<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/How-to-get-cited-by-ChatGPT-The-content-traits-LLMs-quote-most.png" class="attachment-large size-large wp-post-image" alt="How to get cited by ChatGPT: The content traits LLMs quote most" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/11/How-to-get-cited-by-ChatGPT-The-content-traits-LLMs-quote-most.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/How-to-get-cited-by-ChatGPT-The-content-traits-LLMs-quote-most-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>ChatGPT ads are expanding with self-serve buying, and improved measurement, a move toward a scalable advertising platform.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/How-to-get-cited-by-ChatGPT-The-content-traits-LLMs-quote-most.png" class="attachment-large size-large wp-post-image" alt="How to get cited by ChatGPT: The content traits LLMs quote most" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/How-to-get-cited-by-ChatGPT-The-content-traits-LLMs-quote-most.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/How-to-get-cited-by-ChatGPT-The-content-traits-LLMs-quote-most-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
OpenAI is taking the next step in building its ChatGPT ads platform — introducing self-serve buying, CPC bidding and improved measurement to bring more advertisers into the ecosystem



What’s happening. Ads in ChatGPT are moving beyond a limited pilot, with new ways for businesses to buy and manage campaigns. Advertisers can now access inventory through agency and tech partners — or directly via a new beta Ads Manager rolling out in the U.S.



This marks a shift from a controlled test environment to a more scalable ad platform.



Why we care. Until now, access to ChatGPT ads has been restricted and expensive, limiting participation to large advertisers. These updates lower the barrier to entry, opening the door for SMBs, startups and a wider range of brands to test the channel.



At the same time, <a href="https://searchengineland.com/openai-adds-cpc-ads-to-chatgpt-475148">introducing CPC bidding</a> brings ChatGPT closer to established performance platforms, allowing advertisers to optimise for actions — not just impressions.



Self-serve Ads Manager. The new Ads Manager gives advertisers direct control over campaigns, including budgeting, bidding, creative uploads and performance tracking.



While still in beta, it signals OpenAI’s intention to build a full-service ad platform — not just a partner-led ecosystem.



Between the lines. This is a familiar playbook. Platforms typically start with high-touch, partner-led campaigns before moving to self-serve tools that unlock scale. ChatGPT is now entering that second phase.



CPC bidding arrives. Previously, ChatGPT ads were sold on a CPM basis. The addition of CPC means advertisers can now align spend with user actions — a critical step for performance marketers.



Given the nature of ChatGPT queries — often exploratory, comparative and decision-driven — clicks could become a strong proxy for intent.



Measurement catches up. OpenAI is also rolling out pixel-based tracking and a Conversions API, allowing advertisers to measure actions like purchases, sign-ups and leads.



Importantly, this data is aggregated, with no access to individual conversations — reinforcing OpenAI’s emphasis on privacy.



Why this is a big deal. Measurement has been one of the biggest gaps in early ChatGPT ads. Without it, advertisers struggled to justify spend. These updates begin to close that gap and make optimisation more viable.



The ecosystem grows. OpenAI is also expanding its partner network, working with agencies like WPP and Publicis Groupe, as well as tech platforms such as Criteo and Adobe.



This allows advertisers to buy ChatGPT ads through tools and workflows they already use.



What to watch:



<ul class="wp-block-list">
<li>How quickly self-serve adoption scales</li>



<li>Whether CPC performance holds as competition increases</li>



<li>How measurement evolves to match advertiser expectations</li>
</ul>



Bottom line. <a href="https://openai.com/index/new-ways-to-buy-chatgpt-ads/">ChatGPT ads are moving from experiment to platform</a> — and with self-serve tools, CPC bidding and better measurement, OpenAI is laying the groundwork for scale.

OpenAI is taking the next step in building its ChatGPT ads platform — introducing self-serve buying, CPC bidding and improved measurement to bring more advertisers into the ecosystem
What’s happening. Ads in ChatGPT are moving beyond a limited pilot, with new ways for businesses to buy and manage campaigns. Advertisers can now access inventory through agency and tech partners — or directly via a new beta Ads Manager rolling out in the U.S.
This marks a shift from a controlled test environment to a more scalable ad platform.
Why we care. Until now, access to ChatGPT ads has been restricted and expensive, limiting participation to large advertisers. These updates lower the barrier to entry, opening the door for SMBs, startups and a wider range of brands to test the channel.
At the same time, introducing CPC bidding brings ChatGPT closer to established performance platforms, allowing advertisers to optimise for actions — not just impressions.
Self-serve Ads Manager. The new Ads Manager gives advertisers direct control over campaigns, including budgeting, bidding, creative uploads and performance tracking.
While still in beta, it signals OpenAI’s intention to build a full-service ad platform — not just a partner-led ecosystem.
Between the lines. This is a familiar playbook. Platforms typically start with high-touch, partner-led campaigns before moving to self-serve tools that unlock scale. ChatGPT is now entering that second phase.
CPC bidding arrives. Previously, ChatGPT ads were sold on a CPM basis. The addition of CPC means advertisers can now align spend with user actions — a critical step for performance marketers.
Given the nature of ChatGPT queries — often exploratory, comparative and decision-driven — clicks could become a strong proxy for intent.
Measurement catches up. OpenAI is also rolling out pixel-based tracking and a Conversions API, allowing advertisers to measure actions like purchases, sign-ups and leads.
Importantly, this data is aggregated, with no access to individual conversations — reinforcing OpenAI’s emphasis on privacy.
Why this is a big deal. Measurement has been one of the biggest gaps in early ChatGPT ads. Without it, advertisers struggled to justify spend. These updates begin to close that gap and make optimisation more viable.
The ecosystem grows. OpenAI is also expanding its partner network, working with agencies like WPP and Publicis Groupe, as well as tech platforms such as Criteo and Adobe.
This allows advertisers to buy ChatGPT ads through tools and workflows they already use.
What to watch:




How quickly self-serve adoption scales
Whether CPC performance holds as competition increases
How measurement evolves to match advertiser expectations
Bottom line. ChatGPT ads are moving from experiment to platform — and with self-serve tools, CPC bidding and better measurement, OpenAI is laying the groundwork for scale.

ChatGPT ads are expanding with self-serve buying, and improved measurement, a move toward a scalable advertising platform.

ChatGPT ads expand with self-serve buying

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>UCP-powered checkout initially went live only in AI Mode but it now seems to be available for some retailers in Search.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2016/01/google-shopping-2016a-ss-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
Earlier this year, Google announced <a href="https://searchengineland.com/google-universal-commerce-protocol-467290">Universal Commerce Protocol</a> (UCP), a protocol to allow AI-agents to buy directly from search. This went live only in Google&#8217;s <a href="https://blog.google/products/ads-commerce/digital-advertising-commerce-2026/">AI Mode interface in February</a>. But now, it seems to be rolling out in the main Google Search results where there are retailers that support UCP.



What it looks like. Brodie Clark <a href="https://brodieclark.com/notes/#may-2026">posted</a> a screenshot, which I can replicate, of the UCP-powered &#8220;Buy&#8221; button in the product detail overlay within Google Search, specifically for the retailer Wayfair. Here is his screenshot:



<figure class="wp-block-image size-full"><img decoding="async" width="1782" height="1020" src="https://searchengineland.com/wp-content/seloads/2026/05/ucp-google-search-wayfair.jpg" alt="" class="wp-image-476541" srcset="https://searchengineland.com/wp-content/seloads/2026/05/ucp-google-search-wayfair.jpg 1782w, https://searchengineland.com/wp-content/seloads/2026/05/ucp-google-search-wayfair-768x440.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/ucp-google-search-wayfair-1536x879.jpg 1536w" sizes="(max-width: 1782px) 100vw, 1782px" /></figure>



Clicking the Buy button will connect your Google checkout account with Wayfair and make the purchase without having to go to the Wayfair website to checkout. 



About UCP. UCP establishes a shared language between AI agents and commerce systems, removing the need for custom integrations across agents or platforms.



<ul class="wp-block-list">
<li>UCP works with existing standards (e.g., Agent2Agent, Agent Payments Protocol, and Model Context Protocol).</li>



<li>Google co-developed it with partners including Shopify, Etsy, Wayfair, and Target.</li>



<li>More than 20 additional companies across retail and payments have already endorsed it.</li>
</ul>



Why we care. A lot of people are saying AI-agents are the future of the web, well, here is a clear sign of how agents can help retailers make money. While Wayfair did not get any traffic from Google for this query, and there is no click, 0 click-through rates from Google Search. There was an impression in Google Search that led to a purchase without any clicks to the web site.



This won&#8217;t necessarily stop all searchers for wanting to visit the site and learn more about the product before purchasing. Just like it doesn&#8217;t stop all buyers from going into a physical store to touch and feel the product before purchasing. But some may just click &#8220;Buy&#8221; and never visit your site.



