<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-AI-forms-opinions-about-your-brand.png" class="attachment-large size-large wp-post-image" alt="How AI forms opinions about your brand" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-AI-forms-opinions-about-your-brand.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-forms-opinions-about-your-brand-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-forms-opinions-about-your-brand-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-forms-opinions-about-your-brand-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Build a digital footprint that helps AI understand your expertise, recognize your credibility, and recommend 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.

  
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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.




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.
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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.




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.




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.




Part 1, “Rand Fishkin proved AI recommendations are inconsistent, here’s why and how to fix it,” introduced cascading confidence.
Part 2, “AAO: Why assistive agent optimization is the next evolution of SEO,” named the discipline.
Part 3, “The AI engine pipeline: 10 gates that decide whether you win the recommendation,” mapped the full pipeline.
Part 4, “The five infrastructure gates behind crawl, render, and index,” walked through the infrastructure phase.
Part 5, “5 competitive gates hidden inside ‘rank and display’,” covered the competitive phase.
Part 6, “The entity home: The page that shapes how search, AI, and users see your brand,” mapped the raw material.
Part 7, “The push layer returns: Why ‘publish and wait’ is half a strategy,” extended the entry model.
Part 8, “How AI decides what your content means and why it gets you wrong,” covered annotation, the last gate where you’re alone with the machine.
Part 9, “Why topical authority isn’t enough for AI search,” opened the competitive phase proper with topical ownership.
Part 10, “The funnel flip: Why AI forces a bottom-up acquisition strategy,” named the process.
Part 11, “The framing gap: Why AI can’t position your brand,” exposed the gap between evidence and recommendation.
Part 12, “The 10-gate AI search pipeline: Find where your content fails,” showed you how to find (and repair) your F grades in the AI engine pipeline.
Part 13, “The delegation boundary: How AI decides which brands win,” mapped how delegation moves between user and engine across Search, Assistive, and Agent modes.
Part 14, “The funnel query pathway: A framework for measuring AI visibility,” built the measurement instrument.
Part 15, “The micro-macro shift: How to measure AI visibility now that precision is gone,” moved measurement from micro precision to macro trend.
Part 16, “How SEO turns customer success into AI-readable proof,” put SEO inside post-sale operations.
Up next: Why Google isn’t dying: paid and organic just collapsed onto every AI surface.

Build a digital footprint that helps AI understand your expertise, recognize your credibility, and recommend your brand.

How AI forms opinions about your brand

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/What-server-logs-reveal-that-SEO-tools-miss.png" class="attachment-large size-large wp-post-image" alt="What server logs reveal that SEO tools miss" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/What-server-logs-reveal-that-SEO-tools-miss.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/What-server-logs-reveal-that-SEO-tools-miss-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/What-server-logs-reveal-that-SEO-tools-miss-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/What-server-logs-reveal-that-SEO-tools-miss-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Search engines leave behind a detailed record of every visit. Here's how that data helps uncover hidden SEO issues at scale.

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.
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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.
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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.
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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.

Search engines leave behind a detailed record of every visit. Here's how that data helps uncover hidden SEO issues at scale.

What server logs reveal that SEO tools miss

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max-.png" class="attachment-large size-large wp-post-image" alt="Why your brand campaign may not be ready for AI Max" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI Mode eligibility isn't enough reason to expand automation. Learn where to focus before testing AI Max on brand traffic.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max-.png" class="attachment-large size-large wp-post-image" alt="Why your brand campaign may not be ready for AI Max" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Why-your-brand-campaign-may-not-be-ready-for-AI-Max--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Not long ago, broad match was positioned as the future of paid search. Today, that role belongs to <a href="https://searchengineland.com/ai-max-for-search-everything-you-need-to-know-462923">AI Max</a>.</p>



<p>Over the last few months, I&#8217;ve heard repeated recommendations to enable AI Max on brand campaigns, even when those campaigns are already performing exactly as intended.</p>



<p>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.</p>



<p>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.&nbsp;</p>



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



<h2 class="wp-block-heading" id="h-the-promise-and-limitations-of-ai-max">The promise and limitations of AI Max</h2>



<p>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.</p>



<p>Like dynamic search ads (DSA), AI Max can match to queries you didn&#8217;t explicitly target. But it goes further, reaching beyond the intent boundaries defined by your keyword set.</p>



<p>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.</p>



<p>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.</p>



<p>Many accounts haven&#8217;t reached that stage yet.</p>



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<h2 class="wp-block-heading" id="h-why-ai-surface-eligibility-isn-t-a-reason-to-rush-into-ai-max">Why AI surface eligibility isn&#8217;t a reason to rush into AI Max</h2>



<p>Much of the recent interest in AI Max stems from Google&#8217;s push toward AI-powered search experiences.</p>



<p>AI Overviews now reach <a href="https://blog.google/innovation-and-ai/sundar-pichai-io-2026/#conversational-ai" target="_blank" rel="noopener">2.5 billion monthly users</a>, according to Google. Ads appear in <a href="https://www.semrush.com/blog/serps-with-ads-and-ai-overviews/" target="_blank" rel="noopener">25.6%</a> of AI Overview results, Semrush data shows.</p>



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



<p>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.</p>



<p>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&#8217;t accurate.</p>



<p>Ginny Marvin, Google Ads liaison, <a href="https://officialppcchat.com/2026/01/07/all-your-2025-google-ads-questions-answered-ppc-chat-recap-with-guest-ginny-marvin-of-google-ads/" target="_blank" rel="noopener">confirmed</a> that three campaign types are eligible to serve in AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search.</p>



<p>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.</p>



<p>So, the eligibility picture looks like this:</p>



<figure class="wp-block-table"><table><thead><tr><th><strong>Campaign type</strong></th><th><strong>AI Overview eligible</strong></th><th><strong>Query control</strong></th><th><strong>Best use case</strong></th></tr></thead><tbody><tr><td>Exact match</td><td>No</td><td>Highest</td><td>Defensive brand</td></tr><tr><td>Phrase match</td><td>No</td><td>Medium</td><td>Controlled intent expansion</td></tr><tr><td>Broad match</td><td>Yes</td><td>Lower</td><td>Generic scaling</td></tr><tr><td>Performance Max</td><td>Yes</td><td>Low</td><td>Cross-network automation</td></tr><tr><td>AI Max</td><td>Yes</td><td>Lowest</td><td>Mature accounts with strong signals</td></tr></tbody></table></figure>



<p>PMax and AI Max do broadly the same job in terms of AI surface eligibility. So if you run PMax brand campaigns, you&#8217;re already covered. Adding AI Max won’t unlock anything new, as it&#8217;ll only add another automation layer to a setup that&#8217;s already eligible.</p>



<p>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.</p>



<h2 class="wp-block-heading" id="h-test-data-doesn-t-support-google-s-ai-max-claims">Test data doesn’t support Google’s AI Max claims</h2>



<p>When AI Max was in beta, <a href="https://blog.google/products/ads-commerce/google-ai-max-for-search-campaigns/#introduction" target="_blank" rel="noopener">Google stated</a> 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.</p>



<p>Google also <a href="https://business.google.com/us/accelerate/announcements/dsa-upgrade-to-ai-max-2026/" target="_blank" rel="noopener">indicated</a> that advertisers who enable the full AI Max feature suite see 7% more conversions on average. Independent testing has produced more mixed results.</p>



<h3 class="wp-block-heading" id="h-the-evidence-for-ai-max-remains-mixed">The evidence for AI Max remains mixed</h3>



<p>Across 600 accounts, Smarter Ecommerce found that AI Max delivered a <a href="https://www.linkedin.com/feed/update/urn:li:activity:7392205372549148672/" target="_blank" rel="noopener">35%</a> 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.</p>



<p>After running a four-month test, Xavier Mantica found that AI Max had the <a href="https://www.linkedin.com/posts/xavier-mantica_im-done-with-ai-max-after-4-months-of-activity-7392168893257060352-2NyO/" target="_blank" rel="noopener">most expensive conversions</a>. 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 <a href="https://www.linkedin.com/posts/ezrasackett_we-have-a-few-clients-dipping-their-toes-activity-7361450205210112000-38Hu/" target="_blank" rel="noopener">99%</a> of impressions delivered zero conversions.</p>



<p>After a 23-test analysis of 16 advertisers, <a href="https://searchengineland.com/google-ai-max-performance-tests-471366">Andy Goodwin noted</a> 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.</p>



<p>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&#8217;t solved the underlying data problems feeding the automation.</p>



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<h3 class="wp-block-heading" id="h-ai-max-attribution-gets-murky-on-brand">AI Max attribution gets murky on brand</h3>



