Posts

Query Fan-Out: What It Is and How It Affects AI Visibility

/0 Comments/in /by

Your content can rank on the first page of Google and still never be cited or mentioned by LLMs.

This makes sense once you understand query fan-out, a background process AI systems use to build answers.

When someone asks ChatGPT or Perplexity a question, it doesn’t default to the best-ranking page.

Instead, it runs related searches behind the scenes, pulling from the most relevant and reliable sources, regardless of position.

User query

If your brand doesn’t show up in those searches (whether through your own content or third parties), you’re unlikely to make it into the answer.

High rankings don’t hurt, of course.

But in AI search, coverage and retrievability are king.

In this guide, I’ll teach you how to optimize your content strategy for query fan-out to help increase your AI visibility.

You’ll learn:

  • Why LLMs use query fan-out
  • How it behaves differently across major AI platforms
  • Why it changes how you create and structure content
  • A 6-step workflow for earning more citations in AI search

Free template: Our Query Fan-Out Audit Template includes ready-to-use spreadsheets for logging money prompts, sub-queries, and content gaps — plus a checklist to keep you on track. Download it now to follow along.

First, I’ll dive deeper into how query fan-out works.

What Is Query Fan-Out?

Query fan-out is a process AI search systems use to break a single user query into multiple sub-queries to create the most helpful response.

In other words, the AI “fans” the query out into a series of related sub-questions to build a more complete picture of the topic.

How query fan-out works

It then pulls information from multiple sources — editorial sites, Reddit threads, comparison and product pages — and synthesizes it into a single comprehensive answer.

The query fan-out process

AI systems use query fan-out for a few reasons:

  • Confirm information: A single source might be wrong or biased. Running parallel sub-queries allows the system to cross-reference multiple sources and find consensus before committing to an answer.
  • Handle complex, specific queries: When a question has multiple layers, like comparing two products across price, reliability, and long-term value, fan-out breaks it into manageable pieces that the system can research independently.
  • Answer the real question: Someone searching “best toothbrush” probably also wants to know about price, battery life, and durability, even if they didn’t say so. Fan-out anticipates those needs and gathers evidence upfront.

For example, a search for “best toothbrush” might trigger sub-queries like “best electric toothbrushes [year]” and “best toothbrushes for sensitive gums.”

This helps the AI build a more complete and useful answer:

Sub-Query What It Contributes to the AI Response
Best electric toothbrushes Top-rated picks and editorial consensus
Best toothbrushes for sensitive gums Use-case recommendations
Oral-B vs. Philips Sonicare Head-to-head comparison data
Best eco-friendly toothbrushes Value picks and pricing information

The AI then synthesizes those findings into a single answer that covers everything the user might want to know: top picks, price ranges, use-case breakdowns, and comparisons.

In this way, it anticipates the user’s needs, even though the original prompt (best toothbrush) was just two words.

ChatGPT – Best toothbrush

What Query Fan-Out Is NOT

Now that we’ve covered what query fan-out is, let’s clear up a few common misconceptions.

Query fan-out is not:

  • Keyword research: This is the process of finding terms your audience searches for. Query fan-out is something AI systems do automatically, behind the scenes, every time someone asks a question.
  • People Also Ask: PAA is a visible SERP feature that shows users what else they might want to search. Fan-out happens in the background whether you can see it or not.
  • A fixed set of queries: Only 27% of fan-out sub-queries remain consistent across repeated searches, according to a SurferSEO study. Sub-queries vary by phrasing, user context, and platform.

Further reading: AI SEO Myths, Debunked: A No-BS Guide for Marketers


Why Query Fan-Out Matters for AI Visibility

Understanding what query fan-out is only gets you so far. The real question is: What does it mean for your content strategy?

Here are four shifts that should make you rethink how you approach content.

You Don’t Need Top Rankings to Get AI Citations

Top rankings don’t automatically translate to AI citations.

When AI breaks a query into sub-queries, it pulls the most relevant and complete source for each one, regardless of where it ranks.

ChatGPT cites pages in position 21+ almost 90% of the time, according to a Semrush study.

Perplexity and Google show the same pattern.

Ranking Positions of LLM-Cited Search Results

AI Retrieves Passages, Not Pages

Rather than directing users to a page, AI systems scan your content and synthesize the exact passage that resolves a query.

This means that the earlier you answer a question, the better your chances of being extracted.

The data backs this up.

44.2% of citations in ChatGPT responses come from the first 30% of a page, while 31.1% come from the middle, and 24.7% from the final third, according to growth advisor Kevin Indig’s analysis of 1.2 million ChatGPT responses.

ChatGPT – Citations from intros

You’re Competing Across a Whole Topic, Not Individual Keywords

SEO often revolves around individual keywords. Query fan-out revolves around comprehensive coverage.

That’s why broad, well-connected coverage across a topic (think pillar pages and topic clusters) can help you earn more AI visibility.

Topic clusters

Pro tip: Pages that rank for fan-out queries (not just the main query) are 161% more likely to get cited, according to a SurferSEO AI Overviews study.

Query Fan-Out Collapses the Buying Journey

We were taught that buyers move linearly — awareness, consideration, decision — and have long optimized content for each stage.

The Marketing Funnel

With AI, those stages collapse into one.

A single high-intent question triggers the system to fan out.

It pulls awareness-level context, consideration-level comparisons, and decision-level specifics into one answer.

The entire buying journey can now happen in a single interaction. So your content needs to work across the full funnel, not just the stage you’re targeting.

Pro tip: Want to work through these steps as you read? Our free Query Fan-Out Audit Template has spreadsheets for tracking your money prompts, sub-queries, intent buckets, and content gaps — plus a checklist to keep the full workflow on track.

The Query Fan-Out Workflow: 6 Steps to Earn More AI Citations

This six-step workflow shows you how to earn more AI citations by identifying and targeting high-impact sub-queries.

It’s repeatable, so you can follow these steps for every topic that matters to your business.

Note: Each AI platform handles fan-out differently, from the number of sub-queries it runs to how it cites sources. We cover the platform differences in depth after the workflow.

Step 1: Find Your Money Prompts

Money prompts are the conversational phrases or questions your ideal customer would ask an AI tool when trying to solve the problem your product or service addresses.

Money prompts are:

  • Typically long-tail and highly specific
  • Tied to a real use case or constraint
  • Close to a decision, not just browsing

Think of money prompts as the AI SEO equivalent of money keywords: high-commercial-intent keywords designed to drive sales.

For example, “noise-canceling headphones ” is a keyword.

“What noise-canceling headphones are best for working from home with kids around, and cost under $300?” is a money prompt.

Noise canceling headphones

Look for money prompts where your audience asks questions:

  • Customer support tickets
  • Community forums
  • Sales call transcripts
  • Internal chat logs
  • Google Search Console queries

For example, when I searched for noise-canceling headphones on Reddit, I found multiple money prompts in real users’ posts.

Like this one that asks for the best noise-canceling headphones for telehealth:

Reddit – Telehealth noise cancelling headphones

And this one asking for durable headphones that will last longer than 2 years:

Reddit – Durable noise cancelling headphones

Forums and transcripts are a good starting point. But you’ll need a dedicated tool to find money prompts using real AI search data.

Semrush’s AI Visibility Toolkit tells you exactly what users type into AI tools, along with the AI’s response.

To show you how it works, I’ll use Bose, a well-known headphone brand, as an example.

Note: I’ll be using Semrush to show you how to complete the query fan-out workflow. If you don’t have a subscription, sign up for a free trial of Semrush One, which includes the AI Visibility Toolkit and Semrush Pro.


First, I searched Bose’s domain in the Visibility Overview tool.

The “Topics & Sources” report revealed over 123.7K prompts where the brand already appears in AI answers.

Visibility Overview – Bose – Prompts

Filtering by “noise canceling” let me dig deeper into topic-specific money prompts like “noise-canceling headphones for sensory issues.”

Visibility Overview – Bose – Prompts – Noise canceling

Clicking the prompt provides a full breakdown: the AI’s response, every brand mentioned alongside yours, and the exact sources it cited.

Visibility Overview – Bose – Prompt details

Follow the same process for your own domain.

These prompts are your highest-priority money prompts — your audience is already searching them, and AI is already answering them.

Don’t have AI visibility yet? Use the Prompt Research tool.

Enter a broad topic to see the prompts that generate the most AI results in your industry.

Prompt Research – Noise canceling headphones

As you find relevant prompts, add them to your spreadsheet.

Even a few money prompts give you enough to work with for the next step.

Fan-Out Audit Template – Money Prompts

Step 2: Generate Your Fan-Out Set

There are two ways to generate fan-out sets: manually or with a dedicated fan-out tool.

The manual approach is free and helps you understand how fan-out behaves, while tools are faster and better suited to working at scale.

I’ll start with the manual method.

Paste this prompt template into any AI platform to get a fan-out set:

Expand this question into the sub-queries an AI system might search to answer it: [your money prompt].

When I ran my Reddit money prompt through ChatGPT, it returned sub-queries grouped into categories:

  • “Core Product Category”
  • “Durability & Longevity”
  • “Battery & Hardware Lifespan”
  • “Reliability & Failure Rates”

ChatGPT – Money prompt

Each category is a potential content gap you’ll address in Step 4.

Run your money prompt through multiple AI tools to get a more complete picture, since each platform tends to expand prompts differently.

Pro tip: Manual research is a solid starting point, but outputs can contain inaccuracies or hallucinations. A dedicated fan-out tool simulates how different AI platforms expand your query and returns an organized list of sub-queries you can act on immediately.

For a faster option, Backlinko’s free ChatGPT Query Fan-Out Tool is worth trying.

Install the Chrome extension, open ChatGPT, and ask your money prompt. The extension captures the response in real time and breaks down every sub-query ChatGPT ran behind the scenes.

When I ran a prompt through it, the panel showed:

  • Each sub-query the model generated
  • The metadata behind the response, including model version
  • Every URL cited, categorized by type: sources, products, images, and news

As you gather sub-queries, assign a query type to each — this tells you what kind of content you’ll need to create in the next step.

Use these definitions to categorize them.

Query Type What It Means
Reformulation A reworded version of the original prompt
Comparative Weighs two or more options against each other
Implicit Addresses a need the user didn’t explicitly state
Personalized Tailored to a specific situation, constraint, or preference
Entity expansion Drills into a specific brand, product, or person mentioned
Related A connected topic the AI anticipates the user might want next

Step 3: Bucket Sub-Queries by Intent Type

Bucketing by intent tells you what types of content to create and the ideal format for each.

To categorize a sub-query, answer this question: What does the person actually want to do after getting an answer?

Consider an example from the noise-canceling headphones query fan-out set: “Sony vs Bose Noise Canceling Headphones.”

Someone asking this is weighing two specific products against each other, so it’s a “comparison” query.

Fan-Out Audit Template – Intent Buckets

The right format for this query is a head-to-head comparison page or table, not a general buying guide or listicle.

The intent isn’t always this obvious, and some sub-queries may fit more than one bucket.

When that happens, place it where the strongest intent lies.

Here’s a general guide to the main intent buckets and what each one calls for:

Bucket Description Example Sub-Query Content Format
Definitions / Basics What is X? How does X work? “how do noise canceling headphones work” Explainer article, glossary section
Comparisons / Alternatives X vs Y, alternatives to X “apple airpods max vs sony wh 1000xm4” Comparison page, head-to-head section
Best for X / Recommendations Best option for a specific use case “best noise canceling headphones for working from home” Listicle, buying guide
Problems / Troubleshooting How to fix X, why does X happen “how to get rid of background noise in audio” How-to guide, FAQ section
Pricing / Value How much does X cost, is X worth it “are there any good wireless headphones with noise cancellation under $150?” Pricing page, value comparison section
Social Proof / Discussions Reviews, Reddit opinions, user experience “best earbuds for calls in noisy environment reddit” Review roundup, user feedback section

Step 4: Audit Your Existing Content for Gaps

Once you’ve bucketed your sub-queries by intent and format, check which ones your site already covers and which ones it doesn’t (aka content gaps).

Start by searching your own site.

Type “site:yourdomain.com [sub-query topic]” into Google.

For example, running “site:bose.com noise canceling headphones” surfaces all their pages on that topic.

Google SERP – Bose – Noise canceling headphones

From here, evaluate each page against the sub-query it should cover:

  • Coverage: Does it directly answer the sub-query, or just mention the topic in passing?
  • Format: Is it the right content format for the intent?
  • Self-contained answers: Can the answer stand on its own, without the reader needing to look anywhere else?

Categorize each page by its coverage level:

Coverage Level What It Looks Like What to Do
Not covered No page on your site addresses this sub-query at all Create new content targeting this sub-query directly
Partially covered A page mentions the topic in passing but doesn’t resolve the sub-query directly Add a dedicated section to the existing page that fully answers the sub-query
Fully covered A dedicated section or page answers the sub-query completely and can be extracted and cited by AI without needing surrounding context Monitor for AI citations and update regularly to stay current

For each sub-query, you’ll also want to know which competitors are showing up for your money prompts.