Now that it is rolling out in the main search results, it is something to keep a closer eye on.

Earlier this year, Google announced Universal Commerce Protocol (UCP), a protocol to allow AI-agents to buy directly from search.  This went live only in Google’s AI Mode interface in February.  But now, it seems to be rolling out in the main Google Search results where there are retailers that support UCP.
What it looks like. Brodie Clark posted a screenshot, which I can replicate, of the UCP-powered “Buy” button in the product detail overlay within Google Search, specifically for the retailer Wayfair.  Here is his screenshot:




Clicking the Buy button will connect your Google checkout account with Wayfair and make the purchase without having to go to the Wayfair website to checkout. 
About UCP. UCP establishes a shared language between AI agents and commerce systems, removing the need for custom integrations across agents or platforms.
UCP works with existing standards (e.g., Agent2Agent, Agent Payments Protocol, and Model Context Protocol).
Google co-developed it with partners including Shopify, Etsy, Wayfair, and Target.
More than 20 additional companies across retail and payments have already endorsed it.
Why we care. A lot of people are saying AI-agents are the future of the web, well, here is a clear sign of how agents can help retailers make money. While Wayfair did not get any traffic from Google for this query, and there is no click, 0 click-through rates from Google Search.  There was an impression in Google Search that led to a purchase without any clicks to the web site.
This won’t necessarily stop all searchers for wanting to visit the site and learn more about the product before purchasing. Just like it doesn’t stop all buyers from going into a physical store to touch and feel the product before purchasing.  But some may just click “Buy” and never visit your site.
Now that it is rolling out in the main search results, it is something to keep a closer eye on.

UCP-powered checkout initially went live only in AI Mode but it now seems to be available for some retailers in Search.

Google expands UCP checkout to main search shopping results

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is rolling out new measurement tools to help advertisers better connect data, prove impact and make smarter decisions.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
Google is rolling out new tools to help advertisers better understand performance across increasingly complex customer journeys.



What’s happening. As AI continues to transform campaigns, creatives and targeting, Google is introducing updates focused on data integration, experimentation and media mix modelling — all aimed at helping marketers turn fragmented signals into actionable insights.



Why we care. Automation has made it easier to run campaigns, but harder to understand what’s actually working. These updates make it easier to connect data, prove what’s actually driving results, and make smarter budget decisions across channels. As AI handles more of the execution, having strong measurement in place becomes the key differentiator for performance and growth.



Data is the starting point. Google is expanding its Data Manager to give advertisers a clearer view of how their data flows across platforms like BigQuery, HubSpot and Shopify.



A new map-based interface will help marketers visualise connections between data sources and identify gaps in tracking or configuration. At the same time, updates to the Google tag aim to simplify setup, allowing advertisers to upgrade existing tags without additional coding.



The goal: make it easier to unify signals and improve data quality — which directly impacts campaign performance.



Between the lines. Google is acknowledging a long-standing issue — advertisers struggle more with data setup and integration than with campaign execution itself.



By simplifying tagging and data flows, Google is trying to remove one of the biggest blockers to effective AI adoption.



Proving what actually works. Google is also introducing Meridian GeoX, a new geo-experimentation tool designed to measure incremental impact across regions.



Built on an open-source framework, GeoX feeds into Google’s broader Marketing Mix Model, Meridian, giving advertisers a more defensible way to validate performance — especially when presenting results to finance teams.



<figure class="wp-block-image size-full"><img decoding="async" width="1000" height="565" src="https://searchengineland.com/wp-content/seloads/2026/05/GML_Meridian_GeoX_Map_-_Option_1.width-1000.format-webp.webp" alt="" class="wp-image-476503" srcset="https://searchengineland.com/wp-content/seloads/2026/05/GML_Meridian_GeoX_Map_-_Option_1.width-1000.format-webp.webp 1000w, https://searchengineland.com/wp-content/seloads/2026/05/GML_Meridian_GeoX_Map_-_Option_1.width-1000.format-webp-768x434.webp 768w" sizes="(max-width: 1000px) 100vw, 1000px" /></figure>



This signals a shift toward causal measurement, not just correlation.



Why it matters. As privacy changes reduce visibility and attribution becomes more complex, marketers are under pressure to prove impact. Tools like GeoX aim to provide that “ground truth” — something many attribution models struggle to deliver.



Simplifying media mix modelling. To address the complexity of Marketing Mix Models (MMMs), Google is launching Meridian Studio — a Google Cloud-powered platform that helps teams build, customise and scale models more easily.



The focus is on operationalising MMMs, making them less resource-intensive and more accessible for enterprise teams managing large datasets.



What to watch:



<ul class="wp-block-list">
<li>Whether advertisers adopt MMMs more widely with simplified tools</li>



<li>How effective GeoX is in proving incremental impact</li>



<li>If improved data visibility translates into better campaign performance</li>
</ul>



Bottom line. <a href="https://blog.google/products/ads-commerce/google-marketing-live-2026-turn-your-data-into-decisions/">Google is making a strategic shift</a>: in an AI-driven world, better measurement — not just better automation — will determine who wins.

Google is rolling out new tools to help advertisers better understand performance across increasingly complex customer journeys.
What’s happening. As AI continues to transform campaigns, creatives and targeting, Google is introducing updates focused on data integration, experimentation and media mix modelling — all aimed at helping marketers turn fragmented signals into actionable insights.
Why we care. Automation has made it easier to run campaigns, but harder to understand what’s actually working. These updates make it easier to connect data, prove what’s actually driving results, and make smarter budget decisions across channels. As AI handles more of the execution, having strong measurement in place becomes the key differentiator for performance and growth.
Data is the starting point. Google is expanding its Data Manager to give advertisers a clearer view of how their data flows across platforms like BigQuery, HubSpot and Shopify.
A new map-based interface will help marketers visualise connections between data sources and identify gaps in tracking or configuration. At the same time, updates to the Google tag aim to simplify setup, allowing advertisers to upgrade existing tags without additional coding.
The goal: make it easier to unify signals and improve data quality — which directly impacts campaign performance.
Between the lines. Google is acknowledging a long-standing issue — advertisers struggle more with data setup and integration than with campaign execution itself.
By simplifying tagging and data flows, Google is trying to remove one of the biggest blockers to effective AI adoption.
Proving what actually works. Google is also introducing Meridian GeoX, a new geo-experimentation tool designed to measure incremental impact across regions.
Built on an open-source framework, GeoX feeds into Google’s broader Marketing Mix Model, Meridian, giving advertisers a more defensible way to validate performance — especially when presenting results to finance teams.




This signals a shift toward causal measurement, not just correlation.
Why it matters. As privacy changes reduce visibility and attribution becomes more complex, marketers are under pressure to prove impact. Tools like GeoX aim to provide that “ground truth” — something many attribution models struggle to deliver.
Simplifying media mix modelling. To address the complexity of Marketing Mix Models (MMMs), Google is launching Meridian Studio — a Google Cloud-powered platform that helps teams build, customise and scale models more easily.
The focus is on operationalising MMMs, making them less resource-intensive and more accessible for enterprise teams managing large datasets.
What to watch:




Whether advertisers adopt MMMs more widely with simplified tools
How effective GeoX is in proving incremental impact
If improved data visibility translates into better campaign performance
Bottom line. Google is making a strategic shift: in an AI-driven world, better measurement — not just better automation — will determine who wins.

Google is rolling out new measurement tools to help advertisers better connect data, prove impact and make smarter decisions.

Google rolls out new data, experimentation and MMM tools to improve measurement

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920.png" class="attachment-large size-large wp-post-image" alt="ChatGPT growth" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Early ChatGPT ad data shows high engagement, with strong intent queries like Mother’s Day driving significantly more ad exposure.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920.png" class="attachment-large size-large wp-post-image" alt="ChatGPT growth" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-growth-1920-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
Initial reports from SimilarWeb indicate ChatGPT ads are outperforming traditional benchmarks on engagement — but with limited inventory and small-scale tests, it’s too early to call this a long-term trend.



What’s happening. According to early analysis, ads appearing in ChatGPT conversations are generating strong click-through rates vs Display and Podcast channels, likely driven by high-intent user queries and the native way ads are integrated into responses.



<figure class="wp-block-image size-full"><img decoding="async" width="1902" height="1034" src="https://searchengineland.com/wp-content/seloads/2026/05/Screenshot-2026-05-05-at-15.55.39.png" alt="" class="wp-image-476499" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Screenshot-2026-05-05-at-15.55.39.png 1902w, https://searchengineland.com/wp-content/seloads/2026/05/Screenshot-2026-05-05-at-15.55.39-768x418.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Screenshot-2026-05-05-at-15.55.39-1536x835.png 1536w" sizes="(max-width: 1902px) 100vw, 1902px" /></figure>



Unlike traditional search ads, these placements appear directly within conversational answers, making them feel more contextual and less disruptive.



