<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Google Ad Manager" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Advertisers may see reporting discrepancies in Google Ad Manager, affecting how they track performance and optimize campaigns.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Google Ad Manager" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/01/Google-Ad-Manager.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google is investigating a disruption affecting Google Ad Manager, according to an update posted on the Google Ads Status Dashboard.</p>



<p>The incident began at 13:49 UTC on March 4. By 13:54 UTC, Google said it was reviewing reports that some users could access Ad Manager but weren’t seeing the most up-to-date data.</p>



<p><strong>What’s happening</strong>. The issue appears to impact reporting consistency. Specifically, Ad Exchange match rate and Ad Exchange request values are not aligning between Ad Manager’s interactive reports and the legacy reporting query tool (now deprecated).</p>



<p><strong>Why we care. </strong>Reporting discrepancies in Google Ad Manager can directly impact how you evaluate performance and optimize campaigns. If Ad Exchange match rates and request data don’t align across reporting tools, it becomes harder to trust the numbers driving pacing, forecasting and revenue decisions.</p>



<p><strong>What it means</strong>. Users can still log into Ad Manager, but reporting discrepancies may affect data accuracy — at least temporarily. There’s no indication yet of a full outage, but for publishers and advertisers relying on real-time reporting, mismatched metrics could complicate performance monitoring and optimization decisions.</p>



<p><strong>What’s next</strong>. Google says it’s actively investigating and will provide further updates. In the meantime, affected users are advised to monitor the <a href="https://ads.google.com/status/publisher/incidents/AtfCWVaqtJ4bcfjALwDd">status dashboard</a> and contact support if they’re experiencing issues not listed there.</p>


Google is investigating a disruption affecting Google Ad Manager, according to an update posted on the Google Ads Status Dashboard.
The incident began at 13:49 UTC on March 4. By 13:54 UTC, Google said it was reviewing reports that some users could access Ad Manager but weren’t seeing the most up-to-date data.
What’s happening. The issue appears to impact reporting consistency. Specifically, Ad Exchange match rate and Ad Exchange request values are not aligning between Ad Manager’s interactive reports and the legacy reporting query tool (now deprecated).
Why we care. Reporting discrepancies in Google Ad Manager can directly impact how you evaluate performance and optimize campaigns. If Ad Exchange match rates and request data don’t align across reporting tools, it becomes harder to trust the numbers driving pacing, forecasting and revenue decisions.
What it means. Users can still log into Ad Manager, but reporting discrepancies may affect data accuracy — at least temporarily. There’s no indication yet of a full outage, but for publishers and advertisers relying on real-time reporting, mismatched metrics could complicate performance monitoring and optimization decisions.
What’s next. Google says it’s actively investigating and will provide further updates. In the meantime, affected users are advised to monitor the status dashboard and contact support if they’re experiencing issues not listed there.

Advertisers may see reporting discrepancies in Google Ad Manager, affecting how they track performance and optimize campaigns.

Google Ads status dashboard flags Ad Manager reporting issue

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg" class="attachment-large size-large wp-post-image" alt="Google Shopping Ads - Google Ads" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google has added a “build to order” availability option in GMC giving vehicle sellers a clearer way to list customizable, factory-order models.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg" class="attachment-large size-large wp-post-image" alt="Google Shopping Ads - Google Ads" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/05/Google-Shopping-Ads-Google-Ads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google introduced a new availability value in Google Merchant Center — built specifically for vehicle sellers who don’t carry every model on the lot. The new attribute, “build to order,” lets dealers flag vehicles that aren’t physically in inventory but can be customized and ordered by customers.</p>



<p><strong>What needs to change</strong>. Sellers must update two areas: their structured data (set availability to <code>BuildToOrder</code>) and their Merchant Center feed (set availability to <code>build to order</code>). Consistency between structured data and feed submissions is critical to avoid disapprovals.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="772" height="587" src="https://searchengineland.com/wp-content/seloads/2026/03/Build-to-order.png" alt="" class="wp-image-470817" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Build-to-order.png 772w, https://searchengineland.com/wp-content/seloads/2026/03/Build-to-order-768x584.png 768w" sizes="(max-width: 772px) 100vw, 772px" /><figcaption class="wp-element-caption">Instruction on when to use the availability <code>[availability]</code> attribute in GMC </figcaption></figure>
</div>


<p><strong>Why we care</strong>. Until now, sellers had limited ways to signal that a vehicle wasn’t available for immediate pickup. The new value better reflects how many modern automakers operate — especially direct-to-consumer brands like Tesla and Rivian, where buyers configure features before production. For dealers offering factory orders or custom builds, this means clearer expectations for shoppers — and cleaner data for Google.</p>



<p><strong>The fine print</strong> Vehicles marked “build to order” must have the condition attribute set to “new.” If a listing is marked “used,” it will be disapproved — Google considers build-to-order vehicles to be newly configured, not pre-owned.</p>



<p><strong>Bottom line</strong> If you sell customizable or factory-order vehicles, this update gives you a more accurate way to reflect availability — but only if your feed, structured data and condition fields are properly aligned.</p>



<p>First spotted. This update was shared by Google Shopping specialist Emmanuel Flossie, where he shared  how to implement this update on his <a href="https://feedarmy.com/kb/google-merchant-center-adds-build-to-order-availability-attribute-for-vehicle-listings/">blog</a>. </p>



<p><strong>Dig deeper.</strong> <a href="https://support.google.com/merchants/answer/6324448?hl=en#:~:text=The%20vehicle%20isn't%20immediately,ordered%20by%20the%20customer%20directly.">&#8220;Availability [availability]&#8221; Google Merchant Centre help doc</a></p>


Google introduced a new availability value in Google Merchant Center — built specifically for vehicle sellers who don’t carry every model on the lot. The new attribute, “build to order,” lets dealers flag vehicles that aren’t physically in inventory but can be customized and ordered by customers.
What needs to change. Sellers must update two areas: their structured data (set availability to BuildToOrder) and their Merchant Center feed (set availability to build to order). Consistency between structured data and feed submissions is critical to avoid disapprovals.
Instruction on when to use the availability [availability] attribute in GMC 



Why we care. Until now, sellers had limited ways to signal that a vehicle wasn’t available for immediate pickup. The new value better reflects how many modern automakers operate — especially direct-to-consumer brands like Tesla and Rivian, where buyers configure features before production. For dealers offering factory orders or custom builds, this means clearer expectations for shoppers — and cleaner data for Google.
The fine print Vehicles marked “build to order” must have the condition attribute set to “new.” If a listing is marked “used,” it will be disapproved — Google considers build-to-order vehicles to be newly configured, not pre-owned.
Bottom line If you sell customizable or factory-order vehicles, this update gives you a more accurate way to reflect availability — but only if your feed, structured data and condition fields are properly aligned.
First spotted. This update was shared by Google Shopping specialist Emmanuel Flossie, where he shared  how to implement this update on his blog. 
Dig deeper. “Availability [availability]” Google Merchant Centre help doc

Google has added a “build to order” availability option in GMC giving vehicle sellers a clearer way to list customizable, factory-order models.

Google Merchant Center adds “build to order” for vehicle listings

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/The-PPC-AI-integrity-hierarchy-Where-to-draw-the-line-on-synthetic-ad-imagery.png" class="attachment-large size-large wp-post-image" alt="The PPC AI integrity hierarchy- Where to draw the line on synthetic ad imagery" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/The-PPC-AI-integrity-hierarchy-Where-to-draw-the-line-on-synthetic-ad-imagery.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/The-PPC-AI-integrity-hierarchy-Where-to-draw-the-line-on-synthetic-ad-imagery-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/The-PPC-AI-integrity-hierarchy-Where-to-draw-the-line-on-synthetic-ad-imagery-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/The-PPC-AI-integrity-hierarchy-Where-to-draw-the-line-on-synthetic-ad-imagery-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI can generate endless ad variations, but not every edit is ethical or policy-safe. Here’s a framework for responsible AI imagery in PPC.

PPC platforms are asset-hungry. What began as simple text ads and keyword bidding has evolved into an AI-driven ecosystem.
Tools inside Google Ads can now remove backgrounds, generate lifestyle scenes, and even create synthetic humans in minutes. But just because the technology allows it doesn’t mean every brand should use it.
That shift forces PPC advertisers to confront difficult questions: 
Are you willing to trade efficiency for authenticity? 
How far up the stack should your brand let AI operate? 
If clients knew exactly where and how you were using AI, would they trust you, or would they question you?
A brand integrity hierarchy offers a way to navigate those decisions — a four-level framework that helps determine how much AI manipulation your brand, industry, and audience can tolerate.
Why PPC needs its own AI ethics framework
Generic AI ethics guidelines don’t account for the operational realities of paid search. PPC isn’t a brand storytelling channel. It’s a high-volume, high-velocity system that demands constant image production across dozens of audiences, formats, and placements.
You must generate fresh lifestyle imagery at a pace traditional creative workflows can’t sustain.
At the same time, Google and Bing enforce strict policies around accurate product representation, especially in Merchant Center, where even minor visual inaccuracies can trigger disapprovals or account risk.
Layer on top of that the platform pressure. Google Ads added Nano Banana Pro, turning Asset Studio into an AI co-creation environment. Performance Max actively pushes you toward AI-generated backgrounds, variations, and lifestyle images to improve performance. Demand Gen and Merchant Center also now have capabilities to change product images at scale.
Most brands can’t afford the photoshoots required to keep up with this demand, yet the volume and placement of images across channels make them unavoidable if you want to compete.
This combination of policy risk, creative pressure, and platform-promoted tools is unique to PPC — which is exactly why the industry needs its own AI ethics framework.
Dig deeper: What’s next for PPC: AI, visual creative and new ad surfaces
  

    

