OpenAI is rolling out the ability to upload audience lists to ChatGPT Ads. The option is under the “Tools” section and is named “Audiences.” I assume this lets ChatGPT Ads target based on the audience lists you upload to the ad platform for better ad targeting.
More details. You can upload raw or hashed emails and phone numbers to use as audience filters for your campaigns on ChatGPT Ads.
What it looks like. We spotted screenshots of this from Craig Graham and Joss Froggatt on LinkedIn – here are those screenshots:
Why we care. OpenAI continues to add more customizations and targeting options to its new ChatGPT Ads platform.
This should make for a more robust ad platform for advertisers and marketers and improve the overall conversions and ROI of the ads.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/audiences-chatgpt-ads-6N5Akr.jpg?fit=833%2C577&ssl=1577833Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-07 11:27:152026-07-07 11:27:15ChatGPT Ads rolling out audience lists
OpenAI rolled out a new feature to let ChatGPT Ads generate ads for you – I suspect using AI. The feature is available under the “Add new ad” option, and says “generate ads for you.”
The advertiser or marketer can then opt to let ChatGPT create the ad, and then review, edit and approve the ad for delivery on the ChatGPT Ad platform.
What it looks like. Anthony Higman posted a screenshot of this feature on the social media platform named X. Here is that screenshot:
As you can see, it says, “We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you’re ready.” Then you can “Review and create.”
There is also a quick duplicate ad option that Higman spotted as well:
Why we care. It makes sense that OpenAI would leverage AI to help advertisers create ads. The hope is it would lead to more ads being created and submitted on ChatGPT Ads sooner. This way the AI company can earn more money on ChatGPT.
As a marketer, you should be very careful when using AI to generate ads. Make sure to review the ads that were generated for you carefully so they meet your branding and marketing criteria, including your ROI goals.
Be the brand AI recommends.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
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Twelve months ago, the industry was betting on which AI platform would win discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. Neither bet paid off.
Previsible (disclosure: I’m its CPO and co-founder) just published its third AI Traffic Study, analyzing 6.77 million LLM-driven sessions. The data shows consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026. And 92.4% of that traffic comes from one platform.
The plateau was a pause
In mid-2025, AI traffic appeared to be peaking in some sectors. It wasn’t.
Sessions rose from 65,249 in November 2024 to 396,278 by August 2025, then dropped sharply in November 2025, before hitting new highs of 428,203 in February 2026 and 644,478 in May.
That November dip needs context.
Sessions fell 50% in one month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms held steady. This was likely a model-related change; we’ve seen modest product tweaks massively swing referral traffic, like last fall when many sites lost half their ChatGPT traffic because the model began favoring Wikipedia and Reddit. Sessions recovered to 442,609 by December.
The lesson: one vendor’s product decisions can halve your AI traffic overnight. Plan for the volatility.
Consolidation, not competition
When we last published in December 2025, ChatGPT held roughly 84% share, followed by Perplexity at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field has collapsed toward the leader.
Across the full dataset, ChatGPT commands 92.4% of trackable LLM referral traffic, growing 12.8x over 19 months with no sign of slowing. It’s the only LLM sending meaningful referral volume at scale. Optimizing for “AI visibility” without prioritizing ChatGPT means optimizing for an abstraction.
Important framing: this measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined, but it operates on a different measurement paradigm and is excluded here.
The challengers flipped
The surprise isn’t at the top. It’s who’s moving underneath.
Claude
Claude grew 64x, from 133 sessions in November 2024 to 8,528 in May 2026, and overtook Perplexity in March 2026 for the first time. It stayed ahead.
Claude was flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage the industry expected Copilot to win may be materializing for Claude instead.
If your audience includes technical buyers, developers, or professional services, Claude visibility is now material, and the window for early positioning is open.
Gemini
Gemini is the quiet number two: 3.2x growth with almost no volatility. Its Workspace and Android integration mean referral numbers likely undercount its real discovery footprint.
Perplexity & Copilot
Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot collapsed 96% from its August 2025 peak, from 8,651 sessions to 339.
Neither is a growth bet for traffic acquisition anymore. Both are shifting toward keeping users inside their own experiences: browsers, agents, and modes where they don’t need to send you traffic at all.
Where LLMs send users, and why it should change your roadmap
The study’s most actionable finding isn’t market share. It’s landing pages.
ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search.
The model trusts your domain but can’t pick the right page, so it sends users to your search box and lets them navigate. This pattern persists across verticals and time periods, suggesting it’s structural to retrieval-augmented generation rather than a temporary quirk.