Why we care . If these early CTRs hold at scale, ChatGPT could become a serious performance channel — especially for advertisers looking to reach users at the moment of intent.



But there’s a catch: inventory is still limited, and early performance often looks better before wider rollout introduces more competition and variability.



Between the lines. High CTRs don’t necessarily mean high performance. Conversion quality, cost efficiency and scalability will ultimately determine whether ChatGPT ads can compete with established platforms like Google Ads.



There’s also the novelty factor — users may be more likely to engage simply because the format is new.



Zoom in. Some categories are already showing stronger signals than others.



Mother’s Day-related prompts are far more likely to trigger ads—about three times more than average—because they signal strong purchase intent, with brands like Etsy, Nordstrom and flower retailers already showing strong visibility.



What to watch:



<ul class="wp-block-list">
<li>Whether CTRs hold as inventory expands</li>



<li>How conversion rates compare to search and social</li>



<li>If pricing models evolve beyond early testing phases</li>
</ul>



Bottom line. ChatGPT ads are off to a strong start on engagement — but until scale, cost and conversion data catch up, advertisers should treat this as a promising test channel, not a proven one.



Dig deeper. <a href="https://www.similarweb.com/corp/reports/advertising-in-ai/">Advertising in AI: Insights from Real User Behavior</a>

Initial reports from SimilarWeb indicate ChatGPT ads are outperforming traditional benchmarks on engagement — but with limited inventory and small-scale tests, it’s too early to call this a long-term trend.
What’s happening. According to early analysis, ads appearing in ChatGPT conversations are generating strong click-through rates vs Display and Podcast channels, likely driven by high-intent user queries and the native way ads are integrated into responses.




Unlike traditional search ads, these placements appear directly within conversational answers, making them feel more contextual and less disruptive.
Why we care . If these early CTRs hold at scale, ChatGPT could become a serious performance channel — especially for advertisers looking to reach users at the moment of intent.
But there’s a catch: inventory is still limited, and early performance often looks better before wider rollout introduces more competition and variability.
Between the lines. High CTRs don’t necessarily mean high performance. Conversion quality, cost efficiency and scalability will ultimately determine whether ChatGPT ads can compete with established platforms like Google Ads.
There’s also the novelty factor — users may be more likely to engage simply because the format is new.
Zoom in. Some categories are already showing stronger signals than others.
Mother’s Day-related prompts are far more likely to trigger ads—about three times more than average—because they signal strong purchase intent, with brands like Etsy, Nordstrom and flower retailers already showing strong visibility.
What to watch:




Whether CTRs hold as inventory expands
How conversion rates compare to search and social
If pricing models evolve beyond early testing phases
Bottom line. ChatGPT ads are off to a strong start on engagement — but until scale, cost and conversion data catch up, advertisers should treat this as a promising test channel, not a proven one.
Dig deeper. Advertising in AI: Insights from Real User Behavior

Early ChatGPT ad data shows high engagement, with strong intent queries like Mother’s Day driving significantly more ad exposure.

ChatGPT ads show strong early CTRs — but scale is still the question

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/05/The-10-gate-AI-search-pipeline-Find-where-your-content-fails.png" class="attachment-large size-large wp-post-image" alt="The 10-gate AI search pipeline- Find where your content fails" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/The-10-gate-AI-search-pipeline-Find-where-your-content-fails.png 1672w, https://searchengineland.com/wp-content/seloads/2026/05/The-10-gate-AI-search-pipeline-Find-where-your-content-fails-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/The-10-gate-AI-search-pipeline-Find-where-your-content-fails-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/The-10-gate-AI-search-pipeline-Find-where-your-content-fails-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>AI search is a multiplicative system where one weak signal limits results. Diagnose bottlenecks, prioritize fixes, and improve visibility.

AI search is a multiplicative system where one weak signal limits results. Diagnose bottlenecks, prioritize fixes, and improve visibility.

The 10-gate AI search pipeline: Find where your content fails

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>This is just a limited test now for some AI agents hosted on Google's infrastructure but it may roll out more widely sometime in the future.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-finger-print-logo-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
Google is trying a new method of bot authentication named Web Bot Auth. Google posted a <a href="https://developers.google.com/crawling/docs/crawlers-fetchers/web-bot-auth">new help document</a> that explains that Web Bot Auth is a &#8220;new cryptographic protocol that helps websites to validate that bots are authentic.&#8221;



The goal of Web Bot Auth is to help you automate the process of authenticating which AI Agent bots are authentic and which are fraud.



Limited test. Google said the search compan is &#8220;testing the protocol with some AI agents hosted on Google infrastructure.&#8221; Not all Google user agents are using Web Bot Auth and Google is not yet signing every request of agents using the protocol.



Thus Google recommends that in addition to Web Bot Auth you continue relying on <a href="https://developers.google.com/crawling/docs/crawlers-fetchers/verify-google-requests">IP addresses, reverse DNS, and user-agent strings</a> as Google gradually rolls out signed traffic.



What is Web Bot Auth. Google defined Web Bot Auth as &#8220;Web Bot Auth is an experimental cryptographic protocol used to authenticate requests sent by bots. Instead of relying solely on self-reported headers and IP addresses, Web Bot Auth allows agents to cryptographically sign their requests.&#8221;



Web Bot Auth can bring the following benefits according to Google:



<ul class="wp-block-list">
<li>Future-proofing: Help establish a web where agent providers and websites can build mutual trust and make informed access decisions.</li>



<li>Cryptographic certainty: Move beyond easily spoofed headers to a verified identity and decouple agent identity from IP addresses.</li>



<li>Better observability: Gain clearer insights into how agents interact with your content.</li>
</ul>



Why we care. As AI Agents become more and more common across the web, managing which Agents can access your site and web pages may become more and more of a challenge. This new method of authentication may help you allow authentic AI Agents and block the inauthentic AI Agents.



Again, this is an &#8220;experimental&#8221; feature right now, so keep track of its progress.

Google is trying a new method of bot authentication named Web Bot Auth. Google posted a new help document that explains that Web Bot Auth is a “new cryptographic protocol that helps websites to validate that bots are authentic.”
The goal of Web Bot Auth is to help you automate the process of authenticating which AI Agent bots are authentic and which are fraud.
Limited test. Google said the search compan is “testing the protocol with some AI agents hosted on Google infrastructure.” Not all Google user agents are using Web Bot Auth and Google is not yet signing every request of agents using the protocol.
Thus Google recommends that in addition to Web Bot Auth you continue relying on IP addresses, reverse DNS, and user-agent strings as Google gradually rolls out signed traffic.
What is Web Bot Auth. Google defined Web Bot Auth as “Web Bot Auth is an experimental cryptographic protocol used to authenticate requests sent by bots. Instead of relying solely on self-reported headers and IP addresses, Web Bot Auth allows agents to cryptographically sign their requests.”
Web Bot Auth can bring the following benefits according to Google:
Future-proofing: Help establish a web where agent providers and websites can build mutual trust and make informed access decisions.
Cryptographic certainty: Move beyond easily spoofed headers to a verified identity and decouple agent identity from IP addresses.
Better observability: Gain clearer insights into how agents interact with your content.
Why we care. As AI Agents become more and more common across the web, managing which Agents can access your site and web pages may become more and more of a challenge. This new method of authentication may help you allow authentic AI Agents and block the inauthentic AI Agents.
Again, this is an “experimental” feature right now, so keep track of its progress.

This is just a limited test now for some AI agents hosted on Google's infrastructure but it may roll out more widely sometime in the future.

Web Bot Auth, Google’s new experimental method to validate authentic bots

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Query-intent-vs.-conversion-intent-Why-the-difference-matters.png" class="attachment-large size-large wp-post-image" alt="Query intent vs. conversion intent- Why the difference matters" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Query-intent-vs.-conversion-intent-Why-the-difference-matters.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Query-intent-vs.-conversion-intent-Why-the-difference-matters-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Query-intent-vs.-conversion-intent-Why-the-difference-matters-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Query-intent-vs.-conversion-intent-Why-the-difference-matters-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>What users type isn’t always what they want. Learn how aligning intent signals, behavior, and brand improves performance.

One of the major reasons PPC practitioners hold onto syntax-oriented keyword strategies is the disconnect between “query intent” and “conversion intent.” For years, you’ve likely relied on keywords to show you understand what your customers want and to prequalify traffic using syntax-oriented signals.
As user behavior shifts to more conversational queries and AI becomes an increasingly relevant part of the user journey, the distinction between these two intents becomes even more critical to understand and act on.
Here, we’ll define query and conversion intent and explore strategies to apply them effectively. This isn’t prescriptive. You should make decisions based on what will serve your business well. However, it provides a framework for analyzing your data and optimizing for the right humans.
Disclosure: I’m a Microsoft employee, and I’ll be sharing some examples that pull from Microsoft tooling. However, most of the strategies reflect platform-agnostic approaches.



