Brands often invest in influencer and affiliate promotions but stop short of giving the content additional reach, assuming the creator’s audience is enough.
Using paid marketing, adding it to your site, and sharing it across your channels isn’t doing their job for them.
It’s a way to grow your company by using their brand recognition and strengthening the relationship.
Yes, you pay an influencer an upfront fee, a commission, or send them a product in exchange for a promotion, but that doesn’t mean the relationship stops there.
And that’s where amplification becomes a real advantage. It unlocks more value from the creator relationships you already have.
Why amplifying creator content pays off
Before getting into the tactics, here are the reasons amplifying creator content pays off.
Trusted validation
When a trusted third party verifies that your product, store, or company is legitimate, you gain credibility with anyone who recognizes or relates to them.
This is especially important in competitive industries where trust is uncertain and consumers have many options, such as jewelry or insurance.
A clear example is choosing a hotel at Disney or on a Caribbean island.
With so many choices and mixed pros and cons, something needs to break the tie.
If a trusted individual chooses your brand, that alone can influence the decision.
You can use this content in ads to reach a new audience, and you can test it with people on your newsletter or SMS lists who haven’t converted yet.
The same applies to remarketing.
If someone visited a page or category on your site but didn’t convert from your usual remarketing, show them a video that reviews the same product or offers a fair comparison between options.
You can say how great you are all day, but a third party validating that message may help convert that traffic.
Lower media costs
Some influencers are out of budget, but guaranteeing that their ads will reach a new, like-minded audience may help bring their price down.
You can also allow them to use their affiliate links in the amplified content so they can earn commissions.
The commissions put risk on both sides – they lower their fees, and you spend money instead of relying on commission only.
It’s a fairer approach, especially when their fees are higher than usual.
As the influencer starts making money, they may waive their fees if their commissions exceed them and choose to become an affiliate instead. This frees up your media budget to test new partners.
You can also opt for a hybrid deal, where you pay part of their media fee and they earn commissions to cover the rest, which opens up more budget for testing new partners and outlets.
When there’s a natural reason for people to share the content – like food that can go viral or something funny – consumers may start taking the amplified content from the ad or your website and sharing it to their personal accounts or groups.
If their accounts are set to public view, search engines and LLMs like ChatGPT can find these links.
This creates more paths back to your site and gives them more content to discover, reference, and send traffic to.
Affiliate recruitment
When the big or known accounts start promoting a vendor, it means there is money to be made.
By amplifying the content to an audience that likely knows them, other creators, traditional affiliates, and marketers will notice the affiliate links if you allow the creator to use affiliate links for the network.
Some will reach out asking for a “collab,” which means money up front, and others will apply to become an affiliate.
Having the big names builds trust for new partners.
It means they are risking their personal brands on your company, products, and services, and that goes far with other partners.
This exposure may help the new partners feel confident that your program is legit.
This is one of the things we encourage with our clients who are dedicated to the affiliate marketing channel.
It helps everyone win, as affiliate recruitment and affiliate activation are the two most challenging parts of the channel.
When ambassadors and influencers approach the client and ask for money up front, we start them as an affiliate first to keep things fair for all creators, and if it makes sense, we move them to hybrid models.
It’s less risky for you as a brand and gives the creator a foot in the door.
Not all of our clients are open to this, but those who are do see the benefits.
It’s easier to build a network of partners, and both parties are taking risks instead of it always being one-sided.
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.
Automation has shaped PPC for decades, and the landscape keeps shifting.
I’ve seen that evolution firsthand, from helping build the first AdWords Editor to developing early Google Ads scripts and writing about automation layering.
Now we’re entering another major transition.
As AI changes how we search and get answers, it’s also transforming how automation itself gets built.
And this time, the momentum isn’t coming from ad platforms like Google – it’s coming from AI companies like OpenAI.
Until recently, AI mostly helped with human language tasks like writing ad copy, summaries, or reports.
But the latest generation of LLMs can increasingly generate computer language too, including the software and workflows that streamline how we work.
At OpenAI’s DevDay in San Francisco, the company introduced AgentKit, a new way to build AI that can take action.
It marks the start of a phase where the automation mindset that powered PPC optimization can extend far beyond campaigns and into entire workflows.
Imagine if AI could handle your everyday busywork
Picture this:
A client sends a CSV with weekly results, and before you’ve even opened the email, the file is saved to the right folder and added to your dashboard.
A client asks for a meeting – AI checks your calendar, drafts an agenda, and schedules it.
You start writing new ad copy with AI, and the system automatically pulls your brand guidelines and checks for tone and compliance.
This is all possible today, and you don’t need an engineering degree to make it happen.
If you can define how your work is broken down into distinct tasks, you can create an agent that does those steps for you.
An AI agent is a smart helper that can figure out what needs to happen and then take action using connected tools.
Software has historically been built around deterministic steps. If X, do Y, else do Z. It’s predictable, but inflexible.
And it requires humans to define every possible scenario that should be covered, which makes writing a helpful program time-consuming and difficult.
But just like an LLM is flexible in how it answers your questions, it can use that flexibility to automatically figure out a reasonable next step to complete a task.
Instead of replying with text, agents can reason through steps, call APIs, and perform tasks.
I’ve explained early versions of this before:
You ask ChatGPT for restaurant ideas while planning a trip.
It suggests a few places.
It then uses an app like Resy to book the reservation.
That’s what an agent does: it can understand your intent and take a real-world step.
This concept builds on earlier OpenAI features, such as GPT Actions and function calling, which gave models controlled access to outside data.
Agents are the next evolution – they combine reasoning with execution, meaning they can plan and act in the same flow.
Now, think about that in PPC terms.
An agent could pull campaign data, summarize results, and even reference brand or policy docs before generating compliant creative.
That’s a big step up from traditional “AI writing assistants.”
Many marketers, myself included, have experimented with them for more than a year, but it used to take a lot of technical work.
About a year and a half ago, I built an agent based on the two books I’d written that could answer questions in my tone and reference my ideas.
I used LangChain, one of the first frameworks for connecting large language models to data and tools. It worked, but it wasn’t quick.
I had to learn vector databases, RAG, and several other moving parts to get it working – not something most PPC pros want to tackle on a Monday morning.
Since then, several companies have made it easier to build agents like these, and some even feature them with a digital clone of a person, such as HeyGen.
But when OpenAI introduces a way to create agents, I pay attention – and that’s what they did with AgentKit.
It brings a visual interface for building agents directly on the platform of the most used chatbot.
What used to take hours or days of development can now be done in minutes, and you don’t need to know how to code.
AgentKit: ‘Zapier for AI’
AgentKit is OpenAI’s new toolkit for creating agents that can connect to tools and take actions through those tools.
It’s a visual builder where you link services like Gmail, Dropbox, or Slack, and describe what the agent should do using tools you already use every day.
If you’ve ever used Zapier, n8n, Make, or Rule Engine, the concept will feel familiar: you connect blocks in sequences that represent what you want to happen.
But because a flexible AI model sits at the core of these flows, AgentKit is different – it can use reasoning instead of rigid rules.
If that sounds scary, you can add a simple human-in-the-loop approval step to any flow.
Instead of “If X happens, do Y,” you can say, “If a client sends a campaign report, summarize it and save it to the right folder.”
The AI figures out how to do that by making reasonable requests that help it understand what you mean by vague instructions like “the right folder.”
For PPC marketers, this opens the door to automating work around campaigns (think reporting, documentation, and creative preparation), without waiting for a platform feature or a developer.
Get the newsletter search marketers rely on.
The unsung hero: Model Context Protocol (MCP)
Under the hood, much of the power that enables agents to take action comes from the Model Context Protocol, or MCP.
It’s not brand-new, but it’s the key piece that makes all of this work.
MCPs are the connectors that let agents talk to your tools or data in a structured way.
If you think of APIs as the connectors of the web, MCPs are similar, but built as a standard that any LLM can use.
Some are built by OpenAI, like the connectors for Dropbox or Gmail.
Others come from third-party developers, like Box.
And you can create your own to connect private data or internal systems.
You can think of it this way: MCPs are the plumbing. AgentKit is the faucet.
The plumbing defines what data can flow where. The faucet is how you turn that into something usable.
Without MCPs, an agent would be like a brilliant intern with no logins to any of the systems they need.
With them, the agent can safely use your data and tools with clear permissions.
If this still sounds abstract, think of an MCP as a menu of what an AI can do inside a given flow.
For example, the Google Ads MCP currently includes actions like:
Search for entities.
List connected customers.
That’s it for now. It can read data, but it can’t change bids or create ads yet.
That limitation is a good illustration that MCPs don’t open the door to entire systems for an LLM to go wild.
Instead, they provide a defined set of capabilities created by the MCP developer.
It’s an important guardrail. And even with MCPs that offer broader capabilities, you still control exactly which actions your agent can access when you integrate them into a flow.
Even in this early state, it’s a clear preview of how AI might eventually interact with Google Ads data through well-defined, secure interfaces.
Example: A ‘brand-safe ad assistant’
Here’s what this looks like in practice.
Imagine you want an AI assistant that writes Google Ads while automatically following your brand voice and legal disclaimers.
In AgentKit, you could create an agent with two connected tools:
Dropbox, where your brand guidelines live.
A vector store with your agency’s tone and policy docs.
You could then ask the agent to “write new RSA headlines for our fall campaign using our style and disclaimers,” and it would connect with the right data to complete the task.
Behind the scenes, it reads the files, extracts the rules, and generates compliant ad copy. You still approve the final version, but the prep work is automated.
It may sound simple, especially since you can already do this with a custom GPT, but it shows how these building blocks can be expanded.
For example, you could integrate an MCP for your email platform and have the agent send a client an approval request for the creatives it generated.
Connecting data sources in AgentKit
Here are the steps to create an agent connected to the two data sources mentioned above.
In Agent Builder, click the + icon next to Tools to give your agent a new capability, such as connecting it to an MCP.
Choose an existing MCP, like the ones shown here, or connect a custom MCP by clicking + Server.