Think about what that means. The model did the hard work of choosing your domain. Your internal search UX now determines whether that high-intent visit converts or bounces.
For most sites, internal search is a neglected navigation feature, not an acquisition surface. That has to change.
The vertical view tells several different stories:
SaaS traffic lands on search pages (34.6%).
Publisher traffic lands on news pages (54%), yet against 120+ million organic sessions, publisher penetration is 0.11%; publishers produce the content LLMs cite and capture almost none of the resulting traffic.
Ecommerce traffic lands on product pages, with purchase intent already formed.
Health traffic lands on About pages (42.1%), with users evaluating the source before the content.
Legal traffic spreads across blog, about, contact, and location pages: the full evaluation arc.
The platforms have personalities, too:
ChatGPT and Gemini are search-pattern models: domain trust, page-level uncertainty.
Perplexity and Claude are content-selection models that pick specific pages and over-index on long-form.
If your strategy depends on editorial content driving qualified traffic, Perplexity and Claude matter disproportionately to their share.
What to do now
Optimize for ChatGPT first. Expand elsewhere when volume justifies it.
Monitor Claude. It overtook Perplexity in March. Early positioning compounds.
Treat product pages as AI entry points. Product pages capture 43% of e-commerce LLM traffic. Structured, comparable product data is a discoverability requirement now.
Make pricing machine-readable. “Contact us for pricing” gives AI systems nothing to summarize, compare, or recommend.
Prioritize internal search. It’s an acquisition tool, not a navigation feature.
Track AI traffic by page type, not site-wide. Your site average hides where AI traffic concentrates. Your pricing page might run 3x your site-wide penetration.
The next question is the one nobody has answered: conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce?
We built this dataset to answer that. If the last 19 months are any indication, the answers will change faster than most teams are ready for.
About the data
166 GA4 properties, November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so the trajectories reflect behavioral change rather than sample expansion.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/ai-traffic-search-EtDxTJ.webp?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-06 16:15:002026-07-06 16:15:00ChatGPT commands 92% of AI referral traffic. Here’s what 6.77 million sessions reveal.
AI visibility and AI profitability are not the same thing. Most teams are growing one without building the other.
The four most common failure modes are optimizing for mentions over conversions, measuring AI visibility like rankings, chasing tactics without a revenue connection, and running AEO/GEO in a silo.
AI-referred visitors convert at 8.3 times the rate of traditional traffic, close 62 percent faster, and generate 7 times more revenue per visitor. Those numbers only hold if your conversion architecture is built to receive them.
The highest-performing campaigns share four traits: retrieval-ready content, strong authority signals, multi-channel distribution, and conversion systems designed for low-click environments.
You can start building toward profitability in 90 days without a full overhaul, but the phases have to run in order.
You might be showing up in ChatGPT answers. Getting cited in Google’s AI Overviews. Watching your brand mentions climb across the web.
And still not seeing it move the revenue needle.
That’s the problem a lot of marketing teams are grappling with right now. AI visibility is growing. Profitability isn’t keeping pace. After analyzing more than 100 AEO and GEO campaigns at NP Digital, I can tell you the issue isn’t the strategy itself. Most teams are simply optimizing for the wrong outcomes.
If you already know what AEO and GEO are and you’re ready to actually make money from them, this post is for you. I’m going to break down exactly where the profitability gap comes from, what the winning campaigns have in common, and how to build toward revenue, not just visibility.
Why Most AEO/GEO Efforts Don’t Make Money
Getting cited is not the same as getting paid. That distinction sounds obvious, but most AEO/GEO programs are structured around the former and hope the latter follows automatically. It doesn’t.
After auditing campaigns across industries, NP Digital identified four failure modes that consistently prevent AI visibility from converting into revenue.
Optimizing for mentions and citations. Mentions don’t pay the bills; conversions do. If your entire AEO/GEO program is oriented around getting named in AI responses, you’re measuring a proxy, not an outcome. A citation that doesn’t connect to a conversion path is brand awareness you can’t prove.
Measuring AI visibility like rankings. Citation volume tells you nothing about pipeline. Teams that treat AI mention counts the same way they used to track keyword rankings end up with
dashboards full of activity metrics and no way to show leadership what any of it is worth.
Chasing AI-specific tactics in isolation. Schema updates, prompt engineering, entity optimization do matter, but tactics without distribution don’t compound. Teams that bolt on AEO/GEO tactics without building content and authority infrastructure underneath them tend to see short-term citation spikes that fade quickly.