What are query and conversion intents?
Query intent is the underlying need driving the text put into a search function. This search function can be on a SERP (search engine results page), video/social/gaming/email/site search bar, or AI surface.
Conversion intent is the human need to achieve some outcome, understood through stated and inferred data points. These range from text entered in various search experiences, content consumed, and tracked actions taken.
Different examples of query and conversion intent will have higher or lower rates of confidence based on how explicit text is, as well as patterns in content consumed.
For example, if I search “Microsoft ads login,” both query and conversion intent are clear — I want to log in. It’s easy to match ads and organic content to that query. Videos shown in any video query would have to do with logging in, and emails would be focused around login information.
Google SERP







Bing’s SERP







YouTube results







The query “Microsoft ads” is more nebulous, as such, needs to draw from other signals like previously engaged content and search history. While I might get a login page, I’d likely also see blog/sales content, third-party advice on Microsoft ads, and potentially competitor info trying to capitalize on the general nature of the query.
Google SERP







Bing SERP







YouTube results







Let’s look at a non-branded example as well. “Purple hair dye” has a clear transactional intent. While the user might not have a brand in mind, they know they want a specific color. 
We don’t know if the user is looking for a semi-permanent or permanent color. We also don’t know the user’s pronouns, so matching them to a specific demographic to entice a purchase is a gamble. 
Google SERP







Bing SERP







YouTube results







In the query “purple hair dye for long wavy hair,” the transactional intent is maintained. However, the query focuses more on the core needs of the person behind the text. Long, wavy hair means there needs to be enough dye to cover long hair.
Additionally, while some men have long wavy hair, the person behind the query is more likely to identify as female. 
Wavy hair has a different composition than straight or curly hair, so products specifically for wavy hair will be more relevant than those without hair type identifiers.
Google SERP







Bing SERP







YouTube results







In all of these examples, there was clear conversion intent. The human behind the query clearly wanted to achieve something. However, if we relied only on the text (i.e., query intent), we might miss a meaningful opportunity to connect with customers. 
This is why close variants (which have been available on both Google and Microsoft for ~10 years) represent a useful way to unshackle ourselves from syntax alone.
Additionally, by limiting our understanding of queries to SERPs, we ignore critical insights from where our customers connect, work, and play. Microsoft’s internal data from March 2024 shows that brands that use both Audience ads (display, native, and video) and Search see a 6x conversion rate. Part of this is brand recognition, and the power of brand media buys influencing performance.
Yet there’s also the pragmatic piece that some marketers refuse to engage with video and social. By being where your competitors refuse to be, you can shape and capture desire while they fight over a shrinking share of voice.
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How to optimize for each intent
Once you understand the difference between query and conversion intent, you can begin mapping out the actions needed to capitalize on both.
Conversion intent is much easier to understand than query intent. This is why AI systems typically run queries in the background to understand human input and get at the conversion intent behind the query. 
To succeed at shaping queries and capturing conversions, it’s critical to understand the input points for humans and the AI systems that will be serving them results.
Let’s revisit the “purple hair dye for long wavy hair” query:




Copilot surfaces how it arrived at the output by looking up information and finding the best matches. This is similar to the SEO concept of E-E-A-T.
Yet you’ll notice that the results for my personal Copilot are different than the traditional SERP (chiefly that ads aren’t the dominant result — ads serve at the bottom of clearly transactional conversations after organic listings).
This is where the “Details” function comes into play and can help you know where to focus content, feed, and messaging functions:




This product is pretty flat on price, save for some deep summer dips. If I’m desperate for color, I might buy now, or I might wait for what seems like a regular summer sale. I’m also getting insights into why this product is wonderful (hair conditioning, cruelty-free, vibrant, and customizable color, etc.). 
These are things I’ve shown interest in through past purchases, conversations with Copilot, and other signals it has access to.
Brands that want to optimize for query intent need to make sure the following are in good order:
Feed/landing page clarity

It should be incredibly easy to map what the product/service is to the query. While there is value in some 1:1 matching of language, it’s much more important that the core offering be understood as aligned with what the human is looking for. 
For example, DUI and DWI are technically two different charges and have geo implications. However, DUI tends to be the universal legal charge and service.
Images adding context 

Visual content is critical to engage humans. However, if the image isn’t clear or is duplicative of another service/product page, you might confuse the user and the machine attempting to understand and position you for queries. This is why it’s critical to add alt text (even on paid landing pages) for images and videos. 
A good way to test whether your visuals are serving you well is to put the landing page into a PMax campaign creator. If you see the images and they match the correct service text, you’ve done a good job.
Invest time in understanding how humans and AI are querying

Free tools like Google Trends, Microsoft Clarity, and Bing Webmaster offer insights into search trends, citations, grounding queries, and which AI systems and humans are successfully engaging with your content.
Conversion intent is more straightforward, though debatably harder because it requires more creative and critical thinking: 
Matching messages to personas

The reason one person says yes to you might be completely different from the reason someone else does. Locking in conversion intent includes being mindful of how you’re selling yourself. If you ignore what matters to your customers in reviews, intake from customer success or sales, and other signals, you risk selling yourself badly and losing the customer.
This is where AI-powered creative and audience mapping can be helpful, since platforms have access to more insights than a brand does during the auction.
Honor the impulse nature of visual content

Someone coming to you from a display spot or short video is very different than someone coming from a text-laden SERP. They were inspired to act and need frictionless paths to conversion. 
One-click checkout (including solutions like Copilot Checkout) ensures humans don’t need to think to do business with you.
Ultimately, both query and conversion intent need brand and performance marketing to be successful, and it’s critical to understand how the success metrics manifest.
The converging roles of brand and performance
For a long time, brand and performance marketing were treated as separate motions, with separate owners, budgets, and success metrics. 
Brand was about reach, recall, and long-term connection. 
Performance was about efficiency, conversion rate, and immediate return. 
That separation made sense when channels, measurement, and user journeys were cleaner than they are today. It’s much harder to maintain in an environment where AI systems infer intent continuously and across surfaces. 
A user doesn’t experience brand and performance as separate. They experience confidence, familiarity, relevance, and ease. Those signals are created over time through exposure, engagement, and trust, and they often determine whether conversion intent ever materializes, regardless of how “high intent” a query might appear on its own.
From a metrics perspective, this convergence is clear. Brand-oriented activity influences performance outcomes even when it isn’t the final touch. Exposure to display, native, or video doesn’t always produce an immediate click, but it changes how humans and systems interpret future behavior. 
When someone later performs a search, engages with an AI assistant, or compares options on a marketplace, prior brand interactions act as accelerators. They reduce hesitation, shorten decision cycles, and increase the likelihood that a conversion signal will be credited downstream.
From a strategy standpoint, this means brand work should no longer be evaluated solely on isolated upper-funnel KPIs, and Performance work can’t be evaluated purely on last-click efficiency. 
Audience-based formats, contextual placements, and visual storytelling directly shape conversion intent by shaping preferences and expectations before a query even occurs. Search and shopping formats then serve as capture mechanisms, translating that latent intent into action.
This is particularly relevant in AI-assisted experiences, where systems synthesize multiple inputs before presenting options or recommendations. Content, feeds, reviews, images, and historical engagement all influence how brands are represented and when they appear. 
In these environments, strong brand signals don’t compete with performance outcomes. They enable them by making the brand easier to understand, trust, and choose.
Brand and performance don’t need to use the same tactics, but they must be planned together. Measurement frameworks should account for assistive value, not just final interactions. 
Creative strategies should recognize that inspiration and conversion often happen at different moments. Optimization should focus less on forcing intent into rigid buckets and more on supporting the full decision journey.
When we recognize that query intent and conversion intent are related but not identical, the convergence of brand and performance becomes less a philosophical debate and more an operational necessity. 
Success comes from designing systems that reflect how humans actually decide, not just how they type.
Key takeaways




Query intent describes what is said; conversion intent reflects what the human needs to accomplish. They overlap, but they aren’t interchangeable.
Brand activity shapes conversion intent long before a query is expressed and influences how future interactions are interpreted.
Performance outcomes improve when Brand signals reduce friction, uncertainty, and choice overload.
AI-driven experiences amplify this convergence by relying on cumulative signals rather than single actions.
Sustainable optimization requires aligning brand and performance strategies, metrics, and expectations around the same human outcomes.

What users type isn’t always what they want. Learn how aligning intent signals, behavior, and brand improves performance.

