Google also announced some new agentic commerce features today in Google Search including Universal Cart, expanding Universal Commerce Protocol and Agent Payments Protocol (AP2).
Plus, Google’s Shopping Graph now contains 60 billion product listings, which is up from 50 billion from earlier this year, announced Vidhya Srinivasan, VP/GM Ads & Commerce.
Universal Cart. Google announced what it is calling the Universal Cart, where you can put products and items from multiple retailers into one single Google Universal Cart and check out on all those items with your Google Wallet with the click of a button.
As you are on Google Search, you can add items directly to your Google Universal Cart without having to go to a specific retailer’s website. This will work across Google Search, Gemini, YouTube and Gmail, so just keep throwing items in your cart – across Google interface and retailer and the cart will maintain your list.
Here is a screenshot of Universal Cart showing multiple retailers:
Google will find the best prices and deals, including which retailer has it in stock and let you check out with your preferred retailer.
Plus, Google said Universal Cart will “anticipate your needs and help solve problems before they.” Google’s example:
“Say you’re building your first custom PC and add a few parts from several retailers to your cart. Your cart will proactively flag any product incompatibilities and suggest alternatives. Since the cart was built on Google Wallet, it understands your payment method perks, loyalty information and merchant offers to help you choose. This lets you quickly find opportunities for hidden savings or points without having to remember them yourself.”
Merchants. Google listed a number of merchants that support this, including Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and Shopify merchants such as Fenty and Steve Madden.
Availability. This is available in Google Search and the Gemini app in the U.S. starting this summer and with YouTube and Gmail later on.
UCP and AP2. Google expanded the Universal Commerce Protocol on Google to Canada and Australia in the coming months and in the U.K. later on. UCP will also be coming to YouTube and more Google verticals including hotel booking and local food delivery.
Agent Payments Protocol (AP2) helps agents make payments for you, securely and with accountability, Google said. “Just tell your agent the specific brands and products you want and how much it can spend, and the agent only makes the purchase when your criteria are met,” Google explained.
Google will launch AP2 to Google products in the coming months, starting with Gemini Spark.
For a long time, SEOreporting revolved around dashboards. When a meeting was on your schedule, you’d spend your day preparing by exporting data from Google Search Console, cleaning it in spreadsheets, and layering charts into Data Studio.
Now, AI coding agents are changing that workflow. Instead of the manual work that would previously take hours, you can use tools like Claude Code to surface customized data with polished visuals in just minutes.
Here’s how to turn Google Search Console data into custom reports and speed up your reporting workflow.
What Claude Code can do with GSC data
Claude Code isn’t the same as using Claude in a browser tab. The standard Claude.ai interface works like a regular chatbot. Claude Code, on the other hand, is Anthropic’s terminal-based AI coding assistant.
It still feels conversational, but instead of living in a browser tab, it can interact directly with files, folders, spreadsheets, and scripts on your machine. It can read exported GSC CSV files, process large datasets locally, generate charts and summaries, analyze trends across pages and queries, and ultimately create structured deliverables from raw data.
Claude Code isn’t simply generating text responses like a chatbot. Instead, it’s creating a local reporting environment that behaves like a lightweight software project.
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There’s a learning curve
Before you can start building beautiful, custom reports, you’ll need to set up Claude Code. If you’re not an engineer or developer, this process can feel overwhelming at first. There is a learning curve, but don’t give up.
Setup is actually the most time-intensive piece of the process, but it’s a one-time process. Depending on your technical experience, the initial setup may take a couple of hours.
The “reports in minutes” concept really applies after the environment is configured. Once you’re past the initial setup and Claude is connected to GSC, you can run any custom SEO report you want in a matter of minutes.
If you’re in an enterprise environment, this setup process can go faster with a little help from the tech team. If you’re an agency or an SEO consultant, you can always lean on the expertise of in-house developers or engineers or an outside contractor.
Getting started
If you don’t already have one, create an account atClaude.ai. You can sign up with Google, email/password, or enterprise SSO.
Most SEOs using Claude Code for reporting have a paid plan or use Anthropic API access. But you can use a free plan at the time of writing.
Install Node.js
Claude Code runs locally on your machine, so you’ll first need Node.js installed. You can also use it on a Chromebook by activating the Linux subsystem.
For the purposes of this tutorial, I used a Mac.
Next, download the current LTS (Long-Term Support) version. Once installed, you’ll have access to npm, which is used to install Claude Code.
To verify the installation, open Terminal (Mac/Linux) or PowerShell (Windows) and run:
node -v npm -v
If both commands return version numbers, you’re ready to continue.
Install Claude Code
Next, install Claude Code globally:
npm install -g @anthropic-ai/claude-code
Once the installation finishes, start Claude Code by running:
claude
The CLI will walk you through authentication and connect to your Anthropic account. After that, Claude Code can work directly with local project folders containing exported SEO data, scripts, spreadsheets, and reporting templates.
At this point, you’ll be able to interact with Claude Code in the terminal using commands much like you would with an AI chatbot.
To kick off the workflow, I gave Claude a prompt:
“I have a marketing meeting coming up, and I want to show our performance from Google Search Console.”
One benefit is that Claude now becomes an onboarding assistant. Claude will ask a handful of clarifying questions to get started. For example, during the setup process, Claude asked:
Whether to use a service account or OAuth credentials to access the Google Search Console API.
Which reporting views or marketing priorities mattered most.
Where the reporting project should live locally on the machine.
Which Google Search Console property to connect to.
Claude also asked where the reporting project should live locally.
(As an aside, we prefer to store it inside a dedicated code directory rather than a standard Documents folder because development projects can sometimes run into file permission or syncing issues when stored inside cloud-synced folders like Documents or Desktop.)
Next, I established how the visuals will be built before connecting to GSC.
We like using Observable Framework, an open-source framework for building data apps, dashboards, and reports.
You don’t necessarily need to follow this exact structure; Claude Code is highly customizable, and you’ll settle into what works for you.
And remember: if you’re unsure about any next steps, you can just ask Claude, and it will help guide the setup.
Connecting to GSC
Before Claude Code can start generating reports from live GSC data, you’ll need to connect it to the Search Console API.
This is another technical part of the process, but the good news is that Claude can walk you through much of the setup interactively.
To establish the connection, you’ll need to create a Google Cloud Project (GCP) and configure API credentials.
That setup process typically includes:
Creating a Google Cloud project.
Enabling the Search Console API.
Generating OAuth credentials or API secrets.
Adding those credentials to a local environment file.
In larger organizations, your IT or development team may already manage this infrastructure.
If not, you can still configure it yourself using a standard Google account or Google Workspace account.
Generating reports
Once you’ve finished connecting to GSC, congratulations! You made it through the hardest part. Once setup is complete, your reporting process changes entirely.
You can now focus on the reporting views you want to create, such as:
“Show me the top 10 landing pages that gained traffic this month.”
“Create a chart of declining nonbrand queries over the last 90 days.”
“Compare CTR trends by device type.”
“Show me the top-performing pages from New York last month.”
Claude is now like an on-demand reporting assistant. You simply open the project folder, launch Claude Code, and ask for the charts you need.
In addition, you can be more dynamic in your meetings.
Instead of building a rigid dashboard ahead of time and hoping stakeholders ask predictable questions, you can generate new views dynamically as questions come up.
That means you can walk into a meeting, ask Claude for a completely new chart or segmentation, and generate it in minutes rather than rebuilding an entire dashboard manually.
Now let’s look at some reports you might quickly run before your next meeting.
Here’s an example of a custom SEO performance dashboard generated from Google Search Console data.
While some of these metrics are available inside GSC, building your own report gives you much more flexibility in how trends, comparisons, and supporting metrics are visualized together.
You could also generate a bar chart with YoY rankings, or a heat map of rankings for keywords by month. Both examples are below.
What we like to include in our reporting is a combination of scorecards, time-series charts, year-over-year bar chart comparisons, and heat maps that break down the key drivers behind a metric.
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Claude Code completely transforms SEO reporting
SEO reporting has always been a push and pull between speed and flexibility.
Dashboards are fast once they are built, but they are often rigid. Custom analysis is powerful but historically has been time-intensive.
Claude Code changes everything.
Now you can interact with your GSC data more dynamically, explore new questions as they arise, and create reporting views that would have previously taken hours to build manually.
Once the initial setup is complete, reporting becomes far more adaptable to the needs of you and your stakeholders.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-19 14:00:002026-05-19 14:00:00How to build custom SEO reports with Claude Code and Google Search Console
SEO is a white-collar job. So does that mean our jobs will be eliminated, too? The answer isn’t as obvious as you might think.
Yes, the world is changing. But if you’ve been doing SEO for a while, you should be used to that by now.
SEOs have always been forced to wear strange combinations of hats: part technical analyst, part content strategist, part UX researcher, part marketer, and part analyst.
I don’t think AI will make SEO expertise obsolete. But it will make shallow SEO obsolete.
The people who thrive will be the ones who understand search behavior, business outcomes, technical systems, content strategy, analytics, and how to turn all of that into better decisions.
The old version of SEO stopped working years ago
I’ve been doing SEO since before there was a word for “SEO.” Every few years, there’s a viral article declaring that “SEO is dead.” One of the first to catch fire was a 2005 article by Jeremy Schoemaker, repeating something he’d heard from Jason Calacanis.
We know the reality. SEO never died. But over the years, it’s changed a lot.
Look at this screenshot of a Google search for [flowers] in 2007 versus the same search in 2026.
Google’s “flowers” SERP in 2007, when a No. 1 organic ranking controlled most of the visible page.
Google’s “flowers” SERP in 2026, where organic listings compete with ads, shopping results, local packs, AI features, and other search elements.
This example is near and dear to my heart because I wrote that title tag in 2007. I was fortunate enough to lead SEO at 1-800-Flowers at a time when a No. 1 organic ranking meant significant traffic and revenue.
Twenty years later, their team has maintained the No. 1 organic ranking. However, today it’s so buried on the SERP that I wonder whether it gets any clicks at all.
This phenomenon isn’t limited to searches for “flowers.” Search for any competitive head term these days, and chances are you’ll see the organic result buried.
Is SEO “dead”? That really depends on your definition of “SEO.”
If your definition is “getting to the top of Google organic search” by spending your whole day writing title tags, then yeah, SEO is pretty much dead. It has been for a long time.
If your definition of SEO is understanding that people are looking for your goods and services, understanding their needs, answering their questions, and meeting them wherever they go to find information, then your journey as an SEO expert — or whatever you eventually decide to call yourself — is only beginning.
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Why true SEO experts are uniquely positioned to thrive
There’s one phenomenon I’ve noticed with AI, not just in SEO, but across every industry. You might have noticed it too.
On social media, you’ll see a lot of AI-generated videos. The vast majority are silly “look what I can do with AI” videos. You see them, maybe press “Like,” and then forget about them. But the ones with staying power are made by people who understand filmmaking: pacing, framing, lighting, composition, camera movement, editing, sound design, and how to build toward an emotional payoff.
In other words, even though everyone can generate videos with AI now, the differentiator is no longer how “cool” the visuals are. It’s how skillfully creators use AI as a tool to achieve their vision.
There’s an analogous situation happening with SEO and AI. I’ve noticed a lot of people typing simplistic prompts and, like Neo in “The Matrix,” declaring, “I know SEO.”
What these folks don’t realize is that SEO is a lot more than title tags, and it was never just about reverse-engineering search engines. It was always about reverse-engineering the human brain, drawing on knowledge and experience across keyword lists, user behavior, content strategy, technical systems, analytics, persuasion, UX, and business outcomes.
When others are typing simplistic prompts into their LLMs, SEO experts will be having deep conversations with their LLMs, teaching them, challenging them, and finding ways to get the best out of them. Those who excel in this new world won’t be the ones who have all the answers. They’ll be the ones who have the right questions.
While it’s still early, and I’m convinced we haven’t even scratched the surface of ways to use LLMs in SEO, here are just a few ways I’ve been using AI in my SEO work to make it more efficient and effective than ever.
1. Performing SEO basics with unprecedented efficiency and effectiveness
I’m generally not a fan of AI-generated long-form writing. You end up with generic, inauthentic slop that, in the words of Shakespeare, is “full of sound and fury, signifying nothing.”
I predict that a year from now, most people will be able to spot the clear signs of AI-generated copy: not just obvious tells like excessive use of em dashes and repetitive phrasing (“That’s not X … it’s Y!”), but a lack of authentic personality and stories.
Metadata is one of the places where I don’t mind AI assistance because its job isn’t to invent original thought. It’s to compress the page’s value, intent, and positioning into the right format for the right surface.
The big mistake I see people making with AI-generated metadata is that their prompts are far too generic: “Write a title tag for this page.”
A seasoned SEO knows the goal isn’t to create a “pretty title tag.” It’s to create the most effective title tag possible for human, search engine, and AI discovery. It takes into account various search intents, brand positioning, competitor gaps, conversion drivers, and practical space limitations.
AI opens up new opportunities that weren’t practical before. Not many people know that ideally, your title tag, Open Graph tag, and Twitter card should be distinct from one another because they’ll be shown to different audiences on Google, Facebook, and X. And it took me a few tries to remind AI that title tag length isn’t based on character count, but on pixel width.
Those “in the know” will start using AI to generate everything: title tags, meta description tags, OG tags, Twitter cards, and the right structured data.
Someone without SEO experience will write generic prompts and wonder why their perfectly polished title tags aren’t doing anything for them a year from now.
2. Turning SEO recommendations into dev-ready tickets
One “edge” I’ve had throughout my career is the ability to translate vague marketing goals into precise technical requirements developers can actually execute.
But as technology has become more complex, I found myself hitting my own limits. I understood the principles of coding, but had a hard time articulating exactly what I needed developers to do. Googling hardly ever helped because I’d just find high-level articles written by consultants, some of whom clearly didn’t understand it either.
A practical example is modern React or single-page app architecture, where a page may look complete to users while key SEO content is assembled after load from JavaScript rather than appearing as crawlable HTML.
In the past, I might’ve written a vague recommendation like “we need more crawlable content on this page,” forcing my poor developer to figure out what that means.
With AI, I can turn that into a real implementation ticket: grounding the LLM in the site’s tech stack, translating the SEO need into concepts like server-side rendering, hydration, DOM content, and crawlable links, and adding examples, test cases, edge cases, and acceptance criteria.