Running AEO/GEO separately from revenue goals. This is the biggest one. Visibility disconnected from business outcomes is overhead. The teams getting budget approved for AI search have tied it to pipeline, not impressions.
NP Digital data tells the story clearly. AI visibility index climbed to 133 across tracked brands, while the profitability outcomes index reached 174. The gap between those two numbers is the opportunity this post addresses.
The Profitability Gap: What Changes When Buyers Use AI
Buyers who find you through AI tools are not the same as buyers who find you through traditional search. They arrive differently, they behave differently, and they convert differently.
The traditional funnel started with discovery through search, a click-through to compare options, an early-stage arrival that needed nurturing, and multiple touchpoints before a decision. The AI-influenced funnel runs differently. Research happens inside AI tools. Buyers validate brands before they ever click. They arrive later, already informed, and convert faster when trust exists.
That shift is an advantage, but only if your conversion architecture is built to receive it.
NP Digital data across 40-plus B2B and B2C campaigns makes the opportunity concrete. AI-referred visitors convert at 5.97 percent. Traditional traffic converts at 0.72 percent. Time to conversion drops from eight days to three. Revenue per visitor rises from $2.56 to $18.04.
The volume is still small. AI traffic accounts for about 0.58 percent of total traffic but drives 5.09 percent of sales. Lifetime value is also stronger at $325, up from $271 for Google-referred traffic.
The math works. But capturing those numbers requires a funnel built for visitors who arrive intent-driven rather than still in the research phase.
What the Profitable Campaigns Have in Common
Across the campaigns NP Digital analyzed, the ones generating real pipeline from AI search shared four traits. These traits reinforce each other, which is why building them together matters.
Content Built for Retrieval
The content types that drive both AI citations and conversions are high-intent formats that answer specific questions buyers ask when they’re close to a decision. Not top-of-funnel awareness pieces.
Comparison pages and alternatives content convert AI-referred traffic at 6.8 percent, the highest of any page type NP Digital tracked. First-party research and original data earn citations because they can’t be replicated elsewhere; they become reference points AI engines return to repeatedly. Bottom-funnel educational content and FAQ frameworks round out the top performers.
Format is as important as topic. Lists and listicles account for 48 percent of AI citations in NP Digital’s research. Step-by-step guides come in at 17 percent. AI engines pull from content structured for easy parsing. Content not formatted for retrieval tends not to get retrieved.
Strong Authority Signals
NP Digital scored six trust signals across ChatGPT, Gemini, Copilot, Claude, and Perplexity on a one-to-five scale. Third-party citations scored between 4.5 and 4.8, the single most consistent signal across every platform. Expert authorship scored between 4.0 and 4.6.
AI engines reward signals that are difficult to manufacture: named, credentialed authors; external sources citing your content; consistent brand presence across multiple platforms. Publishing on your own site still matters, but earning coverage and mentions outside it is what drives citations.
Multi-Channel Distribution
NP Digital tracked 75 brands across AI platforms and found a direct correlation between monthly publishing channels and AI visibility score. AI engines validate authority through repetition and consistency. Presence across YouTube, LinkedIn, Reddit, and PR channels signals to AI tools that your brand is real and relevant, not just self-published.
Conversion Architecture for Low-Click Environments
AI-referred visitors arrive pre-qualified. They’ve already done the research, compared options, and formed an opinion. A landing page designed for someone at the top of the funnel is the wrong tool for a visitor who’s already at the bottom.
The brands capturing revenue from this traffic have built accordingly: fast pages, strong trust indicators placed prominently, simplified calls to action, bottom-funnel calculators and tools, and conversational paths that confirm a decision rather than explain a product category.
How to Measure AEO/GEO for Revenue, Not Just Visibility
The metrics most teams track are measuring the wrong thing. Rankings, raw traffic, click-through rate, AI mention counts, these are visibility metrics. They tell you whether people are seeing your brand. They don’t tell you whether it’s generating revenue.
The teams getting AEO/GEO budgets renewed are the ones connecting citations to pipeline. That requires a different measurement stack.
Stop tracking: raw rankings, organic traffic volume as a primary metric, click-through rate, AI mention counts, raw citation tracking, vanity impressions.
Start tracking: influenced conversions, brand search lift, assisted pipeline, returning visitor quality, and conversion rate by intent source.
NP Digital’s outcomes-first measurement framework organizes this into three tiers. At the foundation: visibility and influence signals, including brand search volume, share of voice, community engagement, and earned media. In the middle: demand signals, including multi-touch attribution, AI-driven lead scoring, behavioral intent, and consumption depth. At the top: business outcomes, including revenue, CAC:LTV ratio, retention, expansion, and advocacy.