Query intent vs. conversion intent: Why the difference matters

<div><img width="1920" height="1081" src="https://searchengineland.com/wp-content/seloads/2026/05/How-Chinas-fragmented-search-ecosystem-is-reshaping-SEO-in-2026.png" class="attachment-large size-large wp-post-image" alt="How China’s fragmented search ecosystem is reshaping SEO in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-Chinas-fragmented-search-ecosystem-is-reshaping-SEO-in-2026.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-Chinas-fragmented-search-ecosystem-is-reshaping-SEO-in-2026-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-Chinas-fragmented-search-ecosystem-is-reshaping-SEO-in-2026-1536x865.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-Chinas-fragmented-search-ecosystem-is-reshaping-SEO-in-2026-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Baidu still drives B2B demand, while AI and ecommerce platforms reshape discovery. Here’s how to stay visible across both.

In February 2025, the world watched as a small group of humanoid robots took the stage at the CCTV Chinese New Year show for the very first time. It was a charming performance, even if the steps were shaky and the movements were mostly limited to the arms.
Just one year later, at the Spring Festival Gala, the shaky steps were gone and the humanoid robots were able to actually run and do standing somersaults and full kung fu routines with swords and nunchaku. The message was clear: in just one year, we have witnessed a decade’s worth of advancement.
The 10-year leap in technology is real and not limited to robotics. Which raises a critical question for every digital marketer eyeing the world’s largest web population: How has search in China progressed in recent years?
A parallel in the Chinese search landscape
The answer is that we’re witnessing the first, calculated tremors of a massive shift. AI models have not yet replaced traditional search. The evolution isn’t happening through a single “big bang,” but through a constant, iterative pulse. 
New LLM models are surfacing every few months, each more specialized than the last. Chinese tech giants are increasingly open-sourcing their models, and even industry leaders are hedging their bets. Baidu, for example, is integrating DeepSeek into its search experience, even as its own Ernie (Wenxin) model remains a formidable powerhouse.
Let’s look at how users actually search in China today — and what this nuanced shift from links to reasoning means for your 2026 SEO strategy.

  
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The great narrative fallacy: Is web search dead in China?
In many marketing circles, a specific narrative has been repeated so often it has become an article of faith: “Traditional search on Baidu is dead — and has been for years. Websites are obsolete. In China, everything is WeChat.”
This narrative is almost always driven by service providers whose business models depend on WeChat, Douyin, Weibo, or Xiaohongshu marketing. To them, the “open web” is a ghost town. But is this actually true?
The social supremacy argument
There’s a grain of truth in the hype. The Chinese web is a mobile-first multiverse. Users access and explore the web through super-apps:
RedNote (Xiaohongshu / Little Red Book): This is the de facto engine for lifestyle research and travel planning.
Pinduoduo and Douyin: These are the juggernauts of social commerce and impulse buying.
WeChat: The absolute center of daily life, where everything from a quick message to a utility bill payment via QR code happens.
In this environment, social media isn’t just a channel. It’s the air people breathe. For B2C brands, social ads can — and often do — exceed website-driven sales by orders of magnitude.
The B2B reality check
For those of us working with B2B companies that need real visibility in China, the “Baidu is dead” narrative falls apart the moment you look at the analytics. Clients who invest in Baidu SEO and Baidu search engine advertising (SEA) continue to see a steady, high-volume stream of real human visitors — in many cases generating more qualified leads and higher conversion rates than their counterparts in the UK or Germany.
Why? Because when a B2B procurement officer or a technical engineer needs a specific industrial solution, they don’t just scroll until they find it on a social media feed. They search for a verified, authoritative source. In other words, they look for a website.
Is the social media narrative a lie? No. But ignoring a channel that — at least in the B2B sector — remains more effective in China than in many search-first Western countries is simply bad business. The goal isn’t to choose one over the other; it’s to understand how they coexist. 
And just as we’ve settled the debate between web marketing versus app marketing, a new challenger — the LLM — has entered the battleground to disrupt both.
Mapping the 2026 landscape: Intent-based specialization
To a Google-first marketer, the idea of searching anywhere but a search engine feels like a detour. In China, it’s the standard operating procedure. Users don’t just “Google it.” Instead, they choose the tool that fits the intent.
As a Baidu specialist living and working in China, I see this daily. While I might be optimizing a B2B landing page for Baidu, my wife is likely on Pinduoduo, finding household deals, or on Xiaohongshu, planning our next weekend trip. 
The “everything app” exists, but the “right app” always wins the click.
1. Traditional web search: The authority tier
Despite the “death of the web” narrative, traditional web search remains the primary battleground for B2B and high-authority research. If a user needs a technical whitepaper, a government regulation, or a verified corporate headquarters, they go here.
Baidu: Still the mobile heavyweight, with a ~70% mobile market share. Its structural advantage is massive: The Baidu app is installed on over 724 million monthly active devices (as of early 2026). It has evolved into an AI-first portal, but for SEOs, it remains the place where the open web lives and breathes.
Microsoft Bing: The professional’s sanctuary. It has claimed a massive chunk of desktop search for those seeking a cleaner, international, or technical experience.
Haosou (360 Search): The enterprise default, often pre-installed on corporate PCs and known for its security focus.
Sogou: Deeply integrated with WeChat, it’s the bridge between the walled garden and the web.
Google: Yes, Google. Despite the firewall, a significant population of tech-savvy professionals and researchers use it via VPN for global technical data and academic resources.
2. Social discovery: The inspiration tier
This is where search becomes discovery. Users don’t always have a keyword, but they do have an interest. In this context, SEO is about social indexing: ensuring your brand appears when a user looks for proof and not just products.
WeChat (Weixin): The internal search for official brand news and private traffic.
Xiaohongshu (RED): The ultimate product-discovery engine. If you aren’t on RED, you don’t exist in the lifestyle or luxury sectors.
Douyin: Visual, video-first search. Users search Douyin to see how something works.
Kuaishou: The powerhouse for lower-tier cities and raw, authentic grassroots content.
Weibo: Real-time search — what is happening right now in the public eye.
Bilibili: Long-form video search for deep dives, tutorials, and Gen Z subcultures.
3. Ecommerce: The transactional tier
In the West, users often start on Google and end on Amazon. In China, the journey frequently starts and ends in the same place.
Taobao / Tmall: The grand bazaar. If you want variety and brand stores, this is the first stop.
JD.com: The Amazon of China for logistics and high-end electronics.
Pinduoduo: The favorite for daily essentials and group-buy deals. Its search logic is entirely driven by value for money.
Douyin Mall: The rising star of “impulse search,” merging entertainment with immediate checkout.
Xianyu (Goofish): The go-to for the thriving second-hand market and hobbyist niches.
4. Generative AI (LLMs): The reasoning tier
This is the newest layer of the map — the “thinking” search. These AI models don’t just produce lists of links. They are assistants that synthesize the web for the user.
Doubao (ByteDance): Currently the most popular consumer AI assistant, used for casual, conversational queries.
DeepSeek (Domestic): The choice for developers and those in need of “deep thinking” logic. It’s the engine currently getting tested inside WeChat’s search bar.
Kimi (Moonshot AI): The king of long-context. Users use Kimi to search through 50-page PDFs or complex financial reports.
Qwen (Alibaba): Powerfully integrated into the Alibaba ecosystem for business and coding tasks.
Tencent Yuanbao: The “AI brain” for WeChat content.
Wen Xiaoyan (Baidu): The AI-facing evolution of Baidu search.
5. Hyper-local and logistics: The utility tier
For the physical world, search is about “now” and “near me.”
Meituan / Dianping: If you’re hungry or want to see a movie, you don’t use Baidu. You use Dianping for reviews and Meituan for transactions.
Amap (Gaode) / Baidu Maps: The “search engines of the real world.” SEO on these platforms is purely about point-of-interest (POI) optimization.
Ctrip (Trip.com) / Railway 12306: The specialized gates for the massive domestic travel market.
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From mapping to maneuvering: The Baidu specialist’s edge
Baidu SEO isn’t dead; your website just isn’t the sole focus of web search anymore.
The ‘walled garden’ SERP: A decade of distraction
If you’re a Google-centric SEO, there are some notable differences when working with Baidu:
The ad-heavy layout: It isn’t uncommon to see ads claiming the top, middle, and bottom of a Baidu search engine results page (SERP), occupying nearly 50% of the visible real estate.
The Baidu monopoly: The most coveted organic positions are almost always reserved for Baidu’s own properties. Baidu Baike (the encyclopedia), Baidu Zhidao (the Q&A hub), and Baijiahao (the news/blogging arm) are the permanent residents of Page 1.
The portal giants: High-authority giants like Zhihu (China’s Quora), Bilibili, and Sohu take up whatever space is left.
Riding the Chinese SERP dragon
In this environment, ranking a corporate homepage for a high-volume keyword is a fool’s errand. Instead, we’ve mastered the art of the “long-tail dragon.”
In the West, we talk about the long tail of search as a small, niche opportunity. In China, with its linguistic complexity and massive user base, the long tail is a winding, multi-layered beast that is often more lucrative than the head terms. 
And we don’t just rank a website; we piggyback on the authority of the platforms Baidu already trusts. If you can’t beat Baidu Baike, you become the verified entry inside it.
Interestingly, it is these very platforms — the ones we’ve been using to bypass the “blue link problem” — that have now become the primary focus of the next generation of search.
What is changing in Baidu SEO?
In China, there is no brand loyalty toward particular AI models, as Westerners have toward platforms like ChatGPT and Claude.
The AI-switching reality
Chinese users are restless. They don’t stick with one model. They switch — sometimes because a hyped model hits a downtime wall, and sometimes because a new model claims the throne of the “most intelligent AI.” In this cycle of competition and user preference, an SEO can’t just focus on the “big sources.”
If you’re following the Western playbook, you’re likely chasing Reddit, Quora, and YouTube as your “sources of truth” for AI training. But in China, that focus is dangerously narrow. To win the reasoning battle, you must understand the investor-source connection.
Brainstorming the wisdom platforms
If you want to train AIs to see your brand in China, you have to look at the platforms they were built on:
Tencent is invested in Sogou. In 2021, Tencent fully privatized Sogou. This means Sogou Baike is no longer just a Baidu alternative — it is now a core training set for Tencent’s Yuanbao. If you ignore Sogou Baike, you’re invisible to the AI search bar inside WeChat.
Bytedance owns Baike.com. Bytedance bought Baike.com (formerly Hudong Baike) specifically to fuel its search ambitions. If you want to get cited by Doubao, your content needs to be mirrored here and not just on Baidu.
The neutral giants: Keep an eye on Zhihu. Because both Tencent and Baidu are heavy investors in Zhihu, it remains one of the few neutral high-authority sources that almost every Chinese LLM uses for opinionated or expert reasoning.
The new SEO commandment
We’re no longer just optimizing for a search engine. We’re optimizing for a data pedigree.
If your client is B2B, you might still prioritize the Baidu ecosystem. But if your client is in ecommerce and you aren’t feeding the Qwen engine via Alibaba’s ecosystem, or the Doubao engine via Baike.com, you’re limiting your visibility across key AI systems.
The 2026 China SEO/GEO blueprint: From keywords to semantic saturation
If you’re waiting for a “DeepSeek optimization checklist” or a “Doubao ranking guide,” you’ve already missed the point. Because users switch models as often as they switch takeout apps, you can’t afford to be “Baidu-only” or “WeChat-centric.”
Here is what’s actually working for SEO in China in 2026:
Optimize for citations and not just clicks
While SEO in the West is focused on generative engine optimization (GEO), in China, it’s all about fact density. 
The logic: When Kimi or DeepSeek performs a reasoning query, the AI looks for verifiable facts.
The tactic: Stop writing marketing fluff. Start using the inverted pyramid writing style. Lead with a direct, data-backed answer in your first paragraph. Use hard statistics, expert quotes, and structured lists. If a model can’t extract a fact from your content in 200 milliseconds, it might hallucinate a competitor’s data instead.
Build an entity moat across wisdom platforms
As we brainstormed earlier, every AI has a “parent” with a preferred data source. But since models are now open-sourcing their weights and distilling each other’s intelligence, your brand must achieve entity consistency.




