any time soon.

Google is changing how it charges for certain Demand Gen campaigns on Discover, signaling a closer link between billing models and campaign optimization goals.

What happened. Google Ads has notified advertisers that Demand Gen campaigns using view-through conversion (VTC) optimization on Discover will move from cost-per-click (CPC) billing to cost-per-thousand impressions (CPM) beginning July 15th.

The change affects a limited number of advertisers and applies only to campaigns with VTC optimization enabled. Advertisers not using VTC optimization will see no change.

The transition will happen automatically, with no action required from advertisers.

Why we care. The change could alter how advertisers evaluate efficiency within Demand Gen campaigns. Campaigns optimized for view-through conversions may see differences in spend pacing, impression volume, and reporting metrics once billing transitions from clicks to impressions.

Advertisers focused primarily on click-driven performance may want to reassess whether VTC optimization remains the right fit for their objectives.

Why Google is making the change. According to Google, the update is designed to better align billing with campaign objectives.

View-through conversions measure actions taken after a user sees an ad but does not click it. Because impressions play a central role in generating those conversions, Google argues that CPM billing more accurately reflects the value being delivered.

The company also says the change will allow its systems to optimize more effectively for view-through conversion goals.

Opt-out option. Advertisers who do not want to transition to CPM billing can opt out by disabling view-through conversion optimization in campaign settings.Doing so will prevent the billing change from taking effect for those campaigns.

The bottom line. Google is tying payment more closely to the behavior its Demand Gen campaigns are designed to optimize for. For advertisers using view-through conversions, impressions—not clicks—will soon become the basis for both optimization and billing on Discover.

First spotted. The update was shared by Adsquire founder, Anthony Higman, who shared the comms he received on X.

Advertisers using Microsoft Ads can now target users based on job seniority, adding another layer of B2B audience precision powered by LinkedIn data.

What’s happening. Microsoft Advertising expanded its LinkedIn Profile targeting capabilities to include job seniority targeting across Search and Audience campaigns, according to Product Liaison Navah Hopkins.

The update allows advertisers to target or observe users based on 10 standardized seniority levels: CXO, VP, Director, Manager, Senior, Entry, Owner, Partner, Training, and Volunteer.

The feature is available at both the campaign and ad group level, giving advertisers more flexibility when segmenting audiences.

Why we care. B2B marketers have long struggled to distinguish between decision-makers and practitioners within search campaigns. The addition of job seniority targeting gives advertisers a way to better align messaging, bidding strategies, and reporting with specific audience segments.

For organizations with longer sales cycles or multiple stakeholders involved in purchasing decisions, understanding who is engaging with ads can be as important as the conversion itself.

Between the lines. Unlike many audience targeting options available across advertising platforms, Microsoft’s integration with LinkedIn data offers a professional identity layer that can help advertisers better understand who is behind a click.

The new seniority filters can be applied directly within campaign settings or used in observation mode to gather performance insights without restricting reach.

How marketers can use it:

Tailor messaging by seniority

Advertisers can create separate ad groups for executives, managers, and individual contributors, adapting tone and messaging based on audience expectations.

An executive-focused campaign might emphasize strategic outcomes and business growth, while messaging aimed at practitioners could focus on workflows, implementation, or efficiency gains.

Identify who is actually converting

Observation mode allows marketers to analyze conversion performance across seniority levels without narrowing targeting.

This can help answer questions such as:

• Are conversions coming from decision-makers or influencers?
• Is budget being spent on audiences that rarely close?
• Which seniority levels generate the highest-quality leads?

Improve audience testing

The additional reporting layer provides another signal for optimization and expansion decisions.

Advertisers importing campaigns from other platforms may find performance patterns differ on Microsoft Ads, making seniority reporting a useful source of testing and audience discovery.

Availability. The feature is currently available in selected markets across the Americas, EMEA, and APAC regions.

• Americas: Argentina, Brazil, Canada, Chile, Colombia, Ecuador, Mexico, Peru, and the United States.
• EMEA: Egypt, Nigeria, Saudi Arabia, and South Africa.
• APAC: Australia, India, Indonesia, Japan, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Vietnam.

The bottom line. Microsoft Ads continues to lean into its LinkedIn integration as a differentiator in the B2B advertising market. The addition of job seniority targeting gives advertisers another way to connect search intent with professional identity, helping them better understand not just what audiences are searching for, but who is doing the searching.

Google updated its AI Search optimization guide to clarify that llms.txt files neither help nor hurt Google search rankings. It also confirmed that Google Search does not use llms.txt files.

What Google wrote. I bolded the portion that is new, where Google wrote that Google Search does not use AI text files, markup or Markdown files.

• “You don’t need to create new machine readable files, AI text files, markup, or Markdown to appear in Google Search (including its generative AI capabilities), as Google Search itself doesn’t use them. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn’t mean that the file is treated in a special way.”

Google also added a new note that reads:

• “It’s completely fine if you decide to create and maintain LLMS.txt files (or other similar files) for other services or systems that use these files. Doing so won’t harm (nor help) your visibility or rankings in Google Search, as Google Search ignores them.”

Here’s a screenshot of this section:

Dig deeper.