<p>AI Max doesn’t always find genuinely new search terms, <a href="https://searchengineland.com/google-ai-max-performance-tests-471366">according to Adalysis</a>. In some cases, it simply takes credit for the queries that exact and phrase campaigns were already winning.</p>



<p>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.</p>



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



<p>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.</p>



<h3 class="wp-block-heading" id="h-brand-controls-don-t-work-consistently">Brand controls don’t work consistently</h3>



<p>Google&#8217;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.</p>



<p>Adalysis notes that competitor terms occasionally slip through and brand terms sometimes match to non-brand queries. <a href="https://www.dacgroup.com/insights/blog/paid-media/ai-max-and-the-next-chapter-of-paid-search/" target="_blank" rel="noopener">DAC reports</a> overlap between brand and non-brand terms as well as unintended language matching. And <a href="https://lbbonline.com/news/AI-Max-and-the-Changing-Story-of-Paid-Search" target="_blank" rel="noopener">LBBOnline finds</a> relevance hovering around 50% in some campaigns.</p>



<p>Brand controls could improve over time. But the available evidence doesn&#8217;t support treating AI Max as a low-risk switch for tightly controlled defensive brand campaigns.</p>



<h2 class="wp-block-heading" id="h-what-to-consider-before-testing-ai-max-on-brand">What to consider before testing AI Max on brand</h2>



<p>Before expanding automation into a defensive brand campaign, ask these questions.</p>



<h3 class="wp-block-heading" id="h-1-are-the-conversion-signals-trustworthy">1. Are the conversion signals trustworthy?</h3>



<p>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?</p>



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



<h3 class="wp-block-heading" id="h-2-have-you-already-explored-generic-growth">2. Have you already explored generic growth?</h3>



<p>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.</p>



<h3 class="wp-block-heading" id="h-3-does-the-account-give-automation-enough-useful-learning-data">3. Does the account give automation enough useful learning data?</h3>



<p>AI Max isn&#8217;t magic. It reflects the quality of the signals underneath it.</p>



<p>If most of the account&#8217;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.</p>



<h3 class="wp-block-heading" id="h-4-are-brand-modifier-searches-already-structured-properly">4. Are brand + modifier searches already structured properly?</h3>



<p>&#8220;Brand + reviews,&#8221; &#8220;Brand + pricing,&#8221; &#8220;Brand + near me,&#8221; and product intent variations often deserve their own campaign strategy entirely. AI Max shouldn&#8217;t become a substitute for good account architecture.</p>



<h3 class="wp-block-heading" id="h-5-do-you-have-a-strategic-reason-to-expand-the-brand-campaign">5. Do you have a strategic reason to expand the brand campaign?</h3>



<p>If so, test carefully using experiments. That&#8217;s a business decision, not a checkbox recommendation from a rep who hasn&#8217;t looked deeply enough at the account to understand where the real opportunities actually are.</p>



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<h2 class="wp-block-heading" id="h-ai-max-only-works-as-well-as-the-signals-feeding-it">AI Max only works as well as the signals feeding it</h2>



<p>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.</p>



<p>But automation only works as well as the signals feeding it. Right now, the issue is that the foundations underneath the automation still aren&#8217;t strong enough. Better conversion frameworks, measurement, account structure, and feedback loops make automation smarter.</p>



<p>If brand remains the best-performing campaign in the account, the bigger question is why the rest of the account hasn&#8217;t caught up yet.&nbsp;</p>



<p>Above all else, don&#8217;t confuse Google&#8217;s automation priorities with your account priorities.</p>

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.
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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 typeAI Overview eligibleQuery controlBest use case

Exact matchNoHighestDefensive brand
Phrase matchNoMediumControlled intent expansion
Broad matchYesLowerGeneric scaling
Performance MaxYesLowCross-network automation
AI MaxYesLowestMature 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.
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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.
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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.

AI Mode eligibility isn't enough reason to expand automation. Learn where to focus before testing AI Max on brand traffic.

Why your brand campaign may not be ready for AI Max

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png" class="attachment-large size-large wp-post-image" alt="Google Ads may be over-crediting your conversions- A 7-day test tells a different story" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is giving advertisers more ways to evaluate creative performance and make data-driven optimization decisions.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png" class="attachment-large size-large wp-post-image" alt="Google Ads may be over-crediting your conversions- A 7-day test tells a different story" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>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.</p>



<p><strong>What&#8217;s happening</strong>. Google is rolling out new asset experiments for Performance Max campaigns, allowing advertisers to test how different creative assets affect results.</p>



<p>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.</p>



<p>Advertisers will also be able to test assets generated through Google&#8217;s Asset Studio.</p>



<p><strong>The big picture</strong>. Performance Max has long automated campaign optimization across Google&#8217;s inventory, but advertisers have had limited visibility into the impact of creative changes.</p>



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



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



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



<p><strong>What else is new:</strong>



<ul class="wp-block-list">
<li>Conversion lift studies and experiments are being brought together under one Experiments page.</li>



<li>Additional experiment and measurement capabilities are planned for future releases.</li>



<li>Expanded support for manager accounts (MCCs) and the Google Ads API is expected to begin rolling out in the coming weeks.</li>
</ul>



<p><strong>Why we care.</strong> 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.</p>



<p><strong>What to watch.</strong> 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.</p>



<p><strong>The bottom line</strong>. <a href="https://business.google.com/us/accelerate/announcements/scale-results-with-new-asset-experiments-in-performance-max/">Google is giving Performance Max advertisers more sophisticated testing capabilities</a>, making it easier to evaluate creative changes, measure results across multiple KPIs, and manage experiments from a centralized location.</p>



<p><strong>First spotted. </strong>The update was first spotted by PPC News Feed. </p>


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.

Google is giving advertisers more ways to evaluate creative performance and make data-driven optimization decisions.

Google adds new Performance Max asset testing tools

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png" class="attachment-large size-large wp-post-image" alt="OpenAI ChatGPT ad platform" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png 1920w, https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>OpenAI is expanding ChatGPT ads to five new markets including the UK, &#038; introducing new campaign management features.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png" class="attachment-large size-large wp-post-image" alt="OpenAI ChatGPT ad platform" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png 1920w, https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>OpenAI is expanding its advertising ambitions inside ChatGPT, beginning an early test that allows multiple advertisers to appear within a single ad placement.</p>



<p><strong>What&#8217;s happening</strong>. The company is testing multi-advertiser ad units across a small subset of ChatGPT ads, according to a product update sent to advertisers.</p>



<p>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.</p>



<p>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.</p>



<p><strong>Meanwhile, in Ads Manager Beta</strong>. OpenAI also announced several new campaign management features for advertisers:</p>



<ul class="wp-block-list">
<li>Advertisers can now convert existing campaigns from lifetime budgets to daily budgets.</li>



<li>CPM campaigns can be cloned and converted to CPC bidding with one click.</li>



<li>Impression-based campaigns now support custom CPM max bids.</li>



<li>Bulk editing is available directly within the Ads Manager interface.</li>



<li>Daily budgets will transition to an average daily budget model with weekly pacing flexibility.</li>



<li>Geographic targeting is expanding beyond the U.S., Canada, Australia, and New Zealand to include the U.K., Japan, South Korea, Brazil, and Mexico.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="539" height="990" src="https://searchengineland.com/wp-content/seloads/2026/06/chatgpt-expandsion.jpeg" alt="" class="wp-image-479657"/></figure>



<p><strong>Why we care.</strong> The updates bring OpenAI&#8217;s ad platform closer to the functionality marketers expect from mature advertising ecosystems, reducing campaign management friction while expanding targeting opportunities internationally.</p>



<p><strong>What to watch</strong>. 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&#8217;s ad inventory strategy while offering advertisers more opportunities to reach users during purchase and research journeys.</p>



<p><strong>The bottom line</strong>. 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.</p>


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.

OpenAI is expanding ChatGPT ads to five new markets including the UK, & introducing new campaign management features.