The point isn’t to become a React engineer. It’s to communicate SEO requirements in a way that developers can execute without forcing them to think too much about it. Trust me, your developer will thank you.
3. Mining GSC, GA4, and Semrush or Ahrefs data for actual user needs
The holy grail of SEO has always been to read your users’ minds and create content that meets their needs. Anyone who’s spent a lot of time with SEO data knows that there are enormous amounts of insights locked within this data. The first problem is unlocking them. The second problem is getting them into a format that will get people to pay attention.
In the past, I would literally lock myself in a room with a giant spreadsheet open on my screen. I’d go through search terms one by one, categorizing and clustering them, and, if I was lucky, end up with a handful of insights days later.
I might start with a list of 30,000 keywords and get through maybe a few hundred before getting completely exhausted. And when I’d present my insights, along with my giant pivot table, to stakeholders, they’d nod their heads, and then everyone would forget about them.
LLMs are changing the game. You can simply upload data from GSC, GA4, and Semrush and Ahrefs, along with your own business and market insights, and then simply ask your LLM questions.
Here are just a few recent examples of analyses I’ve done for my clients. These would once have taken days or weeks. Now I can get to a strong first pass in minutes.
Analyze our GSC keyword data and organize the keywords into topical clusters. Which topics do we clearly have a “right to own” in Google’s eyes?
Review our top competitors and uncover keywords within this topical neighborhood that they rank for but we don’t. What kind of content do we need to “break in”?
Surface GSC queries that get lots of impressions but few clicks. What improvements can we make to our titles, snippets, or positioning to drive more clicks?
Examine organic landing pages that attract a lot of traffic but fail to convert. What is the search intent behind the keywords driving traffic to these pages, and how can we improve conversion?
Find keywords where we’re in “striking distance” of stronger rankings. What additional content do we need to create or adjust to push us to the top?
Analyze the queries people type into our on-site search. What are examples of searches they might perform on Google or prompts they might use in LLMs when looking for this information?
There are literally an endless number of questions you can ask. I didn’t present these as sample prompts because they’re thought starters. While you’ll probably get a decent answer, the real value from AI comes only when you:
Dig deep into specific concepts, pages, and keywords.
4. Prototyping page layouts, content modules, and more
Something else I’ve found LLMs can do really well is generate a solid wireframe of a page or page module that you can pass on to your web designer and developer. But this is another area where the quality of the output depends almost entirely on the quality of your prompt and the context you provide the LLM.
Most people will simply type “design me a web page,” perhaps with a few “wish list” items they’d like to see. AI may produce something that looks “complete” on the surface, perhaps a hero section, a list of benefits, some FAQs, and a call to action (CTA). But when executed, it’ll feel lifeless, generic, and disconnected from the actual business problem.
The better approach is to ground the LLM with as much background information as possible. This doesn’t need to include every SEO report, but rather the ones that provide the highest-quality signals, such as the ones we discussed above: topic clusters, competitor gaps, conversion data, and on-site search data. Add other useful information like sales objections, customer reviews, your brand’s unique value propositions, and a clear explanation of what the page needs to accomplish.
With proper context, AI can help lay out something that transcends a generic landing page. For example, it can propose a strong hero section with suggested wording, recommendations for CTAs, section order, comparison tables, proof blocks, FAQs based on real questions, trust elements, and paths for different stages of intent.
Remember that it works in reverse, too. Upload a screenshot of an existing page, either yours or your competitor’s, tell the LLM what your goals are for the page, and ask it to critique the page.
AI can also open up other SEO opportunities that have previously been roadblocks.
Want to do A/B testing? Tell the LLM the hypothesis you want to test, and have it come up with variants for you.
Want to prototype a simple interactive tool? Provide your requirements, provide the underlying data, and see what your LLM can do.
In some cases, it can go beyond a static mockup and produce a working prototype that a developer can evaluate, harden, and turn into production code.
Your edge as an SEO is knowing what information to feed the model, what problems the page actually needs to solve, and which ideas are strategically useful versus just AI-generated decoration.
The one thing that I haven’t seen AI do very well yet is generate professional-quality design and production-quality code. But everything up to that point is at your fingertips now.
5. Making analytics useful again
As I’m sure it was for many of you, July 1, 2024, was a dark day for me. That’s when Google shut down Universal Analytics and forced us all onto GA4.
Since it was called Urchin, I’d all but mastered UA. Then one day, all of my reports and dashboards were simply gone. And I had no interest in spending another decade on a learning curve just to recreate reports that they’d once given me by default.
But with the arrival of LLMs, you can simply ask the LLM to walk you through building whatever report you want.
The first report I had to re-create was the on-site search report, one that’s inexplicably missing from GA4. I wrote my own prompt to walk me through creating this, but for the purposes of this article, I had ChatGPT write the prompt:
Act as a senior GA4 analytics consultant.
I want to rebuild a useful onsite search report in GA4/Looker Studio. GA4 does not provide the same dedicated Site Search report that Universal Analytics had, but I can use the `view_search_results` event, the `search_term` parameter, and any custom parameters needed.
Create a practical, implementation-ready plan that covers:
1. How to confirm onsite search tracking is working.
2. Recommended event name and parameters, including which should be registered as custom dimensions.
3. How to track searches when the site does not use URL query parameters.
4. The most useful report sections, including:
- total searches
- unique searchers
- top search terms
- zero-result searches
- refined or repeated searches
- searches followed by exits
- searches followed by conversions
- searches by page, device, and user type
5. Step-by-step instructions for building the report in GA4 Explore and Looker Studio.
6. A QA checklist to make sure the data is accurate.
Keep the answer concise, practical, and usable by both a marketer and a developer.
The key to writing these prompts, or prompts that generate prompts, is including the phrase “step by step.” One of the nice things about AI is that it doesn’t judge.
Take as long as you need, ask it to break the setup down into steps as granular as you like, and feel free to ask “dumb” questions. It’ll oblige enthusiastically.
You can imagine what this opens up. One of the classic issues with SEO analytics is that all too often, they’re merely vanity metrics.
Conversions, clicks, impressions, and rankings may look impressive at first, but eventually the dreaded “so what” question will arise. Who really cares if you see impressions and rankings growing like wildfire if your revenue isn’t increasing?
This is where you want to ask your AI to help you tie data to business performance.
Which unbranded keywords are actually driving revenue?
Which are leading to soft conversion goals like email signup, account creation, or pricing page visits?
Which search queries bring in engaged visitors who come back later through brand search, direct traffic, or email?
Again, the sky’s the limit. You can build a report or dashboard to answer just about any question your stakeholders have, provided you’re collecting the right data, and if you’re not, AI can help you create tickets for your web developer to collect that data.
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The work is changing. The need for expertise isn’t.
Like I said, this is only scratching the surface of how AI can help transform the work we do as SEOs.
But let’s get to the question everyone is really asking: Is your job safe?
I don’t have a crystal ball. But one thing is pretty clear to me. Not every SEO job will survive unchanged. Big companies will likely cut roles. Teams will likely get smaller. A lot of tactical work that used to require specialists may be done faster, cheaper, or “good enough” by someone using AI.
If your value is limited to tasks that AI can perform on command, there may be challenges ahead.
But if your value is understanding customers, interpreting search behavior, connecting data to business outcomes, translating strategy into execution, and helping companies become more findable, useful, and trusted, then AI isn’t the end of your career. It may be the best leverage you’ve ever had.
And there’s another reason I’m optimistic. The same AI disruption hitting SEO is hitting every other white-collar profession, too. If large companies do lay off significant numbers of talented people, many of those people aren’t just going to disappear from the economy.
Some will start businesses. Some will finally pursue ideas they’ve had in their heads for years. Some will use AI to build prototypes, launch products, test markets, and create companies in ways that would have required far more capital and staff just a few years ago.
That should give us hope.
Many of the great companies we know today started with little more than a few people, an idea, and the willingness to figure things out as they went. Steve Jobs and Steve Wozniak, Bill Gates and Paul Allen, Mark Zuckerberg, Jeff Bezos, Larry Page and Sergey Brin, Michael Dell, and many others did not begin with massive corporations behind them. They began with ideas, persistence, and the tools available to them at the time.
If they were able to accomplish what they did with their tools, imagine what a new generation of entrepreneurs will be able to do with AI.
Maybe you’ll be one of those entrepreneurs. Or maybe your role will be helping one of them turn their ideas into businesses people can actually discover, understand, trust, and choose.
Either way, the products, services, brands, and businesses built with AI will still need to be found. They will still need to explain why they matter. They will still need to earn attention, authority, and trust.
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Google Discover has publisher profile pages. They live at profile.google.com/cp/ and appear when someone taps a publisher’s name on a Discover card. These pages aren’t new. They launched in August 2025 with the Follow button rollout, and by November 2025 Google’s documentation referred to them as “source overviews.”
For most of the 47,000+ publishers we monitored, the pages are auto-generated: a name, follower count, social links pulled from the Knowledge Graph, recent posts, and a footer label that reads “Profile generated by Google.”
Since March 2026, though, something changed for a small subset of publishers. A group gained access to enhanced profiles: custom banner images, a configurable links shelf, and the ability to pin posts (labeled “Pinned” in the publisher interface, formerly “Featured Posts”).
They also gained control over the order of their social links, website, and content tabs — something standard profiles don’t allow. On standard profiles, social links are sorted algorithmically by follower count, with the website listed last. On claimed profiles, the publisher decides.
The “Profile generated by Google” label also disappeared entirely, replaced by nothing — a quiet signal that the profile had been claimed.
There’s no public documentation explaining how to get access. No Search Console toggle. No application form. Google appears to have hand-selected participants for what is effectively an invitation-only pilot program.
We identified 54 publishers in this cohort. All are U.S.-based. All publish in English. And what they have — and haven’t — done with the feature over two months of monitoring reveals patterns every publisher should watch before the program scales.
How we found the 54
Our Profile Features Monitor tracks 46,926 publishers across seven languages: English, French, German, Italian, Spanish, Dutch, and Portuguese. To isolate the enhanced cohort, we filtered for publishers that showed persistent enhanced-profile signals across multiple snapshots: active links, full banner headers, or both.
The result: 54 domains with stable access to the enhanced profile surface. The composition of that group offers clues about Google’s intentions:
Tier
Publishers
Examples
National
15
WSJ, Fox News, NY Post, Newsweek, Inquirer
Regional Paper
13
Boston Globe, SFGate, CT Insider, Times Union
Local TV
14
KTLA, PIX11, MyFox8, WSMV, Atlanta News First
Lifestyle Brand
6
Delish, The Dodo, Country Living, House Beautiful
Specialty
6
Pew Research, The Athletic, Gothamist, Civil Beat
The skew toward local news and community publishers is striking and aligns with Google’s public emphasis on supporting local journalism. Nearly half the cohort — 27 of 54 publishers — consists of regional newspapers and local TV stations. National brands are included too, but they’re not the majority.
The two-tier profile system
Under the hood, Google operates two distinct profile architectures. Understanding the difference matters because this isn’t just a cosmetic upgrade. It’s a structural split.
Standard profile (99.9% of publishers):
Auto-generated from public sources.
“Profile generated by Google” label visible.
No publisher control over content or layout.
Claimed profile (the 54 publishers):
No generation label.
Publisher can configure the banner, links shelf, and pinned post.
Publisher controls the order of social links, website, and content tabs (standard profiles sort them by follower count).
This isn’t Search Console verification, structured data markup, or any existing publisher tool. It’s a separate, invitation-only system.
What the 54 publishers actually did
This is where it gets interesting. Access to a feature and its effective use are different. Here’s what the data shows across each configurable surface.
Forty-one of the 54 publishers uploaded a banner image. The remaining 13 have the capability — a “prepared” state — but haven’t used it yet.
What stands out is the production quality. There are no amateur banners in the cohort. Every uploaded image reflects clear professional design investment.
Five distinct visual archetypes emerged:
Brand-pattern: No photography, just the wordmark or abstract identity repeated as a tile. Pure prestige.
Tier predicts archetype. National publishers cluster around brand-pattern banners. Local outlets lean into civic identity and city imagery. Lifestyle brands showcase their content directly.
One anomaly: The Athletic uploaded a solid black square — 656×656 pixels. Whether that reflects deliberate minimalism aligned with The Athletic’s dark UI or simply a broken upload is unclear. It’s the only non-image banner in the cohort.
The format split is revealing: 71% used square banners — likely Google’s recommended ratio — while 29% used wide landscape formats. None used portrait layouts. Based on CDN serving patterns, the minimum recommended resolution appears to be 512 pixels on the longest side.
Publishers that chose wide formats made deliberate design decisions: SecretNYC uses a manifesto-style collage, the New York Post uses a headline grid, and Barron’s uses a geometric pattern. Square appears to be the default safe option.
Links: local TV dominates, nationals ignore it
Thirty-three of the 54 publishers enabled the links feature. Of those, 31 added at least one link, for a total of 65 configured links across the cohort.
The content is overwhelmingly focused on on-site navigation: 85% of links point to the publisher’s own sections, weather pages, live streams, or app downloads. This functions more like a mini site navigation layer than a promotional surface.
The tier gap is enormous:
Local TV: 31 links across 14 hosts (average 2.2 per publisher). Fox affiliates consistently shelve: Watch Live, Weather, Local News, Sub-region, Contact.
National: 9 links across 15 hosts (average 0.6 per publisher). Most nationals didn’t bother.
Three outliers worth noting:
PIX11 published “How to make PIX11 a preferred source on Google,” meta-promoting Discover follows from within the Discover profile itself.
Gothamist funneled donations through `pledge.wnyc.org` with a purpose-specific utm_campaign=discover-profile tag.
Fox Nation placed a direct subscription conversion link (“Subscribe to Fox Nation”) on what most publishers treat as a navigational surface.
Pinned posts (formerly Featured Posts): capability granted, rarely used
Fifty-two of the 54 publishers enabled the Pinned feature. Only 13 currently use it with an active pinned post.
Lifestyle brands were the strongest adopters: five of six had the feature active. Among national publishers, only 2 of 15 used it. The capability exists across nearly the entire cohort. Adoption does not.
About text: Wikipedia out, self-branding in
On standard profiles, the “About” section is auto-generated by Google, usually sourced from Wikipedia. On claimed profiles, publishers write their own.
Within the cohort, 38 of 54 use a custom-written description, while only 16 retain a Wikipedia-sourced version — a surprisingly low number for publishers of this size and prominence.
The tone splits cleanly by publisher tier.
