The follow-up query: Rethinking SEO for conversational search

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The follow-up query- Rethinking SEO for conversational search

Today, the most revealing search query may be the follow-up. The question people ask after they learn, compare, or reconsider often reveals what they actually need.

For brands, that next question represents a valuable opportunity to earn visibility, secure trust, and support a meaningful action. The objective isn’t to predict every possible prompt. It’s to understand the audience well enough that your content, evidence, and experience remain useful as the conversation evolves.

The first query doesn’t tell the whole story

Traditional SEO strategy has focused primarily on the query that brings someone to a result. But what happens after the first answer is delivered?

A user can now begin with a broad question, narrow the request, add a photo, ask for a comparison, and move toward an action without restarting the search process. The follow-up query is the connective tissue in that journey.

This dynamic more closely mirrors how people actually think. We rarely begin with a perfectly formed question. We learn, reconsider what matters, add context, and proceed down the rabbit hole.

The biggest change isn’t that people suddenly began asking related questions. It’s that the interface can now remember the relationship between them.

Traditional SEO best practices still matter. The strategic workflow has simply expanded. Instead of treating a single query and landing page as the whole assignment, brands must consider the first question, likely follow-ups, credible proof, and the next useful action.

The goal is simple: Help people answer the question they have now and make it easy to answer the question they’re likely to have next.

Be the brand AI recommends.

See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.

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What conversational search actually means

Conversational search allows people to ask natural questions, carry context from one request to the next, and refine their needs along the way. It can happen in a search engine, AI assistant, voice interface, on-site chatbot, shopping assistant, or visual search tool.

Three qualities distinguish conversational search from conventional search:

  • Context carries forward: “What about one under $200?” makes sense because the system remembers what “one” refers to.
  • Intent can evolve: A user may move from learning to comparing to buying within one interaction.
  • The format can change: A journey may combine typed text, speech, images, video, maps, charts, or product feeds.

Not every voice query or AI summary is conversational. The defining question is: Can the person continue the task without rebuilding the context?

Conversational search also overlaps with personalization, but the two aren’t identical. Personalization changes an answer based on what a system knows about the individual. 

Conversational search changes an answer based on what the individual reveals during the exchange. Increasingly, search systems combine both forms of context to deliver more individualized responses.

Here’s a simple example:

  • Initial query: “What is the best carry-on for a five-day work trip?”
  • Follow-up: “I need a laptop sleeve and I fly budget airlines.”
  • Visual turn: The user uploads a photo of a bag and asks, “Is this likely to fit?”
  • Decision turn: “Compare two options that are under $250.”
  • Action turn: “Which choice can arrive by Friday?”

Traditional keyword research might stop at “best carry-on luggage.” Conversational strategy follows the full decision arc. Specifications, comparisons, images, policies, inventory, delivery data, and expert guidance all have a role to play.

A sample search can move from question to image, and follow-up to action.
A sample search can move from question to image, and follow-up to action.

How search became conversational

Search has gradually evolved from keyword-driven interactions to contextual, multimodal conversations. 
Search has gradually evolved from keyword-driven interactions to contextual, multimodal conversations. 

Conversational search didn’t begin with ChatGPT or other AI platforms. Its building blocks developed over decades alongside changing user behavior.

Keywords and reformulation

Early web search encouraged short noun phrases that were often stripped of natural grammar. If the results missed the central need, users manually reformulated the search: “running shoes,” then “running shoes flat feet,” then “best stability running shoes women.”

The user carried the context between searches. SEO centered on keyword matching and landing pages built around primary phrases.

Semantic and contextual understanding

Search engines gradually improved at understanding entities, relationships, intent, and natural phrasing. Google’s 2019 BERT announcement emphasized the context and relationships among words in a query. Small terms such as “to,” “for,” and “no” could materially change intent.

For SEO, that shift reduced the value of repetitive exact-match language and increased the importance of satisfying underlying needs.

Voice and answer-first interfaces

Voice assistants normalized complete questions and concise spoken answers. They also introduced local, immediate, and hands-free situations. Related searches could have been “Where is the nearest pharmacy open now?” or “How long do I bake salmon at 400 degrees?”

Many voice interactions remained single-turn, but the lasting lesson was clear: Provide concise answers, accurate facts, and content that works when heard rather than read on a page.

Complex and multimodal understanding

Google’s 2021 MUM announcement framed complex tasks as journeys that could require multiple searches. In 2022, Lens multisearch enabled people to combine an image with text such as a color, attribute, or question.

The direction was already clear: Let people authentically express their needs while the system performs more of the work behind the scenes.

What conversational search looks like now

Generative AI can interpret natural language, retrieve up-to-date information, combine sources, and retain context within a single interface. That dynamic changes both how people express their needs and how platforms search on their behalf.

One question can trigger many searches

Google says AI Overviews and AI Mode may use query fan-out, running multiple related searches across subtopics and data sources. For example, a lawn care question may prompt research into treatment, prevention, safety, cost, climate, and timing.

The visible prompt doesn’t tell the whole story. A page can support part of an answer even when it doesn’t mirror the wording of the initial question. Keyword datasets still reveal demand, but they represent only part of the picture. Support questions, on-site searches, reviews, sales conversations, and prompt testing can expose the needs that come next.

Dig deeper: Query fan-out optimization guide: How to rank in AI searches

Follow-ups turn results into journeys

Google has made the shift visible by connecting follow-up questions in AI Overviews to a continuing conversation in AI Mode. ChatGPT search similarly blends conversational responses with timely web information and source links.

On a practical level, people can reveal more information with every turn:

  • “Explain heat pumps.”
  • “Would one work in a 1920s house?”
  • “What if the electrical panel is only 100 amps?”
  • “Estimate the trade-offs in Southern California.”
  • “What should I ask contractors?”

Each question changes the best answer. A page that handles only the definition may support the opening request and disappear from the rest of the journey. The initial query identifies the topic. The follow-ups reveal what truly matters: budget, risk, location, use case, or deadline.

This shift is already affecting the search interface. In January, Barry Schwartz documented how follow-up questions can move users directly from AI Overviews into AI Mode, a feature that Google later pushed worldwide across desktop and mobile.

Multimodal inputs make the conversation more natural

People don’t need to translate everything they see into keywords. They can show a system an object, screen, plant, product, room, or broken part and ask a direct question.

In May 2025, Google reported that Lens handles more than 25 billion queries per month. Search Live has further expanded this behavior by allowing people to discuss a live camera view and ask free-flowing follow-ups.

For brands, visual SEO can’t stop at filenames and alt text. The image or video must be useful. Clear angles, close-ups, scale, labels, captions, demonstrations, and transcripts can help a person and a search system understand what the visual is trying to prove.

A photograph showing exactly where a reset button sits on an appliance is more useful than a polished lifestyle image of the same appliance. 

Dig deeper: How multimodal discovery is redefining SEO in the AI era

Search is moving closer to action

Conversational systems increasingly connect research with execution. Search experiences can already assist with tickets, reservations, local appointments, shopping, and forms. 

As agentic capabilities develop, accurate availability, pricing, policies, product details, and accessible conversion paths become an increasingly important part of discoverability.

A great article can’t rescue inaccurate inventory or a broken booking flow.

More needs are being satisfied before a click

Visits containing a Google AI summary resulted in a traditional result click 8% of the time, compared with 15% when no AI summary appeared, per a Pew Research Center study. Links inside the summaries received clicks in only 1% of visits.

One study can’t provide a universal CTR forecast, but the direction matters. A brand can influence a decision without receiving a visit. The clicks that remain may also represent a later, more qualified need.

When an AI response covers the basics, people need a stronger reason to click. They may want to verify a claim, see the original demonstration, use a tool, join a community, or check current availability. The click increasingly means: “Give me the proof, experience, or utility the summary can’t.”

That dynamic elevates:

  • Original reporting, testing, research, and firsthand experience.
  • Transparent authorship, methodology, dates, sources, and corrections.
  • Calculators, datasets, templates, maps, and interactive tools.
  • Newsletters, saved lists, communities, and other reasons to return directly.
  • Clear next actions that respect the person’s stage and risk level.

If an AI answer can repeat everything on the page, the page needs to offer something more.

Trust becomes part of the conversion

Increased automation can make human judgment feel more valuable. People don’t require a person to handle every interaction, but they do want to feel understood rather than processed — especially when nuance, emotion, or risk enters the conversation.

Good self-service respects people’s time. It should answer routine questions clearly, admit uncertainty, and provide a direct path to a knowledgeable human when empathy or accountability matters.

Search isn’t only an acquisition channel delivering anonymous traffic. It can be the beginning of a relationship that continues through a useful tool, newsletter, expert response, or community. That relationship can begin before the click and deepen after it.