You can also add a file search capability and select the files to include directly in the pop-up dialog.
Now you can interact with the agent to see how it uses its new abilities to produce better answers and, where enabled, how it uses other tools to take actions.
If you’ve been in PPC for a while, you’ve seen this script before.
We went from manual optimizations, to automated rules, to scripts, to automation layering – and each wave changed the skill set needed to stay ahead. Agents are the next wave.
Instead of writing scripts or building workflows with APIs, we’ll soon describe them in plain English and let AI generate the logic.
That amplifies what marketers can do.
The core skills stay the same – strategy, measurement, and judgment – but the way we build automation is about to get much faster, more flexible, and far more accessible.
The current tools for building AI agents are still early.
Setting up an MCP takes some configuration, and the Google Ads connector is limited to reading data.
But the potential is clear: AI will move beyond generating text to running workflows, checking rules, and getting work done.
If you want to stay ahead of this shift, start small.
Experiment with simple automations that connect your email, files, or reports.
Learn what agents can and can’t do yet.
Just as marketers who adopted scripts early will be the ones setting the standard later, those who learn this now will be the ones setting the standard later.
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.
https://zoomyourtraffic.com/wp-content/uploads/2025/11/wp-header-logo-57.png9001600Team ZYThttps://www.zoomyourtraffic.com/wp-content/uploads/2024/01/ZYT-1.pngTeam ZYT2025-11-21 07:55:502025-11-21 07:55:50From scripts to agents: OpenAI’s new tools unlock the next phase of automation
AI search evolves every month. This constant flux is reshaping which brands get visibility and which sources AI models trust most.
We now have three months of data in the AI Visibility Index, tracking ChatGPT and Google AI Mode.
The key takeaway: AI search is volatile. This is likely to be normal for the immediate future.
The brands that win are monitoring and adapting to these changes in real-time.
The research tracks 2,500 real-world prompts across five key verticals: Business & Professional Services, Digital Technology & Software, Consumer Electronics, Fashion & Apparel, and Finance. revealing seismic shifts in source diversity, brand mentions, and model behavior that no marketer can afford to ignore.
What changed at a model level?
ChatGPT: Unique brand mentions fluctuated. Meanwhile, sources cited by ChatGPT surged 80% in October alone – a fundamental shift toward greater source diversity.
Google AI Mode: Brand mentions dropped 4% from August to October, suggesting tighter controls on recommendations. Source diversity increased more moderately at 13%, indicating a more conservative approach than ChatGPT.
Key trends over three months
Reddit’s correction and resurgence: ChatGPT reduced Reddit citations by 82% between August and October. However, it remains the fourth most-cited source in ChatGPT. During the same period, Google AI Mode increased Reddit usage by 75%, making it the second most-used source. The platforms are converging on Reddit’s value, just from opposite directions.
Brand diversity varies by vertical and model: In ChatGPT, Consumer Electronics saw a 20% increase in unique brands mentioned, while Finance dropped 15%. Google AI Mode showed universal declines across almost every vertical. More proof that each model requires its own approach.
Top brands remain relatively stable: Among the top 100 brands, there were 25 new entrants over three months—but only two broke into the top 50. For leading brands, changes in visibility stayed within a ~20% range, much narrower than the broader market volatility.
Source strategies must be model-specific: ChatGPT and Google AI Mode agree on which brands to mention 67% of the time, but only 30% of the time on which sources to use. Wikipedia, Forbes, and Amazon dominate ChatGPT, while Amazon and YouTube lead in Google AI Mode.
The update confirms that AI visibility requires constant monitoring. Both platforms are experimenting with diversity, correcting for overreliance, and refining their approaches.
What this means for your strategy
In AI search, yesterday’s visibility doesn’t guarantee tomorrow’s.
Sixty-one of the top 100 brands appear in both ChatGPT and Google AI Mode’s results, showing high brand similarity. But source similarity is much lower and actually decreased from August to October.
Translation: build your brand visibility across both platforms, but tailor your source strategy to each model individually.
Explore the AI Visibility Index to discover the complete rankings, interactive leaderboards, and deeper trends across all five industries. Then download the proven tactics to build visibility in this rapidly evolving landscape. All for free.
Opinions expressed in this article are those of the sponsor. Search Engine Land neither confirms nor disputes any of the conclusions presented above.
Semrush Enterprise helps mid- to large-sized businesses boost online visibility across traditional and AI-powered search. Equip your teams with industry-leading data and AI automation to drive efficiency, collaboration, and ROI. Manage SEO, AI search, and content from a single platform. Uncover growth opportunities, monitor competitors, and simplify reporting with built-in collaboration tools and on-demand expert support.
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Google says it’s dramatically cut down on mistaken advertiser suspensions — a long-standing frustration for many legitimate marketers using its platform.
By the numbers:
Incorrect account suspensions are down over 80%.
Suspension appeals are being processed 70% faster.
99% of appeals are now resolved within 24 hours.
Why we care. Advertisers depend on uninterrupted access to Google Ads to reach customers and drive revenue. Erroneous suspensions can derail campaigns and business operations, especially for small and mid-size advertisers.
How they did it:
Clarified policy language to make compliance simpler.
Used Google’s Gemini AI to sharpen detection systems and reduce false positives.
Improved internal review and appeal processes to get legitimate advertisers reinstated more quickly.
The big picture. Google Ads processes millions of advertiser accounts globally and faces constant threats from scammers and policy violators. Balancing enforcement with fairness has been a persistent challenge — one the company hopes these AI-driven improvements will finally stabilize.
Search Engine Land is owned by Semrush. We remain committed to providing high-quality coverage of marketing topics. Unless otherwise noted, this page’s content was written by either an employee or a paid contractor of Semrush Inc.
Anu Adegbola has been Paid Media Editor of Search Engine Land since 2024. She covers paid search, paid social, retail media, video and more.
In 2008, Anu started her career delivering digital marketing campaigns (mostly but not exclusively Paid Search) by building strategies, maximising ROI, automating repetitive processes and bringing efficiency from every part of marketing departments through inspiring leadership both on agency, client and marketing tech side. Outside editing Search Engine Land article she is the founder of PPC networking event – PPC Live and host of weekly podcast PPC Live The Podcast.
She is also an international speaker with some of the stages she has presented on being SMX (US, UK, Munich, Berlin), Friends of Search (Amsterdam, NL), brightonSEO, The Marketing Meetup, HeroConf (PPC Hero), SearchLove, BiddableWorld, SESLondon, PPC Chat Live, AdWorld Experience (Bologna, IT) and more.
Google is rolling out asset-level reporting for Display campaigns, giving advertisers a clearer view of how individual creative assets perform — a move that brings Display closer to the visibility already seen in Performance Max campaigns.
Why we care. Until now, Display campaign insights have been limited to overall ad performance. With this update, advertisers can analyze results at the asset level — images, headlines, descriptions — to pinpoint what’s driving engagement and what’s not.
How it works. A new Assets tab in Google Ads will let users:
Compare performance of each creative asset.
View when assets were last updated to track iteration history.
Decide which assets to keep, refresh, or remove based on data.
The details. A new Google support page, “About asset reporting in Display,” outlines the update with links to:
Get started
How it works
Asset reporting for your Display campaigns
Evaluating asset performance
Between the lines. This upgrade mirrors reporting tools available in Performance Max, signaling Google’s continued effort to unify insights across campaign types and improve transparency in automated advertising.
What’s next. The feature hasn’t been spotted live yet, but its appearance on Google’s help center — first noticed by PPC News Feed founder Hana Kobzová — suggests a wider rollout is imminent.
Search Engine Land is owned by Semrush. We remain committed to providing high-quality coverage of marketing topics. Unless otherwise noted, this page’s content was written by either an employee or a paid contractor of Semrush Inc.
Anu Adegbola has been Paid Media Editor of Search Engine Land since 2024. She covers paid search, paid social, retail media, video and more.
In 2008, Anu started her career delivering digital marketing campaigns (mostly but not exclusively Paid Search) by building strategies, maximising ROI, automating repetitive processes and bringing efficiency from every part of marketing departments through inspiring leadership both on agency, client and marketing tech side. Outside editing Search Engine Land article she is the founder of PPC networking event – PPC Live and host of weekly podcast PPC Live The Podcast.
She is also an international speaker with some of the stages she has presented on being SMX (US, UK, Munich, Berlin), Friends of Search (Amsterdam, NL), brightonSEO, The Marketing Meetup, HeroConf (PPC Hero), SearchLove, BiddableWorld, SESLondon, PPC Chat Live, AdWorld Experience (Bologna, IT) and more.
The shift away from fully keyword-targeted search campaigns has been building for years – but this week, it reached a tipping point.
Two account managers on my team, each handling different clients in different industries, came to me with the same uneasy admission.
They were leaning toward dropping some of their keyword search campaigns in favor of Performance Max.
Not all of them. But some. These weren’t impulsive calls.
They were data-backed decisions made after months of testing, optimization, and watching Performance Max consistently outperform keyword-targeted campaigns.
Are we heading toward keywordless targeting? Not quite.
But we’ve reached a stage where some accounts are no longer keyword-dominant – and that shift changes everything.
When seasoned PPC marketers who’ve built their careers on keyword strategies start making this recommendation, it’s time to pay attention.
The landscape has changed, and if you’re still resisting, you’re not being cautious – you’re forfeiting growth.
The stars have aligned for AI-forward advertising
Here’s what’s happening right now:
AI Overviews are appearing more frequently in search results.
The ones who are still waiting? They’re watching their competitors pull ahead while they sit at the station.
The holistic approach you’ve been missing
Across client accounts – and in conversations with prospects – I’m seeing a clear pattern:
B2B companies that focused too heavily on performance marketing are now admitting they have a brand trust problem.
On the flip side, companies that invested only in awareness are struggling to convert.
The answer isn’t choosing one or the other. It’s both.
I used to call it awareness campaigns. Now I’m calling it what it really is: brand trust campaigns.
Because when you show up consistently across platforms, you’re building more than awareness – you’re building trust that your brand exists, matters, and can solve your customers’ problems.