Local TV stations lean promotional (“Your trusted source for breaking news, accurate weather forecasts and local sports across Greensboro…” ).
National and digital-native publishers stay more factual (“Gothamist is a website about New York City news, arts, events and food, brought to you by New York Public Radio”).
One publisher takes a mission-driven approach: Delish — “you don’t have to know how to cook, you just have to love to eat!”
The implication for publishers preparing for this feature: once you claim the profile, you take control of the About section. It becomes your pitch on a Google-owned page.
Notably, the most visible publishers in the cohort chose factual descriptions over promotional copy.
UTM tracking: the blind spot
Only three of the 65 configured links include analytics parameters. Gothamist tagged its donation link with utm_campaign=discover-profile, making it the only publisher in the cohort treating the profile as a measurable acquisition channel.
The Philadelphia Inquirer instrumented two links, but one reused an Instagram bio campaign tag (mktg_acq_ig_organic_bio_offer), meaning Discover traffic from that link will be misattributed to Instagram in analytics.
The other 62 links have no tracking at all. In practice, 95% of the cohort has no way to measure whether profile links generate traffic.
Social platform priorities
On claimed profiles, publishers control the display order of social links and content tabs. Standard profiles don’t: Google sorts links algorithmically by follower count and places the website last. That means the ordering we observe on claimed profiles reflects deliberate editorial choices, not algorithmic defaults:
Local TV stations list Facebook first: 86% (12 of 14). Zero list X/Twitter first.
National publishers spread their bets: Facebook 33%, Instagram 20%, X 20%, YouTube 13%.
Concrete examples: Newsweek places YouTube first and Articles second. Delish leads with Website, followed by Instagram. These are active editorial decisions about which audience channel matters most.
The local TV finding is particularly notable. Despite news media’s historical reliance on X/Twitter, not a single local station in this cohort places it as their primary social link.
Sister-site coordination
For media groups with multiple properties in the cohort, setup patterns reveal whether profile management is centralized or handled locally:
Hearst Connecticut, which has five papers in the cohort, shows near-identical configuration across all profiles. The links structure is the same, including a shared Hearst checkout funnel with publication-specific site IDs. The setup points to a centralized digital team managing profile operations across the group. Even so, each masthead still uses distinct banner art.
Dow Jones, across The Wall Street Journal and jp.wsj.com, uses shared banner artwork: the same wordmark tile, confirmed through perceptual hashing. That points to brand coordination at the asset level.
Everyone else Everyone else — including Fox affiliates, Dotdash Meredith properties, and the Fox News group — shows completely different setups across properties, even within owned-and-operated chains. Profile management appears to be handled locally rather than centrally.
The rollout is still active
Comparing snapshots #9 and #12 — taken 19 days apart — confirms this isn’t a frozen experiment. During that window, four publishers added banners (jp.wsj.com, New York Post, SecretNYC, and Everyday Health), one activated Links for the first time (New York Post), and jp.wsj.com (The Wall Street Journal’s Japanese edition) entered the cohort entirely.
No publishers lost features. The program is still expanding within the cohort, and new participants continue to appear.
The adoption paradox
We scored each publisher on a composite 0–6 scale, assigning one point for each of the following:
Banner uploaded
Links feature active
Featured Posts active
At least one configured link
Four or more social platforms listed
Any UTM tracking present
Nobody scored 6. The distribution:
Score
Publishers
%
2
22
41%
3
10
19%
4
14
26%
5
8
15%
6
0
0%
National publishers with the largest audiences are the least engaged with the configurable surface, with a mean score of 2.93. Most uploaded a banner and stopped there.
Local TV stations — despite having the smallest Discover footprints — are the most engaged, with a mean score of 3.57. Lifestyle brands score highest overall at 3.83, yet their Discover visibility trajectory is the flattest in the cohort.
And here’s the critical finding: feature adoption shows no correlation with visibility trajectory.
Across the cohort, the 180-day late/early capture ratio ranges from 0.23x for Prevention — down 77% — to 4.27x for NewsNation — up 327%. Variance is massive within every tier.
KTLA scores high on adoption, with seven links, a full banner, and active profile engagement, and grew 3.69x. But Delish also scores high and declined to 0.90x. MyFox8 configured five links and fell to 0.52x.
Publishers that fully utilized the configurable surface show no better visibility trajectory than those who used it minimally.
This feature gives publishers a controlled surface for branding and navigation, not a ranking lever. It’s a profile page, not an algorithm input.
What this means for publishers
The program is U.S.-only and invitation-only for now. Across the six other language markets we monitor — French, German, Italian, Spanish, Dutch, and Portuguese — we found zero enhanced profile deployments: not a single banner or configured link outside the English-language cohort.
But the underlying infrastructure is already in place. All 47,000+ publishers we track already have profile pages with follower counts, social links, and content feeds. The enhanced features sit on top of that existing architecture. Google isn’t rebuilding the system. It’s selectively unlocking capabilities within it.
If — or when — Google scales this, here’s how publishers should prepare:
Audit your structured data now. Profile social links are pulled from your sameAs/JSON-LD markup. Errors there will carry over to your profile. Verify what Google will display before you’re given control.
Design a banner. Use a square format (1:1 ratio) with a minimum resolution of 512px, and treat it as a professional brand asset. The 54 publishers in this cohort set a clear standard: there were no amateur images. Think about which archetype fits your brand: a wordmark tile for prestige brands, local landmarks for regional publishers, or content-driven imagery for vertical and lifestyle outlets.
Plan your link strategy. The data suggests that section navigation and utility content — weather, live streams, and similar recurring destinations — drive the most engagement. Local TV stations treating the profile as a mini site navigation layer are the clearest power users. Decide now which five to seven links represent your most valuable entry points.
Instrument from day one. Almost nobody in the current cohort tracks profile link performance. Adding a dedicated UTM campaign parameter — utm_campaign=discover-profile, for example — would put you ahead of 95% of the pilot group on attribution alone.
If you’re a media group, decide your operating model. Should profile management be centralized or handled newsroom by newsroom? The cohort shows both models. Hearst Connecticut runs one coordinated setup across five papers, while Fox affiliates manage profiles independently at the station level. The important part is that the choice is deliberate — not something decided accidentally when individual newsrooms start receiving invitations.
Methodology
Data comes from the 1492.vision Profile Features Monitor, which tracks roughly 47,000 publishers across seven languages through recurring snapshots of profile metadata. The 54-publisher cohort was identified through persistent enhanced-feature signals observed across multiple snapshots between March and May 2026.
Visibility trajectories are based on proprietary capture data. All findings are descriptive only: the cohort reflects Google’s selection criteria, not a random sample, and this dataset does not support causal claims about feature impact.
The full analysis — including the complete 10-phase timeline, banner image gallery, snapshot-by-snapshot evolution, and tier-by-tier breakdowns — is available at 1492.vision/research/discover-publisher-profiles-en.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/05/google-discover-publishers-aeLVnD.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-12 17:20:512026-05-12 17:20:51Google quietly gave 54 publishers control over their Discover profiles. Here’s what they did with it.
Won is the gate that pays for everything: the click happens, the recommendation lands, and the agent transacts.
Won has changed beyond recognition in the last 24 months. It used to mean a click on a search result, the human doing the picking, the brand competing for attention against nine blue links. It still means that, sometimes.
It can also mean an assistive engine naming your brand to a user who accepts the recommendation. Or it can mean an Agent transacting on the user’s behalf.
All of this is about delegation: how much we delegate to machines, and when. Delegation in the context of search and AI is far from new. We’ve delegated “finding the books to the librarian” since AltaVista.
What’s new is that the boundary of what and where we delegate is now flexible: the user can hand more of the journey to the engine than ever before, and the brand that wants to win has to be ready for every option on the delegation spectrum.
What hasn’t changed: The point of search
Underneath the three mechanisms sits the same commercial truth that’s been the point of search since Sergey Brin first articulated it: get the user to the best solution to their problem as efficiently as possible.
AI hasn’t changed the point of search. AI assistive engines and agents simply get the user to the best solution to their problem significantly more efficiently than search: a 15-minute purchase journey through ChatGPT that would have taken a week through traditional Search. In essence, AI removes an enormous amount of friction inherent in search.
The delegation boundary is the line between what the user does for themselves and what they hand to the engine. The further the user pushes it toward the engine, the less work the user does, and the faster won arrives. The further the user holds it back, the longer won takes.
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From problem to purchase in 15 minutes with ChatGPT
I’m a double bass player, and I have a huge bass amp in my living room. I also have a guitar my father gave me when I was 18, which I’ve never played much, because I became a professional double bass player in the 1990s, so the guitar has lived as a keepsake for most of its life.
A few months back, I got an offer to play a solo gig at the jazz club opposite my flat. I had a guitar, but no guitar amp, and I didn’t want to buy a guitar amp for one gig. I figured I could just use the bass amp.
Here’s my conversation with ChatGPT:
Me: Can I play guitar through my bass amp? Will I break it?
ChatGPT: No, you won’t break it. But it’ll sound terrible.
Me: How do I make it sound good?
ChatGPT: Three pedals: reverb, compression, equalization.
Me: Which ones?
ChatGPT: Boss, JHS, TC Electronic.
Me: Price?
ChatGPT: Around $250 for the set.
Me: Cheaper. I’m more of a singer than guitarist.
ChatGPT: I can get you there for $125.
Me: I need delivery by Friday, guaranteed. Saturday gig.
ChatGPT: Thomann in Europe, Sweetwater in the US. Both will hit Friday.
Me: Europe.
ChatGPT:[Thomann link]
Me: Clicks. Buys.
The Google team specifically asked me to make this point in the keynote, and I want to land it here too because they care about it for a reason most marketers haven’t worked out yet.
The engine made decisions for me all the way down the funnel. It decided whether the question even had an answer, which pedals were worth shortlisting at which price tier, and who could meet a Friday deadline.
My delegation boundary sat at the perfect click. ChatGPT owned the entire research-and-recommendation funnel. I owned the buy button, and only because Thomann doesn’t yet have an agential checkout. If they had, the agent would have transacted while I was making coffee.
The point isn’t that the funnel compressed from a week to fifteen minutes. The point is what happened inside the compression: the engine made dozens of small decisions on my behalf, each one quietly closing off options that might otherwise have stayed open.
A different engine might have surfaced different brands, recommended a different price tier, or picked a different supplier. The brand that wins isn’t the one the user chose. It’s the one that survived every one of those upstream decisions the engine made before the user ever saw a recommendation.
Two things put my boundary that far to the right.
Emotional weight: The pedals just needed to be good enough, not perfect.
Domain expertise: I’m not a guitarist. I have no opinions about boutique pedal brands. The comparison work a serious guitarist would relish is friction I’d happily have someone else do.
A working professional would have approached the same purchase entirely differently. A studio musician whose tone is their living would have gone into the shop, plugged the pedals in, and decided in the room. A geeky enthusiast would have spent the week on Reverb forums comparing JHS to Strymon to Walrus, because for them, the research is the fun, the comparing is the point.
The point here is that you have the same purchase made by three buyer personas, each with a completely different position on the delegation boundary, and Thomann needs to be ready to win all three.
I’ve bought over €2,000 of equipment from Thomann since.
The single-mode assumption is dead, three modes coexist now
For two decades, “optimize for search” was the whole of the job: get in the top 10 and win the click at each stage of the funnel. Exhausting work, and it’s no longer enough.
That single approach has been replaced with three modes running in parallel. Search hasn’t gone anywhere. It just has assistive and agential sitting alongside as alternatives for all or part of the journey for each use.
Search tolerates a fuzzy or unclear brand because the human will do the sorting.
Assistive tolerates less of it, because the AI is recommending you to the user, and the AI’s credibility is on the line every time it uses your name.
Agent tolerates none of it, because the agent transacts without asking, and a fuzzy brand is exactly the kind of risk the agent will quietly route around, and the user will never know.
Won looks different in each mode.
In search, won is the click, and then the user finishes the journey on your site, and you need to deal with friction (objections, questions, and clarifications).
In assistive, won is the AI engine naming you and the user accepting the name without further verification, and so most of the friction has already been dealt with.
In agent, won is the transaction completed without anyone consulting the user, friction reduced to zero from the user’s point of view.
The pedal-buying journey sat at the assistive end, and could have been at the agential end. The professional studio musician sits at the search end. The geeky enthusiast sits at the search end too, because for him, the friction is the entertainment.
The delegation boundary moves with every purchase, person, and culture
A user might delegate a coffee order to an agent without thinking, ask an assistive engine for advice on a kitchen renovation but make the final supplier call themselves, then spend an hour happily window-shopping for a bracelet that’s “just what I always wanted.” Same person, same week, three positions on the boundary, and all three engines doing useful work.
The diagram shows that one person sets the delegation boundary at completely different positions depending on the purchase. A wedding venue lives at the far left of search mode, because the decision is emotional and irreversible, and you wouldn’t want AI to make that journey for you.
A few notches to the right, still in search mode, you’ve got someone who’s a sock collector and is window-shopping the listings because the choosing is the point. Further right, still in search mode but inching toward assistive, you’ve got the strategic business contract where only the human holds the strategic context that the AI doesn’t have access to.
Cross over into assistive mode, and you’ve got the holiday I’m planning, where I’m asking the AI for advice but cross-checking through search to challenge the results before I commit. A notch further right, still inside assistive mode, you’ve got the kitchen renovation where I’m taking the AI’s advice on what’s possible, but I’m picking the supplier myself, because the supplier relationship is one I’m going to live with for years.
Cross again into agent mode, and you’ve got the holiday I already know: the agent books the cheapest familiar hotel without consulting me, because I’ve stayed there before and it’ll be fine.
Further right, the pragmatic sock purchase where I just need the right pair in the right size by Tuesday, and the brand doesn’t matter a great deal. And right at the far end, the taxi from A to B, where I genuinely don’t think about it, the agent picks the ride, the agent pays, and I get to my destination with zero fuss or thought.
Same person, eight different positions on the boundary. The variability is the whole AAO game: you have to have a strategy that will win the click at every stage across the Delegation Boundary. The boundary tracks risk, human preference, emotional weight, reversibility, and a half-dozen other things.
You have to wrap that into your strategy on top of the cascading confidence, the 10 gates, the entity home, the push-layer entry modes, the framing gap, and the funnel flip this series has been piling up since February 2026.
