As I navigate through the ever-evolving world of search engine optimization, I’ve come across a revelation that I believe could change the game. It’s something I like to call ‘YBYS’: Your brand equals your SEO.
The simplest approach to staying relevant, even in the face of AI-driven search changes, is surprisingly straightforward: focus on building a real brand.
Every day, I hear two questions repeatedly in meetings with various businesses.
“How do we get back our Google clicks?”
“How do we show up in all the LLMs?”
The answer, although not always welcomed, is simple: build your brand. The old tactics like keyword stuffing and excessive backlinking have lost their long-term effectiveness.
While search-and-answer bots can indeed be manipulated in the short term, the real, lasting value comes from being genuine and credible.
Let’s take the example of two brands: Crayola and Monday Mandala. Crayola may be the well-known brand you think of for crayons, but Monday Mandala brings more traffic for coloring-related searches. It’s remarkable, but true!
Even though Crayola wins in brand recall, Monday Mandala excels in attracting clicks. This dynamic shows how brand recognition can be just as important as clicks in the world of AI-driven search engines.
We’re in an age where building a memorable brand is invaluable, extending its impact beyond the fluctuations of search engine algorithms.
Search has fragmented, yet a brand’s strength hasn’t. In past years, search meant asking Google, clicking a link, and landing on a website. Nowadays, the landscape is far more complex, with answers appearing across multiple platforms.
So, what stands the test of time when users no longer click links? It’s brand memory. Users remember names, trust established relationships, and value recommendations. These aspects travel with them, transcending the traditional boundaries of your website.
Your brand essentially becomes your SEO. SEO tactics are still useful, but the underlying core of a brand makes you unforgettable.
I strive to integrate this philosophy, highlighting that your brand lives beyond just your online presence. Recognizable brands endure, driving loyalty and sustainable 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.
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’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.
AI has infiltrated nearly every industry, becoming an integral part of apps, company processes, and even daily life. As someone who’s been navigating the local SEO landscape since its inception, I’m witnessing a significant change in user search behavior and the types of responses they receive.
Back in the day, a local business could achieve high rankings simply by optimizing its website, polishing up the Google Business Profile, securing around 50 citations, and soliciting customer reviews. However, in today’s AI-driven search world, these efforts are just foundational.
To succeed in AI-driven local searches, it’s crucial to influence what the wider web communicates about your business, or in simpler terms, build brand awareness.
Consider local search as a form of digital word-of-mouth.
These questions are at the core of what AI systems evaluate when users request local business recommendations. Here’s how I work on shaping the reputation signals these advanced search engines rely on.
How to Conduct Competitor Research for AI Visibility
One initial step in developing an AI search strategy is figuring out which brands large language models (LLMs) recommend most frequently and understanding their strategies.
Identify Businesses Frequently Mentioned in AI Responses
Since AI responses change frequently, I found it essential to run the same query multiple times to discern patterns.
I run the most common brand-related searches at least 20 times in my chosen LLM. Whether you do this manually or employ software like Gumshoe or Waikay, these tools can help synthesize prompts based on your business details and indicate how often your brand appears.
Pinpoint the Sites AI Cites Most Often
After identifying competitors, I turn my attention to the sources LLMs tap into. Analyzing results can be done manually or with the aforementioned tools.
Getting Your Brand Mentioned on Key Sites
Armed with a list of essential sites, I strive to have my brand featured there.
If blogs are primary AI sources, I offer to contribute expert content. For mentions in podcasts or on YouTube, I seek opportunities to guest feature. The ultimate aim is brand amplification.
Building Reviews for AI Consideration
For years, Google has dominated as the primary channel for discovery, leading businesses, like mine, to focus primarily on garnering Google reviews. However, to excel in AI outcomes, reviews across multiple platforms are vital.
Diversify Your Review Collection Strategy
I recommend seeking reviews on various platforms such as Yelp, BBB, Facebook, and others pertinent to your industry. Regular reviews on multiple sites can bolster your brand’s visibility and enhance rankings in traditional search results.