      
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&amp;amp;lt;br>  @media (max-width: 768px) {&amp;amp;lt;br>    .headline-responsive {&amp;amp;lt;br>      font-size: 30px !important;&amp;amp;lt;br>      line-height: 1.3 !important;&amp;amp;lt;br>    }&amp;amp;lt;br>  }&amp;amp;lt;br>
Level 1 – The core (zero risk): The absolute truth
Definition: The product and the human exactly as they exist in reality.
Permitted activities:
Upscaling resolution.
Cropping for fit.
Color correction.
Non-generative background cleanup (removing dust, adjusting lighting).
PPC context: This level is fully compliant with Google and Microsoft’s “accurate representation” policies. Merchant Center explicitly permits technical edits that don’t alter the product itself. This is the safest zone for regulated industries such as finance, healthcare, legal services, and brands with strict authenticity standards.
Client talk-track: “We’re using AI to make your reality look its best on every screen size. We aren’t changing what the product is, only how it’s displayed.”
Risk assessment: Zero brand risk. Zero policy risk. Maximum consumer trust.
I think about Level 1 the same way I think about working with a graphic designer in Photoshop. You’re not changing the product, the setting, or the truth — you’re simply cleaning up what already exists.
This level is about technical refinement, not creative invention. It’s the equivalent of adjusting lighting, removing dust, fixing a crooked crop, or correcting color balance. Nothing about the image becomes “untrue.” You’re enhancing reality, not altering it.
Level 2 – The inner ring (low risk): Contextual narrative
Definition: AI-generated environment, not AI-generated product.
Permitted activities:
Generative backgrounds (e.g., placing a watch on a mountain backdrop).
Removing visual distractions (e.g., power lines, litter, unrelated objects).
Seasonal or thematic settings (e.g., holiday scenes, office environments).
Generic commodity generation (e.g., coffee beans, grain, raw materials, not branded products).
Google Ads context: Performance Max’s AI background generation is designed for this level. Google allows contextual enhancement as long as the product remains unchanged. This approach is useful for scaling creative variations without expensive location shoots or studio rentals.
Risks:
Cultural mismatch. AI-generated settings may not reflect the target audience’s reality.
Unrealistic or off-brand environments.
Requires human review for brand consistency.
Client talk-track: “We’re using AI to build a world for your product to live in. The product the customer receives is identical to the one in the ad.”
Risk assessment: Low brand risk. Low policy risk. Maintains consumer trust if executed thoughtfully.
Level 2 sits in an odd psychological space. The manipulations themselves are still low-risk. You’re creating scenes, composites, or enhanced environments the same way a graphic designer would in Photoshop.
Brands have been doing this manually for decades. But the moment AI performs the same task, something shifts. To customers, and even to some advertisers, the exact same edit can feel more artificial simply because an algorithm did it instead of a human.
That perception gap matters. 
Even when the output is identical, AI-assisted scene creation can trigger a sense of “this looks fake” that traditional Photoshop work never did. It’s irrational, but it’s real and worth acknowledging at this second tier. The actual risk is still low, but the emotional risk is higher than Level 1.
Dig deeper: AI tools for PPC, AI search, and social campaigns: What’s worth using now
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See terms.
Level 3 – The outer ring (high risk): Subject augmentation
Definition: Altering the “hero” — the product or the person.
Activities:
Beautification filters on models.
Slimming or reshaping human subjects.
Altering food textures to appear more appealing.
Removing “imperfections” from products.
Making products appear more premium than they are.
PPC industry context: The platforms prohibit misleading or manipulated product imagery. Merchant Center disapprovals often occur at this level. High sensitivity exists in beauty, apparel, food, and health categories, where consumer expectations are tied directly to visual accuracy.
Recent consumer trust studies show that users feel deceived when they discover product images have been significantly altered. This is a policy concern, more so a brand reputation issue.
Half of U.S. adults (51%) believe AI-generated and edited content needs better labeling, CNET reports. One in five (21%) believe AI content should be prohibited on social media with no exceptions.
Risks:
High PR risk (e.g., press call-out moments).
High policy risk (e.g., disapprovals, account suspension).
High consumer trust risk (e.g., returns, negative reviews).
Client talk-track: “This is where we risk the ‘press call-out.’ If we remove a model’s birthmark or make a burger look like a 3D render, we aren’t optimizing — we’re fabricating.”
Risk assessment: High brand risk. High policy risk. Potential for long-term damage to consumer trust.
Level 3 moves into territory where the image no longer reflects the real person or product. And yes, brands have been doing this in Photoshop for years, and they’ve been called out for it just as long. There’s precedent, and there’s backlash.
What changes at Level 3 is scale. AI lets you make edits instantly, repeatedly, and across entire product catalogs or campaigns. The ethical risk isn’t new, but the volume and speed at which AI enables these distortions make the consequences far bigger. A single questionable Photoshop edit is one thing. Hundreds of AI-altered images pushed across every channel is something else entirely.
This is where the risk stops being theoretical and starts becoming reputational — and where paid search teams need a clarified stance.
Level 4 – The edge (critical risk): Full fabrication
Definition: Synthetic humans, synthetic products, or fully AI-generated scenes.
Activities:
AI-generated models.
Virtual influencers.
Products that don’t exist.
Entirely fabricated lifestyle scenes with no real-world basis.
PPC context: Synthetic humans are allowed in some formats with proper disclosure, but Merchant Center prohibits listing products that don’t exist. There is a high risk of disapproval for “inaccurate representation.” This level may be acceptable for creative testing or conceptual campaigns, but it’s dangerous as a primary brand identity.
Legal precedents regarding copyright protection for non-human-authored creative works remain murky. Using fully synthetic assets may cause challenges if ownership disputes arise or if synthetic models are mistaken for real individuals without proper disclosure.
Risks:
Maximum brand risk.
Maximum policy risk.
Maximum consumer trust risk.
Potential long-term damage to “trust equity.”
Client talk-track: “This is for high-speed testing or fringe creative. If we use this for our main brand identity, we must be prepared for the ‘inauthentic’ label.”
Risk assessment: Critical brand risk. Critical policy risk. Use with extreme caution and full disclosure.
Level 4 is where AI stops enhancing reality and starts inventing it. The image becomes a construction. While I haven’t personally worked with brands operating at this tier, it’s absolutely where the industry could be headed, and it deserves serious consideration.
Fully fabricated imagery can mislead customers, violate platform policies, and erode trust at scale. When AI creates people, products, or environments from scratch, the line between creative expression and consumer deception becomes razor-thin. The reputational fallout from getting this wrong is far greater than anything in Levels 1 through 3.
This is the highest-risk tier because it asks a fundamental question: Are you still advertising your product or an AI-generated fiction of it?
Brand alignment: Defining your North Star
Not every brand should operate at the same level of the brand integrity scale. Your acceptable AI usage depends on four factors.
1. Define your non-negotiables
Every brand must choose its acceptable level(s) on the scale and document it in a brand AI manifesto for PPC.
Examples:
Dove (authenticity-driven beauty brand): Level 1 only.
Tech-forward DTC brand: Levels 2-3 acceptable with clear disclosure.
Ecommerce aggregator: Levels 1-2 for product listings, Level 3 for lifestyle content.
Action: Create a PPC brand AI manifesto in collaboration with creative, legal, and executive leadership.
2. The press test vs. the policy test
Two critical questions should guide every AI decision:
Policy test: “Will the platform approve this?”
Press test: “Would we be proud if The Verge covered this?”
The press test is the real guardrail. Google’s policies change. Public perception is permanent.
3. Human-in-the-loop protocol
Every AI-assisted asset must be checked for:
Material deception: Does this misrepresent the product or service?
Identity erasure: Does this erase diversity or cultural authenticity?
Cultural hallucinations: Does this AI-generated scene reflect reality or stereotype?
Product accuracy: Does the ad show what the customer will actually receive?
Automated AI generation should never bypass human review, especially in regulated verticals.
4. Align with your customer base
Different audiences have different tolerances for AI manipulation:
Gen Z: Values “perfectly imperfect” authenticity. Responds negatively to over-polished imagery.
B2B: Prioritizes clarity and utility. AI-generated backgrounds are acceptable. Synthetic humans less so.
Retail: Authenticity directly impacts conversion rates. Product accuracy is non-negotiable.
Dig deeper: Why creative, not bidding, is limiting PPC performance
Operationalizing the brand integrity circle inside PPC ads
Creative workflow
Implement a pre-flight checklist for AI-generated assets:
Identify the level: Core, inner ring, outer ring, or edge
Apply the press test: Would we defend this publicly?
Check for bias: Does this asset represent your audience accurately?
Verify product accuracy: Is this what the customer will receive?
Document disclosure: If synthetic humans are used, is this disclosed?
Media workflow 
Safe placements for AI-generated assets
Performance Max (with contextual backgrounds).
Demand Gen (lifestyle scenes).
YouTube thumbnails (conceptual creative).
Unsafe placements
Merchant Center product images (Level 1 only).
Regulated verticals (finance, healthcare, legal).
Sensitive categories (beauty, weight loss, medical devices).
Legal workflow
Legal teams should:
Review synthetic human usage for disclosure compliance.
Validate product accuracy claims.
Approve the brand AI manifesto.
Maintain documentation for regulatory audits.
Industry standards and emerging frameworks, such as the Coalition for Content Provenance and Authenticity (C2PA), are establishing transparency protocols for AI-generated media. Monitor these developments and align your practices accordingly.
What the PPC community thinks
Some PPC professionals are already experimenting with the tools discussed in this framework.
Ameet Khabra, owner of Hop Skip Media, tested Nano Banana when it first appeared inside the Google Ads interface. She found the tool useful for ideation and quick edits, but noted that strong results often required highly specific prompts.
That level of prompt detail may be realistic for experienced advertisers, but it’s less likely for many SMBs experimenting with AI-generated assets.
“I think it’s a great tool to use for ideation and potentially quick edits,” Khabra said. “But I would still have a graphic designer creating the final product.”
Even when AI imagery is available, some advertisers remain skeptical of how it appears to audiences.
Julie Friedman Bacchini, owner of Neptune Moon, says AI-generated images often look noticeably artificial.
“I don’t like AI images because they look like AI and that’s off-putting to me,” Bacchini said. “It can be hard to avoid. Even when you’re trying to use stock photos, there are so many AI images on those sites too.”
To understand how people outside the industry view these changes, I also polled the community on Threads.