Build reporting from the bottom up. Track from the top down. The goal is a dashboard leadership reads as a business document, not a marketing activity report.
NP Digital research shows how much KPI priorities have shifted. Leadership priority for rankings dropped from 88 to 63 between 2024 and 2026. Pipeline contribution rose from 23 to 70. Revenue growth held steady at 96 to 98. Your measurement framework needs to reflect where leadership attention already sits.
A practical starting point: for every vanity metric on your current dashboard, add one outcome metric alongside it. That shift is often enough to change the budget conversation.
The 90-Day Plan to Turn AEO/GEO Into Revenue
You don’t need to overhaul everything at once. You do need to run the phases in order. Each phase builds on the one before it, and skipping ahead consistently produces weaker results.
Days 1 to 30: Audit and Fix the Foundation
Start by auditing your current AI visibility across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. Search your brand name and core topics. Note where you appear, where competitors appear instead, and where no one appears. Those gaps are your priority list.
From there, identify high-intent content gaps where competitors are getting cited and you aren’t. Improve structured formatting across your highest-traffic pages with clear headers, FAQ sections, and concise direct answers. Strengthen author and entity signals. Clean up trust indicators including reviews, third-party citations, and brand consistency across platforms. Apply schema and retrieval-friendly formatting throughout.
One consistent finding across NP Digital’s audits: brand authority, PR and mentions, and community visibility are almost always the lowest-scored areas. Start there before investing more in content production.
Days 31 to 60: Create and Distribute for Profitability
Create the content types that drive both citations and conversions: comparison pages, original research and proprietary data, buyer guides, and FAQ expansions. These formats earn citations and convert the traffic those citations send.
Distribute across LinkedIn, YouTube, PR placements, expert commentary opportunities, and community channels like Reddit. The goal is consistent presence across multiple ecosystems. AI engines validate authority through repetition across platforms, not just depth on your own site.
Days 61 to 90: Optimize Conversion and Measurement
With the foundation fixed and the content layer built, optimize for what happens when AI-referred visitors arrive.
Improve bottom-funnel UX for high-intent visitors. Add calculators, tools, and simplified calls to action. Optimize assisted conversion flows. On the measurement side, track influenced pipeline from AI-assisted traffic, compare conversion quality across platforms, and build an executive dashboard tied to revenue rather than visibility metrics.
The window to establish AI search presence is real and won’t stay open indefinitely. The brands building this infrastructure now are accumulating authority signals that compound over time and become increasingly difficult for competitors to overcome.
FAQs
How do you connect AEO/GEO to revenue?
The connection runs through your measurement framework and your conversion architecture. On the measurement side, track influenced conversions, assisted pipeline, and brand search lift rather than citation counts. On the conversion side, build landing pages and CTAs designed for visitors who arrive already informed. AI-referred visitors are pre-qualified and need a fast path to a decision, not an introduction to your product category.
What metrics should you track for AEO/GEO profitability?
Move away from rankings, raw traffic, and citation volume as primary KPIs. The metrics that connect to profit are influenced conversions, brand search lift, assisted pipeline, returning visitor quality, and conversion rate by intent source. Build toward a three-tier measurement stack: visibility and influence at the foundation, demand signals in the middle, and business outcomes at the top.
What content converts best from AI-referred traffic?
Comparison pages and alternatives content convert AI-referred traffic at 6.8 percent, the highest of any page type in NP Digital’s research. First-party research, bottom-funnel educational content, and FAQ frameworks also perform well. Format matters as much as topic. Lists and listicles account for 48 percent of AI citations because they’re structured for easy extraction.
Conclusion
The winners in AI search don’t just focus on earning the most citations but make sure they can turn citations into pipeline.
That requires connecting visibility to conversion architecture, measuring outcomes rather than activity, and building the content and authority signals that AI engines reward consistently over time. None of those things happen by accident.
The brands doing this work now are building compounding advantages. Authority signals accumulate. Citation patterns stabilize. Conversion infrastructure improves with data. Starting later means starting behind.
If you want support building an AEO/GEO strategy tied to revenue rather than just visibility, NP Digital’s team works through exactly this kind of profitability infrastructure with clients.
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Each month, we host the SEO update by Yoast covering the latest in search and AI. In this edition, Carolyn Shelby and Alex Moss discussed Google’s evolving stance on AI-driven search, publisher controls in the UK, and how to navigate visibility in an era where traditional SEO tactics are being reconsidered.
Watch the full recap on YouTube to dive deeper into these topics, hear some examples, and hear the answer to audience questions.
