What it looks like. This was spotted by Kamlesh Shukla posted about this on X and shared this screenshot:

As you can see, Google is asking this searcher to “Sign in to continue.” It says:

• “Sign in to verify you’re a human and see more results”

This happened after this searcher conducted a Google Search and when past the first few pages of the search results. I suspect most humans do not click past the first or second page of the search results but still, normally Google would serve a captcha and not request the user to sign in.

Why we care. This is a new experience from Google Search to verify human activity. If rolled out, it can cause issues for many of the SEO tracking tools and even tools like SerpAPI which was sued by Google for scraping its search results, which Google lost in a court.

This seems to be a limited test right now, so we are not sure if Google will release this more widely.

AI visibility metrics help uncover pre-click influences that might not be obvious from search terms, conversion tracking, landing page analysis, or other conventional data points. They add context that helps explain why campaigns attract the right customers, the wrong customers, or no customers at all.

Performance marketers are paid to find the most valuable customers with the most efficient spend. The baseline is understanding what happened after someone searched, clicked, or converted. But focusing exclusively on cleaning up existing demand is a short-sighted goal.

AI experiences (and what goes into serving them) are just as important to performance workflows because AI can shape what customers know, which brands they consider, and the language they use before they ever reach an ad or website.

What AI visibility metrics reveal

AI visibility metrics help answer two questions:

• What information did an AI system retrieve to support its response?
• Was your brand part of the information that shaped that response?

Three signals are especially useful:

• Grounding queries show the retrieval searches AI systems use to gather supporting information and the broader topics your brand appears in.
• Citations show when your content is referenced in an AI-generated response.
• Share of authority shows how much citation activity belongs to your domain compared with other cited domains for the same topic or query set.

How to put AI visibility to work

Grounding queries show how AI interprets human intent

Search terms show what a person typed. Grounding queries show the information an AI system retrieved to help answer that person’s question.

A single prompt can generate several grounding queries covering comparisons, pricing, reviews, product details, availability, implementation questions, or other supporting topics. Those queries show how AI translates a human need into retrievable information.

It gives you another way to evaluate whether landing pages, product descriptions, and campaign messaging communicate the right value.

If AI systems consistently associate your brand with services you don’t offer, audiences you don’t want, or use cases that don’t convert well, that mismatch may eventually show up in paid campaigns as traffic that looks relevant but performs poorly.

For example, a B2B company offering executive coaching may not want to be interpreted the same way as a company offering tactical sales training. Both ideas may be semantically similar, but they can attract different buyers, budgets, expectations, and conversion paths.

If grounding queries consistently associate the company with lower-value training searches instead of higher-value advisory or coaching needs, that’s a signal worth investigating.

The useful signal isn’t whether one phrase is universally better than another. It’s whether grounding queries, search terms, landing page behavior, and conversion quality point to the same interpretation of the same offer.

What to do with this insight 

If you see a lot of relevant grounding queries, that can be a strong opportunity to test AI-powered query matching with Performance Max, AI Max, or other AI-supported campaign types.

At minimum, grounding queries can inform keyword testing, search themes, creative ideas, and landing page updates. The key is to treat grounding queries as inputs, not instructions.

A grounding query isn’t the same as a keyword. It’s a clue about how AI systems interpret intent. Before acting on it, ask:

• Does this query reflect a product or service we actually want to sell?
• Does it match the customers we want more of?
• Do we have a landing page that supports this intent?
• Would this work better as a keyword, search theme, creative test, or content update?
• Can we measure whether the test improves conversion quality?

If grounding queries and search terms overlap in valuable ways, that may validate that AI systems and customers are interpreting your offer similarly

If they don’t overlap, investigate before changing bids or budgets. The issue may be messaging, landing page clarity, or content gaps rather than campaign settings.

Dig deeper: SEO vs. PPC vs. AI: The visibility dilemma

Citations and topics help you preview how AI understands your brand

A citation doesn’t mean you won the customer, the final answer, or the conversion. It means your content helped shape the AI experience. That matters because consideration can happen before a measurable click.

If your brand is cited for topics that align with your paid campaigns, your content and campaign messaging may reinforce each other. If your brand is cited for topics that don’t match what you sell, who you serve, or where you win, that may explain why some campaign traffic looks relevant on the surface but doesn’t convert well.

Topics add another layer by showing the themes, attributes, and categories AI systems associate with your brand. That can be especially useful when you’re trying to understand whether AI systems accurately represent your brand.

For example, if a cybersecurity platform wants to be known for enterprise identity protection but AI visibility reporting consistently associates it with small-business antivirus comparisons, that mismatch could be leading to lower-quality “conversions” that translate into bad data for the ad platform. What looks like a campaign settings or targeting issue could actually be a phrasing or content problem.

This is why landing page content matters. AI-powered campaign features such as final URL expansion, asset optimization, and broader matching systems also rely on how your brand, products, services, and pages are interpreted.

If AI visibility data shows that AI systems misunderstand your brand, there’s a reasonable chance campaign automation could inherit some of that confusion.