Run your money prompts through AI platforms to gather this information manually. Or refer back to your research from the AI Visibility Toolkit in Step 1.

Click any prompt to see which brands were mentioned and the exact sources the AI cited.

Bose – Prompt details – Brands & Sources

Already showing up alongside competitors? That’s a prompt worth protecting — focus on strengthening your coverage so you stay in the answer.

If competitors are showing up and you’re not, that’s a gap worth closing before they own it.

Fan-Out Audit Template – Content Audit

Step 5: Structure Your Content So AI Can Extract It

Creating the right content is only half the job. The other half is making it easy for AI to find, parse, and use.

Start by filling the gaps you identified in Step 4.

For sub-queries with no coverage, create dedicated pages or sections that target them directly.

For partial coverage, add self-contained answers to existing pages that resolve the sub-query without needing surrounding context.

Then, structure everything so AI can extract it cleanly:

  • Address specific questions directly — lead with the answer, not background context
  • Use content chunking: Break content into focused sections with clear headings, short paragraphs, and bullet points
  • Front-load key information early in the page or section
  • Use clear, precise language, including specific product names, figures, and use-case-specific wording
  • Add FAQ sections

Here’s what this looks like in action.

Bose has over 63.9K mentions across AI platforms in the U.S. alone:

Visibility Overview – Bose

It helps that they’re a household name. But their content is also built to be extracted.

Their product pages front-load specific claims as scannable elements — “24 hours of battery life” and “legendary noise cancelation” — rather than burying them in copy.

Bose – Product features

Key specs are organized into structured comparison tables:

Bose – Product specs

And they build dedicated landing pages for use cases like flying, using descriptive, scenario-specific language.

This matters because AI fans out into use-case-specific sub-queries.

Bose – Noise cancelling headphones for flights

When I searched “best noise-canceling headphones for flight anxiety,” AI Mode recommended Bose, using nearly identical language from Bose’s flight landing page.

Google AI Mode – Noise canceling headphones

When a user’s prompt matches the scenario your page was built for, AI systems may be more likely to pull from it.

This is a clear example of that in action.

You don’t need a complete site overhaul to make this work.

Even restructuring a few high-priority pages to address your fan-out gaps can improve your chances of being extracted and cited.

Step 6: Measure Your Performance in AI Search

Once your content is structured and live, track your performance in LLMs.

Start with the money prompts you identified in Step 1.

For each one, you want to know:

  • Are you showing up? Is your brand mentioned or recommended in the response?
  • Is what it says accurate? Are the claims the AI makes about your brand correct, or is it pulling outdated or wrong information?
  • How do you compare? Which competitors appear in the same response, and how are they positioned relative to you?

If you’re tracking manually, run them through multiple LLMs (in a private or incognito window) and record what you find.

ChatGPT – Bose headphones

But once you’re tracking dozens of sub-queries across platforms, manually tracking gets messy (and time-consuming).

I use Semrush’s Prompt Tracker to automate the process.

It alerts you to changes in mentions for your money prompts, so you don’t have to keep re-running them yourself.

Position Tracking – Keywords

Another helpful tool is the Visibility Overview.

It provides an AI visibility score that tracks how often you’re showing up in AI answers compared to competitors.

Visibility Overview – Bose

The Perception tool tracks sentiment so you know how LLMs describe your brand — and if they mention competitors more favorably.

Perception – Bose – Sentiment

It also breaks down the factors driving that sentiment.

For Bose, “industry-leading noise cancellation” shows up as a strength, while “over-the-ear models not sweatproof” flags a use-case they could address with targeted content.

Perception – Bose – Key sentiment drivers

Tracking should be an ongoing process.

Revisit your money prompts regularly and update your content as new sub-queries emerge or competitors gain ground.

How Query Fan-Out Works Across Different Platforms

How content surfaces in an AI answer depends on several factors:

  • Whether the system searches the live web or draws from its training knowledge
  • How many sub-queries it runs
  • Which sources it favors, and how it cites them

Understanding those patterns helps you make smarter decisions about content structure, format, and where to focus your optimization effort.

Plus, if a competitor outperforms you in a specific LLM, understanding how that platform handles fan-out can help you figure out why.

Platform How Fan-Out Works
ChatGPT Reasons internally, then runs live web searches when a question requires fresh data, comparisons, or current information
Perplexity Combines conversation context with real-time web search
Claude Clarifies intent first; relies mostly on training data
Google AI Overviews Synthesizes Google’s index into condensed, featured-snippet-style summaries
Google AI Mode Breaks complex prompts into multiple searches across Google’s index

Note: Some of the behavior described below is based on how each system describes its own reasoning when prompted. LLMs aren’t always reliable narrators of their own processes, so treat these observations as directional rather than definitive.

ChatGPT

For simple, informational queries, ChatGPT usually responds from its training data without running a live search.

ChatGPT – Compound interest

But that changes when the question requires fresh information, comparisons, or real-world data.

When I asked which car I should buy (Toyota vs. Honda) in Thinking mode, ChatGPT spent about 22 seconds reasoning through the question.

Then, it produced an answer drawn from 41 cited sources

ChatGPT – Toyota vs Honda

That’s query fan-out in action: one prompt, varied sources, and multiple sub-queries running behind the scenes.

By default, you can’t see the sub-queries ChatGPT runs. But I’ll show you how to find them (don’t worry — it’s easier than it looks).

Note: This DevTools method only works in the web version of ChatGPT. You can’t access sub-query data on mobile or in the desktop app.

First, search a money prompt in ChatGPT.

Then, look at your browser’s address bar and copy the slug that appears after chatgpt.com/c/ — that’s the unique ID for your conversation

ChatGPT – URL

Next, right-click anywhere on the page and select “Inspect.”

ChatGPT – Inspect

A developer panel will open on the side of your screen:

  • Click “Network” at the top of that panel
  • Paste the slug you copied into the filter bar
  • Refresh the page

Click on the fetch version of the slug (here, it’s the second option under the Name column).

Chrome DevTools – Network

Then, open the Response tab.

Chrome DevTools – Network – Response

Once it loads, press Ctrl+F (or Cmd+F on Mac) and search for the word “queries.”

Chrome DevTools – Network – Response – Queries

What appears is the exact set of internal searches ChatGPT ran before producing its answer.

For the Toyota vs Honda prompt, ChatGPT generated queries around:

  • Vehicle specifications
  • Fuel economy
  • Reliability
  • Safety ratings
  • Long-term ownership costs

Once you have the sub-queries, cross-reference them against your content.

Are you targeting each one? Do your pages use the same language ChatGPT is searching for — “long-term ownership costs” rather than just “value”?

ChatGPT often pulls from third-party sources like Reddit threads, review sites, and comparison pages.

So topical authority matters here — not just what’s on your site, but whether your brand shows up across the sources ChatGPT is likely to retrieve.

Perplexity

Perplexity runs two types of fan-out simultaneously:

  1. Internal fan-out — scans your prior conversation history for relevant context
  2. External fan-out — searches the external web for relevant information

The final answer draws on both layers, which means your content needs to work for a range of user situations, not just one.

For the Toyota vs. Honda question, Perplexity’s first batch of sub-queries had nothing to do with the cars.

Perplexity – Toyota vs Honda

Instead, it checked whether I’d previously mentioned anything that could shape its recommendation.

Perplexity – Toyota vs Honda – Subqueries

Like budget constraints, driving habits, or past questions about either brand.

Perplexity – Toyota vs Honda – Subqueries – Details

Only after that internal scan did it launch external searches about reliability, ownership cost, and safety ratings.

What this means for your content: Perplexity may pair your page with context you can’t predict: a user’s past questions, constraints, or preferences.

Your content needs to be specific and self-contained enough to remain accurate and useful no matter the surrounding context.

Claude

Claude takes a different approach.

Rather than immediately running sub-queries, it asks clarifying questions first. Then, it generates a response tailored to your answers.

When I asked the Toyota vs. Honda question, Claude presented a preference widget before producing an answer.

Claude – Toyota vs Honda

Once I responded, it generated a recommendation tailored to my priorities.

Claude – Toyota vs Honda – Answer

Because it clarifies intent before searching, Claude tends to generate fewer, more targeted fan-out sub-queries than other platforms.

The implication for your content: Answer specific, well-defined use cases directly rather than trying to cover every angle on a single page.

Google AI Overviews and AI Mode

AI Overviews appear as concise, AI-generated summaries with sources listed in a clickable sidebar.

Google SERP – Toyota vs Honda – AI Overview

They work by synthesizing Google’s existing web index into a tighter, more contained summary.

AI Mode, by contrast, is a dedicated conversational search tab designed for complex, multi‑part questions.

Google AI Mode – Toyota vs Honda

Like AI Overviews, it draws on Google’s index to generate answers, but it offers more interaction and depth.

Neither platform exposes the sub-queries it runs.

But SEOs have found a way to extract Google’s fan-outs using Screaming Frog configured with a Gemini API. Watch Dan Hinckley’s tutorial for a full walkthrough.

For both, the optimization focus is the same: Front-load your answers, use descriptive subheadings, and structure content so individual passages stand on their own.

AI Search Runs on Query Fan-Out — Your Content Strategy Should Too

High rankings alone won’t earn AI mentions.

The brands showing up are the ones covering the questions their audience is actually asking and making that content easy for AI to extract and cite.

You’ve got the query fan-out framework. Now it’s about execution.

Start with one money prompt, map the sub-queries, and audit where your content stands.

Then work through the gaps, one topic at a time.

Next, dive deeper into how to get your brand seen and trusted across AI platforms with our AI search strategy guide.

The post Query Fan-Out: What It Is and How It Affects AI Visibility appeared first on Backlinko.

Read more at Read More

Using AI to Support and Defend Your Brand

/0 Comments/in /by

Key Takeaways

  • AI-generated answers have compressed brand discovery into a single moment. One summary can now serve as a customer’s entire first impression.
  • AI systems pull from a wide range of sources, including forums, review sites, and outdated content, not just your owned properties.
  • The most repeated claim tends to surface in AI outputs, not necessarily the most accurate one.
  • Inconsistent messaging gets amplified by AI, not smoothed over.
  • Content governance, proactive publishing, and continuous monitoring are the new foundations of brand reputation management.

    Brand management has a new problem. Everything you have built, your positioning, your messaging, your reputation, can now be summarized by an AI system before a customer ever visits your site, reads your content, or talks to your team. That summary may be accurate. It may not be. The person reading it likely has no way to tell the difference.

    This is not a hypothetical risk. It is happening continuously, across every major AI platform, for brands of every size. The question is not whether AI is shaping how people perceive your brand. It is whether you are doing anything to influence what AI says.

    The First Impression Problem

    People used to form impressions of brands gradually. They encountered coverage, read reviews, visited a website, spoke with someone. Perception built up over multiple interactions, giving brands time to shape it.

    That process is being compressed. An AI-generated answer can now stand in for all of those touchpoints. A prospective customer asks ChatGPT or Perplexity about your company, gets a two-paragraph summary, and walks away with a complete impression, accurate or not, before ever interacting with anything you control.

    A graphic showcasing brand hijackings in AI search ads on ChatGPT.

    What makes this genuinely difficult is how AI builds those summaries. It does not prioritize your owned content. It pulls from whatever it can find: your website, press coverage, review platforms, social media, forum discussions, complaint boards. It weighs those sources by factors that are not always intuitive. A high volume of low-quality negative content can outweigh a smaller volume of accurate positive content. Old information that has not been addressed or replaced sits alongside current content, with no timestamp visible to the user.

    Your brand’s AI reputation is shaped by your entire content footprint, not just the parts you have invested in carefully.

    The Risk Goes Beyond False Information

    Most brands are not facing outright fabrication. The more common risk is partial truths: accurate statements pulled out of context, outdated information that was once correct, nuanced positions simplified into something that no longer reflects where you actually stand.

    Partial truths are more insidious than false information because they are harder to dispute and easier to spread. Once an AI system has assembled a narrative from the sources it has found, that narrative gets reinforced every time someone asks a related question. It becomes what people know about you, and correcting it requires more than just publishing accurate content. It requires replacing the sources the AI is drawing from.

    A ChatGPT query about the best plumbing companies in the Chicago area.

    There is also a compounding effect to be aware of. AI-generated summaries get shared across platforms. Screenshots get posted. Those shares become new inputs that reinforce the same narrative in future AI outputs. A problematic summary does not stay contained.

    The practical consequence is straightforward: the most accurate claim does not automatically rise to the top in AI outputs. The most repeated claim does.

    Content Governance Is Brand Protection Now

    The practical response to this challenge starts with content governance, and governance needs a different frame than it typically gets in marketing organizations.

    Most brands treat governance as an internal process concern: who approves content, how brand guidelines get followed, what templates teams use. Those things matter. In an AI-mediated environment, though, governance is the mechanism that determines whether AI systems can accurately summarize who you are. It is infrastructure, not administration.