7 factors tell you where the delegation boundary sits in your category
Score yourself against seven broad factors (and add your own, if you like):
Emotional weight: The more the purchase touches identity, family, or values, the harder to delegate.
Domain expertise required: The more specialized the decision, the more users either delegate fully (because they know they don’t know) or refuse to delegate (because they think they do).
Price relative to income: A $2 coffee delegates easily, a $20,000 car doesn’t.
Purchase frequency: Habitual purchases delegate readily, one-offs need scrutiny.
Regulatory context: Financial, medical, and legal categories carry compliance constraints.
Cultural context: Trust in agents varies by market and demographic.
Evaluate your category and you get a credible read of where your audience sits, how much friction they’re willing to hand to the engine, and where you need to focus your work. You’ve also done something less obvious, but even more important than scoring: you’ve grouped your audience by behavior at the decision moment rather than by demographic or category labels.
For two decades, we built paid and organic strategies based on how humans see the world: by category, by demographic, by geography. In Google Ads, five-star hotels in Bali went in one ad group, hostels in Bali in another, and five-star hotels in Thailand in a third, because that’s how a marketer thinks.
With AI, the engines stopped thinking that way. For Performance Max and AI Max, you absolutely must group by intent to get performance. Five-star hotels in Bali sit in the same cohort as five-star hotels in Thailand, because the intent (luxury accommodation) holds the cohort, and the geography doesn’t.
Gemini thinks in intent cohorts, not human categories, and the same lesson applies across Organic, ChatGPT, Claude, and other LLMs.
What matters here: AI groups by intent, AI builds the cohorts, and the brand optimizing against the old human structure is competing in categories the engines have left behind.
For years, Google representatives have told us that SEO (or assistive agent optimization, the newer discipline that extends SEO to cover assistive engines and agents) is all about intent, and the point they perhaps didn’t make clear enough is: that’s because it’s how AI thinks.
The user delegates, the engine commits
Here’s the point most brands haven’t seen yet: the user and the engine work in tandem, and your strategy needs to account for that.
Nothing happens without the user’s mandate. The agent doesn’t take over because the assistive engine has decided it’s time. The user pushes the boundary by handing the engine a mandate, and the engine acts within that mandate. The delegation boundary isn’t drifting toward the engine on its own: it is being intentionally moved by the user on a case-by-case basis.
Every engine has been delegated to, and delegation forces commitment. We hand search the job of finding the 10 best links for our query, and Search has to commit to which 10. We hand the assistive engine the job of recommending one brand from a comparison set, and it has to commit to which one. We hand the agent the job of executing the transaction, and it has to commit to which transaction.
Different layers of delegation, different consequences when the commitment is wrong, but the act of commitment is universal across the delegation spectrum, because every engine has been handed a job that ends in an answer (even if that answer is giving us a choice of 10).
The algorithms learn at three levels, and they learn differently across the three engines (search, assistive, and agential):
Individual level: What this specific user has accepted, overridden, ignored, repeated. The narrowest scale, fully personalized. Heaviest in Agent mode (where the agent acts on this specific user’s behalf), significant in Assistive mode, negligible in Search.
Cohort level: What users with this combination of intent signals have accepted on average across thousands of comparable journeys. Wider than the individual, narrower than everyone. Decisive in Assistive mode (the engine needs to know “what works for users like this one”), important in Agent mode as a fallback when individual signal is sparse, lighter in Search.
Global level: What the algorithmic trinity has encoded about the brand from the world’s record. Not user behavior but encoded knowledge, stored in three places simultaneously: the LLM as parameters baked into model weights during training, the search engine as the indexed corpus and ranking signals, and the knowledge graph as entity nodes, relationships, and attributes. Three storage layers, three update cadences, three signal types, all encoding the same fundamental thing: what the AI has come to believe about the brand from everything brands publish and everything others say about them.
Three concentric circles. The user at the center. The cohort around the user. The global world around the cohort. The first two layers are about behavior: what users do with you.
The third layer is about knowledge: what the algorithmic trinity has encoded about you across LLM parameters, search index, and knowledge graph.
The brand has direct leverage at the third layer (everything you publish, everything written about you, everything that becomes part of the corpus the trinity reads from) and indirect leverage at the first two (through the experiences that shape what users do).
Important: The third level is the one that compounds the longest. Every won event Thomann has earned across every customer in every cohort over the last 28 years isn’t just training the cohort signal: it’s feeding the global priors that every future model trained on widely available data will inherit.
Brand confidence work done today (and every “today” past and present) compounds into future models on substrates the brand never directly trains. That’s the deeper structural reason systematic effort compounds in the AI era. Train the substrate, not just the signal.
Why the cohort signal isn’t destiny, and where your opportunity lies
If the engines weighted what they had already learned as the only voices that mattered, no challenger brand could ever break in.
The dominant brands would dominate forever, the individual histories, the cohort patterns, and the global priors would lock in, and AAO would be a closed game where only the incumbents win.
Luckily, that isn’t the reality. What the engine has already learned is heavy, but it isn’t absolute. The brand that establishes a stronger claim than the brands the engine recommends and advocates for can break in, and the layers the brand can move are cohort and global. (The individual layer can be influenced through other communications channels, but is effectively closed in the AI engines because they are increasingly walled gardens.)
The route in is to claim, frame, and prove. Make your claim, frame the claim, and prove it with evidence across enough sources that the engine has the corroboration to move you up.
Underneath that work sits the understandability, credibility, and deliverability process:
Understandability built through clear knowledge of who you are and what you do.
Credibility built through N-E-E-A-T-T.
Deliverability built through topical ownership.
Get all three. Once your confidence clears the threshold:
The cohort signal that currently favors your competitor stops being the only voice the engine hears.
The global layer starts absorbing your contribution alongside the incumbents’.
Your claim becomes framed and proved in a way the engine can resolve and prioritize.
That’s the only way an outsider gets in. It’s also the route every dominant brand took to become dominant in the first place.
What wins at won is confidence
When push comes to shove, what decides the outcome at won is the confidence the algorithm has in your brand at the moment of decision.
Confidence at the moment of:
Ranking in search.
Recommending in assistive.
Action in agent.
The mode, channel, and cohort change, but the question doesn’t.
Content and context are table stakes today. Content is what you’ve published, where, in what structure: every brand doing digital marketing seriously has been doing it for years, since Bill Gates wrote “Content is king” in 1996.
Context is the match between your content and the user’s intent at the moment they’re asking. Both are table stakes, so neither differentiates at the recruitment, grounding, display, and won gates.
Confidence is what’s left, and confidence is what we can measure at the bottom of the funnel. Three things, you measure at the bottom of the funnel brand results (the due diligence rabbit hole):
You measure the accuracy of the results about your brand.
You measure the positive sentiment in the results about your brand.
You measure the consistency of the results about your brand across all of the engines.
How right, how positive, how consistent: that is your measurement of confidence across the AI spectrum.
This is exactly where Thomann won my pedal purchase. ChatGPT was confident enough in Thomann’s information to commit to a Friday delivery on Thomann’s behalf, on a Tuesday, with my Saturday gig riding on it.
Hundreds of European suppliers could, in theory, have gotten those pedals to me by Friday. ChatGPT picked Thomann because they had published the stock data, shipping times, warehouse logistics, country-by-country delivery commitments, returns policy, price, and credibility signals, in the kind of structured, accurate, consistent detail that makes an algorithm willing to put its name behind a recommendation.
Confidence work laid down years before the moment ChatGPT needed it was compounded into a single won event in 15 minutes flat.
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Who’s making the decision at won — and when
Map your intent cohorts. Score each one against the seven factors. Work out which mode each cohort will use to make which decision. Then train the seven AI employees (Google, ChatGPT, Perplexity, Claude, Copilot, Siri, and Alexa) to represent you with positive sentiment, accurate facts, and consistent narrative across every one of those moments, because they’re already working 24/7, they’re already talking to your customers, and the only question left is whether they’re recommending you or your competitor.
Untrained employees cost you money every day they’re untrained. Trained employees generate revenue every day they’re trained. Won is the moment you’ve trained them for, or the moment they’ve handed to your competition.
The next question is how you measure all of this, and it turns out my answer rewrites how brands should think about measuring AI-era search-assistive-agential success entirely. That’s the next article in the series.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/05/Search-according-to-Sergey-Brin-UVHCj5.png?fit=960%2C540&ssl=1540960Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-12 15:00:002026-05-12 15:00:00The delegation boundary: How AI decides which brands win
One of the major reasons PPC practitioners hold onto syntax-oriented keyword strategies is the disconnect between “query intent” and “conversion intent.” For years, you’ve likely relied on keywords to show you understand what your customers want and to prequalify traffic using syntax-oriented signals.
As user behavior shifts to more conversational queries and AI becomes an increasingly relevant part of the user journey, the distinction between these two intents becomes even more critical to understand and act on.
Here, we’ll define query and conversion intent and explore strategies to apply them effectively. This isn’t prescriptive. You should make decisions based on what will serve your business well. However, it provides a framework for analyzing your data and optimizing for the right humans.
Disclosure: I’m a Microsoft employee, and I’ll be sharing some examples that pull from Microsoft tooling. However, most of the strategies reflect platform-agnostic approaches.
What are query and conversion intents?
Query intent is the underlying need driving the text put into a search function. This search function can be on a SERP (search engine results page), video/social/gaming/email/site search bar, or AI surface.
Conversion intent is the human need to achieve some outcome, understood through stated and inferred data points. These range from text entered in various search experiences, content consumed, and tracked actions taken.
Different examples of query and conversion intent will have higher or lower rates of confidence based on how explicit text is, as well as patterns in content consumed.
For example, if I search “Microsoft ads login,” both query and conversion intent are clear — I want to log in. It’s easy to match ads and organic content to that query. Videos shown in any video query would have to do with logging in, and emails would be focused around login information.
Google SERP
Bing’s SERP
YouTube results
The query “Microsoft ads” is more nebulous, as such, needs to draw from other signals like previously engaged content and search history. While I might get a login page, I’d likely also see blog/sales content, third-party advice on Microsoft ads, and potentially competitor info trying to capitalize on the general nature of the query.
Google SERP
Bing SERP
YouTube results
Let’s look at a non-branded example as well. “Purple hair dye” has a clear transactional intent. While the user might not have a brand in mind, they know they want a specific color.
We don’t know if the user is looking for a semi-permanent or permanent color. We also don’t know the user’s pronouns, so matching them to a specific demographic to entice a purchase is a gamble.
Google SERP
Bing SERP
YouTube results
In the query “purple hair dye for long wavy hair,” the transactional intent is maintained. However, the query focuses more on the core needs of the person behind the text. Long, wavy hair means there needs to be enough dye to cover long hair.
Additionally, while some men have long wavy hair, the person behind the query is more likely to identify as female.
Wavy hair has a different composition than straight or curly hair, so products specifically for wavy hair will be more relevant than those without hair type identifiers.
Google SERP
Bing SERP
YouTube results
In all of these examples, there was clear conversion intent. The human behind the query clearly wanted to achieve something. However, if we relied only on the text (i.e., query intent), we might miss a meaningful opportunity to connect with customers.
This is why close variants (which have been available on both Google and Microsoft for ~10 years) represent a useful way to unshackle ourselves from syntax alone.
Additionally, by limiting our understanding of queries to SERPs, we ignore critical insights from where our customers connect, work, and play. Microsoft’s internal data from March 2024 shows that brands that use both Audience ads (display, native, and video) and Search see a 6x conversion rate. Part of this is brand recognition, and the power of brand media buys influencing performance.
Yet there’s also the pragmatic piece that some marketers refuse to engage with video and social. By being where your competitors refuse to be, you can shape and capture desire while they fight over a shrinking share of voice.
Once you understand the difference between query and conversion intent, you can begin mapping out the actions needed to capitalize on both.
Conversion intent is much easier to understand than query intent. This is why AI systems typically run queries in the background to understand human input and get at the conversion intent behind the query.
To succeed at shaping queries and capturing conversions, it’s critical to understand the input points for humans and the AI systems that will be serving them results.
Let’s revisit the “purple hair dye for long wavy hair” query:
Copilot surfaces how it arrived at the output by looking up information and finding the best matches. This is similar to the SEO concept of E-E-A-T.
Yet you’ll notice that the results for my personal Copilot are different than the traditional SERP (chiefly that ads aren’t the dominant result — ads serve at the bottom of clearly transactional conversations after organic listings).
This is where the “Details” function comes into play and can help you know where to focus content, feed, and messaging functions:
This product is pretty flat on price, save for some deep summer dips. If I’m desperate for color, I might buy now, or I might wait for what seems like a regular summer sale. I’m also getting insights into why this product is wonderful (hair conditioning, cruelty-free, vibrant, and customizable color, etc.).
These are things I’ve shown interest in through past purchases, conversations with Copilot, and other signals it has access to.
Brands that want to optimize for query intent need to make sure the following are in good order:
Feed/landing page clarity
It should be incredibly easy to map what the product/service is to the query. While there is value in some 1:1 matching of language, it’s much more important that the core offering be understood as aligned with what the human is looking for.
For example, DUI and DWI are technically two different charges and have geo implications. However, DUI tends to be the universal legal charge and service.
Images adding context
Visual content is critical to engage humans. However, if the image isn’t clear or is duplicative of another service/product page, you might confuse the user and the machine attempting to understand and position you for queries. This is why it’s critical to add alt text (even on paid landing pages) for images and videos.
A good way to test whether your visuals are serving you well is to put the landing page into a PMax campaign creator. If you see the images and they match the correct service text, you’ve done a good job.
Invest time in understanding how humans and AI are querying
Free tools like Google Trends, Microsoft Clarity, and Bing Webmaster offer insights into search trends, citations, grounding queries, and which AI systems and humans are successfully engaging with your content.
Conversion intent is more straightforward, though debatably harder because it requires more creative and critical thinking:
Matching messages to personas
The reason one person says yes to you might be completely different from the reason someone else does. Locking in conversion intent includes being mindful of how you’re selling yourself. If you ignore what matters to your customers in reviews, intake from customer success or sales, and other signals, you risk selling yourself badly and losing the customer.
This is where AI-powered creative and audience mapping can be helpful, since platforms have access to more insights than a brand does during the auction.
Honor the impulse nature of visual content
Someone coming to you from a display spot or short video is very different than someone coming from a text-laden SERP. They were inspired to act and need frictionless paths to conversion.
One-click checkout (including solutions like Copilot Checkout) ensures humans don’t need to think to do business with you.