What to do with this insight

If citations and topics are misaligned, audit your landing page content before assuming the issue is bidding, budget, or audience targeting.

Look at how your priority pages describe:

• The problems you solve and whether you attempt to tackle more than one.
• The proof points that support the claims made in your advertising.
• The customer types you want more of based on how you describe your services.

Then compare that language with the topics where AI systems cite or associate your brand.

If AI systems are pulling your brand into the wrong topics, tighten the content. Make the product category, audience, proof points, and next step easier to understand. If the right topics appear but your citations are weak, look for places where your content may be too thin, too generic, or missing the evidence needed to be useful.

Better content can strengthen paid media campaigns because AI-powered campaign tools are increasingly tasked with interpreting pages, assets, and customer context. The clearer your landing pages and content are, the easier it is for those systems to understand where your offer is relevant. 

Dig deeper: 4 CRO strategies that work for humans and AI

Share of authority reveals where competitors are beating you in AI recommendations

Citations show whether your content is referenced in a given grounding query.

Share of authority shows how much citation activity belongs to your domain compared with other cited domains for the same topic or grounding query. This can be a useful competitive signal because it shows where other brands may be influencing the customer’s research journey before a search, click, or conversion happens.

If competitors are cited more often on a priority topic, they may be doing a better job answering the questions customers ask before they convert. This might mean:

• More accessible landing pages.
• Better proof points, such as reviews and awards.
• More useful product information that aligns with their feed.

Fundamentally, ask:

• Are competitors doing a better job proving their value in the places AI systems use to shape recommendations?

If the answer is yes, the issue may not be that your campaigns are broken. Your content may not be giving AI systems enough clear, useful, or verifiable information to understand why your brand belongs in the recommendation set.

What to do with this insight 

Use share of authority to identify where competitors are outperforming you on topics that matter to the business. Then invest in the content and landing page experiences that prove your value.

That might mean:

• Building new landing pages for high-value use cases.
• Expanding product or service details to include ideas mentioned in grounding queries.
• Adding clearer proof points, such as reviews and awards.
• Updating creative and messaging to reflect what customers and AI systems are already asking about.

Not every share-of-authority gap deserves action. If you’re underrepresented on a topic that doesn’t matter to your business, that may be fine.

But if you’re underrepresented on a high-value category, core product, branded initiative, or use case that your paid campaigns depend on, that gap deserves attention.

The performance question isn’t: “How do we become visible everywhere?” A better question is: “Where do we need stronger evidence so qualified customers are more likely to consider us?”

What AI visibility adds to PPC reporting

AI visibility reporting isn’t a replacement for conversion reports, bidding reports, placement reports, or behavioral analytics. Those tools still tell you whether campaigns are working and where performance is coming from.

AI visibility adds a different perspective. It helps explain how customers, AI systems, and competitors interact with information before traditional performance metrics exist.

Google updated the Data Manager API with new audience management tools, more flexible data ingestion and expanded support for user-provided address data, making it easier for developers to maintain Customer Match lists and improve data quality.

The release is aimed at reducing manual work while providing better visibility into data issues that don’t require an ingestion request to fail.

What’s new. The headline addition is a new RemoveAllAudienceMembers method, which allows developers to clear an entire audience list in a single operation. An optional timestamp parameter also lets advertisers remove only members added before a specified date, making full audience refreshes much easier.

Google has also introduced field-level ingestion warnings. Rather than failing an entire request when optional fields contain invalid data, the API now processes valid records while returning detailed warnings that identify the problematic fields and explain why they failed validation.

The update also expands the address information that can be sent to Google Analytics destinations. Developers can now include street address, city and state or province alongside existing fields such as name, postal code and region, while user-provided data can also satisfy identifier requirements for certain multi-source events when other identifiers aren’t available.

Google has additionally released new AI agent skills in its Google Skills GitHub repository to help developers build Data Manager API integrations more efficiently within AI-assisted coding environments.

Why we care. The update removes several friction points for developers managing first-party data. Audience refreshes become simpler, ingestion issues are easier to troubleshoot without interrupting workflows, and the expanded address fields provide more flexibility when sending user data to Google Analytics.

Bottom line. The latest Data Manager API release streamlines audience management, improves error handling and expands data collection capabilities, giving developers more efficient tools for managing customer data across Google Ads, Display & Video 360 and Google Analytics.

Microsoft has updated its analytics tool, Clarity, to add branded and non-branded breakdowns to the AI Citations dashboard and AI reports. Clarity lets you filter branded versus non-branded information across query analysis and filtering.

What Microsoft said. “To make that analysis easier, Microsoft Clarity now adds branded query segmentation to the AI Citations dashboard,” Microsoft wrote. “You can now distinguish branded and non-branded grounding queries AI systems use to look up supporting information for a response,” Microsoft added.