This principle has guided my work in news SEO. The first search often identifies the event, while the next wave of questions reveals what readers actually need. For instance:

  • A coaching announcement may begin with a name and team, then expand into contract terms, career history, replacement candidates, and what the move means for the upcoming season. 
  • A wildfire search sequence may begin with the fire’s name and location, then expand into evacuation zones, road closures, shelter information, containment levels, air quality, and the neighborhoods at greatest risk. 

The opportunity isn’t to publish a thin article for every variation. It’s to build a connected resource that can serve the evolving story.

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The strategic shift: Optimize the conversation, not just the keyword

This change can feel bigger than it needs to be. We must always remember: Simple isn’t stupid when it comes to audience engagement.

We don’t need to predict every prompt or rebuild an entire content program overnight. We need to listen more closely, connect related needs, and make our best information easier to find, understand, trust, and use.

Start with the audience journey, not the newest AI feature. Platforms will change. The need to be useful, credible, and genuinely responsive won’t.

Map follow-up paths around real decisions

Choose a high-value audience need and brainstorm what a person would naturally ask next. Map at least five types of follow-up:

  • Clarify: “What does that mean?”
  • Constrain: “What if I have a small budget?”
  • Compare: “How is option A different from option B?”
  • Validate: “What evidence supports that?”
  • Act: “What should I do, buy, book, or ask next?”

Use search data, support tickets, sales calls, reviews, community discussions, and prompt testing to your advantage, but keep the order straight. AI can suggest questions. Human behavior should tell us whether those questions matter.

Conversation maps should also reflect how different audiences express the same need. Test important journeys with native-language experts, accounting for regional phrasing, cultural context, and moments when people switch languages during an exchange. Translation alone may not reveal the same follow-up intent.

Not sure where to begin? Take five real user questions and write the most likely follow-up under each one. With that framework, you already have the foundation of a conversation map.

Five follow-up paths can reveal the full decision journey.
Five follow-up paths can reveal the full decision journey.

Build topic systems rather than prompt pages

Create a strong hub around the main decision and connect it to a small number of useful supporting resources. These might include:

  • A definitive overview.
  • A comparison or alternatives page.
  • A decision checklist.
  • A firsthand case study.
  • A visual demonstration.
  • A tool or calculator.
  • Product, location, policy, or service pages with current facts.

Don’t publish 30 weak articles that say nearly the same thing. Create one excellent explanation and support it with assets that serve distinct needs.

Internal links should follow the user’s questions, not just mirror the site’s organizational chart. On the anchor text front, “compare plans,” “check compatibility,” and “understand the risks” provide more direction than “learn more.”

Decide whether each follow-up requires a section, separate page, reusable component, or no new asset at all. Let the task (not a minor keyword variation) drive that decision. 

Dig deeper: How to build topic clusters that AI will cite

Make every key claim easy to verify

State the answer first, then provide the evidence behind it. When the answer depends on specific circumstances, explain those conditions and include the relevant source, date, test method, or limitation.

  • Weak example: “This laptop has all-day battery life.”
  • Stronger example: “In our battery test, this laptop lasted 14 hours while streaming video over Wi-Fi at 50% screen brightness. Battery life may be shorter when gaming or running power-intensive applications.”

The stronger version gives a search system a clear, well-supported answer to assess and potentially cite. More importantly, it tells a person what the claim means and when it may not apply.

Before publishing, always ask: “What evidence would a skeptical reader need to believe this?”

Design for retrieval and reading

Keep important information in crawlable text even when a video, graphic, or app provides the richer experience. Use descriptive headings, concise definitions, and logical sections to help users extract information more easily.

Maintain the fundamentals:

  • Allow intended crawling and indexing.
  • Use canonical URLs and descriptive internal links.
  • Keep important information out of images alone.
  • Match structured data to visible content.
  • Maintain Merchant Center, product feeds, and Business Profile data where relevant.
  • Provide a fast and accessible mobile experience.

There’s no secret AI markup. Google says AI Overviews and AI Mode don’t require special optimization. Search systems can’t confidently use what they can’t access or understand.

Test whether people can continue the journey across voice, keyboard, screen-reader, and visual interfaces. Clear headings, descriptive alt text, captions, transcripts, accessible forms, and understandable error messages do more than satisfy technical requirements. They determine who can participate in the conversation.

Treat images and video as answer assets

For every important visual, ask: “What can this answer that text can’t?”

A product image can show scale and fit. A repair video can demonstrate motion and sequence. A safety graphic can clarify a warning sign. Captions, transcripts, and expert reviews can provide context the visual can’t carry alone.

Avoid decorative imagery that adds no information. Use descriptive filenames and alt text for accessibility without keyword stuffing. Place visuals near the relevant explanation and provide stable pages where they can be discovered.

Could someone learn, compare, or complete a step from this visual? If not, reconsider whether it’s necessary.

Dig deeper: Your images have a new job in AI search

Create a strong next turn on owned surfaces

When someone arrives from an AI answer, assume they may already know the basics. Provide the next layer immediately:

  • A comparison after an overview.
  • A calculator after an explanation.
  • A “what changes for your situation?” breakdown.
  • Primary documents and methodology after a summary.
  • Availability and booking steps after local advice.

On-site search and chat should preserve useful context, cite source material, offer escalation routes, and admit uncertainty. Don’t send a well-informed visitor back to the beginning of the discovery process.

Preserve only the context needed to reduce repetition, not every detail a person shares. Sensitive information requires clear consent, limited retention, and an obvious path to human help.

Build authority people can remember

AI systems often synthesize multiple sources. Generic prose is easy to replace, but distinctive evidence and recognizable expertise aren’t.

Build assets people can associate with your brand: a named dataset, annual benchmark, expert rubric, original test, or trusted decision tool. Newsletters, alerts, memberships, proprietary tools, events, and communities can then reduce dependence on any single interface.

Search value increasingly incorporates visibility, trust, and direct audience relationships — not only the immediate click.

Dig deeper: Utility news content: How to win beyond clicks in AI search

Conversational search is a cross-functional responsibility

SEO and content teams can’t deliver the full journey alone. Conversational search touches:

  • Content and subject-matter experts, who provide accurate explanations and proof.
  • UX and product teams, who build usable paths and context-aware experiences.
  • Customer service and sales, who address real questions, objections, and anxieties.
  • Ecommerce and operations, who maintain prices, inventory, policies, and availability.
  • Legal, privacy, and accessibility partners, who establish responsible boundaries.
  • Analytics teams, who connect discovery with engagement and outcomes.

The strongest strategy synthesizes these functions around the audience’s decision-making process rather than forcing each individual to navigate the organization’s internal silos.

Follow this practical workflow for teams:

  • Choose one important journey: Define the audience, decision, risk, and desired outcome. Combine keyword data with support transcripts, sales objections, on-site searches, reviews, and expert interviews.
  • Map the conversation: Branch the initial need into clarification, constraints, comparison, validation, and action. Mark what already exists, pinpoint weaknesses, and establish what requires a new asset or data source.
  • Match the answer to the format: Use prose for explanation, tables for comparison, images for recognition, video for motion, tools for calculation, and feeds for changing facts. Add firsthand evidence, expert review, dates, and limitations.
  • Connect the journey: Align the hub, supporting pages, visuals, operational data, and conversion experience. Decide when self-service should become a context-aware human exchange.
  • Test and improve: Sample initial prompts, follow-ups, and image-based inputs across relevant interfaces. Track accuracy, citations, gaps, and competitors. Improve weak branches instead of aimlessly publishing new content.

Measurement: A scorecard for conversational search

No single metric tells the whole story of conversational search. Begin with four questions:

  • Can people find you? Track topic-level visibility, multimodal discovery, branded demand, and accurate presence within a fixed sample of AI answers.
  • Do the right people engage? Monitor tool use, video completion, evidence-module clicks, return visits, saved items, and newsletter sign-ups alongside pageviews.
  • Do they trust you? Monitor citation accuracy, corrections, earned references, direct traffic, freshness, and successful human interactions.
  • Do they act? Measure assisted conversions, qualified leads, bookings, purchases, support resolution, time to decision, and retention.

Treat AI citation testing as directional, not as definitive market share. Outputs vary by interface, conversation history, time, location, account context, and model. 

Document your definitions and maintain a consistent prompt sample before comparing periods. 

Measure the journey from discovery through action
Measure the journey from discovery through action.

Dig deeper: How to measure prompt-level visibility in AI search

What comes next

The next evolution is likely to move from conversational answers toward conversational action. A person may have a broken appliance, troubleshoot it, compare replacements, check local stock, and book installation within one search session.

As AI agents compare, filter, schedule, and transact under user direction, accurate prices, clear policies, consistent product information, accessible workflows, and source transparency will become even more important.

Original evidence, named expertise, clear methods, and current source pages will help people verify what they receive. Don’t chase every interface change or create thin content for every prompt variation. The goal is to add value, not volume.

Discovery will remain distributed across search engines, AI assistants, social video, communities, commerce platforms, and brand-owned experiences. The goal isn’t to put all of your search strategy eggs into one chatbot’s basket. It’s to become the clearest, most useful, and verifiable source for the decisions your audience needs to make.