The sentiment was strikingly consistent: while the industry focuses on efficiency, the public is increasingly wary of fantasy versus reality.
One commenter wrote:
“False advertising. That seems like a pretty big concern. As a consumer, I actually would like to see the real thing I’d be buying.”
Another described the issue more bluntly:
“Bait and switch. Fantasy versus reality. Falsehood versus the truth.”

  
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Master the spectrum, don’t avoid it
AI isn’t inherently deceptive. Nor is it inherently transparent. It’s a tool. Like all tools, its ethical impact depends on how it’s used. As PPC experts with access to these technologies and advisory roles with brands, we need a clear point of view to guide these decisions.
The brand integrity scale outlined above provides a structured approach to AI use in PPC, helping you navigate the tension between automation and authenticity. By defining your brand’s position on this spectrum today, you ensure tomorrow’s campaigns are remembered for their resonance.
Adopt ethical AI standards — define your brand AI manifesto, implement the press test, and ensure every AI-generated asset passes human review before it reaches your audience. Your brand’s integrity depends on it.

AI can generate endless ad variations, but not every edit is ethical or policy-safe. Here’s a framework for responsible AI imagery in PPC.

Are your PPC ads still authentic in the age of AI creative?

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>This change was made because the advice was "out of date" and Google handles JavaScript fine.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/google-code-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google has removed the &#8220;design for accessibility&#8221; section from within the <a href="https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics">Understand the JavaScript SEO basics documentation</a>.  Google <a href="https://developers.google.com/search/updates#removed-outdated-information-about-javascript-and-accessibility">said</a> this was removed because the information was &#8220;out of date and not as helpful as it used to be.&#8221;</p>



<p>The old text said that using JavaScript for page content &#8220;may be hard for Google to see.&#8221;  But Google now says that has not been true for many years, thus why Google removed the section.</p>



<p><strong>The old section. </strong>The old section read:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>&#8220;Design for accessibility: Create pages for users, not just search engines. When you&#8217;re designing your site, think about the needs of your users, including those who may not be using a JavaScript-capable browser (for example, people who use screen readers or less advanced mobile devices). One of the easiest ways to test your site&#8217;s accessibility is to preview it in your browser with JavaScript turned off, or to view it in a text-only browser such as Lynx. Viewing a site as text-only can also help you identify other content which may be hard for Google to see, such as text embedded in images.&#8221;</p>
</blockquote>



<p><strong>Why it was removed. </strong>Google <a href="https://developers.google.com/search/updates#removed-outdated-information-about-javascript-and-accessibility">explained</a>:</p>



<ul class="wp-block-list">
<li>&#8220;The information was out of date and not as helpful as it used to be. Google Search has been rendering JavaScript for multiple years now, so using JavaScript to load content is not &#8220;making it harder for Google Search&#8221;.&#8221; </li>



<li>&#8220;Most assistive technologies are able to work with JavaScript now as well.&#8221;</li>
</ul>



<p><strong>Why we care. </strong>While Google Search can handle JavaScript super well, it is still important for you to double check what Google Search sees by using the URL inspection tool within Google Search Console.</p>



<p>Keep in mind, Google can handle JavaScript very well, Microsoft Bing likely can as well. But many of the new AI engines might not be able to render JavaScript as well as Google or Bing.</p>


Google has removed the “design for accessibility” section from within the Understand the JavaScript SEO basics documentation.  Google said this was removed because the information was “out of date and not as helpful as it used to be.”
The old text said that using JavaScript for page content “may be hard for Google to see.”  But Google now says that has not been true for many years, thus why Google removed the section.
The old section. The old section read:
“Design for accessibility: Create pages for users, not just search engines. When you’re designing your site, think about the needs of your users, including those who may not be using a JavaScript-capable browser (for example, people who use screen readers or less advanced mobile devices). One of the easiest ways to test your site’s accessibility is to preview it in your browser with JavaScript turned off, or to view it in a text-only browser such as Lynx. Viewing a site as text-only can also help you identify other content which may be hard for Google to see, such as text embedded in images.”
Why it was removed. Google explained:
“The information was out of date and not as helpful as it used to be. Google Search has been rendering JavaScript for multiple years now, so using JavaScript to load content is not “making it harder for Google Search”.” 
“Most assistive technologies are able to work with JavaScript now as well.”
Why we care. While Google Search can handle JavaScript super well, it is still important for you to double check what Google Search sees by using the URL inspection tool within Google Search Console.
Keep in mind, Google can handle JavaScript very well, Microsoft Bing likely can as well. But many of the new AI engines might not be able to render JavaScript as well as Google or Bing.

This change was made because the advice was "out of date" and Google handles JavaScript fine.

Google removes accessibility section from JavaScript SEO section

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop.png" class="attachment-large size-large wp-post-image" alt="Google Ads tactics to drop" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From April 1, 2026, Google will block inactive developer tokens from uploading Customer Match data via the Google Ads API.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop.png" class="attachment-large size-large wp-post-image" alt="Google Ads tactics to drop" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Ads-tactics-to-drop-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is communicating that starting April 1st, Customer Match uploads through the Google Ads API will stop working for certain users, in a message sent to API developers.</p>



<p>Specifically, developers who haven’t uploaded Customer Match data in the past 180 days using their developer token will no longer be able to do so via the Ads API.</p>



<p><strong>What’s changing</strong>. If you fall into that inactive bucket, any attempt to upload Customer Match lists through the Google Ads API after April 1 will fail. Instead, Google wants you to move those workflows to the <a href="https://searchengineland.com/google-launches-data-manager-api-465903">Data Manager API</a>. The change applies only to Customer Match uploads — all other campaign management and reporting tasks should continue as normal in the Google Ads API.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="688" height="1084" src="https://searchengineland.com/wp-content/seloads/2026/03/Customer-Match-upload.jpeg" alt="" class="wp-image-470800"/></figure>
</div>


<p><strong>Why Google says it’s doing this</strong>. Google positions the Data Manager API as a more modern, unified data ingestion solution across its platforms, with stronger security protocols. It also includes features not available in the Ads API, such as confidential matching and enhanced encryption — signaling a push to centralize and better secure audience data handling.</p>



<p><strong>Why we care.</strong> If you or your developers haven’t touched Customer Match uploads in the last six months, this could catch you off guard. After April 1, 2026, the old workflow simply won’t work — and errors will replace uploads. </p>



<p><strong>The takeaway.</strong> Check whether your developer token has been used for Customer Match recently and plan a migration to the Data Manager API now, before Google flips the switch.</p>



<p><strong>First spotted.</strong> This announcement was shared by Paid Search specialist Arpan Banerjee who shared the message he got from Google on <a href="https://www.linkedin.com/posts/arpan301_googleads-ppc-digitalmarketing-share-7434955721114427392-9yND/">LinkedIn</a>. </p>


Google is communicating that starting April 1st, Customer Match uploads through the Google Ads API will stop working for certain users, in a message sent to API developers.
Specifically, developers who haven’t uploaded Customer Match data in the past 180 days using their developer token will no longer be able to do so via the Ads API.
What’s changing. If you fall into that inactive bucket, any attempt to upload Customer Match lists through the Google Ads API after April 1 will fail. Instead, Google wants you to move those workflows to the Data Manager API. The change applies only to Customer Match uploads — all other campaign management and reporting tasks should continue as normal in the Google Ads API.




Why Google says it’s doing this. Google positions the Data Manager API as a more modern, unified data ingestion solution across its platforms, with stronger security protocols. It also includes features not available in the Ads API, such as confidential matching and enhanced encryption — signaling a push to centralize and better secure audience data handling.
Why we care. If you or your developers haven’t touched Customer Match uploads in the last six months, this could catch you off guard. After April 1, 2026, the old workflow simply won’t work — and errors will replace uploads. 
The takeaway. Check whether your developer token has been used for Customer Match recently and plan a migration to the Data Manager API now, before Google flips the switch.
First spotted. This announcement was shared by Paid Search specialist Arpan Banerjee who shared the message he got from Google on LinkedIn.

From April 1, 2026, Google will block inactive developer tokens from uploading Customer Match data via the Google Ads API.

Google to disable Customer Match uploads in Ads API

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png" class="attachment-large size-large wp-post-image" alt="Click fraud in Google Ads: Where exposure rises and how to reduce it" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From Video Partners to Search, fraud exposure isn’t equal. Learn where invalid clicks spike and how to shift toward higher-intent traffic.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png" class="attachment-large size-large wp-post-image" alt="Click fraud in Google Ads: Where exposure rises and how to reduce it" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google has long been considered the gold standard for ad spend compared to social platforms. But scale doesn’t equal immunity. Click fraud remains a persistent risk, and the safety of your budget depends entirely on where your ads are running.</p>



<p>While Google Ads offers immense reach, its campaigns aren’t created equal. Some are significantly more exposed to malicious activity than others. To protect your margins, you must understand what constitutes click fraud, where it originates, and how to shield your campaigns.</p>



<h2 class="wp-block-heading" id="h-what-are-invalid-clicks">What are invalid clicks?</h2>



<p>Invalid clicks are interactions that lack legitimate consumer intent. Because they aren’t driven by real human interest, they skew performance data and drain budgets without any possibility of conversion. These clicks generally originate from four primary sources:</p>



<ul class="wp-block-list">
<li><strong>Botnets:</strong> Networks of hijacked devices controlled by a “botmaster.” They generate massive volumes of automated traffic that mimic human behavior to inflate metrics or carry out DDoS attacks.</li>



<li><strong>Click farms:</strong> Large groups of low-paid workers or automated scripts tasked with manually clicking ads. They create an illusion of high engagement, misleading brands into believing a campaign is more effective than it truly is.</li>



<li><strong>Ad injection and malware:</strong> Malicious software that “injects” unauthorized ads into websites or forcibly redirects users. This hijacks legitimate revenue and erodes consumer trust.</li>