These campaigns use first-party data and website engagement signals, and they’re currently the only campaign type that can target lookalike segments across the web.
With high-impact images and videos, they function like social ads with strong engagement and brand recall.
When you pair brand trust campaigns like Demand Gen with Performance Max – which will likely soon appear in AI Overviews, AI Mode results, and across Google’s entire ecosystem – you’re building a program with staying power.
Performance Max is replacing keyword-dominant strategies
I’m not saying keywords are dead – but 100% keyword-targeted search campaigns are no longer the dominant strategy.
In those two accounts I mentioned earlier, the path to dropping some keyword campaigns wasn’t overnight. It was methodical.
While our clients initially wanted to advertise every service, it wasn’t feasible to promote everything through keyword-targeted campaigns – it was simply too costly.
We went with a keyword-based strategy for top-tier services, and tested Performance Max for tier-two and tier-three services.
The results were promising, so we were given more budget.
After several months, conversion rates and sales data made it clear.
Performance Max was a rising star. We added top-tier services to PMax to complement existing search efforts.
Is the final step completely dropping those keyword campaigns and going all-in on Performance Max? Maybe someday.
Now, this isn’t an all-or-nothing scenario for every account.
In many cases, Performance Max and keyword campaigns work beautifully together.
But what I am seeing is a clear trend: Performance Max is earning more budget, more trust, and more results.
Think about what these AI-powered campaigns actually do.
They create opportunities for your brand to show up consistently across search, Google, and YouTube.
By the time someone is ready to convert, they may search directly for your brand or use a high-intent keyword – and your Performance Max campaign will be there.
But you’ve already built awareness, trust, and consideration long before that moment.
Google’s newest offering, AI Max for Search campaigns, represents yet another evolution.
Early results from our testing are mostly flat – which is actually fine at this stage.
But here we are again, facing the same hesitation we saw with Performance Max when it first launched.
However, what’s exciting about AI Max is its location interest targeting at the ad group level and new brand controls that we haven’t seen before.
These are meaningful additions that signal where Google is heading.
The cost of resistance
Here’s what I hear from the market:
“I tried Performance Max and it didn’t work.”
“I’m seeing too many junk keywords.”
“I’m not ready to give up control.”
I get it. Change is uncomfortable.
Letting AI optimize assets feels like relinquishing control. Trusting the algorithm with your budget requires a leap of faith.
But every single Google product launch faces this same resistance.
Each time, the advertisers who adopt early, test thoroughly, and push through the learning curve are the ones who win.
If something didn’t work six months ago, that doesn’t mean it won’t work today. Performance Max has evolved significantly.
The platform has more controls, more transparency, and more ways to guide the algorithm toward your goals.
Dismissing it based on outdated testing is like refusing to get on the train because it was delayed last year.
How to prepare for an AI-first future
If AI Mode and AI Overviews are changing how people search – and they are – then you need an AI-forward ad program to show up in those experiences.
If you’re not testing these tools now, you won’t be ready when your competitors are already established.
Here’s what you can do today.
Audit your current campaigns: Are you still running 100% keyword-targeted campaigns? If so, test Performance Max alongside them and compare performance over at least two months.
Invest in brand trust campaigns: Whether through Demand Gen, LinkedIn, or YouTube, make sure you’re building awareness and trust alongside your conversion campaigns.
Create high-quality assets: Video and images that convey why customers trust you aren’t optional anymore. They’re essential for AI-powered campaigns to succeed.
Adopt a test-and-learn mindset: Everything takes at least two weeks to settle. Some tests take months. If you give up too quickly, you’ll never know what could have worked. We’ve stopped tests that weren’t performing, only to revisit them months later with better results because the platforms evolve.
Stop viewing these tools as threats to your control: They’re tools to expand your reach and improve your results. The fundamentals of great marketing – strong messaging, understanding your audience, and stellar content – still matter. AI just helps you reach more of the right people.
The Google Ads landscape is shifting toward AI-powered campaigns, and data from real accounts confirms this trend.
Advertisers who are testing, learning, and adapting are seeing results. Those who wait for certainty or cling to outdated strategies are losing ground.
You don’t need to abandon everything overnight.
But you do need to start testing.
Commit to the learning curve, knowing that initial results may not be extraordinary but that the platform will improve – and so will your results.
The train has left the station. You can wait for the next one and arrive hours late, or you can pivot, adapt, and find a faster route.
My team and I? We’re on that train, and we’ll keep riding.
That’s what a growth mindset looks like – continuous testing, continuous learning, and staying open to what’s next.
Because in this industry, standing still is the only way to lose.
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.
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Google’s AI Overviews and AI-driven search are reshaping content creation, SEO, and user behavior.
As we watch this fascinating evolution of search – and continue to debate what we call this new marketing discipline (HubSpot is opting for AEO, or answer engine optimization) – I interviewed Aja Frost, senior director of global growth and paid media at HubSpot. Some of the topics covered in our interview:
The need to redefine success metrics for AEO, prioritizing visibility and share of voice
HubSpot’s experimental journey, including creating hyperspecific, data-rich content and optimizing for LLMs.
Traffic directly from LLMs converts about 3x better than traditional search traffic for HubSpot.
This transcript has been edited for length and clarity.
Danny Goodwin: Hey everybody, this is Danny Goodwin, editorial director of Search Engine Land, and, today I’m being joined by Aja Frost. We have an interesting discussion coming up about GEO, AEO, AI, and all the good hot topics. It’s great to meet you Aja. ’cause I’ve actually never, uh, run into you on the conferences or anywhere. So it’s really nice to connect with you.
Aja Frost: You know, Danny, I was gonna say, it’s nice to see you, which is my go-to if I’m not sure whether I’ve seen someone, I met someone before. I figured we had met because we definitely run in the same circles. But I’m delighted to be finally, officially making your acquaintance.
Danny Goodwin: Absolutely. Before we dive in for the people watching or listening, do you want to introduce yourself? Tell us a little bit about who you are and what you do?
Aja Frost: Yep. I am Senior Director of Global Growth and Paid Media at HubSpot. Global Growth is our catch-all for top-of-funnel non-paid demand, which largely translates to SEO and now AEO. And I’ve been at HubSpot for a little over nine years, which is about eight years longer than I thought I would be.
For those who don’t know, HubSpot is the customer platform that powers 268,000 teams. And it changes, I would say, as a company, every few years, which is what has kept me there. I think we have had a really interesting journey to this point, and we are embarking on what I believe is the most interesting era of SEO, AEO, and really marketing yet.
Danny Goodwin: Absolutely. So, yeah, it is a very fun time and you’ve been around for a few years at this point, so very curious to get your take. So, we had SMX Advanced a while back, our conference returned in person and at that point in time I’m like, oh, this whole AEO versus GEO versus whatever we’re gonna call a debate – it’s gotta be settled by the time like October, November comes around. And I’m surprised that it has not still been settled.
So I’m curious from your perspective, where do you stand on that whole name debate? What are you calling it, you know, this new form of SEO, or if it’s some, even if you consider it a new form of SEO, you know, has been GEO, AEO, some people call it AI SEO. What are you kind of calling this practice right now internally and, and why have you settled on whatever term that is?
Aja Frost: Yeah, great question because this was the topic of much debate internally at HubSpot. I think we debated all of the names that you just mentioned and probably 10 more. And we ultimately landed on AEO, or answer engine optimization, because we think it best reflects how people are using AI and what businesses/brands should be doing in response.
So I think SEO, you wanted to rank in the results, like that was pretty clear. Now you wanna be a part of the answer. And so answer engine optimization is the tactics, the plays that you run to show up as part of that answer.
Also, it just sounds cooler than GEO in my opinion, but we’ll see how long the debate rages on. I have learned not to underestimate how long people in our particular world can spend haggling and debating this type of thing.
Danny Goodwin: Yes, I know it’s, it’s sort of like subdomains versus subfolders. If you’ve been around long enough, you’ll know what that means and how long that debate has been going on. And I can’t even tell you, uh, more than a decade, I’m safe in assuming. Whatever we call it ultimately or whatever it gets decided it is called, this does feel like a big transition point for search from traditional ranking search to AI search is more about retrieval. So for you, how has it changed the way you’re thinking about visibility and strategy?
Aja Frost: Yeah, we are very much thinking about AEO as an evolution of SEO, which I did my homework and I’m just a Danny Goodwin fan, so I know that I think we’re on the same page there. And yes, that was an intentional pun.
I think one thing that has actually always been a very HubSpot philosophy is do what’s best for the customer. And that’s always overlapped really neatly with our SEO strategy. It’s also what Google has preached for many years – do what’s best for the customer. You may miss out on some short-term wins, but in the long run, your site is going to perform better.
And that is at the heart of our AEO strategy. I also think that the three buckets of plays that we’re running are familiar from SEO. So the what hasn’t changed, but the how has, and I’ll go a click deeper there. Those three buckets for us are content, technical, and offsite.
Our content for AEO looks fairly different than it does for SEO. It’s much more specific. It’s much nicher and deeper. It’s structured differently. It’s written differently. But it’s always intended to be what’s best for the customer or best for the reader.
The second bucket is technical. And again, I think that Google indexes/ingests content differently than AI bots do. And so we need to adjust our technical strategies to match while not doing anything that’s harmful for GoogleBot, because of course we still care about Google.
And then offsite, one thing that is probably the clearest from SEO to AEO is the emphasis on brand mentions rather than links. And so we’re really shifting our offsite strategy to be much more about positive mentions in the places that AI is training and citing versus getting backlinks on high domain authority websites.
Danny Goodwin: That is a big shift. I think still a lot of people aren’t ready for. So much of the stuff the tactics have been ingrained for – and I forget, how long have you been doing SEO roughly?
Aja Frost: I’ve been doing SEO for a little over a decade.
Danny Goodwin: So SEO is probably about near 30 years old at this point.
Aja Frost: Oh, Danny, we didn’t say we were gonna talk about my age on the podcast.
Danny Goodwin: Hey. But yeah. Um, sorry about that.