What it looks like. Here is a screenshot from the Microsoft Clarity blog of these new updates:

What is new. Microsoft added branded vs. non-branded view across query analysis and filtering to the Clarity reports including:

• Branded labels in the queries card: Individual queries are now clearly marked as branded in the queries view, so you can quickly identify brand-specific queries and understand what the AI systems looked up at a glance. 
• Share of Authority breakdown by query type: The Share of Authority card now breaks out results by branded and non-branded queries, giving you a clearer view of where your authority is strongest: queries that mention your brand versus more general queries.  
• Branded and non-branded filters: Filter dashboard data by branded or non-branded queries to compare how your visibility performs when an AI system looks up your brand directly versus broader, more general topics. 
• More precise citation analysis: By separating brand-led demand from generic discovery, you can better assess brand strength, identify discovery and consideration opportunities, and interpret changes in citation performance with greater confidence. 

• Branded labels in the queries card: Individual queries are now clearly marked as branded in the queries view, so you can quickly identify brand-specific queries and understand what the AI systems looked up at a glance. 
• Share of Authority breakdown by query type: The Share of Authority card now breaks out results by branded and non-branded queries, giving you a clearer view of where your authority is strongest: queries that mention your brand versus more general queries.  
• Branded and non-branded filters: Filter dashboard data by branded or non-branded queries to compare how your visibility performs when an AI system looks up your brand directly versus broader, more general topics. 
• More precise citation analysis: By separating brand-led demand from generic discovery, you can better assess brand strength, identify discovery and consideration opportunities, and interpret changes in citation performance with greater confidence. 

Why we care. Being able to break down and filter your analytics reports by branded and non-branded queries can help you zone in to the data that matters to you. Having more clarity on how people find your content through AI search and chat experiences are important and useful. This update gives you a little more information to work with when it comes to understanding your AI visibility.

On the latest episode of PPC Live the Podcast, Ana Kostic shared how one account restructure taught her a lesson that still shapes how she manages paid media today: successful PPC isn’t about building the perfect campaign structure—it’s about protecting the business behind it.

The mistake: A “perfect” account restructure

Early in her career, Kostic inherited a Google Ads account with a messy structure. Determined to apply PPC best practices, she rebuilt the account from the ground up, confident that cleaner campaigns and better keyword organisation would improve performance. Instead, traffic and sales dropped by around 40%.

The hidden cost of starting over

The issue wasn’t the new structure itself—it was wiping away years of historical performance data that Google’s systems had learned from. The account eventually recovered, but only after roughly two and a half months, with the full benefits taking closer to six months to materialise.

The lesson wasn’t about Google Ads

For Kostic, the biggest takeaway wasn’t technical. It was recognising that businesses can’t afford major revenue dips while platforms relearn campaign performance. “You have to think about the business first,” she said.

Ask business questions before platform questions

Today, Kostic starts every onboarding by understanding the business rather than the account. She asks about cash flow, margins, growth goals and how much short-term disruption the company can realistically absorb before recommending major structural changes.

Why slow beats perfect

Rather than replacing campaigns overnight, Kostic now introduces changes gradually, allowing new structures to learn while existing campaigns continue delivering results. Her philosophy is simple: “We like it slow and boring.”

Communication is part of optimisation

Kostic also credits her former manager for helping navigate the difficult conversations that followed. Instead of assigning blame, the agency focused on transparency, created a recovery plan and supported both the client and the team until performance stabilised.

PPC doesn’t stop inside Google Ads

One of Kostic’s biggest pieces of advice is to spend more time talking to sales teams. Those conversations often reveal the language customers actually use, helping advertisers build better campaigns than platform data alone can provide.

AI doesn’t change the fundamentals

While she’s a strong advocate for products like Performance Max, Kostic says AI shouldn’t replace good decision-making. Advertisers should still put guardrails in place, test gradually and prioritise business stability over chasing every new feature.

Bottom line

The biggest PPC mistakes aren’t always campaign mistakes—they’re business mistakes. For Kostic, rebuilding an account too aggressively became the experience that transformed her from a platform specialist into a business-first strategist.

When I first started working in Google Ads, I wished someone had explained which recommendations to trust and which to question. Some can improve performance. Others can increase costs or make changes that don’t align with your strategy.

Google Ads changes quickly, and trial and error is part of the job. Here are four recommendations and settings I’d review carefully before enabling them.

1. Auto-apply settings

Shortly after Google introduced the Recommendations tab, it also introduced the auto-apply feature, encouraging advertisers to let Google automatically implement many of the suggested changes. We’ve had several close calls with auto-apply. 

One Google rep insisted we fully enable it before he’d get off a strategy call, but he couldn’t explain how it would improve the account’s performance. More recently, I received an email on behalf of a client referring to the feature as “enabling recommendations” instead of auto-apply.

To clarify, every account has a Recommendations tab, and it generates recommendations whether you accept them or not.

Auto-apply is a separate setting that allows Google to automatically implement whichever recommendations you select. These range from relatively routine options, such as Use Optimized Ad Rotation, to more controversial ones, such as Improve Your Responsive Search Ads (allowing Google to automatically generate new headlines or descriptions) or Use Display Expansion.

Only opt into recommendation types that align with your client’s strategy and brand guidelines. For example, regulated industries generally need to avoid auto-generated ad copy. Likewise, if a client has strict CPA or ROAS targets, don’t enable “Set a target CPA” or “Set a target ROAS” unless you’re comfortable letting Google determine those targets. This will become even more important as Google’s targeted bidding changes roll out on Aug. 17.