    As one brand governance expert put it: this “ensures that the core signals of your brand are clear enough to survive the compression that happens through an AI component.” When brand signals are inconsistent or vague, AI amplifies that inconsistency rather than resolving it.

    Messaging consistency across every touchpoint. If different teams, regions, or channels are publishing different descriptions of your product, your mission, or your positioning, AI will find all of them and combine them into something that may not accurately represent any of them. A unified source of truth that every piece of external content draws from is the foundation.

    Content that explains rather than claims. AI systems have no way to evaluate vague marketing language. Terms like “industry-leading” or “innovative” mean nothing to an AI summarizing your brand. What does register is specific, plain-language explanation of what you do, how you work, and why it matters. Replace generic claims with clear explanations throughout your owned content.

    Your website treated as AI infrastructure, not just a marketing asset. Most organizations still build their websites primarily as human-facing experiences. For AI systems, your website is often the first place used to understand your organization. Review your key pages with one question in mind: could an AI produce an accurate summary of your brand from what we have published here? If the answer is no, you have content work to do.

    Taking an Active Role in What AI Says About You

    Governance handles internal consistency. The external picture requires a more active approach.

    Start by auditing what AI systems are currently saying about your brand. Prompt ChatGPT, Google AI Overview, and Perplexity with the questions a prospective customer, investor, or journalist would ask. Capture those outputs. Then trace the narrative back to its sources. Are those sources accurate? Current? Are there negative or outdated sources being weighted heavily because you have not published sufficient structured content to counter them?

    Using our Chicago plumber example from before, we see Angi is heavily weighted as a source in that ChatGPT answer.

    An Angi landing page dedicated to Chicago plumbers.

    That audit gives you a content agenda. Gaps in AI representation can often be addressed by publishing clear, well-structured content that gives AI systems better information to pull from. If outdated claims are being surfaced, identify the sources driving them and address those sources directly. Claims spreading on Reddit or social platforms can be addressed on those platforms. 

    A Reddit post axsking about Chicago plumbers with responses.

    Structured explanations published through FAQs and policies give AI systems better, more current information to draw from.

    Third-party credibility carries significant weight. Earned media, analyst coverage, and credible reviews are treated as high-trust signals by AI systems that evaluate external validation. Proactive brand publishing and digital PR work are not just marketing tactics in this environment; they are inputs that shape what AI says about you before a narrative hardens.

    Spokespeople and executives also need to think about this. In a traditional media environment, journalists contextualize statements. In an AI-mediated environment, those statements get pulled directly into summaries. Specificity and context matter more than polished soundbites. Complete explanations travel better than compressed talking points.

    Monitoring Cannot Be Periodic

    One of the most common mistakes brands make with AI reputation management is treating it as a project with a completion date. You audit, fix the gaps, and move on. That approach misses how dynamic the AI reputation environment actually is.

    New coverage, a viral social post, a competitor’s messaging shift, or a change in how your content is indexed can all alter what an AI says about your brand. The only way to stay ahead of narrative shifts before they harden is to monitor consistently, not quarterly.

    Brand-based prompts in Writesonic.

    Build a standing practice of prompting major AI tools with brand-relevant queries on a regular cadence. Track what changes. Create workflows for responding to misinformation on the platforms where it originates, before it has time to proliferate. Think of AI reputation management the same way you think about SEO: something that requires continuous attention, not a one-time fix.

    FAQs

    How often should I audit what AI says about my brand?

    Monthly at minimum, with closer attention during periods of significant company news, product launches, or any event that generates substantial external coverage. AI systems update as the web updates, so the outputs you capture today may not reflect what users see in six weeks.

    What content is most effective at influencing AI summaries?

    Clear, specific, well-structured content that directly addresses the questions people ask about your brand. FAQs, plain-language product explainers, executive Q&As, and detailed company descriptions all register more effectively than vague marketing copy. Third-party coverage from credible sources also carries high signal weight.

    What should I do if AI is saying something inaccurate about my brand?

    Identify the sources driving the inaccurate narrative. Address misinformation directly on the platforms where it originated (forums, review sites, social media). Publish structured, authoritative content that provides AI systems with better information to draw from. Building third-party credibility through earned media helps establish accurate narratives as the dominant signal over time.

    Conclusion

    The question brand managers need to be asking has shifted. It is no longer just “what message do we want to put out?” It is “what will AI tell someone about us, and is that accurate?” Answering that question requires consistent messaging, clear content, active monitoring, and a willingness to treat AI reputation as a standing business function rather than a marketing add-on.

    The brands that build that infrastructure now will have a meaningful advantage as AI-mediated discovery continues to grow. The brands that do not will find their reputation increasingly shaped by whatever AI happens to find first.

    Read more at Read More

    Google Is Testing Sponsored Shops in SERPs: What This Means for Advertisers

    /0 Comments/in /by

    Key Takeaways

    1. Google is testing “Sponsored Shops,” a format that groups multiple products from a single retailer into one branded unit inside Shopping results.
    2. This moves competition from the product level to the retailer level, changing what it takes to win visibility.
    3. Feed quality, seller ratings, and assortment depth become more critical than ever.
    4. The format introduces multiple click paths within one ad unit, which could complicate attribution and traffic flow.
    5. Performance Max is a likely vehicle through which Sponsored Shops placements will be accessible when the format formally launches, but nobody knows for sure.
    6. Brands that build strong store-level signals now will be better positioned if and when this rolls out broadly.

    Google is running a Shopping test that could change how brands compete for visibility in product search. If it scales, the rules shift, and advertisers who see it coming will have a head start.

    Here’s what’s happening and what you should be doing about it right now.

    What Is Google Actually Testing?

    Google’s Sponsored Shops test groups several products from one retailer into a single ad unit inside Shopping results, alongside the store name, ratings, and brand signals. Think of it as a mini storefront sitting directly inside the search results page, rather than a row of individual competing products.

    Sponsored shops results for backpack.

    Source

    It is still a test. Google has not confirmed a broad rollout. The direction it points toward matters, though, and Shopping advertisers should be paying close attention.

    The test does not exist in isolation. It is part of a broader shift Google has been building toward for a while: more brand-centric, discovery-oriented, and AI-mediated shopping experiences. In 2025, Google introduced the Merchant Brand Profile feature, which lets retailers build brand-presence pages in search with lifestyle images, videos, and business descriptions. 

    An example business in Google Sponsored shops.

    Source

    Sponsored Shops looks like the logical next step in that direction, bringing brand identity directly into the Shopping ad unit itself.

    Why the Format Change Is a Bigger Deal Than It Looks

    Right now, Shopping competition is largely a product-level game. Your listing competes against a competitor’s listing. Better feed, stronger bid, you take the placement.

    Sponsored Shops changes the terms of that competition. Instead of a single product earning a spot, your entire store is on display at once: assortment, brand presence, and ratings together. A competitor with a stronger catalog and better seller signals will have a structural advantage that no amount of bid optimization can fully offset.

    That’s a meaningful shift. Brands that have been winning through finely tuned individual product listings will need to think harder about how their store presents as a whole. Brands that have invested in feed quality, customer experience, and assortment depth will find that investment paying off in ways it didn’t before.

    There’s also a measurement angle worth flagging. A single ad unit with multiple clickable elements (store name, individual products, ratings) creates multiple potential click paths. How traffic splits across those paths, and how that maps to your current attribution model, is an open question every Shopping advertiser should be thinking through before this format scales.

    What This Signals About Where Google Is Headed

    Google has been explicit about where it wants Shopping to go. In its own communications about 2026 priorities, the company described its goal as making search “a more powerful tool for discovery, where ads can inspire and answer all at once.” AI Mode already surfaces organic shopping recommendations based on query relevance, and Google has confirmed it is testing a new ad format inside AI Mode that showcases retailers offering relevant products, clearly marked as sponsored.

    A ChatGPT result for men's running shoes black.

    Source

    Sponsored Shops fits squarely into that roadmap. It moves Shopping slightly up the funnel, making it as much about brand discovery as product comparison. Rather than a format designed purely to capture demand-ready buyers, it is designed to let brands show up with range and identity in front of people who are still forming their consideration set.

    For users, the format is intuitive. Browsing several products from the same retailer without leaving the results page is a better experience than clicking in and out of individual listings. Google tends to expand formats that improve user experience. That’s worth taking seriously.

    The PMAX Connection

    As of right now, we don’t know what vehicle is going to power sponsored shops. Performance Max is a likely bet based on volume and Google’s push for PMax adoption, but nothing is confirmed. PMax already accounts for roughly 62 percent of Google Shopping spend among major advertisers, and it is already designed to surface both store-level and product-level assets dynamically across Google’s ecosystem.

    With this said, though, AI Max for shopping is still in beta, so that might impact what plays a role. We also know that Google does tend to favor some of their newer products which likely helps adoption rate (e.g. AI Max, PMax, & Broad being eligible for AIO ad placements).

    What to Do Before This Rolls Out

    You do not need to wait for a full launch to get ahead of it.

    Start with your product feed. Feed quality has always mattered in Shopping, but a storefront format makes weak data much more visible. Every title, description, image, and availability signal is part of how your store presents in that unit. Get it right now. Research consistently shows that product titles, images, and product identifiers are the three highest-impact feed optimizations, and all three will matter even more in a store-level display format.

    Google results for gymshark tshirts.

    Source

    Take stock of your seller ratings. In a storefront format, ratings are far more prominent than they are in individual listings. If you have not been actively managing reviews and customer experience signals, that needs to change. A store-level placement that leads with a weak rating is a self-defeating ad.

    Look at assortment depth. A Sponsored Shops unit showing three products when a competitor shows ten is a losing presentation. Review whether your full catalog is properly represented in your feed and close any gaps.

    Audit your PMax asset groups. Given that PMax is the likely vehicle for Sponsored Shops placements, your asset groups should be fully built out with all image formats, high-quality lifestyle images alongside product images, accurate brand descriptions, and audience signals that represent your full customer base rather than just buyers of individual products.

    Revisit your attribution setup. Multiple click paths inside a single unit means your current reporting may not capture traffic flow accurately. Think about how you will measure this before the format exists in your account at scale.

    FAQs

    What exactly is a Sponsored Shops unit?

    A Sponsored Shops unit groups multiple products from a single retailer into one ad block inside Google Shopping results, displayed alongside the store name, ratings, and brand signals. Rather than individual product listings competing side by side, the format presents a mini storefront for a single brand.

    Is Sponsored Shops live now?

    As of now, Sponsored Shops is still in testing. Google has not confirmed a broad rollout timeline. The format is worth preparing for regardless, since the steps that improve your eligibility for it also strengthen your existing Shopping performance.

    Which campaign type will Sponsored Shops use?

    Performance Max is the most likely vehicle, given that it already accounts for the majority of Shopping spend and dynamically surfaces store-level and product-level assets across Google’s ecosystem. Making sure your PMax asset groups are fully built out is the right preparation move.

    Will smaller retailers be disadvantaged?

    Formats that reward assortment breadth, seller ratings, and feed quality tend to favor established retailers with larger catalogs and more customer reviews. That said, a well-optimized feed and a strong seller rating matter more than raw catalog size. Smaller retailers with tight assortments and excellent customer experience signals are not automatically excluded.

    What should I do right now?

    Focus on feed quality, seller ratings, and PMax asset completeness. These are the fundamentals that will determine Sponsored Shops eligibility and performance when the format expands, and they are also the fundamentals that determine your current Shopping performance.

    Conclusion

    Sponsored Shops is still in testing. Google Shopping is clearly moving toward a model where brands compete as storefronts, not just as individual products. The shift fits a broader pattern: more AI-mediated discovery, more brand-level visibility signals, more emphasis on the full store experience rather than the individual listing.

    The time to build those store-level signals is before the competition catches up, not after. The good news is that everything you do to prepare for Sponsored Shops makes your existing Shopping campaigns stronger right now. There’s no downside to starting.

    Read more at Read More

    Google zero-click searches hit 68% in early 2026: Study

    /0 Comments/in , /by
    Google zero

    Google searches ended without a click 68.01% of the time in the U.S. during the first four months of 2026, according to new SparkToro research based on Similarweb clickstream data. That’s up from 60.45% in 2024, a 7.56-point increase in two years.

    Fewer searches result in clicks. The share of searches generating at least one click fell 9.51 percentage points between 2024 and 2026 (a 22.9% decline), according to SparkToro. This includes clicks to organic results, paid ads, and Google-owned properties such as Maps and YouTube, but excludes follow-up searches within Google.

    • Over the same period, the share of searches that led to another Google search rose 7.2 percentage points.
    • This trend reflects Google’s growing ability to answer questions directly in search results while encouraging users to refine or continue their searches within Google, according to SparkToro.