The goal: Your brand name, headquarters, and core product claims must be identical across Baidu Baike (Baidu), Sogou Baike (Tencent), and Baike.com (ByteDance).
The result: When these models cross-check their reasoning, they find a consensus. In 2026, consensus is the new authority.
Leverage information gain
Chinese AI models have a well-observed recency bias — they prefer sources that are roughly 25% fresher than traditional search results.
The tactic: Don’t just regurgitate what’s already on Zhihu. Provide a “unique data slice.” If everyone says “The best time to post on Douyin is 6 PM,” and you publish a case study proving “11 AM is better for B2B industrial leads,” the AI will cite you as the “nuanced exception.” That citation is worth more than ten #1 rankings.

  
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The era of the entity architect
We’ve come a long way from the shaky steps of the 2025 CCTV Gala.
In 2026, China’s search ecosystem is no longer a directory of links. It’s a living, reasoning entity. 
For the Western search specialist, the lesson is clear: The “super app” was a distraction. The real story is the fragmentation of intent.
My wife still goes to Pinduoduo for the best price. My colleagues still go to Bing for technical sanctuary. And the “I, Robot” enthusiasts of 2026 are using a rotating door of LLMs to find their answers.
As a Baidu specialist, my job has shifted from “ranking a website” to “architecting an entity.” We no longer build for the bot; we build for the source. If you’re the undeniable source of truth across the platforms that shape China’s information ecosystem, it doesn’t matter which model delivers the answer.
You’ll be the one they’re cheering for.

Baidu still drives B2B demand, while AI and ecommerce platforms reshape discovery. Here’s how to stay visible across both.

How China’s fragmented search ecosystem is reshaping SEO in 2026

<div><img width="2048" height="1152" src="https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-scaled.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-scaled.jpg 2048w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-1200x675.jpg 1200w" sizes="(max-width: 2048px) 100vw, 2048px" /></div>Your customers see the whole SERP. You should too.

<div><img width="2048" height="1152" src="https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-scaled.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-scaled.jpg 2048w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-1200x675.jpg 1200w" sizes="(max-width: 2048px) 100vw, 2048px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1152" src="https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-scaled.jpg" alt="" class="wp-image-475001" srcset="https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-scaled.jpg 2048w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/05/LevelAgency-20250505-v3-1200x675.jpg 1200w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


In February 2024, <a href="https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents" target="_blank" rel="noopener sponsored nofollow">Gartner predicted</a> that traditional search volume would drop 25% by 2026. It didn&#8217;t. <a href="https://blog.google/company-news/inside-google/message-ceo/alphabet-earnings-q4-2025/" target="_blank" rel="noopener sponsored nofollow">Google&#8217;s search revenue</a> accelerated to 17% year-over-year growth, crossing $63 billion in Q4 2025 alone. But clicks per search are falling while query volume explodes. The pie got bigger. The slices got redistributed. And most search teams are still optimizing for the old pie.



Are you still poring over spreadsheets full of organic keyword rankings like it&#8217;s 2003? Your customers don&#8217;t care where they&#8217;re getting their answers. They&#8217;re just looking for answers they can trust. And they&#8217;re finding those answers across more surfaces than your rank tracker knows exist.



If your organic strategy lives in one spreadsheet, your paid strategy in another, and your AI search strategy in a third (or nowhere), you&#8217;re optimizing for a search experience that no longer exists.



<h2 class="wp-block-heading" id="h-what-search-actually-looks-like-now">What &#8220;search&#8221; actually looks like now</h2>



Google &#8220;best tax software&#8221; right now. Go ahead, I&#8217;ll wait.



Count the surfaces on that single results page. Sponsored ads across the top. An AI Overview with its own recommendations and citations. A Reddit thread (because Google knows people trust other people more than brands). Organic listings from CNET, H&amp;R Block, and others. A video carousel. Discussion forum links. A product carousel with images and prices. More sponsored results at the bottom. And a &#8220;People also search for&#8221; section feeding the next query.



That is one search. One keyword. And nobody owns it.



Now think about how different people actually use that page. I scroll past everything to find the Reddit thread, because I want to know what real humans recommend. My dad clicks the first sponsored ad because he doesn&#8217;t understand paid advertising (sorry, dad!) and just trusts Google to surface the best option up top. Someone else reads the AI Overview, gets a good-enough answer, and never clicks anything at all. A fourth person watches the Smart Family Money video and leaves.



Same query. Four completely different paths. Four different &#8220;winners.&#8221; And if you&#8217;re the brand celebrating a number-three organic ranking on this page, you may be missing that most of the real estate, and most of the user attention, lives somewhere other than those blue links.



This is what I mean by the total SERP experience. Your customer sees the whole page. You should too.



<h2 class="wp-block-heading" id="h-the-ai-layer-changes-the-math">The AI layer changes the math</h2>



AI Overviews now appear on <a href="https://www.semrush.com/blog/ai-search-seo-traffic-study/" target="_blank" rel="noopener sponsored nofollow">roughly 25% to 48%</a> of Google queries, depending on the study. <a href="https://techcrunch.com/2025/07/21/chatgpt-users-send-2-5-billion-prompts-a-day/" target="_blank" rel="noopener sponsored nofollow">ChatGPT processes 2.5 billion prompts a day</a>. <a href="https://www.businessofapps.com/data/perplexity-ai-statistics/" target="_blank" rel="noopener sponsored nofollow">Perplexity is up 239% year over year</a>. These are real numbers from real platforms where real buyers are forming opinions about your brand, or not forming opinions because you&#8217;re nowhere to be found.