• Meet llms.txt, a proposed standard for AI website content crawling
• llms.txt isn’t robots.txt: It’s a treasure map for AI
• Does llms.txt matter? We tracked 10 sites to find out
• Google adds llms.txt check to Chrome Lighthouse

Why we care. There’s been a lot of confusion about how Google Search handles llms.txt, markdown, and other AI-related files. In short, Google Search may discover, crawl, and index these files, but it does not use them in any special way. Having them on your site won’t help your rankings, and it won’t hurt them either.

Watch this video on YouTube

While managing a major B2B SaaS account, Hallam PPC Lead, Simran Harichand tightened a target CPA to improve efficiency but failed to monitor the impact. The change dramatically reduced spend, leaving the account €30,000 short of its monthly budget target.

When underspending becomes a business problem

Underspending isn’t just a media issue — it can affect a client’s future budgets. In this case, unused funds had to be returned to finance, making it harder for the marketing team to justify similar investment levels in future planning cycles.

The hardest part wasn’t the mistake

The most difficult moment came when Simran had to explain the situation to the client. Rather than making excuses, she took full responsibility for the error and acknowledged the impact it had on their goals.

Trust is built after the mistake

Although the client was understanding, trust had been damaged. Simran rebuilt confidence by introducing weekly budget pacing updates, showing transparency and proving the issue wouldn’t happen again.

Why the “brilliant basics” matter

The experience reinforced the importance of fundamentals such as budget pacing, account monitoring and conversion tracking. No matter how advanced advertising platforms become, strong basics remain the foundation of good performance.

What she’d do differently today

Looking back, Simran says she underestimated how much influence a target CPA change could have on delivery. Today, she treats any spend-related adjustment as a significant account change that requires close monitoring.

The danger of relying on AI without oversight

Simran supports testing AI-powered tools but warns against blindly adopting every new feature. She believes advertisers should balance experimentation with human oversight and strategic thinking.

Why conversion tracking remains the industry’s biggest blind spot

One of the most common issues she sees in account audits is poor tracking implementation. Inaccurate conversion data can lead to flawed optimisation decisions, making reliable measurement more important than ever.

The human side of client relationships

Strong client relationships can help teams navigate difficult moments when mistakes happen. Building trust through communication and honesty often matters just as much as delivering strong performance.

The bottom line

Mistakes are inevitable in PPC, but accountability and learning from them are what matter most. For Simran, the experience was a reminder that long-term success is built on mastering the fundamentals and maintaining trust.

💾

You’ve probably seen some version of these three claims:

• Quote-led headlines outperform plain declarative ones by nearly 29%.
• Question headlines underperform both, sometimes by 24%.
• Format drives the result: Rewrite a statement as a quote, or add that magic word, and you should expect a real lift.

We tested all three against 1,674,518 English editorial articles and 1,690,295 French articles from the 1492.vision Discover corpus (November 2025 to May 2026): about 3.4 million editorial articles with at least one capture across our fleet.

They share a deeper flaw than any of their numbers.

All three treat headline format as a cause — a lever you pull to gain visibility. But the data shows, layer after layer, that a format’s measured effect is almost entirely a proxy for something else: which publisher used it, for which audience, and on which Discover surface.

The headline is a symptom of those choices, not an independent driver.

The clearest demonstration is Simpson’s paradox. Once you see it, you find it throughout the dataset.

A note on what we measure

Our metric isn’t clicks from Discover; no third party has that data. It’s hits per article: how often an article appears across the 1492.vision fleet we observe, a proxy for visibility.

The corpus is limited to editorial articles. YouTube and X are excluded because their headlines follow different conventions. We’ll return to both at the end—they sharpen the point more than anything else.

A word on why the volume matters: the entire argument depends on being able to slice 3.4 million articles by publisher, Discover surface, topic, and language while still retaining enough data in each segment for meaningful comparisons. That’s the difference between a number and an insight — and between a real format effect and a statistical mirage.

The number is real, at the wrong altitude

Pool all publishers together, and a clean gradient emerges: quote-led headlines at the top, statements at the bottom.

Lang	Format	Articles	Mean hits	Median	vs statement
EN	Quote-led	38,044	13.0	4	+37%
EN	Quote inside	75,463	11.5	4	+21%
EN	Question	53,081	10.2	4	+7%
EN	Statement	1,674,518	9.5	3	baseline
FR	Quote-led	179,472	52.8	13	+48%
FR	Quote inside	223,052	49.9	12	+40%
FR	Question	103,117	41.3	11	+16%
FR	Statement	1,690,295	35.7	9	baseline

The commonly cited +29% is conservative for pure editorial articles: quote-led headlines show a +37% lift in English and +48% in French. Questions, far from underperforming, also outperform statements (+7% EN, +16% FR).

At this level of aggregation, claim 1 looks understated and claim 2 looks plainly wrong.

This is the level of aggregation where most headline advice is born. Hold onto that +37% figure — the rest of this piece is about what it’s actually measuring.

Hidden variable 1: which publisher

The aggregate can’t answer a crucial objection on its own: the publishers that use quotes aren’t the same publishers that don’t.

Celebrity media, regional dailies, and buzz-driven sites lean heavily on quotes and earn more Discover hits per article regardless of headline format. Pure-play publishers, wire services, and utility-focused sites favor declarative headlines and tend to sit lower.

