I recently discovered that OpenAI is set to introduce conversion-optimized ad campaigns starting in early June. This marks a significant step towards creating a performance advertising ecosystem within ChatGPT.
Why does this matter to us? This move by OpenAI, as reported by The Information, confirms the development of conversion-focused ads along with necessary tracking infrastructure and performance measurement tools for advertisers like us.
What’s the current update? OpenAI has communicated with advertisers, stating that those who set up the OpenAI Pixel or Conversions API in advance will get early access to these campaigns in June.
According to the company:
Advertisers configuring conversions by June 1 will gain early access by June 5.
Advertisers can already start tracking conversions using Ads Manager today.
This system enables advertisers to measure actions triggered by ads, enhancing campaign effectiveness.
A deeper look. OpenAI is setting up an infrastructure akin to performance platforms like Google and Meta. With the OpenAI Pixel, advertisers can track website activity post-ad interaction, while the Conversions API allows them to send first-party conversion data back into OpenAI’s systems directly.
This capability allows OpenAI to optimize campaigns for measurable business outcomes, beyond just engagement metrics.
What’s at stake? The future of OpenAI’s advertising strategy largely hinges on measurement accuracy and gaining advertisers’ trust.
With browser restrictions and privacy changes eroding traditional tracking methods, OpenAI’s Conversions API could play a crucial role in demonstrating campaign performance and attribution within AI-driven ad experiences.
I’ve discovered that AI Overviews are changing the way Google Search displays paid ads. Nowadays, it seems like there’s more pressure to get my ads to appear in AI-generated responses, as direct search results provide fewer opportunities for clicks.
Google suggests that Shopping, Performance Max, and AI Max for Search campaigns are best suited for this evolution. However, just choosing the right campaign isn’t enough. I need to ensure the quality of my feeds, optimize my landing pages, and use effective audience signals and creative content strategies to boost my ads’ chances.
Enable Google-Recommended Campaigns for AI Overviews
I’ve found that Google is quite clear about which campaign types are most likely to appear in AI Overviews. Interestingly, these opportunities are often overlooked by experienced marketers due lack of full control.
Despite this, I’ve come to understand that combining control with data and an understanding of search intent will benefit both me, as an advertiser, and the searcher. This involves strategizing beyond picking the right campaign types, focusing instead on fully optimized feed data and content alignment.
To boost my visibility in AI Overviews, I’ve enabled Google’s recommended campaigns to sync with the feature, particularly Shopping, Performance Max, and AI Max for Search, utilizing broad match keywords and smart bidding with final URL expansion.
Shopping Campaigns
Learning that the original keywordless campaign relies heavily on my data feed quality, I’ve focused on creating a well-built and optimized product data feed, using high-quality images, and ensuring my titles and descriptions are thorough.
I’ve realized how crucial the product data feed is in determining ad visibility for specific queries. When high-intent questions are asked, the AI Overview can feature a product carousel, enhancing the prominence of shopping results.
Performance Max Campaigns
In Performance Max, I’ve seen how keywordless campaigns utilize page content, data feeds, and audience insights to decide ad display. These inputs are key in determining ad visibility for queries.
Enabling Final URL expansion has allowed my ads to appear in more searches by leveraging page content for user query relevance.
AI Max for Search Campaigns
By using existing keywords as a starting point, AI Max for Search expands beyond to determine ad delivery strategies. This means keywords signal intent rather than dictate ad display.
I’ve noticed that AI Max uses search term matching and asset optimization to target queries unaddressed by traditional keyword targeting.
6 Best Practices for Ad Campaigns
To improve my chances of being featured in an AI Overview, I’ve optimized my campaigns by focusing on creative, copy, schema, and link-building techniques to reinforce brand authority.
1. Diversify Your Assets
With campaigns like AI Max and Performance Max, I’ve realized the importance of using varied creative assets. Incorporating informative headlines, descriptions, and visuals in multiple formats allows for diverse ad placements.
2. Use a Conversational Tone
Understanding Google’s approach, I’ve shifted from generic sales pitches to a conversational tone in my Responsive Search Ads, using language that assists the user rather than typical sales jargon.
3. Be Clear and Informative
By answering key questions succinctly, my ads now have a better chance of being highlighted in AI Overviews. A focus on information-rich landing pages has proven essential.
4. Check Schema Markup and Links
I ensure my schema markup is thorough and aligned with my content. Linking to reputable sources builds authority, and collaborating with my SEO team has enhanced these practices.
5. Guide Automation with Audience Signals
I recognize the lack of control in these campaigns, so I’ve guided automation using strong audience signals, exclusions, and negative keywords to refine my targeting strategies.