Aja Frost: No, they’re all good.
Danny Goodwin: So yeah, I mean, it’s just like, there’s this kind of, this whole playbook I think that a lot of people are attached to. And change is scary for a lot of people. Rethinking that stuff is important because nothing is static. And especially right now things are just kind of chaotic. The amount of changes we’re seeing, it’s crazy.
Aja Frost: Oh my God. Change is so scary.I think change is scary for us. We also had the pressure of not just figuring this out for our own internal strategy, but for figuring it out for our customers. The strategy that we are shipping right now, I have a very direct line to our VP of product for our marketing hub. I also spend a lot of time with the head of product for content hub. Those two products basically represent your website and content strategy and HubSpot. Everything that we’re doing. I’m telling them about the stuff that’s working, the stuff that’s not working, so they can turn that into product learnings as quickly as possible. I think it is terrifying and exhilarating and exciting all at once.
Danny Goodwin: Yeah. And with that change, I think there’s a lot of rethinking about how we define success, right? So AEO is not going to be the same success metrics that we had with SEO. So how are you actually thinking about that right now? It used to be like, how many links can I acquire? But what are you thinking about now? What’s important? Is it visibility in a AI answers, getting citations or mentions the actual conversions from the traffic, which again, is not as large as traffic from search, but – there is debate over whether it’s higher quality at this point, which maybe we’ll get into a little bit later. How are you sort of defining success with AEO?
Aja Frost: This was also a topic of much debate, and we actually published the results on our Loop Marketing page. We have a new scorecard for how companies should be thinking about marketing in the age of AI. And AEO, which fits into this loop marketing framework has a few new North Star metrics.
The first, and the one that I would argue is the most important, is visibility. And it’s visibility and not traffic, or not citations, because visibility is what’s going to ultimately inform whether someone converts. And they might not convert in that session. They’re probably not gonna convert directly from their interaction with the LLM. We know that LLMs just are really bad at navigational search. And so they’re probably opening up a new tab or maybe two days later, five days later, going to the website. But the, the visibility is what informs what we care about, which is the conversion. So that’s number one.
That takes, by the way, a lot of education with your exec leadership. And I am very lucky to work at a company, whose leadership is deeply embedded in all these conversations, and I think gets it. But if you are at a company where your CEO is not reading Search Engine Land, it’s definitely worth doing a deep dive to help them understand why visibility is the number one.
Second is share of voice. So what is your visibility like relative to your competitors? And I think that’s a really useful benchmark. I know that there was a lot of coverage back in mid-September when ChatGPT really turned down the dial on visibility for brands. And if you are just looking at visibility, you might think, oh, something’s going haywire with my strategy. If you look at share voice and share voice is constant or growing, you know that you’re doing the right thing, agnostic of some of the algorithmic changes.
Then we get to mentions, or sorry, mentions goes into visibility, then we get to citations. How many times is your website used as a source in answer engine responses? And I think this is really important. I think a lot of brands go after citations first. I’m putting it third on our list. I think it is important because if you get the citation, what we have found is your average ranking and the response and the sentiment of that description, they’re both better, which makes a ton of sense. If you control the source, you’re always gonna say the nicest things about yourself and put yourself first. If you overindex on citations, however, you’re gonna miss out on a wide swath of visibility that I think is pretty critical.
Danny Goodwin: You’ve done a lot of experimenting, which I want to get into in a minute, with optimizing for LLMs and AI-generated answers. What ways do you see SEO and AEO being similar? And then maybe where do you see them separating a little bit?
Aja Frost: Yeah, I think this goes back to what I was talking about – solving for the customer or doing what is good for the end user. I think that is shared for SEO and AEO. And one of the questions you probably get, ’cause I get it all the time, is, well, if I do this for AEO, will it be bad for SEO? And my answer is always no. If you are doing, if you were rolling out an AEO strategy that is good for the end user.
So an example of what would be bad for the end user would be burying secret instructions in content for an AI agent. A good thing would be creating really helpful specific content that’s going to answer a really niche query that someone is asking ChatGPT. And as long as you are solving for that end user, I think that you’ll benefit in both disciplines. You’ll, benefit in answer engines as well as Google.
And then I think the three higher level categories of plays are similar, but where I think things get very different are, again, the content is just, we’re going from these very broad, high level topics, these ultimate guides, which HubSpot – this is a, I don’t know, a dubious claim to fame. But when I started an SEO at HubSpot, then I was telling the blog team what keywords I thought we should target and, and recommending search friendly titles. And I really liked Ultimate Guide. I just thought it sounded nice. So every title I recommended was Ultimate Guide, this Ultimate Guide that. And then of course, a lot of websites started using Ultimate Guide, and now I’ll click through the SERPs and I see Ultimate Guide. I’m like, I think this is my fault.
So you’re going from the ultimate guide to, you know, this is the exact use case that this exact persona wants to accomplish, and here’s how to do it, and here’s some original data that we’ve gotten from customers just like you. And if you come from an answer engine, it’s gonna be tailored exactly to what we know about you. And so it’s a very different style of content and content journey.
Danny Goodwin: Yeah, for sure. ’cause I, I feel like, and I’ve, I had this conversation not publicly, but there were conversations after the whole bruhaha about all the traffic HubSpot lost. And just how much traffic they were losing. Everybody was losing their minds over it. And I was like, wow. You know, you kind of forget the influence that HubSpot had on content marketing as a whole. Your playbook that you guys came up with was used by so many other websites. Like there’s just, you know, repurposed for their specific topic or niche or whatever. But yeah, like HubSpot, that playbook was huge for a lot of years. Right. I think that’s, that was started like right before COVID around that time and then just sort of exploded., Is that the right timeframe?
Aja Frost: I think it depends on what you are talking about. If you’re talking about inbound, inbound I think is really at the heart of the web. At least for a lot of companies that were publishing educational content and inbound goes way, way back.
I think we have always been very much a build and public company and, and we share our successes and our strategies along the way. Which is what we’re doing right now with Loop Marketing. I think that has led to a lot of companies saying, oh, this was really successful for HubSpot, I’m gonna adopt it as well, which is good. That’s what we wanted.
But I also think that when we started seeing declines from the emergence of AI Overviews and the changing nature of Google, that was a bit of a bellwether for what I think a lot of websites are now seeing. And so one response could have been, oh, we’re not gonna build in public anymore. We’re gonna be very cagey about what we’re doing and what’s working. So that doesn’t happen again. But that’s obviously not what we’re doing.
We’re trying to be even more transparent and helpful. I really hope and believe that loop marketing, which is not a replacement of inbound, but meant to be, again, an extension of and, and a really helpful framework for companies can play that role.
Danny Goodwin: So just going back to that, that traffic drop. I was basically told it was about an 80% traffic drop and you kind of helped the company through that. And now in LLM world, HubSpot is the most cited CRM, is that correct?
Aja Frost: Or the most visible CRM
Danny Goodwin: Most visible. Okay. Gotcha. All right. And, and obviously this is, again, this is a fairly new technology. So, when you were starting to approach optimization on LLMs and AEO, how did you start that journey? Like, what were the first few things that you maybe either thought about or tried that did or did not work?
Aja Frost: Yeah. Well, the first thing I did that I would really recommend folks do if they don’t have an AEO function already stood up was I, um, pulled together some of the ICS on our team that were already doing a lot of experimentation and research in their own time. In my day-to-day, I am usually working with managers or directors. I’m not super close to the work. But I knew that I needed to be really close to this and really help guide it. And so I said, the three of us, we’re gonna meet once a day. We are going to launch one experiment per week if we can. I’m working with the dev team so that whatever we need to do, we can execute as quickly as possible. And so we took a very experimental mindset from the get go.
What we started out with was how do we scale good quality data-rich content? We had been thinking, and I think most people thought about content, maybe in a month you put out 30 pieces. If you’re a news publication, you could be putting out hundreds. But we’re thinking in multipliers of tens most teams. And I think we need to be thinking in multipliers of hundreds or thousands.
And so with the team, I wanted to figure out how do we create that content? How do we start relatively small? So like batches of 10, generated with AI reviewed by a human, and then how do we scale that over time? That I think has been very successful.
We’re still experimenting with the types of content that get the most visibility in answer engines. And so that’s what a lot of experimentation revolves around. We also did a lot of what I think of as good clean AEO. Making sure that we were using all the available schema types across our website, making sure that things were really well structured and that we’re leading with the answer. And each section of the page is semantically complete and things are formatted in a Q and A format. You know, a lot of things that I think are now becoming like the standard AEO playbook.
Danny Goodwin: So you mentioned content types. I know there’s been a lot of noise about how some people are abusing top X lists – the top 10 best insert thing here. Is that the sort of stuff you’ve been playing around with? When you say content form, is there anything you can share about what you found that works maybe better?
Aja Frost: Yeah, so I’m not thinking so much about top X for Y, although I think that that still very much has a place in people’s content playbooks. But what we’re really experimenting with is – Danny, what’s the last thing you did research with ChatGPT to buy?
Danny Goodwin: Oh, to buy?
Aja Frost: Yeah.
Danny Goodwin: Uh, it’s, it’s probably researching to find a hotel for Christmas.
Aja Frost: Okay. Find a hotel for Christmas. So the context that ChapGPT is going to have when it recommends a hotel for you is probably about how much money you typically spend based on some demographic data it’s collected about you, if you’ve done any hotel research in the past, where you’re going, obviously how long you’re gonna stay. Hotels, we wanna provide the answers for all of those contextual clues.
So if I were a hotel and I was trying to show up in answer engines, I would be creating content that spoke to your particular persona type and your particular use case. Now, I think the challenge is doing that without that content being duplicative or spammy. And to do that, this is what we spend a lot of time on. What are all the data sources that we can ingest to feed these systems essentially, so that all the content is unique, it’s grounded in what we know the persona needs, and it’s not repetitive from page to page.
Danny Goodwin: As, as you’ve gone through this process, were there any maybe big surprises like, oh my God, I didn’t think that would work. Or is there just like any kind of aha! moments, um, as you’ve been doing all this optimization for AI answers?