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

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg" class="attachment-large size-large wp-post-image" alt="Google Local Services Ads vs. Search Ads- Which drives better local leads?" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google will update its Local Services Ads policies on July 6, renaming them as "requirements" and aligning advertiser guidelines.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg" class="attachment-large size-large wp-post-image" alt="Google Local Services Ads vs. Search Ads- Which drives better local leads?" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is changing the rules framework that governs Local Services Ads, updating policy language and aligning advertiser requirements with its new badge system.</p>



<p><strong>What&#8217;s happening</strong>. On July 6th Google will update its Local Services Ads policies to improve readability, revise terminology, and remove requirements that no longer apply to advertisers.</p>



<p>As part of the update, Google will rename &#8220;Local Services platform policies&#8221; as &#8220;Local Services Ads requirements.&#8221;</p>



<p>The changes build on the company&#8217;s recent overhaul of the Local Services Ads badge system, including updates to Google Guarantee badges and advertiser verification standards.</p>



<p><strong>Why we care. </strong>While these changes are mostly administrative, advertisers should pay attention because the new &#8220;requirements&#8221; framework could make it easier for Google to tie compliance standards directly to badge status in the future. For agencies and local businesses, it&#8217;s another indication that maintaining verification credentials and meeting platform standards will remain critical for competing in LSAs.</p>



<p><strong>The big picture</strong>. Google says the policy refresh is intended to better align advertiser requirements with the new badge framework while making compliance guidance easier to understand.</p>



<p>The company is not positioning the update as a major policy crackdown. Instead, the focus appears to be on simplifying existing rules and modernizing the way requirements are communicated to businesses.</p>



<p><strong>The bottom line</strong>. <a href="https://support.google.com/adspolicy/answer/6245891">Google is refreshing the policy framework for Local Services Ads</a>, replacing &#8220;platform policies&#8221; with &#8220;requirements&#8221; and aligning advertiser guidance with a new badge-driven approach to trust and eligibility.</p>


Google is changing the rules framework that governs Local Services Ads, updating policy language and aligning advertiser requirements with its new badge system.
What’s happening. On July 6th Google will update its Local Services Ads policies to improve readability, revise terminology, and remove requirements that no longer apply to advertisers.
As part of the update, Google will rename “Local Services platform policies” as “Local Services Ads requirements.”
The changes build on the company’s recent overhaul of the Local Services Ads badge system, including updates to Google Guarantee badges and advertiser verification standards.
Why we care. While these changes are mostly administrative, advertisers should pay attention because the new “requirements” framework could make it easier for Google to tie compliance standards directly to badge status in the future. For agencies and local businesses, it’s another indication that maintaining verification credentials and meeting platform standards will remain critical for competing in LSAs.
The big picture. Google says the policy refresh is intended to better align advertiser requirements with the new badge framework while making compliance guidance easier to understand.
The company is not positioning the update as a major policy crackdown. Instead, the focus appears to be on simplifying existing rules and modernizing the way requirements are communicated to businesses.
The bottom line. Google is refreshing the policy framework for Local Services Ads, replacing “platform policies” with “requirements” and aligning advertiser guidance with a new badge-driven approach to trust and eligibility.

Google will update its Local Services Ads policies on July 6, renaming them as "requirements" and aligning advertiser guidelines.

Google to update Local Services Ads policies in July

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png" class="attachment-large size-large wp-post-image" alt="The problem with AI share of voice and 3 metrics that matter more" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Many AI visibility platforms extrapolate from a small subset of prompts. Explore three metrics designed for an infinite-query environment.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png" class="attachment-large size-large wp-post-image" alt="The problem with AI share of voice and 3 metrics that matter more" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Traditional <a href="https://searchengineland.com/guides/share-of-voice">share of voice</a> (SOV) is effectively obsolete, yet many organizations have replaced it with an equally flawed successor: AI share of voice.</p>



<p>Software vendors now claim to measure brand visibility across ChatGPT, Gemini, Claude, Perplexity, and other AI platforms using a single percentage score. The problem is that these metrics rely on a hidden denominator. </p>



<p>Unlike traditional search, where visibility could be measured against a known keyword set, the universe of possible AI prompts is effectively infinite.</p>



<p>Traditional SOV had limitations, but at least its assumptions were transparent. Marketers defined a fixed keyword set, tracked visibility against competitors, and used that list as a stable denominator. Everyone understood the measurement&#8217;s boundaries.</p>



<p>That model no longer exists. Search results are dynamic and personalized, and are increasingly being replaced by conversational interfaces. Yet many AI visibility platforms continue to present precise-looking percentages that can&#8217;t be audited or validated.</p>



<p>To stop presenting fictional metrics to leadership teams, we must rethink how we define and measure visibility in AI search.</p>



<h2 class="wp-block-heading" id="h-why-traditional-sov-metrics-now-fail">Why traditional SOV metrics now fail</h2>



<p>The basic assumptions of search engine optimization and digital brand tracking have been broken by two major shifts: the disappearance of the static results page and the rapid rise of personalized, conversational answers.</p>



<p>Search engines have become highly dynamic, personalized landscapes that change shape continuously based on real-time data.</p>



<p>Between AI-generated summaries, localized results, continuous scrolling, interactive merchant grids, and real-time social feeds, no two users will encounter the same interface, even when entering the exact same query at the exact same moment. </p>



<p>Because the search environment changes constantly, attempting to calculate a precise &#8220;share&#8221; of that screen has become a mathematical impossibility.</p>



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<h2 class="wp-block-heading" id="h-the-new-volatile-normality-of-rankings">The new volatile normality of rankings</h2>



<p>Securing the top ranking position in the older marketing model meant capturing a highly predictable percentage of user click-through rates.</p>



<p>In the modern search landscape, however, ranking first organically might place a brand below several sponsored listings, an AI-generated overview, interactive question accordions, and featured discussions from community platforms.</p>



<p>Because search engines now construct layouts dynamically in response to immediate user intent and past search history, rankings fluctuate by the hour.</p>



<p>Measuring share of voice based on static positions is as unproductive as trying to measure the volume of an ocean wave with a wooden ruler.</p>



<h2 class="wp-block-heading" id="h-the-modern-ai-share-of-voice">The modern AI share of voice</h2>



<p>When marketing teams realized that traditional rank tracking was losing its utility, software vendors quickly introduced alternative metrics, branded as LLM Visibility or AI share of voice.</p>



<p>These dashboards present highly polished, authoritative percentage scores that suggest a brand&#8217;s footprint has been successfully mapped across platforms like ChatGPT, Claude, Gemini, and Perplexity.</p>



<p>These tools fail to deliver on this promise, exposing a fundamental methodology problem that we must address directly.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Legacy tracking (transparent)</strong></td><td><strong>LLM visibility (black box)</strong></td></tr><tr><td>– Define fixed keyword list (known).<br>– Measure rank on static SERPAuditable denominator.</td><td>– Infinite possible user prompts.<br>– Vendor runs small, arbitrary subset.<br>– Subjective denominator.</td></tr></tbody></table></figure>



<h2 class="wp-block-heading" id="h-the-infinite-tail">The infinite tail</h2>



<p>Legacy <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> tools relied on a user-defined keyword list that served as a transparent denominator, whereas modern conversational engines present an entirely different mathematical reality where the universe of possible user prompts is effectively infinite.</p>



<p>Buyers no longer search for solutions using simple, two-word phrases. Instead, they enter highly specific, conversational queries that describe their exact organizational context, integration needs, and feature requirements.</p>



<p>Because no marketing tool can realistically sample this infinite universe of natural language, software vendors instead select a small, arbitrary subset of static prompts, run them through AI models behind the scenes, and aggregate those limited outputs into a representative global percentage.</p>



<p>This process creates a metric that only measures share of voice within a contrived and artificial environment, presenting a closed sandbox as if it were the open web.</p>



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<h2 class="wp-block-heading" id="h-the-issue-with-black-box-metrics">The issue with black-box metrics</h2>



<p>Marketers maintained full visibility into the data they were analyzing with legacy tracking tools, which meant that if a system reported a specific percentage of visibility, the underlying keyword list could be audited and adjusted. Modern LLM visibility tools obscure their denominator within proprietary, vendor-defined systems that are almost certainly incomplete.</p>



<p>This structural flaw became incredibly clear in September 2025, when OpenAI updated to its ChatGPT 5.0 model. Following this release, the platform-wide volume of outbound citations and source links dropped.</p>



<p>For marketing teams relying on LLM tracking dashboards, this model change resulted in a sudden, sharp decline in their reported visibility metrics. The decline had nothing to do with a loss of brand relevance or a failure in marketing strategy. ChatGPT had simply changed how it presented source data to users.</p>



<p>This update demonstrates that modern AI metrics are highly volatile and largely out of your control. While software vendors are genuinely trying to solve an incredibly complex engineering problem, the underlying methodology simply cannot support the high-confidence dashboards they deliver, meaning these metrics should be treated as directional signals rather than hard numbers.</p>



<h2 class="wp-block-heading" id="h-beyond-ai-share-of-voice-3-metrics-that-matter-more">Beyond AI share of voice: 3 metrics that matter more</h2>



<p>We must shift our focus from measuring pure search volume to measuring how effectively a brand is integrated into the broader context of digital discussions.</p>