The raw comparison, then, isn’t quote versus statement. It’s one publisher population versus another.

This is a textbook Simpson’s paradox: a strong trend in the aggregate that weakens, disappears, or reverses once you segment by group.

To get anywhere near the effect of headline format itself, the grouping variable has to be the publisher.

So make each publisher its own baseline: compare quote versus statement within the same site, holding audience and topic mix constant.

Across 324 English and 439 French publishers with enough of both formats — at least 50 quote and 200 statement articles each:

Lang	Publishers	Quote wins (median site)	Quote wins (mean site)	Median within-publisher Δ
EN	324	31.5%	55.9%	+3.1%
FR	439	47.6%	57.4%	+5.5%

In English, statements outperform quotes at 68% of publishers by the median; quote-led headlines hurt more often than they help. In French, the result is close to a coin flip.

That leaves the underlying format effect at roughly +3% to +5%—about five to nine times smaller than the aggregate figure.

(The mean is higher than the median because a minority of publishers see large gains from quotes. The median is the more reliable measure of the typical publisher.)

Stop here and the lesson sounds like “segment your data.” But the collapse points to something larger.

If three-quarters of a +37% effect was really a publisher effect, the obvious next question is: what else is the headline metric standing in for?

The rest of this article is a tour of those hidden variables. And by this point, the answer to claim 3 is already coming into view: the format itself isn’t the driver.

The same substitution, in reverse: questions

The conventional advice says questions underperform by roughly 24%. The aggregate view of our data says the opposite: questions outperform statements (+7% EN, +16% FR).

Both conclusions are wrong for the same reason. Question headlines are disproportionately used by high-engagement publishers, which inflates their aggregate performance.

Within publishers, the picture settles.

In English, question headlines show a modest real underperformance (-3.7%), winning at only 29.3% of sites. In French, the effect is essentially neutral (-0.5%), with questions outperforming at 46.2% of sites.

The conventional advice gets the direction roughly right in English and neutral in French, but its usual magnitude is about sixfold too large.

The question mark isn’t the cause. The kind of publisher using it is. Same hidden variable, opposite sign.

The effect won’t even hold still

Even that modest within-publisher effect drifts from month to month.

In English, it peaks at +2.5% and turns negative in March 2026, while statements outperform questions at 55% to 60% of sites each month. In French, it ranges from +3% to +12% — strongest in December and February, weakest in March — with no clear trend.

A genuine causal lever shouldn’t wobble like this. A correlation tied to a shifting content mix should.

Hidden variable 2: Which audience

The +3-5% average hides a sharp, consistent split. In English:

• Gainers: International general news (BBC +85%, Forbes +46%, CBS News +43%, Boston Globe), Yahoo aggregators, mass-market magazines (Parade, Good Housekeeping), Gizmodo.
• Losers: Specialist sport (RugbyPass, Planet F1, ThisIsAnfield), entertainment (IMDb, TVInsider, People), and factual-leaning dailies (Standard, Washington Post).

French data follows the same pattern in a different market.

• Gainers: Regional newspapers (La Dépêche, La Montagne, L’Écho Républicain) and general-interest magazines (Grazia).
• Losers: Specialist sports outlets (Foot National, le10sport, MadeInFoot), technology publishers (Les Numériques), and service-oriented titles (Journal des Femmes, Femme Actuelle).

The pattern is editorial, not algorithmic. Quotes tend to work where the audience comes for commentary, reaction, and framing, and fail where the audience comes for facts.

A publisher built around “what someone said” benefits from a quoted headline. One built around “what just happened” usually doesn’t.

The convergence between English and French is the giveaway. This isn’t a language effect; it’s a reader-intent effect.

What looks like a headline-format effect is, in this case, an audience effect wearing the clothes of a headline.

Hidden variable 3: Which Discover surface

Discover isn’t a single feed. It’s a collection of pipelines, each selecting articles in different ways:

• Editorial curation (moonstone, mustntmiss).
• The main topic-personalization engine (aura).
• Related-reading context (paginationpanoptic, content).
• Similarity-based recommendation (relatedcontentruby, userpersonascontent).

First, rule out the obvious alternative explanation. Are quote-led articles simply being routed to higher-value Discover surfaces, making the apparent bonus a placement effect rather than a headline effect?

The data says no.

Comparing where quote and statement articles actually appear, the distributions are nearly identical. In English, the largest differences are small: content.f (+2.2 percentage points), aura.f (-1.9), and moonstone.f (+0.6).

The bonus isn’t about placement: quotes and statements appear on the same surfaces in the same proportions. It’s about intensity — how each format performs once it’s on a surface. There, the overall +3% to 5% breaks into a wide range: from +22% to -14% in EN and from +25% to -12% in FR.

Grouped into functional families, the pattern is readable:

Pipeline family	EN	FR
Editorial curation (moonstone, mustntmiss, astria, news…)	+3.4%	+9.7%
Related reading / context (paginationpanoptic, content…)	+2.0%	+6.7%
Trends / freshness (deeptrends, freshvideos…)	+4.4%	+2.3%
Main personalization (aura)	+0.6%	+1.8%
Similarity-based recommendation (relatedcontentruby, userpersonas…)	-1.6%	-1.9%

Quote-led headlines win where multiple headlines compete for attention at once — curation carousels, news clusters, and other surfaces where the title carries a social signal: someone said this. They lose on similarity-based recommendations, where the surface sells continuity (“because you read X, you’ll read Y”) and a quote disrupts the topic-clear promise with an out-of-context citation.