Refine Your Approach to Requesting Reviews
Generic review requests are ineffective. Providing clear direction enhances the quality of feedback, steering customers toward experiences or product aspects AI models might query.
For instance, if you run a plumbing service, a polished review request could resemble this:
Hi [Name],
Thank you for choosing us for your hot water tank repair. If you could take a moment, please leave a review on [Link to Platform] and share how we met your needs:
— What plumbing issue did we resolve?
— Was our service up to your expectations?
— Did our plumber arrive punctually and display professionalism?
— Was the cost justifiable for the service quality?
Your review is invaluable to us and beneficial for others seeking quality plumbing services.
Thank you!
[Your Name]
AI systems directly reference review content, so securing detailed feedback is crucial.
Always Respond to Reviews
If you haven’t started responding to reviews, now’s the time. AI systems evaluate the content in review responses.
Establish an Everywhere Presence
AI systems scour the web for even rare mentions of your business. Thus, maintaining a presence across multiple platforms is essential, including:
YouTube.
Reddit.
Industry forums.
Social media, especially LinkedIn.
Industry publications.
Local and hyperlocal blogs.
Local news sites.
Local and industry podcasts and video channels.
Best-of lists in your city or industry.
Press releases.
Engage actively on platforms that resonate with your audience. Tools like Sparktoro can help identify where your audience is most active, enabling focused efforts.
Creating AI-Optimized Content That Stands Out
Today’s content strategies must cater to both humans and machines, demanding alterations in content structuring.
Research by Dan Petrovic into Google’s “grounding snippets” reveals that Google prioritizes sentences closely aligned semantically with the query and those positioned early in the text.
Deliver Key Information Promptly
While humans might savor a thoughtfully crafted introduction, LLMs laser focus on specific answers.
To cater to this, I ensure that my crucial points shine in the opening paragraphs, with the rest of the content bolstering these points.
Addressing the Right Questions
This revolves around keyword research and understanding query fan-out. It’s about pinpointing what queries bring visitors to my business and ensuring my site acts as an answer hub for these inquiries.
For local outfits, essential questions might include:
What do you do?
What services or products are available?
Who is your target audience?
What problems do you address?
Where are you located?
Which neighborhoods or cities do you serve?
Is service delivery on-site, or do clients visit your premises?
What are your business hours?
Do you provide emergency or immediate services?
Do you operate during weekends and holidays?
How can clients contact you?
What’s the booking procedure?
Do you provide quotes or consultations?
Is it appointment-only, or do you allow walk-ins?
Why should someone opt for your services?
What differentiates you from the competition?
Do you hold any awards or certifications?
Are you renowned for a specific product or service?
What are the costs involved?
Are there discounts or packages available?
What do other clients say about you?
Can you share reviews and testimonials?
Do you provide case studies or before-and-after visuals?
Answers to common queries.
Demonstrating authority and expertise:
What’s your process like?
Do you impart knowledge through tips, guides, or blog posts?
Incorporating tools like AlsoAsked can enhance this question discovery process.
Once addressed on your site, ensure consistency of answers across the web, including citations, guest posts, and press releases.
Craft Machine-Friendly Content Structures
Local businesses often list their services as follows: “Services include: plumbing, drain cleaning, pipe repair, etc.”
To improve, I utilize semantic triples for better machine comprehension.
A semantic triple comprises:
[Subject] + [predicate] + [object]
The subject pertains to what’s being defined, the predicate explains its relation to the object, and the object elaborates on the subject.
For instance:
[Rescue Plumbing] [is] [a plumbing company in Denver].
Swapping out “we” with the brand name provides machines the unambiguous signals they need, improving clarity about your services.
Introduce Fresh Perspectives
AI searches rely heavily on information gain. Thus, I ensure my content contributes new insights rather than restating existing details.