Aja Frost: The hardest part has been the measurement. I think that we are still very much as an industry, and I know this ’cause I talked to a lot of AEO vendors, figuring out how to correlate the actions that we are taking with specific visibility increases. And it’s highly dependent on the prompts you are tracking. I think that leaves the room for uncertainty and ambiguity because what if you’re tracking the wrong prompts? Or what if you’re tracking the right prompts, but not enough of them?
It’s far less clear to say “I did X and Y happened” than it was with SEO. And even with SEO, you know, we couldn’t run A/B tests. We are always doing look backs. There’s so many variables at play.
I talked about education with execs around why visibility is the most important. I think the other really important piece of education, not just for executive leadership, but for, SEO/AEO teams is getting comfortable with less data and fewer direct lines between what we’re doing and the results.
So that’s been, I don’t know if that’s been surprising ’cause I think I knew going in that that was going to be hard. But as we’ve progressed and we’ve done more and more teasing apart, the impact of individual experiments has gotten harder and harder.
Danny Goodwin: So I heard through on background of getting this interview set up that you sort of have a formula for getting ChatGPT to recommend a brand. So I want to hear all about that. What can you tell us about that?
Aja Frost: Well, I think that many of the best tactics that we are successfully using are ones that I’ve already mentioned. So we’ve spent a lot of time talking about hyper-specific persona-centric content. What we’ve talked about a little less is the off-site tactics that we’re using. And what we’ve done is identified ChatGPT and Google, because those are priority engines, we’ve identified their top training and citation sources.
And then we have put together a concerted strategy to show up as positively and frequently as possible in those places. And two big areas for us have been YouTube and Reddit, which probably won’t surprise anyone as being very influential for answer engines. I can go a little bit more into some of the things we’ve done there, if that’s useful?
Danny Goodwin: Yeah, I think so. There’s been some research done around how heavily cited Reddit and YouTube and a few other sites are. So yeah, I’d be kinda curious to know, like from a strategic standpoint, maybe like how you guys are approaching Reddit and YouTube.
Aja Frost: Yeah. Very different strategies for each and one big learning for us, I wouldn’t say this is in the last year because we’ve been very active on both platforms for several years, but, um, treating every social media platform as its own beast and really getting to know the lay of the land and understanding the culture and the rules and the unspoken rules before we engage. I mean, that’s just a general best practice for any community or social media site.
But on YouTube, we have a large slate of owned channels from Marketing Against the Grain and HubSpot Marketing, to How to HubSpot, Science of Scaling. It really runs the gamut. And we, the global growth or SEO AEO team works really closely with the teams creating those conthat content to weave in organic mentions of the products where they make sense and make sure that we are creating content on topics that we know answer engines and people care about. We also have a lot of creator partnerships with folks who speak to our relevant audience and somewhat similar playbook there. We want organic, relevant, contextual mentions of HubSpot.
Danny Goodwin: So that’s like influencer marketing, that sort of thing when you say creator?
Aja Frost: Yeah. I think you could call it influencer marketing. I mean, we, we sign, um, multi-month sometimes one-year contracts with creators and, and say, you know, we will pay you X, Y, Z and, in exchange you will create content on these wide topics. Well, we give them a lot of editorial freedom, but you know. You’ll mention HubSpot in X videos, that sort of thing.
And then on Reddit, it is a much more advocacy and community-centric approach. And I should have shouted out HubSpot Media on the YouTube front. They are a fantastic partner to my team. On the Reddit front, we work really closely with HubSpot community, another internal team. And in the last year we became the co-moderator of HubSpot’s subreddit. And we have spent most of our time making that subreddit as productive and engaging as possible because what we’ve seen, which is really interesting, is that the more activity that happens in our HubSpot, the more positive mentions of HubSpot there are across Reddit.
Because basically you’re creating a team of advocates who are really excited about your brand, your product, and then they organically go out into conversations on our sales, our marketing, our CRM, and they say good things about HubSpot. So, very, very different strategies, but both focused on getting the right people to say nice things about HubSpot.
Danny Goodwin: I think we touched on this a little bit earlier. Google search versus traffic you get from AI engines, it’s very different. It’s not as large. We’ve actually reported, in the last couple months, three different stories basically saying that traffic that you get from LLMs is either worse or about on par with Google search in terms of converting. I’m curious what you’ve seen there. Do you see that to be the case or do you see quality traffic coming through?
Aja Frost: Yeah, the traffic that directly comes from LLMs converts at about three times better than traditional search for us. So we’re definitely seeing higher conversion rates. And I, I’ve read the SEL stories. I was looking at the one you most recently published, which was like 900 e-comm website over the course of a year. I shared that with my team last week. I was curious whether the difference in conversion rates had anything to do with the difference in the type of product and the buying journey.
Like, I think by the time someone is coming to hubspot.com from an LLM, they’ve done a lot of research, at least that’s what our analysis suggests. And so they’re much readier to convert than someone who might in the old world have been coming to the blog to download an ebook on content marketing. It’s been another really fascinating area to watch the industry debate because I’ve also seen several different, uh, different stats.
Danny Goodwin: Right. Yeah. Again, it’s very early and these are not large scale studies, it’s just sort of anecdotal I guess we would say. But any data, I think is useful ’cause at least it gets people thinking about all of these things and it’s gonna always go back to, it depends. It may be different for ecomm versus B2B or whatever the case may be. I think there’s still a lot that’s going to change from where AI is now. I even today was seeing somebody saying we’re at peak AI already. Like really? Like it’s, it’s two years old. Like, come on.
Aja Frost: Yeah. I would disagree with that. Yeah. I think there are, to your point, some things that could be step function increases in conversion rates. Obviously instant checkout, that’s huge. I think that, yeah, I mean this was obviously over the course of a year and I do remember seeing in the study that conversion rates had increased over time, maybe as people got more comfortable or familiar with ChatGPT.
But instant checkout’s huge. I don’t know what adoption for Atlas is going to be or for any of these ad browsers to be fair. But agent mode or agentic checkout would definitely improve conversion rates. So I think we’re at the very early innings of this.
Danny Goodwin: Where do you think AEO as a practice will be at maybe a year from now? Do you think it’ll be kind of its own thing? Do you think it’ll be part of SEO and is there anything that you were maybe kinda excited to see happen from ChatGPT or some of these other engines that could make these systems even better?
Aja Frost: I think a lot hinges on when Google makes AI Mode more of the primary search experience. I don’t believe that you are going to get an AI-powered answer for every search. My belief is for navigational queries, at the very least, you’re probably always gonna have something that feels like the traditional SERP and that it gets you from point A to point B very quickly. But I think for a lot, if not most other searches, you will probably be in some form of AI Mode and at that point, SEO and AEO become merged because there is no real traditional SERP to optimize for anymore.
Danny Goodwin: Yep. Exactly. That’s sort of been my problem with this whole naming debate. If you’re gonna call it AI SEO, what happens if that search engine goes away? There’s no more, there’s no more SE in SEO.
Aja Frost: Totally. Yeah. But yeah, and also that doesn’t exactly roll off the tongue. Like I don’t wanna stand up and and say I am an AI SEO.
Danny Goodwin: Right. Exactly. So if you could maybe give people one AEO type of experiment you think maybe they could run before the end of the year to kinda get a feel for it or just anything that you think might be helpful for them to kinda experiment with. Is there anything maybe you could suggest to people like, try this tactic or this strategy or whatever?
Aja Frost: I think if you want a real project, then I would try creating those hyper-specific, very persona-focused pages. I think if you’re looking for something that you could run with and get live by the end of the week, use one of the many query fan-out tools that are available online. Take a page that already exists on your website, plug like a, a likely reasonable query that would lead someone to that page into a query fan-out pool, and then assess whether your page answers or has content for all of the subqueries that that pool provides. And if it doesn’t add them and then see does your visibility for that head question increase.
Danny Goodwin: Awesome. Any final thoughts? Anything we didn’t talk about that you’d love to comment on or leave people with some parting words of wisdom?
Aja Frost: Yeah, I would, I would be remiss not to direct people to hubspot.com/loopmarketing. We have spent a lot of time on AEO. Of course, AEO is one of the tactics in this new growth framework for the AI era, but there’s a lot more that we believe businesses can and should be doing to not just survive but thrive. Check it out. I think there’s a lot there.
Danny Goodwin: Awesome. And just, just for anyone who’s listening and doesn’t know what is loop marketing like, can you give us just a quick overview of what that is? ’cause you mentioned a couple times.
Aja Frost: Yeah. Loop marketing is a growth framework for businesses. There are four phases: express, tailor, amplify, and evolve. Each of those four phases has a host of plays and tactics. But the general idea is that, as the web changes, as folks go from progressing through this ever-narrowing funnel to getting an answer in an LLM, then going to your Instagram, then reading a review and, and really having like a much more messy, much less linear journey, we need a new framework for marketing. And so this framework is an ever-evolving, much more flexible dynamic framework.
Danny Goodwin: Right. So it’s sort of like that old bendy straw, the messy middle as Google put it, I think. Right?
Aja Frost: Yes. Yes. I will say messy middle came up many times in our conversations around the loop.
Danny Goodwin: Yeah. Awesome. Alright, well that is all the time I have for you for today. It was a great conversation. I really appreciate you taking the time to chat with us. Look forward to seeing more from you in the future and wishing you nothing but success heading forward.
Aja Frost: Thanks so much, Danny. This was really fun.
Danny Goodwin: All right. Thanks. Aja. Bye everybody.
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https://zoomyourtraffic.com/wp-content/uploads/2025/11/wp-header-logo-15.png00Team ZYThttps://www.zoomyourtraffic.com/wp-content/uploads/2024/01/ZYT-1.pngTeam ZYT2025-11-06 07:53:172025-11-06 07:53:17Aja Frost on AI search, content strategy, and AEO success metrics
With Black Friday, Cyber Monday, and the holiday season fast approaching, you’re probably knee-deep in holiday account planning.