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

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Make the next question count

The follow-up query changes SEO because it exposes the full shape of intent. People don’t experience their needs as isolated keywords. They learn, revise, compare, validate, and act.

We don’t need to predict every sentence someone might type. We need to understand the decision journey well enough that our content, evidence, visuals, data, and experience remain useful as the conversation changes. That means creating useful content rather than commodity AI-generated drafts, keeping important information accessible, and never fabricating firsthand experience, reviews, or citations.

The brands that stand out won’t simply answer the first question. They’ll listen, adapt, and earn the next one — proving that even as search becomes more automated, lasting authority is still built through useful information, credible evidence, and genuine human connection.

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What happens before search strongly shapes the decision

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What happens before search strongly shapes the decision

Search isn’t shrinking, but its role in the purchase funnel is changing.

People are searching in fundamentally different ways, using cameras, conversations, and keywords to find what they want. At the same time, discovery is happening before many searches, as recommendation engines curate content and help people develop preferences before they ever type a query.

The searches marketers see may be less about discovering what to buy and more about confirming a decision that’s already taking shape. Those earlier stages are difficult to observe, so the activity that helped someone develop a preference can disappear from the purchase path.

A few weeks ago, I was on a walk in my neighborhood, enjoying a Pacific Northwest summer day. Along my usual route, I noticed a beautiful plant that I loved but didn’t recognize. Just a few years ago, I would have had to return home, type a clumsy description like “purple flower Northwest beautiful” into a search engine, and then scroll through dozens of pictures before finding the plant’s name.

This time, I simply pointed my phone camera at the plant, had Google Lens identify it for me, and then bought one at my local plant store the next day. No ad. No search bar. Just a camera and an answer.

Nothing about my experience is unusual anymore, and it should change how every digital marketer views search’s role in the purchase funnel.

Search is changing alongside the funnel

The demand generation funnel has become flatter, noisier, and more complicated. The touchpoints have sorted themselves into a consistent sequence.

The traditional funnel was often described as a straight line from awareness → interest → desire → action, with search activity doing the most work in the interest stage, when people were still figuring out what they wanted.

Now the funnel looks something like passive exposure → preference development → confirmation searches → purchase. Search is still a critical component of a buying decision, but it plays a smaller role in discovery. 

That job has shifted to recommendation engines that can predict user intent and curate personalized content before the first query. By the time searches happen, they’re confirming a decision that’s already been made.

I see this play out in client accounts. The queries that are closest to purchase look more like caveman language, i.e., “brand name product model,” indicating that consumers know exactly what they’re looking for when it’s finally time to buy.

The catch here is that the first two stages of the new funnel are ones we can’t really observe. If someone watches a series of videos on social media, develops an interest in a product, and then conducts a Google search before purchase, that activity will appear to be a branded search conversion. The content that helped them form their decision never appears in the purchase path.

Consequently, most marketers still budget as if search does the discovery work because that’s what their data tells them.

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Examining YouTube’s unique position

As search plays a different role in the funnel, YouTube is taking on a larger role in discovery. Two things make it different: machine trust and human trust. 

  • Machine trust: A study of over 100 million AI citations found that YouTube was the second most-cited social media platform, behind only Reddit. AI answer engines trust YouTube so much because of its massive library of content, including elements like video transcripts, which provide AI models with structured text they can display in answers.
  • Human trust: Consumers have more confidence in YouTube than other social platforms. Studies show that more than two-thirds of consumers find YouTube “somewhat or very trustworthy” when it comes to making purchase decisions.

Creators on YouTube also supply social proof, which now plays a critical role in purchasing decisions, particularly among younger buyers. Up to 70% of Gen Z follow an influencer, and 44% made a purchase based on an influencer’s recommendation.

YouTube enjoys the unique position of being highly trusted by both consumers and the machines making recommendations, and Google has taken steps to ensure the platform gets credit for its role in discovery. In January, it rolled out the Attributed Branded Search metric, which enables marketers to link YouTube ad exposure to a branded search within a specific timeframe.

ABS only captures exposure to YouTube advertising. The organic layer featuring review videos, product unboxings, and comparison videos remains invisible. If the measurable portion is driving tangible results, imagine what the rest of the channel is doing.

Dig deeper: YouTube is no longer optional for SEO in the age of AI Overviews

Discovery extends well beyond YouTube

This huge invisible discovery layer extends well beyond YouTube. Much of that discovery happens outside the channels brands control.

AI tools don’t repeat your version of the brand. They repeat what the entire web says about you. Earned media drove 84% of AI citations, according to a Muck Rack review of 25 million ChatGPT, Claude, and Gemini responses across multiple industries.

Those third-party mentions, YouTube review videos, and Reddit threads form the raw material for the AI answers and feed recommendations that drive today’s purchasing decisions.

The practical outcome of this shift is that brand reputation management and search visibility have merged into a single role.

Dig deeper: Why social search visibility is the next evolution of discoverability

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How do brands drive discovery?

Brands still have an enormous role to play in driving discovery. However, those activities must extend beyond paid advertising. Here are three ways you can influence discovery:

  • Build a video library that answers questions about your product and your brand regardless of whether you’re running YouTube ads. These will become a database AI answers can draw from.
  • Data health has become a non-negotiable. The algorithm only knows what you tell it. Consequently, your product feeds should provide the algorithms with as much information as possible. It’s also critical to build brand consistency across the channels you control.
  • Show up in conversations you don’t own. Reddit threads involving your brand are feeding AI Overviews whether you participate or not.

Providing consistent and complete information across channels will give algorithms and AI tools something reliable to work with.

Don’t abandon search

A reshaped funnel shouldn’t be the reason you pull back from search. Google query volume reached an all-time high in early 2026 even as search’s position in the funnel changed.

The mistake won’t come from overinvesting in search. Instead, the mistake will come from expecting search to accomplish something it’s no longer suited for.

Rather than retreating from search, this is an opportunity to reimagine your marketing mix within a new framework. If search now occupies a different funnel position, how can you allocate your marketing resources to cover the upper-funnel work that still must be done? 

The answer to this question will be different for every marketing team, but it’s a conversation that everyone should be having right now.

Search behavior won’t stand still

A few years ago, I never would have imagined that I could search for a plant’s name with a camera built into my phone, much less have a back-and-forth conversation with my computer. But here we are.

Marketers are working on the cutting edge, watching new technology change user behavior right before our eyes. Everything could shift again by this time next year. Nobody knows for sure. But the larger trend is clear: people are increasingly searching to confirm what they’ve already decided.

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Claude for SEO: 5 ways to automate repetitive work

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Claude for SEO- 5 ways to automate repetitive work

After working on my hreflang XML sitemap project with Gemini, my company decided to invest in paid Claude accounts. I took the opportunity to see what else Claude could do through its desktop app and found some great uses for it.

Here are five ways I’ve been using it in my everyday SEO work.

1. Implement a daily briefing

The first thing I set up was a daily intelligence brief. Using lessons learned from other AI tools, I crafted a detailed outline of the types of news I wanted to see, including industry updates, competitor news, mergers, and acquisitions.

The key to getting more useful information and less fluff is to be specific and use the output as a guide for improvement. Don’t accept schlock — you’re paying for the tool, so teach it to do what you need.

For instance, I have Gemini checking atmospheric conditions for radio wave propagation in my amateur radio hobby, and it took several adjustments to get useful output.

Dig deeper: How to turn Claude Code into your SEO command center

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2. Connect to data sources for faster analysis

Yes, I can dig into Google Analytics and analyze the data, but how much faster would it be if I could just ask Claude to look at the data and answer questions for me? It turns out it’s much faster.

The folks at Rank Math shared instructions on how to make a direct connection without using third-party middleware. This tip has saved me a lot of time by allowing me to ask for what I need and quickly get an answer.

3. Create a better and faster hreflang XML sitemap

I decided to see how Claude would tackle the hreflang XML sitemap project. Like I did in my Gemini example, I told it what I wanted, fed it the latest versions of the sitemaps, and asked how it would approach the problem.

Claude surprised me by asking only for links to XML sitemaps as input. Using its own resources, Claude gathered all the pages and built the sitemap for me without any further intervention.

I double-checked the output and found it to be excellent on the first go, avoiding the need for the back and forth I experienced with Gemini — a huge time saver.

Get the newsletter search marketers rely on.

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4. Generate, translate, and localize content

Our team supports three main businesses across several regions. This requires content updates to be replicated across websites, translated, and localized as appropriate. We need to track this work as it goes through the pipeline so nothing falls through the cracks.

This is where Claude has been a tremendous benefit. I took an example of content that had been generated, vetted, and approved by one of our in-house experts and decided to see if Claude could handle automating some of the international SEO process. The results were far beyond my expectations.