Ultimately, both query and conversion intent need brand and performance marketing to be successful, and it’s critical to understand how the success metrics manifest.
The converging roles of brand and performance
For a long time, brand and performance marketing were treated as separate motions, with separate owners, budgets, and success metrics.
Brand was about reach, recall, and long-term connection.
Performance was about efficiency, conversion rate, and immediate return.
That separation made sense when channels, measurement, and user journeys were cleaner than they are today. It’s much harder to maintain in an environment where AI systems infer intent continuously and across surfaces.
A user doesn’t experience brand and performance as separate. They experience confidence, familiarity, relevance, and ease. Those signals are created over time through exposure, engagement, and trust, and they often determine whether conversion intent ever materializes, regardless of how “high intent” a query might appear on its own.
From a metrics perspective, this convergence is clear. Brand-oriented activity influences performance outcomes even when it isn’t the final touch. Exposure to display, native, or video doesn’t always produce an immediate click, but it changes how humans and systems interpret future behavior.
When someone later performs a search, engages with an AI assistant, or compares options on a marketplace, prior brand interactions act as accelerators. They reduce hesitation, shorten decision cycles, and increase the likelihood that a conversion signal will be credited downstream.
From a strategy standpoint, this means brand work should no longer be evaluated solely on isolated upper-funnel KPIs, and Performance work can’t be evaluated purely on last-click efficiency.
Audience-based formats, contextual placements, and visual storytelling directly shape conversion intent by shaping preferences and expectations before a query even occurs. Search and shopping formats then serve as capture mechanisms, translating that latent intent into action.
This is particularly relevant in AI-assisted experiences, where systems synthesize multiple inputs before presenting options or recommendations. Content, feeds, reviews, images, and historical engagement all influence how brands are represented and when they appear.
In these environments, strong brand signals don’t compete with performance outcomes. They enable them by making the brand easier to understand, trust, and choose.
Brand and performance don’t need to use the same tactics, but they must be planned together. Measurement frameworks should account for assistive value, not just final interactions.
Creative strategies should recognize that inspiration and conversion often happen at different moments. Optimization should focus less on forcing intent into rigid buckets and more on supporting the full decision journey.
When we recognize that query intent and conversion intent are related but not identical, the convergence of brand and performance becomes less a philosophical debate and more an operational necessity.
Success comes from designing systems that reflect how humans actually decide, not just how they type.
Key takeaways
Query intent describes what is said; conversion intent reflects what the human needs to accomplish. They overlap, but they aren’t interchangeable.
Brand activity shapes conversion intent long before a query is expressed and influences how future interactions are interpreted.
Performance outcomes improve when Brand signals reduce friction, uncertainty, and choice overload.
AI-driven experiences amplify this convergence by relying on cumulative signals rather than single actions.
Sustainable optimization requires aligning brand and performance strategies, metrics, and expectations around the same human outcomes.
In February 2025, the world watched as a small group of humanoid robots took the stage at the CCTV Chinese New Year show for the very first time. It was a charming performance, even if the steps were shaky and the movements were mostly limited to the arms.
Just one year later, at the Spring Festival Gala, the shaky steps were gone and the humanoid robots were able to actually run and do standing somersaults and full kung fu routines with swords and nunchaku. The message was clear: in just one year, we have witnessed a decade’s worth of advancement.
The 10-year leap in technology is real and not limited to robotics. Which raises a critical question for every digital marketer eyeing the world’s largest web population: How has search in China progressed in recent years?
A parallel in the Chinese search landscape
The answer is that we’re witnessing the first, calculated tremors of a massive shift. AI models have not yet replaced traditional search. The evolution isn’t happening through a single “big bang,” but through a constant, iterative pulse.
New LLM models are surfacing every few months, each more specialized than the last. Chinese tech giants are increasingly open-sourcing their models, and even industry leaders are hedging their bets. Baidu, for example, is integrating DeepSeek into its search experience, even as its own Ernie (Wenxin) model remains a formidable powerhouse.
Let’s look at how users actually search in China today — and what this nuanced shift from links to reasoning means for your 2026 SEO strategy.
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The great narrative fallacy: Is web search dead in China?
In many marketing circles, a specific narrative has been repeated so often it has become an article of faith: “Traditional search on Baidu is dead — and has been for years. Websites are obsolete. In China, everything is WeChat.”
This narrative is almost always driven by service providers whose business models depend on WeChat, Douyin, Weibo, or Xiaohongshu marketing. To them, the “open web” is a ghost town. But is this actually true?
The social supremacy argument
There’s a grain of truth in the hype. The Chinese web is a mobile-first multiverse. Users access and explore the web through super-apps:
RedNote (Xiaohongshu / Little Red Book): This is the de facto engine for lifestyle research and travel planning.
Pinduoduo and Douyin: These are the juggernauts of social commerce and impulse buying.
WeChat: The absolute center of daily life, where everything from a quick message to a utility bill payment via QR code happens.
In this environment, social media isn’t just a channel. It’s the air people breathe. For B2C brands, social ads can — and often do — exceed website-driven sales by orders of magnitude.
The B2B reality check
For those of us working with B2B companies that need real visibility in China, the “Baidu is dead” narrative falls apart the moment you look at the analytics. Clients who invest in Baidu SEO and Baidu search engine advertising (SEA) continue to see a steady, high-volume stream of real human visitors — in many cases generating more qualified leads and higher conversion rates than their counterparts in the UK or Germany.
Why? Because when a B2B procurement officer or a technical engineer needs a specific industrial solution, they don’t just scroll until they find it on a social media feed. They search for a verified, authoritative source. In other words, they look for a website.
Is the social media narrative a lie? No. But ignoring a channel that — at least in the B2B sector — remains more effective in China than in many search-first Western countries is simply bad business. The goal isn’t to choose one over the other; it’s to understand how they coexist.
And just as we’ve settled the debate between web marketing versus app marketing, a new challenger — the LLM — has entered the battleground to disrupt both.
Mapping the 2026 landscape: Intent-based specialization
To a Google-first marketer, the idea of searching anywhere but a search engine feels like a detour. In China, it’s the standard operating procedure. Users don’t just “Google it.” Instead, they choose the tool that fits the intent.
As a Baidu specialist living and working in China, I see this daily. While I might be optimizing a B2B landing page for Baidu, my wife is likely on Pinduoduo, finding household deals, or on Xiaohongshu, planning our next weekend trip.
The “everything app” exists, but the “right app” always wins the click.
1. Traditional web search: The authority tier
Despite the “death of the web” narrative, traditional web search remains the primary battleground for B2B and high-authority research. If a user needs a technical whitepaper, a government regulation, or a verified corporate headquarters, they go here.
Baidu: Still the mobile heavyweight, with a ~70% mobile market share. Its structural advantage is massive: The Baidu app is installed on over 724 million monthly active devices (as of early 2026). It has evolved into an AI-first portal, but for SEOs, it remains the place where the open web lives and breathes.
Microsoft Bing: The professional’s sanctuary. It has claimed a massive chunk of desktop search for those seeking a cleaner, international, or technical experience.
Haosou (360 Search): The enterprise default, often pre-installed on corporate PCs and known for its security focus.
Sogou: Deeply integrated with WeChat, it’s the bridge between the walled garden and the web.
Google: Yes, Google. Despite the firewall, a significant population of tech-savvy professionals and researchers use it via VPN for global technical data and academic resources.
2. Social discovery: The inspiration tier
This is where search becomes discovery. Users don’t always have a keyword, but they do have an interest. In this context, SEO is about social indexing: ensuring your brand appears when a user looks for proof and not just products.
WeChat (Weixin): The internal search for official brand news and private traffic.
Xiaohongshu (RED): The ultimate product-discovery engine. If you aren’t on RED, you don’t exist in the lifestyle or luxury sectors.
Douyin: Visual, video-first search. Users search Douyin to see how something works.
Kuaishou: The powerhouse for lower-tier cities and raw, authentic grassroots content.
Weibo: Real-time search — what is happening right now in the public eye.
Bilibili: Long-form video search for deep dives, tutorials, and Gen Z subcultures.
3. Ecommerce: The transactional tier
In the West, users often start on Google and end on Amazon. In China, the journey frequently starts and ends in the same place.
Taobao / Tmall: The grand bazaar. If you want variety and brand stores, this is the first stop.
JD.com: The Amazon of China for logistics and high-end electronics.
Pinduoduo: The favorite for daily essentials and group-buy deals. Its search logic is entirely driven by value for money.
Douyin Mall: The rising star of “impulse search,” merging entertainment with immediate checkout.
Xianyu (Goofish): The go-to for the thriving second-hand market and hobbyist niches.
4. Generative AI (LLMs): The reasoning tier
This is the newest layer of the map — the “thinking” search. These AI models don’t just produce lists of links. They are assistants that synthesize the web for the user.
Doubao (ByteDance): Currently the most popular consumer AI assistant, used for casual, conversational queries.
DeepSeek (Domestic): The choice for developers and those in need of “deep thinking” logic. It’s the engine currently getting tested inside WeChat’s search bar.
Kimi (Moonshot AI): The king of long-context. Users use Kimi to search through 50-page PDFs or complex financial reports.
Qwen (Alibaba): Powerfully integrated into the Alibaba ecosystem for business and coding tasks.
Tencent Yuanbao: The “AI brain” for WeChat content.
Wen Xiaoyan (Baidu): The AI-facing evolution of Baidu search.
5. Hyper-local and logistics: The utility tier
For the physical world, search is about “now” and “near me.”
Meituan / Dianping: If you’re hungry or want to see a movie, you don’t use Baidu. You use Dianping for reviews and Meituan for transactions.
Amap (Gaode) / Baidu Maps: The “search engines of the real world.” SEO on these platforms is purely about point-of-interest (POI) optimization.
Ctrip (Trip.com) / Railway 12306: The specialized gates for the massive domestic travel market.
From mapping to maneuvering: The Baidu specialist’s edge
Baidu SEO isn’t dead; your website just isn’t the sole focus of web search anymore.
The ‘walled garden’ SERP: A decade of distraction
If you’re a Google-centric SEO, there are some notable differences when working with Baidu:
The ad-heavy layout: It isn’t uncommon to see ads claiming the top, middle, and bottom of a Baidu search engine results page (SERP), occupying nearly 50% of the visible real estate.
The Baidu monopoly: The most coveted organic positions are almost always reserved for Baidu’s own properties. Baidu Baike (the encyclopedia), Baidu Zhidao (the Q&A hub), and Baijiahao (the news/blogging arm) are the permanent residents of Page 1.
The portal giants: High-authority giants like Zhihu (China’s Quora), Bilibili, and Sohu take up whatever space is left.
Riding the Chinese SERP dragon
In this environment, ranking a corporate homepage for a high-volume keyword is a fool’s errand. Instead, we’ve mastered the art of the “long-tail dragon.”
In the West, we talk about the long tail of search as a small, niche opportunity. In China, with its linguistic complexity and massive user base, the long tail is a winding, multi-layered beast that is often more lucrative than the head terms.
And we don’t just rank a website; we piggyback on the authority of the platforms Baidu already trusts. If you can’t beat Baidu Baike, you become the verified entry inside it.
Interestingly, it is these very platforms — the ones we’ve been using to bypass the “blue link problem” — that have now become the primary focus of the next generation of search.
What is changing in Baidu SEO?
In China, there is no brand loyalty toward particular AI models, as Westerners have toward platforms like ChatGPT and Claude.
The AI-switching reality
Chinese users are restless. They don’t stick with one model. They switch — sometimes because a hyped model hits a downtime wall, and sometimes because a new model claims the throne of the “most intelligent AI.” In this cycle of competition and user preference, an SEO can’t just focus on the “big sources.”
If you’re following the Western playbook, you’re likely chasing Reddit, Quora, and YouTube as your “sources of truth” for AI training. But in China, that focus is dangerously narrow. To win the reasoning battle, you must understand the investor-source connection.
Brainstorming the wisdom platforms
If you want to train AIs to see your brand in China, you have to look at the platforms they were built on:
Tencent is invested in Sogou. In 2021, Tencent fully privatized Sogou. This means Sogou Baike is no longer just a Baidu alternative — it is now a core training set for Tencent’s Yuanbao. If you ignore Sogou Baike, you’re invisible to the AI search bar inside WeChat.
Bytedance owns Baike.com. Bytedance bought Baike.com (formerly Hudong Baike) specifically to fuel its search ambitions. If you want to get cited by Doubao, your content needs to be mirrored here and not just on Baidu.
The neutral giants: Keep an eye on Zhihu. Because both Tencent and Baidu are heavy investors in Zhihu, it remains one of the few neutral high-authority sources that almost every Chinese LLM uses for opinionated or expert reasoning.
The new SEO commandment
We’re no longer just optimizing for a search engine. We’re optimizing for a data pedigree.
If your client is B2B, you might still prioritize the Baidu ecosystem. But if your client is in ecommerce and you aren’t feeding the Qwen engine via Alibaba’s ecosystem, or the Doubao engine via Baike.com, you’re limiting your visibility across key AI systems.
The 2026 China SEO/GEO blueprint: From keywords to semantic saturation
If you’re waiting for a “DeepSeek optimization checklist” or a “Doubao ranking guide,” you’ve already missed the point. Because users switch models as often as they switch takeout apps, you can’t afford to be “Baidu-only” or “WeChat-centric.”
Here is what’s actually working for SEO in China in 2026:
Optimize for citations and not just clicks
While SEO in the West is focused on generative engine optimization (GEO), in China, it’s all about fact density.
The logic: When Kimi or DeepSeek performs a reasoning query, the AI looks for verifiable facts.
The tactic: Stop writing marketing fluff. Start using the inverted pyramid writing style. Lead with a direct, data-backed answer in your first paragraph. Use hard statistics, expert quotes, and structured lists. If a model can’t extract a fact from your content in 200 milliseconds, it might hallucinate a competitor’s data instead.
Build an entity moat across wisdom platforms
As we brainstormed earlier, every AI has a “parent” with a preferred data source. But since models are now open-sourcing their weights and distilling each other’s intelligence, your brand must achieve entity consistency.
The goal: Your brand name, headquarters, and core product claims must be identical across Baidu Baike (Baidu), Sogou Baike (Tencent), and Baike.com (ByteDance).
The result: When these models cross-check their reasoning, they find a consensus. In 2026, consensus is the new authority.
