Hey there! Have you ever wondered what GEO is and how it can supercharge your content’s visibility and engagement in AI-based search engines like ChatGPT and Gemini?
I’m excited to share my insights on optimizing your content specifically for these AI platforms. Think of GEO as the key to getting noticed in the digital realm where AI engines are becoming the norm.
By mastering Generative Engine Optimization (GEO), you can pivot your strategy to cater to AI Overviews, boosting your reach by ensuring your content is relevant and easily discoverable. Let’s dive into this transformative journey together!
Recently, I’ve been delving into an intriguing study by Lily Ray, which reveals some unexpected findings about Google’s AI Overviews. Apparently, these Overviews frequently reference brands’ own listicles but tend to recommend their competitors.
The study highlighted that Google AI Overviews cited these self-promotional listicles in a whopping 69% of B2B software-related queries. Yet, they favored rival brands in their recommendations. This got me thinking about the strategies brands employ to influence AI search outcomes.
Detailed Findings. I discovered that the analysis was quite comprehensive. Ray reviewed 100 B2B queries spanning categories like “best [category] software.” She gathered data across three specific periods: April 15, May 15, and June 8.
The study found that out of 80 queries that triggered an AI Overview, self-serving listicles were referenced 323 times, yet in 224 instances, Google didn’t actually recommend those brands. This mismatch intrigued me.
Analysis of Recommendations. While examining specific cases, it became evident that Google sometimes cited a brand’s listicle but opted to recommend more renowned competitors instead. For instance, in the search for “best LMS for selling courses,” Oasis LMS was mentioned, yet Kajabi and others were pushed forward as the preferred options.
This pattern wasn’t just isolated to LMS software; it appeared in multiple domains like help desk tools, task management, and more. It made me ponder over the dominance of stronger brands in recommendations.
Observing Organic Declines. An interesting trend noted was a drop in organic visibility for websites heavily leaning on self-promotional listicles. I noticed beginnings of these declines back in January and observed further drops post-Google’s May 2026 core update.
Interestingly, these sites also seemed to have expanded into AI-generated content and other “best” pages prominently featuring their own brands.
Rise of Third-party Citations. Ray’s analysis also showed an upsurge in Google comprising third-party content for “best” queries. Platforms like Reddit, Forbes, and YouTube gained traction in citations.
Understanding Impact. I believe it’s crucial to realize that merely having your content cited doesn’t equate to a recommendation. This situation offers competitors the chance to snag attention and, ultimately, valuable visibility.
Keeping Up with Changes. Previously, Search Engine Land shared insights on how some SaaS and B2B businesses witnessed visibility losses after banking on self-ranked “best” lists. The risks are significant when company-driven content doesn’t transparently disclose material relationships as mandated by the FTC’s Consumer Review Rule.
About Ray’s Data. To reach her conclusions, Ray employed Ahrefs Brand Radar to examine numerous AI Overview responses. Her analysis spanned 100 B2B software queries, focusing on citations versus actual recommendations.
Hey there! If you’re anything like me, your backlog is overflowing, your developer is eager to know what to tackle first, and your boss is questioning why months of SEO work haven’t shown results. I’ve been stuck defending my roadmap with gut feelings, and it’s tough.
Without estimating the traffic impact of a fix before it’s live, it’s just a guess—and we both know guesses don’t cut it in budget meetings.
Let me share a framework I use to transform messy data into reliable estimates. It’s not perfect, but it’s solid enough to prioritize with confidence and explain my strategy in any meeting.
Why every recommendation can’t be high priority
I’ve seen teams spend sprints on minor schema issues, ignoring a bigger problem—like a title tag bug affecting thousands of pages. Both were marked as “high priority,” but the traffic impact of one was negligible compared to the other.
Traffic guides true priority. While we can’t neglect brand visibility or UX, traffic offers a universal measure to compare efforts. Without quantified impact, you’re letting the loudest voice, or the most tempting technical puzzle, dictate your roadmap instead of focusing on what truly drives business value.
Plus, SERP landscapes have changed drastically. According to SparkToro, 68% of U.S. Google searches this year ended without a click, up significantly since just two years ago.
With AI Overviews intercepting traffic, the impact of a ranking improvement can vary wildly by SERP layout. Jumping to position three on a commercial keyword might be gold, but on an informational query dominated by AI? Not necessarily.
Your forecasts should account for these dynamics to avoid overpromising.
Step 1: Define the scope
Before making any estimates, I always define the scope. Is the adjustment sitewide, a template fix, or a single-page optimization? Each scenario changes the math.
Sitewide technical fixes
These encompass site speed, mobile usability, HTTPS migrations, and Core Web Vitals. They influence every page, but not uniformly. Address areas with pages on the borderline of failing tests first.