<li><strong>Pixel stuffing and ad stacking:</strong> “Invisible” fraud in which ads are served but never seen. Pixel stuffing compresses an ad into a single 1&#215;1 invisible pixel, while ad stacking layers multiple ads on top of each other in a single slot. You pay for thousands of impressions that have zero chance of being viewed.</li>
</ul>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/branded-search-competitive-ppc-defense-470510">Own your branded search: Building a competitive PPC defense</a></em></strong></p>



<h2 class="wp-block-heading" id="h-the-rising-trend-of-fraud">The rising trend of fraud</h2>



<p>The average invalid click rate across Google Ads is <a href="https://fraudblocker.com/data/invalid-click-rate-benchmarks-for-google-ads" target="_blank" rel="noopener">11.4%</a>, a recent study by Fraud Blocker found. The figure is climbing.</p>



<p>That upward trend becomes clearer over time. In 2010, the average invalid click rate sat at 5.9%. By 2024, that number jumped to 12.3%. This doubling of fraud is likely driven by the increased sophistication of AI-powered bots and malware that can more effectively bypass basic security filters.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1024" height="600" src="https://searchengineland.com/wp-content/seloads/2026/03/Average-invalid-click-rate-by-year.png" alt="Average invalid click rate by year" class="wp-image-470662" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Average-invalid-click-rate-by-year.png 1024w, https://searchengineland.com/wp-content/seloads/2026/03/Average-invalid-click-rate-by-year-768x450.png 768w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>
</div>


<p>Invalid click rates fluctuate based on your campaign setup. Three key factors typically drive these numbers:</p>



<ul class="wp-block-list">
<li><strong>Industry competition:</strong> High CPC industries like legal services, insurance, and real estate are primary targets. Competitors or bots may intentionally target these campaigns to exhaust your daily budget.</li>



<li><strong>Targeting parameters:</strong> Overly broad keywords or targeting regions known for high botnet activity can inadvertently invite “junk” traffic.</li>



<li><strong>Refinement tools:</strong> Negative keywords and audience exclusions act as a shield, reducing the frequency of unintentional clicks.</li>
</ul>



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<h2 class="wp-block-heading" id="h-campaign-hierarchy-which-are-the-biggest-violators">Campaign hierarchy: Which are the biggest violators?</h2>



<p>Not all Google Ads inventory carries the same level of risk. Here’s how campaign types stack up from highest to lowest exposure.</p>



<h3 class="wp-block-heading" id="h-the-biggest-risk-google-video-partners">The biggest risk: Google Video Partners</h3>



<ul class="wp-block-list">
<li>Video Partners show the highest levels of invalid traffic since they reach beyond YouTube to Google’s network of third-party sites. </li>



<li>Many of these sites offer zero control, racking up views from bots or “muted” placements in tiny windows where real people never see them.</li>
</ul>



<h3 class="wp-block-heading" id="h-display-campaigns-highly-vulnerable">Display campaigns: Highly vulnerable</h3>



<ul class="wp-block-list">
<li>Display ads are often plagued by low-quality “made-for-ads” or AI-built sites. </li>



<li>In some cases, more than half of the clicks on a specific site can be invalid. </li>



<li>While major publishers are safer, the risk across the wider network is uneven and requires constant monitoring.</li>
</ul>



<h3 class="wp-block-heading" id="h-shopping-and-demand-gen-the-automation-tax">Shopping and Demand Gen: The automation tax</h3>



<ul class="wp-block-list">
<li>These campaigns face less malicious fraud from price-checking tools, scrapers, and automated bots. </li>



<li>While not always intended to deplete your budget, these clicks still drain spend and skew optimization data, which is particularly damaging for low-margin businesses.</li>
</ul>



<h3 class="wp-block-heading" id="h-performance-max-hidden-exposure">Performance Max: Hidden exposure</h3>



<ul class="wp-block-list">
<li>While PMax scales inventory across all Google properties, it spreads risk across the entire ecosystem. </li>



<li>Because it’s difficult to see exactly where traffic originates, even a low percentage of invalid clicks can result in significant wasted spend.</li>
</ul>



<h3 class="wp-block-heading" id="h-search-the-safest-bet">Search: The safest bet</h3>



<ul class="wp-block-list">
<li>Traditional Search campaigns remain the most secure. </li>



<li>It’s much harder for bots to accurately mimic a human searching for a specific solution. </li>



<li>However, in high CPC industries, even a 2% fraud rate can be financially painful.</li>
</ul>



<h2 class="wp-block-heading" id="h-how-to-mitigate-the-risks">How to mitigate the risks</h2>



<p>Across the diverse range of industries my clients serve, I’ve identified specific patterns in how fraud manifests across different sectors. As a result, the best prescription is proactive. Address these vulnerabilities by shifting from broad, automated settings to a more refined, high-intent strategy.</p>



<p>The following table highlights the specific patterns we monitor to lower invalid click rates:</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Factor</strong></td><td><strong>Higher risk (Aggressive)</strong></td><td><strong>Lower risk (Strict)</strong></td></tr><tr><td><strong>Location</strong></td><td>Global or &#8220;Presence or Interest&#8221;</td><td>&#8220;Presence Only&#8221; (User is physically there)</td></tr><tr><td><strong>Keywords</strong></td><td>Broad match / Generic terms</td><td>Exact match / Long-tail phrases</td></tr><tr><td><strong>Networks</strong></td><td>Including &#8220;Search Partners&#8221; and &#8220;Display&#8221;</td><td>Google Search Network only</td></tr><tr><td><strong>Exclusions</strong></td><td>No negative keywords or placement lists</td><td>Robust negative lists and app exclusions</td></tr><tr><td><strong>Scheduling</strong></td><td>24/7 (Bots often spike at night)</td><td>Custom schedules aligned with business hours</td></tr></tbody></table></figure>



<p>Here are proactive steps you can take to reduce your exposure to fraud.</p>



<ul class="wp-block-list">
<li><strong>Audit placement data:</strong> Regularly review where your ads appear. Immediately exclude low-quality apps or sites that show high click-through rates but zero conversions.</li>



<li><strong>Limit AI Max overreliance:</strong> Automation is powerful, but set and forget is a recipe for wasted spend. Maintain manual guardrails on your automated campaigns.</li>



<li><strong>Review refunds:</strong> Google does issue refunds for detected fraud, but subtle cases often slip through. Compare your internal lead data against Google’s click data to find discrepancies.</li>
</ul>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/ppc-zero-click-search-stay-profitable-463019">PPC in the age of zero-click search: How to stay profitable</a></em></strong></p>



<h2 class="wp-block-heading" id="h-campaign-structure-is-your-first-fraud-defense">Campaign structure is your first fraud defense</h2>



<p>Google is far from a uniform entity. It’s a diverse ecosystem of distinct environments where risk levels can vary by as much as 400%.</p>



<p>Prioritizing high-quality traffic results in superior data integrity, more precise optimization, and reduced acquisition costs. In today’s market, the strategic structure of your campaigns is just as vital to your success as the size of your budget.</p>

Google has long been considered the gold standard for ad spend compared to social platforms. But scale doesn’t equal immunity. Click fraud remains a persistent risk, and the safety of your budget depends entirely on where your ads are running.
While Google Ads offers immense reach, its campaigns aren’t created equal. Some are significantly more exposed to malicious activity than others. To protect your margins, you must understand what constitutes click fraud, where it originates, and how to shield your campaigns.
What are invalid clicks?
Invalid clicks are interactions that lack legitimate consumer intent. Because they aren’t driven by real human interest, they skew performance data and drain budgets without any possibility of conversion. These clicks generally originate from four primary sources:
Botnets: Networks of hijacked devices controlled by a “botmaster.” They generate massive volumes of automated traffic that mimic human behavior to inflate metrics or carry out DDoS attacks.
Click farms: Large groups of low-paid workers or automated scripts tasked with manually clicking ads. They create an illusion of high engagement, misleading brands into believing a campaign is more effective than it truly is.
Ad injection and malware: Malicious software that “injects” unauthorized ads into websites or forcibly redirects users. This hijacks legitimate revenue and erodes consumer trust.
Pixel stuffing and ad stacking: “Invisible” fraud in which ads are served but never seen. Pixel stuffing compresses an ad into a single 1×1 invisible pixel, while ad stacking layers multiple ads on top of each other in a single slot. You pay for thousands of impressions that have zero chance of being viewed.
Dig deeper: Own your branded search: Building a competitive PPC defense
The rising trend of fraud
The average invalid click rate across Google Ads is 11.4%, a recent study by Fraud Blocker found. The figure is climbing.
That upward trend becomes clearer over time. In 2010, the average invalid click rate sat at 5.9%. By 2024, that number jumped to 12.3%. This doubling of fraud is likely driven by the increased sophistication of AI-powered bots and malware that can more effectively bypass basic security filters.




Invalid click rates fluctuate based on your campaign setup. Three key factors typically drive these numbers:
Industry competition: High CPC industries like legal services, insurance, and real estate are primary targets. Competitors or bots may intentionally target these campaigns to exhaust your daily budget.
Targeting parameters: Overly broad keywords or targeting regions known for high botnet activity can inadvertently invite “junk” traffic.
Refinement tools: Negative keywords and audience exclusions act as a shield, reducing the frequency of unintentional clicks.