But before the panic sets in: AI tools still account for <a href="https://sparktoro.com/blog/new-research-google-search-grew-20-in-2024-receives-373x-more-searches-than-chatgpt/" target="_blank" rel="noopener sponsored nofollow">less than 1% of U.S. web traffic</a>. Google sends 300x more referral traffic than all AI platforms combined. The sky isn&#8217;t falling, but the ground is shifting.



The shift that matters most is behavioral. <a href="https://wynter.com/post/how-b2b-saas-cmos-buy-software-in-2026" target="_blank" rel="noopener sponsored nofollow">Wynter&#8217;s 2026 research</a> found 68% of B2B buyers now start their research in AI tools before they ever open Google. They ask ChatGPT to narrow the field, then Google the shortlist to validate. AI evaluates, Google verifies, and your website converts. If your brand is missing from that first AI conversation, you&#8217;re not even on the shortlist when the Googling starts.



<h2 class="wp-block-heading" id="h-why-the-click-data-is-more-interesting-than-scary">Why the click data is more interesting than scary</h2>



A <a href="https://searchengineland.com/google-ai-overviews-drive-drop-organic-paid-ctr-464212" target="_blank" rel="noopener sponsored nofollow">Search Engine Land analysis</a> of 25 million organic impressions across 42 clients found organic CTR drops 61% when an AI Overview appears. In addition, paid CTR drops 68%.



EVERYBODY FREAK OUT!!! Right? Not quite.



Here&#8217;s what the panicked LinkedIn posts leave out: brands <a href="https://searchengineland.com/google-ai-overviews-drive-drop-organic-paid-ctr-464212" target="_blank" rel="noopener sponsored nofollow">cited inside AI Overviews</a> see 35% more organic clicks and 91% more paid clicks. Being in the AI Overview doesn&#8217;t cannibalize your traffic. If anything, it amplifies it. The AI Overview functions like a trust signal, a stamp of &#8220;this brand is relevant to your question&#8221; that makes people more likely to click your listing below.



The real twist, though, is that ranking well in organic doesn&#8217;t guarantee you show up in AI. <a href="https://moz.com/blog/ai-mode-citations" target="_blank" rel="noopener sponsored nofollow">Tom Capper&#8217;s research at Moz</a> found 88% of AI Mode citations are NOT in the organic SERP for the same query. Organic and AI are pulling from different source pools. You can be number one in Google and completely invisible in ChatGPT&#8217;s answer to the same question.



And the small amount of traffic that does come from AI? It <a href="https://www.semrush.com/blog/ai-search-seo-traffic-study/" target="_blank" rel="noopener sponsored nofollow">converts at more than quadruple the rate</a> of organic, according to Semrush. These visitors arrive more informed, more intentional, and more ready to buy. Which makes sense, because they&#8217;ve already done the evaluation inside the AI interface. By the time they click, they&#8217;re just confirming and often converting.



<h2 class="wp-block-heading" id="h-the-org-chart-is-the-problem">The org chart is the problem</h2>



Most companies have SEO reporting to content, PPC reporting to demand gen, and AI search reporting to nobody. <a href="https://www.brightedge.com/news/press-releases/brightedge-survey-reveals-68-marketers-are-embracing-ai-search-shift" target="_blank" rel="noopener sponsored nofollow">BrightEdge found</a> 54% of organizations have handed AI search to the SEO team alone, which is a little like asking your plumber to also handle the electrical work because, hey, it&#8217;s all in the same house.



The waste from this setup is real. One branded Performance Max campaign paid roughly $500,000 for clicks that would have come through organic anyway. <a href="https://research.google/pubs/impact-of-ranking-of-organic-search-results-on-the-incrementality-of-search-ads/" target="_blank" rel="noopener sponsored nofollow">Google&#8217;s own research</a> confirms: when you rank number one organically, only half your paid clicks are truly incremental. The other half? You bought what you already owned.



Meanwhile, <a href="https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search" target="_blank" rel="noopener sponsored nofollow">McKinsey found</a> that a brand&#8217;s own website makes up only 5% to 10% of the sources AI references. AI pulls from Reddit, review sites, affiliates, publishers, and user-generated content. You can have the best SEO program in your category and be completely absent from AI search results because AI is reading what other people say about you, not what you say about yourself.



The <a href="https://searchengineland.com/end-seo-ppc-silos-unified-search-strategy-ai-era-463006" target="_blank" rel="noopener sponsored nofollow">unified approach works</a>. Level cut acquisition costs 18% and boosted SEO leads 22% by merging paid and organic for a B2B SaaS client. And we can use tools in our <a href="https://www.level.agency/level-intelligence-suite" target="_blank" rel="noopener sponsored nofollow">Level Intelligence Suite</a> to connect performance signals across search surfaces. The channels compound each other. Treating them as separate line items on separate P&amp;Ls leaves that compounding on the table.



<h2 class="wp-block-heading" id="h-three-audits-you-can-run-monday-morning">Three audits you can run Monday morning</h2>



You don&#8217;t need a six-month transformation to start seeing the gaps. Three lenses, applied to your top 20 keywords, will show you where the opportunities and the waste are hiding.



Lens 1: Where do you actually appear? Check your organic rankings, paid ad coverage, and AI visibility across ChatGPT, Perplexity, and Gemini for the same set of keywords. <a href="https://www.semrush.com/free-tools/ai-search-visibility-checker/" target="_blank" rel="noopener sponsored nofollow">Semrush has a free AI visibility checker</a>. Most teams have never looked at all three surfaces side by side, and the gaps are almost always larger than they expect.



Lens 2: Where are you paying for traffic you already own? Cross-reference your number-one organic rankings with active PPC bids on the same terms. Start with branded keywords, where the waste is usually largest and the test is cleanest. If you rank first and you&#8217;re still bidding, you&#8217;re probably buying your own clicks.



Lens 3: Where is AI ignoring you? Compare your organic rankings with your AI citation presence. Only 11% of domains get cited by both ChatGPT and Perplexity, so strength in one guarantees nothing in the other. And check your robots.txt while you&#8217;re at it. If you&#8217;re blocking AI crawlers like OAI-SearchBot or PerplexityBot, you&#8217;ve pulled yourself off those shelves entirely.



This diagnostic shows you the full picture. What to do about it, the actual unification framework, is what I&#8217;m laying out at SMX Advanced.



<h2 class="wp-block-heading" id="h-the-window-won-t-stay-open">The window won&#8217;t stay open</h2>



Generative Engine Optimization (GEO) keyword difficulty <a href="https://www.frase.io/blog/what-is-generative-engine-optimization-geo" target="_blank" rel="noopener sponsored nofollow">currently averages 15 to 20</a>, compared to 45 to 60 for equivalent SEO terms. That gap will close. Once an LLM selects a trusted source, it reinforces that choice across related prompts. The brands getting cited now are training the models to keep citing them. Winner-takes-most dynamics are being baked into the weights.



Many companies are seeing search traffic drop significantly. Those same brands, the ones that get it right, are seeing the inverse when it comes to business growth. Rankings and revenue have decoupled. The brands that win from here are the ones that stopped measuring channels in isolation and started measuring the search experience their customers actually have.



We’re presenting a search unification framework at <a href="https://searchengineland.com/smx/advanced/agenda/?sessId=3388" target="_blank" rel="noopener sponsored nofollow">SMX Advanced</a> in our session, &#8220;Organic, paid, and AI search: one strategy to rule them all.&#8221; If you want to stop optimizing for three separate channels and start compounding performance across every search surface, join us for the session or come find the Level team at Booth #9.



Remember: The search experience that existed in 2023 is gone. The strategy should be too.