Template-level changes
Fixes like rewriting title tags can have a major impact, but it’s vital to focus where traffic truly exists. Product templates might garner the majority of clicks, while blogs might trail behind.
Individual page optimizations
Actions like updating meta descriptions can provide quick wins, but their small scale might not significantly impact the business. Focus on these without losing sight of larger opportunities.
Organic clicks serve as a baseline. By filtering affected URLs and reviewing trends, I assess urgency and context.
Impressions and near-win rankings pinpoint real potential. Pages ranked 8-15 are ripe for improvements—push them higher for a CTR boost.
SERP features can greatly influence CTR. Using Search Console’s AI Mode data, I check for AI Overview dominance and adjust expectations.
Step 3: Estimate potential lift
Now, it’s time for educated estimation.
Your own history
When I’ve optimized similar pages before, I use those outcomes as future baselines. Keeping track of past projects builds a valuable benchmarking library.
Competitor benchmarks and SERP analysis
Review competitors and pinpoint their advantages, whether it’s content depth, UX, or backlinks. Aiming to close these gaps can justify a ranking gain.
AI-influenced CTR assumptions
Forecasting can falter without updated CTR assumptions. Seer’s research shows drastic CTR changes due to AI integration. Staying aware of these shifts is essential.
Step 4: Build three scenarios, not one number
One definitive forecast can be deceptive. I prefer building three—conservative, expected, and aggressive—to provide a range that reflects real possibilities.
In the conservative model, expect partial implementations and competition improvements. With the expected model, rely on solid historical benchmarks. The aggressive model accounts for perfect execution and fast indexing.
This comprehensive view guides stakeholders through potential outcomes, ensuring transparency and credibility.
Step 5: Use the forecast to build your roadmap
After forecasting, I compare traffic impact predictions to effort levels using frameworks like RICE. This demonstrates which initiatives offer the most value for the effort and helps align priorities with business goals.
A well-organized roadmap doesn’t just appeal to me but speaks clearly to everyone involved, highlighting efficiency and business impact.
Recently, a German court ruling caught my attention because it asserts that Google can be directly liable for false claims made in their AI Overviews. The Regional Court of Munich’s decision highlights a significant shift, considering AI-generated summaries as Google’s own content rather than just protected search results.
This ruling emerged from a case where AI Overviews mistakenly linked two Munich publishers to scams and dubious practices, despite the linked pages containing no such evidence, as reported by The Decoder.
AI Overviews are not just search tools. According to the court, these Overviews go beyond merely assisting users in finding third-party content. They actually process and present information in their own distinctive manner.
What struck me was the court’s findings that the AI Overview allegedly made standalone accusations regarding questionable business practices, which were not substantiated by the linked sources. Because Google crafts and controls these features and their algorithms, the court ruled these statements to be Google’s own content.
Traditional search protections didn’t apply here. Google argued that they should be protected by German case law, which generally shields search engines as indirect infringers. However, the court disagreed, emphasizing that AI Overviews are distinct as they generate new statements from multiple sources.
The court also dismissed Google’s argument that users could verify claims by reviewing linked content. They highlighted that AI Overviews offer claims that stand as complete answers without needing verification.
Why does this matter to me? The court’s stance implies that AI Overviews aren’t neutral links. If they issue incorrect claims about a company, Google may bear direct responsibility for these words.
Mismatched connections and misinformation. The court determined that misinformation resulted from AI conflating data about other entities with that concerning the publishers.
Given that the contested claims weren’t present on the linked sites, the publishers lacked a clear third party to target legally, should Google be considered only as an intermediary.
Interestingly, the court insisted that Google could compare AI-generated content against primary sources, at least in analogous situations.
Action required from Google. The injunction demands that Google refrains from repeating the disputed claims, which include allegations of scams and nonexistent business practices.
Furthermore, Google is instructed to bear 80% of the legal costs, while each publisher covers 10%. Despite Google’s lack of a cease-and-desist declaration with a penalty clause, the potential for repeat violations was noted, emphasizing the importance of this ruling for future similar claims.
I’ve realized that AI Overviews are fundamentally changing how users interact with search results. Gone are the days of simple, task-oriented searches. Today, AI Overviews encourage users to dive into comprehensive reading sessions right on the search engine results pages (SERPs).
Let’s talk about some critical insights. AI Overviews merge multiple search intents into a single reading session, disrupting the traditional understanding of search behavior. Winning what I call the ‘second impression’ is crucial for different types of web pages.
Recently, I teamed up with Eric Van Buskirk from Clickstream Solutions to analyze vast amounts of anonymized clickstream data. We discovered that time-on-SERP is no longer solely dependent on search intent when AI Overviews are in play.
Historically, search intent—navigational, informational, etc.—predicted user behavior. But with AI Overviews, now users spend similar amounts of time regardless of their initial intent.