Google is aggressively pushing auto-apply, and we’re seeing more incentives to enable it. But these recommendations don’t make sense for every advertiser. Enable them only when they support your goals and strategy.

Dig deeper: The truth about Google Ads recommendations (and auto-apply)

2. Display Expansion

Years ago, Google would automatically opt search campaigns into the Google Display Network, requiring advertisers to opt out. It didn’t make sense then, and it still doesn’t, even though the feature is now opt-in instead of opt-out.

Search and display campaigns serve very different purposes. A good search ad shows users you have what they’re already looking for. It’s a pull ad. Display ads, on the other hand, are push ads. They interrupt users as they browse, build awareness, and encourage them to stop what they’re doing and convert.

As a result, the tactics and success metrics differ significantly. 

• A good search ad has a high click-through rate and, ideally, a strong conversion rate because it matches existing intent. 
• A good display ad typically has a much lower click-through rate because it interrupts the browsing experience. 

It also tends to have a lower conversion rate because users aren’t actively shopping. Display ads usually generate far more impressions than search ads, and their cost per click is much lower because there are nearly unlimited placements in the auction.

As a result, while Display Expansion can generate additional traffic, it’s generally not advisable to run display and search ads in the same campaign, except within Performance Max. Otherwise, you may find that your efficiency metrics suddenly appear much worse.

Dig deeper: Google Search Ads in 2026 require a different kind of audit

3. Network settings

Speaking of additional networks, make sure you know which ones your Demand Gen campaigns are opted into. You have choices. 

Personally, I’ve noticed a decline in traffic quality across three campaigns that inadvertently had the Google Display Network selected, even though Google says performance should improve with Display enabled.

In another campaign, a legacy campaign that performed very well in Google Discover, I noticed it’s now serving impressions only on Gmail and YouTube, not Discover at all. Its CPA has also nearly doubled.

Google has somewhat hidden the setting that controls where your Demand Gen ads run. It’s located at the ad group level rather than the campaign level. 

Make sure you’re opted into only the networks you want to use. If you segment by network in Demand Gen, you can evaluate performance by placement to help inform those decisions.

Beyond Demand Gen placements, be cautious with Search Partners. Google often recommends enabling Search Partners to increase volume. However, it’s important to evaluate Search Partners separately from the Google Search Network. The Network (with Search Partners) segment can help you do that. 

If Search Partners aren’t performing well, it usually makes sense to remain opted out unless you have a traffic goal that can’t otherwise be met. There are exceptions, but efficiency should guide the decision.

Dig deeper: Why you should opt out of Google Search Partners

4. Budget recommendations

Google reps often reach out with opportunities to increase conversions, warnings that you may be losing potential customers, or estimates that you could reach 50% more people. What they don’t always highlight is the additional cost.

Over the years, I’ve received plenty of emails claiming my campaigns could generate 30% more conversions. What they don’t mention is that my budget might have to increase fivefold to make that happen.

Before accepting any recommendation designed to increase volume, make sure you understand the cost and whether it’s one you’re willing to pay. Google also frequently recommends substantial budget increases. When deciding whether to accept one, weigh the cost alongside the potential benefit. 

For example, Google once recommended that a client double its daily budget to gain an estimated 0.75 additional conversions per week.

In that case, one projected additional conversion each week wasn’t worth spending twice as much each day, or 14 times as much each week.

I’ve also seen advertisers forget that once Google increases a budget, it doesn’t lower it again. We’ve seen account owners approve a change, only to wonder a month or two later why they’re still spending so much, even when the higher budget isn’t delivering results. Monitor every change you make, and if it isn’t working, revert it.

Dig deeper: PPC budgeting in 2026: When to adjust, scale, and optimize with data

Review before you enable

While there are other Google Ads settings and recommendations worth reviewing, you can avoid many common issues by understanding where your ads will appear, how much you’re paying, and what you should expect in return, then validating those assumptions with performance data.

Taking a closer look at auto-apply, Display Expansion, network settings, and budget recommendations can help you make more informed decisions and keep your campaigns aligned with your goals.

Google published a new Help Center page for text disclaimers in Search ads, providing advertisers with guidance on a feature that lets required terms, conditions and disclosures appear directly within Search ads.

The feature is available globally and supports campaigns using AI Max.

What’s new. The new documentation explains how advertisers can create and manage text disclaimer assets in Google Ads.

According to the Help page:

• Text disclaimers are available to all Google Ads advertisers worldwide.
• Disclaimers must be added after campaign creation through the Assets menu.
• Each disclaimer can contain up to 90 characters.
• The feature is compatible with AI Max campaigns.

Google also notes that if a disclaimer asset is disapproved, the associated ad will continue serving without the disclaimer rather than stopping delivery.

Why we care. Many advertisers operate in regulated industries where legal disclosures are mandatory. Having dedicated disclaimer assets provides a cleaner way to include required messaging without sacrificing headline or description space. However, advertisers should be aware that disclaimer assets override any pinned Description Line 1, which could affect how carefully structured Search ads are displayed.