In February 2024, Gartner predicted that traditional search volume would drop 25% by 2026. It didn’t. Google’s search revenue accelerated to 17% year-over-year growth, crossing $63 billion in Q4 2025 alone. But clicks per search are falling while query volume explodes. The pie got bigger. The slices got redistributed. And most search teams are still optimizing for the old pie.
Are you still poring over spreadsheets full of organic keyword rankings like it’s 2003? Your customers don’t care where they’re getting their answers. They’re just looking for answers they can trust. And they’re finding those answers across more surfaces than your rank tracker knows exist.
If your organic strategy lives in one spreadsheet, your paid strategy in another, and your AI search strategy in a third (or nowhere), you’re optimizing for a search experience that no longer exists.
What “search” actually looks like now
Google “best tax software” right now. Go ahead, I’ll wait.
Count the surfaces on that single results page. Sponsored ads across the top. An AI Overview with its own recommendations and citations. A Reddit thread (because Google knows people trust other people more than brands). Organic listings from CNET, H&R Block, and others. A video carousel. Discussion forum links. A product carousel with images and prices. More sponsored results at the bottom. And a “People also search for” section feeding the next query.
That is one search. One keyword. And nobody owns it.
Now think about how different people actually use that page. I scroll past everything to find the Reddit thread, because I want to know what real humans recommend. My dad clicks the first sponsored ad because he doesn’t understand paid advertising (sorry, dad!) and just trusts Google to surface the best option up top. Someone else reads the AI Overview, gets a good-enough answer, and never clicks anything at all. A fourth person watches the Smart Family Money video and leaves.
Same query. Four completely different paths. Four different “winners.” And if you’re the brand celebrating a number-three organic ranking on this page, you may be missing that most of the real estate, and most of the user attention, lives somewhere other than those blue links.
This is what I mean by the total SERP experience. Your customer sees the whole page. You should too.
The AI layer changes the math
AI Overviews now appear on roughly 25% to 48% of Google queries, depending on the study. ChatGPT processes 2.5 billion prompts a day. Perplexity is up 239% year over year. These are real numbers from real platforms where real buyers are forming opinions about your brand, or not forming opinions because you’re nowhere to be found.
But before the panic sets in: AI tools still account for less than 1% of U.S. web traffic. Google sends 300x more referral traffic than all AI platforms combined. The sky isn’t falling, but the ground is shifting.
The shift that matters most is behavioral. Wynter’s 2026 research found 68% of B2B buyers now start their research in AI tools before they ever open Google. They ask ChatGPT to narrow the field, then Google the shortlist to validate. AI evaluates, Google verifies, and your website converts. If your brand is missing from that first AI conversation, you’re not even on the shortlist when the Googling starts.
Why the click data is more interesting than scary
A Search Engine Land analysis of 25 million organic impressions across 42 clients found organic CTR drops 61% when an AI Overview appears. In addition, paid CTR drops 68%.
EVERYBODY FREAK OUT!!! Right? Not quite.
Here’s what the panicked LinkedIn posts leave out: brands cited inside AI Overviews see 35% more organic clicks and 91% more paid clicks. Being in the AI Overview doesn’t cannibalize your traffic. If anything, it amplifies it. The AI Overview functions like a trust signal, a stamp of “this brand is relevant to your question” that makes people more likely to click your listing below.
The real twist, though, is that ranking well in organic doesn’t guarantee you show up in AI. Tom Capper’s research at Moz found 88% of AI Mode citations are NOT in the organic SERP for the same query. Organic and AI are pulling from different source pools. You can be number one in Google and completely invisible in ChatGPT’s answer to the same question.
And the small amount of traffic that does come from AI? It converts at more than quadruple the rate of organic, according to Semrush. These visitors arrive more informed, more intentional, and more ready to buy. Which makes sense, because they’ve already done the evaluation inside the AI interface. By the time they click, they’re just confirming and often converting.
The org chart is the problem
Most companies have SEO reporting to content, PPC reporting to demand gen, and AI search reporting to nobody. BrightEdge found 54% of organizations have handed AI search to the SEO team alone, which is a little like asking your plumber to also handle the electrical work because, hey, it’s all in the same house.
The waste from this setup is real. One branded Performance Max campaign paid roughly $500,000 for clicks that would have come through organic anyway. Google’s own research confirms: when you rank number one organically, only half your paid clicks are truly incremental. The other half? You bought what you already owned.
Meanwhile, McKinsey found that a brand’s own website makes up only 5% to 10% of the sources AI references. AI pulls from Reddit, review sites, affiliates, publishers, and user-generated content. You can have the best SEO program in your category and be completely absent from AI search results because AI is reading what other people say about you, not what you say about yourself.
The unified approach works. Level cut acquisition costs 18% and boosted SEO leads 22% by merging paid and organic for a B2B SaaS client. And we can use tools in our Level Intelligence Suite to connect performance signals across search surfaces. The channels compound each other. Treating them as separate line items on separate P&Ls leaves that compounding on the table.
Three audits you can run Monday morning
You don’t need a six-month transformation to start seeing the gaps. Three lenses, applied to your top 20 keywords, will show you where the opportunities and the waste are hiding.
Lens 1: Where do you actually appear? Check your organic rankings, paid ad coverage, and AI visibility across ChatGPT, Perplexity, and Gemini for the same set of keywords. Semrush has a free AI visibility checker. Most teams have never looked at all three surfaces side by side, and the gaps are almost always larger than they expect.
Lens 2: Where are you paying for traffic you already own? Cross-reference your number-one organic rankings with active PPC bids on the same terms. Start with branded keywords, where the waste is usually largest and the test is cleanest. If you rank first and you’re still bidding, you’re probably buying your own clicks.
Lens 3: Where is AI ignoring you? Compare your organic rankings with your AI citation presence. Only 11% of domains get cited by both ChatGPT and Perplexity, so strength in one guarantees nothing in the other. And check your robots.txt while you’re at it. If you’re blocking AI crawlers like OAI-SearchBot or PerplexityBot, you’ve pulled yourself off those shelves entirely.
This diagnostic shows you the full picture. What to do about it, the actual unification framework, is what I’m laying out at SMX Advanced.
The window won’t stay open
Generative Engine Optimization (GEO) keyword difficulty currently averages 15 to 20, compared to 45 to 60 for equivalent SEO terms. That gap will close. Once an LLM selects a trusted source, it reinforces that choice across related prompts. The brands getting cited now are training the models to keep citing them. Winner-takes-most dynamics are being baked into the weights.
Many companies are seeing search traffic drop significantly. Those same brands, the ones that get it right, are seeing the inverse when it comes to business growth. Rankings and revenue have decoupled. The brands that win from here are the ones that stopped measuring channels in isolation and started measuring the search experience their customers actually have.
We’re presenting a search unification framework at SMX Advanced in our session, “Organic, paid, and AI search: one strategy to rule them all.” If you want to stop optimizing for three separate channels and start compounding performance across every search surface, join us for the session or come find the Level team at Booth #9.
Remember: The search experience that existed in 2023 is gone. The strategy should be too.

Your customers see the whole SERP. You should too.

Unifying the search experience for real growth in 2026 by Level Agency

<div><img width="360" height="200" src="https://searchengineland.com/wp-content/seloads/2026/04/mt_360x200_smxnow_april.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" /></div>Join us May 6 for the second SMX Now, featuring Ameet Khabra on how to spot and stop PPC drift before it hurts performance.

<div><img width="360" height="200" src="https://searchengineland.com/wp-content/seloads/2026/04/mt_360x200_smxnow_april.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" /></div>
Automation doesn’t fail on its own — it does exactly what it’s trained to do. The problem is that when Google Ads is fed incomplete, misaligned, or overly broad signals, it can optimize toward the wrong outcome faster than most advertisers realize.



In our second installment of SMX Now, our new monthly series, Ameet Khabra of Hop Skip Media will break down a real account where a 417% jump in conversions turned out to be the wrong kind of success. She’ll use that case study to explain the four key ways automation drift enters an account: signal drift, query drift, inventory drift, and creative drift.



You’ll leave with a practical framework for diagnosing drift early, understanding where human oversight matters most, and managing automation more deliberately so it works toward real business goals — not just platform-reported wins.



Join us May 6 at noon ET.



<a href="https://www.bigmarker.com/third-door-media/the-automation-drift-and-how-to-correct-course?utm_bmcr_source=Editorial" type="link" id="https://www.bigmarker.com/third-door-media/the-automation-drift-and-how-to-correct-course?utm_bmcr_source=Editorial" target="_blank" rel="noopener">Save your spot</a>

Automation doesn’t fail on its own — it does exactly what it’s trained to do. The problem is that when Google Ads is fed incomplete, misaligned, or overly broad signals, it can optimize toward the wrong outcome faster than most advertisers realize.
In our second installment of SMX Now, our new monthly series, Ameet Khabra of Hop Skip Media will break down a real account where a 417% jump in conversions turned out to be the wrong kind of success. She’ll use that case study to explain the four key ways automation drift enters an account: signal drift, query drift, inventory drift, and creative drift.
You’ll leave with a practical framework for diagnosing drift early, understanding where human oversight matters most, and managing automation more deliberately so it works toward real business goals — not just platform-reported wins.
Join us May 6 at noon ET.
Save your spot

Join us May 6 for the second SMX Now, featuring Ameet Khabra on how to spot and stop PPC drift before it hurts performance.

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All About Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

Free Consultation

Reach out with any questions you might have regarding how RSO can help you with digital marketing.

All About
Digital Marketing