These insights are crucial. Consider Google’s change in approach: it’s less about presenting links and more about providing exact answers. This requires us to think differently about how we engage users.
For operators like me, understanding the significance of the ‘second impression’ helps us adapt our strategy for product, category, and blog pages.
In product detail pages (PDPs), it’s important to manage schemas and compare competitors’ offerings. On category detail pages (CDPs), having visible filters and vast product arrays can make all the difference.
As for blog content, I’m focusing on credibility signals like publication dates and author names within schema markup to gain trust and validation clicks.
Instead of predicting user behavior as before, the new focus is on optimizing my content’s visibility and trustworthiness in an AI-influenced SERP landscape. This shift doesn’t change our core content strategy but adds new layers of intricacy to how we optimize for SERP.
As someone who eagerly follows Google’s updates, I was thrilled to learn about the latest developments in Google Search Console. Recently, Google has started to roll out new Search Generative AI performance reports. These reports, along with a feature to block your content in AI responses, are designed to give website owners more control.
Currently, these features are being introduced to a select group of website owners in the UK, but there are plans to expand access in the near future. This gradual rollout allows us to get accustomed to these changes before they become widely available.
Exploring the Search Generative AI Performance Report
The new AI performance report in Google Search Console is something I’ve been anticipating. Although it doesn’t cover everything, it does provide some important insights into how our content is performing within AI responses, AI Mode, and AI Overviews on Google Search. The report includes data on impressions, pages, countries, devices, and dates. However, a notable omission is click data, so we’re left guessing about the exact number of searchers clicking through to our sites from AI responses.
Google stated:
– We’re rolling out new insights for website owners regarding their pages’ appearances in generative AI Search features. These insights include impressions metrics and information on which pages appear in AI responses and in which countries. We’re working closely with website owners to determine what insights would be most helpful and will expand the metrics available over time.
Additionally, Google shared more details about the metrics we can expect:
– Impressions: Frequency of your site’s URLs appearing in generative AI features in Search and Discover.
– Pages: Identifying URLs that appeared within AI features.
– Countries: Understanding visibility on a country basis.
– Devices: Identifying the devices used to view your website. Available for Search results.
– Dates: Monitoring performance with hourly, daily, weekly, and monthly granularity.
I inquired about click data from a Google representative, who mentioned that they are exploring additional metrics that will help inform our strategies in the future.
Initially, this report is available to a subset of users in the UK, with plans to expand globally in the future.
Another exciting feature Google introduced is the ability to block your content from appearing in AI search features like AI Overviews, AI Mode, or AI Discover. Google described this as a “new toggle” within Google Search Console, allowing us to decide whether or not our site should be part of these AI search features.
Google notes that opting out will prevent your site from receiving traffic or impressions from these features. Importantly, this control won’t affect your ranking in standard search results outside of generative AI Search features, so there’s no risk of negatively impacting core web search visibility.
Again, like the performance report, this toggle is currently available to a subset of UK website owners, with plans to widen access as they complete further testing. Google had promised these controls after facing some backlash from the EU, and it’s promising to see them starting to roll out now.
One study even showed that 1/3rd of SEOs are willing to block Google from showcasing their content in AI search features.
Why It Matters
As site owners and publishers, many of us have been asking for control over how and if our content appears in Google’s AI features. Now, we have just that. Although it’s initially limited, I’m hopeful these features will eventually be available to all.
Moreover, we’ve been requesting AI Search reporting from Google from day one. With Google’s announcement following Bing’s release of its own AI performance report, we’re taking a significant step forward. While Google’s report currently targets UK site owners and lacks click data, it holds promise for a global rollout soon.
Hey there! So, you’re interested in tracking traffic from AI overviews, right? Well, you’re in the right place. I’ve explored how we can utilize Google Search Console, GA4, and GTM text fragments to get a complete picture of our brand’s visibility in the AI sphere.
The process might seem a bit daunting at first, but trust me, once you get the hang of it, it’s incredibly insightful. Let’s dive into each tool and see how they can enhance our understanding of AI-driven traffic.
Starting with Google Search Console, it’s our go-to for understanding search queries and how they drive traffic to our site. By analyzing these queries, we can uncover the impact of AI overviews on our search visibility.
Next up is GA4. It’s fantastic for tracking user interactions and gaining a deeper insight into how AI-driven traffic engages with our content. We can set up specific events to see which AI overview delivers the most value.
Finally, Google Tag Manager helps us implement text fragments seamlessly. These fragments allow us to track specific sections and elements on our website, providing granular data that’s essential for optimization.
By leveraging these tools, we can significantly enhance our AI visibility strategy. So, are you ready to make your brand stand out in the AI world?
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.
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.