  
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See terms.
Campaign hierarchy: Which are the biggest violators?
Not all Google Ads inventory carries the same level of risk. Here’s how campaign types stack up from highest to lowest exposure.
The biggest risk: Google Video Partners
Video Partners show the highest levels of invalid traffic since they reach beyond YouTube to Google’s network of third-party sites. 
Many of these sites offer zero control, racking up views from bots or “muted” placements in tiny windows where real people never see them.
Display campaigns: Highly vulnerable
Display ads are often plagued by low-quality “made-for-ads” or AI-built sites. 
In some cases, more than half of the clicks on a specific site can be invalid. 
While major publishers are safer, the risk across the wider network is uneven and requires constant monitoring.
Shopping and Demand Gen: The automation tax
These campaigns face less malicious fraud from price-checking tools, scrapers, and automated bots. 
While not always intended to deplete your budget, these clicks still drain spend and skew optimization data, which is particularly damaging for low-margin businesses.
Performance Max: Hidden exposure
While PMax scales inventory across all Google properties, it spreads risk across the entire ecosystem. 
Because it’s difficult to see exactly where traffic originates, even a low percentage of invalid clicks can result in significant wasted spend.
Search: The safest bet
Traditional Search campaigns remain the most secure. 
It’s much harder for bots to accurately mimic a human searching for a specific solution. 
However, in high CPC industries, even a 2% fraud rate can be financially painful.
How to mitigate the risks
Across the diverse range of industries my clients serve, I’ve identified specific patterns in how fraud manifests across different sectors. As a result, the best prescription is proactive. Address these vulnerabilities by shifting from broad, automated settings to a more refined, high-intent strategy.
The following table highlights the specific patterns we monitor to lower invalid click rates:

FactorHigher risk (Aggressive)Lower risk (Strict)
LocationGlobal or “Presence or Interest”“Presence Only” (User is physically there)
KeywordsBroad match / Generic termsExact match / Long-tail phrases
NetworksIncluding “Search Partners” and “Display”Google Search Network only
ExclusionsNo negative keywords or placement listsRobust negative lists and app exclusions
Scheduling24/7 (Bots often spike at night)Custom schedules aligned with business hours





Here are proactive steps you can take to reduce your exposure to fraud.
Audit placement data: Regularly review where your ads appear. Immediately exclude low-quality apps or sites that show high click-through rates but zero conversions.
Limit AI Max overreliance: Automation is powerful, but set and forget is a recipe for wasted spend. Maintain manual guardrails on your automated campaigns.
Review refunds: Google does issue refunds for detected fraud, but subtle cases often slip through. Compare your internal lead data against Google’s click data to find discrepancies.
Dig deeper: PPC in the age of zero-click search: How to stay profitable
Campaign structure is your first fraud defense
Google is far from a uniform entity. It’s a diverse ecosystem of distinct environments where risk levels can vary by as much as 400%.
Prioritizing high-quality traffic results in superior data integrity, more precise optimization, and reduced acquisition costs. In today’s market, the strategic structure of your campaigns is just as vital to your success as the size of your budget.

From Video Partners to Search, fraud exposure isn’t equal. Learn where invalid clicks spike and how to shift toward higher-intent traffic.

Click fraud in Google Ads: Where exposure rises and how to reduce it

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Think retargeting in Google Ads is just banner ads? The real takeaway: your first-party data now powers AI performance — so use it.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/week-40-landscape-cover-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-9-16 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="Are you making the most of your first-party data in Google Ads?" width="422" height="750" src="https://www.youtube.com/embed/-ImGWYB2yzI?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p>One of the most profitable Google Ads targeting tactics is retargeting: showing ads to people who are already familiar with your business. But if you still think that “retargeting” means a Display campaign chasing users around the web with banner ads, you’re missing out on how &#8220;Your data segments&#8221; actually function today.</p>



<p>Let’s explore how you can leverage your proprietary audience data in new ways, and what mistakes to avoid in 2026 and beyond.</p>



<h2 class="wp-block-heading" id="h-what-are-your-data-segments-in-google-ads">What are “Your data segments” in Google Ads?</h2>



<p>Retargeting means showing ads to people who are already familiar with your business. Google uses the euphemistic name &#8220;Your data segments&#8221; to refer to all the retargeting lists in your account.</p>



<h3 class="wp-block-heading" id="h-what-types-of-retargeting-can-you-do-in-google-ads">What types of retargeting can you do in Google Ads?</h3>



<p>A variety of different retargeting methods are available in Google Ads. They mirror what you’ll find on other ad platforms like Meta, LinkedIn, or TikTok. I find it helpful to group them into four categories:</p>



<ul class="wp-block-list">
<li><strong>Website Visitors</strong>: This is the standard one — people who have visited your website. You collect this data using Google Tag Manager or Google Analytics.</li>



<li><strong>App Users</strong>: If you have a mobile app, you can pull data from Firebase or other third-party analytics tools into Google Ads for retargeting.</li>



<li><strong>Customer Match</strong>: This is the &#8220;holy grail” of retargeting. You take your business’s first-party data (email addresses, phone numbers, etc.) and upload it directly to Google Ads, so that Google can find those same users across its platforms.</li>



<li><strong>Content Engagers</strong>: People who have interacted with your content on Google-owned properties. Examples include a segment of users who have watched your YouTube videos, or a segment of users who have clicked through to your site from search results (this is called the <a href="https://searchengineland.com/google-ads-engaged-audiences-454293">Google Engaged Audience</a>, which we explored in another article).</li>
</ul>



<h2 class="wp-block-heading" id="h-should-you-upload-your-data-segments-if-you-re-not-planning-to-do-retargeting">Should you upload “your data segments” if you’re not planning to do retargeting?</h2>



<p>Many practitioners overlook this detail: your data segments aren&#8217;t just about ad <em>targeting.</em></p>



<p>Even if you don&#8217;t have a single retargeting campaign running, the mere existence of these lists in your account provides a vital signal for Smart Bidding and Optimized Targeting.</p>



<p>For example, when you upload a customer list, you&#8217;re telling Google, &#8220;These are the people who actually buy from me.&#8221; Even if you never add that list to your audience signal in Performance Max, Google will still use it to understand likely converters and adjust bidding/targeting accordingly.</p>



<p>Similarly, let’s say you only run Search and Shopping campaigns, and you use Target ROAS bidding. When Google is trying to set the right bid for the right user at the right time, their presence (or lack thereof) on a “your data segment” list is one of many signals incorporated into that bidding calculation.</p>



<h2 class="wp-block-heading" id="h-how-can-you-use-retargeting-lists-in-google-ads">How can you use retargeting lists in Google Ads?</h2>



<p>Different campaign types handle audience data differently. It’s important to know the distinction so you can plan your targeting strategy accordingly.</p>



<ul class="wp-block-list">
<li><strong>Search, Shopping, Display</strong>: In these campaigns, you have three options with Your data segments: Targeting, Observation, Exclusion.
<ul class="wp-block-list">
<li><strong>Targeting</strong> means your ads will only show if the user is a member of your data segment</li>



<li><strong>Observation</strong> allows you to see your campaign data segmented by list, without narrowing your reach</li>



<li><strong>Exclusion</strong> means your ads will only show if the user is NOT a member of your data segments.</li>
</ul>
</li>
</ul>



<ul class="wp-block-list">
<li><strong>Performance Max and App Campaigns</strong>: In these AI-powered campaigns, you can include Your data segments as part of your audience signal. Performance Max recently added the ability to exclude Your data segments as well.</li>



<li><strong>Demand Gen</strong>: In Demand Gen, you can Target and Exclude Your data segments, but there is no “Observation” option.</li>
</ul>



<p>If you’re new to retargeting, I find Demand Gen the best place to start. It’s built for visual storytelling and works well with the Google Engaged Audience or basic website visitor lists.</p>



<p>If you have some experience with retargeting campaigns, you might want to try New Customer Acquisition or Customer Retention mode in PMax or Shopping, as these are powered by Your data segments.</p>



<h2 class="wp-block-heading" id="h-what-s-the-biggest-retargeting-mistake-to-avoid">What’s the biggest retargeting mistake to avoid?</h2>



<p>Over-segmenting. I know it can be tempting to create 50 different lists: &#8220;People who visited the cart on a Tuesday,&#8221; or &#8220;People who looked at three pages but didn&#8217;t click the &#8216;About&#8217; section.&#8221;</p>



<p>Unless you&#8217;re spending six figures or more every month, this level of granularity doesn’t help, and may actually hurt your campaigns. Google’s AI needs data density to learn. When you slice your audience into tiny slivers, you don&#8217;t have enough &#8220;matched records&#8221; for the system to optimize.</p>



<p>Upload your unique data to Google Ads, keep your strategy simple, and let the bidding algorithms do the heavy lifting in driving returning customers for you.</p>



<p><em>This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.</em></p>


One of the most profitable Google Ads targeting tactics is retargeting: showing ads to people who are already familiar with your business. But if you still think that “retargeting” means a Display campaign chasing users around the web with banner ads, you’re missing out on how “Your data segments” actually function today.
Let’s explore how you can leverage your proprietary audience data in new ways, and what mistakes to avoid in 2026 and beyond.
What are “Your data segments” in Google Ads?
Retargeting means showing ads to people who are already familiar with your business. Google uses the euphemistic name “Your data segments” to refer to all the retargeting lists in your account.
What types of retargeting can you do in Google Ads?
A variety of different retargeting methods are available in Google Ads. They mirror what you’ll find on other ad platforms like Meta, LinkedIn, or TikTok. I find it helpful to group them into four categories:
Website Visitors: This is the standard one — people who have visited your website. You collect this data using Google Tag Manager or Google Analytics.
App Users: If you have a mobile app, you can pull data from Firebase or other third-party analytics tools into Google Ads for retargeting.
Customer Match: This is the “holy grail” of retargeting. You take your business’s first-party data (email addresses, phone numbers, etc.) and upload it directly to Google Ads, so that Google can find those same users across its platforms.
Content Engagers: People who have interacted with your content on Google-owned properties. Examples include a segment of users who have watched your YouTube videos, or a segment of users who have clicked through to your site from search results (this is called the Google Engaged Audience, which we explored in another article).
Should you upload “your data segments” if you’re not planning to do retargeting?
Many practitioners overlook this detail: your data segments aren’t just about ad targeting.
Even if you don’t have a single retargeting campaign running, the mere existence of these lists in your account provides a vital signal for Smart Bidding and Optimized Targeting.
For example, when you upload a customer list, you’re telling Google, “These are the people who actually buy from me.” Even if you never add that list to your audience signal in Performance Max, Google will still use it to understand likely converters and adjust bidding/targeting accordingly.
Similarly, let’s say you only run Search and Shopping campaigns, and you use Target ROAS bidding. When Google is trying to set the right bid for the right user at the right time, their presence (or lack thereof) on a “your data segment” list is one of many signals incorporated into that bidding calculation.
How can you use retargeting lists in Google Ads?
Different campaign types handle audience data differently. It’s important to know the distinction so you can plan your targeting strategy accordingly.
Search, Shopping, Display: In these campaigns, you have three options with Your data segments: Targeting, Observation, Exclusion.