<p>As search queries morph into conversational discovery, a brand&#8217;s visibility is no longer defined by the keywords it owns, but by how deeply it is embedded in the conceptual models used by AI.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1024" height="559" src="https://searchengineland.com/wp-content/seloads/2026/06/The-modern-brand-visibility-trial.png" alt="The modern brand visibility trial" class="wp-image-479613" srcset="https://searchengineland.com/wp-content/seloads/2026/06/The-modern-brand-visibility-trial.png 1024w, https://searchengineland.com/wp-content/seloads/2026/06/The-modern-brand-visibility-trial-768x419.png 768w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>
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<h3 class="wp-block-heading" id="h-1-share-of-mentions">1. Share of mentions</h3>



<p>AI models synthesize relationships between concepts rather than simply indexing pages, meaning a brand must exist within the model&#8217;s training data, fine-tuning datasets, or real-time retrieval sources to be surfaced at all.</p>



<p>Share of mentions tracks how frequently your brand name, products, or key executives are naturally included in the responses generated across the broader information ecosystem.</p>



<p>This metric shifts the operational focus from ranking positions to vocabulary inclusion, ensuring that a brand is recognized by the model even when it is not explicitly prompted for a vendor list.</p>



<p>To influence this metric, organizations must focus on securing organic mentions across high-trust forums, developer communities, and authoritative industry publications where AI models actively gather and update their information.</p>



<h3 class="wp-block-heading" id="h-2-share-of-recommendations">2. Share of recommendations</h3>



<p>When buyers use conversational engines to make purchasing decisions, they regularly ask for direct comparisons, shortlists, and product recommendations to simplify their research process.</p>



<p>Share of recommendations measures how often your product or service is explicitly featured when a user asks an AI engine to act as an advisor on a specific business challenge.</p>



<p>This approach shifts our focus from raw traffic acquisition to winning the buyer&#8217;s consideration set, which is critical because conversational engines filter out the noise of the web to deliver a highly curated list of options.</p>



<p>If your product positioning is overly generic, the model will struggle to categorize your offering and will default to recommending competitors that have established a much clearer, highly documented use case.</p>



<h3 class="wp-block-heading" id="h-3-share-of-narrative">3. Share of narrative</h3>



<p>Merely securing a mention in an AI response is insufficient if the context of that mention portrays your brand poorly, as high visibility within a negative framework can quickly become a strategic liability.</p>



<p>Share of narrative measures the qualitative attributes, adjectives, and associations linked to your brand name in conversational outputs, allowing you to understand how your business is being framed.</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Narrative</strong></td><td><strong>What it tracks</strong></td><td><strong>The core strategic question</strong></td></tr><tr><td>The “best” narrative</td><td>How often you are framed as the premium, gold-standard market leader.</td><td>Is the model positioning our brand as the most capable solution available?</td></tr><tr><td>The “popular” narrative</td><td>How often you are cited as the default, widely adopted industry standard.</td><td>Is the model identifying our brand as the most commonly used option?</td></tr><tr><td>The “budget” narrative</td><td>How often you are categorized as the cost-effective, value, or entry-level alternative.</td><td>Is the model framing our brand primarily as a low-cost, entry-level alternative?</td></tr></tbody></table></figure>



<p>If an AI engine includes your brand frequently but consistently describes your product as a complex, legacy system, your high share of voice may actually be damaging your sales pipeline.</p>



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<h2 class="wp-block-heading" id="h-reframing-your-success-metrics">Reframing your success metrics</h2>



<p>Leadership teams require competitive benchmarks to evaluate market performance, meaning you cannot simply stop reporting on share of voice without offering a viable alternative.</p>



<p>Transitioning your executive reporting smoothly requires a structured, three-step plan.</p>



<p>Reframing the executive narrative involves educating your leadership team on the limitations of modern AI dashboards.</p>



<p>This means explaining the hidden denominator problem and demonstrating why treating these figures as absolute metrics introduces unnecessary risk.</p>

Traditional share of voice (SOV) is effectively obsolete, yet many organizations have replaced it with an equally flawed successor: AI share of voice.
Software vendors now claim to measure brand visibility across ChatGPT, Gemini, Claude, Perplexity, and other AI platforms using a single percentage score. The problem is that these metrics rely on a hidden denominator. 
Unlike traditional search, where visibility could be measured against a known keyword set, the universe of possible AI prompts is effectively infinite.
Traditional SOV had limitations, but at least its assumptions were transparent. Marketers defined a fixed keyword set, tracked visibility against competitors, and used that list as a stable denominator. Everyone understood the measurement’s boundaries.
That model no longer exists. Search results are dynamic and personalized, and are increasingly being replaced by conversational interfaces. Yet many AI visibility platforms continue to present precise-looking percentages that can’t be audited or validated.
To stop presenting fictional metrics to leadership teams, we must rethink how we define and measure visibility in AI search.
Why traditional SOV metrics now fail
The basic assumptions of search engine optimization and digital brand tracking have been broken by two major shifts: the disappearance of the static results page and the rapid rise of personalized, conversational answers.
Search engines have become highly dynamic, personalized landscapes that change shape continuously based on real-time data.
Between AI-generated summaries, localized results, continuous scrolling, interactive merchant grids, and real-time social feeds, no two users will encounter the same interface, even when entering the exact same query at the exact same moment. 
Because the search environment changes constantly, attempting to calculate a precise “share” of that screen has become a mathematical impossibility.
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The new volatile normality of rankings
Securing the top ranking position in the older marketing model meant capturing a highly predictable percentage of user click-through rates.
In the modern search landscape, however, ranking first organically might place a brand below several sponsored listings, an AI-generated overview, interactive question accordions, and featured discussions from community platforms.
Because search engines now construct layouts dynamically in response to immediate user intent and past search history, rankings fluctuate by the hour.
Measuring share of voice based on static positions is as unproductive as trying to measure the volume of an ocean wave with a wooden ruler.
The modern AI share of voice
When marketing teams realized that traditional rank tracking was losing its utility, software vendors quickly introduced alternative metrics, branded as LLM Visibility or AI share of voice.
These dashboards present highly polished, authoritative percentage scores that suggest a brand’s footprint has been successfully mapped across platforms like ChatGPT, Claude, Gemini, and Perplexity.
These tools fail to deliver on this promise, exposing a fundamental methodology problem that we must address directly.

Legacy tracking (transparent)LLM visibility (black box)
– Define fixed keyword list (known).
– Measure rank on static SERPAuditable denominator.– Infinite possible user prompts.
– Vendor runs small, arbitrary subset.
– Subjective denominator.





The infinite tail
Legacy SEO tools relied on a user-defined keyword list that served as a transparent denominator, whereas modern conversational engines present an entirely different mathematical reality where the universe of possible user prompts is effectively infinite.
Buyers no longer search for solutions using simple, two-word phrases. Instead, they enter highly specific, conversational queries that describe their exact organizational context, integration needs, and feature requirements.
Because no marketing tool can realistically sample this infinite universe of natural language, software vendors instead select a small, arbitrary subset of static prompts, run them through AI models behind the scenes, and aggregate those limited outputs into a representative global percentage.
This process creates a metric that only measures share of voice within a contrived and artificial environment, presenting a closed sandbox as if it were the open web.
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The issue with black-box metrics
Marketers maintained full visibility into the data they were analyzing with legacy tracking tools, which meant that if a system reported a specific percentage of visibility, the underlying keyword list could be audited and adjusted. Modern LLM visibility tools obscure their denominator within proprietary, vendor-defined systems that are almost certainly incomplete.
This structural flaw became incredibly clear in September 2025, when OpenAI updated to its ChatGPT 5.0 model. Following this release, the platform-wide volume of outbound citations and source links dropped.
For marketing teams relying on LLM tracking dashboards, this model change resulted in a sudden, sharp decline in their reported visibility metrics. The decline had nothing to do with a loss of brand relevance or a failure in marketing strategy. ChatGPT had simply changed how it presented source data to users.
This update demonstrates that modern AI metrics are highly volatile and largely out of your control. While software vendors are genuinely trying to solve an incredibly complex engineering problem, the underlying methodology simply cannot support the high-confidence dashboards they deliver, meaning these metrics should be treated as directional signals rather than hard numbers.
Beyond AI share of voice: 3 metrics that matter more
We must shift our focus from measuring pure search volume to measuring how effectively a brand is integrated into the broader context of digital discussions.
As search queries morph into conversational discovery, a brand’s visibility is no longer defined by the keywords it owns, but by how deeply it is embedded in the conceptual models used by AI.