Remembering Bruce Clay
In this month’s SEO Update, we honor Bruce Clay, who recently passed away. He was a pioneer in SEO whose work shaped the industry. His mentorship and leadership left a lasting impact on professionals worldwide.
Google warns against manipulating brand mentions for AI
Google issued a clear warning: stop manipulating brand mentions to game AI systems. This includes tactics such as paying for unrelated brand citations, think dog food brands mentioned on sports betting sites, to artificially inflate perceived authority.
Why it matters:
Google’s message is simple: if your brand mentions are irrelevant or forced, they won’t help your authority. Worse, they might backfire as AI systems get better at detecting manipulation. Focus on earning genuine mentions from relevant sources instead.
Actionable takeaway:
Avoid paid or spammy brand mentions.
Build authority through contextually relevant citations.
If your mentions feel unnatural, they probably are.
UK forces Google to give publishers control over AI use
The UK’s Competition and Markets Authority (CMA) struck a deal with Google, requiring the company to let publishers block their content from being used in AI features, without hurting their standard search rankings.
Why it matters:
Publishers can now opt out of AI training data, but there’s a catch. If you block Google’s AI from using your content, you might lose citations in AI overviews, even if you rank well in traditional search. Users will instead see synthesized answers from other sources.
Actionable takeaway:
If your content is truly unique and proprietary, blocking AI access might make sense, but only if you have a monetization strategy beyond search traffic.
For most sites, allowing AI access is better for visibility. Ensure your content is structured and crawlable so AI systems can cite you accurately.
If you block AI access, provide a teaser, like Amazon’s “Look Inside” feature, to encourage clicks.
New AI visibility insights in Google Search Console and Bing Webmaster Tools
Both Google and Bing rolled out new reporting features to help you understand how your content appears in AI-driven search.
Google Search Console grounding queries
Google now shows grounding queries, the specific searches where your content was cited by AI. This helps you see which topics are driving AI visibility.
Why it matters:
Grounding queries indicate that AI systems are using your content to generate answers. If you’re not seeing citations, your content might not be structured or visible enough for AI to reference.
Actionable takeaway:
Check Search Console weekly for grounding queries.
Focus on visible, structured content, so avoid hiding key info in accordions or tabs.
Use this data to refine your content strategy, so double down on what’s working or fix what’s not.
Bing Webmaster Tools: AI performance reports
Bing’s new reports include intents, topics, citation share, and performance comparisons for AI-driven search. This gives you a clearer picture of how your content performs in Bing’s AI experiences, like Copilot.
Why it matters:
Bing’s AI integrations, such as Copilot in Windows, reach millions of business users. Ignoring Bing means missing out on a growing segment of AI-driven traffic.
Actionable takeaway:
Set up Bing Webmaster Tools if you haven’t already.
Compare Bing’s data with Google’s to spot gaps or opportunities.
Use LLMs like ChatGPT or Claude to analyze exports from both tools for deeper insights.
Google’s new publisher profiles and business data integrations
Google introduced publisher profiles and enhanced business data integrations, giving creators and businesses more control over how their content appears in search.
Why it matters:
These tools help you fill out your knowledge graph, which improves visibility across Google’s ecosystem, including Gemini. Think of it as Google+ for publishers, but with a focus on entity authority rather than social networking.
Actionable takeaway:
Create or update your publisher profile in Google Search Console.
Ensure your Google Business Profile is complete and accurate.
Use structured data to connect entities such as authors, brands, and products to your content.
Google updates SEO guidance: Don’t blindly trust AI or SEO tools
Google’s latest guidance warns against blindly following AI-generated SEO advice or third-party tool recommendations. The example? An AI suggested changing “consultant” to “advisor” for a site, only for the site to start competing with financial advisors instead of its actual audience.
Why it matters:
AI and SEO tools can misinterpret context. Always verify recommendations before implementing them.
Actionable takeaway:
Trust but verify, so use AI and tools for ideas, but apply critical thinking.
Check multiple sources, so compare Google’s data with Bing’s, or use tools like Semrush/Ahrefs for cross-referencing.
Prioritize human judgment, because if a recommendation feels off, it probably is.
Schema.org released data showing that 95% of websites use only 12 of the 958 available schema types. Meanwhile, fewer than 1,000 sites use 485+ schema types.
Why it matters:
Schema helps search engines understand your content, but most sites aren’t leveraging its full potential. Using more schema types can improve visibility in AI-driven search and rich results.
Actionable takeaway:
Audit your current schema usage to identify any missed opportunities.