Targeting means your ads will only show if the user is a member of your data segment
Observation allows you to see your campaign data segmented by list, without narrowing your reach
Exclusion means your ads will only show if the user is NOT a member of your data segments.
Performance Max and App Campaigns: In these AI-powered campaigns, you can include Your data segments as part of your audience signal. Performance Max recently added the ability to exclude Your data segments as well.
Demand Gen: In Demand Gen, you can Target and Exclude Your data segments, but there is no “Observation” option.
If you’re new to retargeting, I find Demand Gen the best place to start. It’s built for visual storytelling and works well with the Google Engaged Audience or basic website visitor lists.
If you have some experience with retargeting campaigns, you might want to try New Customer Acquisition or Customer Retention mode in PMax or Shopping, as these are powered by Your data segments.
What’s the biggest retargeting mistake to avoid?
Over-segmenting. I know it can be tempting to create 50 different lists: “People who visited the cart on a Tuesday,” or “People who looked at three pages but didn’t click the ‘About’ section.”
Unless you’re spending six figures or more every month, this level of granularity doesn’t help, and may actually hurt your campaigns. Google’s AI needs data density to learn. When you slice your audience into tiny slivers, you don’t have enough “matched records” for the system to optimize.
Upload your unique data to Google Ads, keep your strategy simple, and let the bidding algorithms do the heavy lifting in driving returning customers for you.
This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.

Think retargeting in Google Ads is just banner ads? The real takeaway: your first-party data now powers AI performance — so use it.

Google Ads retargeting: A guide to your data segments

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/Welcome-to-Claude-Code.jpg" class="attachment-large size-large wp-post-image" alt="Claude Code" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Welcome-to-Claude-Code.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/03/Welcome-to-Claude-Code-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/03/Welcome-to-Claude-Code-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/03/Welcome-to-Claude-Code-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Most SEO work means tab-switching between GSC, GA4, Ads, and AI tools. What if one setup could cross-reference them all?

Lately, I’ve been spending most of my day inside Cursor running Claude Code. I’m not a developer. I run a digital marketing agency. But Claude Code within Cursor has become the fastest way for me to handle many tasks I want to do, including pulling and analyzing data from Google Search Console, GA4, and Google Ads.
The setup takes about an hour. After that, you can ask things like “which keywords am I paying for that I already rank for organically?” and get an answer in seconds instead of spending an afternoon with spreadsheets. (I wouldn’t have been the one spending an afternoon with spreadsheets anyway, but now nobody has to.)
Here’s the step-by-step process I developed while analyzing data for our agency clients. If this looks too technical, paste the URL of this article into Claude and ask it to walk you through it step by step.
What you’re building
What you end up with is a project directory where Claude Code has access to Python scripts that pull live data from your Google APIs. You fetch the data, it lands in JSON files, and then you just talk to it.
No dashboards to build. No Looker Studio templates to maintain. You’re basically giving Claude Code the same data your team would look at, and letting it do the cross-referencing.
seo-project/
├── config.json              # Client details + API property IDs
├── fetchers/
│   ├── fetch_gsc.py         # Google Search Console
│   ├── fetch_ga4.py         # Google Analytics 4
│   ├── fetch_ads.py         # Google Ads search terms
│   └── fetch_ai_visibility.py  # AI Search data 
├── data/
│   ├── gsc/                 # Query + page performance
│   ├── ga4/                 # Traffic by channel, top pages
│   ├── ads/                 # Search terms, spend, conversions
│   └── ai-visibility/       # AI citation data
└── reports/                 # Generated analysis

  
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Step 1: Set up Google API authentication
Everything runs through a Google Cloud service account. One service account covers both GSC and GA4, which is nice. Google Ads needs its own OAuth setup, which is less nice but manageable.




Service account (for GSC + GA4)
Create a project in Google Cloud Console.
Enable the Search Console API and Google Analytics Data API.
Create a service account under IAM & Admin > Service Accounts.
Download the JSON key file.
Add the service account email as a user in your GSC property (read access is enough).
Add it as a Viewer in your GA4 property.
The service account email looks like your-project@your-project-id.iam.gserviceaccount.com. You’ll add this email address to each client’s GSC and GA4 properties, same way you’d add any team member.
For agencies: one service account works across all clients. Add it to each property, update a config file with the property IDs, and you’re set.
Google Ads authentication
Google Ads is different. You need:
A developer token from the Google Ads API Center (under Tools & Settings > Setup > API Center).
OAuth 2.0 credentials from Google Cloud (not the service account, a separate OAuth client).
A one-time browser authentication to generate a refresh token.
The developer token requires an application. For agency use, describe it as “automated reporting for marketing clients.” Approval usually takes 24-48 hours.
If you’re using a Manager Account (MCC), one developer token and one refresh token cover all sub-accounts. You just change the customer ID per client.
If you don’t have API access or MCC, maybe it’s a new client and you’re still getting set up, you can skip the API entirely. Download 90 days of keyword and search terms data as CSVs from the Google Ads UI, drop them in your data directory, and Claude Code will work with those just as well. That’s how we handle clients who aren’t in our MCC yet.
Install the Python dependencies
All the examples below assume you’re working in the terminal on a Mac or Linux machine. If you’re on Windows, the easiest path is Windows Subsystem for Linux (WSL).
pip install google-api-python-client google-auth google-analytics-data google-ads
Step 2: Build the data fetchers
Each fetcher is a short Python script that authenticates, pulls data, and saves JSON. I didn’t write these from scratch. I described what I wanted to Claude Code and it wrote them.
One thing that genuinely surprised me: I never had to read the API documentation. Not for GSC, GA4, or Google Ads. 
I’d say something like “I want to pull the top 1,000 queries from Search Console for the last 90 days,” and Claude Code would figure out the authentication, endpoints, and query parameters. It already knows these APIs. You just tell it what data you want.
Here’s what the scripts look like.
Google Search Console fetcher
from google.oauth2 import service_account
from googleapiclient.discovery import build

SCOPES = ['https://www.googleapis.com/auth/webmasters.readonly']

def get_gsc_service():
    credentials = service_account.Credentials.from_service_account_file(
        'service-account-key.json', scopes=SCOPES
    )
    return build('searchconsole', 'v1', credentials=credentials)

def fetch_queries(service, site_url, start_date, end_date):
    response = service.searchanalytics().query(
        siteUrl=site_url,
        body={
            'startDate': start_date,
            'endDate': end_date,
            'dimensions': ['query'],
            'rowLimit': 1000
        }
    ).execute()
    return response.get('rows', [])
You get back queries with clicks, impressions, CTR, and average position. Save it as JSON.
GA4 fetcher
from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import (
    RunReportRequest, DateRange, Metric, Dimension
)

def get_ga4_client():
    credentials = service_account.Credentials.from_service_account_file(
        'service-account-key.json',
        scopes=['https://www.googleapis.com/auth/analytics.readonly']
    )
    return BetaAnalyticsDataClient(credentials=credentials)

def fetch_traffic_by_channel(client, property_id, start_date, end_date):
    request = RunReportRequest(
        property=f"properties/{property_id}",
        date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
        dimensions=[Dimension(name="sessionDefaultChannelGroup")],
        metrics=[
            Metric(name="sessions"),
            Metric(name="totalUsers"),
            Metric(name="bounceRate"),
        ]
    )
    return client.run_report(request)
Google Ads fetcher
Google Ads uses something called Google Ads Query Language (GAQL). If you’ve ever written a SQL query, this will look familiar. If you haven’t, don’t worry, Claude Code will write it for you:
from google.ads.googleads.client import GoogleAdsClient

client = GoogleAdsClient.load_from_storage("google-ads.yaml")
ga_service = client.get_service("GoogleAdsService")

query = """
    SELECT
        search_term_view.search_term,
        metrics.impressions,
        metrics.clicks,
        metrics.cost_micros,
        metrics.conversions
    FROM search_term_view
    WHERE segments.date DURING LAST_30_DAYS
    ORDER BY metrics.impressions DESC
"""

response = ga_service.search(customer_id="1234567890", query=query)
This pulls the same data as the Search Terms report you’d download from the Google Ads UI: impressions, clicks, cost, conversions, match type, campaign, and ad group.
Get the newsletter search marketers rely on.
 
				
								
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						// form.onSubmit(function(){
						// });
						
						// form.onSuccess(function (values, followUpUrl) {
						// });
					});
				
			
See terms.
Step 3: Create a client config
One JSON file per client. Nothing fancy, just the property IDs and some context:
{
  "name": "Client Name",
  "domain": "example.com",
  "gsc_property": "https://www.example.com/",
  "ga4_property_id": "319491912",
  "google_ads_customer_id": "9270739126",
  "industry": "Higher Education",
  "competitors": [
    "https://competitor1.com/",
    "https://competitor2.com/"
  ]
}
Step 4: Ask cross-source questions
So now you’ve got JSON files from GSC, GA4, and Ads sitting in your project directory. Claude Code can read all of them at once and answer questions that would normally mean a lot of tab-switching and VLOOKUP work.
The paid-organic gap analysis
The single most valuable question I’ve found:
“Compare the GSC query data against the Google Ads search terms. Find keywords where we’re paying for clicks but already have strong organic positions. Also, find keywords where we’re spending on ads with zero organic visibility. Those are content gaps.”