1. Share of mentions
AI models synthesize relationships between concepts rather than simply indexing pages, meaning a brand must exist within the model’s training data, fine-tuning datasets, or real-time retrieval sources to be surfaced at all.
Share of mentions tracks how frequently your brand name, products, or key executives are naturally included in the responses generated across the broader information ecosystem.
This metric shifts the operational focus from ranking positions to vocabulary inclusion, ensuring that a brand is recognized by the model even when it is not explicitly prompted for a vendor list.
To influence this metric, organizations must focus on securing organic mentions across high-trust forums, developer communities, and authoritative industry publications where AI models actively gather and update their information.
2. Share of recommendations
When buyers use conversational engines to make purchasing decisions, they regularly ask for direct comparisons, shortlists, and product recommendations to simplify their research process.
Share of recommendations measures how often your product or service is explicitly featured when a user asks an AI engine to act as an advisor on a specific business challenge.
This approach shifts our focus from raw traffic acquisition to winning the buyer’s consideration set, which is critical because conversational engines filter out the noise of the web to deliver a highly curated list of options.
If your product positioning is overly generic, the model will struggle to categorize your offering and will default to recommending competitors that have established a much clearer, highly documented use case.
3. Share of narrative
Merely securing a mention in an AI response is insufficient if the context of that mention portrays your brand poorly, as high visibility within a negative framework can quickly become a strategic liability.
Share of narrative measures the qualitative attributes, adjectives, and associations linked to your brand name in conversational outputs, allowing you to understand how your business is being framed.

NarrativeWhat it tracksThe core strategic question
The “best” narrativeHow often you are framed as the premium, gold-standard market leader.Is the model positioning our brand as the most capable solution available?
The “popular” narrativeHow often you are cited as the default, widely adopted industry standard.Is the model identifying our brand as the most commonly used option?
The “budget” narrativeHow often you are categorized as the cost-effective, value, or entry-level alternative.Is the model framing our brand primarily as a low-cost, entry-level alternative?





If an AI engine includes your brand frequently but consistently describes your product as a complex, legacy system, your high share of voice may actually be damaging your sales pipeline.
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Reframing your success metrics
Leadership teams require competitive benchmarks to evaluate market performance, meaning you cannot simply stop reporting on share of voice without offering a viable alternative.
Transitioning your executive reporting smoothly requires a structured, three-step plan.
Reframing the executive narrative involves educating your leadership team on the limitations of modern AI dashboards.
This means explaining the hidden denominator problem and demonstrating why treating these figures as absolute metrics introduces unnecessary risk.

Many AI visibility platforms extrapolate from a small subset of prompts. Explore three metrics designed for an infinite-query environment.

The problem with AI share of voice and 3 metrics that matter more

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png" class="attachment-large size-large wp-post-image" alt="Google AI Brief may be the replacement keywords never had" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>After 15 years of premature obituaries, the keyword may finally have a replacement. Here's why Google AI Brief matters.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png" class="attachment-large size-large wp-post-image" alt="Google AI Brief may be the replacement keywords never had" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google. </p>



<p>Advertisers weren&#8217;t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn&#8217;t disappear until Google had something better to replace it.</p>



<p>At <a href="https://searchengineland.com/google-marketing-live-2026-everything-you-need-to-know-478167">Google Marketing Live</a> (GML) last month, we may have seen that replacement. <a href="https://blog.google/products/ads-commerce/ai-max-new-features/" target="_blank" rel="noopener">AI Brief</a> is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.</p>



<p>At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn&#8217;t advertisers simply ignore it and stick with keywords?</p>



<p>Probably not.</p>



<p>When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.</p>



<p>Consider the other announcements from GML. <a href="https://blog.google/products-and-platforms/products/search/search-io-2026/" target="_blank" rel="noopener">AI Mode</a> surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.</p>



<p>Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.</p>



<p>With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword&#8217;s obituary much easier to believe.</p>



<h2 class="wp-block-heading" id="h-the-keyword-is-dying-because-users-stopped-using-it">The keyword is dying because users stopped using it</h2>



<p>Most &#8220;keywords are dead&#8221; arguments over the past decade were supply-side stories. Google reduced broad match&#8217;s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.</p>



<p>Now it&#8217;s different. The pressure is coming from the demand side. </p>



<p>People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The <a href="https://searchengineland.com/googles-new-intelligent-search-box-its-biggest-change-to-the-search-box-in-25-years-477968">new search box, the biggest upgrade in 25 years</a>, dynamically expands as you type. You no longer pick a &#8220;mode&#8221; before you ask. The interface itself is telling consumers that &#8220;running shoes&#8221; is no longer the only way to ask for what they really want.</p>



<p>If you&#8217;re an advertiser, the question stops being &#8220;Do I want to use keywords?&#8221; It becomes &#8220;How do I show up in a query a keyword can&#8217;t possibly match?&#8221; Trying to capture a paragraph of context with three positive match types and one negative is, let&#8217;s be real, increasingly absurd.</p>



<p><a href="https://www.optmyzr.com/blog/google-ads-match-type-performance/" target="_blank" rel="noopener">Optmyzr&#8217;s 2026 Match Type Study</a> shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. <em>(</em><strong><em>Disclosure: </em></strong><em>I’m the cofounder and CEO of Optmyzr.)</em>



<p>Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget.&nbsp;</p>



<p>Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments.&nbsp;</p>



<p>Advertisers are clearly growing more comfortable trusting Google&#8217;s AI with broader targeting, a shift attributed to Smart Bidding&#8217;s maturation rather than exact match losing its performance edge.</p>



<p>The other tell is that Google isn&#8217;t alone here. We recently started managing ads on ChatGPT, and <a href="https://openai.com/index/testing-ads-in-chatgpt/" target="_blank" rel="noopener">OpenAI&#8217;s ad surface</a> is keyword-optional from day one.&nbsp;</p>



<p>When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we&#8217;re just arguing about how fast the keyword is dying.</p>



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<h2 class="wp-block-heading" id="h-ai-brief-is-a-technical-replacement-for-keywords">AI Brief is a technical replacement for keywords</h2>



<p>Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google&#8217;s CEO, even said that Google&#8217;s migration to become an <a href="https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/cloud-next-2026-sundar-pichai/" target="_blank" rel="noopener">AI-first company</a> was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.</p>



<p>AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn&#8217;t say, the searches you want to capture or avoid, and the audience you&#8217;re trying to reach.&nbsp;</p>



<p>Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you&#8217;d tell a new media buyer on their first day.</p>



<p>Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it&#8217;s off. You iterate. When you&#8217;re happy, you lock in the brief.</p>



<p>That&#8217;s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.</p>



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<p>There&#8217;s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech.&nbsp;</p>



<p>Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.</p>



<p>This is what Sam Altman called <a href="https://interestingengineering.com/innovation/openai-chatgpt-5-software-on-demand" target="_blank" rel="noopener">&#8220;software on demand&#8221; at the GPT-5 launch</a>, the idea that AI can &#8220;instantaneously create an entire piece of computer software for you.&#8221; </p>



<p>Google echoed the same vision at <a href="https://blog.google/innovation-and-ai/sundar-pichai-io-2026/" target="_blank" rel="noopener">I/O 2026</a>, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.</p>



<p>People need to be purposeful about work. Your purpose at work isn&#8217;t to write emails and work with spreadsheets. It&#8217;s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.</p>



<p>Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”</p>



<h2 class="wp-block-heading" id="h-how-to-try-ai-brief-now">How to try AI brief now</h2>



<p>AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines.&nbsp;</p>



<p>So yes, this is starting as an AI Max feature, and you may not be using AI Max because <a href="https://www.linkedin.com/posts/frederickvallaeys_ppc-google-googleads-activity-7463423763141844993-5qAU?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAANNEYBt9NB1q5YjxuhKuNA_mWp-rL1BQc" target="_blank" rel="noopener">several practitioners note</a> that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.</p>



<p>The practical playbook shared during a recent <a href="https://www.youtube.com/watch?v=FnIwHT0VuD8&amp;t=5m30s" target="_blank" rel="noopener">PPC Town Hall</a> is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data.&nbsp;</p>



<p>With the advent of AI Brief&#8217;s matching guidelines, advertisers can further tweak their targeting by saying, &#8220;prioritize searches for X, avoid Y.&#8221; But this strategy still requires a human who knows the account to pull that lever. So don&#8217;t unplug your keyboard just yet.</p>



<h2 class="wp-block-heading" id="h-the-new-funnel-and-why-short-keywords-still-have-a-job">The new funnel, and why short keywords still have a job</h2>



<p>Andrew Lolk and Kirk Williams <a href="https://www.linkedin.com/feed/update/urn:li:activity:7463423763141844993?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7463423763141844993%2C7463579684237266945%29&amp;dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287463579684237266945%2Curn%3Ali%3Aactivity%3A7463423763141844993%29" target="_blank" rel="noopener">pushed me</a> on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing &#8220;newborn photographer&#8221; and converting on the first ad that shows.</p>



<p>Short, transactional queries won&#8217;t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it&#8217;s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.</p>