6. Regularly Monitor Campaigns
Regular monitoring is crucial for brand safety and profitability. Reviewing search terms, landing pages, and ad assets ensures my message remains consistent and aligned.
Adapt Your Approach for AI Overviews
Adapting to conversational AI Overviews requires me to focus on maximizing visibility on the SERP. Emphasizing data feed quality, content alignment, and creative diversity turns this shift into an opportunity for growth.
Looking at data from 10 websites, I discovered why original research, innovative tools, and answer-focused content often outperform generic educational articles in the GEO realm.
Some marketers believe GEO might replace SEO, while others say robust SEO is enough for AI visibility. So, I decided to dig into both perspectives by examining LLM referral traffic and organic traffic across 10 different sites.
Here’s what I found out about how AI search leans towards specific content patterns that differ from traditional organic search.
3 Key Findings from the Dataset
1. Traditional SEO Content Strategies Fall Short for GEO
I noticed blog content themes were a strong predictor of LLM traffic. Educational “comprehensive” guides often underperformed compared to shorter posts with unique data.
Trends and analysis posts were cited by LLMs 78% of the time. Posts featuring unique data held a significant lead in the citation pool, while educational how-to content lagged behind at a mere 12%.
It became clear that producing content rich in data and measurements significantly boosts your chances of entering the LLM citation pool. On the other hand, generic educational content might not make the cut.
2. Organic Success Doesn’t Ensure LLM Traffic
In my analysis, the top 10 organic pages captured over half the organic sessions but only 29% of LLM sessions.
Your most successful organic content may not necessarily perform well with LLM traffic. Among the top 100 organic pages, nearly half didn’t receive any LLM traffic at all!
Although there’s some correlation between organic performance and LLM traffic, the two aren’t equivalent.
3. Service/Product Pages Excel in LLM Traffic
While articles and blogs brought in most LLM referrals by session count, service and product pages outperformed others when LLM sessions are considered per 1,000 organic sessions, making them significant performers.
Page type
LLM sessions per 1,000 organic
Service/product
29.4
Article/content
23.4
FAQ/support
14.0
Tool/demo
9.8
Homepage
5.6
Turning my attention to practical insights, it was evident that crafting authoritative content that offers specific answers can significantly enhance LLM traffic. Integrating interactive tools emerged as another powerful approach. When LLMs recommend tools, they drive targeted traffic effectively.
The Methodology Behind My Case Study
I analyzed GA4 data from 10 diverse websites, covering 150,000 indexed pages in March 2026 to gather these findings.
The domains, handpicked for their varied industries and consistent SEO performance, ranged across healthcare, technology, retail, and more, ensuring a balanced view.
I meticulously isolated LLM-referral traffic using GA4 channel groupings and segmenting referrer paths, focusing on sessions from major AI platforms like ChatGPT.
Content type categorization helped me compare LLM citations, while I used per-page averages from GA4 for engagement time analysis.
It’s worth mentioning that LLM bot crawls aren’t captured by GA4, as they make server-level requests before client-side JavaScript loads. Thus, the organic session data reflects only human visitors.
What LLM Traffic Patterns Reveal About Engagement
LLM Referral Behavior vs. Organic Traffic
Analyzing engagement time across traffic types revealed averages were similar—yet disparities emerged across different page types.
Page type
Organic avg. time
LLM avg. time
Tool/demo
101 seconds
146 seconds
Homepage
36 seconds
82 seconds
Service/product
69 seconds
63 seconds
Article/content
56 seconds
40 seconds
Tools and homepage content saw heightened engagement from LLM users, suggesting they look for actionable insights rather than merely seeking information.
Recognizing the Potential of Interactive Tools with LLM Traffic
Interactive tools received the highest per-page LLM citations, and these tools were prominently featured by LLMs in response to relevant user queries.
Emergence of LLM-only Traffic
Interestingly, some LLM-receiving pages recorded no organic clicks, which could signify unique discovery mechanisms. This study showed engagement quality on these pages was notably high, driven by LLM-directed users ready to engage.
GEO Tactics Supported by Data
Answer Questions LLMs Can’t Address Themselves
It was evident that generic educational content is often redundant for LLMs. Content differentiation comes from original research and proprietary insights.
Investing in research and verifiable data can significantly enhance your content’s GEO impact.
Implement Answer Capsules
Research shows answer capsules, concise responses placed prominently, are strongly favored by LLMs for citation.
By providing direct answers early, the pages excelled in LLM traffic.
Maximize Named Interactive Tools
If your site includes calculators or assessments, highlight them for GEO success. Ensure they are easily found and provide valuable, targeted insights.