LLMs are drawn to articles that expand their understanding of your brand, industry, and locality.
I leverage personal and vocational expertise to answer niche questions and share unique job experiences, ensuring I rank for AI searches where my competitors don’t feature.
AI Visibility Checklist
Enhancing AI visibility requires more than focusing on your website and Google Business Profile. This checklist covers reviews, citations, content, and brand signals crucial for AI evaluation.
Revamp your local SEO strategy. Continue refining your website and Google Business Profile while enhancing brand visibility online.
Identify and analyze your competitors’ content and citation methodologies.
Find sources LLMs cite within your niche and location; ensure your brand features on these platforms.
Seek reviews across varied platforms, optimize your review requests, and respond to all feedback.
Boost your presence on blogs, social media, forums, YouTube channels, podcasts, and in the press.
Offer unique, informative, and comprehensive content on your website and across web platforms. Use semantic triples to deliver essential information concisely.
This exploration of localized AI search can be far more expansive, but I hope I’ve held your interest. Ensure you check back for upcoming discussions!
I’m realizing more and more how crucial it is for enterprise SEO teams to track website changes meticulously. Without visible updates, we might be unaware of risky changes until they’ve negatively impacted our traffic and revenue. This is where changelogs become invaluable.
Working within large enterprise websites, I collaborate with various stakeholders including SEO teams, developers, and product managers. It’s always a challenge to discover changes only after they’ve already affected our site’s performance—a frustrating reality.
Consider how a quiet CMS update might strip core content from pages or how product rollouts generate canonical mismatches. By the time I identify the problem, rankings, traffic, and KPI reports are already suffering.
That’s why I advocate for SEO changelogs. They are more than just records; they build visibility, accountability, and teamwork around website changes that can tweak search performance.
Why I Believe Enterprise SEO Teams Can’t Do Without Changelogs
In enterprise settings, SEO decisions often come last. Despite strong workflows, website changes may still occur away from SEO purview. By implementing an SEO changelog, I can bridge that gap, ensuring all impactful changes are documented and shared.
For me, a comprehensive changelog includes metadata tweaks, schema updates, and internal link changes. It’s crucial for identifying risks quickly, understanding deployment impacts, and reducing unexpected SEO pitfalls. Documenting what changed, where, and the expected outcomes is vital.
Organizations usually have deployment records through various logs, but these often lack an SEO perspective, which makes proactive monitoring challenging. My goal is clear: integrate SEO with enterprise changelogs for holistic site governance.
The 2023 Lumar study found about 53% of teams face misalignment issues. With dynamic Google SERPs, improved operational visibility is key, and robust changelogs aid in tackling these challenges.
Using tools like SEMrush, I can ensure brand visibility everywhere customers search. The SEO toolkit, enriched with AI data, becomes indispensable for me. It’s time to leverage these resources as I optimize my site’s search presence.
The Anatomy of an Enterprise SEO Changelog
I aim to create a clear and informative SEO changelog by focusing on these key areas:
Specific changes and their locations.
The context.
The stakeholders involved.
Expected and observed impacts.
Defining the Changes Clearly
It’s important for me to provide a clear definition and scope of changes. For instance:
Updated schema markup on product pages to include AggregateRating.
Modified hreflang tags across 10 European markets.
Updated robots.txt to disallow paths.
Understanding the Context
I need to note why a change was made and its intended aim, essential for retrospective analysis. For example:
Implemented schema markup to enhance rich snippet potential.
Updated hreflang tags for accurate regional page delivery.
Robots.txt update to refine crawl behavior per Search Console insights.
Identifying the Stakeholder
I ensure transparency by identifying who made changes, which assists in efficient follow-up if necessary. This fosters a culture of SEO awareness.
Expected Impact
Although not always comprehensive, detailing the expected impact is valuable. Larger deployments might include a business rationale, like improving site speed, while smaller changes might target specific metrics.