<p>Here&#8217;s how I see it shaping up:</p>



<ul class="wp-block-list">
<li><strong>AI prompts for discovery:</strong> &#8220;I just had a baby and I want to remember this period. What are some ideas?&#8221;</li>



<li><strong>AI prompts for research:</strong> &#8220;Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.&#8221;</li>



<li><strong>Short keyword to buy:</strong> &#8220;Newborn photographer Los Altos.&#8221;</li>
</ul>



<p>If you only show up at the bottom of that funnel, you&#8217;re betting your entire business on being the first short-keyword click. If you&#8217;re not present in the discovery and research prompts above it, you&#8217;re not in the consideration mix when the short query happens.&nbsp;</p>



<p>The reason a user may do that short query is that they already know more or less who they&#8217;d buy from, and they&#8217;re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.</p>



<p>This is also why I don&#8217;t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working.&nbsp;</p>



<p>Further, AI-first campaign types will soon be great at converting agentically, using the <a href="https://searchengineland.com/google-universal-commerce-protocol-ecommerce-seo-470460">Universal Commerce Protocol</a> and other new methods being developed to allow agents to transact for their humans.</p>



<h2 class="wp-block-heading" id="h-what-ai-brief-does-to-the-four-human-ppc-roles">What AI Brief does to the four human PPC roles</h2>



<p>I&#8217;ve argued for years that PPC pros take on four roles in an automated world:&nbsp;</p>



<ul class="wp-block-list">
<li>Teacher.</li>



<li>Doctor.</li>



<li>Pilot.</li>



<li>Restaurateur.&nbsp;</li>
</ul>



<p>These roles continue to explain the PPC manager&#8217;s world quite well, but with some new nuance.</p>



<h3 class="wp-block-heading" id="h-the-teacher-nbsp">The teacher&nbsp;</h3>



<p>This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword.&nbsp;</p>



<p>The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output.&nbsp;</p>



<p>AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.</p>



<h3 class="wp-block-heading" id="h-the-doctor">The doctor</h3>



<p>The shift is from &#8220;prescribe Drug X&#8221; to writing down, in structured language, what the patient actually needs.&nbsp;</p>



<p>The treatment can then evolve as the patient&#8217;s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription.&nbsp;</p>



<p>Briefs and prompts allow freedom and evolution. That&#8217;s what good medicine looks like, and that&#8217;s what good targeting looks like now.</p>



<h3 class="wp-block-heading" id="h-the-pilot">The pilot</h3>



<p>We need a new instrument panel. If we&#8217;re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent.&nbsp;</p>



<p>We&#8217;ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.</p>



<h3 class="wp-block-heading" id="h-the-restaurateur-nbsp">The restaurateur&nbsp;</h3>



<p>You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief.&nbsp;</p>



<p>You define the cuisine, the values, the things the chef must never serve, and the kind of guest you&#8217;re cooking for. Then you taste, correct, and iterate. The kitchen runs.</p>



<p>If you want the longer-form version of where I think digital marketing automation is heading, I wrote it up earlier this year as <a href="https://searchengineland.com/ai-skills-the-next-layer-of-marketing-automation-477308">AI skills, the next layer of marketing automation</a>.</p>



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<h2 class="wp-block-heading" id="h-why-ai-brief-feels-different">Why AI Brief feels different</h2>



<p>The keyword isn&#8217;t dying because Google decided to kill it. It&#8217;s dying because consumers stopped phrasing their needs in a couple of words.</p>



<p>AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That&#8217;s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.</p>



<p>Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.</p>



<p>We don&#8217;t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does.&nbsp;</p>



<p>Push back. Lock it in. Then you can move on to the parts of the job a machine can&#8217;t do, like knowing your customers and working on the goals that move their business in the direction they want.</p>

People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google. 
Advertisers weren’t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn’t disappear until Google had something better to replace it.
At Google Marketing Live (GML) last month, we may have seen that replacement. AI Brief is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.
At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn’t advertisers simply ignore it and stick with keywords?
Probably not.
When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.
Consider the other announcements from GML. AI Mode surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.
Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.
With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword’s obituary much easier to believe.
The keyword is dying because users stopped using it
Most “keywords are dead” arguments over the past decade were supply-side stories. Google reduced broad match’s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.
Now it’s different. The pressure is coming from the demand side. 
People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The new search box, the biggest upgrade in 25 years, dynamically expands as you type. You no longer pick a “mode” before you ask. The interface itself is telling consumers that “running shoes” is no longer the only way to ask for what they really want.
If you’re an advertiser, the question stops being “Do I want to use keywords?” It becomes “How do I show up in a query a keyword can’t possibly match?” Trying to capture a paragraph of context with three positive match types and one negative is, let’s be real, increasingly absurd.
Optmyzr’s 2026 Match Type Study shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. (Disclosure: I’m the cofounder and CEO of Optmyzr.)



Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget. 
Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments. 
Advertisers are clearly growing more comfortable trusting Google’s AI with broader targeting, a shift attributed to Smart Bidding’s maturation rather than exact match losing its performance edge.
The other tell is that Google isn’t alone here. We recently started managing ads on ChatGPT, and OpenAI’s ad surface is keyword-optional from day one. 
When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we’re just arguing about how fast the keyword is dying.

  
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AI Brief is a technical replacement for keywords
Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google’s CEO, even said that Google’s migration to become an AI-first company was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.
AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn’t say, the searches you want to capture or avoid, and the audience you’re trying to reach. 
Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you’d tell a new media buyer on their first day.
Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it’s off. You iterate. When you’re happy, you lock in the brief.
That’s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.
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There’s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech. 
Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.
This is what Sam Altman called “software on demand” at the GPT-5 launch, the idea that AI can “instantaneously create an entire piece of computer software for you.” 
Google echoed the same vision at I/O 2026, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.
People need to be purposeful about work. Your purpose at work isn’t to write emails and work with spreadsheets. It’s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.
Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”
How to try AI brief now
AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines. 
So yes, this is starting as an AI Max feature, and you may not be using AI Max because several practitioners note that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.
The practical playbook shared during a recent PPC Town Hall is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data. 
With the advent of AI Brief’s matching guidelines, advertisers can further tweak their targeting by saying, “prioritize searches for X, avoid Y.” But this strategy still requires a human who knows the account to pull that lever. So don’t unplug your keyboard just yet.
The new funnel, and why short keywords still have a job
Andrew Lolk and Kirk Williams pushed me on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing “newborn photographer” and converting on the first ad that shows.
Short, transactional queries won’t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it’s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.
Here’s how I see it shaping up:
AI prompts for discovery: “I just had a baby and I want to remember this period. What are some ideas?”
AI prompts for research: “Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.”
Short keyword to buy: “Newborn photographer Los Altos.”
If you only show up at the bottom of that funnel, you’re betting your entire business on being the first short-keyword click. If you’re not present in the discovery and research prompts above it, you’re not in the consideration mix when the short query happens. 
The reason a user may do that short query is that they already know more or less who they’d buy from, and they’re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.
This is also why I don’t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working. 
Further, AI-first campaign types will soon be great at converting agentically, using the Universal Commerce Protocol and other new methods being developed to allow agents to transact for their humans.
What AI Brief does to the four human PPC roles
I’ve argued for years that PPC pros take on four roles in an automated world: 
Teacher.
Doctor.
Pilot.
Restaurateur. 
These roles continue to explain the PPC manager’s world quite well, but with some new nuance.
The teacher 
This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword. 
The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output. 
AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.
The doctor
The shift is from “prescribe Drug X” to writing down, in structured language, what the patient actually needs. 
The treatment can then evolve as the patient’s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription. 
Briefs and prompts allow freedom and evolution. That’s what good medicine looks like, and that’s what good targeting looks like now.
The pilot
We need a new instrument panel. If we’re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent. 
We’ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.
The restaurateur 
You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief. 
You define the cuisine, the values, the things the chef must never serve, and the kind of guest you’re cooking for. Then you taste, correct, and iterate. The kitchen runs.
If you want the longer-form version of where I think digital marketing automation is heading, I wrote it up earlier this year as AI skills, the next layer of marketing automation.

  
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Why AI Brief feels different
The keyword isn’t dying because Google decided to kill it. It’s dying because consumers stopped phrasing their needs in a couple of words.
AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That’s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.
Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.
We don’t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does. 
Push back. Lock it in. Then you can move on to the parts of the job a machine can’t do, like knowing your customers and working on the goals that move their business in the direction they want.

After 15 years of premature obituaries, the keyword may finally have a replacement. Here's why Google AI Brief matters.