When I ran this for a higher education client, it identified:
2,742 search terms with wasted ad spend (impressions, zero clicks).
351 opportunities to reduce paid spend on terms where organic was already strong.
33 high-performing organic queries that paid could amplify.
41 content gaps where paid was the only presence (no organic).
That analysis took about 90 seconds. The equivalent manual process (downloading CSVs from GSC and Ads, VLOOKUPing across them, categorizing the overlaps) takes most of an afternoon.
Other questions worth asking
Once you have GSC + GA4 + Ads data loaded:
“Which pages get the most impressions in GSC but have low CTR? What’s the traffic from GA4 for those same pages?” (identifies meta description/title opportunities)
“What are the top 20 organic queries by impression that we’re not running ads against?” (paid amplification candidates)
“Group the GSC queries by topic cluster and show me which clusters have the most impressions but lowest average position.” (content investment priorities)
“Which pages in GA4 have high bounce rates but strong GSC positions? Those might need content improvement.”
Claude Code isn’t doing anything a human couldn’t do with spreadsheets. It’s doing it in seconds, and you can follow up with another question without rebuilding the whole analysis from scratch.
Step 5: Add AI visibility tracking
Traditional SERP positions aren’t the whole picture anymore. Between Google’s AI Overviews, AI Mode, Copilot, ChatGPT, and Perplexity, you need to know whether AI systems are citing your content. 
This is especially true in verticals like higher education, where prospective students increasingly start their research in AI search tools.
If you have a tracking platform
Tools like Scrunch, Semrush’s AI Visibility toolkit, or Otterly.ai will track your brand’s presence across ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot. 
Export the data as CSV or JSON and drop it in your data directory. Claude Code can then cross-reference AI citations against your GSC and Ads data.




When I did this for our own site, we discovered two blog posts competing for the same AI citations on GEO-related queries.
One had 12 times as many Copilot citations as the other, despite both targeting similar intent. That led to a consolidation decision we wouldn’t have made based solely on traditional rank data. This kind of AI search cannibalization is something most SEO teams aren’t yet checking for.
If you don’t have a tracking platform
You don’t need an enterprise tool to start. There are several APIs that let you pull AI search data directly, and the costs are lower than you’d think.
DataForSEO AI Overview API: The most accessible option. Pay-as-you-go at about $0.01 per query, with a $50 minimum deposit. You send a keyword, and it returns the full AI Overview content from Google SERPs, including which URLs are cited. It also has a separate LLM Mentions API that tracks how LLMs reference brands across platforms.
# DataForSEO AI Overview — simplified example
payload = [{
    "keyword": "best higher education marketing agencies",
    "location_code": 2840,  # US
    "language_code": "en"
}]

response = requests.post(
    "https://api.dataforseo.com/v3/serp/google/ai_overview/live/advanced",
    headers=auth_headers,
    json=payload
)
# Returns: AI Overview text, cited URLs, references
SerpApi: Starts at $75/month for 5,000 searches. Returns structured JSON for the full Google SERP, including AI Overviews. Good documentation, Python client library, and a free tier for testing.
SearchAPI.io: Similar to SerpApi, starts at $40/month. Also offers a separate Google AI Mode API that captures AI-generated answers with citations.
Bright Data SERP API: Pay-as-you-go starting around $1.80 per 1,000 requests. Set brd_ai_overview=2 to increase the likelihood of capturing AI Overviews. Also has an MCP server if you want tighter agent integration.
Bing Webmaster Tools: Free, and the only first-party AI citation data available from any major platform right now. Shows how often your content appears as a source in Copilot and Bing AI responses, with page-level data and the “grounding queries” that triggered citations. No API yet (Microsoft says it’s on the backlog), but you can export CSVs.
DIY: Direct LLM API Calls: The cheapest approach for small-scale monitoring. Write a Python script that sends a consistent set of prompts to the OpenAI, Anthropic, and Perplexity APIs, then parses responses for brand mentions. Perplexity’s Sonar API is especially useful here because it includes web citations in responses, and citation tokens are free. Total cost: under $20/month for a modest prompt library.
The general pattern: Pick one SERP API for Google AI Overview data, use Bing Webmaster Tools (it’s free), and supplement with direct LLM API calls or a dedicated tracker if budget allows.
The workflow in practice
So what does this actually look like on a Tuesday morning?
Setup: Once per client, ~15 minutes
Add service account email to client’s GSC and GA4
Get their Google Ads customer ID (or export search terms if they’re not in the MCC)
Create a config.json with property IDs
Monthly data pull: ~5 minutes
python3 run_fetch.py --sources gsc,ga4,ads
Analysis (as needed): Open Claude Code in the project directory and ask questions. The data is right there.
Output: Claude Code generates a markdown report. When I need something client-facing, I push it to Google Docs using a separate tool I built called google-docs-forge. It converts markdown into a properly formatted Google Doc, so the output doesn’t look like it came from a terminal.




The whole process takes about 35 minutes for a new client: setup, fetch, analysis. Monthly refreshes take about 20 minutes, including analysis time. Compare that to the manual alternative of downloading CSVs from three different platforms, cross-referencing in spreadsheets, and writing up findings.
What this doesn’t replace
I don’t want to oversell this. Claude Code is reading your data and finding patterns across sources faster than you can manually. It’s not telling you what to do about those patterns. You still need someone who understands the client’s business, their competitive situation, and what they’re actually trying to accomplish. The tool finds the interesting data. The strategist decides what to do with it.
You also need to verify what it gives you. LLMs can hallucinate, and that includes data analysis. I’ve seen Claude Code confidently report a number that didn’t match the JSON file. It’s rare, but it happens. 
Treat the output like you’d treat work from a new analyst: trust but verify, especially before anything goes to a client. Spot-check the numbers against the source data. If something looks too clean or too dramatic, go look at the raw file.
It also doesn’t replace your existing platforms. If you need historical trend data, automated alerts, or a client-facing dashboard, you still want a Semrush or an Ahrefs. What this gives you is the ability to ask ad hoc questions across multiple data sources, which none of those platforms does well on their own.
And the GEO/AI visibility tracking space is still immature. The data from AI citation tools is directionally useful. Wind sock, not GPS. Google doesn’t publish AI Overview or AI Mode citation data through any official API, so every third-party tool is approximating. Bing’s Copilot data is the most reliable because it’s first-party, but it only covers the Microsoft ecosystem.

  
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Start with GSC, layer in the rest
If you want to give this a shot:
Start with GSC only. It’s the easiest API to connect (service account, read-only access, free). Fetch your queries and pages for the last 90 days. Ask Claude Code to group queries by topic, identify page-2 ranking opportunities, and find pages with high impressions but low CTR.
Add GA4 second. Same service account. Now you can ask cross-source questions: “Which pages rank well in GSC but have high bounce rates in GA4?”
Add Google Ads when you’re ready. The OAuth setup is more involved, but the paid-organic gap analysis alone justifies the effort.
Layer in AI visibility last. Start with Bing Webmaster Tools (free) and one SERP API for AI Overview data.
Each layer builds on the last. You don’t need all four to get value. The GSC + GA4 combination alone surfaces insights that take hours to find manually.

Most SEO work means tab-switching between GSC, GA4, Ads, and AI tools. What if one setup could cross-reference them all?

How to turn Claude Code into your SEO command center

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/WebMCP-explained-Inside-Chrome-146s-agent-ready-web-preview.png" class="attachment-large size-large wp-post-image" alt="WebMCP explained- Inside Chrome 146’s agent-ready web preview" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/WebMCP-explained-Inside-Chrome-146s-agent-ready-web-preview.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/WebMCP-explained-Inside-Chrome-146s-agent-ready-web-preview-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/WebMCP-explained-Inside-Chrome-146s-agent-ready-web-preview-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/WebMCP-explained-Inside-Chrome-146s-agent-ready-web-preview-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>WebMCP exposes structured website actions for AI agents. See how it works, why it matters, and how to test it in Chrome 146.

Chrome 146 has introduced an early preview of WebMCP behind a flag. WebMCP (Web Model Context Protocol) is a proposed web standard that exposes structured tools on websites, showing AI agents exactly what actions they can take and how to execute them.
Here’s some context around what that actually means.
The internet was originally built for humans. We designed buttons, dropdowns, and forms for people to read, understand, and use. But now there’s a new type of user emerging: AI agents. Soon, they’ll be able to complete registrations, buy tickets, and take any action needed to complete a goal on a website.
Right now, AI agents face a major challenge. They must crawl websites and reverse-engineer how everything works. For example, to book a flight, an agent needs to identify the right input fields, guess the correct data format, and hope nothing breaks in the process. It’s inefficient.
The WebMCP standard will solve this issue by exposing the structure of these tools so AI agents can understand and perform better.
A deeper understanding of WebMCP
Let’s say you need to book a flight.
Without WebMCP: An AI agent would crawl the page looking for a button that would say something like “Book a Flight” or “Search Flights.” The agent reads the screen, guesses which fields need what information, and hopes the form accepts its input.
With WebMCP: Instead of thinking “I need to find a ‘Book a Flight’ button,” the agent thinks “I need to call the bookFlight() function with clear parameters (date, origin/destination, passengers) and receive a structured result. The agent doesn’t search for visual elements. It calls a function, just like developers do when working with APIs.
How WebMCP works
WebMCP provides JavaScript APIs and HTML form annotations so AI agents know exactly how to interact with the page’s tools. It works using basically three steps:
Discovery: What tools does this page support? Checkout, BookFlight, searchProducts.
JSON Schemas: The exact definitions of what inputs are expected and what outputs come back.

For example, a bookFlight tool might expect:

Input: {origin: “LON”, destination: “NYC”, outboundDate: “2026-06-10”, passengers: 2}
Output: {confirmationNumber: “ABC123”, totalPrice: “$850”, status: “confirmed”}
State: Tools can be registered and unregistered based on the current page state. For example, a checkout tool only appears when items are in the cart, or a bookFlight tool becomes available after a user selects dates. This ensures agents only see relevant actions for the current situation.
Your website exposes a list of actions, each one describing what it does, what inputs it accepts, what outputs it returns, and what permissions it requires.
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Why this new standard matters
AI agents are quickly becoming part of our daily workflows. Soon we won’t book our own flights, fill out forms, or publish content — we’ll ask an AI to do it for us.
But right now, AI agents struggle to interact with websites reliably. They currently use two imperfect approaches:
Automation (fragile and unreliable): In this approach, the AI agent reads the screen, clicks buttons, and types into fields, just like a human would. However, websites are constantly updated. Button colors change. Field names change. Classes change. A/B tests create different versions of the same page. What worked yesterday may not work today.
APIs (limited availability): APIs provide a direct, structured way for agents to interact with websites. The problem is that most websites don’t have public APIs, and those that do are often missing key features or data that’s available through the user interface.
WebMCP: The missing middle ground
WebMCP fills the gap between those two approaches. It lets websites expose actions in a way that matches how the web actually works. Think of it as making your existing web interface readable by AI agents — without the fragility of UI automation or the overhead of maintaining a separate API.