As usual, beginning with the end in mind, I uploaded examples of previous translation and localization work to show what I needed done. Next, I uploaded a new item and gave it specific instructions:

  • Look at the content and identify the page on the main .com website to understand the service and its context.
  • Check the regional websites and determine if that same service is offered.
  • If it is, create a translated/regionalized version using our standard template.
  • Create tasks in our workflow tracker (we use Asana) and space out due dates according to a standard formula.
  • Upload the content docs to the created task.

I was pleasantly surprised by how quickly Claude was able to do this. It accomplished everything I needed except uploading the document to the ticket. The translations were on par with what we were getting with Google Translate, as verified by our in-country experts.

Strike another blow against inefficiency.

Dig deeper: Turn your SEO process into AI-powered tools

5. Replace an outdated WordPress plugin

While setting up a new WordPress website for one of our businesses, I found that one of the plugins we were using hadn’t been updated in several years and had known security issues. Thankfully, other security measures in place significantly mitigated the risk, but it’s always better to fix the problem than rely on other means.

I found a suitable substitute and thought about how long it would take my team and me to go through the 20+ websites we support and update the plugins. Even if it only took an hour per website, that’s more than 20 hours of dedicated, hands-on, fingers-to-keys work.

Before starting the process, I asked Claude if it could do it for me. Again, I was pleasantly surprised.

Using Claude’s Chrome extension, I logged into a staging environment for one of our smaller websites and told Claude what to do. It found all the places the plugin was being used, swapped it out with the new plugin, and did a quick visual check. There was one small discrepancy with the first attempt, which was quickly fixed when I pointed out the problem to Claude.

“Would you like me to check the other plugins for any security issues?” it asked when completed. Of course I would. It found an additional plugin that was unused and no longer supported. That was quickly deactivated and deleted.

My only time requirement was about five minutes per site of hands-on work as I:

  • Pulled a copy of each production website from staging.
  • Turned Claude loose.
  • Visually checked URLs that were updated.
  • Pushed the newly updated staging version to production.

Everything went on in the background as I worked on other tasks, and I didn’t have to get anyone else from my team involved.

I bet you’d be willing to trade getting the same result from five minutes of work vs. an hour any day of the week.

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

See your AI visibility

How to work effectively with AI

AI tools can feel overwhelming, but they’re an incredibly useful asset when used properly. Follow these practical guidelines when implementing AI, and you’ll be blazing through those tedious SEO tasks in no time.

Be the strategist and let AI be the doer

Don’t just demand a final product. Discuss the architecture, the edge cases, and the logic first, and be specific. Treat the AI like an intern by showing examples of final products when possible.

Use the proper tool for the proper job

Not all AIs are alike, and some are better at some tasks than others. Read, learn, and experiment. There are a lot of us out there using AI tools, and people have found some amazing results using one platform that completely failed on another.

Check, double-check, and correct when needed

Much like embracing the iterative process I referred to in my last article about using AI tools, don’t be afraid to check thoroughly and correct when needed.

The reality that AI is really still in its infancy becomes readily apparent when it fails. You may have to spoon-feed information at first to get the result you want. Remember, you don’t have to be polite — be direct and clear for faster results.

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AI Overviews and paid search: When to fight, influence, or generate demand

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AI Overviews and paid search- A three-bucket framework

AI Overviews reshaped the search results page, yet most paid search playbooks still run as if nothing happened.

You still hear the same default rule: bid up where performance looks strong, bid down where it looks weak. That assumes every query is the same kind of opportunity, a small auction for the same kind of click. In B2B and industrial accounts, where sales cycles run for months, conversion data is thin, and much of the demand is informational, that assumption falls apart.

When Google answers a question directly on the results page, the value of a paid click varies across your keywords.

  • Some queries still lead to a real commercial decision, and winning the top spot above the answer is worth paying for.
  • Others get resolved inside the AI answer itself, so the goal shifts from buying a click to influencing the answer and building a brand strong enough to be included.
  • A third group has moved so far toward zero-click that paid search on its own can no longer rebuild the journey.

Those are three different problems, and a single bid lever can’t solve all three. Trying to is how accounts spend budget on the wrong surface. I separate them into three buckets: fight, influence, and generate demand.

The framework: Fight, influence, and generate demand

Each bucket describes a different mechanism on the results page, so each one calls for a different approach.

Bucket 1: Fight

Fight is for bottom-of-funnel queries where intent is clearly commercial, and a click still turns into direct leads and sales: product + modifier, supplier shortlists, “buy,” “quote,” “distributor,” and brand terms combined with buying intent. Here, the job hasn’t changed much. You want to win the auction that sits above the answer, and you want the ad itself to answer the buying question.

This is where paid competition concentrates, and the data show it. The likelihood of an ad appearing rose steadily with cost per click, according to a July SE Ranking study of commercial queries in Google’s U.S. AI Mode:

  • About 54% at $10 or more.
  • Around 24% for keywords under $2.
  • 32% between $2 and $10.

AI Mode isn’t the same surface as AI Overviews, but what they share is the pattern: paid inventory follows commercial weight. Where the market already pays more for a click, AI-era ad surfaces appear more often. You rarely compete alone, since about seven in 10 answers with an ad in that study showed two advertisers in the block.

Fight is expensive for a reason, so treat it that way.

  • Reserve the premium for terms where winning absolute top still beats your incremental cost per acquisition. Use bid simulators to estimate the cost of that jump in position first.
  • Watch Absolute Top impression share and impression share lost to rank, because that’s how you see the AI block pushing you down the page.

What you shouldn’t do is pour Fight budget into informational queries the Overview has already answered.

See exactly how your competitors win.

Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.

Analyze your competitors

Bucket 2: Influence

Influence is for queries where the Overview, or a longer conversational answer, does the educating. Very often, the user won’t click.

The goal here isn’t to gain a cheap visit anymore. It’s to be part of how the answer gets built: cited as a source or presented as one of the brands named inside the answer.

As Shashi Thakur put it at Google Marketing Live EMEA 2026, the best ads must be answers. In an Influence query, your ad brief starts from the question the user is really asking, rather than from a keyword list, and it means accepting that SEO and PPC address the same challenge, not competing against each other.

In B2B and industrial accounts, the queries that trigger AI answer blocks are still mostly informational. In one of our accounts, the intent split of the terms surfacing these blocks looked like this:

Intent Share
Informational 83.3%
Learn and solve 10.0%
Commercial 6.7%

For “learn and solve” and some “informational” terms, plan for share of the answer instead of maximum CPC.

There is a budget consequence worth stating plainly. On influence queries, we work very closely with SEO, and we’re usually not interested in paying to appear where organic is already winning. Being visible twice, in paid and in the answer, barely happens anyway.

When the data pushes us to move money into the Fight and Generate demand buckets, we deliberately avoid being aggressive on terms where SEO already earns the visibility. That keeps paid spend pointed at the gaps we’re interested in covering.

Bucket 3: Generate demand

Generate demand is for queries where zero-click has already won and paid search alone won’t rebuild the top of the funnel. Informational demand that used to create assisted journeys now often ends on Google, with no visit to anyone.

You have two options. You can:

  • Keep bidding while your CPCs climb and volume falls.
  • Fund the work that happens before the search: Demand Gen, YouTube, first-party content, and the review and community presence that shows up later when the same person searches with commercial intent.

This work is about recognizing that some keywords have stopped being capture channels and have become a signal of a demand gap. The capture still happens later, in Fight, once the person is ready to act.

Generate demand fills the pool that Fight then fishes from. Measured only on last-click Search ROAS, this work always looks weak because its payoff appears as brand searches and better-qualified pipeline weeks later.

Bucket The job The paid response
Fight Clear purchase intent, where the click still feeds the sales pipeline directly Pay for absolute top when your brand isn’t already winning the answer box
Influence Top and mid-funnel, where the answer shapes consideration Win share of the answer through citation and SEO coordination, not raw CPC
Generate demand Zero-click is winning and Search alone can’t rebuild the funnel Build demand before the search, through Demand Gen, YouTube, and awareness

Dig deeper: What happens when AI Overviews contradict paid search ads?

Why one bid rule fails after AI Overviews

AI Overviews sit at the top of the page, sharing space that used to belong to ads and organic results. The answer often satisfies the user before they scroll, changing the economics of paid search in three ways.

1. Click-through falls on informational and mid-funnel queries

When users get a usable answer without leaving Google, both organic and paid lose clicks. The effect is stronger when your brand isn’t mentioned in the AI Overview.

The clicks that remain tend to be better qualified, but the drop in volume usually exceeds the lift in quality, so total conversions decline.

2. Cost per click goes up

There are fewer premium positions at the top. More advertisers are shifting their budgets to paid search to offset lost visibility.

The pressure is strongest in B2B and services, and on comparative queries such as “best X” or “X reviews.” Pure transactional queries hold up better because users still need to click through to buy or request a quote.