The bigger picture. As Google introduces more AI-powered campaign features, it’s also expanding the supporting tools advertisers need to remain compliant. The addition of AI Max compatibility suggests Google intends disclaimer assets to work consistently across both traditional Search campaigns and its newer AI-driven experiences.

Worth noting. The Help page also states that disclaimer text may be truncated in certain situations, such as when larger font sizes are used or in some languages.

Bottom line. Google’s new Help documentation provides long-awaited guidance on text disclaimers for Search ads, clarifying how advertisers can add legal disclosures while continuing to use AI Max and other modern Search campaign features.

First spotted. The documentation was first highlighted by PPC specialist Hannah Gillespie.

Unlike rankings, impressions, or clicks, Gemini brand mentions don’t appear in Search Console or Google Analytics. A buyer might discover your company in a Gemini response, continue researching through Google Search, and later return through a branded search or direct visit.

By then, the AI interaction itself is largely invisible to traditional reporting. That makes brand visibility something you have to measure directly.

The most effective approach combines manual monitoring, AI visibility platforms, and analytics data to answer two questions:

• How often does your brand appear in Gemini?
• Is that visibility influencing business outcomes?

Here’s how to build that process.

Why Gemini brand mentions are harder to measure

Traditional SEO measurement assumes there’s a consistent result to measure. But Gemini doesn’t work that way.

A single query can produce different responses based on factors like follow-up questions, search context, location, personalization, and changes to Google’s underlying models. 

That variability is only increasing. Google has already begun introducing more personalized Gemini experiences, including features that can incorporate signals from products like Gmail, Photos, and Search for users who opt in. 

Two people asking the same question may see meaningfully different responses. Your brand might be recommended in one conversation, omitted in the next, or appear alongside a different competitive set a week later.

There’s no single “Gemini ranking” to monitor. Instead, marketers should be measuring patterns: 

• How often your brand appears across an important set of prompts.
• How consistently it’s recommended.
• How it’s positioned relative to competitors.
• Whether those trends improve over time.

Keeping up with those patterns comes down to combining a few complementary approaches.

3 ways to track brand mentions in Gemini-powered search

Since there isn’t one source of truth for Gemini visibility, effective measurement is about assembling the right signals.

Some are qualitative, like how your brand is positioned in AI-generated responses. Others are quantitative, like inclusion rates across tracked prompts or downstream engagement in analytics.

Together, they provide a more reliable picture than any individual metric can.

Method 1: Manually monitor important prompts

Manual monitoring is still one of the best ways to understand how Gemini presents your brand. It won’t scale forever, but it gives you something automated tools can’t: context. 

Do the following to evaluate not just whether you’re mentioned, but how you’re described, which competitors appear alongside you, and whether Gemini accurately reflects your positioning.

Build a prompt library

Start by identifying the prompts your audience is most likely to ask throughout the buying journey.

For example:

• Awareness
  • What is customer data platform software?
  • Best CRM software.
  • How does endpoint detection work?
• Consideration
  • Salesforce vs. HubSpot.
  • Best CRM for manufacturing companies.
  • Top cybersecurity vendors.
• Decision
  • Is HubSpot worth it?
  • Salesforce pricing.
  • HubSpot alternatives.

Don’t stop with category keywords. Include:

• Branded searches.
• Competitor comparisons.
• Industry questions.
• Use-case queries.
• Location-specific prompts if local visibility matters to your business.
• Common follow-up questions that build on an initial prompt.

Gemini conversations often unfold across multiple turns, so a brand that doesn’t appear in the first response may be introduced later as users refine their questions or ask for recommendations.

A consistent prompt library makes it a lot easier to see those results over time.

Record more than whether you’re mentioned 

A simple yes-or-no spreadsheet tells you that your brand appeared — but not why that mention matters.  For each prompt, capture details like:

These details often reveal trends that raw inclusion rates miss.

For example, maybe your brand appears frequently but is consistently listed after competitors. Or perhaps Gemini mentions your company but describes an outdated product offering.

It’s worth paying attention to why Gemini is describing your brand that way. Gemini frequently supports its responses with citations or links to the sources behind its answers. Track which pages are being cited — not just whether your brand appeared.

Over time, those citations can reveal which content Google considers authoritative for a topic and identify pages worth expanding or updating.

Repeat the process regularly

Gemini responses evolve. New content is published. Google’s models change. Competitors update their websites.

Review your priority prompts on a regular cadence: weekly for highly competitive markets, or monthly for most organizations. 

Watch for meaningful shifts in inclusion, positioning, citations, and competitor comparisons rather than reacting to individual responses.

Method 2: Use AI visibility monitoring tools

Manual reviews work well when you’re tracking dozens of prompts.

Once you’re monitoring hundreds or thousands, consistency becomes just as important as scale. That’s where AI visibility monitoring tools become valuable.

Popular options include dedicated platforms like Profound, Scrunch AI, Otterly.AI, and Peec AI, alongside AI visibility features now rolling out across SEO platforms like Semrush and Ahrefs. 