    AI Overviews and zero click. SparkToro believes AI Overviews are likely contributing to the increase in zero-click searches, though the study doesn’t isolate the extent to which the overall rise between 2024 and 2026 can be attributed specifically to AI Overviews.

    • AI Overviews now appear on more than 20% of Google searches, according to the research. When they do, click-through rates drop by nearly 60%.

    AI Mode and zero click. It appears to have played only a limited role during the January to April study period. SparkToro found that just 0.34% of searches transitioned into AI Mode during that time.

    • However, Google said at I/O 2026 that AI Mode had surpassed 1 billion monthly users and that query volume was more than doubling each quarter, suggesting its impact on search behavior could grow significantly.

    Zero click history. SparkToro has tracked zero-click search behavior for years, though its underlying data sources have changed over time. Because the studies rely on different providers, panels, and methodologies, long-term comparisons are not directly equivalent. Still, the available data consistently points to a rise in zero-click behavior over time, according to SparkToro.

    Why we care. The findings suggest Google is increasingly satisfying user needs without sending users to external websites. However, you should interpret direct comparisons across years cautiously because SparkToro’s historical analyses rely on different clickstream data providers and panels.

    SEO still matters, but… SEO alone may be insufficient for many publishers seeking to regain historical levels of Google-referred traffic. SparkToro co-founder Rand Fishkin recommended investing in brand awareness and influence on the platforms where your audience already spends time, regardless of whether those efforts drive direct website visits.

    • Some categories continue to benefit significantly from SEO, including branded searches, local business queries, and high-intent transactional searches, Fishkin said.

    About the data. The study used Similarweb desktop and mobile web panel data covering U.S. Google searches from January through April 2026. SparkToro assumed that two-thirds of searches occurred on mobile devices and one-third on desktops. The analysis excludes searches conducted in Google’s mobile search app, where SparkToro said zero-click behavior may be even higher.

    The study. In 2026, Less than One Third of Google Searches Still Send a Click

    Read more at Read More

    Web Design and Development San Diego

    How AI forms opinions about your brand

    /0 Comments/in , /by
    How AI forms opinions about your brand

    AI forms opinions about your brand from what it can see online. That’s your digital footprint.

    The problem is that AI often sees only fragments of your business. It sees your website, content, reviews, and mentions, but much of the expertise, customer insight, and operational knowledge that makes your business valuable never makes it into the digital footprint.

    The solution is to surface that knowledge, organize it into a single source of truth, and turn it into machine-readable signals. Here’s how to collect it, organize it into a single source of truth, and distribute it across the channels AI uses to understand, evaluate, and recommend brands.

    What you feed the machines is understandability, credibility, and deliverability (UCD)

    Everything you put into your footprint is fodder for three things AI has to decide about you. Together, they provide the fodder for the whole funnel.

    Understandability

    Does AI know who you are, what you do, and who you serve? You already know where your understandability comes from: 

    • Your about page.
    • Your product pages.
    • Your structured data. 

    What often gets missed is the operational detail that explains what you actually do once a client is inside.

    Credibility

    Does AI believe you’re good at it? This is N-E-E-A-T-T credibility — notability, experience, expertise, authoritativeness, trustworthiness, and transparency, an extension of Google’s E-E-A-T.

    You know what credibility signals you currently feed: your case studies, your credentials, and your testimonials. What many businesses don’t realize is how much N-E-E-A-T-T credibility is already embedded in their day-to-day operations.

    Deliverability

    Does the AI engine have the content to hand you to the subset of its users who are your audience? 

    You know where your deliverability comes from: the topical content, the marketing, and the authority pieces you commission. Deliverability is often hiding in plain sight, in the content generated by your business operations and offline activities.

    Your customers search everywhere. Make sure your brand shows up.

    The SEO toolkit you know, plus the AI visibility data you need.

    Start Free Trial
    Get started with

    Semrush One Logo

    5 streams of business data feeding every commercial surface

    All three elements of the UCD trio are fed by the five inputs below, and how much each contributes varies by business.

    The point isn’t to file each input under one letter. Organized and codified, the five together give AI the fodder it needs from top to bottom of the funnel.

    5 streams of business data feeding every commercial surface

    1. Products and services: What you sell, and you already do it

    Your products and services data: what you sell, at what price, under what conditions, and with consistent names and identifiers. This is mostly about understandability, with credibility riding alongside it.

    Most businesses already do this, so the work is in the depth, not the effort. Don’t just list what you sell. Describe who each offering is for, what problem it solves, what it costs, what it doesn’t do, and how it differs from the next option.

    A thin product page tells AI a product exists. An exhaustive one tells it when to recommend that product and to whom.

    Keep it accurate, complete, and consistent with everything else in your footprint. A price or product name that differs across pages reads as doubt.

    2. Authority content: Your expertise, and almost everybody does it

    This is the marketing you already create to show you know your field: your articles, videos, guides, data studies, and the thought leadership you publish to tick the box marked “content created.”

    People put effort into it to build authority, rank, do SEO, and position themselves as experts. That’s fine. It leans toward deliverability because it’s what tells AI which territory to surface you in.

    But everybody does it, which is exactly why it’s the least differentiating of the five on its own. It earns its weight only when it’s tied to the rest: the same expertise proven by your operations and corroborated by third parties, not just asserted in a blog post.

    It’s necessary, but it’s not where your advantage hides.

    3. Brand narrative and voice: Who you are, who you serve, and why you’re the best

    All marketers create brand narratives, so the work here is about consistency and clarity rather than invention. Everybody communicates who they are, what they do, and who they serve, and keeping that clear and consistent matters enormously. 

    But three things are often left out, and AI needs all of them.

    • Intent: It isn’t enough to name your ideal customer profile (ICP). You have to pair your ICP with what they’re after: the cohort-to-intent combinations from the funnel query pathway. AI has to know not just whose problem you solve, but which problem, and at which moment, before it can hand you to them.
    • Credibility: The thing that feeds your N-E-E-A-T-T. Many people leave it out because they feel awkward saying it. You have to set it out because AI won’t work out your true value on its own. Be clear and bold about why you’re credible, then make sure you can back it up with evidence.
    • Making the relationship with your clients explicit: Validation from the people you serve that you deliver on what your narrative and cohort-to-intent mapping promise. Say who you are, what you do, and who you serve. Then explain why a customer should choose you and prove it.

    Voice is the part corporations get wrong most often. Narrative is what you say. Voice is how you say it. One team may write the narrative once, but voice escapes through every rep, every support reply, every social post, and every deck. 

    When it drifts, and in most large companies it drifts constantly, AI reads the same brand as five different brands and loses confidence in all five.

    So standardize your voice and keep it consistent everywhere. Consistency is a credibility signal in itself. Inconsistency is a tax you pay without seeing the bill.

    In short, make sure your brand narrative clearly sets out your ICP, who you are, and why you’re the best fit for them, in a voice that stays consistent wherever AI finds it.

    4. OPID business operations: The stream almost nobody harvests

    This is everything your business generates by running: onboarding, performance, integration, devotion, and all the day-to-day activity around them. 

    It’s the most powerful of the five because the material comes from your clients and from the work your team does to serve them, which is exactly the material that rarely makes it online. It sits behind closed doors, buried in a CRM, parked on a platform nobody values, and almost nobody harvests it.

    It feeds all three elements of understandability, credibility, and deliverability more effectively than anything else you own. 

    • Understandability comes from the granular detail of what you actually do and the exact circumstances in which you help. Most of that is only ever discussed inside the business. A review where a client describes precisely what they got from you puts something on the record you’d never say about yourself, and the machine reads it as fact.
    • Credibility is your N-E-E-A-T-T, and this is the most convincing kind because it comes from clients themselves, not from your marketing.
    • Deliverability comes from the match. The content here aligns exactly with your cohort-to-intent combinations because it was created around the clients you attracted and served well. Whether it comes from you or from them, it fits the audience and intent you need to communicate to the engines.

    Once you start looking, you’ll find the richest material you own:

    • Customer voice is the highest signal because it’s real questions in real language: reviews across every platform, written and video testimonials, FAQs, unpublished support questions that should become FAQs, support and sales call transcripts, onboarding and churn-exit interviews, and free-text survey responses.
    • Evidence and outcomes provide the proof you need: case studies with real before-and-after numbers, patent filings, academic deposits that are public but underused, and independent third-party studies that corroborate your claims.
    • Methodology covers the rest. SOPs, playbooks, training materials, glossaries you currently keep private, and long-form spoken content such as webinars, keynotes, and podcast appearances, transcribed.

    Look for material that answers a question an assistive engine or agent actually gets asked, in the questioner’s own words, with a verifiable fact attached. 

    A support ticket, churn interview, or sales call transcript will often outperform polished marketing copy in that test because it’s already phrased the way real people ask questions.

    That’s the whole point of harvesting OPID business operations: taking information from a place AI can’t see and moving it to a place where it can, while making it visible to your human audience, too. It’s convincing to both because it’s true and because it matches the cohort-to-intent combination exactly.

    5. Bringing the offline online: The stream almost nobody runs

    This section is all about the marketing and audience engagement you do offline: the talks you give, the festivals or hackathons you sponsor to support your community, the interviews, the panels, and the rooms full of clients. It’s obvious to you, but largely invisible to AI.

    Bring the offline online and feed it to the machines by publishing self-reporting content and linking to the social posts and summary articles others write. That’s a huge win most brands miss.

    But it works the other way, too. Your codified source of truth can feed your offline communication, so the story a client hears from you at a conference, in a newspaper, on the radio, or face to face is consistent with the story you’re telling AI on the web.

    That matters more than it seems. If the two differ, you lose the person because the gap reads as doubt to a human and as low confidence to a machine.

    Clarity and consistency over time, online and offline, is the name of the game.

    Get the newsletter search marketers rely on.

    See terms.

    Organize and codify the five into one source of truth

    Once you’ve harvested all five streams, organize and codify them into a single source of truth: a database you build to output whatever format each surface needs, including HTML, schema, MCP, RDF, prose, audio, video, and images.

    Organize the data once, centralize it, set up a system that codifies it on the way out, and from there you can distribute it in a few clicks while your digital footprint stays clear and consistent as it grows.

    Then distribute it across your digital ecosystem in the format your human audience expects and packaged so machines can ingest it cleanly.

    Where you publish affects how much the machine believes you, and the rule is simple: the less of you there is in it, the more it trusts it. You’re working across three tiers.

    First-party: You claim 

    You publish on your own properties, in your own voice. You state who you are and set the frame. It’s the baseline, and on its own it proves nothing because you wrote it and you published it.

    Second-party: You corroborate

    Here, you’re still publishing, but across a broader footprint and with other voices in the mix. Two things widen here.

    • The platform: In addition to your own entity home website, you publish on platforms where you own the account, such as YouTube, LinkedIn, Medium, and press releases. You’re stating your case the same way you would on your website, just on another property you control.
    • The voice: You can publish your own words, or you can publish what a client or user said, such as a review, quote, or case study, on your own site and across those other accounts.

    It’s a step up from first-party because the substance is no longer solely your own assertion, even though you’re still the one choosing it and publishing it.

    Third-party: They prove you

    A third party publishes in its own voice, on its own site or social accounts, or on a neutral platform such as Trustpilot, with no involvement from you. 

    Think clients and partners sharing their experiences, journalists, analysts, academics, and the long tail of user-generated content that assistive engines lean on.

    It’s the strongest evidence because you had no hand in creating it.

    You can’t write that third tier, but you can feed it. Your clients publish because you’ve served them well enough that they want to, so earn it.

    Independent publishers can’t see inside your business, so give them something to work with: a client story they can build on, a view into your operation, or data about your business and industry they can cite.

    Giving outside parties a true, detailed version of your business to publish is what PR, marketing, and content teams have always done. The only thing that’s changed is that now you do it so machines read the result as proof, not just so humans read it as coverage.

    Point all three tiers at the same picture — you, your audience, and the independents — and they align into one answer the machine can’t miss.

    Author x Publication

    Read the grid by how much of you is in the publication.

    • First-party is all you. Your words on your own site. It’s pure claim, and the machine treats it as the baseline because you wrote it and you published it.
    • Third-party is none of you. Someone else’s words on a platform you don’t control. That’s why it’s the strongest proof.
    • Everything in between is second-party corroboration. Your own words carried onto an account you run elsewhere, or someone else’s words that you chose to publish on your own page.

    The same review is second-party when you surface it on your site and third-party when the client publishes it on their own account. The words are identical. The weight is different. The difference is determined entirely by who publishes it.

    Step back, and you have a powerful loop: You harvest your operations, codify them into a single source of truth, and distribute them across the tiers machines read. Then the machines recommend you, your ICP arrives, and serving them generates the next round of operations to harvest.

    Each turn feeds the next, so your digital footprint compounds instead of resetting.

    A simplified version of the flywheel

    The mirror principle is why this is the whole game

    When an AI engine recommends a brand, think of it as an impartial broker. Much as a travel agent carries every airline or a mortgage broker has the whole market on screen, an AI engine carries every brand in your category and recommends whichever it judges to be the best solution for the person asking.