Google AI Brief may be the replacement keywords never had

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png" class="attachment-large size-large wp-post-image" alt="Stop looking for the perfect PPC budget split" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Fixed budget ratios rarely survive real-world conditions. Here's how to evaluate funnel health and adjust spend as market dynamics change.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png" class="attachment-large size-large wp-post-image" alt="Stop looking for the perfect PPC budget split" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Most <a href="https://searchengineland.com/guide/what-is-paid-search">PPC</a> <a href="https://searchengineland.com/guide/ppc/budgeting-and-bidding-strategies">budget</a> discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That&#8217;s usually the wrong goal.</p>



<p>The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.</p>



<p>Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.</p>



<p>Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn&#8217;t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months. </p>



<p>Both are right, which is what makes this so difficult.</p>



<h2 class="wp-block-heading" id="h-the-lower-funnel-case-is-easy-to-make">The lower-funnel case is easy to make</h2>



<p>When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search.&nbsp;</p>



<p>Someone typing &#8220;buy running shoes new york&#8221; has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.</p>



<p>The problem is that this demand already exists. These campaign types harvest intent. They don&#8217;t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else:&nbsp;</p>



<ul class="wp-block-list">
<li>A YouTube pre-roll.</li>



<li>A friend&#8217;s recommendation.</li>



<li>A social post.</li>



<li>Years of brand presence in the market.&nbsp;</li>
</ul>



<p>You&#8217;re collecting fruit from a tree you didn&#8217;t plant.</p>



<p>Search is worth treating separately here because it doesn&#8217;t sit neatly at the bottom of the funnel. A query like &#8220;best running shoes for marathon training&#8221; is informational.&nbsp;</p>



<p>The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it&#8217;s configured and which queries it actually captures.&nbsp;</p>



<p>It&#8217;s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?</p>



<p>This works until it stops working. And the signal that it&#8217;s stopping usually arrives too late.&nbsp;</p>



<p>When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that&#8217;s been living off existing demand without replenishing it.</p>



<p>Lower-funnel efficiency is real. But it&#8217;s also borrowing against the future.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/ppc-budget-planning-aligning-business-goals-ad-spend-performance-458150">PPC budget planning: Aligning business goals, ad spend, and performance</a></em></strong>



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<h2 class="wp-block-heading" id="h-the-reseller-trap-when-your-lower-funnel-depends-on-someone-else-s-brand">The reseller trap: When your lower funnel depends on someone else&#8217;s brand</h2>



<p>There is a version of this problem that&#8217;s specific to resellers and multi-brand ecommerce, and it doesn&#8217;t get discussed enough.</p>



<p>If you sell branded products you don&#8217;t own, your lower funnel can work extremely well in the short term.&nbsp;</p>



<p>Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You&#8217;re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.</p>



<p>The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows.&nbsp;</p>



<p>You can&#8217;t counter it with your own PPC spend because the underlying interest isn&#8217;t there to harvest. The tree stops producing fruit, and you never owned it.</p>



<p>This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well.&nbsp;</p>



<ul class="wp-block-list">
<li>Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently.&nbsp;</li>



<li>Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.</li>
</ul>



<p>Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That&#8217;s only within Google&#8217;s ecosystem.&nbsp;</p>



<p>To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.</p>



<p>Neither of these investments shows up in this month&#8217;s ROAS report. Both show up in next year&#8217;s business resilience.</p>



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<h2 class="wp-block-heading" id="h-upper-funnel-is-inventory-management">Upper funnel is inventory management</h2>



<p>Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.</p>



<p>Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn&#8217;t click isn&#8217;t a failed impression. They&#8217;re a potential high-intent searcher in three weeks. You&#8217;re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.</p>



<p>Google&#8217;s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don&#8217;t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable.&nbsp;</p>



<p>Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.</p>



<p>Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn&#8217;t &#8220;What is the ROAS on this campaign?&#8221; It&#8217;s &#8220;How much qualified demand am I creating for my Shopping and Search campaigns to close?&#8221;</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/paid-media-efficiency-cut-waste-improve-roas-474032">Paid media efficiency: How to cut waste and improve ROAS</a></em></strong>



<h2 class="wp-block-heading" id="h-why-a-fixed-split-is-the-wrong-answer">Why a fixed split is the wrong answer</h2>



<p>The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They&#8217;re useful as a starting point and useless as a long-term policy.</p>



<p>Consider what changes the optimal split.</p>



<ul class="wp-block-list">
<li>A new product launch needs heavy upper-funnel investment upfront because awareness is zero.&nbsp;</li>



<li>A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool.&nbsp;</li>



<li>A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn&#8217;t respond fast enough to be built in-season.</li>
</ul>



<p>Equally, a business in financial distress or facing a short-term revenue target can&#8217;t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.</p>



<p>The point is that both of these decisions are correct in context. A fixed split ignores context entirely.</p>



<h2 class="wp-block-heading" id="h-building-a-dynamic-split-logic">Building a dynamic split logic</h2>



<p>Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.</p>



<p><strong>Shift budget toward upper funnel when:</strong>



<ul class="wp-block-list">
<li>Branded search volume is flat or declining quarter over quarter.</li>



<li>New customer acquisition cost is rising while retention metrics hold.</li>



<li>You&#8217;re entering a new market or launching a new product.</li>



<li>Competitors are visibly increasing their brand presence.</li>



<li>You&#8217;re approaching a peak season with at least six to eight weeks of runway.</li>



<li>You&#8217;re a reseller whose top brands are showing declining search interest or reduced marketing activity.</li>
</ul>



<p><strong>Shift budget toward lower funnel when:</strong>



<ul class="wp-block-list">
<li>You have a short-term revenue target that can&#8217;t wait.</li>



<li>Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.</li>



<li>Cost per acquisition on Shopping or Search is below target, and scaling makes sense.</li>



<li>Audience saturation on Demand Gen is high, meaning you&#8217;re reaching the same people repeatedly without expanding reach.</li>
</ul>



<p>Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn&#8217;t.</p>



<p>The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you&#8217;ve already lost several weeks of pipeline-building time.</p>



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<h2 class="wp-block-heading" id="h-the-conversation-nobody-wants-to-have">The conversation nobody wants to have</h2>



<p>The reason funnel balance stays broken in most organizations isn&#8217;t analytical. It&#8217;s political.</p>



<p>Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue.&nbsp;</p>



<p>Upper-funnel spend requires a different kind of argument: &#8220;This investment will make our Shopping and Search campaigns work better in six weeks.&#8221; That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.</p>



<p>The answer isn&#8217;t to stop making the argument. It&#8217;s to change the evidence you bring to it.&nbsp;</p>



<ul class="wp-block-list">
<li>Track branded search volume as a leading indicator.&nbsp;</li>



<li>Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other.&nbsp;</li>



<li>Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.</li>
</ul>



<p>Budget allocation isn&#8217;t a one-time decision. It&#8217;s an ongoing signal about what kind of growth you&#8217;re building.&nbsp;</p>



<p>Optimizing purely for this month&#8217;s ROAS is a choice. So is investing in the demand that will drive next quarter&#8217;s revenue.&nbsp;</p>



<p>And if you&#8217;re a reseller, it&#8217;s also a decision about whether your business is built on a foundation you control or one you&#8217;re renting from brand owners who have their own priorities.</p>



<p>The best PPC teams do both, and they know when to lean in each direction.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/how-to-optimize-b2b-ppc-spend-when-budgets-and-confidence-are-low-454592">How to optimize B2B PPC spend when budgets and confidence are low</a></em></strong>

Most PPC budget discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That’s usually the wrong goal.
The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.
Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.
Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn’t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months. 
Both are right, which is what makes this so difficult.
The lower-funnel case is easy to make
When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search. 
Someone typing “buy running shoes new york” has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.
The problem is that this demand already exists. These campaign types harvest intent. They don’t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else: 
A YouTube pre-roll.
A friend’s recommendation.
A social post.
Years of brand presence in the market. 
You’re collecting fruit from a tree you didn’t plant.
Search is worth treating separately here because it doesn’t sit neatly at the bottom of the funnel. A query like “best running shoes for marathon training” is informational. 
The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it’s configured and which queries it actually captures. 
It’s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?
This works until it stops working. And the signal that it’s stopping usually arrives too late. 
When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that’s been living off existing demand without replenishing it.
Lower-funnel efficiency is real. But it’s also borrowing against the future.
Dig deeper: PPC budget planning: Aligning business goals, ad spend, and performance




  
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The reseller trap: When your lower funnel depends on someone else’s brand
There is a version of this problem that’s specific to resellers and multi-brand ecommerce, and it doesn’t get discussed enough.
If you sell branded products you don’t own, your lower funnel can work extremely well in the short term. 
Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You’re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.
The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows. 
You can’t counter it with your own PPC spend because the underlying interest isn’t there to harvest. The tree stops producing fruit, and you never owned it.
This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well. 
Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently. 
Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.
Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That’s only within Google’s ecosystem. 
To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.
Neither of these investments shows up in this month’s ROAS report. Both show up in next year’s business resilience.
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Upper funnel is inventory management
Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.
Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn’t click isn’t a failed impression. They’re a potential high-intent searcher in three weeks. You’re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.
Google’s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don’t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable. 
Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.
Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn’t “What is the ROAS on this campaign?” It’s “How much qualified demand am I creating for my Shopping and Search campaigns to close?”
Dig deeper: Paid media efficiency: How to cut waste and improve ROAS