Observed Impact
I add this information retrospectively, after collecting sufficient data, such as clicks or impressions, to foster a culture of testing and learning.
The Tools Assisting in Managing Changelogs
Automation is my goal, and several tools assist in logging changes effectively. Here’s what I use:
GitHub/GitLab Webhooks
Setting these up to post deployment summaries to SEO channels like Slack or email keeps me up-to-date.
Jira/Linear Automation
Using rules that log entries once a ticket is marked “Done” allows me to streamline the changelog process.
CMS Change Logs
Platforms like Contentful and Adobe Experience Manager maintain logs I can integrate into the central changelog using APIs.
Third-party SEO Tool Alerts
Leveraging tools like Botify and Lumar for immediate alerts helps me swiftly address crawl anomalies and metadata changes.
Establishing a Changelog Workflow
After defining core changelog elements, I plan a scalable workflow through phased implementation.
Initiate a Pilot Program
Starting small, I pick a team and simple logging method as a proof of concept, maybe using Slack or Google Sheets.
Expand and Standardize
Recognizing changelog value across teams allows me to standardize formats, enhancing cross-departmental integration.
Include SEO Context
Adding context helps my team understand changes better, facilitating proactive SEO management and effective deployment.
Leveraging SEO Changelogs for Stakeholder Buy-in
Enterprise SEO requires buy-in across organizations, often challenging due to stakeholder management gaps. An effective SEO changelog strategy aids in securing support by demonstrating its role in broader risk management, not just SEO.
Highlight Business Risk Mitigation
I position changelogs as business risk tools, emphasizing prevention of costly disruptions like faulty URL updates.
Champion Internal Participation
Identifying champions within development, content, or QA teams streamlines changelog integration into daily processes, converting potential threats into manageable business concerns.
Celebrate Changelog Achievements
I ensure that wins from changelog use, like stopping visibility issues, are shared, reinforcing its value across teams.
Measuring Changelog Success
For continuous improvement, I measure metrics like the percentage of changes captured, detection speed, and issue interception rate.
Embedding SEO into Brand Culture
I strive for more than documentation; it’s about fostering awareness of SEO’s impact on digital channels. By integrating SEO visibility as a business standard, brands strengthen their competitive edge, making SEO a shared responsibility across teams.
On a recent Thursday, I logged into Google Search Console expecting the usual link report, only to discover a significant issue—it had broken. For some, it displayed zero links, while others saw their reported links drop by nearly 90% from the previous week.
Google acknowledged the problem and decided to revert to older data temporarily as they worked on a fix. This means the link data you’re seeing might be weeks old.
Google’s Response: John Mueller of Google mentioned, “Thanks for the heads-up, Barry. We’ll take a look to see if there’s anything unexpected happening (given the long weekends, it might take a bit of time).”
By Saturday, the links seemed to reappear, but as Mueller explained, they had merely switched back to previous data as a temporary measure. “They’re working on resolving the actual issue and in the meantime switched back to the data from the week before.”
Old Data: If you check your link report now, it displays old information. This is crucial to keep in mind if you’re using this data for reports to clients or stakeholders.
The Bug’s Impact: Many folks noticed either zero links or a drastic drop exceeding 85%. Here’s a screenshot highlighting the problem:
OK, this takes the cake. Hahaha. Yeah, something is very off with the links reporting in GSC. pic.twitter.com/KIYmFPm1fX— Glenn Gabe (@glenngabe) May 21, 2026
Why It Matters: For those relying on this link data for generating reports, the inaccuracy can be problematic. Data pulled on that Thursday might not be reliable.
While Google is addressing the issue, be prepared to work with data that’s temporarily outdated.
From March to May 2026, I dove into a deep analysis of over 50 agencies to unveil the top medical device SEO agencies of the year. I meticulously evaluated them based on the following pivotal factors:
Notable Clients (35%): To me, an agency’s past collaborations with medical device clients speaks volumes about its potential success. So, the history of these relationships carries the most weight in my ranking.