    That impartiality is why buyers trust it. It’s also why the engine recommends your competitor without hesitation. It was never on your side. It’s on the buyer’s.

    That’s good news once you see it the right way. An AI engine can only recommend what it clearly understands and trusts. You don’t need to trick a rigged system. You need to provide the clearest, most complete picture of who you are, what you do, who you serve, and why you’re the right fit.

    Build a clearer, better-corroborated case than your competitors, and, on merit, you become the name the engine reaches for throughout the funnel. Many brands aren’t losing because they’re being outspent. They’re losing because the picture AI has of them is incomplete.

    And that picture comes from your digital footprint. AI forms its view of you from the world’s view of you: the reviews, coverage, and corroboration scattered across the market. What it shows about you is its opinion of the world’s opinion of you. That’s the mirror principle.

    You can try to flatter the system, trick it, or lean on it, and that might work for a while. But the approach that lasts is changing what the world can see. When you do that, you’re not manipulating anything. You’re providing proof: something that was always true, but underrepresented or invisible.

    That’s exactly what this article has laid out. Harvest the five streams, organize and codify them into a single source of truth, and distribute them across the channels AI reads. Do that, and you’ve provided the fullest, truest, and best-corroborated picture of your business at the moment that matters most: when someone is looking for what you sell, and AI is deciding what to recommend.

    Do it consistently, across everything AI can see, and you shape how it understands your business over time.

    This is the 17th piece in my AI authority series.

    Read more at Read More

    Web Design and Development San Diego

    What server logs reveal that SEO tools miss

    /0 Comments/in , /by
    What server logs reveal that SEO tools miss

    For large websites, server logs often reveal technical SEO problems long before rankings decline. They show how search engines crawl your site, where crawl budget gets wasted, how quickly servers respond, and whether important pages remain accessible.

    Unlike Google Search Console, analytics platforms, and third-party crawlers, server logs capture every request search engines make to your infrastructure. 

    Yet many organizations never analyze them — missing one of the most valuable sources of technical SEO data available.

    Why server logs reveal what other SEO tools miss

    Many SEO teams rely on Google Search Console, Bing Webmaster Tools, third-party crawlers, and analytics platforms. Those tools help, but they all rely on data samples, delayed reporting, or simulated crawls. 

    Server logs capture direct interactions between crawlers and infrastructure. That distinction matters on websites with hundreds of thousands or millions of URLs.

    A log file records every request processed by a server. For SEO purposes, the most useful entries come from crawlers such as Googlebot, Bingbot, GPTBot, Applebot, and other verified search engine bots. 

    Each request generates operational data, including the requested URL, response code, timestamp, user agent, and response timing. Over time, those records form a detailed crawl history.

    Your customers search everywhere. Make sure your brand shows up.

    The SEO toolkit you know, plus the AI visibility data you need.

    Start Free Trial
    Get started with

    Semrush One Logo

    Hidden SEO issues in crawl data

    Most technical SEO issues begin as crawl inefficiencies that gradually compound over time. A search engine crawler may:

    • Request a page and receive an unexpected response.
    • Encounter a category section that slows under heavy load.
    • Follow redirect chains that expanded after a deployment. 

    In other cases, product pages disappear from inventory while still returning a 200 status code. These problems rarely occur as isolated incidents. 

    Search engines encounter them repeatedly across thousands or millions of crawl requests, creating patterns that can quietly erode crawl efficiency, indexing, and visibility.

    Server logs expose those patterns clearly. 

    • On large ecommerce platforms, logs often show crawlers spending excessive time on filtered navigation URLs while strategic product pages receive limited recrawling. 
    • On publisher websites, crawlers sometimes revisit outdated archive paths more aggressively than newly updated content. 
    • SaaS platforms frequently expose staging environments or parameter-driven duplicate URLs through internal systems without realizing how heavily those URLs consume crawl activity. 

    Without logs, those problems remain hidden behind aggregate reporting.

    Server logs also provide historical visibility. Unlike Google Search Console data, which expires over time, retained logs reveal crawl trends tied to migrations, infrastructure changes, indexing shifts, and platform redesigns.

    Where crawl resources go

    Search engines don’t crawl every page equally. Large websites compete internally for crawl attention. 

    Search engines allocate resources based on perceived importance, internal linking, infrastructure quality, content freshness, and historical performance. Logs reveal those crawl decisions directly.

    A retailer with five million URLs may assume high-value category pages receive regular crawling because they appear in XML sitemaps and navigation systems. Log file analysis may show Googlebot spending a disproportionate share of crawl resources on parameterized URLs created through faceted filtering instead.

    Another site may discover crawlers revisiting redirected legacy URLs years after a migration. These situations are common because search engines work from observed behavior rather than internal assumptions.

    Server logs also help identify sources of crawl waste that quietly consume large portions of crawl activity. Common examples include:

    • Infinite URL combinations.
    • Session parameters.
    • Crawlable internal search pages.
    • Open faceted navigation systems.
    • Duplicate mobile URLs.
    • Exposed staging environments.
    • Broken canonical structures. 

    As web platforms expand over time, crawl efficiency increasingly becomes an infrastructure challenge as much as a traditional SEO problem.

    When infrastructure limits crawling

    Response timing data is among the most valuable information in server logs. Search engines monitor how efficiently servers respond during crawling. Slow or unstable infrastructure affects how aggressively crawlers move through a site.

    A difference between 300 milliseconds and 3 seconds may appear minor on a single request, but across hundreds of thousands of crawler requests, the impact becomes substantial. Response timing analysis helps isolate infrastructure bottlenecks under real crawl conditions and exposes performance issues that traditional SEO tools often miss.

    In production environments, these patterns appear frequently. Product pages may bypass cache layers and generate database-heavy responses, image optimization services can slow down media crawlers, and API-driven templates often create inconsistent latency during crawl spikes. JavaScript rendering systems may delay crawler access to content, while regional CDN routing can introduce performance issues in specific markets.

    Synthetic monitoring tools often miss these patterns because simulated testing doesn’t fully replicate crawler behavior. Logs capture what crawlers experience at the request level. Timing analysis also helps separate isolated incidents from persistent operational issues.

    A temporary deployment issue differs from a structural bottleneck. Logs reveal the difference through historical request patterns.

    Search engines, particularly Google, tend to reward reliable infrastructure with more consistent crawling. Fast, stable responses support efficient crawl allocation and improve recrawl frequency on important pages.

    On enterprise systems, response timing analysis frequently influences infrastructure planning beyond SEO. Operations teams use log data to prioritize cache improvements, CDN adjustments, scaling decisions, and deployment scheduling.

    Get the newsletter search marketers rely on.

    See terms.

    Soft 404s at scale

    Soft 404s remain one of the most overlooked yet highly consequential SEO issues for large online brands. Unlike a standard 404 page, which correctly returns an HTTP 404 status code, a soft 404 returns a 200 OK response while serving thin, empty, or functionally useless content.

    To search engines, these pages appear crawlable and indexable despite offering little or no value, which can quietly waste crawl budget and dilute overall site quality signals.

    Common soft 404 examples include:

    • Out-of-stock product pages that remain live without meaningful replacement content.
    • Empty category templates created through faceted navigation.
    • Broken internal search result pages.
    • Placeholder inventory URLs with little usable information.
    • Expired listings that still return a 200 OK status code. 

    Failed rendering can create similar issues when JavaScript content doesn’t fully load for crawlers. On large web platforms, these low-value pages often accumulate quickly and consume significant crawl activity without contributing meaningful search visibility.

    Search engines eventually classify many of these pages as low quality. The issue becomes operational when crawlers continue revisiting those URLs repeatedly. Document size analysis within logs provides one way to identify potential soft 404 patterns at scale.

    Landing pages with nearly identical response sizes can sometimes indicate templated low-value responses. A group of 60,000 product URLs all returning responses smaller than 100 bytes after inventory expiration usually points toward placeholder templates rather than meaningful content.

    Internal search systems create another common example. Empty search result pages often generate highly consistent response sizes because the template loads correctly while no actual content appears.

    Response codes alone rarely expose the full pattern of crawl behavior. A clearer operational picture emerges when HTTP status codes are analyzed alongside response sizes, crawl frequency, and URL patterns. Together, these signals reveal how search engines interact with different sections of a web platform and where crawl inefficiencies begin to accumulate.

    Large publishers, such as news websites, also encounter soft 404 issues through broken pagination systems or empty archive states. 

    SaaS platforms sometimes expose onboarding placeholders through crawlable public URLs. 

    Marketplace websites frequently generate thin pages for inactive listings while still returning successful responses. Document size analysis helps identify these patterns quickly across large datasets.

    The case for log retention

    Short log retention periods limit the quality of server log analysis. Many crawl patterns develop gradually, with search engines adjusting crawl allocation over weeks or months rather than days. 

    Historical log data reveals long-term shifts in crawl behavior, including:

    • Changes in crawl frequency.
    • Legacy URL activity.
    • Migration effects.
    • Infrastructure instability.
    • Seasonal crawl patterns.
    • Redirect persistence.
    • Broader crawl budget fluctuations.

    For large websites, six to 36 months of logs often provide meaningful operational history.

    Historical data is especially valuable during migrations. Teams compare crawler behavior before and after structural changes to determine whether important sections gained or lost crawl visibility. Without retained logs, those comparisons disappear permanently.

    Many organizations still overwrite logs quickly or don’t retain them at all. Once lost, historical crawl data can’t be reconstructed later.

    Separating search crawlers from bot noise

    Raw server logs contain large volumes of automated traffic unrelated to SEO. Many bots impersonate Googlebot or Bingbot, making accurate filtering essential before meaningful analysis can begin. Effective validation typically combines user agent analysis, reverse DNS checks, and trusted IP verification to separate legitimate crawlers from scrapers, monitoring systems, and malicious automation.

    Once filtered correctly, server logs reveal clear behavioral differences between crawler types, including Googlebot Smartphone, Googlebot Image, Bingbot, Applebot, AdsBot, and newer AI-oriented crawlers. Each interacts with web platforms differently, creating distinct crawl patterns, resource demands, and indexing behavior.

    Image crawlers place heavier demands on media infrastructure. Mobile crawlers focus more heavily on rendering consistency. AI-focused crawlers often revisit large archive sections repeatedly.

    Crawler segmentation helps technical teams prioritize infrastructure improvements based on actual crawl demand rather than assumptions.

    Monitoring migrations with log data

    Migrations are one of the highest-risk periods in technical SEO, as even well-tested launches can introduce crawl instability. 

    Server logs provide direct visibility into how search engines respond after deployment, including which redirects crawlers continue to follow, whether redirect chains form, which legacy URLs remain active, and where 404 spikes occur. 

    Logs also reveal how crawl allocation shifts across the platform, whether response times begin to deteriorate, and which sections search engines continue to prioritize after the migration goes live.

    A migration may appear successful during browser testing while crawlers encounter entirely different behavior through caching systems, CDN routing, or redirect logic.

    Large ecommerce migrations often reveal persistent crawl activity on old URL structures weeks or months after launch. International platforms sometimes discover regional redirect inconsistencies affecting only certain crawlers. Logs expose those failures early enough to correct them.

    Collecting the right log data

    Useful log analysis depends on complete records. At a minimum, logs should include:

    • Remote IP address, including originating IP and optional (X-)Forwarded-For information.
    • User agent string.
    • Request protocol, such as HTTP, HTTPS, or WSS.
    • Request hostname.
    • Request path.
    • Request parameters.
    • Request time, including date, time, and time zone.
    • Request method.
    • Response HTTP status code.
    • Response timings.

    These fields create the operational baseline required for meaningful crawl analysis.

    Hostname and protocol fields often receive less attention than they deserve. Missing these values creates blind spots on multilingual websites, subdomain-heavy platforms, and CDN-driven architectures.

    Many organizations simplify analysis by storing the full request URL as a normalized field containing protocol, hostname, path, and parameters.

    Additional fields can further improve analysis quality:

    • Response byte size.
    • Cache status.
    • Referrer.
    • CDN edge location.
    • Upstream timing.
    • Compression type.

    Response size data becomes especially valuable during soft 404 investigations and duplicate content analysis.

    Why logs remain underused

    Server logs often fall between departments. Infrastructure teams view them as operational data. Security teams use them for threat monitoring. SEO teams focus on crawling and indexing. Analytics teams prioritize user behavior reporting.

    As a result, one of the most valuable technical SEO datasets within an organization often remains completely unused. Yet server logs answer operational questions that few other systems can.

    They reveal which pages absorb the largest share of crawl resources, which sections return unstable responses, and which deprecated URLs continue receiving heavy crawler activity years later. 

    Logs also expose latency issues affecting specific crawler groups and low-value pages that dilute crawl efficiency. These insights directly influence rankings, crawl allocation, and search visibility.