Today, we’re zeroing in on a Google Ads feature that you can use all year ‘round, but may be particularly useful to you and your customers during the holidays: Promotion assets.
Promotion assets (formerly known as promotion extensions) are an optional addition to your Search or Performance Max campaigns. They allow you to highlight special deals, sales, and discounts your business is currently running, alongside your standard ad headlines and descriptions.
Promotion assets can show on Google Search, and on Google Maps if you have a local promotion offer.
While you absolutely can edit your search ad text to include promotional messaging, promotion assets offer a few unique benefits. Specifically, they:
Stand out in search results: Promotion assets may be bold or have a box around them, so they can really capture a user’s attention and increase your click-through rate.
Allow for flexibility: You can highlight deals and offers without constantly editing and re-submitting your core ad text. You can even schedule them to go live (and turn off) in advance, which saves you lots of time.
To show special deals and sales with your Shopping ads, you need to set that up in Google Merchant Center rather than Google Ads. These are called Merchant Promotions, and they run through a separate system.
If you’re an ecommerce business, head over to Merchant Center to set up your deals and promotions.
To create a new promotion asset, go to Assets > Assets on the left-side navigation in Google Ads, tap the + button, and choose Promotion.
You can add promotion assets at the account, campaign, or ad group level.
If you define multiple assets across the various levels, Google will use the most specific level available. For example, ad group-level promotion assets would be selected over campaign-level promotion assets.
Part of creating a promotion asset is selecting an “occasion” for your promotion. While you must select from Google’s list of occasions, there is a wide variety of options.
Some are seasonal (like Halloween or Valentine’s Day), but many are generic and flexible, allowing you to run promotions year-round. For example:
Fall Sale
Winter Sale
Spring Sale
Summer Sale
This means that although you need an “occasion,” promotion assets are not just for big tentpole holidays. Any kind of business can have promotions, sales, and discounts throughout the year.
To ensure your promotion assets get approved, and provide a good user experience, you need to pay close attention to several “fiddly” details. Inconsistency between your asset and your landing page is a common reason for disapproval.
Here’s a checklist of some of those details:
Language and Currency
Discount Type: For example
Monetary Discount: e.g., “$20 off”
Percent Discount: e.g., “20% off”
Up to Discount: e.g., “Up to $100 off”
Item Text (20 Characters): This is where you specify the actual product or service on sale. Keep it brief and relevant, e.g., “shoes” or “house cleaning.”
Final URL: This link must take the user directly to the specific promotion for that specific item. Do not link to a generic homepage.
Promo code (optional)
Minimum order value (e.g., “Valid on orders over $50”) (optional)
Terms and Conditions (optional)
Promotion assets give you three layers of scheduling control. While we appreciate Google’s thoroughness, the use cases can be a bit confusing! Here’s what you need to know:
1. Promotion Scheduling: the dates your promotion is active
Understandably, the promotion asset is only eligible to show when the promotion is actually running. This means you cannot advertise a promotion before it has started or after it has ended.
2. Asset Scheduling: the dates when your promotion asset can run
This controls when your promotion asset will be eligible to run, which may or may not be the same as the actual promotion dates.
For example, if your business is running a sale for six weeks but only wants to advertise it via Google Ads during the last two weeks, you can set the promotion asset to be live only during those final 14 days.
3. Ad Scheduling: time of day and day of week
You can set additional scheduling rules to show your promotion asset only on specific days or times of the week (e.g., only during evenings or on weekends).
This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.
Jyll Saskin Gales is a Google Ads coach, teacher and consultant. She advises agencies, business owners and in-house marketers, training them to get the best results from Google Ads. She hosts the Inside Google Ads podcast, her own “Inside Google Ads” and “Google Ads for Beginners” courses, and wrote the bestselling book “Inside Google Ads: Everything you need to know about Audience Targeting.” Jyll worked at Google for 6 years and has an MBA from Harvard Business School.
https://zoomyourtraffic.com/wp-content/uploads/2025/11/wp-header-logo-5.png00Team ZYThttps://www.zoomyourtraffic.com/wp-content/uploads/2024/01/ZYT-1.pngTeam ZYT2025-11-03 07:53:042025-11-03 07:53:04How to use Google Ads Promotion assets (a step-by-step checklist)
Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude?
LLM optimization is taking shape as a new discipline focused on how brands surface in AI-generated results and what can be measured today.
For decision makers, the challenge is separating signal from noise – identifying the technologies worth tracking and the efforts that lead to tangible outcomes.
The discussion comes down to two core areas – and the timeline and work required to act on them:
Tracking and monitoring your brand’s presence in LLMs.
Improving visibility and performance within them.
Tracking: The foundation of LLM optimization
Just as SEO evolved through better tracking and measurement, LLM optimization will only mature once visibility becomes measurable.
We’re still in a pre-Semrush/Moz/Ahrefs era for LLMs.
Tracking is the foundation of identifying what truly works and building strategies that drive brand growth.
Without it, everyone is shooting in the dark, hoping great content alone will deliver results.
The core challenges are threefold:
LLMs don’t publish query frequency or “search volume” equivalents.
Their responses vary subtly (or not so subtly) even for identical queries, due to probabilistic decoding and prompt context.
They depend on hidden contextual features (user history, session state, embeddings) that are opaque to external observers.
Why LLM queries are different
Traditional search behavior is repetitive – millions of identical phrases drive stable volume metrics. LLM interactions are conversational and variable.
People rephrase questions in different ways, often within a single session. That makes pattern recognition harder with small datasets but feasible at scale.
These structural differences explain why LLM visibility demands a different measurement model.
This variability requires a different tracking approach than traditional SEO or marketing analytics.
The leading method uses a polling-based model inspired by election forecasting.
The polling-based model for measuring visibility
A representative sample of 250–500 high-intent queries is defined for your brand or category, functioning as your population proxy.
These queries are run daily or weekly to capture repeated samples from the underlying distribution of LLM responses.
Tracking tools record when your brand and competitors appear as citations (linked sources) or mentions (text references), enabling share of voice calculations across all competitors.
Over time, aggregate sampling produces statistically stable estimates of your brand visibility within LLM-generated content.
Early tools providing this capability include:
Profound.
Conductor.
OpenForge.
Consistent sampling at scale transforms apparent randomness into interpretable signals.
Over time, aggregate sampling provides a stable estimate of your brand’s visibility in LLM-generated responses – much like how political polls deliver reliable forecasts despite individual variations.
Building a multi-faceted tracking framework
While share of voice paints a picture of your presence in the LLM landscape, it doesn’t tell the complete story.
Just as keyword rankings show visibility but not clicks, LLM presence doesn’t automatically translate to user engagement.
Brands need to understand how people interact with their content to build a compelling business case.
Because no single tool captures the entire picture, the best current approach layers multiple tracking signals:
Share of voice (SOV) tracking: Measure how often your brand appears as mentions and citations across a consistent set of high-value queries. This provides a benchmark to track over time and compare against competitors.
Referral tracking in GA4: Set up custom dimensions to identify traffic originating from LLMs. While attribution remains limited today, this data helps detect when direct referrals are increasing and signals growing LLM influence.
Branded homepage traffic in Google Search Console: Many users discover brands through LLM responses, then search directly in Google to validate or learn more. This two-step discovery pattern is critical to monitor. When branded homepage traffic increases alongside rising LLM presence, it signals a strong causal connection between LLM visibility and user behavior. This metric captures the downstream impact of your LLM optimization efforts.
Nobody has complete visibility into LLM impact on their business today, but these methods cover all the bases you can currently measure.
Be wary of any vendor or consultant promising complete visibility. That simply isn’t possible yet.
Understanding these limitations is just as important as implementing the tracking itself.
Because no perfect models exist yet, treat current tracking data as directional – useful for decisions, but not definitive.
Measuring LLM impact is one thing. Identifying which queries and topics matter most is another.
Compared to SEO or PPC, marketers have far less visibility. While no direct search volume exists, new tools and methods are beginning to close the gap.
The key shift is moving from tracking individual queries – which vary widely – to analyzing broader themes and topics.
The real question becomes: which areas is your site missing, and where should your content strategy focus?
To approximate relative volume, consider three approaches:
Correlate with SEO search volume
Start with your top-performing SEO keywords.
If a keyword drives organic traffic and has commercial intent, similar questions are likely being asked within LLMs. Use this as your baseline.
Layer in industry adoption of AI
Estimate what percentage of your target audience uses LLMs for research or purchasing decisions:
High AI-adoption industries: Assume 20-25% of users leverage LLMs for decision-making.
Slower-moving industries: Start with 5-10%.
Apply these percentages to your existing SEO keyword volume. For example, a keyword with 25,000 monthly searches could translate to 1,250-6,250 LLM-based queries in your category.
Using emerging inferential tools
New platforms are beginning to track query data through API-level monitoring and machine learning models.
Accuracy isn’t perfect yet, but these tools are improving quickly. Expect major advancements in inferential LLM query modeling within the next year or two.
Get the newsletter search marketers rely on.
Optimizing for LLM visibility
The technologies that help companies identify what to improve are evolving quickly.
While still imperfect, they’re beginning to form a framework that parallels early SEO development, where better tracking and data gradually turned intuition into science.
Optimization breaks down into two main questions:
What content should you create or update, and should you focus on quality content, entities, schema, FAQs, or something else?
How should you align these insights with broader brand and SEO strategies?
Identify what content to create or update
One of the most effective ways to assess your current position is to take a representative sample of high-intent queries that people might ask an LLM and see how your brand shows up relative to competitors. This is where the Share of Voice tracking tools we discussed earlier become invaluable.
These same tools can help answer your optimization questions:
Track who is being cited or mentioned for each query, revealing competitive positioning.
Identify which queries your competitors appear for that you don’t, highlighting content gaps.
Show which of your own queries you appear for and which specific assets are being cited, pinpointing what’s working.
From this data, several key insights emerge:
Thematic visibility gaps: By analyzing trends across many queries, you can identify where your brand underperforms in LLM responses. This paints a clear picture of areas needing attention. For example, you’re strong in SEO but not in PPC content.