Together, they make it possible to automate prompt testing, monitor your visibility in Gemini, and compare how your brand performs across other AI search experiences over time.

What AI visibility tools can measure

Capabilities vary by platform, but many can track metrics like:

• Answer inclusion rate.
• Competitive share of voice.
• Citation frequency.
• Response position.
• Historical visibility trends.
• Prompt coverage.
• Competitor comparisons.

Instead of rerunning hundreds of searches by hand, you can quickly see how your brand’s visibility changes across tracked Gemini prompts over time.

Understand the limitations

AI visibility tools offer a standardized way to measure performance, not a perfect representation of every user’s experience.

Most platforms test a predefined set of prompts under consistent conditions. But actual Gemini responses can still vary based on:

• Personalization.
• Location.
• Conversation history.
• Ongoing model updates.

That’s especially key to keep in mind as Google continues making Gemini more personalized across its products. A visibility platform can help you measure directional performance, but it can’t recreate every context in which a real user might encounter your brand.

Ultimately, no platform can perfectly model a dynamic AI system. What matters is measuring the same prompts consistently enough to make confident optimization decisions.

Method 3: Track analytics for signs of AI influence

Visibility tells you whether you’re showing up. Analytics helps you understand whether that visibility is making a difference.

The real value comes from connecting those two views. One shows whether Gemini is including your brand in the conversation. The other helps determine whether that visibility is translating into meaningful business results.

Getting the full picture means looking for both direct and indirect signs of AI influence.

Watch referral traffic when it exists

Some Gemini experiences do generate website visits. When they do, monitor:

• Referral traffic
• Landing page performance
• Engaged sessions
• Conversions
• Assisted conversions

These visits may only represent a portion of AI-driven discovery. Still, they can help flag which pages are benefiting from Gemini exposure and whether visitors are taking valuable actions once they arrive.

Look beyond referrals

To understand Gemini’s broader impact, also monitor signals like:

• Increases in branded search volume.
• Growth in direct traffic.
• Higher assisted conversion rates.
• Improved performance on pages frequently cited by Gemini.
• Changes in demand following sustained improvements in AI visibility.

None of these metrics can prove AI influence on its own. But when those trends align with stronger visibility across your tracked Gemini prompts, they can provide compelling evidence that Gemini is influencing awareness, consideration, and conversion.

The future of Gemini visibility measurement

For years, SEO has largely measured outcomes. Rankings, clicks, sessions, and conversions all tell you what happened after someone found your brand.

Gemini opens the door to measuring something earlier: whether your brand was present while a buying decision was still taking shape. 

In other words, marketers can begin measuring consideration, not just acquisition.

That’s a different kind of signal. Instead of waiting for traffic to tell you your marketing worked, you’ll increasingly be able to see whether your brand earned consideration in the first place.

As AI-powered search becomes a larger part of how people research products and services, that earlier visibility may become one of the most valuable indicators marketers have.

I’ve spent most of my career treating keyword research as the foundation of organic strategy. It tells you what people type into a search box, how often, and with what intent.

Now there’s a second demand signal alongside it: what people ask AI assistants. Those conversations reveal demand that keyword research alone doesn’t always capture.

When your audience opens ChatGPT, Perplexity, or Google’s AI Mode and describes a problem in a full sentence, that query often looks very different from the short phrase they’d type into traditional Google Search.

Instead of treating those as separate research exercises, I started putting keyword demand and prompt demand in the same table. The gap between them often determines what kind of content a topic actually needs.

Two research disciplines, one table

For every topic I’m considering for a new SEO campaign, I pull two numbers.

• Keyword volume is how many times people type a keyword into search. I use Google Ads Keyword Planner and cross-reference the data with a third-party tool, usually Semrush or Ahrefs. I start with a list of target keywords, then use each suggested keyword to uncover related opportunities.
• Prompt volume is how many times people ask AI assistants. I use Profound’s Prompt Volumes tool for this. It pulls from a dataset of real prompts people submit to ChatGPT, Gemini, Claude, and Perplexity, then models how often a topic appears across those conversations. You enter a keyword (or a batch of them), and it returns volume, related phrasing, and how that demand trends over time. That’s the prompt-side number I put next to traditional monthly search volume data.

I use this data to build a simple spreadsheet. The keyword column represents search demand. The prompt column represents AI-answer demand. It’s also important to understand some nuances in the tool outputs. Keyword Planner merges close variants, so near-duplicate phrases report the same figure.

I wouldn’t count them as separate demand. Prompt volume is generally more directional. I find it reliable for comparing orders of magnitude, which is what this method needs, but less reliable if you rely too heavily on specific numbers.

Dig deeper: Prompt research: The next layer of SEO and GEO strategy

What the gap actually tells you

Once both columns are filled in, topics can fall into a few strategic content buckets, depending on the relationship between keyword and prompt volume.

Here’s a real sample from a batch I ran recently. The topics are anonymized, and the numbers are rounded to the values reported by the tools.

Keyword-strong, prompt-weak: Write the classic SEO page

Topic A pulls roughly 20,000 monthly searches and almost no prompt demand. People search for it, but they aren’t asking an assistant to walk them through it.