Separate Tracking for Organic and LLM Pages
Recognizing that organic and LLM hits don’t always align, thoughtful mapping based on AI queries can reveal high-quality LLM traffic opportunities.
Pages that solely receive LLM attention can still hold value, as users arrive prepared for deeper engagement, driven by AI direction.
Same Strategies, Different Tactics in GEO and SEO
This analysis highlighted that while GEO coexists with SEO, it demands distinct page tactics. As zero-click searches grow, understanding and leveraging these nuances becomes crucial.
By constructing content that answers specific questions with original data and strategic uses of GEO tactics, you can optimize for both systems. Keep in mind, mastering one does not automatically ensure success in the other.
AI visibility has transformed into a macro measurement challenge, and I’m here to guide you through building a foolproof framework to track recommendation trends effectively.
Through my experiences, I’ve learned that the funnel query pathway (FQP) is the ideal framework for measuring AI visibility. By assessing the FQP quarterly, I can derive actionable strategic insights.
I’ve coined this transformation the micro-macro shift. Traditional micro (ranking) metrics from search engines are no longer sufficient to measure AI visibility due to the opaque nature of AI engines.
In the AI-driven world, we must embrace a macro measurement approach, akin to economics evolving new measurement disciplines for broader economic systems.
The AI landscape operates under a brand-user-algorithm (BUA) opacity, where four layers veil every AI-era brand recommendation process.
The multi-layered opacity impacts everything from brand perception to conversion rates, and understanding this opacity is crucial.
Utilizing micro-strategies in an AI environment is futile. Instead, my focus shifts to macro-level insights, acknowledging that consistency over time is key, not momentary precision.
In 2026, search remains micro, while assistive and agent modes adopt macro approaches. The right measurement strategy for your business hinges on understanding each mode’s environment and data.
Search enables user control with clear metrics. Having been trained in this mode, I recommend maintaining micro strategies for search-based operations, supplemented by macro methodologies.
Assistive recommendations come from engines like ChatGPT. Unfortunately, I can’t see the decision data, making micro assessments impossible and macro the only viable option.
Agents autonomously make purchases, providing a clear but limited view of their decision-making. The conversion insight remains macro, even if initiation is observable.
Given buyers’ ever-changing reliance on different surfaces, adopting a macro approach remains inevitable, ensuring I stay adaptable to any environment they opt into.
As I shift forward with macro metrics, measuring becomes more about trends. Tracking consistent methodologies over eight quarters offers reliable strategic clarity.
In the busy world of AI decision-making, patience and consistency are key to staying ahead. I prioritize stable methodologies to gain competitive insights over time.
I’m excited to share that Google has introduced a new feature designed to streamline the ad approval process called Real-Time Policy Reviews. During the creation of campaigns, this system offers instant feedback, making it faster and easier to get ads up and running.
The feature is currently tailored for Responsive Search Ads, but Google has plans to expand it to other campaign types within the year. This means as I create ads within Google Ads, I receive immediate policy feedback, eliminating the need to wait in a post-submission review queue.
The real magic happens in two phases. First, as I draft my ad, the system flags any editorial issues instantly, like typos or errors with destination links, allowing me to correct these before finalizing my ad.
Once I’ve saved the ad, Google provides a policy decision immediately. Ads that pass without any issues can go live almost instantly, whereas those with more complicated violations are redirected to a post-save review page, detailing the problem and outlining possible solutions.
I find this update crucial because it reduces campaign launch delays, especially during promotions or product launches that demand immediate action and can’t afford postponements.
Google has segmented policy issues into two main categories: ‘editable,’ which are simple problems I can fix on the spot like formatting errors, and ‘complex,’ which need further certifications or appeals.
This aligns with Google’s ongoing mission to make campaign management smoother by integrating it into our day-to-day tasks, especially essential for those rapid-response campaigns.
As Real-Time Policy Reviews become available across more campaign types, I anticipate a faster transition from creation to delivery. However, it also emphasizes the importance of addressing compliance throughout my creative process.
Starting in June, Google Ads will implement a policy that deletes any reporting data older than 37 months, unless we take action to export and preserve it.
As someone who heavily relies on historical data for reporting and forecasting, I recognize the urgency to revamp my data management strategies before access to older records is lost.
What’s Changing. From June 1st, only data from periods shorter than a month—such as hourly, daily, and weekly reports—will be accessible for 37 months. For longer spans like monthly, quarterly, and annual reports, we will enjoy access for up to 11 years.
Once those retention periods lapse, the data will no longer be available in the Google Ads interface or through APIs.