Explore less common schema types, like FAQPage, HowTo, or Event to stand out.
Use Yoast SEO’s schema blocks to simplify implementation.
German court rules Google liable for false AI overview claims
If Google’s AI cites false or harmful information about your business, you now have legal recourse in Germany. However, prevention is better than litigation.
Actionable takeaway:
Monitor AI overviews for inaccuracies about your brand.
Publish accurate, crawlable content to counteract misinformation.
If you find false claims, correct them at the source, such as on Reddit or in forums, and report them to Google.
Google’s open knowledge format: A new way to structure content
Google introduced the Open Knowledge Format (OKF), a way to catalog site content in markdown for AI consumption. This is part of Google’s push for structured, AI-friendly content.
Why it matters:
While Google’s search team advises against duplicate markdown versions of pages, the engineering team is building tools like OKF. This suggests structured content will play a bigger role in AI-driven search.
Actionable takeaway:
Wait and watch, as OKF is new, and adoption isn’t urgent yet.
Focus on structured content, like schema, clear headings, visible text.
Avoid gating critical information behind interactive elements, such as accordions and tabs.
Yoast news: Performance upgrades and new features
We rolled out performance improvements in versions 27.8 and 27.9, of Yoast SEO, including:
Faster admin pages and post editor for large sites.
Speed boosts for SEO analysis. For instance, a sitemap query on a 2M-page site dropped from 300 seconds to 25 milliseconds.
Yoast Duplicate Post plugin upgrades, including improved Rewrite and Republish functionality for easier content repurposing.
Sign up for the next SEO Update by Yoast
The next SEO Update by Yoast is on August 25, 2026, at 4:00 PM CET (10:00 AM EST). Sign up to join live!
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Microsoft is bringing experimentation to Performance Max campaigns, giving advertisers new ways to test campaign changes and measure incremental impact without disrupting live performance.
What’s new:
Uplift experiments let advertisers measure the incremental impact of Performance Max campaigns against a control group.
Upgrade experiments allow advertisers to compare an existing campaign with an upgraded Performance Max version before fully rolling out changes.
Both experiment types are available under Campaigns > Experiments for eligible accounts.
Why we care. Until now, Microsoft Ads experiments were limited to Search campaigns. Expanding testing to Performance Max gives advertisers a safer way to validate campaign changes, optimize performance, and make data-driven decisions before committing budget.
Between the lines. As experimentation expands, Microsoft has also renamed its existing experiment offering to Search optimization experiments, distinguishing it from the new Performance Max testing capabilities. The move reflects Microsoft’s broader push to provide advertisers with more sophisticated optimisation tools across automated campaign formats.
The bottom line. Microsoft is closing a key gap in its Performance Max offering by introducing dedicated experiment types, giving advertisers more confidence when testing upgrades and measuring the true impact of automated campaigns.
First spotted. The help docs were spotted by PPC News Feed founder, Hana Kobzová.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/microsoft-pmax-experiments-3oEFUi.jpg?fit=1280%2C720&ssl=17201280Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-30 16:38:252026-06-30 16:38:25Microsoft expands Performance Max testing with new experiment types
Google is updating the All Campaigns selector with a redesigned interface that makes it easier for advertisers to navigate large and complex account structures.
What’s happening. The new All Campaigns selector is rolling out across Google Ads, bringing a refreshed layout and improved navigation tools.
What’s new:
The selector has been moved to a new location in the interface.
Campaigns now appear in an expandable hierarchical view, making campaign groups and nested structures easier to browse.
A new search function lets advertisers quickly locate campaigns and campaign groups.
Why we care. The update could save time for advertisers managing large accounts by making it faster to navigate between campaigns, particularly in accounts with multiple campaign groups or complex organisational structures.
The bottom line. Google’s redesigned All Campaigns selector aims to streamline campaign management with a clearer hierarchy and built-in search, helping advertisers navigate complex accounts more efficiently.
First spotted. The update was identified by performance marketer Vivek Gupta on LinkedIn, and is rolling out gradually, so it may not yet be available in every Google Ads account.
Google is updating its advertising policy to clarify how it limits certain ads while estimating a user’s age, offering advertisers more transparency as it expands age assurance technology worldwide.
What’s happening. Google has renamed its Default Ads Treatment policy to “Categories restricted while Google is estimating a user’s age.” The change better reflects that the restrictions are temporary and only apply while Google’s systems determine a user’s age.
What’s changing:
The policy has a new name to more clearly describe its purpose.
Google has updated the policy language to emphasise that the restrictions are interim protections during the age estimation process.
Enforcement remains unchanged.