The growth opportunity
Just as websites optimized for search engines in the 2000s, WebMCP represents the next evolution: optimization for AI agents. Early adopters who implement WebMCP could gain a competitive advantage as AI-powered search and commerce become mainstream.
But this isn’t just about SEO anymore. It’s about seizing a broader growth opportunity. SEO, AEO (AI engine optimization), and agentic optimization are all knowledge areas with one common goal: improving revenue. WebMCP opens the door to being not just discoverable, but directly actionable by the agents your future customers will use.
Real-world examples
To make this more concrete, here are some scenarios where WebMCP changes the game:
B2B scenarios
Quote and proposal requests: Industrial suppliers expose a request_quote tool. A buyer’s agent can submit identical RFQs across multiple vendors without adapting to each site’s unique form.
Vendor qualification filtering: Service providers expose a search_capabilities tool. Before making contact, a procurement agent can query multiple vendors to filter for specific certifications or geographic coverage.
Freight and logistics rate shopping: Carriers expose get_shipping_rate and schedule_pickup tools. A logistics agent can query multiple carriers and book the best option without navigating unique quoting interfaces.
Commercial insurance quoting: Insurance carriers expose request_policy_quote tools. A broker’s agent can submit the same business information across multiple carriers to compare coverage options without re-entering details on each insurer’s portal.
Wholesale and distribution ordering: Distributors expose check_inventory and get_volume_pricing tools. A purchasing agent can query stock levels and pricing across multiple distributors and place an order with whichever offers the best combination of availability and price.
B2C scenarios
Multi-retailer price comparison: Retailers expose search_products and check_price tools. A consumer’s agent can query multiple stores, compare options, and add items to the cart at whichever retailer offers the best deal — all in seconds.
Restaurant discovery and booking: Restaurants expose browse_menu and reserve_table tools. An agent can find availability and search menus across multiple restaurants before booking a table at the best match for your preferences.
Local service provider quoting: Service businesses expose check_availability and request_quote tools. If you need a plumber or electrician, for example, an agent can collect quotes from multiple providers without you having to fill out intake forms on each company’s website.
Travel planning across providers: Airlines, hotels, and car rentals expose search_availability and book_reservation tools. An agent can query multiple providers directly and assemble a complete itinerary without an aggregator like Expedia or Kayak.
Real estate search and tour scheduling: Listing sites expose search_properties and schedule_showing tools. A buyer’s agent can search across different platforms and book property tours without navigating unique forms.
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See terms.
How to implement WebMCP
WebMCP gives developers two ways to make their websites agent-ready:
Imperative API
Declarative API




The Imperative API
The Imperative API lets developers define tools programmatically through a new browser interface called navigator.modelContext. You register a tool by giving it a name, a description, an input schema, and an execute function.
Here’s a simplified example of an ecommerce product search tool:




The agent sees the tool, understands what it does, knows what input it needs, and can call it directly.
Developers can register tools one at a time with registerTool(), replace the full set with provideContext() (useful when your app’s state changes significantly), or remove them with unregisterTool() and clearContext().
The Declarative API
The Declarative API transforms standard HTML forms into agent-compatible tools by adding a few HTML attributes.
Here’s a simplified example of a restaurant reservation form:




By adding toolname and tooldescription to a form, the browser automatically translates its fields into a structured schema that AI agents can interpret. When an agent calls the tool, the browser populates the fields and, if toolautosubmit is set, it submits the form automatically.
The big takeaway: Existing websites with standard HTML forms can become agent-compatible with minimal code changes.
Implementation best practices from Google’s documentation
Google’s early preview documentation includes some practical guidance on designing good WebMCP tools. A few highlights worth noting:
Use specific action verbs: Name tools based on what they actually do. Use create-event if the tool immediately creates an event. Use start-event-creation-process if it redirects the user to a UI form. Clear naming helps agents choose the right tool for the task.
Accept raw user input: Don’t ask the agent to perform calculations or transformations. If a user says “11:00 to 15:00,” the tool should accept those strings, not require the agent to convert them to minutes from midnight.
Validate in code, not just in schema: Schema constraints provide guidance, but they’re not foolproof. When validation fails, return descriptive error messages so the agent can self-correct and retry.
Keep tools atomic and composable: Each tool should do one specific thing. Avoid creating overlapping tools with subtle differences. Let the agent handle the workflow logic.
Return after the UI updates: When a tool completes an action, make sure the UI reflects that change before returning. Agents often verify success by checking the updated interface, then use that information to plan their next step.
How to try WebMCP today
WebMCP is currently available as an early preview behind a feature flag in Chrome 146. It’s not production-ready yet, but developers and curious teams can already experiment with it.
Requirements
Chrome version 146.0.7672.0 or higher.
Basic familiarity with Chrome flags.
Setup steps
Open Chrome and navigate to chrome://flags/#enable-webmcp-testing
Find the “WebMCP for testing” flag and set it to “Enabled”
Relaunch Chrome to apply the changes
Once the flag is enabled, you can install the Model Context Tool Inspector Extension to see WebMCP in action. The extension lets you inspect registered tools on any page, execute them manually with custom parameters, or test them with an AI agent using Gemini API support. Google also has a live travel demo where you can see the full flow, from discovering tools to invoking them with natural language.
What all this means going forward
In the same way that mobile-first design changed how we build websites, agent-ready design could define the next generation of web applications.
That said, WebMCP is still in early preview. The final version will likely change. The Chrome team is actively discussing rolling back parts of what they’ve been building with the embedded LLM API (like summarization and other features). So what we’re seeing now is a starting point, not the finished product.
WebMCP is simply the next chapter in AI optimization. While aiming for discoverability and citation is still essential, WebMCP opens up a new opportunity for brands — making entire web experiences and functionality accessible to AI agents. It’s not just about being found or cited. It’s about being usable by the next generation of web users.
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What to do now
Start experimenting with WebMCP, but don’t bet your roadmap on it yet. The standard is evolving, and early adopters will have an advantage, but only if they stay flexible as the standard matures.
The websites that win in an agent-driven web will be those that make it easy for AI to complete tasks, not just find information.
This article was originally published on LOCOMOTIVE (as WebMCP: The Standard That Lets AI Agents Call Website Functions Directly) and is republished with permission.

WebMCP exposes structured website actions for AI agents. See how it works, why it matters, and how to test it in Chrome 146.

WebMCP explained: Inside Chrome 146’s agent-ready web preview

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google has a new help document on how Google's crawlers work.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google posted a <a href="https://developers.google.com/crawling/docs/about-crawling">new help document</a> on &#8220;Things to know about Google&#8217;s web crawling.&#8221; While many of those &#8220;things to know&#8221; are already known, Google felt it would be a good idea to make this document in order to provide &#8220;basic educational information about crawling to better highlight various resources about crawling that are available to site owners.&#8221;</p>



<p>The document has 9 items posted in it right now including:</p>



<p><strong>Frequent crawling is a good sign</strong>! Google wrote, </p>



<ul class="wp-block-list">
<li>&#8220;If we&#8217;re crawling your site a lot, it&#8217;s an indication your pages have fresh or highly relevant content that people want to find, and that our systems are recognizing that demand. Online shopping is a great example: we crawl ecommerce sites often so that our results will display retailers&#8217; most up-to-date prices, promotions, and inventory status.&#8221;</li>
</ul>



<p><strong>Other items. </strong>Here is the full list, but make sure to check out the help document to read it all. None of it is new but it is a helpful refresher:</p>



<ul class="wp-block-list">
<li>What is crawling? In short, crawling is how Google &#8220;sees&#8221; the web</li>



<li>We have many crawlers; they each have important jobs</li>



<li>We perform repeat crawls to find the latest updates and to provide the freshest search results</li>



<li>Frequent crawling is a good sign!</li>



<li>Google&#8217;s crawling has grown over time as pages have become more complex</li>



<li>We optimize crawling automatically</li>



<li>Google crawlers never go into paywall or subscription content without permission</li>



<li>Site owners have control over what gets crawled, and how</li>



<li>Our standard crawlers always respect websites&#8217; choices about how their content is accessed and used</li>
</ul>



<p><strong>Why we care. </strong>Crawling is a fundamental requirement for SEO and being found in Google Search and other Google surfaces. This help document might help you quickly understand how Google crawling works and what you can aim to do to improve your site&#8217;s crawlability. </p>



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Google posted a new help document on “Things to know about Google’s web crawling.” While many of those “things to know” are already known, Google felt it would be a good idea to make this document in order to provide “basic educational information about crawling to better highlight various resources about crawling that are available to site owners.”
The document has 9 items posted in it right now including:
Frequent crawling is a good sign! Google wrote, 
“If we’re crawling your site a lot, it’s an indication your pages have fresh or highly relevant content that people want to find, and that our systems are recognizing that demand. Online shopping is a great example: we crawl ecommerce sites often so that our results will display retailers’ most up-to-date prices, promotions, and inventory status.”
Other items. Here is the full list, but make sure to check out the help document to read it all. None of it is new but it is a helpful refresher:
What is crawling? In short, crawling is how Google “sees” the web
We have many crawlers; they each have important jobs
We perform repeat crawls to find the latest updates and to provide the freshest search results
Frequent crawling is a good sign!
Google’s crawling has grown over time as pages have become more complex
We optimize crawling automatically
Google crawlers never go into paywall or subscription content without permission
Site owners have control over what gets crawled, and how
Our standard crawlers always respect websites’ choices about how their content is accessed and used
Why we care. Crawling is a fundamental requirement for SEO and being found in Google Search and other Google surfaces. This help document might help you quickly understand how Google crawling works and what you can aim to do to improve your site’s crawlability. 
complete picture of your search visibility.
      
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Google has a new help document on how Google's crawlers work.

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