Nitty-gritty Details. Metrics that measure reach and frequency will have even shorter retention limits, staying available for just three years. These metrics include:
unique users,
average impression frequency per user,
7-day and 30-day average impression frequency,
and frequency distribution metrics.
The Larger Impact. The policy change means I need to export and securely store historical Google Ads data soon, or it’ll become permanently inaccessible.
I acknowledge that long-term trend analysis and benchmarking depend heavily on years of granular data, which may no longer be directly accessible in Google Ads.
Looking Ahead. If I rely on external BI tools or customized reporting systems, I need to set up automated data export pipelines to maintain continuity before the new retention limits take effect in 2026.
For More Information. Read more about Google’s data retention changes on their official support page.
I recently came across some exciting updates from Google that are designed to enhance the way we search for and interact with content. Google is introducing new features to its AI experiences, including AI Mode and AI Overviews, by incorporating preferred sources along with a perspectives carousel and highly cited labels.
Preferred Sources in AI Mode and AI Overviews. One of the updates brings preferred sources to AI search results. According to Duncan Osborn, Product Manager at Google Search, users will now be able to easily identify links in AI responses from sources they have selected. I find this particularly beneficial as it helps me quickly access content from sources I trust.
I saw Google testing this feature recently, and now we have the final version that’s rolling out. There will be a label highlighting preferred sources within AI results, making it noticeable to us. It’s fascinating how this is now available globally and in all languages. Google mentions that users have selected over 345,000 unique sources, and these sources receive double the click-through rate. For those interested in trying it out, you can find more details in Google’s documentation.
Perspectives Carousel. Another interesting addition is the perspectives carousel. Google will present a new carousel for certain searches, tailored to help us dive deeper into specific topics, especially when they’re rapidly evolving. The carousel will prominently feature our preferred sources, making recent articles more accessible across various search queries.
In addition to this, there’s also a carousel that shows helpful perspectives from online discussions, forums, and social media. This is a wonderful way for us to tap into diverse viewpoints, broadening our understanding of topics that interest us. These features are being rolled out in AI Mode and AI Overviews.
Highly Cited Label. Finally, Google is expanding the highly cited label to more web article links within search results. This feature makes it easier to find articles that many other stories refer to. It’s a fantastic tool for me to trace a story back to its primary reporting, ensuring that I am viewing the original source of information. This feature will be available across Google Search, beyond just AI-specific functions.
I find it fascinating that users interact differently when faced with AI Overviews compared to AI Mode. New clickstream data reveals that AI Overviews significantly alter user behavior—from reverse scrolling to extended evaluation of search results across various intents.
Take Netflix, for example. The average user spends about 18 minutes just browsing. They skim through tiles, watch trailers, and often circle back. It turns out, searching isn’t much different these days, thanks to new insights.
This week, I’m diving into:
Four notable behavioral shifts observed with AI Overviews, gathered from over 846,000 Google sessions.
The evolving role of brand-name searches and why they no longer offer the same shortcuts.
An insight that might change how you craft title tags and meta descriptions this quarter.
Eric Van Buskirk from Clickstream Solutions mined anonymized clickstream data supplied by Surfer SEO. The study analyzed around 846,000 U.S.-based Google searches from February and March of 2026.
This marks the fifth study on user behavior with Google’s AI features over the past year. Earlier, a UX study on 70 users in May 2025 utilized think-aloud and screen recording methods, while a study from October 2025 examined AI Mode specifically. This research trades depth for scale, uncovering patterns too subtle for smaller studies.
For a bit of context, previous SERP mouse-tracking studies involved only a handful of people—this one, however, evaluates queries from tens of thousands of users.
A fascinating contrast surfaces: User behavior in AI Overviews starkly opposes that in AI Mode, where AI Mode is akin to autoplay, while AI Overviews replicate the browsing experience.
This article outlines four major findings from this recent study and how they might influence your title tags and meta descriptions in 2026. Full methodology available here.
With groundbreaking insights, like how nearly half of AI Overview interactions involve reverse scrolling and how search types no longer reliably predict behavior, this data is invaluable. It challenges traditional assumptions and has meaningful implications for e-commerce and decision-heavy categories.
Surprising findings include brand searches losing their shortcut advantage, implying even users searching specifically for brands might pause to consider adjacent content on the SERP.
Read more intriguing insights on how the AI landscape shifts user engagement and strategy in SEO.
I’ve recently discovered that Google is reshaping our approach to Display Ads by integrating them into Demand Gen campaigns, providing us with wider reach and innovative AI-driven features.