    Technical SEO and GEO increasingly overlap with infrastructure engineering because search engines continuously evaluate operational quality. Server logs expose those operational realities in detail. 

    For large websites, log analysis stops being optional once crawl scale reaches enterprise complexity. The data already exists. The advantage comes from retaining it, structuring it properly, and using it consistently.

    See the complete picture of your search visibility.

    Track, optimize, and win in Google and AI search from one platform.

    Start Free Trial
    Get started with

    Semrush One Logo

    The business value of server logs

    Ultimately, server log retention delivers value far beyond SEO alone. In particular, preserved log data can strengthen buyer confidence by providing verifiable operational evidence of site performance, infrastructure stability, and historical activity. 

    That additional transparency can materially support due diligence and even contribute positively to company valuation, making a compelling case that the cost of recording and retaining server logs is often outweighed by their long-term strategic value.

    Read more at Read More

    Web Design and Development San Diego

    Why your brand campaign may not be ready for AI Max

    /0 Comments/in , /by
    Why your brand campaign may not be ready for AI Max

    Not long ago, broad match was positioned as the future of paid search. Today, that role belongs to AI Max.

    Over the last few months, I’ve heard repeated recommendations to enable AI Max on brand campaigns, even when those campaigns are already performing exactly as intended.

    The problem is that many accounts still lack the foundations AI Max needs to work well. Conversion tracking is unreliable, offline conversion imports are missing, and generic campaigns remain constrained by budget or structure.

    AI Max depends on strong conversion signals, sufficient volume, and enough variation for the system to learn effectively. In many accounts, brand campaigns provide most of that signal. 

    But using AI Max on brand means introducing additional automation into your most predictable and efficient traffic source.

    The promise and limitations of AI Max

    AI Max expands search targeting beyond your existing keyword list by using keywords, landing pages, and site content as signals rather than strict targeting parameters.

    Like dynamic search ads (DSA), AI Max can match to queries you didn’t explicitly target. But it goes further, reaching beyond the intent boundaries defined by your keyword set.

    Google has positioned AI Max as the next step in Search automation, with DSA, automatically created assets, and campaign-level broad match settings scheduled to transition into AI Max in September.

    The platform includes controls such as brand exclusions, URL exclusions, text guidelines, and location targeting. In accounts with strong conversion tracking, sufficient search volume, and reliable performance signals, AI Max may uncover incremental growth opportunities.

    Many accounts haven’t reached that stage yet.

    Your customers search everywhere. Make sure your brand shows up.

    The SEO toolkit you know, plus the AI visibility data you need.

    Start Free Trial
    Get started with

    Semrush One Logo

    Why AI surface eligibility isn’t a reason to rush into AI Max

    Much of the recent interest in AI Max stems from Google’s push toward AI-powered search experiences.

    AI Overviews now reach 2.5 billion monthly users, according to Google. Ads appear in 25.6% of AI Overview results, Semrush data shows.

    As Google continues expanding AI-driven search experiences, advertisers are understandably focused on maintaining visibility across those surfaces.

    That concern is reasonable. The problem is that AI Max is often presented as the solution before advertisers address the measurement, conversion, and account structure issues that determine whether the automation can succeed.

    Google Ads representatives typically pitch AI Max for brand campaigns by claiming it’s necessary for eligibility in AI Mode and AI Overviews on brand searches. But this isn’t accurate.

    Ginny Marvin, Google Ads liaison, confirmed that three campaign types are eligible to serve in AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search.

    However, exact match keywords aren’t eligible to serve in AI Overviews at all, even when identical broad match keywords exist in the same account.

    So, the eligibility picture looks like this:

    Campaign type AI Overview eligible Query control Best use case
    Exact match No Highest Defensive brand
    Phrase match No Medium Controlled intent expansion
    Broad match Yes Lower Generic scaling
    Performance Max Yes Low Cross-network automation
    AI Max Yes Lowest Mature accounts with strong signals

    PMax and AI Max do broadly the same job in terms of AI surface eligibility. So if you run PMax brand campaigns, you’re already covered. Adding AI Max won’t unlock anything new, as it’ll only add another automation layer to a setup that’s already eligible.

    So, when reps position AI Max on brand as the answer to AI surface eligibility, advertisers should stop and ask why this feature takes priority over fixing the account’s foundation.

    Test data doesn’t support Google’s AI Max claims

    When AI Max was in beta, Google stated that advertisers who activate the feature would see 14% more conversions, and those running exact and phrase match keywords would likely see a 27% increase in conversions.

    Google also indicated that advertisers who enable the full AI Max feature suite see 7% more conversions on average. Independent testing has produced more mixed results.

    The evidence for AI Max remains mixed

    Across 600 accounts, Smarter Ecommerce found that AI Max delivered a 35% lower return on ad spend (ROAS) than traditional match types. AI Max accounted for just 0.57% of total ad spend in those accounts, indicating that advertisers kept the budget to a minimum.

    After running a four-month test, Xavier Mantica found that AI Max had the most expensive conversions. While AI Max cost $100.37 per conversion, phrase match cost $43.97 per conversion, and exact match cost $52.69 per conversion. And Ezra Sackett tested 30,000 search terms with AI Max, only to find that 99% of impressions delivered zero conversions.

    After a 23-test analysis of 16 advertisers, Andy Goodwin noted improved Quality Score and ROAS when advertisers used the AI Max full feature suite. But he tested mature advertisers and used text customization in only 50% of tests and URL optimization in just 44%. This suggests advertisers were cautious about enabling every AI Max feature.

    However, none of this data is brand-specific. AI Max may deliver value in the right context, but an exact match defensive brand campaign that already performs well isn’t the ideal place to test a new automation product that depends heavily on signal quality. This is especially true for accounts that haven’t solved the underlying data problems feeding the automation.

    Get the newsletter search marketers rely on.

    See terms.

    AI Max attribution gets murky on brand

    AI Max doesn’t always find genuinely new search terms, according to Adalysis. In some cases, it simply takes credit for the queries that exact and phrase campaigns were already winning.

    Because AI Max treats keywords as signals rather than targeting parameters, impressions that would previously have been attributed to your exact match keyword can end up attributed to AI Max instead.

    This reporting issue can be significant for brand campaigns. Brand traffic is already the highest converting traffic in most accounts.

    Flip on AI Max, and suddenly you see an uplift. But it’s difficult to tell if it’s incremental or if preexisting branded performance simply appears in a different automation bucket.

    Brand controls don’t work consistently

    Google’s pitch leans heavily on brand controls. AI Max offers inclusions, exclusions, and guardrails that supposedly keep the match type tightly focused. In practice though, these controls don’t always work well.

    Adalysis notes that competitor terms occasionally slip through and brand terms sometimes match to non-brand queries. DAC reports overlap between brand and non-brand terms as well as unintended language matching. And LBBOnline finds relevance hovering around 50% in some campaigns.

    Brand controls could improve over time. But the available evidence doesn’t support treating AI Max as a low-risk switch for tightly controlled defensive brand campaigns.

    What to consider before testing AI Max on brand

    Before expanding automation into a defensive brand campaign, ask these questions.

    1. Are the conversion signals trustworthy?

    Have you separated macro and micro conversions? Do offline imports work correctly? Does lead quality feed back into the platform, or does Google still optimize equally toward every form fill?

    If the signal quality underneath the account is poor, AI Max will amplify it instead of fixing it.

    2. Have you already explored generic growth?

    In many of the accounts I audit, budget, weak landing page alignment, poor structure, and outdated query management limit generic campaigns. This is where you usually find incremental growth, not inside an already dominant brand campaign.

    3. Does the account give automation enough useful learning data?

    AI Max isn’t magic. It reflects the quality of the signals underneath it.

    If most of the account’s meaningful conversion volume comes from brand, then turning AI Max on in a brand campaign may reinforce existing dependency on branded traffic rather than helping the account grow beyond it.

    4. Are brand + modifier searches already structured properly?

    “Brand + reviews,” “Brand + pricing,” “Brand + near me,” and product intent variations often deserve their own campaign strategy entirely. AI Max shouldn’t become a substitute for good account architecture.

    5. Do you have a strategic reason to expand the brand campaign?

    If so, test carefully using experiments. That’s a business decision, not a checkbox recommendation from a rep who hasn’t looked deeply enough at the account to understand where the real opportunities actually are.

    See the complete picture of your search visibility.

    Track, optimize, and win in Google and AI search from one platform.

    Start Free Trial
    Get started with

    Semrush One Logo

    AI Max only works as well as the signals feeding it

    AI Max may grow into something genuinely useful over time. Remember, PMax went through a similar evolution and is in a much stronger place now than it was early on.

    But automation only works as well as the signals feeding it. Right now, the issue is that the foundations underneath the automation still aren’t strong enough. Better conversion frameworks, measurement, account structure, and feedback loops make automation smarter.

    If brand remains the best-performing campaign in the account, the bigger question is why the rest of the account hasn’t caught up yet. 

    Above all else, don’t confuse Google’s automation priorities with your account priorities.

    Read more at Read More

    How to Build Topical Authority in the AI Search Era (7 Steps)

    /0 Comments/in /by

    You can be a strong brand, publish high-quality content, and still not have topical authority.

    Just look at Great Jones, a kitchenware company.

    Their Dutch oven (called The Dutchess) is beautiful, well-reviewed, and featured in industry-leading sites like Vogue, the New York Times, Bon Appétit, and The Kitchn.

    The Kitchn – Great Jones Dutch oven

    But search “best Dutch ovens” on Google or ask an LLM for recommendations, and the brand rarely appears.

    Google AI Mode – Best Dutch ovens

    It’s not that Great Jones lacks content or press.

    What’s missing is the pattern — a consistent, positive framing that ties the brand to Dutch ovens across its own site and third parties.

    Without this, search engines and large language models (LLMs) can’t confidently connect the brand to the topic, so they default to the names with stronger signals.

    Many brands have some version of this gap. And AI search has only made it more visible.

    The good news: You can build this pattern.

    In this guide, I’ll show you how using the Topical Authority Pyramid, a framework I created to turn your brand into the go-to name in your niche.

    This framework builds on conversations with Amanda Milligan, Content and Growth Manager at Semrush, and my work in brand positioning across ecommerce, SaaS, and finance.

    What Is Topical Authority?

    Topical authority is your site’s earned reputation for expertise on a specific subject. It forms when your brand and topic appear together repeatedly across the sources that buyers, search engines, and LLMs trust.

    Think about the brands you automatically connect with certain topics.

    Like these:

    Topical Authority

    You didn’t consciously decide to make those associations.

    They formed because those brands kept showing up with the same message, in the same spaces, around the same topic.

    That’s topical authority — and it’s also how search engines and LLMs learn which brands are most strongly associated with a topic.

    The Topical Authority Pyramid Framework

    Topical authority has traditionally been defined by content volume and breadth of coverage.

    Publish comprehensively on a subject, and you’d own it.

    That’s no longer enough.

    As Amanda explains:

    The phrase “topical authority” has been around for a long time, but the thinking around it has evolved significantly. At its core, it’s always been about your brand becoming associated with specific topics. What’s changed is how we try to build that association.

    Today, search engines and LLMs look for more than coverage. They look for a clear position on the topic and external evidence that supports it.

    To address this, I created the Topical Authority Pyramid:

    Topical Authority Pyramid

    The Pyramid breaks topical authority into three layers:

    • Foundational authority: On-site content and credibility signals that demonstrate experience, expertise, authoritativeness, and trustworthiness (E-E-A-T), and category fit. (Think category pages, about pages, author bios, comparison content, FAQs, customer reviews, case studies, and more.) Still important, but not enough on its own.
    • Point of view (POV-led authority): A specific, consistent angle that separates you from every other brand covering the same ground. It gives buyers a reason to choose you and AI systems the confidence to recommend you over competitors.
    • Proof-backed authority: Third-party signals (mentions, reviews, citations, and data) that back up your POV across the wider web. It turns your POV from self-declared to independently verified.

    Each layer works alongside the others to establish your brand as the expert in your niche and earn more visibility in search engines and LLMs.

    Many brands, including Great Jones, have strong foundational authority and scattered proof, but no consistent POV tying it all together.

    Here’s how to build all three.

    Free resource: Download our free Topical Authority Audit template to audit your topics, score competitor authority, and track your progress. Fill it out as you work through each step below or at your own pace.

    Step 1: Audit Your Topic Reputation

    Your brand likely already has a topical reputation, whether you’ve shaped it intentionally or not.

    Audit it before deciding what to build.

    Topical Authority Pyramid – Step 1

    Research Your Current On-Site Associations

    The gap between what you publish and what you want to be known for may be wider than you expect.

    This is something Amanda has experienced firsthand:

    When I did content audits, I’d inventory every piece of content by topic. You might find you have dozens of pieces on something that isn’t even your priority, and only five on the topic you actually want to own. That mismatch is exactly what a topic audit is designed to surface because what you’ve published is what you’re telling Google and buyers your priorities are.