The largest pipeline by volume, Aura, ranks on topic affinity and barely reacts to format at all, with gains of just +0.6% to +1.8%.

Why is the net effect so small?

A single quote-led FR article doesn’t get one number; it gets a blend:

• +10 to +25% on its curation share (moonstone, mustntmiss, astria)
• ~0% on its aura share, the largest slice of volume
• -3% on its relatedcontentruby share (≈ 10% of captures)
• -2 to -6% on shopping/viewer-related surfaces

Integrate those and you land at +4% to +7% net. The curatorial gains are real but partly offset by recommendation losses, which is why the aggregate is nowhere near +29%. The same format is both an asset and a liability, depending entirely on the surface serving it.

And +4–7% overstates how much the format itself matters because each pipeline’s ranking is a compound of signals unrelated to the title: engagement, scroll depth, topic affinity, E-E-A-T, entities, reading history, location, timing, and prior interactions.

A quote in the headline is, at best, one weak signal competing with all of those. Long before an article reaches a feed, it’s largely swamped by everything else.

Questions by pipeline, same story sharper

These are within-publisher medians (each publisher against itself), so they aren’t a crude artifact of FR using more questions. The format follows the same pipeline logic as quotes, but in a more polarized form:

• FR curation leans positive on questions; EN curation leans negative. astria.f, the same pipeline in both languages, runs +9% in FR and -1% in EN; FR mustntmiss.f is +14%, EN moonstone.f is -13%.
• Similarity-based recommendation penalizes questions everywhere, harder than quotes: relatedcontentruby.f FR -11.5% (306 publishers), EN -6.1% (119); itemitemcollaborativefiltering.f FR -14.5%.
• aura stays neutral in both (+3.5% FR, -0.6% EN).

Two caveats point in the same direction:

• A fleet-capture metric can’t distinguish an algorithmic penalty from an audience-eviction effect: readers see a question mark, decide “not now,” and scroll past. The fact that relatedcontentruby — which serves already-engaged readers — penalizes questions this heavily points to a behavioral signal, not just ranking.
• Within-publisher pairing controls for each publisher against itself, but the median is still computed across a different set of publishers in FR and EN, on partly different surfaces. So “FR rewards questions, EN doesn’t” describes the publishers and topics occupying each cell, not an inherent property of the language or the question mark. It’s another hidden variable mistaken for a format effect.

Hidden variable 4: Which editor, and which judgment

Even the honest +3% to 5% comes with a caveat that outweighs its size. When a publisher writes a headline as a quote, they choose the best available quote for that story. So the within-publisher figure compares the best quote an editor selected with the average of all that publisher’s statements, not the same article written two ways.

It’s the subject-line A/B testing problem: a good alternative beats a bad one, but the average alternative doesn’t. Convert every headline to quote-led and you’d be writing average quotes, so most of the gain would disappear. The +3–5% is an upper bound on a selective practice, not the return from a blanket rule.

That’s the final reason “do it everywhere” fails:

• Not every article has a quote. A sports result, a press release, a market analysis, a product test: forcing one means fabricating it.
• The editor-selection bias above: The measured bonus is the best quote chosen, not a property of the format.
• Recommendation pipelines are long-tail levers. relatedcontentruby and friends are how an article redeploys after its initial peak, the main mechanism for extending Discover lifetime. Optimizing the headline for the curation peak while breaking the promise on these surfaces can net negative.
• The largest pipeline barely reacts. aura is 11% to 15% of FR captures and 7% to 9% of EN, with a +0.6% to 1.8% quote effect. A universal quote rule optimizes secondary surfaces while ignoring that the biggest one runs on topic affinity.

The clincher: the same format, opposite meaning

We excluded YouTube and X from the main corpus, but their results are the clearest proof of the thesis. The same quote-led format produces opposite effects depending entirely on what the title is trying to do.

Domain	Lang	Quote articles	Statement	Mean hits quote	Mean hits stmt	Δ
YouTube	EN	43,476	734,986	11.6	10.2	+14%
YouTube	FR	16,509	93,912	59.0	29.1	+103%
x.com	EN	34,156	268,175	5.2	4.9	+6%
x.com	FR	32,201	114,914	21.4	24.6	-13%

On YouTube, the title is effectively a text thumbnail that has seconds to create curiosity. A quote serves as a content promise — “here’s the line worth hearing” — which helps explain the +103% result in French. On X, the title is the post itself, and a detected quote usually indicates that someone is repeating or responding to another person’s words, diluting the original message. That correlates with a -13% result.

Same characters. Same regex. Opposite outcome. The format didn’t change; the job it was doing did.

(Methodological footnote: a naive audit that folded YouTube into the editorial corpus would inflate the overall quote bonus by 20–30 points, while one that folded in X would dilute it. Any serious headline study has to isolate editorial articles before measuring headline effects.)