What’s happening? Now, I can effortlessly manage my placements on the Google Display Network (GDN) through Demand Gen campaigns. Interestingly, I still have the option to keep my ads running exclusively on GDN if that’s more suitable for my needs.
Through Demand Gen campaigns, I’m able to extend my ad reach across YouTube, Discover, Gmail, Maps, and a vast array of Display Network sites, all within a more centralized system.
Why do I care? This strategic shift by Google is crucial because it centralizes more inventory, harnesses automation, and leverages AI for enhanced campaign optimization. It’s become an essential factor for my performance and discovery ad strategies.
As a Display advertiser, these adjustments mean I gain access to advanced AI features, greater cross-platform reach, and potentially increased efficiency. I see this as a shift towards less reliance on traditional standalone Display management over time.
The bigger picture. Google is steering Demand Gen to be the go-to campaign type for visual discovery advertising, merging creative social-style distribution with its powerful AI targeting capabilities.
Google claims an average ROI increase of 9.5% for those who’ve added GDN inventory to their Demand Gen campaigns, and I’m intrigued by the potential benefits.
Between the lines. These changes provide me with access to the latest Demand Gen features announced at Google Marketing Live, including enhanced channel controls and forward-looking AI campaign tools.
What to watch. With Google’s ongoing journey towards consolidating campaign management under AI-led products, I find myself reevaluating my strategies for upper-funnel discovery, Display, and performance-centric media purchasing.
I recently followed an intriguing conversation with Google’s CEO, Sundar Pichai, where he explored the transformative journey that awaits Google’s AI, Search, and digital tools. The path forward envisions these elements coalescing into a unified powerhouse capable of executing tasks seamlessly.
In a detailed exchange with Nilay Patel from The Verge, Pichai addressed concerns about an evolving Search landscape. He firmly reiterated Google’s commitment to connecting users with the open web, assuaging publisher concerns about potential traffic declines.
Pichai assured, “Through it all, we are very committed to both meeting user expectations and also connecting them to what’s out on the web.” Yet, it’s clear why some fears persist as Google steers towards an AI-driven future where Search evolves to include conversational agents and task-oriented tools, reducing the need for traditional clicks.
Why we care. It’s important to recognize the emerging landscape, one where Google’s Search, Gemini, and agent technologies blend into a singular AI layer. This shift points toward a revamped approach to discovering information, creating content, and handling tasks.
Agents are the future. These AI agents are poised to drive the next evolution on the web. According to Pichai, “I look at agents, and that is the next evolution of the web. I think it will evolve the web pretty profoundly.”
In the background, Google’s efforts in developing agentic tools across Search, Gemini, Spark, and Antigravity aim to bring these innovations together for a more cohesive user experience. Acknowledging this unified trajectory, Pichai envisions Google’s ecosystem as evolving into an ‘agent manager’ model.
One product. When asked if Google’s suite of AI search and app-building tools might eventually merge into one, Pichai affirmed, “It will.” This convergence means Google agents will quietly assist users in planning and executing tasks, a vision for which Google is diligently assembling essential building blocks.
Pichai elaborated, “We are laying a lot of the primitives of what we need for agents to work end to end, and more importantly, for AI to work.”
Dig deeper. Explore perspectives on how Google’s Search and Gemini might converge or continue to diverge in the discussion led by Google’s Liz Reid.
Google rejects Google Zero. In the face of concerns about Google’s evolving role in web traffic, Pichai illustrated his view of an expansive information ecosystem, far broader than Google alone.
Addressing Condé Nast’s apprehension about declining search traffic, he highlighted the dynamism of the current landscape, where publishers adapt continually to shifts in user behaviors and new digital formats.
“It’s exceptionally dynamic, and so it makes sense to me every publisher is adapting to this new world,” he observed.
Google says some clicks are going away. While Pichai refrained from advising publishers on business planning, he emphasized that as technology improves, low-quality clicks naturally dwindle, alongside metrics reflecting a decline in bounce clicks.
Google points to subscriptions. By highlighting Google’s adjustments to support subscription models, Pichai acknowledged this as a key adaptation amid evolving publisher strategies.
“We are adapting to the fact that publishers are increasingly turning to subscription offerings, too,” he stated, promoting Google’s efforts to highlight subscribed content as preferred sources for users.
It’s worth noting that the drive towards subscriptions was, in part, a response to diminishing reliance on search traffic.
Search had to move faster. The decision to reorganize Google Search was a strategic move to enhance agility in the rapidly advancing AI era, positioning the platform for rapid decision-making and innovation under new leadership.
For more insights into Sundar Pichai’s thoughts on AI, search, and the future of the web, consider listening to the full interview here.