3. Position gets harder to read

There are often only a few useful slots above the AI Overview or AI Mode answer, and falling below the Overview can sharply reduce click-through, especially on mobile. Absolute Top impression share and impression share lost to rank become especially useful for understanding that pressure.

Underneath these changes is a shift in behavior. People can research and decide within a single answer experience, shortening the window to capture and persuade them. Intent still lives in your account, but the economics attached to each type of intent have changed. That’s why you should classify a query before you touch the bid.

Dig deeper: 4 strategic paid search pivots to survive Google’s AI Overviews

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See terms.

How to classify your terms in practice

The framework only pays off if your account can distinguish between valuable and low-value traffic, so two foundations come first.

One is semantic coherence: keyword, ad, and landing page as a single unit of meaning, with each ad group built around one clear concept. Mixing concepts teaches the system the wrong matches, and that gets worse the moment you hand control to broad match, Performance Max, or AI Max.

The other is conversion quality: primary conversions that reflect real business value, lead scoring, and offline or CRM signals wherever you can pass them back. Smart Bidding and the AI surfaces amplify whatever you give them, so thin data makes every bucket noisier.

With those in place, classification becomes a routine you can repeat:

  • Pull your search terms and map intent, separating clear commercial intent from research and from purely informational queries.
  • Use SEO data, Search Console, AI Overview Reports, plus manual SERP checks as a proxy for where Overviews appear and whether your brand is cited.
  • Sort each term into Fight, Influence, or Generate demand, and revisit the sorting regularly because the SERP keeps changing.
Step Action Data Sources Output
1. Map intent Pull search terms and classify by intent type. Google Ads keywords and search terms (+ Search Console). Terms grouped by: commercial, research, informational.
2. Identify AI presence Check where AI Overviews appear and whether your brand is cited. AI Overview reports, manual SERP checks. Map of AI Overview presence by term.
3. Sort into buckets Assign each term to a strategic bucket. Intent mapping + AI presence data. Fight, Influence, or Generate demand list.
4. Revisit Regularly Re-sort terms as SERP landscape changes. Ongoing SERP monitoring, AI Overview reports. Update bucket assignments and review budget assigned to each.

On Fight terms, accept a position premium only where absolute top still wins your incremental cost per acquisition.

On Influence terms, align ad copy with the question the Overview answers, coordinate with SEO on content and citation, and pull back paid pressure where organic already wins.

On Generate demand terms, move budget into YouTube, Demand Gen or content when the Search clicks are gone but the query still matters and measure through brand search and assisted pipeline.

AI Max and broad match sit on top of this classification. They don’t replace it. Google needs broad match or keywordless campaigns for your ads to appear around AI answer surfaces, which expands reach while reducing control over what you show.

Use them where you already know the bucket and your tracking is clean, and introduce them through small, gradual tests.

Dig deeper: How to get your Google Ads seen in AI Overviews

Every click they win is a customer you lose.

See where competitors are investing, which keywords drive their results, and how to capture more of the market.

See who’s stealing your traffic

Put the budget where the opportunity is

Accounts that apply one rule to every query will keep funding the wrong mechanism, seeing it as rising costs they can’t explain. Accounts that re-segment and move money between buckets and into channels outside the auction spend less time arguing with a results page that has already answered the user.

In B2B and industrial search especially, shifting from optimizing bids to reallocating budget across the three buckets is the difference between defending last year’s setup and building for how people search now.

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3 ways to make AI safer in a live ad account by Optmyzr

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It’s been a pretty crazy few weeks in AI safety. We’ve watched agents go off the rails and do things nobody expected them to do, in production, on other people’s systems. So it’s a reasonable moment to ask how you use AI safely when the thing it’s touching is a live ad account with real money moving through it every hour.

Almost every conversation I have about agents in ad accounts opens the same way.

Do you trust the AI?

While it’s a good question, it’s often asked to shirk responsibility and conclude that AI shouldn’t be used. A better question is: How can we trust AI?

Then the conversation turns to what we can build around the AI model to make it safe for our business to use.

To explain, it’s always helpful to frame it the way you’re already evaluating collaborators. Nobody asks whether they trust a new PPC agency in the abstract. They ask what the team has access to, what they’re allowed to change without checking first, and who reviews the work. Three different questions with three different answers, and you’d never accept “I simply trust them” as a substitute.

Same three questions for an agent:

  • What can it see? An agent working off a thin data layer will give you a confident answer built on a third of your account. It won’t tell you it’s guessing when it doesn’t know it’s guessing.
  • What is it allowed to do? Not what you told it to do. What it is structurally prevented from doing, no matter what anyone tells it.
  • Who signs off? Not “we review the change history afterward.” Who has to say yes before?

Most agentic PPC setups I look at have a decent answer to the first question, but there’s a lot of benefit in becoming more stringent with the answers for the other two.

We’ve been thinking about safe AI for PPC a lot at Optmyzr, so let’s take a look at what we’ve learned so you can get yourself to safer AI faster.

Each layer pays for itself on its own

The reason I’d rather write this as three techniques than as one system: you don’t have to do all three to get value. Each one closes a different failure mode, and each one is useful the day you turn it on.

  • Layer 1: Better grounding means fewer confidently wrong answers. That’s worth having whether you ever let an agent write anything. Most people should start here.
  • Layer 2: A policy layer means the changes that do get made stay inside limits you set. That holds whether the change came from an agent, from a script, or from a person having a bad Tuesday.
  • Layer 3: A review step means nothing reaches the account without someone seeing it first. And as a nice side benefit, you end up with a record of why. That’s worth having even if your data layer is thin and you have no policies at all.

Then these layers compound, which is the part I find genuinely satisfying. Grounding makes the agent’s proposals worth reviewing, so the review step feels like leverage instead of homework. Policies filter the obvious non-starters before a human ever sees them, so the queue stays short enough that people keep opening it. Each layer makes the next one work better than it would on its own.

So, if you’re starting from zero, start with grounding. It has the fastest payoff and the least process to stand up. If you’re already letting an agent make changes, add policies this week. If you have both, the review queue is what turns it from something you use into something your team uses.

Order matters less than accumulation. Any one of these leaves you better off than you were yesterday, and each one you add compounds with the last.

Layer 1: Ground it

A blind agent is a dangerous agent.

Ask an agent connected to a thin data layer why your CPA went up last month. It will answer you. Fluently. Immediately. Based on whatever slice of your account it could actually reach.

What it couldn’t see might have been the entire answer. It’ll make do with what it has.

Realize that grounding is a safety feature, not a convenience feature. Every gap in what an agent can see is a place where it will make something up, and it will do it in exactly the same confident tone as everything else it says. There is no tonal tell.

So here’s what I think you should expect from an MCP, or any data layer, that you’re going to let an agent reason over for PPC.

The full query layer for Google Ads. Real GAQL. Any resource, field, segment or metric the API exposes, including the ones no packaged report covers. Not a curated summary of what a product manager thought was interesting. The moment your data layer is a curated subset, you’ve limited which questions the agent can answer well, and you haven’t told anyone which ones those are.

  • GA4 sitting alongside the ads data. So “what happened after the click” is part of the same question, instead of a second tool and a manual join. A lot of the diagnosis questions people actually ask are cross-boundary questions. If the boundary is still there, the agent guesses across it.
  • Complete change history, every actor. UI edits, scripts, Optmyzr, other tools. So “who made the change that moved our ROAS in March” is a question with an answer, rather than a group chat.
  • Negative keywords consolidated across all four levels. Account level, shared lists, campaign, ad group, plus a deterministic check of whether a given query is already blocked and by which negative, and a list of campaigns sitting with no negative protection at all. Negatives are a common place to watch an agent reason confidently and wrongly, because the true state is scattered across four places and nobody assembles it.
  • Auction Insights, with a drill-down into one competitor domain, shows your own performance on every keyword you share with them. Competitive questions are the ones where a hallucination is hardest to catch, because you have no independent read on the answer.
  • Vertical benchmarks. Your CTR, CPC, conversion rate, and impression share as a percentile against other accounts in your industry, rather than against a blog post average from three years ago that everyone quotes and nobody sources.
  • Multiple ad platforms. Google, Microsoft, Meta, Amazon, LinkedIn, OpenAI, TikTok, Yahoo. Budget questions are rarely single-platform questions, even when the person asking works mostly in one.
  • A stored profile of each account. Business model, economic posture, bid strategy mix, structure, budget behavior, what’s already been tried and what happened when you tried it. The agent reads this before it opens its mouth.

Optmyzr’s MCP has all of the above today, and it’s a one-click install from the Claude directory rather than an API console, a developer token, or an engineer on speed dial.

Grounding raises the quality of everything the agent says. The next layer decides what it’s allowed to do about it.

Layer 2: Gate it

How do you stop an AI from blowing through a month of ad budget by mistake?

Not by asking it nicely. Not in the prompt.