The headline was never the variable

Put the layers together. Three-quarters of the +37% raw bonus was explained by publisher differences. What remained split again by audience, then by Discover surface, then by which quote the editor selected, and finally reversed entirely when the title served a different function on another platform. At every step, removing context shrank or flipped the apparent format effect.

There’s no clean residue at the bottom where the headline acts independently. The effect is inseparable from the context that creates it.

That’s not a measurement failure; it’s the finding. We just saw the mechanism. Headline format is one weak signal among many stronger ones, all moving through pipelines that often pull in opposite directions.

The consequence is the point. An article’s visibility is the running score of that entire contest, not the verdict of any headline rule. A number measured across publishers is downstream of everything that travels with the format: who published it, what topic it covers, what the audience expects, the newsroom’s style and habits, and the conventions of the language itself.

So when an aggregate reports “+29% for quotes,” it isn’t isolating the quotation marks. It’s measuring a correlation with that whole bundle of factors and quietly relabeling it as causation.

None of this means aggregate data is the enemy. Everything above comes from aggregate data, just analyzed at the right level.

The trap is narrower: treating a single cosmetic variable, averaged across publishers that don’t belong in the same category, as a causal lever.

The same index that exposes that mistake also reveals the signals that genuinely drive Discover: which topics a publisher wins on, which entities are accelerating, who dominates a given surface, and what’s trending before it peaks. Those signals aren’t cosmetic, and they aren’t drowned out by stronger forces. They’re the underlying demand that headline format only weakly approximates.

The lesson isn’t “ignore the data.” It’s “stop averaging the wrong variable across the wrong population.”

This is why no cross-publisher average, corrected or not, converts into a rule for your site:

• Visibility isn’t traffic. Two sites can earn identical Discover visibility on the same article and see very different CTRs because their audiences click for different reasons.
• No two audiences are the same. A quote that reads as insider commentary to a magazine reader may read as vague or irrelevant to someone scanning sports scores.
• A cross-publisher average of one cosmetic feature is the average of audiences you don’t have. Segment by your audience, your topics, and your surfaces, and it becomes information again.

The only test that answers your question is the one you run on your own site, with your own audience. Know who you’re writing for, then measure them. Slice the data by your audience, your topics, and your surfaces — not by a single number averaged across everyone.

So what about the three claims?

Each is real as a correlation and useless as a cause:

• “Quotes beat statements by ~29%”: True in aggregate — larger than +29%, in fact — but mostly explained by publisher differences. At the publisher level, the residue is +3% to 5%, and even that compares the best quote an editor selected against the average of all statements, not the format itself.
• “Questions underperform”: Directionally true in EN, neutral in FR, but the magnitude is about 6x too large. The actual effect is roughly -4% in EN and ~0% in FR.
• “The format itself is the driver”: The claim the dataset refutes. The same article from the same publisher, mechanically rewritten as a quote, would not gain the aggregate effect.

The honest version, if you want one sentence to keep:

A quote-led headline can earn roughly +3% to 7% additional Discover visibility for audiences that value commentary and framing (general news, magazines, regional press), especially on curation surfaces, and lose for factual audiences (sports, tech, utility) and on similarity-based recommendation surfaces. There is no universal gain from quotation marks; the popular ~+29% figure overstates the format effect by roughly an order of magnitude. The useful question isn’t “Should I use a quote?” but “Who am I writing for, and which Discover surface drives my traffic?” The only place to answer that is with your own site, not anyone else’s average.

Methodology

• Data and period: 1,674,518 EN and 1,690,295 FR editorial articles with Discover visibility from 1492.vision proprietary data, collected between 2025-11-01 and 2026-05-19. Editorial articles only; excludes ads, videos, AI Overviews, and showcases. Domain exclusions: x.com, twitter.com, m.twitter.com, youtube.com, www.youtube.com, and m.youtube.com (reported separately above).
• Headline format detection (regex): Quote-led: title starts with a multi-word quoted phrase (“…”, «…», ‘…’, or ‘X…’:). Quote inside: a quoted phrase appears but not at the start. Question: ends with ?. Statement: everything else. Titles under 20 or over 300 characters are excluded. Detection deliberately errs toward false negatives in the quote bucket, biasing against finding a quote effect, so the +3–5% is conservative.
• Three layers of analysis: (1) Raw aggregate: all publishers pooled, producing +37% / +48%. (2) Within-publisher: quote vs. statement inside each publisher with ≥50 quote and ≥200 statement articles; we report the share of publishers favoring quotes and the median per-publisher Δ. This neutralizes publisher-mix bias. (3) Monthly evolution: the same pairing, recomputed monthly with relaxed thresholds (≥10 quote, ≥40 statement).
• Pipeline layer: Captures come from 1492.vision proprietary data, with each row representing one capture on a specific pipeline. For each (pipeline, format, publisher), captures per article = pipeline captures ÷ distinct articles. Within-publisher pairing includes publishers with ≥20 quote (or question) and ≥60 statement articles on that pipeline. A pipeline is shown only if ≥5 publishers qualify. Pipeline families are an empirical grouping (editorial curation, related reading, trends, similarity-based recommendation, and main personalization) that reflects how each surface behaves.
• Metric: A “hit” is one capture of an article on Discover by the 1492.vision device fleet. It is a visibility proxy, not a visit.
• Known limitations: (1) No traffic data: the metric is Discover visibility, not clicks, so a format could affect CTR independently without appearing here. (2) Regex detection misses edge cases and is biased toward under-counting quotes. (3) Within-publisher effects compare the best quote an editor selected against the average statement, not the counterfactual of making every headline quote-led. (4) Some negative pipelines have small publisher samples (<10); the consistent direction matters more than any individual magnitude.