Third-party resource mapping: These tools also reveal which external resources LLMs reference most frequently. This helps you build a list of high-value third-party sites that contribute to visibility, guiding outreach or brand mention strategies.
Blind spot identification: When cross-referenced with SEO performance, these insights highlight blind spots; topics or sources where your brand’s credibility and representation could improve.
Understand the overlap between SEO and LLM optimization
LLMs may be reshaping discovery, but SEO remains the foundation of digital visibility.
Across five competitive categories, brands ranking on Google’s first page appeared in ChatGPT answers 62% of the time – a clear but incomplete overlap between search and AI results.
That correlation isn’t accidental.
Many retrieval-augmented generation (RAG) systems pull data from search results and expand it with additional context.
The more often your content appears in those results, the more likely it is to be cited by LLMs.
Brands with the strongest share of voice in LLM responses are typically those that invested in SEO first.
Strong technical health, structured data, and authority signals remain the bedrock for AI visibility.
What this means for marketers:
Don’t over-focus on LLMs at the expense of SEO. AI systems still rely on clean, crawlable content and strong E-E-A-T signals.
Keep growing organic visibility through high-authority backlinks and consistent, high-quality content.
Use LLM tracking as a complementary lens to understand new research behaviors, not a replacement for SEO fundamentals.
Redefine on-page and off-page strategies for LLMs
Just as SEO has both on-page and off-page elements, LLM optimization follows the same logic – but with different tactics and priorities.
Off-page: The new link building
Most industries show a consistent pattern in the types of resources LLMs cite:
Wikipedia is a frequent reference point, making a verified presence there valuable.
Reddit often appears as a trusted source of user discussion.
Review websites and “best-of” guides are commonly used to inform LLM outputs.
Citation patterns across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews show consistent trends, though each engine favors different sources.
This means that traditional link acquisition strategies, guest posts, PR placements, or brand mentions in review content will likely evolve.
Instead of chasing links anywhere, brands should increasingly target:
Pages already being cited by LLMs in their category.
Reviews or guides that evaluate their product category.
Articles where branded mentions reinforce entity associations.
The core principle holds: brands gain the most visibility by appearing in sources LLMs already trust – and identifying those sources requires consistent tracking.
On-page: What your own content reveals
The same technologies that analyze third-party mentions can also reveal which first-party assets, content on your own website, are being cited by LLMs.
This provides valuable insight into what type of content performs well in your space.
For example, these tools can identify:
What types of competitor content are being cited (case studies, FAQs, research articles, etc.).
Where your competitors show up but you don’t.
Which of your own pages exist but are not being cited.
From there, three key opportunities emerge:
Missing content: Competitors are cited because they cover topics you haven’t addressed. This represents a content gap to fill.
Underperforming content: You have relevant content, but it isn’t being referenced. Optimization – improving structure, clarity, or authority – may be needed.
Content enhancement opportunities: Some pages only require inserting specific Q&A sections or adding better-formatted information rather than full rewrites.
Leverage emerging technologies to turn insights into action
The next major evolution in LLM optimization will likely come from tools that connect insight to action.
Early solutions already use vector embeddings of your website content to compare it against LLM queries and responses. This allows you to:
Detect where your coverage is weak.
See how well your content semantically aligns with real LLM answers.
Identify where small adjustments could yield large visibility gains.
Current tools mostly generate outlines or recommendations.
The next frontier is automation – systems that turn data into actionable content aligned with business goals.
Timeline and expected results
While comprehensive LLM visibility typically builds over 6-12 months, early results can emerge faster than traditional SEO.
The advantage: LLMs can incorporate new content within days rather than waiting months for Google’s crawl and ranking cycles.
However, the fundamentals remain unchanged.
Quality content creation, securing third-party mentions, and building authority still require sustained effort and resources.
Think of LLM optimization as having a faster feedback loop than SEO, but requiring the same strategic commitment to content excellence and relationship building that has always driven digital visibility.
From SEO foundations to LLM visibility
LLM traffic remains small compared to traditional search, but it’s growing fast.
A major shift in resources would be premature, but ignoring LLMs would be shortsighted.
The smartest path is balance: maintain focus on SEO while layering in LLM strategies that address new ranking mechanisms.
Like early SEO, LLM optimization is still imperfect and experimental – but full of opportunity.
Brands that begin tracking citations, analyzing third-party mentions, and aligning SEO with LLM visibility now will gain a measurable advantage as these systems mature.
In short:
Identify the third-party sources most often cited in your niche and analyze patterns across AI engines.
Map competitor visibility for key LLM queries using tracking tools.
Audit which of your own pages are cited (or not) – high Google rankings don’t guarantee LLM inclusion.
Continue strong SEO practices while expanding into LLM tracking – the two work best as complementary layers.
Approach LLM optimization as both research and brand-building.
Don’t abandon proven SEO fundamentals. Rather, extend them to how AI systems discover, interpret, and cite information.
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.
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Branding wasn’t something SEOs traditionally thought much about. The real wins were in non-branded keywords, where the traffic and conversions lived.
However, that changed when Google and OpenAI turned most of these queries into zero-click searches.
For the remaining queries, search platforms directly reward authoritative and popular brands, so branding can no longer be ignored for SEO.
Here’s your brand SEO playbook for getting seen, trusted, and chosen in the future of AI-powered search.
Brand SEO is about clarifying and amplifying your brand’s voice everywhere people search. It starts with a solid brand foundation. Without that, you’ll struggle to improve visibility in AI-powered search systems.
If branding is new to you, think of it as the process of creating a distinct identity in the minds of consumers. It differentiates your business from competitors and builds a lasting impression through your name, messaging, visuals, and reputation.
So when you do brand SEO, it’s about creating consistency and ensuring accuracy in how your brand presents itself everywhere people search (Google, ChatGPT, Reddit, and beyond).
You’ll be able to define and control some aspects of your brand. For example, here’s Ahrefs’ media kit, where we make it easy for others to reference our brand the same way we do.
But you are not in control of the impression your brand makes in consumers’ minds (and how AI summarizes those impressions).
Why SEOs can’t ignore branding anymore
As AI is integrated into search, brand signals are becoming a part of Google’s ranking algorithm.
For instance, Mark Williams-Cook discovered that Google uses a site quality score to classify websites, and those that fall under a certain benchmark (0.4 on a 0-1 scale) do not qualify for visibility in rich snippets.
This score is calculated based on:
Brand strength, measured by how many searches are made that include the brand’s name
User interactions (like clicks), especially when a brand does not rank in the top position
Branded anchor text, determining topic-to-brand connections from around the web
Brands are also being vectorized as entities in LLMs and semantic search engines’ embedding models.
This means that machines treat your brand as a distinct organization. Then, they map other topics related to your brand to understand what you’re all about so they can summarize this information directly in search results.
When visualized, it looks like this:
Notice how the brand Lucasfilm is connected to its sub-brand Star Wars, which is connected to characters, actors, genres, and more?
The same network of connections is built around your brand, too.
This is the foundation of how AI systems understand your brand and how to summarize it best. So brand SEO is crucial for ensuring your brand:
Shows up as a distinct entity, separate from other similar-sounding entities, like Apple the company vs apple the fruit.
Is connected to appropriate and accurate topics for your products and services, like how Dyson is connected to vacuum cleaners and seen as an authority for that topic.
Has no gaps that can lead to misinformation or hallucinations in AI summaries. If your brand entity isn’t connected to topics and other entities that matter, those are gaps you need to close.
Brand SEO is not just about rankings (which only care about if you show up). It’s about how you show up to ensure favorable and accurate mentions in AI-generated responses.
Here’s the exact 7-step brand SEO framework I use.
1. Set up your brand’s online foundation
Start by defining your brand and any key topics or things you want to connect it to. I use the “5 W’s and How” framework to get the ball rolling:
The “who” element
There are two aspects here: who you help and who you hire.
For your audience (who you help), tailor your branding to speak their language and give them the “what’s in it for me” factor upfront. For example, Obsidian is a knowledge management app. But its tagline is 100% focused on the benefit it delivers to users, and it shows up where people search:
Also, show the team behind the brand (i.e., who you hire) and create profile pages for each of them, showcasing their industry experience and expertise.
The “what” element
What does the business do? What topics or product categories does it want to be known for? Create dedicated landing pages for the brand’s flagship products or services.
For example, instead of having a single page with all your services, split these up into separate landing pages and add the main ones in your navigation.
You could also have separate pages for unique features and attributes that matter to your audience, showcasing the things that make your brand, products or services different. For example, here’s a turf company promoting the unique qualities of its grass varieties:
It helps with SEO and search ads since you can direct visitors to the exact service or feature they’re interested in.
The “when” element
Is time a potential factor influencing your brand? If so, include this in your brand messaging, such as “24/7 support” or “up-to-the-minute” updates.
Depending on your product or service, you could also create dedicated landing pages about this USP with details like:
Locations open 24/7, and their contact details
Mobile services you offer for emergencies and the areas you cover
How you collect up-to-the-minute updates to report on
Express shipping you offer for “last-minute” purchases
The “where” element
Think of physical locations (like cities and suburbs), virtual (like the metaverse), or conceptual (like fictional worlds) that are relevant to your brand.
Are the reasons why you started the brand or why you do things in a particular way important to your audience? Connect these to your unique selling proposition as part of your key messaging.
For example, purpose-driven brands can inspire loyalty among their audiences, take Who Gives a Crap as an example:
Their branding is very loud when it comes to why they do what they do. On the surface, they just sell toilet paper. However, they’ve had huge success on the sales and promotion side because of their “why”, earning thousands of links and mentions in premium publications:
The “how” element
For most brands, who they serve, what they do, or why they do it is often enough to unify their brand vision. But there are rare occurrences where it all comes down to how they do things.
For example, a facilities management company I worked with struggled to define its brand. Its services spanned multiple categories (security, cleaning, labor hire, and investigations), and its audience ranged from small pubs to international government bodies.
This made both the “what” and “who” too broad to unify, a rare situation.