You use a policy layer that controls what an AI can and cannot do, and you separate it from the AI itself, so the AI can never change the rules.

Set the rules once. Enforce them everywhere, always.

This is automation layering, which I’ve been writing about across my books for years, applied to a new first layer. The original idea is simple: one system does the work, like Google’s own bidding and budget automations, and a second system, your own automations, scripts or rule engine strategies, validates that what the first system did actually makes sense for your business before it sticks.

AI just took over the first job. It’s now the thing making the recommendation. The second layer didn’t become less necessary; it became more necessary because the first layer is more creative and sometimes unpredictable.

Account policies are how you write down the “never do this.” Rules you set once, on the account itself, about what is allowed to happen there. No bid increase above 10% in a single move. No budget change beyond a set threshold. These campaigns don’t get touched. No competitor brand terms added. Whatever your version of “absolutely not” happens to be.

And the policy doesn’t care who’s asking.

An agent proposing a 20% bid increase gets blocked. A hallucination gets blocked. An instruction hidden in a document gets blocked. A junior with a misplaced decimal gets blocked. You, at 11pm on a Friday, in a hurry, on your phone, get blocked.

Same rule, same verdict, no exemption for good intentions or seniority.

Want it through anyway? Override it deliberately. It goes on the record with your name attached. An override you can perform without noticing is not a guardrail but a speed bump made of paint.

This structural detail matters: this isn’t a setting for the AI. It’s a setting on the account, and the AI is one more thing subject to it. Same as a script. Same as a person.

What PPC account policies look like. Screenshot from Optmyzr.com, September 2026.

A rule that lives in the prompt is a rule the model can be talked out of by a clever user, by an injected instruction sitting in a document it was asked to read, or by its own drift over a long session. A rule that lives on the account holds under every path into the account.

Layer 3: Keep a human in the loop

Everyone says they’re in the loop.

Few people can tell you which screen, which person, or which queue enforces being in the loop. Ask, and you usually get “we check the change history afterward,” which means nobody’s checking.

And good luck getting a quick answer when someone asks you to produce the reasoning behind an AI-assisted decision from three months ago.

So when we built the write path into our MCP, we didn’t leave the loop to good intentions. There is no route from the agent to your ad account that doesn’t first stop at a human. 

We took a pattern from engineering, where it’s been settled practice for decades: nobody pushes code to production without a change request that another person reviews. Why the account where you spend six figures a month deserves less process than a CSS tweak is a question our industry has never really answered.

Here’s the actual sequence.

  1. The agent proposes. Every write, whether a bid, a budget, a paused campaign, or a new negative, becomes a draft change request. Nothing reaches the ad platform yet. The agent’s job ends at “here’s what I think you should do, and why.”
  2. Policies evaluate it. Each row is checked against your account policies and carries its verdict with it, so a blocked row shows up as blocked, with the reason attached rather than in a log somewhere.
  3. A human opens the review. The rows, the stated reasoning for each one, the policy warnings, a timeline of what happened when, and the list of people eligible to approve it.
  4. You preview exactly what would go live. The exact deterministic changes, like your target ROAS for “Brand Campaign” will go from 200% to 220%, rather than a natural-language paraphrase of what the agent believes it’s about to do.
  5. You confirm. Only then does anything change in the ads account.
What change requests look like in a PPC account. Screenshot from Optmyzr.com, September 2026.

Whether it’s an AI or a colleague suggesting the change, everything goes through the same pipe. In the case of a colleague, the change request is a second pair of eyes. In the case of an AI, it’s the first pair.

An audit trail for AI in PPC

The change request queue turned out to be more valuable than just the safety it was built for.

Because it’s not just a list you go to in order to be the human in the loop. It’s a complete record of intent.

When a client asks in November why their target CPA was moved in March, you have the proposal, the rationale, the policy verdicts, who approved it, and when. Was it a human? Was it an AI? What was the data behind the recommendation? Who made the final call?

Try assembling that from a chat transcript six months later. Try assembling it from change history, which tells you what changed but never why.

We built this for safety, and it turned into the best account documentation we’ve ever had. If you’re an agency, that’s a credibility argument as much as a safety one.

What good looks like

Put the three layers together, and you get something I’d describe, approvingly, as boring.

Not underpowered. Boring. 

As in: you know what it can see, you know what it structurally cannot do, and you know nothing reaches the account without you. The excitement belongs in the findings, not in wondering what it got up to while you were at lunch.

That’s the bar I’d hold any agentic PPC setup to, ours included, and it’s a bar you climb one rung at a time:

  • It sees the whole account because gaps in what an agent can see are where it starts inventing, confidently.
  • It’s bounded by rules you wrote, which live on the account rather than in the prompt, and which apply to everyone identically.
  • It can’t act alone, because every write becomes a change request with policy verdicts attached, reviewers named, and a confirmation step no agent can fabricate.

You don’t have to arrive there in one move. Pick the layer that closes your biggest gap, ship it, then add the next one.

We’ve been building this against real accounts and genuinely stupid edge cases since well before MCP was a term marketers used. It’s a one-click install from the Claude directory now.

If you tried agentic PPC once, got a confidently wrong answer, and quietly shelved it, that’s the failure I’d most like you to come back and retest.

Having safe AI for our ad accounts can’t be something we expect to get just from picking the right model. It’s something we achieve by layering in processes and technologies we control.

Try Optmyzr’s safe AI for PPC free for 14 days.

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SMX Now: Find the entity gaps holding back your content strategy

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Your Schema Says One Thing. Google's NLP Sees Another. That Gap Is Your Content Strategy

Your schema tells search engines what your brand is. But that doesn’t mean Google’s systems understand it the same way.

That gap can reveal where your content strategy is falling short.

Our next SMX Now on Sept. 16 at 1 p.m. ET will feature Ray Martinez, VP of SEO at Archer Education. He’ll show you how to measure the difference between the entities you define and the ones Google’s natural language processing (NLP) actually recognizes.

You’ll learn how to build an entity audit using schema.org markup, the Google Cloud Natural Language API, and an agentic coding tool such as Antigravity, Claude Code, or Codex. The process turns your existing schema into a queryable knowledge graph, then compares it with competitor content to uncover topics they cover, gaps they miss, and entities Google doesn’t yet connect with your brand.

Martinez will also show you how to turn those findings into action. You’ll learn how to create content around under-recognized entities, strengthen those connections with structured data and internal data, and make your pages more retrievable and citable across search and AI engines.

You’ll leave with a repeatable way to measure discoverability, find content opportunities, and track whether Google and AI systems are getting better at understanding what your brand actually does.

Save your spot

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Google Ads AI Dashboards start appearing in advertiser accounts

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Google Ads’ new AI-powered Dashboards are rolling out to some accounts, letting advertisers create visual performance reports with simple text prompts.

Driving the news. AI Dashboards started appearing in some Google Ads accounts following Google’s announcement of the feature in August. You can use simple text prompts to turn account data into visual reports, Google said.

  • Instead of manually building a report to investigate a performance change, you can describe what you want to analyze and have Google generate the visualization.

Why we care. Building reports in Google Ads has traditionally required you to manually select metrics, dimensions, and visualizations. AI Dashboards shift much of that work to Gemini, letting you describe what you want to analyze while Google builds the report.

The details. The feature goes beyond building charts.

Each report also includes a real-time AI summary that explains the “why” behind the data, according to Google. That lets you use the dashboard to understand what changed in campaign performance and what may be driving those changes, rather than simply reviewing the underlying numbers.

The big picture. Google is increasingly putting AI between advertisers and their campaign data. It has also introduced AI-powered insights on the Google Ads homepage and Ask Advisor, its in-product AI agent, as it shifts toward workflows where advertisers can ask performance questions in natural language instead of manually navigating reports.

Bottom line. AI Dashboards reduce the work of building Google Ads reports, letting you spend more time interpreting insights rather than creating them.

First spotted. This update was first spotted by paid search expert Thomas Eccel, who shared it on LinkedIn.

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Microsoft Advertising removes Max CPC from new standalone bidding campaigns

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Microsoft Advertising will stop allowing advertisers to set Max CPC limits on new campaigns using several standalone automated bidding strategies starting Oct. 1, as the platform pushes advertisers toward conversion-based targets and other automated bidding controls.

Existing campaigns with Max CPC limits won’t immediately lose them, while portfolio bid strategies and several other bidding strategies will retain the option.

Why we care. This removes another manual control from Microsoft’s automated bidding strategies. Advertisers that use Max CPC as a safeguard against unexpectedly expensive clicks will have less direct control when creating certain new campaigns and will instead need to rely more heavily on budgets and conversion-based targets.

Whether that produces better results will depend on the quality of an advertiser’s conversion data, the targets they set and how effectively Microsoft’s bidding system responds to those signals.

What’s changing. Starting Oct. 1st, advertisers creating new campaigns using standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks bidding strategies will no longer be able to add a Max CPC.