Leverage information gain
Chinese AI models have a well-observed recency bias — they prefer sources that are roughly 25% fresher than traditional search results.
The tactic: Don’t just regurgitate what’s already on Zhihu. Provide a “unique data slice.” If everyone says “The best time to post on Douyin is 6 PM,” and you publish a case study proving “11 AM is better for B2B industrial leads,” the AI will cite you as the “nuanced exception.” That citation is worth more than ten #1 rankings.
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The era of the entity architect
We’ve come a long way from the shaky steps of the 2025 CCTV Gala.
In 2026, China’s search ecosystem is no longer a directory of links. It’s a living, reasoning entity.
For the Western search specialist, the lesson is clear: The “super app” was a distraction. The real story is the fragmentation of intent.
My wife still goes to Pinduoduo for the best price. My colleagues still go to Bing for technical sanctuary. And the “I, Robot” enthusiasts of 2026 are using a rotating door of LLMs to find their answers.
As a Baidu specialist, my job has shifted from “ranking a website” to “architecting an entity.” We no longer build for the bot; we build for the source. If you’re the undeniable source of truth across the platforms that shape China’s information ecosystem, it doesn’t matter which model delivers the answer.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-05 12:00:002026-05-05 12:00:00How China’s fragmented search ecosystem is reshaping SEO in 2026
In February 2024, Gartner predicted that traditional search volume would drop 25% by 2026. It didn’t. Google’s search revenue accelerated to 17% year-over-year growth, crossing $63 billion in Q4 2025 alone. But clicks per search are falling while query volume explodes. The pie got bigger. The slices got redistributed. And most search teams are still optimizing for the old pie.
Are you still poring over spreadsheets full of organic keyword rankings like it’s 2003? Your customers don’t care where they’re getting their answers. They’re just looking for answers they can trust. And they’re finding those answers across more surfaces than your rank tracker knows exist.
If your organic strategy lives in one spreadsheet, your paid strategy in another, and your AI search strategy in a third (or nowhere), you’re optimizing for a search experience that no longer exists.
What “search” actually looks like now
Google “best tax software” right now. Go ahead, I’ll wait.
Count the surfaces on that single results page. Sponsored ads across the top. An AI Overview with its own recommendations and citations. A Reddit thread (because Google knows people trust other people more than brands). Organic listings from CNET, H&R Block, and others. A video carousel. Discussion forum links. A product carousel with images and prices. More sponsored results at the bottom. And a “People also search for” section feeding the next query.
That is one search. One keyword. And nobody owns it.
Now think about how different people actually use that page. I scroll past everything to find the Reddit thread, because I want to know what real humans recommend. My dad clicks the first sponsored ad because he doesn’t understand paid advertising (sorry, dad!) and just trusts Google to surface the best option up top. Someone else reads the AI Overview, gets a good-enough answer, and never clicks anything at all. A fourth person watches the Smart Family Money video and leaves.
Same query. Four completely different paths. Four different “winners.” And if you’re the brand celebrating a number-three organic ranking on this page, you may be missing that most of the real estate, and most of the user attention, lives somewhere other than those blue links.
This is what I mean by the total SERP experience. Your customer sees the whole page. You should too.
But before the panic sets in: AI tools still account for less than 1% of U.S. web traffic. Google sends 300x more referral traffic than all AI platforms combined. The sky isn’t falling, but the ground is shifting.
The shift that matters most is behavioral. Wynter’s 2026 research found 68% of B2B buyers now start their research in AI tools before they ever open Google. They ask ChatGPT to narrow the field, then Google the shortlist to validate. AI evaluates, Google verifies, and your website converts. If your brand is missing from that first AI conversation, you’re not even on the shortlist when the Googling starts.
Why the click data is more interesting than scary
A Search Engine Land analysis of 25 million organic impressions across 42 clients found organic CTR drops 61% when an AI Overview appears. In addition, paid CTR drops 68%.
EVERYBODY FREAK OUT!!! Right? Not quite.
Here’s what the panicked LinkedIn posts leave out: brands cited inside AI Overviews see 35% more organic clicks and 91% more paid clicks. Being in the AI Overview doesn’t cannibalize your traffic. If anything, it amplifies it. The AI Overview functions like a trust signal, a stamp of “this brand is relevant to your question” that makes people more likely to click your listing below.
The real twist, though, is that ranking well in organic doesn’t guarantee you show up in AI. Tom Capper’s research at Moz found 88% of AI Mode citations are NOT in the organic SERP for the same query. Organic and AI are pulling from different source pools. You can be number one in Google and completely invisible in ChatGPT’s answer to the same question.
And the small amount of traffic that does come from AI? It converts at more than quadruple the rate of organic, according to Semrush. These visitors arrive more informed, more intentional, and more ready to buy. Which makes sense, because they’ve already done the evaluation inside the AI interface. By the time they click, they’re just confirming and often converting.
The org chart is the problem
Most companies have SEO reporting to content, PPC reporting to demand gen, and AI search reporting to nobody. BrightEdge found 54% of organizations have handed AI search to the SEO team alone, which is a little like asking your plumber to also handle the electrical work because, hey, it’s all in the same house.
The waste from this setup is real. One branded Performance Max campaign paid roughly $500,000 for clicks that would have come through organic anyway. Google’s own research confirms: when you rank number one organically, only half your paid clicks are truly incremental. The other half? You bought what you already owned.
Meanwhile, McKinsey found that a brand’s own website makes up only 5% to 10% of the sources AI references. AI pulls from Reddit, review sites, affiliates, publishers, and user-generated content. You can have the best SEO program in your category and be completely absent from AI search results because AI is reading what other people say about you, not what you say about yourself.
The unified approach works. Level cut acquisition costs 18% and boosted SEO leads 22% by merging paid and organic for a B2B SaaS client. And we can use tools in our Level Intelligence Suite to connect performance signals across search surfaces. The channels compound each other. Treating them as separate line items on separate P&Ls leaves that compounding on the table.
Three audits you can run Monday morning
You don’t need a six-month transformation to start seeing the gaps. Three lenses, applied to your top 20 keywords, will show you where the opportunities and the waste are hiding.
Lens 1: Where do you actually appear? Check your organic rankings, paid ad coverage, and AI visibility across ChatGPT, Perplexity, and Gemini for the same set of keywords. Semrush has a free AI visibility checker. Most teams have never looked at all three surfaces side by side, and the gaps are almost always larger than they expect.
Lens 2: Where are you paying for traffic you already own? Cross-reference your number-one organic rankings with active PPC bids on the same terms. Start with branded keywords, where the waste is usually largest and the test is cleanest. If you rank first and you’re still bidding, you’re probably buying your own clicks.
Lens 3: Where is AI ignoring you? Compare your organic rankings with your AI citation presence. Only 11% of domains get cited by both ChatGPT and Perplexity, so strength in one guarantees nothing in the other. And check your robots.txt while you’re at it. If you’re blocking AI crawlers like OAI-SearchBot or PerplexityBot, you’ve pulled yourself off those shelves entirely.
This diagnostic shows you the full picture. What to do about it, the actual unification framework, is what I’m laying out at SMX Advanced.
The window won’t stay open
Generative Engine Optimization (GEO) keyword difficulty currently averages 15 to 20, compared to 45 to 60 for equivalent SEO terms. That gap will close. Once an LLM selects a trusted source, it reinforces that choice across related prompts. The brands getting cited now are training the models to keep citing them. Winner-takes-most dynamics are being baked into the weights.
Many companies are seeing search traffic drop significantly. Those same brands, the ones that get it right, are seeing the inverse when it comes to business growth. Rankings and revenue have decoupled. The brands that win from here are the ones that stopped measuring channels in isolation and started measuring the search experience their customers actually have.
We’re presenting a search unification framework at SMX Advanced in our session, “Organic, paid, and AI search: one strategy to rule them all.” If you want to stop optimizing for three separate channels and start compounding performance across every search surface, join us for the session or come find the Level team at Booth #9.
Remember: The search experience that existed in 2023 is gone. The strategy should be too.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-05 11:00:002026-05-05 11:00:00Unifying the search experience for real growth in 2026 by Level Agency
Your analytics dashboard tracks clicks, but it doesn’t convey the complete picture.
When a buyer reads an AI answer that mentions your competitor, or scrolls through a Reddit thread where your brand doesn’t appear, that’s lost visibility. And it won’t show up anywhere in your traffic data.
Share of voice (SoV) captures what traffic metrics can’t.
It measures your brand’s visibility against competitors across channels where buyers actually research and make decisions.
While SoV spans social, PR, and paid media, search is where most brands should start. It’s the channel where buyers with the strongest purchase intent show up, and it’s the easiest to measure competitively. That’s what this guide focuses on.
I’ll walk you through four steps to measure your share of voice in organic and AI search. Then, I’ll show you how to turn that data into decisions that move the needle where it matters.
What Is Share of Voice?
Share of voice measures your brand’s visibility relative to competitors across multiple marketing channels.
That includes organic and AI search, social media, review sites, communities, and more.
Traditionally, brands used SoV to track their share of ad spend in a market.
Now it’s evolved into something even more valuable. It can measure your brand’s presence across every touchpoint where buyers research and make decisions.
In simple terms: SoV tells you what percentage of the conversation you own in your category, compared to competitors.
This guide focuses on search SoV — both organic and AI — because that’s where buyer discovery is shifting fastest and where the measurement tools have matured enough to give you actionable data.
I find that search SoV also tends to be the foundation: once you understand your visibility in organic and AI results, layering in other channels becomes much simpler.
What Counts as a “Good” Share of Voice?
While there’s no universal benchmark for SoV, establishing one for your brand comes down to:
Market position: Market leaders have a higher share of voice since they own the conversation. Challengers aim for a mid-range SoV when competing against players with decades of brand equity.
Competitive context: In a fragmented market with 20+ active competitors, 8% SoV could put you in the top five. But in a three-player market, anything below 30% could mean you’re behind the leader.
Beyond these two factors, look at the broader market shifts within your category.
High SoV in a declining market can be a vanity metric. The real win is growing your share as the category grows.
How SoV Works in Traditional vs AI Search
Both SEO and AI SoV answer the same question: What percentage of category demand does your brand own?
You track 100 target keywords. Those keywords generate 50,000 total monthly visits across all ranking sites. You capture 15,000 of those visits.
That’s 30% organic share of voice.
AI SoV measures brand mentions in LLM responses from ChatGPT, Perplexity, Google AI Mode, and similar tools.
For example, you test 100 category-related prompts. Your brand is mentioned in 45 responses and cited in 15. Your competitor shows up in 30 responses with 10 mentions.
An AI visibility tool can calculate your weighted AI SoV based on mentions and citations.
Try now: Curious to know how often your brand shows up in AI responses? Try our free AI visibility checker to find out.
Why Is Share of Voice So Important, Especially Now?
Here are three reasons why share of voice should be your core KPI when visibility is scattered across platforms.
And with zero-click searches on the rise, that half is shrinking fast.
When users get their answers directly from AI Overviews and featured snippets, a huge chunk of your visibility is never captured in Google Analytics.
This makes traffic a lagging indicator of visibility.
Share of voice is a better metric because it measures how visible you are in the consideration set, even when users don’t click your site.
Think of it this way:
A user searches for the “best project management software for remote teams.”
They see an AI Overview listing five tools, including yours. The user reads it, takes no action, and later signs up for a product demo on your site.
Traditional traffic data would show this as “direct traffic” since the person went straight to the website. It wouldn’t capture the discovery that occurred in Google.
But SoV reveals that your brand appeared in the consideration set for this high-intent query.
Work Toward One North Star Metric
Your marketing team might be operating in silos.
The SEO team wants more website visits. PR wants more media mentions. The social team wants better engagement.
Each team tracks its own KPIs and optimizes for different outcomes.
But the long-term power of SoV is that it can become the one metric every team rallies around.
When everyone sees how their work contributes to the same visibility percentage, it changes how teams collaborate.
Here’s what this looks like in practice:
SEO team targets specific keywords to boost traffic and visibility via content
PR secures features in industry publications through expert quotes
Social drives brand conversations on Reddit and LinkedIn
Product wins better reviews on G2 and Capterra
This full picture takes time to build.
Start with the foundation by measuring your SoV in organic and AI search.
Once you have that baseline, you can layer in other channels over time.
Let’s see how you can strategically calculate share of voice in four steps.
I’ll use a fictional project management software example to show how each step translates into business insights.
Step 1: Define Your Industry Landscape
Start by outlining the specific competitors and keywords you’ll track for SoV.
Without clear boundaries, you’ll either miss critical gaps or drown in too much noise.
To map your competitive terrain, pick topic clusters tied to revenue.
For a project management software, I picked these clusters:
Category fundamentals (like “project management 101” and “project management for freelancers”)
Use cases (like “agile project management” and “remote team collaboration”)
Industry-specific (like “construction project management” and “marketing project management”)
Pro tip: Don’t pick these topics solely based on search volume. Choose clusters where gaining visibility directly impacts your bottom line.
One way to assess a topic’s revenue potential is to map it to funnel stages.
Categorize your clusters into three stages:
Awareness: Where people are learning and researching, like how to manage projects
Consideration: Where they’re exploring solutions, like the best project management software
Decision: Where they’re comparing options and ready to buy, like Software A vs Software B
Your SoV at each stage tells you where you’re winning and losing in the buyer journey.
This allows you to allocate resources for maximum business impact.
Let’s say this project management software segments the SoV by funnel stage.
It reveals that most of the brand’s visibility is concentrated at the top with almost none at the decision stage.
That’s a problem.
They’re educating the market, but invisible when prospects are actually comparing options and reaching for their wallets.
Strategic takeaway: They need to prioritize comparison pages and case studies to shift visibility toward the decision stage.
Now, define who you’re measuring against.
In search, you’re competing for visibility against two key players:
Direct competitors: Companies selling similar solutions like Asana, ClickUp, Notion, and Trello
Indirect competitors: Review sites capturing the voice of the customer like G2 and industry publishers ranking for your keywords but not competing for customers like HubSpot and Zoho
Tracking them gives you the complete picture of who controls visibility in your market and where you can break through.
Step 2: Build Your Keyword & Prompt Libraries
Create a library of 200-500 queries that capture how people search in your category.
You need both keywords (what people search) and prompts (what people ask LLMs). Together, they reveal your search visibility spectrum.
Pull SEO Data First
Collect queries where you’re already visible to your audience.
Google Search Console (GSC) is a good starting point for this since it captures actual visibility through impressions.