Average Reviews (25%): Another key aspect I considered is customer reviews, particularly those from clients within the medical device industry.
Leadership Experience (15%): Agencies led by individuals with extensive SEO leadership experiences for medical device companies immediately captured my attention.
Company Size (10%): Larger agencies might boast the ability to execute comprehensive strategies using ample resources, but smaller specialized firms shouldn’t be overlooked.
Year Founded (10%): I trust more seasoned agencies that have consistently adapted to evolving SEO practices and maintained client success, even during economic slumps.
Headquarters Location (5%): Although less critical in my evaluation, agencies in major cities such as San Francisco and New York are strategically positioned to draw in exceptional talent.
Based on my research, the following agencies stand out as the frontrunners in medical device SEO for 2026.
I’ve noticed that not every organic visit deserves the same consideration these days. It’s become evident that I need to hone in on high-intent pages to truly measure SEO success and its impact on my business.
Recently, HubSpot rebranded its flagship conference from INBOUND to UNBOUND. This change wasn’t merely cosmetic; it represented a strategic pivot away from old-school SEO strategies that emphasized top-of-funnel traffic.
Modern search dynamics are nudging us closer to a zero-click environment. Trust me, the click-through rate curve is rapidly evolving. Studies show that around 60% of searches now conclude without a single click leading to the open web.
I’ve also observed that the discovery layer of search has shifted significantly. Nowadays, buyers are researching vendors within platforms like ChatGPT and Perplexity before they even consider clicking a traditional blue link.
Attribution has become increasingly complex. The modern buyer journey is fragmented, often starting with AI-assisted search and only finalizing on my website when the prospect is ready to make a decision.
This shifting landscape distorts my SEO reports if I focus solely on traffic as a success indicator. I’ve decided it’s time to pivot and redefine how I present traffic data to marketing leadership, ensuring that my reports align more closely with business impact.
A lively discussion on LinkedIn, led by Peter Rota, debated whether to completely retire organic traffic as an SEO metric. The consensus, I’ve found, is to use traffic with caution, always considering intent and the actual revenue it drives.
While organic traffic isn’t inherently bad, relying on it solely as a KPI lacks context and could be misleading. Adam Heitzman pointed out that it’s essential for traffic metrics to come with intent-based context for more accurate reflections of performance.
In a situation where low-intent traffic is reduced and focus is shifted towards high-intent pages, I’ve noticed that although overall visits might drop, conversions and revenue can actually increase due to better-quality traffic.
This understanding has led me to differentiate between top-of-funnel visits and more meaningful page interactions, thereby filtering out the data noise and focusing on what really matters in my dashboards.
Rand Fishkin beautifully summarized that top-of-funnel marketing feels like ‘rented land’—and he’s right. Buyers are now finding most basic information elsewhere, opting for instant answers on platforms like Reddit, TikTok, and within LLMs.
As of now, generic informational traffic is dwindling. Ironically, many SEO efforts are still devoted to content types most vulnerable to AI-driven change, such as FAQs and long-form articles.
Given this shift, it’s crucial for me to track pages based on their transactional value—those that AI can’t easily replace. I’ve narrowed my focus to four main areas: homepage, pricing pages, products and solutions pages, and money content pages.
Focusing my reporting on these key pages allows me to cut through the noise and concentrate on the traffic truly affecting my business’s bottom line.
For example, when a prospective B2B buyer starts searching for a modern CX platform, they’ll go through AI search, Google verification, and eventually land in the dark funnel for conversion.
Understanding these layers helps me recognize which organic traffic is significant enough to report, enhancing my insights into customer journeys and how they interact with my website content.
I know I must move away from outdated traffic analysis techniques to embrace more effective, modern reporting standards that focus on directional trends and macro shifts indicative of real business impact.
By focusing on page health instead of unreliable keyword-level reporting and monitoring branded search volume as an AI visibility proxy, I can capture a more accurate view of my current impact.