Surprisingly, the answer came from the “how.” By articulating its unique process, it was able to clearly define what tied together its diverse services.
For the first time, the brand’s messaging was in alignment with how they operated offline.
The “5Ws and How” is a simple yet powerful method for defining your brand’s identity and planning how to represent it online, especially if you want people and LLMs to talk about it correctly.
2. Audit your existing brand and its visibility in search
Next, audit the current website, business profiles, social profiles, and the brand’s other owned media.
Look for inconsistencies in brand messaging or core details (like the brand’s name, address, or phone number) that do not align with its current information or style guide.
Start making a list in your project management tool, as you’ll need to clean these inconsistencies up, pronto. Otherwise, they’ll become a significant source of misinformation distributed through LLM responses.
Next, check out Ahrefs’ Brand Radar to assess your earned visibility.
Look for:
Inconsistencies in brand messaging or core details like, incorrect name, address or phone number details could be a problem. As can mentions of old company slogans and taglines.
Brand sentiment (especially negative sentiment): If mentions of your brand are predominantly negative, this could dissuade search engines and LLMs from including your brand in responses.
Weaknesses in brand authority affecting online visibility: If you do not have many brand mentions and links from authoritative sources, your brand’s online authority may be weak.
Brand popularity and traffic from branded keyword searches: If your competitors have more brand searches and demand than you do, this could lead to them also being more visible in search.
To find these potential brand-related visibility issues, start in the “Search demand” tab to get a benchmark of your branded searches:
In the “Web visibility” tab, you can find mentions of your brand around the web. I like to filter out mentions on the brand’s own website here:
It’s also worth checking the other tabs to see the brand’s mentions on different platforms and in AI responses.
You can also look at your analytics or Google Search Console dashboards and filter for branded traffic or impressions. These are great indicators of your current level of brand awareness.
If your brand is fairly new and you want to confirm if it’s seen as a distinct entity by Google, try searching Carl Hendy’s Knowledge Graph API Search Tool. You’ll also be able to see if your brand is getting confused with other things, or if it’s been misclassified:
The idea is to get a robust picture of how machines have classified and interpreted your brand. And if you notice any gaps here or incorrect information, add them to your project management tool.
You’ll need to correct those to ensure accurate information in search responses, especially in AI features. How you go about correcting them depends on the source of the inaccurate information:
If it’s an owned channel (like your social profiles or business citations), you can log in and change it directly.
If it’s on a forum or discussion thread, you can respond and become a part of the conversation, clarifying things for your audience exactly where they’re talking about your brand.
If it’s on a third-party website or news, you could reach out to the author or editor and ask them to correct any misinformation they’ve published.
Your mileage may vary, but it never hurts to try. Here’s an example of Common Room, a brand that undertook such a task recently and what worked for them:
3. Find the topics your audience searches (and on what platforms)
Next, look into untapped opportunities to gain relevant visibility from your audience. With organic traffic going down across the board, clever brands are taking a more holistic view of SEO as “search everywhere optimization”.
I start with Ahrefs’ Keywords Explorer. For example, the topic of “ergonomic chairs” has over 1,400 queries being searched in the US per month, 25,000 times.
This gives me a great overview of what topics I can align the brand to, especially when filtering for commercial or transactional intent. Queries with these intents lead to higher click-through rates from AI-powered search engines compared to informational queries.
However, for brand SEO, I take it a step further by looking at keyword modifiers, features, and attributes mentioned in keywords that can be used in USPs and brand messaging.
For example, for a local aged care home, there were many keywords relating to quality and price:
So, we adapted the brand’s messaging around the USP of “value for money”, making them a top recommended choice in AI responses as a result:
I also go further and assess what platforms are a part of the audience’s search journey to ensure holistic brand visibility everywhere searchers are likely to look.
SparkToro is a great tool for seeing the most popular platforms for a topic. For example, for “ergonomic chair”, Twitch, Github, and Discord are used above average, indicating a strong audience demographic among coders and gamers:
Discussions are happening on these platforms that relevant brands can contribute to. For instance, here’s a thread discussing recommendations for ergonomic chairs on GitHub:
To find the conversations you can join, try using the Web Visibility report in Brand Radar. Filter the data to the platform you care about (like Reddit, in the image below) and then search for mentions of the topic on that platform:
Try out different things here:
Search for your brand mentions on each platform and assess sentiment among your audience
Search for competing products and get your product featured in similar conversations to them
Consider paying for ad real estate on pages or conversations about related topics
The idea is to protect your existing visibility and amplify it everywhere your audience searches for your brand, products, or services.
Remember to keep adding interesting insights and action items as tasks in your project management tool as you go.
4. Analyze competitors and protect your branded real estate
At this stage, you can also do a brand gap analysis.
This is different from a content or link gap analysis. It’s about finding gaps in your brand positioning, messaging, market perception, and visibility compared to competitors while protecting your branded search results.
For example, if you want to be known as the #1 brand for a specific topic or product category, you can see how you compare against competitors. This doesn’t come down to how much content you’ve created about a topic, but rather how closely the market thinks your brand is connected to it.
I use Ahrefs’ Brand Radar for this by adding the brand I’m working on alongside its competitors:
In this example, Toyota is most closely connected to the SUV product category, and (unsurprisingly), Ferrari is the least connected to it.
You can also see the exact terms and responses to get an idea of what topics, features, and attributes each brand is connected to:
For instance, Tesla is lagging behind more established car brands when it comes to it’s connection to the main category of SUV’s, but it’s leading the way for electric SUV’s, it’s specialty.
These AI responses are a great data source for analyzing your positioning against competitors and seeing how LLMs view your brand compared to theirs.
Make sure you also review your branded search results to ensure competitors aren’t hijacking them. For example, Honda is mentioned 482 times in keywords that are specifically about Toyota.
If someone searches for your brand and sees a competitor or affiliate outrank you, that’s a clear sign you’ve left the door open, and they’ve stepped in to claim your visibility.
Keep an eye on who appears in your branded SERPs. Figure out why they’re there, and how to win that space back.
For example, one client of mine, a medico-legal expert, was being outranked by a competitor for her own name. She only had a single-page site. Despite her unique name, it wasn’t enough. So we focused on reclaiming her results by:
Creating a Google Business Profile
Adding an About page
Cleaning up citations and social profiles
Ensuring consistent brand content
Afterwards, her competitor was pushed very far down the page, so she now owns the key areas of the SERPs for her name. Don’t leave the door open for others to control your branded results.
5. Implementing SEO for brand awareness
So far, you’ve done a lot of strategizing, analyzing, and researching. It’s time to start implementing it all.
If you’ve followed the instructions above, you should have some tasks planned out in your project management tool after doing the audit and brand gap analysis. If not, take the time to add specific tasks for you or your team to implement.
For instance, common tasks I plan out for brand SEO include:
Create or update Google, Bing, and Apple business profiles
Create profiles on alternative search platforms, like Reddit
Update branded social media pages with new messaging
Create or update Wikipedia pages (for larger brands)
Clean up inconsistent citations and mentions on third-party sites within our control
Redesign the Home and About pages for consistency and adding EEAT elements
Create individual staff profile pages for leadership and key team members
Add or update organization schema to codify the technical elements of the brand
Optimize branded image files, like logos and favicons, to appear in search results
Create a topical map that aligns specific topics, features, and attributes to the brand
Contribute to relevant conversations on forums and discussion threads
The overall aim is to create a consistent brand footprint online so you’re seen as the go-to brand for your main product or service category.
Clean up as many inconsistencies as are within your control. Then amplify the brand’s messaging and topic alignment through its owned and paid media channels.
6. Promote your brand to build awareness
Once you have all your ducks in a row, your brand’s online footprint has been cleaned and inconsistencies removed, it’s time to promote, promote, promote.
It comes down to embracing “search everywhere optimization” and getting your brand visible on all the platforms you found in Step 3. These will generally consist of:
Traditional search engines
Social media platforms
Marketplaces and aggregators
Forums and discussion threads
Generative AI, LLMs, and chatbots
For example, here are all the platforms I visited when looking for the best laser cutter to buy:
You need to understand what the typical search journeys your audience goes through look like so you can show up with the right message on the right platforms.
Every question you answer on Reddit, every review you reply to on TrustPilot, and every post you make on social media become potential touchpoints, exposing your brand to a high-intent audience that’s actively looking for a solution you can offer.
Brand-focused link building will also help here. Think of it like doing PR. The goal isn’t to sculpt link juice.
It’s about getting your brand mentioned on authoritative and relevant publications your audience read. It focuses on:
Getting linked (or even linked) brand mentions
Aligning your brand mentions with specific topics
Improving the sentiment around your brand
Being seen by the right audiences
These days, even without the link, brand mentions are powerful because they are still recognised by AI systems and contribute to your online brand footprint.
The stronger your footprint across all relevant platforms, the easier it is to attract profitable, repeat customers, too.
Without active promotion and amplification of your brand across these platforms, potential customers are more likely to choose a competitor they have become more familiar with over you instead.
7. Track and monitor your brand’s visibility everywhere people search
The last step is to set up alerts and tracking dashboards to measure brand awareness so you can stay on top of your brand SEO efforts and make future brand SEO audits easier.
It’s already hooked up to all our main tools via the API and makes it easy to create a live, auto-updating dashboard of the key organic brand metrics you care about, like:
Branded traffic over time
Share of Voice for branded keywords
Top pages and how they’re contributing to branded traffic
If you want to be updated on new links and brand mentions more frequently, you can also set up mention alerts that go straight to your inbox:
Final thoughts
As AI reshapes how people find and trust information, brand SEO is no longer optional; it’s foundational.
The sooner you invest in building a clear, consistent, and credible brand across all search surfaces, the more defensible your visibility becomes. It’s not just about showing up anymore. It’s about showing up with authority, accuracy, and credibility.
Start now, and future-proof your brand for the future of AI-powered search.
If you have any questions, feel free to reach out on LinkedIn anytime, or check out our growing portfolio of posts about improving your brand’s visibility in search and LLM responses.