What’s different: Google has also narrowed the list of ad categories restricted during the age estimation process.
Previously, Google restricted ads for:
Adult content and pornography
Alcohol
Gambling
Shocking content
The updated policy now restricts only:
Adult content and pornography
Alcohol
Gambling
Why we care. The update doesn’t introduce new advertising restrictions, but it provides greater clarity on when and why certain ads may not be served. Advertisers in affected verticals can better understand that these limitations are tied to Google’s age estimation process rather than permanent policy changes.
The bottom line. Nothing changes for advertisers operationally, but Google’s updated policy makes it clearer that restrictions on adult, alcohol and gambling ads are temporary safeguards while a user’s age is being estimated.
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Google is expanding measurement capabilities for YouTube brand campaigns, giving advertisers better visibility into how video ads drive engagement, brand interest, and downstream business outcomes.
What’s new:
Shorts Ad Actions for Video View Campaigns: Advertisers running Video View Campaigns that are opted into YouTube Shorts will now automatically benefit from Shorts Ad Actions in budget optimization. Google is also adding new reporting columns to measure these interactions.
Attributed Branded Searches: Now available globally in Google Ads, this new reporting metric measures branded Google searches that occur after users see or view a YouTube ad, helping advertisers quantify how awareness campaigns influence purchase intent.
Why we care. It can be hard for marketers to connect upper-funnel YouTube campaigns with measurable business outcomes. These updates provide stronger signals that link brand advertising to engagement and search intent, making it easier to justify brand investment and optimise campaigns.
By the numbers:
According to Google, YouTube Shorts ads that generated more than 10 seconds of watch time and a like delivered:
15% higher brand consideration
20% higher brand favourability, according to Google.
Google also says that every additional branded search generated is associated with an average $31 increase in sales.
Between the lines. Google continues to blur the distinction between brand and performance marketing by introducing metrics that connect awareness campaigns with downstream actions. Attributed Branded Searches, in particular, gives advertisers another way to demonstrate that YouTube campaigns can influence high-intent behaviour before a conversion takes place.
The bottom line. Google’s latest measurement updates help advertisers better prove the value of YouTube brand campaigns by linking video engagement and branded search activity to business outcomes—offering stronger evidence that upper-funnel advertising can drive measurable results.
Across Google Ads, Meta, and TikTok, platforms are pushing you toward broader, AI-driven targeting. Performance Max, Advantage+ campaigns, and TikTok’s automated audience expansion give algorithms more room to find converters while reducing your control over who sees an ad.
This is fundamentally changing how campaigns are qualified.
As targeting broadens, creative has become one of the most important signals for both users and algorithms. Identifying the right audience is moving out of audience settings and into the message itself.
Broad targeting is making creative your best qualifier.
The shift from audience qualification to creative qualification
For years, performance marketers treated targeting as the primary lever for improving lead quality:
Need prospective graduate students? Layer education interests, demographics, and remarketing audiences.
Need patients seeking specialized care? Build audiences around health-related behaviors and intent signals.
Need insurance shoppers? Narrow targeting by age, life stage, and consumer interests.
These approaches aren’t disappearing, but their influence is shrinking. Platforms increasingly ask you to provide broad audience inputs, strong conversion signals, and compelling creative, then let machine learning determine who’s most likely to convert.
Meta’s Advantage+ ecosystem, Google’s Performance Max campaigns, and TikTok’s recommendation engine all operate on this principle.
The challenge is that algorithms still need signals.
Conversion data remains the strongest signal, but creative is becoming more important in helping platforms understand who should engage with an ad. Every headline, image, video, and call to action provides context about the intended audience and desired action.
Creative is no longer just a persuasion tool.
It’s now a targeting signal.
Why broad targeting requires more intentional creative
Many advertisers still create ads as if targeting will qualify the audience.
Messaging often stays broad because you assume audience settings will narrow who sees the ad. But when platforms expand beyond tightly defined segments, vague creative can attract engagement from people unlikely to become qualified leads.
The consequences are familiar:
Lower lead quality.
Increased cost per qualified lead.
Less efficient optimization.
Noisier conversion data.
Instead, you need creative that clearly communicates who the offer is for—and just as importantly, who it isn’t for.
The goal isn’t simply more clicks or video views.
The goal is engagement from the right people.
When creative clearly identifies the audience, users can self-select. Qualified prospects lean in. Unqualified prospects move on. Both outcomes improve campaign performance and give machine learning systems cleaner signals.
Higher education: When creative becomes the targeting layer
Higher education marketers are already seeing this shift.