Google is rolling out a series of Smart Bidding and budgeting updates designed to help advertisers uncover new demand, capitalize on seasonal opportunities and maintain more predictable campaign performance.

What’s new. The updates include an expansion of Smart Bidding Exploration, a new Promotion Mode beta and changes to bidding target optimization for budget-constrained campaigns.

Driving discovery. Smart Bidding Exploration now allows advertisers to set a return on ad spend (ROAS) tolerance that enables campaigns to pursue additional conversion opportunities from search queries they may not currently be capturing.

Google says campaigns using the feature see, on average, an 18% increase in unique converting search query categories and a 19% increase in conversions.

The company is expanding the capability to Performance Max campaigns without product feeds and opening a beta for Shopping ads across both Performance Max and Standard Shopping campaigns.

Peak period bidding. Promotion Mode allows advertisers to temporarily adjust ROAS targets and allocate additional daily budget during high-demand periods such as seasonal events, product launches and flash sales.

What else is changing. Beginning Aug. 17, Google will update bidding target optimization for campaigns limited by budget, with the goal of delivering more consistent performance that better aligns with advertisers’ CPA and ROAS targets.

Starting July 6, advertisers will begin receiving notifications in Google Ads if campaign adjustments may be needed.

Why we care. These updates give Google’s AI bidding systems more freedom to find incremental conversions beyond existing keyword and audience patterns, potentially unlocking new demand that campaigns might otherwise miss.

The new Promotion Mode is particularly relevant for retailers and seasonal advertisers, as it allows temporary adjustments to ROAS targets and budgets during peak demand periods without requiring major campaign restructuring. Meanwhile, the bidding optimization changes aim to make performance more predictable for campaigns that are constrained by budget.

The bottom line. Google’s latest bidding updates are designed to help advertisers find new conversion opportunities, respond more aggressively during peak demand periods and maintain steadier performance as campaigns scale.

Google is broadening its Limited ad serving policy on Search, giving itself more authority to restrict impressions from advertisers it considers unqualified or potentially confusing to users.

The update could affect how frequently ads appear on certain searches, particularly for newer advertisers, brands with poor user feedback or advertisers whose identity is not clearly communicated in their ads.

What’s changing. Starting this month, Google expanded the policy to cover additional Search scenarios, with implementation rolling out gradually through 2028.

Under the updated rules, Google may limit ad impressions on searches that it believes have a higher risk of creating negative user experiences.

How Google decides. User feedback will play a larger role in determining whether an advertiser is qualified. Advertisers that receive persistent and disproportionate reports about misleading content, products or business practices may see their ads restricted on certain searches.

Google also says it may limit ads that make it difficult for users to identify who the advertiser actually is.

Why we care. Google is applying more discretion to limiting ad visibility, making it based on advertiser trust signals and branding clarity, not just policy compliance. That means advertisers with generic ad copy, unclear brand identity or a history of negative user feedback could see reduced reach on certain searches.

The change also reinforces the growing importance of brand transparency in Search ads. Advertisers may need to revisit ad copy, landing pages and branding elements to ensure users can immediately identify who is behind an ad and why they’re seeing it.

What advertisers should do. Google is encouraging advertisers to strengthen brand visibility across both ads and landing pages, avoid overly generic messaging and clearly communicate any affiliation with other brands.

The company also recommends pinning a domain headline in the first position of responsive search ads to make advertiser identity more obvious to users.

The bottom line. Google’s updated policy gives greater weight to advertiser trustworthiness and clear branding, potentially limiting visibility for advertisers whose identity or business practices create confusion for users.

First spotted. This update was spotted by Founder of Adsquire, Anthony Higman, who shared his displeasure of this update on LinkedIn.

Looking to take the next step in your search marketing career?

Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.

Newest SEO Jobs

(Provided to Search Engine Land by SEOjobs.com)