Impressions show every time your brand appears in results, even when users don’t click.
Go to the “Queries” tab in the “Performance” report.
Click the “Impressions” column header to sort in descending order, and export this list of keywords.
And if you’re running Google Ads, export your PPC keyword list and filter for terms with conversions or high CTR.
You can also repeat this process with tools like Semrush.
Scroll down to the “Top Keywords” section and click the “View all” button.
Adjust the timeline to your preferred range before clicking “Export” to download the full keyword list.
Pro tip: Export all tracked keywords, not just the top money terms. A keyword with 20 monthly searches might seem irrelevant in isolation. But 50 of these collectively represent meaningful category visibility that SoV captures.
Layer in Competitor Intelligence
Besides your own data, track where competitors show up.
This tells you where to compete directly and where to claim ground that they’ve overlooked.
After sourcing keywords, look at how people search for your category in AI tools.
Since AI search queries tend to be more conversational, they often mirror how people talk in community spaces.
Browse Reddit, Facebook groups, and Slack communities to see how your audience phrases their needs and pain points.
For example, this post reveals that agencies want project management tools that aren’t “too corporate or complex for creative teams.”
A question like that can translate directly into an AI prompt: “What’s the most user-friendly project management tool for small creative agencies?”
For decision-stage prompts, review sites G2 and Capterra (or those relevant to your industry) offer a lot of insights.
G2, for instance, lists popular alternatives for every tool.
This is a ready-made list of “[You] vs [Competitor]” and “alternative to [Competitor]” queries your buyers are likely running in AI search.
You can dig deeper with Semrush AI Visibility Toolkit to find prompts where competitors show up in AI answers, but you don’t.
Go to “Prompt Research” and add any of your core topics, like “agile project management.”
Click “Analyze” to get started.
The tool lists real prompts that generate AI responses for your category, such as “best productivity app” and “companies that use agile software development.”
Jot down the prompts relevant to your primary cluster.
Then, repeat for each of your 3-5 clusters.
Document Your Metadata
Finally, organize everything in a master spreadsheet with columns for:
Keyword/Prompt
Topic Cluster
Funnel Stage
Source (SEO/AI)
Once you’re done measuring SoV, this metadata will become your strategic lens.
Use it to decide which clusters to prioritize, which funnel stages are weak, and where SEO and AI visibility diverge.
Here’s what this looks like for the project management software:
Step 3: Calculate Your SoV
Your SoV equals your estimated traffic divided by the total traffic for all tracked brands, multiplied by 100.
Track both SEO and AI SoV to see the full picture of your brand’s visibility.
Calculate SEO Share of Voice
Start by checking your rankings for all the keywords in your tracking list. Track your competitors’ rankings for the same keyword set.
Each ranking position gets an average share of clicks, like position 1 getting roughly 27%.
This will help in estimating the traffic share per keyword.
Note: These benchmarks for organic search CTR shift over time. It’s also crucial to mention that organic CTRs have been declining as AI-generated answers absorb more clicks before users ever reach the results.
Multiply each keyword’s monthly search volume by the click-through rate for your ranking position to estimate your traffic for that duration.
Then, run the same calculation for each competitor.
Use this data to calculate your SoV.
Add up the estimated traffic across all keywords for each brand. Divide your total by the combined total for all tracked brands and multiply by 100.
This manual approach can be time-intensive, especially when tracking hundreds of keywords across multiple competitors.
Semrush handles this math automatically once you set up tracking correctly.
Enter your domain, target search engine, device type, and location.
The location setting matters for SoV tracking because search results vary by location.
If you set the location to the United States, but most of your customers are in New York, your SoV might look different than reality.
Pro tip: Start with country-level tracking to establish your baseline. Only segment by region later if local variations impact your business.
Then, click “Continue to Keywords” to manually add or import your keyword list.
Upload the CSV you made in Step 2 to preserve the data by cluster and funnel-stage categorization.
Then, press “Add keywords to campaign.”
Finally, click “Start Tracking” to begin data collection.
Once this setup is complete, Semrush starts collecting daily ranking data for every target keyword.
Check out the results in the “Share of Voice” tab under “Overview” in the Position Tracking dashboard.
You can also add up to four domains to see how you fare against others in the market.
Semrush tracks every brand’s rankings for your keyword set to aggregate the data into SoV percentages.
Important: While SoV is inherently relative and compares your visibility against others, who you choose as competitors shapes how you interpret your SoV.
Calculate AI Share of Voice
Your AI SoV shows how often LLMs cite your brand when answering questions in your category.
There’s no standardized way to manually measure AI SoV yet, but this two-step process gets you close:
Step 1: Run each prompt from your library through your AI tools of choice, such as ChatGPT, Claude, Google AI Mode, and any other AI tools your audience uses
Step 2: For each response, document every brand that appears — yours and your tracked competitors. Record whether each brand was mentioned, cited as a source, and whether the sentiment was positive, neutral, or negative.
Once you’ve tested all prompts, count how many times each brand appeared across all responses.
Divide each brand’s total mentions by the total number of prompts tested, and multiply by 100.
Keep in mind: This calculation gives you a directional read instead of a live metric. AI responses vary by session, phrasing, location, and platform. That’s why it’s important to test regularly and track trends over time.
Measuring AI SoV manually for 20 prompts across three platforms is doable. Doing it for hundreds of prompts while tracking how recommendations shift week over week isn’t.
That’s what Semrush’s AI Visibility Toolkit is built for.
Go to the Brand Performance report in Semrush’s AI Visibility Toolkit.
Enter your domain and click “Analyze.”
Pick an AI platform between ChatGPT, Google AI Mode, or Perplexity.
Switch among these tools to identify any significant gaps in platform-specific LLM visibility.
Once the report is generated, you’ll see a pie chart visualizing the distribution of SoV for your competitors.
The tool tests hundreds of prompts related to your category across ChatGPT, Google AI Mode, and Perplexity to measure your AI SoV.
For each prompt, it analyzes AI responses for:
Brand mentions: How often your brand appears in the answer
Citations: Whether the AI links to your content as a source
Context: Whether mentions are positive, neutral, or negative
It aggregates this data across all tested prompts to calculate your percentage of total visibility.
You’ll also find a section comparing each competitor against a set of business drivers specific to your industry.
These drivers are the most frequently mentioned topics for your category.
Use this data to identify clusters where you’re stronger and weaker than your competitors.
Interpreting SEO vs AI Share of Voice
SEO share of voice measures organic traffic while AI share of voice tracks LLM mentions and citations.
These might not always align.
You can have a strong organic share of voice (ranking on top for many keywords) but a weak AI SoV if LLMs don’t find your content credible.
And brands with more credible content can win a bigger slice of AI SoV even without much visibility in organic search.
Maintain content freshness and expand into adjacent topics to defend your position.
You rank well, but LLMs don’t cite you.
Implement content chunking to optimize your content for AI search and create citable assets to create credibility that LLMs value.
Low SEO SoV
AI tools cite your content even though you don’t rank at the top on organic search.
Improve SEO fundamentals, including title tags, internal linking, site speed, and keyword optimization.
Focus on depth over breadth.
Create a definitive, well-researched content resource for every core cluster. This is a good start for building visibility on both traditional and AI search.
Dig deeper: Learn more about building visibility in AI search with LLM seeding.
Step 4: Establish Your Baseline and Track Trends
The final step is turning your SoV numbers into an ongoing tracking system that informs decisions.
Create a baseline dashboard to capture three levels of detail:
Overall metrics: Are you gaining or losing ground overall?
Topic cluster performance: Which topics need more investment?
Funnel stage breakdown: Where in the buyer journey are you least visible?
Here’s what this could look like for the project management software:
Once your baseline is locked in, set your tracking cadence strategically.
A monthly frequency allows you to spot trends without the need for reacting to noise.
With quarterly deep dives, you can:
Analyze cluster-specific performance in detail
Correlate SoV changes with past campaigns
Adjust resource allocation based on what’s working
This rhythm prevents you from chasing short-term variations and missing critical shifts that impact your category.
Pro tip: Set up notifications in Semrush Position Tracking to get real-time alerts. You’re notified when SoV drops more than a certain threshold in any core cluster.
How to Improve Share of Voice
Not every fluctuation in your SoV requires action.
Here’s how to strategically diagnose gaps in your SoV and prioritize the right tactics to fix them.
1. Close Visibility Gaps
Clusters with <10% SoV mean you’re almost invisible.
This is especially damaging in decision-stage queries.
If you have less than 10% visibility when buyers search “best project management software,” you’re not in their consideration set.
At the same time, look for opportunities where competitors dominate, but you can compete.
For example, if your project management tool serves creative agencies but you have zero visibility for “project management for creative teams,” that’s your opening.
Potential Solutions
Diagnose the cause:
Search your weak clusters and compare what ranks against what you have
Check if you lack topic coverage, content depth, or basic optimization
Look at which competitors dominate and what formats they use
Build topical authority for major business themes.
Create one pillar page with multiple supporting articles.
Build backlinks to your pillar content to establish visibility across every query in that cluster.
For example, if we learn that the project management software needs to gain decision-stage visibility, we could prioritize comparison content.
Build pages targeting “[Your Brand] vs [Competitor]” and category buyer’s guides.
2. Solve Efficiency Problems
Compare your SoV to actual traffic.
A cluster like “what is project management” might give you a high SoV.
But if only 1% of that traffic converts, you’re likely burning money on the wrong audience.
You’re winning visibility in areas that don’t drive business outcomes. And competitors are capturing high-intent buyers.
Potential Solutions
Diagnose the cause:
Check if you’re ranking for awareness content when you need decision-stage visibility
Look at your traffic-to-conversion ratio by cluster
Identify if your content attracts the wrong audience (students vs. buyers)
Reallocate resources to high-intent clusters.
Instead of producing more awareness content, shift the budget to bottom-of-funnel content.
This includes comparison pages, case studies, and ROI calculators that target buyers ready to evaluate solutions.
Update existing comparison pages with current data and competitive intelligence.
3. Address Competitive Threats
Keep tabs on competitors gaining ground in your strong clusters.
If a competitor gains over 5% SoV in your strong clusters, it’s an early sign that they’re targeting your territory.
That gap can widen unless you respond to maintain your market share.
Diagnose the cause:
Analyze what new content or tactics they launched
Check if they’re winning on review sites, community platforms, or organic search
Identify if they’re capturing a format you’re missing (video, podcasts, tools)
The fix depends on where your competitors are winning.
If competitors actively feature on review sites, optimize your profiles. Run campaigns to source reviews from happy customers.
If they’re visible on community platforms, proactively engage in communities like Reddit and Slack.
Prioritize Based on Effort vs. Impact
Not all gaps matter equally.
Focus on opportunities that will actually move your revenue pipeline.
Start with high-impact, low-effort wins. Then invest in high-effort moves that compound over time.
High Impact
Low Impact
Low Effort
Optimize content ranking #5-10
Claim existing review site profiles
Update comparison pages with current data
Claim industry directory profiles
Minor content refreshes on supporting pages
Social engagement in established channels
Guest commenting on industry blogs
Newsletter mentions in partner publications
High Effort
Build authority in community spaces (Reddit, forums)
Create comprehensive hub content for weak clusters
Earn citations from AI-referenced sources
Develop thought leadership for industry publications
Content for saturated topics without authority
Channels where your audience isn’t active
Platforms AI tools rarely reference
Keywords outside category relevance
Making SoV Your 2026 North Star
Share of voice captures how often you show up across the fragmented platforms where buyers make decisions.
Get started by measuring your current SoV across SEO and AI search with the steps in this guide.
Pick the gap that costs you the most revenue, and strategize the best ways to close it.
Next step: Build your AI optimization gameplan to capture visibility in the fastest-growing search channel.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-30 19:59:142026-04-30 19:59:14How to Calculate Share of Voice (+ Why it Matters for SEO)
Content agencies often specialize in certain industries or subsets of content marketing, such as technical SEO or conversion-focused content.
Our list of some of the best content marketing brands in the business covers a range of services and specialties.
Check out their client lists and portfolios to see if their work aligns with your expectations and preferences.
Knowing what to look for in a content marketing company and the right questions to ask can help you identify the ones with the abilities and capacity to help you expand and improve your content strategy and reach your marketing goals.
The content world is changing, but people still know its value. A 2023 survey from the Content Marketing Institute showed that over three-fourths of marketers indicated that content marketing generates demand and leads.
This is no surprise when you realize 70 percent of people would prefer to learn about a company through an article rather than advertising.
Content marketing can generate huge amounts of traffic, leads, and sales for your business. If you’re a company looking to get started with content marketing, it can be tough to find the resources and expertise you need.
What kind of content do your customers want from you? Is that the same kind of content that creates revenue for your business? Today we’ll take a look at the best content marketing companies in the industry to help you answer those questions and more.
Agency
Best For
Ideal For
Notable Clients
Standout Approach
NP Digital
Immediate and consistent revenue growth
Broad (B2B, e-commerce, SaaS, finance)
CNN, Adobe, Western Union, SoFi
Revenue-focused content with technical SEO built into every campaign from the start
Seer Interactive
Big data search and content
Competitive industries like finance, banking, and mortgages
Asos, Intuit, SendGrid, Terminix
12,000GB proprietary data warehouse surfaces hidden customer trends competitors can’t see
Brainlabs
Technical SEO
Not specified
Formula 1, Estée Lauder, Capital One, Polaroid
Built on a team of mathematicians, scientists, and programmers driving data-backed automation and testing
Fractl
In-depth, research-heavy content
Research-intensive industries
Porch, Fanatics, Superdrug, Healthline
Research published in Harvard Business Review, The Economist, and the NYT, with a dedicated client growth division
Entrepreneurial approach to flipping underperforming businesses through aggressive conversion optimization
The Content Bureau
B2B content marketing
Technology, venture capital, and financial sectors; global corporations
American Express, PayPal, Microsoft, Cisco
Woman-owned agency with 80 percent of staff at 10+ years tenure, offering premium, high-attention client service
Webprofits
Growing challenger brands
E-commerce, consumer, and retail brands scaling fast
Logitech, Philips, Nespresso, HP
“Fluid marketing” methodology blends digital strategy and performance marketing to find hidden growth opportunities
Siege Media
Scalable SEO content
Fortune 500 companies down to small startups
Zillow, Airbnb, TripAdvisor, Asana
Passive link generation through content, backed by a proprietary link management tool maintained monthly
Directive
Performance marketing for tech companies
Tech companies of all sizes
Amazon, Bill.com, Matillion, SentinelOne
Generated $10B+ in client revenue by acting as an embedded extension of in-house marketing teams
1. NP Digital – Best for Immediate and Consistent Revenue Growth
NP Digital is my content marketing company. We created NP Digital in 2017 to serve the millions of people who needed help with their content marketing to grow revenue.