Topic B follows the same pattern at a smaller scale: decent search demand but a weak prompt signal.

For these, I start with a keyword-led plan and stay honest about where the demand is. People are searching for this in Google, not asking AI about it, at least not yet. So the brief looks like a traditional SEO brief.

I look at what’s already ranking:

• What does the SERP reward for this query?
• What questions are the top pages actually answering, and where are they thin?

Then I ask the only question that matters for the page I’m about to build:

• What can I say here that nobody else is saying, and how do I answer the searcher’s question completely?

From there, I work top-down through the entire document.

• The title and H1 match the keyword and intent.
• The opening answers the question early.
• The rest of the page covers related questions, definitions, and supporting information the SERP rewards.
• HTML is structured in a way that makes the answer easy to parse through clear headings, short definition blocks, and conclusions near the top of each section.

The point for this bucket isn’t to force an AEO rewrite onto a search query. It’s to win the keyword based on how the SERP works today while making the page substantive enough that, if AI search starts citing this topic later, there’s something meaningful to pull from.

Dig deeper: The infinite tail: When search demand moves beyond keywords

Prompt-strong, keyword-weak: Write for the answer, not the SERP

This is the group keyword research alone that you will miss. It’s also the part of the method I rely on most.

Topic C reports about 5,000 monthly searches. Based on keyword volume alone, I’d probably deprioritize it. But it pulls roughly 250,000 in prompt volume. Keyword search volume understates demand for this topic by about 50 times. People aren’t typing exact-match keywords into Google. They’re describing the problem to an assistant and asking what to do about it.

Topic D shows about 4,000 searches against 16,000 prompts. Topic E follows the same pattern at a smaller scale.

For these, I want content built to be the answer itself: clear definitions, direct responses to the questions people actually ask, and a structure an LLM can extract and cite. The goal is to get cited within the answer.

This is where teams leave the most demand on the table. Topics can look “small” in Keyword Planner yet dominate prompt volume, and teams that rely only on keyword research never even put them on the roadmap.

Strong on both: Build the flagship

Topic F reports roughly 12,000 searches and 16,000 prompts. Topic G sits in a similar range on both sides. When a topic shows demand on both surfaces, I fund it like a pillar. These are the pages worth building to rank in search and get cited in AI answers.

This is also where the two research disciplines stop feeling like separate exercises. Keyword research tells me how to structure and title the page for search. Prompt research tells me which questions to answer and how to phrase them so an assistant will pull from the page. Together, they help me build a page that’s designed for both surfaces.

A note on empty cells: When the prompt column comes back empty, don’t read it as zero interest. Often, the demand is there but bundled into a broader head term or different prompt categories. A narrow use case might show nothing on its own while rolling up under a much larger prompt topic.

A blank means “no clean matching term here,” not “nobody cares.” Check the head term before you write off a topic. I’ve definitely missed some good early opportunities by not following this rule.

Turning the table into a strategy

The table is only useful if it changes what you ship. Once topics are sorted, the roadmap is straightforward:

• Keyword-strong topics feed the traditional SEO queue: ranking pages matched to search intent.
• Prompt-strong topics feed the answer-engine queue: referenceable, extractable content designed to be cited.
• Strong-on-both topics become your flagships: invest in them and build for both surfaces at once.

Then measure the two surfaces separately. Split your organic reporting so classic search traffic and AI-referred traffic are distinct, rather than blending everything into one “organic” number.

On my team, we separate organic search from AI search sources like ChatGPT and Perplexity, so we can see whether a piece of content is earning traffic on the surface it was built for.

Building great content helps you perform across SEO and AEO. Looking at both demand signals helps you decide how each topic should be approached. The better your data, the more specific your iterations can become, and the more you’ll learn from every piece you publish.

Dig deeper: How to design content that AI systems prefer and promote

Why I’m running both now

For years, keyword research was enough because search was the only discovery surface that mattered. That stopped being true once a significant part of the journey began before the click, in a conversation that doesn’t look like a keyword.

Running prompt research alongside keyword research lets me stop guessing where demand actually exists. I can see the actual demand for a topic instead of building the same page for every type of query.

Teams that continue treating these as a single demand signal will keep missing the topics where they diverge.

Most GEO and AI search budget recommendations optimize for the wrong KPI. Increased sales and profitability are the objectives. AI search visibility to the right searchers at the right time is simply another way to drive revenue.

A citation is visibility. A booked opportunity, incremental sales, and a new customer. That’s performance. Those outcomes are correlated, but they’re not the same. The gap between them is where a lot of GEO budget goes to die.

That’s why the first job isn’t getting cited more. It’s getting cited in the recommendation prompts that actually precede a purchase in your category.

Approaching GEO differently

I’ve been optimizing for organic search visibility since before there was a Google to optimize for.

In the late 1990s, I helped build some of the earliest paid search bid management technology, back when “search marketing” meant buying keywords on platforms that powered search engines most people have since forgotten.

I mention this for calibration, not nostalgia. I’ve sat through a lot of “everything you know is dead” moments, and that experience taught me to separate the shifts that change the work from the ones that only change the vocabulary.