    The fastest way to assess this is with Semrush’s Organic Rankings tool.

    Enter your domain to automatically see your brand’s strongest topic associations, organized by the topics getting visibility.

    Domain Overview – Greater Jones Goods – Key topics

    When I did this for Great Jones, their strongest topical associations were “recipes” and “celebrity chefs.”

    Dutch ovens barely registered.

    Organic Rankings – Greater Jones Goods – Topics

    Yet, the Dutchess is their primary product.

    Great Jones Goods – The Dutchess

    And “Dutch oven” alone gets over 200,000 monthly Google searches.

    Keyword Overview – Dutch oven

    Great Jones has a big opportunity to increase their topical authority for Dutch ovens and convert some of this search interest into sales.

    These are the kind of topical association gaps you want to surface in this step.

    Two more places to look:

    • Google Search Console: Go to “Performance” > Queries and sort by clicks or impressions. You’ll see the topics that attract users to your site.
    • Branded queries on Google and LLMs: Search “[your brand] + your topic” and “what is [your brand] known for” to see how search engines and LLMs describe you

    ChatGPT – Great Jones cookware

    Audit Your Off-Site Presence

    Next, review your third-party coverage: mentions, reviews, roundups, and editorial press.

    This is where many brands have the biggest gap, and it’s the one AI systems appear to weigh most heavily.

    Run these checks:

    • Search “[your brand] + [topic]” and look beyond your own site: What’s showing? Industry blogs? Reddit? Editorial coverage? Or nothing?
    • Ask an LLM: “What are the best [topic] brands?” and “Where would you recommend buying [topic]?” See whether your brand surfaces and what it’s associated with.
    • Check “best of” lists, roundups, and comparison articles for your topic: Are you in them? If so, where do you rank and how are you described? If not, who is?

    Google SERP – Compare Dutch ovens

    A quick off-site audit for Great Jones showed me they’ve earned coverage any kitchenware brand would envy: features in major lifestyle publications and partnerships with prominent chefs and influencers.

    But when you look specifically at Dutch oven coverage, the off-site gap is obvious.

    Most of the top-ranking articles are a few years old (or older):

    Google SERP – Great Jones Dutch ovens

    And the overall sentiment is inconsistent.

    For example, in Food & Wine’s Dutch oven roundup, the Dutchess appears under the “Other” section (rather than “Top Picks”) with a caveat about heating issues.

    Food & Wine – Best Dutch ovens

    In this Bon Appétit roundup of the best Dutch ovens, Great Jones is categorized under “Dutch ovens we don’t recommend.”

    Bon Appetit – Best Dutch ovens

    They’re also notably missing from some use-case roundups, like this one from Serious Eats:

    Serious eats – Best Dutch ovens

    In Reddit threads where buyers are actively looking for Dutch oven recommendations, Great Jones rarely comes up.

    When it does, many of the threads are from years ago:

    Reddit – Great Jones Dutch ovens

    Great Jones has real brand equity to build on.

    But it’s just not adding up to a solid reputation in Dutch ovens — yet.

    Step 2: Choose the Topic You’ll Build Authority Around

    You can’t build authority on everything at once.

    This step narrows your focus to one topic worth owning based on a few crucial factors:

    • What drives revenue
    • Where competitors are weak
    • Where your brand has room to claim a position

    Topical Authority Pyramid – Step 2

    Build and Prioritize Your Topic List

    Start by listing the topics you want buyers, search engines, and LLMs to associate with your brand.

    Begin with the obvious ones: the products, categories, use cases, and problems you want to be known for.

    Then expand with adjacent topics buyers already care about.

    For Great Jones, that might include slow cooking, one-pot meals, kitchen gifting, or cookware care.

    Look especially for topics where you already have traction, competitors are weak, or your brand should be associated but currently isn’t.

    Once you’ve identified 10 to 15 topics, add them to the “Topic Audit & Scoring” tab in your spreadsheet.

    Topical Authority Template – Scoring topics

    Next, narrow the list down.

    Not every topic on your list is worth building a reputation around right now.

    For each one, ask two questions:

    Do you want to own it? Does it drive revenue, support a product you sell, or build a reputation that brings buyers to you?

    How urgent is it?

    • High: Directly tied to revenue and an opportunity you can act on now
    • Medium: Tied to revenue, but the opportunity or timing isn’t right yet
    • Low: Worth tracking but not acting on yet, or no direct business connection

    You should end up with three to five high-priority topics to investigate next.

    Topical Authority Template – Scoring priority

    Run a Query Audit

    Now test each shortlisted topic to see who already owns the space and where there’s room for your brand to carve out a position.

    For each topic, run four queries on Google and LLMs:

    Query type What to search What it tells you
    Head term The topic as-is (“Dutch ovens”) Who owns the broad topic; what AI defaults to
    Best query Add “best” or a qualifier (“best Dutch ovens under $200”) Where buyer intent lives; which brands AI recommends
    Brand query Your brand + the topic (“Great Jones Dutch oven”) Where you specifically stand; how AI currently describes you
    Specific angle A query tied to an association you might want to own (“Dutch oven for gifting”) Whether that territory is already claimed or still open

    As you run each query, note:

    • Which formats show up most: editorial lists, reviews, Reddit threads, brand pages
    • Whether AI systems name specific brands without being asked (unprompted)
    • Whether community results show buyers asking for recommendations or comparing options

    Record this in the “Query Audit” tab of your spreadsheet.

    Topical Authority Template – Query audit

    If a query shows buying intent but the top results barely address it, that’s a topical authority opportunity.

    For example, when I search “Dutch ovens” and “best Dutch ovens,” the same brands consistently come up: Le Creuset, Staub, Lodge, and Caraway.

    But rarely Great Jones.

    And for “Dutch oven for gifting,” ChatGPT didn’t mention Great Jones at all.

    ChatGPT – Best Dutch ovens

    Great Jones only appears when buyers already know to look for them.

    More importantly, some topics, such as gifting, aesthetics, and non-toxic coating, are not clearly owned by any brand.

    That’s where the opportunity is.

    Score by Association Strength

    After the Query Audit, score your presence on each topic against three competitors on a 0 to 3-point scale.

    The score reflects your overall standing across the Topical Authority Pyramid: foundational, POV, and proof combined:

    Score What it means
    0 Not present anywhere for this topic
    1 Present but weak or negative
    2 Present and positive but inconsistent
    3 Consistently prominent across high-authority sources and AI

    Note: This isn’t a precise measurement. Use your observations, priorities, and market knowledge to guide the score.

    Score your brand first, then each competitor.

    Topical Authority Template – Scoring

    After your scoring is complete, look for high-priority topics where you scored a 1 or 2 and at least one competitor scored a 0 or 1.

    Those are topics where buyer demand is real, you have some footing, and no competitor has locked it down — the conditions for a winnable position.

    For Great Jones, “Dutch ovens for gifting” fits the pattern: high priority, room to claim it, and no clear leader.

    By the end, you should have one topic to focus on.

    • Have more than one? Choose the one closest to revenue or where the gap between your current and desired reputation is smallest.
    • Have none? Go niche. Instead of “Dutch ovens,” try “enameled cast iron Dutch ovens.” A narrower topic is easier to own and still builds toward the bigger one.

    Step 3: Identify Your Topic POV

    You’ve identified one viable topic. Next, decide what reputation to build around it.

    Topical Authority Pyramid – Step 3

    Your POV is the specific angle you own inside that space.

    It’s what makes your brand distinct to buyers, search engines, and AI systems.

    Like these brands — same topic, completely different associations:

    Razors & note-taking tools

    Research What’s Already Owned

    Before identifying your POV, map what dominant brands in your space are already known for.

    These are the POVs to avoid. Going after any of them directly means competing for territory another brand has spent years building.

    Start with your notes from the Query Audit. The patterns there tell you a lot about which competitors own what.

    To go deeper, use the Semrush AI Visibility Toolkit.

    The Brand Performance tool tells you which associations your competitors are winning across AI-generated answers (and how your own brand compares).

    Brand Performance – Great Jones – Key business drivers

    For Great Jones, the obvious territories are taken:

    • Le Creuset owns heritage
    • Staub and All-Clad lean on professional-grade performance
    • Lodge owns value

    No brand has clearly claimed gifting Dutch ovens, visual appeal, or beginner cooking.

    Dutch oven landscape

    (Semrush shows Great Jones is leading on design, which gives them a head start.)

    These gaps are where your POV lives.

    Choose Your POV

    Before committing to a POV, ask three questions:

    • Does it drive revenue or connect to a product or service you sell?
    • Can you defend the POV with what you already have — features, data, customer behavior, and/or expertise?
    • Is the territory open across search and LLMs?

    If a candidate fails any of the three, drop it. It won’t hold up once you start building proof around it.

    For Great Jones, “gifting” passes all three questions.

    People already buy Dutch ovens as gifts.

    Reddit – Dutch oven gift

    Customers already mention its “super attractive,” “modern,” and “beautiful” design in on-site reviews, which aligns perfectly with a gifting POV:

    The Dutchess – Reviews

    And no competitor has clearly made “gifting” their territory yet.

    Write Your POV as One Sentence

    Your POV should be easy to grasp and repeat.

    Writing it as one sentence is the test. If you can’t, it’s likely not sharp enough yet.

    For Great Jones, the POV could be:

    • Gifting: Great Jones is the Dutch oven for the milestone moments: weddings, housewarmings, and “I want this to mean something” gifts
    • Aesthetics: Great Jones is the Dutch oven you give when you want the gift to stay on the counter, not the cabinet
    • Beginner: Great Jones is the Dutch oven that turns beginners into confident home cooks

    Each POV targets a different buyer and a different reason to choose Dutch ovens.

    Topical Authority Template – POV builder

    Step 4: Map Your POV Proof Architecture

    This step is where you plan your proof — the concrete evidence that backs up your POV — across your own site and the wider web.

    You’re not building anything yet.

    You’re mapping what proof you’ll need at each stage of the buyer journey, so you have a clear blueprint to follow.

    Topical Authority Pyramid – Step 4

    Audit Your Proof Across the Buyer Journey

    A POV without proof is just a claim.

    To build credibility, you need evidence that backs up two things:

    You belong in the category

    You’re the go-to brand for the POV you’ve claimed

    And you need to reinforce this at every stage of the buying journey with a different kind of proof:

    Buyer stage What they need to believe Proof assets that help
    Awareness This type of solution solves my problem Research data, industry studies, customer statistics
    Consideration This has the qualities I care about Third-party reviews, expert endorsements, certifications, performance data
    Comparison This is the better choice over alternatives Independent test results, awards, analyst rankings, head-to-head data
    Active Evaluation This will work for my specific situation Case studies, usage data, implementation examples, success metrics
    Decision Other people already trust this Customer numbers, retention rates, repeat purchase data, verified reviews

    To run your audit, go through each belief in the table and identify which proof assets you already have and which are missing.

    Use the POV Proof Planner in your template to record your findings:

    Topical Authority Template – POV planner

    For Great Jones’s gifting POV, a quick proof audit surfaces:

    • Consideration proof exists: The brand has features in the New York Times, Good Housekeeping, and many others, but most aren’t connected to gifting or were published years ago
    • Comparison proof is sparse: Some decision-stage proof tied to gifting exists for Great Jones, but it’s not consistent enough to increase AI recommendations

    InsideHook – Gifting Great Jones cookware

    Step 5: Build Your On-Site Foundation

    Before search engines and LLMs can associate your brand with your POV, you need to establish it on your site.

    This step is about building that foundation: the hub and supporting pages where your topic, POV, and early proof signals all come together.

    Topical Authority Pyramid – Step 5

    Create a Hub Page for Your POV

    Your hub page is the central authority document for your POV.

    It defines the topic, explains why it matters, and routes buyers to supporting pages that go deeper.

    Side note: If you’ve built pillar pages and topic clusters before, this will feel familiar. The structure is similar, but the organizing principle is proof and belief, not coverage and keywords.

    For Great Jones, that could be a “Dutch oven gifting guide.”

    It would link to the Dutch oven product page and explain why Dutch ovens make exceptional gifts.

    Supporting pages, such as gift basket ideas, a gifting FAQ, and a report on cookware gifting would also be linked.

    Hub page and support pages

    If you’ve been publishing for a while, you may already have a page that can serve as the hub: a category page, a subcategory page, or an industry-specific landing page.

    Topical Authority Template – Foundation planner

    Build Supporting Pages

    Supporting pages go deeper than the hub.

    Each one proves a specific aspect of your POV at a specific stage of the buyer journey.

    Go back to the proof assets you mapped in Step 4 — they tell you what you need to prove and at which stage.

    Your supporting pages are how you do it.

    For Great Jones, the comparison stage is a clear gap.

    To convince buyers the Dutchess is a better gift than the alternatives, they need dedicated comparison pages, backed by awards, endorsements from leading industry sites and public figures, and head-to-head data.