Rankings are important, but many marketers still focus obsessively on keywords and content that doesn’t lead to revenue. I’ve always focused on helping readers build a business that generates traffic, leads, and, most importantly, revenue. So we have a big focus on developing high-quality content that ranks high and converts visitors into customers by aligning with user intent.
Today, we’re one of the top content marketing brands in the business. a powerhouse global agency with one of the top 100 blog destinations in the world.
Another thing that’s different about NP Digital is the fact that we incorporate technical SEO into our content marketing planning. SEO — technical, on-page, off-page, local, etc.— it’s always a package deal with content marketing. Our status as one of the top SEO agencies means you get the best of both worlds.
We stay on top of Google’s updates and algorithms and adjust our strategies accordingly. This means the content we create for our clients automatically performs well with Google. here’s no extra work required.
NP Digital is my way of helping everyone achieve the revenue and growth they deserve in their business.
2. Seer Interactive – Best for Big Data Search and Content
Wil Reynolds founded Seer Interactive, which got its start as a search engine optimization company. What makes Seer one of the best content marketing companies on our list is its focus and emphasis on big data.
Using a combination of in-house and third-party tools, they’ve built a massive data warehouse with almost 12,000 gigabytes of data they can analyze to identify new, hidden, and unexpected customer trends.
If you’re in a competitive or cutthroat industry (e.g., finance, banking, or mortgages), this data is what you need to stay ahead of your competitors.
With Seer Interactive, their approach is SEO-heavy. That should be an important priority for every company, whether you’re big or small, but not every company is ready for Big Data.
Brainlabs was founded by Daniel Gilbert in 2012. Understanding that marketing was becoming all about data, he took the unusual tactic of hiring mathematicians, scientists and programmers to support automation and data-driven insights.
His approach paid off: Since 2020, the agency has expanded its services by acquiring other marketing companies, including the SEO-focused Distilled, a leader in the space.
Today Brainlabs is known as one of the top content marketing agencies for technical SEO and helping companies evolve in an increasingly competitive SEO landscape. They are constantly experimenting and testing to improve conversion rates.
4. Fractl – Best for In-Depth, Research-Heavy Content
Fractl is a research-heavy, data-driven content marketing company. They’re focused on rapid, organic growth that’s driven by content marketing, data journalism, digital PR, and search engine optimization.
Research makes Fractl unique.
They’re always researching industry-related topics, and they share their understanding of the art and science behind newsworthy content. They share their research in top publications, leading market resources, scientific journals, and authoritative conferences around the world.
Their research has been published in MarketingProfs, TNW, The Economist, Time, the Harvard Business Review, the New York Times, Pub Con, and many other publications and journals.
If you’re in a research-heavy industry and you’re looking for a high-growth content marketing company, Fractl is a good choice. Aside from being one of the best content marketing brands, they’re one of the few companies that have a division dedicated to client growth.
5. Column Five – Best for Data and Content Visualization
Column Five describes itself as a creative content agency. They’re primarily focused on the visual side of content marketing — storytelling, design, data visualization, video, interactive motion graphics, even exhibition design.
They are most known for their “child of the 90s” viral video on behalf of Internet Explorer, which launched their reputation as one of the best content marketing brands out there.
As a content creation company, Column Five is focused primarily on content strategy, content creation, and content distribution. They rely on a simultaneous mix of organic and paid distribution channels to draw attention to client content.
The company mantra is “the best story wins,” showing their commitment to developing great content that delivers big results. It specializes in content that is “inherently newsworthy,” making it more likely to get traffic, links, and media attention.
6. Single Grain – Best for Conversion-Driven Content Marketing
In 2014, entrepreneur and leading marketing expert Eric Siu made a big gamble. He bought a failing SEO agency for less than the cost of a cappuccino — $2. This wasn’t the first time he’d made a seemingly risky bet — in the past he led the growth strategy for an online education company when it had just a few months of cash left in the bank.
“A month into it, the CEO pulls me aside,” Siu recalls, “and he’s like, ‘Eric, you know, 48 people, their families, they’re riding on your shoulders right now, and if you can’t hit numbers in the next month, we’re gonna have to let you go.’”
Did I mention he was just 25 years old at the time?
Eric leveraged his marketing know-how and entrepreneurial outlook to turn Single Grain around and take it to where it is today: solidly among the ranks of the best content marketing brands out there.
Eric Siu and the Single Grain team can do for your business what they do best: turn it around. They know how to turn a faltering business into a successful one with an approach of optimizing for conversions and focusing on rapid growth.
7. The Content Bureau – Best for B2B Content Marketing
The Content Bureau bills itself as a premier B2B content marketing company. This agency is woman-owned, 100 percent virtual, and their team is 90 percent female, of which a third are women of color. The Content Bureau focuses its attention on the technology, venture capital, and financial sectors, working almost exclusively with global corporations that rely on them year-round.
Many of their clients are long-term, stable clients who prefer their premium approach, exclusive attention, and veteran workforce; 80 percent of their team have been with The Content Bureau for 10+ years.
As an organization, they give their clients lots of handholding; they’re open and transparent with each of their clients, and they deliver amazing service with their extraordinary content.
Webprofits is the content marketing and advertising company that was co-founded by Sujan Patel and Alex Cleanthous. Their company focuses on challenger brands in the e-commerce, consumer, and retail space that want to grow their business fast. They’ve refined their process based on real-life, in-the-trenches experience.
In fact, Patel doesn’t think of Web Profits as an agency. He calls it a marketing “hit squad,” a team of specialists who understand your business inside and out.
What makes Web Profits one of the top content marketing companies? They use a unique “fluid marketing” approach, which combines digital strategy with performance marketing. This enables its team of experts to identify hidden correlations and connections that can point to exciting opportunities for content marketing.
This makes the Web Profits team uniquely qualified to serve challenger brands that want to make a big impact.
Siege Media prides itself on taking a “scientific approach” to scaling SEO-focused content. The agency works with a wide range of companies, from established Fortune 500 businesses to small startups.
The focus of the business is on link-building. Siege Media creates content that serves as passive link generators, a tactic they say is more effective than manual outreach. Their formula results in high-impact content that produces instant results—and it’s a cost-efficient tactic, too.
Siege’s superpower is a proprietary solution for link management. Siege maintains the tool for its clients on a monthly basis, ensuring that websites are always aligned with overall goals and updates.
This commitment to innovation and leveraging technology for content marketing makes Siege one of the best content marketing companies for the future.
10. Directive – Best for Performance Marketing for Tech Companies
CEO Garrett Mehrguth founded Directive when he was just 21, focusing on SEO. Today it works with some of the world’s most prominent tech companies, helping them become more discoverable in a dynamic and often challenging industry. Since its founding, it’s generated more than $10 billion in revenue.
The agency uses a unique data-driven methodology to generate quality leads organically across the marketing funnel. The team prefers to act as a partner rather than a vendor, serving as an extension of its clients’ in-house marketing teams.
4 Characteristics that Make a Great Content Marketing Company
A good content marketing company will have no problem demonstrating that they have the expertise and the resources they need to make your campaign a success. These are some qualities to expect in a high-quality content marketing agency.
1: A Stable Team of Content Creators
Content mills produce poorly written filler content that’s mainly written for search engines. Not only is that a short-sighted approach, but Google’s algorithm is more likely to ding sites that use it—especially now that it is incorporating AI.
The best content marketing companies have a roster of regular and consistent writers on their team. Stable writers are skilled at writing, grammar, logical consistency, and storytelling. These writers can draw your readers in, creating content that moves people towards a specific goal or objective that you have in mind.
These writers don’t need a lot of babysitting, and they’re able to figure things out, to a certain extent, on their own. They’re dependable, and they’re able to match your brand voice.
When you contact a content marketing company, you’ll want to ask them questions about how they run their business.
How many writers do you have on staff?
Are they freelance or W-2? Do you use a mix of both?
How many of your writers are full-time? Part-time?
How do you manage your team of writers?
How many years of experience does the average writer on your team have?
When you ask companies these questions, listen to their answers carefully. Look for any inconsistencies or red flags. If you spot any, bring them up immediately and ask for an answer.
2: Access to Publishers and Influencers
According to Derek Halpern, founder of Social Triggers, you should be spending 20 percent of your time on content creation and 80 percent of your time on content promotion. The content marketing companies you work with are no different. If you’re investing a significant amount of time and money in creating an amazing piece of content, you should be spending 4x as much time on promotion to make sure your target audience sees it.
When you’re working with a content marketing company, they should already have a list of influencers and publishers in their address book. They should also have strong connections and relationships with the right people, so they’re reasonably sure they can drive traffic to your content.
3: Specialized Knowledge About Your Industry
In an ideal world, your content marketing provider has a significant amount of experience in your space, or the ability to connect with experts who do. At a minimum, you’ll want to ensure that the content marketing company you choose can write credibly about the topics that are relevant to your business.
The more specialized the content, the more important these criteria are for your business.
Industries like healthcare, engineering, or finance require large amounts of specialized experience. It’s unrealistic to expect an inexperienced company to write credibly about a highly technical topic.
Specialization requires specialists. The more technical your business, the more important it is to hire a content marketing company with experience and expertise in your field.
4: Content Analysis and Measurement
When you’re investing in the services of a content marketing company, you’ll want to see the numbers. The agency should be able to provide you with a detailed breakdown that includes data outlining your performance as well as the KPIs, metrics, and sentiment surrounding your content.
This information should give you the answers to the following questions:
Does this content move us closer to our campaign goals?
Does this piece of content (e.g., blog post, whitepaper, e-book, infographic) lead to enough conversions?
How far are people reading into your content?
Where in our flywheel are we losing customers?
What do we need to change/optimize to improve our conversion rates?
Which content marketing opportunities are we missing, and where?
Creating content isn’t enough. The content marketing company you choose should provide you with the actionable data you need and a comprehensive strategy to create profitable content for your business.
What To Expect From a Great Content Marketing Company
Top content marketing agencies are able to get you up to speed on their processes and provide you with a consistent and comprehensive set of deliverables. These deliverables ensure that your content marketing campaigns stay on track and that you’re able to achieve the consistent results you need.
To do this, your content marketing provider should provide you with onboarding guidance and specific deliverables throughout the pre-launch, launch, and post-launch phases of your campaign. These should include:
Content samples demonstrating your knowledge and expertise
The information and materials (e.g., credentials, existing content) they need from you to get started
A statement of work and a list of deliverables (e.g., 14 2,500-word articles each month, edits included)
Their process (if they’re not working with you and yours)
Projected campaign milestones, timelines, and calendars
Your point-of-contact, including their name, and contact information
Hours of availability
The best way to communicate (e.g., Slack, email, phone, chat, or text)
Expectations from you
Their process, policies, and procedures
Analysis and reports, including business goals, objectives, KPIs, metrics, strategy, tactics, and risks
Content audits
Consistent updates on your campaign performance
Regular (weekly or monthly) calls to discuss performance
Consistently updated due dates and delivery timelines
Monthly debrief to discuss successes and failures
Here are some additional details you should also expect from your content marketing providers:
Good boundaries (including the ability to say no)
Prompt and clear feedback
Accurate information on various parts of your campaign, including financial, campaign, and performance data
The best content marketing companies ask a lot of questions. They make sure to provide you with the upfront information you need to vet their company and make an informed decision. Once you’ve decided to move forward, they ask you for all of the information and materials they’ll need to produce the results you want.
FAQs
What makes good content marketing?
Good content marketing is different for every business, but in general, it involves creating well-written content that provides valuable information for your target market. It also draws in qualified leads and converts them into customers at a rate that justifies your investment.
How do you track content marketing results?
Tracking content marketing results involves setting clear goals, identifying key performance indicators (KPIs) such as website traffic, inquiries, and conversion rates to use as metrics, and monitoring the results. Most content marketing agencies use analytics tools to track and measure results.
How do you optimize for content marketing?
Optimizing for content marketing involves several steps. First, research who your target audience is and their needs. This will guide you toward topics for content development that can answer their questions and provide valuable information. Incorporate SEO to ensure your content ranks high on search engine results pages and brings in organic traffic. Finally, analyze the results to refine content topics, formats, and overall strategy.
Which content marketing agency is best for B2B companies?
B2B companies should look for agencies that focus on long-form content, SEO, and lead generation. The best partners understand how to create content that nurtures prospects over time, not just drives traffic. Agencies with strong experience in SaaS or professional services tend to perform best here.
Which content marketing company is best for small businesses?
Small businesses need agencies that balance quality with cost. Look for teams that offer flexible packages or project-based work instead of large retainers. The goal is to get consistent, high-quality content without overcommitting your budget early on.
Which agency is best for SEO-driven content?
You want an agency that combines content creation with keyword research and technical SEO. Firms that focus heavily on search performance will build content designed to rank, not just read well. Check for proven results in organic traffic growth and rankings.
Should you hire a specialized content agency or a full-service marketing agency?
Specialized agencies go deeper into content strategy and production. Full-service agencies connect content to SEO, paid media, and conversion optimization, which can drive better overall results. If content is your main bottleneck, go specialized. If growth is the goal, full-service often wins.
How do you choose the right content marketing agency?
Start with their results. Look for case studies showing traffic growth, lead generation, or revenue impact. Then review their content quality and process. The best agencies have a clear system for research, creation, and optimization.
Conclusion
Content marketing produces more leads and revenue than traditional marketing methods. If you’re looking for a good content marketing company to help you get started, it can be tough. Use this list to identify the companies that are a good fit for your business.
With this post, you should have a pretty good idea of the questions to ask, what to expect, and how to select the right content marketing provider.
Invest the right amount of effort with the right company, and your content marketing will grow faster than you expect. It’s tough in the beginning, but it will take effort, push through, and keep creating really helpful content, even if it’s hard.
You’ll see consistent revenue growth once customers realize that you’re serious about helping them solve their problems. Content marketing is the best way to show them that you understand, and you can help. With this said, combining with other disciplines is the best way to unlock your content’s true potential. Check out my lists of the best CRO agencies and top social media agencies for more information.