Historically, campaigns relied heavily on demographic filters, education interests, degree status, and segmented audience lists to reach prospective students.
Today, many strong-performing campaigns use broad lookalike audiences, Advantage+ audiences, or broad prospecting structures designed to maximize audience size and algorithmic learning.
But broader audiences create a challenge.
If a university is promoting an online Master of Science in Data Analytics program, it doesn’t need just any prospective student. It needs prospective students who meet specific admission and career criteria.
Perhaps they already hold a bachelor’s degree.
Perhaps they have professional experience.
Perhaps they want to move into leadership or pivot into a more technical career path.
Rather than relying only on targeting settings to communicate those distinctions, build them directly into the creative.
Consider the difference between these two headlines:
Generic:
“Advance your career with a Data Analytics degree.”
Qualifying:
“Built for bachelor’s degree holders ready to advance into leadership – earn your online M.S. in Data Analytics.”
The second example immediately signals who the program is for. Undergraduate prospects are less likely to engage, while qualified graduate prospects are more likely to click, convert, and reinforce positive optimization signals.
The creative itself becomes the qualification mechanism.
Google Performance Max: Creative guides the algorithm
Google Performance Max may be the clearest example of this industry-wide shift.
Despite the name, audience signals are not strict targeting controls. They’re starting points that help Google’s systems learn. Ultimately, Google determines where and to whom ads are shown across Search, YouTube, Display, Discover, Gmail, and Maps.
Because advertisers have less direct control over audience selection, creative assets become increasingly important in helping Google’s systems understand who should respond.
Imagine a healthcare provider promoting orthopedic services.
A generic headline might read:
“Expert Care for Your Health Needs.”
While technically accurate, it offers little context regarding the intended audience.
A more effective alternative might be:
“Persistent Knee Pain? Meet with Our Orthopedic Specialists.”
The second headline identifies a specific need, a specific audience, and a specific solution. Users immediately understand whether the message applies to them, and Google’s systems receive stronger engagement signals from people actively experiencing that problem.
The same principle applies across insurance, legal services, financial services, and education.
When Performance Max creative clearly identifies the audience and their need state, advertisers help Google’s machine learning systems learn faster and optimize toward more qualified outcomes.
TikTok: The first three seconds matter more than ever
TikTok has always relied heavily on content signals to determine who sees a video.
As the platform continues investing in automation and audience expansion, creative becomes even more critical.
The opening seconds of a video often determine not only whether a user continues watching but also how TikTok categorizes and distributes the content.
For lead generation campaigns, qualification should begin immediately.
A graduate program might open with:
“Already have a bachelor’s degree and looking for your next career move?”
An insurance provider might start with:
“Shopping for Medicare coverage this year?”
A law firm specializing in workplace injury cases could lead with:
“Were you injured on the job within the last 12 months?”
These openings accomplish two objectives simultaneously.
First, they quickly tell viewers whether the content is relevant to them.
Second, they provide TikTok’s algorithm with stronger behavioral signals about who engages with the video. Qualified prospects are more likely to continue watching and take action. Unqualified viewers are more likely to scroll past.
That self-selection process improves audience learning over time.
Creative is now a performance lever
One of the biggest mistakes you can make today is treating creative as something that happens after strategy and targeting are finalized.
In increasingly automated advertising environments, creative is strategy.
The message, visuals, hooks, and calls to action no longer serve only a branding or conversion role. They help platforms determine who should see the ad in the first place.
That means creative and media teams must work together more closely than ever.
When building campaigns, marketers should ask:
Does this creative clearly identify who the offer is for?
Does it communicate relevant qualifications or prerequisites?
Would an unqualified prospect immediately recognize that the message isn’t intended for them?
Are we helping both users and algorithms understand our ideal audience?
If the answer is no, the campaign may be relying too heavily on targeting to solve a problem that creative is now better positioned to address.
The future of qualification is creative
As Google, Meta, and TikTok keep expanding AI-driven targeting, you’ll likely have even less control over audience selection than you do today.
Qualification doesn’t disappear—it shifts into the creative itself.
What once happened primarily through audience settings is increasingly happening through messaging, visuals, and creative strategy.
You must embrace that shift to thrive in this environment. That means:
Writing headlines that identify the intended audience.
Creating videos that establish audience fit in the first few seconds.
Building qualifications, prerequisites, and intent signals directly into the message.
Every ad speaks to two audiences at once: the user and the algorithm.
Platforms are handling more targeting than ever, but they still need direction.
Increasingly, that direction comes from creative. In a world of broad targeting, creative isn’t just the message — it’s the qualifier.
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