Existing campaigns created before the deadline will retain their Max CPC settings. Microsoft Advertising Product Liaison Navah Hopkins also confirmed that Target Impression Share, eCPC and portfolio bidding strategies will continue to support Max CPC controls.

Why Microsoft is making the change. Microsoft says Max CPC limits can interfere with automated bidding by overriding an advertiser’s stated performance goals and potentially creating spend pacing irregularities.

The company says advertisers using conversion-based bidding with target CPA (tCPA) and target ROAS (tROAS) tend to have an easier time achieving their goals than advertisers relying on legacy controls such as Max CPC.

Instead of CPC caps, Microsoft wants advertisers to communicate their objectives through controls more closely tied to business outcomes, including budgets, tCPA, tROAS, conversion value rules and seasonality adjustments.

What happens to existing campaigns. There’s no immediate migration for campaigns already using Max CPC. Campaigns created before Oct. 1 will continue to retain the bidding control.

That creates an important distinction between existing and newly created campaigns: advertisers won’t necessarily lose Max CPC across their accounts on Oct. 1, but their ability to use it when building certain new campaigns will disappear.

Microsoft says further updates about the future of Max CPC will be provided later.

Get ahead of the change. Hopkins is encouraging advertisers to use optimization experiments to test removing Max CPC from existing campaigns before the deadline.

Doing so could give advertisers an indication of how campaigns perform when automated bidding has greater freedom, before the option disappears from newly created standalone campaigns.

That may be particularly relevant heading into the holiday season, when advertisers could otherwise encounter the new restriction while launching or restructuring campaigns.

Targets become the primary lever. Microsoft is encouraging advertisers to think of tCPA and tROAS as their primary volume and value levers, rather than trying to control automated bidding through individual CPC limits.

Hopkins also noted that Microsoft Advertising can allow campaigns to outperform their tCPA or tROAS targets regardless of whether they’re limited by budget. Where appropriate, Microsoft recommends using conversion value rules to provide the bidding algorithm with additional information about which conversions are more valuable to the business.

Bottom line. Microsoft Advertising is making automated bidding more target-driven by phasing Max CPC out of new standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks campaigns from Oct. 1 — while, for now, preserving the control for existing campaigns and portfolio strategies.

September 7th comms. Microsoft Advertising emailed advertisers with more information about this update.

Microsoft’s Max CPC restriction will also apply to Target CPA and Target ROAS campaigns, expanding the bid strategies beyond the Maximize Conversions, Maximize Conversion Value and Maximize Clicks strategies reported in August.

Microsoft also revealed a January 12th deadline for API users, tool providers and Google Import, after which Max CPC won’t be supported for new campaigns or existing campaigns not already using it. While existing campaigns using Max CPC by Oct. 1 can keep the setting, Microsoft now says removing it after that date is irreversible — advertisers won’t be able to add a Max CPC back later.

Dig deeper. Updates to Max CPC for new campaigns

See exactly how your competitors win.

Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.

Analyze your competitors

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Google Ads adds new tools to drive and measure in-store sales

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Google is rolling out two new features designed to help multi-location retailers, restaurants and local service businesses reach nearby customers and connect their ad campaigns with in-store sales.

Driving the news. Google Ads Liaison Ginny Marvin announced Local Customer Optimization for Performance Max store goals campaigns and a new way to bring store sales data into Google Ads through Data Manager.

Both are aimed at reducing the work required to drive and measure offline sales ahead of the holiday rush.

Why we care. These updates make it easier to connect digital advertising with actual store visits and sales. Multi-location businesses can prioritize spend toward nearby, in-market customers across Maps, Waze and local Search, while simpler CRM and Google Sheets connections make it easier for advertisers to feed offline sales data back into Google Ads for measurement and optimization.

Zoom in. Local Customer Optimization is a new campaign-level toggle in Performance Max for store goals campaigns.

When enabled, Google will prioritize budget toward reaching consumers who are actively in-market nearby across Google Maps, Waze and local Search.

The feature is rolling out now.

Meanwhile. Google is also simplifying how businesses can share their offline sales data with Google Ads.

With Store Sales in Data Manager, advertisers will be able to connect their CRM or Google Sheets directly within Data Manager rather than relying on more technically demanding methods of sharing that data.

Advertisers can then use the information to measure in-store sales and optimize campaigns toward their highest-potential walk-in customers.

Google says the feature will begin rolling out in the coming weeks.

The big picture. The two updates are designed to work across opposite ends of the customer journey.

Local Customer Optimization uses real-time navigation and local intent signals to help businesses reach potential customers nearby, while Store Sales in Data Manager gives advertisers a simpler way to feed the resulting offline revenue data back into Google Ads.

For businesses with physical locations, that creates a more direct connection between finding nearby customers online and measuring what happens when they walk through the door.

What to watch: Local Customer Optimization is rolling out now, while Store Sales in Data Manager is expected in the coming weeks.

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Inside Google Maps: 72 ranking signals and the architecture behind local search

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Inside Google Maps- 72 ranking signals and the architecture behind local search

When a business appears in Google Maps, the listing you see is the end product of a much larger system.

Behind it sits a canonical geographic entity assembled from multiple data sources, connected to the Knowledge Graph and the web, scored by several ranking systems, filtered through geographic and semantic retrieval, personalized for the user, and finally passed to a rendering engine that decides what can actually appear on the map.

Our team recently obtained a binary exposing a non-public scope of Geostore, the system Google uses to represent geographic entities. We crossed it with Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and Google’s 2024 leak.

The recovered material includes:

  • 72 Geostore ranking signals.
  • 793 data source providers.
  • 446 local search intent types.
  • 50,998 Mapcore styles.
  • 12,936 label styles.
  • 10,936 searchable Geostore declarations.

The ranking signals will probably attract attention. But they’re only one layer.

The architecture around them tells a more important story about how Google understands places and what local SEO may become as Maps turns into a conversational product.

The first thing to understand: A listing isn’t the entity

A useful mental model starts with Geostore. Google represents geographic objects internally as Features. A Feature can be a business, building, road, city, station, area, transit element, or even a 3D object.

For an establishment, the object can contain identity, geometry, source information, websites, business-chain relationships, Knowledge Graph references, concepts, and ranking information.

The familiar Maps listing is assembled later. What a business owner edits in Google Business Profile isn’t necessarily what Google maintains internally as the entity.

Google builds a canonical representation of the place that can incorporate data from multiple sources, survive changes in geometry, and connect to other Google identifiers, including the Knowledge Graph machine ID (MID).

For local SEO, the entity is the more useful unit to consider. The listing is the interface. The entity sits underneath it.

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Google combines data from 793 providers

One of the most revealing parts of Geostore is its provenance system. 

A business doesn’t simply have one source. Its name might come from one provider, its phone number from another, its category from another, and its geometry from somewhere else entirely.

The corpus exposes 793 source providers, along with mechanisms for provenance, priority, trust, and conflation.

Conflation is the process used when several sources describe the same object and disagree.

Geostore contains generic mechanisms that can pick one value, merge several values, or combine them. It also models trust levels ranging from blocked or untrusted sources to trusted and super-trusted ones.

This gives a different interpretation to a common local SEO problem: changing a field in Google Business Profile doesn’t guarantee that Google’s canonical representation immediately becomes that new value.

The edit becomes another piece of evidence entering a system that may already have competing evidence.

For businesses struggling with persistent incorrect attributes, duplicate information, or changes that repeatedly revert, this architecture helps explain why the problem can be harder than editing a listing.

Dig deeper: The local SEO gatekeeper: How Google defines your entity

The archive makes the leak much more useful

The binary itself gives us structures, field numbers, and complete enumerations. Google’s March 2024 documentation often gives us something different: prose explaining what those structures mean.

We published the two together. The resulting archive contains 10,936 Geostore declarations that can be searched by message name, package, field type, documentation text, tag number, status, and other properties.

In reverse-engineering work, an internal name can easily become a theory once it circulates through the SEO community. The archive lets you inspect the underlying evidence.

If a signal exists, you can find its declaration. If a field existed in the 2024 documentation, you can read the associated description. If a field has been stripped from the newer client scope, its protobuf tag still leaves a numbered hole.

The 2024 leak gave us many descriptions of Google’s systems. The newer binary gives us much more of their actual vocabulary.

Together, they provide a more useful picture than either source does on its own.

Oyster Rank contains 72 ranking signals

Geostore has its own ranking system. Internally, it’s called Oyster Rank. We recovered a complete visible enumeration of 72 signals, including:

  • Google reviews.
  • Web query volume.
  • Listing impressions.
  • Listing opens.
  • Direction requests.
  • Website clicks.
  • Chain membership.
  • Wikipedia signals.
  • Popularity.
  • Prominence.
  • Landmark information.
  • Road usage.

Out of 72 values, 25 are explicitly marked deprecated.

The important limitation is that we recovered the signal names, not their current weights.