    Other supporting pages might include:

    • Dutch oven gift basket ideas: What to pair it with and how to present it, backed by customer photos and a relevant publication feature
    • Gifting FAQ: Sizing, monogramming, return policies, with real customer questions pulled from reviews
    • The Gift-Worthy Dutch Oven Report: Proprietary survey data on how customers buy, give, and display the product

    Pro tip: Strengthen your hub and cluster pages with on-site trust signals. Include author bios that show real niche experience in the topic, named expert sources or contributors, and an About or editorial page that clearly ties your brand and contributors to the category.

    Identify what pages you need, and fill out the rest of the “On-Site Foundation Planner” tab in your template.

    Topical Authority Template – Foundation planner – Supporting pages

    Structure Each Page for Readers and Machines

    Lead with the most important information first — also known as the inverted pyramid.

    It makes your pages easier for readers to scan and for machines to interpret.

    The Inverted Pyramid Approach for Outlining Content

    Then, make sure each page has:

    • Clear section headings: Labeled so readers and machines immediately understand what each section covers
    • POV language: Reuse the same phrases and framing tied to your angle throughout
    • Schema markup: Structured data that helps search engines and AI systems understand your content and context
    • Semantic HTML: Proper use of HTML tags so machines can correctly interpret your page structure

    Non-sematic & Sematic HTML

    Link Your Pages

    Each hub and supporting page proves something on its own.

    Link them together, and you create a proof system.

    Link your proof systems

    Follow these internal linking best practices:

    • Link from the hub to your 5–10 most important supporting pages in the body. Not just in the nav, breadcrumbs, or footer.
    • Link every supporting page back to the hub. Keep key pages within 2–3 clicks of each other.
    • Use descriptive, relevant anchor text to help people and machines understand what the linked page is about

    Vague anchor text

    Step 6: Create an Off-Site Proof System

    A strong POV and foundation won’t get you into AI answers if the association exists only on your site.

    This is one of the biggest shifts in how topical authority works, as Amanda explains:

    Topical authority isn’t just about what’s on your site anymore. You need third-party sources — coverage, mentions, appearances, even reviews — independently reinforcing the same association. If the only place your brand is tied to a topic is your own content, that’s often not enough to build the pattern that AI systems and search engines need to trust you on it.

    This step reinforces your POV in the places buyers and AI systems already trust.

    Topical Authority Pyramid – Step 6

    Start with One Signature Proof Point

    A signature proof point is an original, specific story or finding about your topic.

    Something others outside your brand would want to reference, share, or build on.

    That could be:

    • Proprietary data from your own sales, customer behavior, or research
    • A trend you’ve spotted and named before anyone else
    • A contrarian observation backed by evidence

    For Great Jones and the gifting POV, the insight has to tie Dutch ovens to gifting.

    They might pull data from their own sales — say, a 4x spike in Dutch oven purchases in the two weeks before Mother’s Day — and turn it into a “State of Mother’s Day Gift-Giving” report.

    That report becomes a press pitch to lifestyle publications, a video on their YouTube channel, and a thread on Reddit’s r/gifts.

    One insight, multiple placements, all reinforcing the same association: Great Jones = gifting.

    Google SERP – Dutch oven gift basket

    To find yours, start with your proof assets from Step 4.

    Look for patterns in your data, reviews, industry trends, or customer behavior.

    Distribute Your Proof Point

    Once you have a signature insight, decide where and how to distribute it.

    There are four main buckets:

    • Brand channels: Content you publish directly to audiences you’ve built: email newsletters, marketplaces, review sites, podcasts, social media, SMS or loyalty messaging, local profiles
    • Community: Discussions in spaces your buyers already trust, such as Reddit, niche forums and industry groups, social media comments and communities
    • Partners: Others who extend your reach into new audiences, including affiliates, influencers, retail partners, and integrations
    • Earned: Third-party coverage you pitch but don’t control, such as media mentions, press features, user-generated content, and editorial placements

    Distribute one insight everywhere

    For each bucket, identify the specific publications, platforms, or communities where your insight is most relevant.

    Not sure where to start?

    Run a search on Google or an LLM related to your proof point and look at the sites that rank and the sources that get cited.

    Those are the places worth showing up in. List them in the “Off-Site Proof Planner” tab of your template.

    Topical Authority Template – Off-site planner

    For Great Jones, some of that infrastructure is already in place.

    They already have the social media following, media clout, and collaborations with names like cookbook author Molly Baz.

    Food Network – Great Jones & Molly Baz collaboration

    What they need is a focused distribution of insights around their gifting POV.

    That might look like:

    • Briefing partner creators on a gifting-specific collaboration, like pitching fresh coverage that ties the Molly Baz collab to gifting
    • Pitching their Mother’s Day gifting sales data to lifestyle publications already covering Dutch ovens
    • Reframing existing social content around the gifting angle

    Related reading: LLM Seeding: A New SEO Strategy to Get Mentioned by LLMs

    Step 7: Track Topical Authority Progress

    You’ve built the full Topical Authority Pyramid.

    Now check whether it’s starting to influence how search engines and LLMs describe your brand.

    Topical Authority Pyramid – Step 7

    Use the “Progress Tracker” tab in your spreadsheet to record what you find at 30, 60, and 90-day intervals.

    Topical Authority Template – Progress tracker

    Foundational Layer: Are You Showing Up More?

    Coverage tracking tells you whether your topical footprint is growing:

    Go back to your Step 2 notes. How many of your four query types surfaced your brand unprompted? Run them again and compare.

    Also monitor pages ranking for queries you didn’t directly target, and rising impressions for queries related to your topic.

    For Great Jones, the baseline visibility was weak for many non-brand Dutch oven queries.

    Google SERP – Dutch ovens for gifting

    Showing up in two or three queries at 90 days — especially “Dutch ovens for gifting” — would be a real sign of progress.

    Tools that help:

    • Semrush’s Organic Rankings tool (the Topics report) for association trends
    • Semrush AI Visibility Toolkit: The Visibility Overview tool to see whether your AI Visibility score and mention count are climbing, and Prompt Tracking to re-run your query set on a set cadence
    • Google Search Console for impressions and queries by page
    • Surfer SEO for coverage gaps

    GSC – Performance – Queries – Backlinko

    POV Layer: Are You Being Described Correctly?

    The POV layer tracks language. Specifically, whether mentions of your brand are increasingly paired with your POV.

    Run POV-specific prompts monthly and check the wording.

    For Great Jones, that’s searches like “Dutch oven wedding gift” or “best Dutch oven to give as a gift.”

    And when the Dutchess shows up in reviews, comparisons, and “best of” listicles, watch for the language around it.

    Is it being called “a great house-warming gift,” “splurge-worthy,” or “the kind of gift that gets displayed”?

    That’s the POV landing.

    Tools that help:

    • Brand24 to track web and social mentions
    • Semrush’s Perception tool for sentiment trends, and Narrative Drivers for the attributes and phrases AI ties to your brand

    Perception – Great Jones Goods – Key sentiment drivers

    Proof Layer: Are Others Confirming Your POV?

    The proof layer tracks third-party confirmation.

    Are media mentions, third-party pages, and niche communities backing up the POV you want to own?

    Start with your proof point.

    Are others citing or referencing it? That’s a signal your off-site distribution is working.

    Then, go broader.

    Run [Your Brand] + [POV] queries on Google and an LLM.

    Google SERP – Great Jones Dutch oven gift

    Check whether you’re appearing in more third-party sources associated with your POV.

    Are buyers recommending you unprompted in Reddit or niche communities? Are your hub pages attracting links from relevant sites?

    When your brand appears, is it being described in relation to your POV?

    For Great Jones, that might be a gift guide naming the Dutchess as the go-to Dutch oven for wedding gifts.

    Tools that help:

    • Google Alerts for basic brand mention tracking, or Meltwater for a more robust option
    • Semrush’s Competitor Research tool to surface sites citing competitors but not you, and Narrative Drivers for the Top Cited Domains shaping your topic

    Google Alerts – Great Jones

    Build the Pattern That Wins in AI Search

    Great Jones proves that great press and a great product aren’t enough for topical authority.

    If search engines and LLMs don’t have clear associations attached to your brand, showing up online will be a struggle — no matter what Vogue thinks of you.

    Vogue – Great Jones cookware

    But that’s fixable.

    The Topical Authority Pyramid gives you the framework:

    • A strong foundation that proves you belong in the category
    • A POV that makes you distinct
    • Proof that backs it up across the web

    Once your first topic takes shape, expand.

    Follow the Topical Authority Pyramid for your next topic, claim more territory, and deepen your authority in adjacent spaces.

    Do this well, and search engines and LLMs may just start recommending you by default.

    Want a repeatable way to monitor your AI visibility over time? Our AI visibility audit guide walks you through it step by step.

    The post How to Build Topical Authority in the AI Search Era (7 Steps) appeared first on Backlinko.

    Read more at Read More

    Web Design and Development San Diego

    Google adds new Performance Max asset testing tools

    /0 Comments/in , /by
    Google Ads may be over-crediting your conversions- A 7-day test tells a different story

    Google is expanding experimentation capabilities in Performance Max, giving advertisers more ways to test creative assets and measure campaign performance before making large-scale changes.

    What’s happening. Google is rolling out new asset experiments for Performance Max campaigns, allowing advertisers to test how different creative assets affect results.

    The feature enables marketers to compare entirely new asset groups, evaluate the impact of adding individual assets, or measure the performance of seasonal creative against evergreen content.

    Advertisers will also be able to test assets generated through Google’s Asset Studio.

    The big picture. Performance Max has long automated campaign optimization across Google’s inventory, but advertisers have had limited visibility into the impact of creative changes.

    The new experiments aim to give marketers a more controlled way to evaluate creative decisions before applying them across campaigns.

    Between the lines. The addition of a second success metric could be particularly valuable for advertisers balancing competing objectives, such as maximizing conversions while maintaining efficiency targets.

    Rather than declaring a winner based on a single KPI, marketers will be able to evaluate how changes affect broader campaign performance.

    What else is new:

    • Conversion lift studies and experiments are being brought together under one Experiments page.
    • Additional experiment and measurement capabilities are planned for future releases.
    • Expanded support for manager accounts (MCCs) and the Google Ads API is expected to begin rolling out in the coming weeks.

    Why we care. Creative remains one of the biggest levers available to Performance Max advertisers, yet testing new assets often involves risk. The new experimentation tools provide a structured way to validate creative decisions with data before fully committing budget.

    What to watch. As Google continues investing in automation and AI-generated creative, asset testing is becoming increasingly important. The ability to directly compare human-created, seasonal, evergreen, and AI-generated assets could offer advertisers deeper insight into what drives performance across Performance Max campaigns.

    The bottom line. Google is giving Performance Max advertisers more sophisticated testing capabilities, making it easier to evaluate creative changes, measure results across multiple KPIs, and manage experiments from a centralized location.

    First spotted. The update was first spotted by PPC News Feed.

    Read more at Read More

    Web Design and Development San Diego

    OpenAI to expand ChatGPT ads to new markets & test multi-advertiser placements

    /0 Comments/in , /by
    OpenAI ChatGPT ad platform

    OpenAI is expanding its advertising ambitions inside ChatGPT, beginning an early test that allows multiple advertisers to appear within a single ad placement.

    What’s happening. The company is testing multi-advertiser ad units across a small subset of ChatGPT ads, according to a product update sent to advertisers.

    Rather than displaying a single sponsored result, the new format will group multiple relevant ads together in one placement. Eligible ads will be sold through a second-price auction model, a common pricing mechanism used across digital advertising platforms.

    OpenAI says the goal is to improve product discovery for users while creating more opportunities for advertisers to engage with users during high-intent conversations.

    Meanwhile, in Ads Manager Beta. OpenAI also announced several new campaign management features for advertisers:

    • Advertisers can now convert existing campaigns from lifetime budgets to daily budgets.
    • CPM campaigns can be cloned and converted to CPC bidding with one click.
    • Impression-based campaigns now support custom CPM max bids.
    • Bulk editing is available directly within the Ads Manager interface.
    • Daily budgets will transition to an average daily budget model with weekly pacing flexibility.
    • Geographic targeting is expanding beyond the U.S., Canada, Australia, and New Zealand to include the U.K., Japan, South Korea, Brazil, and Mexico.

    Why we care. The updates bring OpenAI’s ad platform closer to the functionality marketers expect from mature advertising ecosystems, reducing campaign management friction while expanding targeting opportunities internationally.

    What to watch. The multi-advertiser placement test could provide an early signal of how aggressively OpenAI intends to monetize ChatGPT. If successful, the format may become a larger part of the platform’s ad inventory strategy while offering advertisers more opportunities to reach users during purchase and research journeys.

    The bottom line. OpenAI is steadily building out its advertising stack, but the biggest development may be its experiment with showing multiple advertisers in a single ChatGPT ad placement — a move that could reshape how sponsored content appears within AI conversations.

    Read more at Read More