Generative engine optimization, or AI search as many people prefer to call it, is one of those shifts. How people research and decide has changed, and it’s not changing back because even Google and Bing are integrating AI into the SERP.

But most GEO coverage is written by people who don’t carry a quota. The result is coverage that’s either a dictionary entry or a sales deck.

I want to offer the perspective of someone accountable for revenue, not just showing up in a screenshot.

The content changes that matter

Write for prompts

Stop writing for the keyword and start writing for the prompt.

The backlog of “what is” explainer articles that read like every competitor’s adds little to AI answers, since engines can assemble that information from anywhere.

Content designed for buyer selection is different. Build it around the real buyer question: Which provider fits this specific situation?

Include named criteria, honest tradeoffs, and the situations where you’re the wrong choice. Engines reward that kind of candor because it reads like a recommendation.

Publish data only you own

Lean on data only you own. Original, proprietary data is one of the strongest citation magnets because it’s the one thing a model can’t assemble from a hundred other pages.

A single defensible number that nobody else can publish will often outperform a quarter’s worth of generic posts because, once it’s pulled into an answer, it tends to be attributed by name.

If you’re sitting on first-party data, turn one piece of it into a number worth quoting.

Show who’s behind your content

Put real people on the page. Use named authors with real credentials and bios, not a faceless “admin” byline.

The models are working to resolve who you are and whether you’re credible before repeating you, so make that easy.

Remove content that weakens your brand

Prune aggressively. Interchangeable content doesn’t just fail to help. It dilutes your brand’s signal.

The test is simple: If a competitor could replace your logo with theirs and publish the article unchanged, it’s not working for you and may be working against you.

Cutting that kind of content is uncomfortable, and it’s usually the right call.

Dig deeper: 4 types of content decay and how to fix each one

The technical changes that matter, and the ones that don’t

Make your content accessible to AI crawlers

Confirm AI crawlers aren’t blocked and that your pages are indexed in Bing because ChatGPT’s web search relies on Bing’s index. You can’t be quoted from a page the retrieval layer never sees.

Pull key content out of client-side JavaScript so it lives in the HTML.

Make pages easier for AI to cite

Make publish and update dates visible, and keep genuinely refreshed pages current. Freshness is a real factor in AI answers, and materially updated pages often start getting cited again.

Structure pages so the claim comes first and the supporting evidence follows. Use comparison tables where buyers are weighing options because that’s the format engines can lift cleanly.

Ignore the technical hype

The llms.txt file is being promoted as a must-have, but Google has said it doesn’t use one, and there’s no strong evidence that it changes anything.

Schema markup is good technical hygiene, but it’s not the lever many vendors imply.

No amount of technical cleanup rescues thin content. The plumbing helps good content get found. It doesn’t make weak content worth citing.

Dig deeper: Technical SEO for generative search: Optimizing for AI agents

Where the effort gets wasted

The biggest GEO mistakes come down to priorities:

• Making citations on broad informational queries the primary objective. They have limited value, but they’re a worthwhile byproduct of the work you should be doing anyway.
• Over-indexing on a single platform. AI engines draw from overlapping but different sources, so you have to earn visibility across all of them.
• Letting a monitoring tool become the scoreboard. Watching mention counts climb means little if none of those citations reached a buyer. Rising citation counts on the wrong queries aren’t progress. They’re a more expensive vanity metric.

One more correction is worth internalizing: Ranking first in Google no longer guarantees you’ll appear in the AI answer for the same query. The overlap between top organic results and AI-cited sources is much smaller than many people assume.

Treat AI visibility as its own surface and measure it on its own terms. But don’t make AI visibility the KPI. Focus on the marketing and sales initiatives that drive it.

Measure against pipeline, not applause

This is the part most definitional articles skip. Here’s the framework worth holding yourself to.

• Start by defining a money-query set — the specific recommendation prompts real buyers use when they’re close to making a decision. Usually, that’s a few dozen prompts, not a few hundred.
• Track citation share only for that set. Being named in three out of 10 recommendation answers in your category tells you something. Total brand mentions across the web tell you very little.
• Connect those citations to revenue you can defend. Tag AI-referred sessions in analytics, pass them into your CRM, and follow them through to qualified opportunities and closed business the same way you would any other channel.
• Set expectations on timing, too. There’s usually a lag of several weeks between publication and its appearance in AI answers. Don’t panic at week two, and don’t declare victory at week three. Judge performance by the quarter.

The honest test for any citation is simple: Did it create a qualified conversation that wouldn’t have happened otherwise?

If you can’t trace a line from the answer to a real buyer, it’s brand awareness. Fund it and measure it as awareness, not performance.

The part that hasn’t changed

The acronyms have changed more times than I can count. The discipline hasn’t. The brands that win are the ones willing to do work competitors can’t copy and to measure success against revenue rather than applause.

That was true when we were identifying the most valuable PPC keywords in 1998 alongside the best SEO keywords in 1996, and it’s true now. If you have a revenue target to hit, optimize for the answer that puts a buyer in front of you, and ignore the rest of the noise.