• Senior SEO Growth Manager
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• SEO Success Manager
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  Botify’s leading agentic AI search technology and seasoned experts ensure every brand has the power to be found, both in traditional and AI search. With one powerful platform, brands achieve visibility, relevance, and greater control across Google, Bing, ChatGPT, Perplexity, and more. Botify’s technology powers agentic workflows, AI-driven recommendations, and automated cross-platform indexation and deployment. […]
• Growth Marketer
  June 15, 2026
  ABOUT THE ROLE We’re looking for a Growth Marketer to own the entire lead gen cycle. You’ll be the one turning heads and converting them into qualified leads (MQLs), pipeline opportunities (SQLs), and new revenue. This role is focused on building and scaling non-traditional lead gen paths that reach customers where they actually hang out. […]
• VP / Head of Search & AI Visibility
  June 15, 2026
  VP / Head of Search & AI Visibility Location: United States (Remote / Hybrid Preferred) Reports To – President/Founder Company: Milestone Inc. (direct hire) Term: Full-time About Milestone Milestone Inc. is a leading Digital Experience Software and Services company dedicated to providing comprehensive solutions across all touch points that enhance customer engagement and drive business growth. […]
• SEO Strategist
  June 12, 2026
  Clarity is the Global Growth Consultancy for B2B technology brands. As a senior-led consultancy, we align leadership, markets, and execution to turn complex growth ambitions into commercial momentum. Operating from hubs in London, New York, Amsterdam, and Sydney, our 100+ global team helps leaders navigate high-stakes growth tensions across Enterprise Tech, FinTech, Cybersecurity, and HealthTech. […]
• Client Account Manager
  June 9, 2026
  We’re a content and organic discovery agency that helps brands show up in the right places — Reddit, YouTube, editorial, AI search. Our team is small, the work is real, and everyone here actually cares about doing it well. We’re looking for a Client Account Manager to be the connective tissue between our clients and […]
• SEO Link Builder
  June 8, 2026
  ABOUT NOGIGIDDY NoGigiddy is a digital platform built for gig workers, side hustlers, and anyone building an income outside the traditional 9-to-5. We connect our community with real earning opportunities — remote jobs, surveys, gig platforms, and financial tools — all in one place, free to access, no gatekeeping. We built what we wish had […]
• Senior Content Marketer, SEO
  June 7, 2026
  Animalz is a content marketing agency that partners with B2B SaaS companies, venture capital firms, and other tech organizations to drive long-term, sustainable growth through high-quality content. Our fully remote team of strategists and content marketers delivers content strategies tailored to each customer’s goals and context. We pride ourselves on our deep interest and understanding […]
• Director of SEO
  June 6, 2026
  Job Description:   The Director of SEO will be responsible for leading the SEO department, including overseeing the daily operations of the team, and setting the direction for future SEO growth and product efforts. The Director of SEO will lead the SEO management team and be responsible for offering SEO expertise to the team, establishing […]
• SEO & Web Analytics Manager
  June 6, 2026
  Description Are you an SEO professional who enjoys solving technical challenges, uncovering insights in data, and helping organizations improve their visibility in search? Interactive Strategies, a leading digital agency in Washington, DC, is seeking an SEO & Web Analytics Manager to join our Data Insights team. This is a hands-on role focused on technical SEO, analytics reporting, […]

Newest PPC and paid media jobs

(Provided to Search Engine Land by PPCjobs.com)

• Paid Search Analyst
  June 19, 2026
  Description The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the […]
• Global Paid Media Manager
  June 19, 2026
  At NAVEX, we’re transforming the world—making it safer, more ethical, and ensuring every voice is heard. That’s real impact.Our high-performance culture is driven by our values.  We move with speed, passion and purpose — as one team. We are bold in our ideas, accountable in our actions, and committed to doing the right things right. NAVEX is seeking a Senior Global Paid Media Manager to […]
• Growth Marketing Director (Remote)
  June 19, 2026
  The Growth Marketing Director drives sustainable revenue growth through data‑driven acquisition, retention, and lifecycle marketing strategies across Cengage. You’ll have the opportunity to build and scale a high-impact growth engine and shape how we acquire and convert customers at scale, partnering closely with Portfolio, Product, Sales, and Marketing to accelerate customer and revenue growth. What […]
• Senior Associate, Paid Media
  June 19, 2026
  We are in relentless pursuit of an equitable and inspiring workplace that is respectful of all, reflects and represents the world in which we live, and fosters trust, collaboration and belonging. Consistent with this approach, we hire the best qualified candidates for all positions. Are you curious by nature? Do you always seek to understand […]
• Growth Marketing Lead
  June 19, 2026
  Vendelux is transforming how companies discover, evaluate, and maximize the impact of events. Event marketers are the driving force behind pipeline and brand — yet events remain one of the least optimized and most opaque marketing channels. Vendelux changes that. We provide the system of record for event marketing, giving teams the data and insights […]

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• Salary: $95,000 – $115,000
• Serve as the day-to-day owner of all paid digital advertising efforts across the organization.
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• Salary: $75,000 – $90,000
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Search Engine Optimization Manager, Seer Interactive (Remote)

• Salary: $70,000 – $100,000
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• Serve as the primary point of contact for clients, guiding strategic conversations around organic growth, AI Overviews, LLM-powered discovery tools, and how evolving search behavior impacts brand visibility and competitive positioning.

SEO Manager, Prosum (Dallas-Fort Worth Metroplex)

• Salary: $114,000 – $148,000
• Develop and execute multi-brand SEO and search visibility strategies aligned to revenue, traffic, and share-of-voice goals.
• Manage internal and external resources, including contractors and cross-functional partners, to deliver against SEO and search visibility initiatives.

Senior Manager SEO/Gen AI (FTC), Jellyfish (Hybrid, New York, US)

• Salary: $90,000 – $115,000
• Act as the lead interpreter of search performance and visibility quality across both traditional and AI-led discovery experiences
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Note: We update this post weekly. So make sure to bookmark this page and check back.

Microsoft Ads is introducing Product Explorer, a new reporting tool designed to give advertisers a centralized view of product catalog health and performance, according to Microsoft Product Liaison Navah Hopkins.