The schema shows a pipeline in which raw observations are extracted, normalized, and mixed into the Feature’s rank. But the coefficients that would tell us how much each signal contributes are outside the scope we recovered.

So SIGNAL_GOOGLE_REVIEWS proves that reviews belong to the Oyster Rank vocabulary. It doesn’t prove that reviews currently carry a particular weight in a Maps search.

The 72 signals aren’t the Google Maps algorithm

This is probably the most important clarification for SEOs. Oyster Rank appears to characterize the importance of the entity inside Geostore. A user query still has to go through additional systems.

Maps must understand what the person means, identify a geographic context, generate candidates, evaluate semantic relevance, and serve a final result set.

A simplified pipeline looks more like:

Geostore entity -> query understanding -> semantic matching -> candidate generation -> geography and quality -> reranking -> results

There are additional complications. We also found a separate scorer running entirely offline on the device. It has eight signals across 13 tiers and is distinct from both Oyster Rank and server-side Places ranking.

There isn’t a single Maps ranking formula. Different scoring and retrieval systems operate at different stages.

Turning the 72 Oyster Rank signals into a checklist of 72 Google Maps ranking factors would miss most of the architecture.

Local search doesn’t have a fixed radius

We also tested the geographic layer directly. A common local SEO model imagines Google looking within a predefined radius around the user and ranking the businesses found inside it.

Our measurements show something more dynamic. Using the same origin in Paris, the geographic footprint changed considerably depending on the query.

A dense query, such as pharmacie produced a far smaller search area than a brand query, such as Carrefour.

The environment matters, too. The same pharmacy query expanded dramatically when run in a sparsely populated rural area.

Google appears to adapt the candidate space to both the query and what exists around the user.

We then removed geographic weighting from the same engine. Across 5,083 calls and 86,584 results, the median distance moved from 6.87 km with geography to more than 4,000 km without it.

More interestingly, the non-geographic order remained extremely stable.

This suggests geography is doing more than reordering the same list of candidates by distance. It changes what the retrieval system considers in the first place.

Distance is still fundamental in local SEO. But “I’m closer, I should rank higher” is an incomplete model.

Dig deeper: The proximity paradox: Beating local SEO’s distance bias

Maps and the web are connected through entities

The connection between Maps and classic web SEO may be one of the most consequential findings in the corpus.

Geostore Features can connect to the Knowledge Graph through a MID. On the web index side, documents can also carry MIDs.

Google has a layer called webref that associates documents with entities and stores information, including topicality, confidence, geographic metadata, and document-level scores.

The relationship also works at the document-ranking level. The recovered structures describe a relative ranking signal between different documents for the same entity, along with properties such as whether a page is an author page, publisher page, or reference page.

This creates a very different way of thinking about a store locator or location page. Its role may extend beyond ranking for queries such as “shoe shop Paris.” The document can become evidence about the underlying entity.

The SEO objective is then partly to make it easy for Google to establish:

  • Which entity the document describes.
  • How much of the document is actually about that entity.
  • How confident that association should be.
  • Whether the document is a useful reference for it.

Web SEO and local SEO are much less separate inside Google’s infrastructure than their interfaces suggest.

Google understands concepts, not just categories

The semantic layer goes considerably beyond the primary category visible on a listing. Google uses GConcepts, a shared conceptual vocabulary that can describe businesses, dishes, attributes, cuisines, service modes, and other concepts.

We followed a simple ramen query through several parts of the system. The search results themselves didn’t all belong to one category. Google connected the query with ramen restaurants, Japanese restaurants, Asian restaurants, and other related concepts.

Inside listings, the semantic representation goes deeper. Review topics, menu dishes, and other attributes can be represented as entities rather than plain strings.

For an AI system, this is extremely useful. Instead of rereading thousands of reviews every time someone asks whether a restaurant has long waits or good ramen, Google can work from structured themes, entities, and precomputed signals already attached to the place.

Semantic understanding becomes much more important when the interface starts answering complex questions.

Part of Google’s geographic intelligence lives on the phone

Not everything is calculated on Google’s servers. We found on-device structures associated with visits, place candidates, frequent places, trips, home and work, mobility patterns, and user location profiles.

One particularly interesting object is ChainAffinity, which suggests the system can model affinity toward a recurring retail chain.

There’s also the separate offline scorer mentioned earlier.

The exact evidence level differs between components. Some structures are explicitly named in the recovered schema, while parts of the persona layer can only be reconstructed from compiled structures.

But the broader architecture is clear: The phone itself participates in building geographic context.

That means personalization in Maps can combine server-side knowledge of the world with a local model of the user’s own geography.

Ranking still doesn’t guarantee map visibility

Search results are only one of the outputs of Maps. 

The visual map has another problem to solve: Thousands of potentially relevant entities cannot all receive labels simultaneously. That job belongs partly to Mapcore.

We recovered 50,998 Mapcore styles and 12,936 label styles. Label visibility can change with zoom and other rendering conditions.

A business can be eligible or highly ranked and still fail to appear as a visible name on the map. Search ranking and map visibility are separate optimization problems.

This distinction becomes especially important when people measure “Maps visibility” using screenshots or map grids. The visual surface includes a rendering decision after retrieval and ranking have already happened.

Then Google puts Gemini on top

The timing of the recovery is useful. Google is rapidly expanding Ask Maps and other AI-powered experiences, but much of the infrastructure required to answer complex questions was already present.

The system already has:

  • Canonical place entities.
  • Semantic concepts and attributes.
  • Reviews and extracted topics.
  • Knowledge Graph relationships.
  • Web evidence.
  • Geographic retrieval.
  • Behavioral signals.
  • Personal geographic context.
  • Listing composition.
  • Ranking systems.

Gemini adds a conversational interface over these layers. That changes what a local query can be.

“Best ramen near me” is relatively easy. “Where can six people eat near my hotel tonight, with one vegetarian, little waiting time, and good recent feedback about service?” requires a different kind of place representation.

Google needs to know what the restaurant is, what it serves, when it’s open, what people say about it, where it is, how it relates to the user’s route or context, and whether the available evidence is reliable enough to recommend it.

Maps has been building many of those ingredients for years. The AI layer gives Google a new way to use them.

Dig deeper: Google Ask Maps: How to optimize for visibility

What to optimize beyond the Business Profile

The most actionable conclusion from this research isn’t a new list of ranking factors.

Local SEO has traditionally concentrated heavily on optimizing the Google Business Profile: categories, reviews, photos, attributes, opening hours, and other listing fields.

Those remain important.

But Google’s architecture suggests a broader objective: improve the representation Google can build of the entity itself.

For a business or retail brand, I would increasingly ask:

  • What exactly is this place?
  • What does it offer?
  • Which brand or chain does it belong to?
  • Which concepts and attributes describe it?
  • Does its website describe the same entity clearly?
  • Which Web documents provide evidence about it?
  • Does Google see real demand for the brand?
  • What do reviews consistently say about specific aspects of the experience?
  • Which audiences and contexts could make this place relevant?
  • When should Google recommend it rather than another candidate?

The quality of the answer Google can produce depends on the completeness of that representation.

Semantic completeness may become the next local SEO battleground

Proximity, relevance, and prominence remain useful concepts.

AI adds another requirement. The system needs enough structured evidence to reason about a place.

A restaurant can have an optimized profile and hundreds of reviews, yet still be poorly represented for a specific question if Google can’t confidently connect the relevant attributes, concepts, web pages, and review themes to the entity.

A large retail brand has an additional problem.

Google models both chains and individual locations. We observed stores from the same brand in the same metropolitan area carrying different primary concepts, even though the chain model contains a canonical concept structure.

Consistency can’t be assumed simply because every location belongs to the same brand.

For multi-location SEO, the task extends across the brand entity, each local entity, the website, structured data, third-party sources, user-generated content, and the relationships between all of them.

That’s a much larger surface than a Business Profile.

Dig deeper: Multi-location SEO: How to structure geographic pages at scale

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The listing is only the surface

The 72 Oyster Rank signals are fascinating because they expose categories of information Google can use when estimating the importance of a place.

The deeper finding is the system around them:

  • Geostore builds a canonical geographic entity from competing sources.
  • The Knowledge Graph gives that entity semantic meaning.
  • Webref connects Web documents to it.
  • Search and Places interpret the query and retrieve candidates within a dynamic geographic space.
  • On-device systems contribute personal geographic context.
  • Mapcore controls what reaches the visual map.
  • Ask Maps and Gemini can finally reason over the resulting representation in natural language.

The Google Business Profile still matters. But Google’s architecture suggests looking beyond the listing itself and considering the representation Google has built of the business.

The key question is whether that representation is complete enough for Google to confidently recommend the business.

The full study includes the reconstructed architecture, experiments, and technical evidence. The companion archive exposes the 10,936 recovered Geostore declarations alongside the 2024 documentation so the underlying schemas, enums, fields, and signals can be inspected directly.

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