Managing large product feeds can make it difficult to identify which items are eligible to serve, generating impressions or missing critical data. Product Explorer aims to simplify that process.

What’s new. Product Explorer provides a searchable view of an advertiser’s entire product catalog, allowing users to filter products by attributes such as SKU, title, GTIN and product ID.

Advertisers can quickly see which products are active, serving ads and driving performance.

What it does. The tool highlights eligibility issues, metadata gaps and other factors preventing products from serving. It also surfaces recommended actions and allows advertisers to export filtered product lists for further analysis.

Why we care. By combining feed diagnostics and performance reporting in a single interface, Microsoft is making it easier for advertisers to move more products into a servable state and identify underperforming inventory.

Advertisers will now get searchable catalog reporting, product-level performance data covering the previous 30 days, issue detection and actionable recommendations to improve feed quality.

The big picture. Retail advertisers are increasingly focused on feed quality as product-based advertising becomes more automated. Visibility into catalog issues can have a direct impact on campaign reach and performance.

Availability. Hopkins said it should be live in accounts already.

Google Analytics is introducing a new Source Group reporting dimension and hostname filtering controls aimed at improving attribution analysis and data quality.

The updates are designed to help advertisers clean up fragmented traffic source reporting, better analyze cross-channel performance and reduce noise in their analytics data.

What’s new. Source Group is a new reporting dimension that consolidates multiple variations of the same traffic source into a standardized category.

For example, traffic from Facebook can now be grouped under a single reporting value instead of appearing across multiple naming conventions such as “facebook,” “fb” or other variations.

At the same time, Google is updating its existing Source Platform field to align with the new grouping structure and provide more consistent classifications across advertising channels.

Why we care. Cleaner source classification means more accurate attribution and cross-channel reporting. Instead of traffic being fragmented across inconsistent labels, marketers can more easily understand which platforms are actually driving conversions and where budgets are performing best.

The inclusion of AI traffic sources like ChatGPT and Perplexity is particularly noteworthy, as it gives advertisers a standardized way to measure and compare emerging AI-driven referral traffic alongside traditional channels. The new hostname filters also help improve data quality by ensuring only traffic from approved domains is included in reporting.

The big picture. As advertisers manage campaigns across a growing number of platforms, inconsistent source naming can make attribution and budget analysis more difficult. The new reporting structure is intended to simplify performance comparisons across channels.

Between the lines. The update expands source standardization beyond Google’s own properties, creating consistent classifications for platforms including TikTok, Pinterest and Amazon while also introducing support for emerging AI-driven traffic sources such as ChatGPT and Perplexity.

Also new. Google is launching hostname filters within the Admin section, allowing advertisers to exclude events from unapproved domains before they enter reporting.

The feature is designed to help improve data accuracy by preventing unwanted traffic from influencing analysis.

What advertisers get. Standardized source reporting, retroactive access to historical source group data, cleaner attribution analysis and greater control over which domains contribute data to reporting.

The bottom line. Google is adding new tools to help advertisers improve reporting consistency, strengthen attribution analysis and maintain cleaner datasets as traffic sources become more fragmented.

Claude may be more directly tied to Brave Search rankings than other AI answer engines, according to information Jonathan Clark shared on LinkedIn from a Zero Click by Profound session.

Clark, managing partner at Moving Traffic Media, said the session’s key takeaway was that Claude “doesn’t re-rank search results” and instead appears to use Brave’s top 10 results directly in its answers.

Claude searched less often. Claude used web search in 36.6% of prompts, compared with about 90% for ChatGPT, according to Clark.

• Claude was most likely to search when prompts signaled freshness, rankings, location, or comparison intent. Recency-focused prompts such as “best XYZ” triggered search 81% of the time, while ranking-focused prompts triggered search 67% of the time.
• Location-focused prompts triggered search 55% of the time, while comparison prompts such as “X vs. Y” triggered search 51% of the time.

Brave rankings carried weight. Claude’s citations overlapped with ChatGPT’s in only 8% of cases when responding to the same prompts, according to Clark.

• Claude’s results had much higher overlap with Google rankings, at 64%. This suggests that Google SEO efforts may carry over more readily to Claude than strategies focused specifically on improving visibility in ChatGPT, according to Clark.
• The finding also increased the importance of Brave rank tracking. Clark said Claude uses Brave, and that ranking well in Brave gives us “something we can monitor and correlate to data.”

Some prompts stayed in memory. Prompts such as “how does,” “what is,” and “steps to” were less likely to trigger Claude to search the web. When Claude doesn’t search, it can’t cite web pages. Claude searched most often for prompts containing terms such as “best,” “top,” “near me,” and comparison-style queries, according to Clark.

Years showed up often. Clark also noted two patterns that could make Claude easier to test:

• Claude’s query fan-outs were nearly deterministic, producing the same fan-out 65% of the time across users.
• The fan-outs often included years.
  • That means page titles with current-year signals may have an advantage in Claude-triggered searches, especially for ranking and freshness-driven prompts.

Why we care. Claude visibility appears to depend more heavily on ranking in the search results Claude uses. Clark’s takeaway was that Claude may be one of the most optimizable AI answer engines today because its search behavior appears more consistent and more closely tied to observable search rankings.