Why a fixed split is the wrong answer
The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They’re useful as a starting point and useless as a long-term policy.
Consider what changes the optimal split.
A new product launch needs heavy upper-funnel investment upfront because awareness is zero. 
A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool. 
A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn’t respond fast enough to be built in-season.
Equally, a business in financial distress or facing a short-term revenue target can’t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.
The point is that both of these decisions are correct in context. A fixed split ignores context entirely.
Building a dynamic split logic
Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.
Shift budget toward upper funnel when:




Branded search volume is flat or declining quarter over quarter.
New customer acquisition cost is rising while retention metrics hold.
You’re entering a new market or launching a new product.
Competitors are visibly increasing their brand presence.
You’re approaching a peak season with at least six to eight weeks of runway.
You’re a reseller whose top brands are showing declining search interest or reduced marketing activity.
Shift budget toward lower funnel when:




You have a short-term revenue target that can’t wait.
Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.
Cost per acquisition on Shopping or Search is below target, and scaling makes sense.
Audience saturation on Demand Gen is high, meaning you’re reaching the same people repeatedly without expanding reach.
Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn’t.
The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you’ve already lost several weeks of pipeline-building time.

  
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The conversation nobody wants to have
The reason funnel balance stays broken in most organizations isn’t analytical. It’s political.
Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue. 
Upper-funnel spend requires a different kind of argument: “This investment will make our Shopping and Search campaigns work better in six weeks.” That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.
The answer isn’t to stop making the argument. It’s to change the evidence you bring to it. 
Track branded search volume as a leading indicator. 
Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other. 
Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.
Budget allocation isn’t a one-time decision. It’s an ongoing signal about what kind of growth you’re building. 
Optimizing purely for this month’s ROAS is a choice. So is investing in the demand that will drive next quarter’s revenue. 
And if you’re a reseller, it’s also a decision about whether your business is built on a foundation you control or one you’re renting from brand owners who have their own priorities.
The best PPC teams do both, and they know when to lean in each direction.
Dig deeper: How to optimize B2B PPC spend when budgets and confidence are low

Fixed budget ratios rarely survive real-world conditions. Here's how to evaluate funnel health and adjust spend as market dynamics change.

Stop looking for the perfect PPC budget split

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>We've analyzed nearly 1 million queries to show you what's really happening. Most search teams are missing it completely.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png" alt="" class="wp-image-479302" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p>Here&#8217;s something worth sitting with for a moment.</p>



<p>Your competitors are running ads on ChatGPT. You can&#8217;t see them. You don&#8217;t know which prompts they&#8217;re bidding on, what creative they&#8217;re running, or how their presence compares to yours. And unlike Google Ads (where Auction Insights at least gives you a rear-view mirror), there&#8217;s currently no native way to get any of that picture on ChatGPT.</p>



<p>That&#8217;s the blind spot. And it&#8217;s bigger than most search teams realize.</p>



<p>OpenAI launched advertising inside AI-generated responses earlier this year. Brands moved fast, all in within weeks. The minimum spend dropped, the Ads Manager launched, and a genuinely new ad channel was born. With ChatGPT advertising expected to expand to U.K. markets soon, the window for early-mover advantage is closing faster than it looks.</p>



<p>We&#8217;ve been watching since day one. Here&#8217;s what we&#8217;ve found.</p>



<h2 class="wp-block-heading" id="h-what-does-chatgpt-ads-actually-look-like-right-now">What does ChatGPT Ads actually look like right now?</h2>



<p>We analyzed nearly 1 million query indexes across 20 industries and five markets (the U.S., U.K., Australia, New Zealand, and Canada) between March 2026 and May 2026. The data tells a clear story.</p>



<h3 class="wp-block-heading" id="h-it-s-a-u-s-first-channel-everywhere-else-is-still-warming-up">It&#8217;s a U.S.-first channel. Everywhere else is still warming up.</h3>



<p>In the U.S., ChatGPT served ads on 4.5% of queries. Across roughly 170,000 U.K. indexes in the same period? We found zero ads. The U.S. accounts for around 90% of all ChatGPT ad placements in our dataset. Canada and New Zealand are active. Australia&#8217;s at 1.6%. The U.K. hasn&#8217;t flipped yet, but it will.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="420" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-1.png" alt="Horizontal bar chart on dark navy background showing ChatGPT ad frequency by market. Canada leads at 4.57%, U.S. at 4.47%, New Zealand 3.85%, Australia 1.61%, United Kingdom at effectively zero. Adthena branding bottom right." class="wp-image-479307" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-1.png 800w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-1-768x403.png 768w" sizes="(max-width: 800px) 100vw, 800px" /><figcaption class="wp-element-caption"><em>ChatGPT ad frequency by market</em></figcaption></figure>
</div>


<p>For U.K. search teams, that&#8217;s a two-sided finding. The channel isn&#8217;t live here yet. But your U.S. competitors have had months to figure out which prompts convert, what creative works, and where the real opportunity sits. When U.K. advertising opens up, they won&#8217;t be starting from scratch. You might be.</p>



<h3 class="wp-block-heading" id="h-there-s-only-one-ad-per-response-the-majority-of-cases">There&#8217;s only one ad per response the majority of cases</h3>



<p>In the U.S., ChatGPT averages just 1.06 ad items per ad-bearing answer. That means in the vast majority of responses, there&#8217;s a single sponsored slot. Not three. Not a carousel. One.</p>



<p>That changes the stakes completely. In Google Ads you can hold position two or three and still get clicks. On ChatGPT, you&#8217;re either in the answer or you&#8217;re not. Share of voice here is binary in a way paid search has never quite been before.</p>



<h3 class="wp-block-heading" id="h-some-industries-are-blocked-for-now">Some industries are blocked for now</h3>



<p>Four categories returned zero ChatGPT ads across the entire dataset: Legal, Pharma, Banking, and Nonprofit. Healthcare was near-zero at 0.45%. This looks like deliberate OpenAI policy rather than lack of demand. These restrictions will evolve. When they do, the teams that are already watching will have a head start.</p>



<h3 class="wp-block-heading" id="h-the-hottest-categories-aren-t-the-ones-you-d-expect">The hottest categories aren&#8217;t the ones you&#8217;d expect</h3>



<p>Logistics tops the chart at 12.4% ad frequency, followed by Home &amp; Garden at 12% and Beauty &amp; Cosmetics at 10%. These are categories building serious ground on ChatGPT right now, well above a platform average of around 3.3%. Media &amp; Entertainment (8%), Insurance (7.2%), and Energy &amp; Utilities (6.4%) aren&#8217;t far behind.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-2.png" alt="Horizontal bar chart showing ChatGPT ad frequency across 19 industries. Logistics leads at 12.41%, Home &amp; Garden 11.99%, Beauty &amp; Cosmetics 10.03%. Blocked verticals shown at zero with platform average line in lime. Dark navy Adthena-branded design." class="wp-image-479308" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-2.png 800w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-2-768x432.png 768w" sizes="(max-width: 800px) 100vw, 800px" /><figcaption class="wp-element-caption"><em>ChatGPT ad frequency by industry, all markets</em></figcaption></figure>
</div>


<h3 class="wp-block-heading" id="h-retail-is-where-the-real-money-is-flowing">Retail is where the real money is flowing</h3>



<p>Retail &amp; Fashion makes up 24% of U.S. query volume but accounts for 39% of all U.S. ad items. That&#8217;s not a scrape artifact. It&#8217;s genuine advertiser demand. With an ad frequency of 6.55% against a U.S. average of 4.5%, retail brands are competing hard for ChatGPT presence. The category accounts for more than a third of all ad placements in our full dataset.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="440" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-3.png" alt="Grouped bar chart comparing share of U.S. queries vs share of U.S. ad items. Retail &amp; Fashion over-indexes: 24.1% of queries, 38.9% of ad items. Dark navy Adthena-branded design." class="wp-image-479309" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-3.png 800w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-3-768x422.png 768w" sizes="(max-width: 800px) 100vw, 800px" /><figcaption class="wp-element-caption"><em>Retail &amp; Fashion: share of queries vs share of ad items, U.S.</em></figcaption></figure>
</div>


<h3 class="wp-block-heading" id="h-so-what-s-the-actual-problem"><strong>So what&#8217;s the actual problem?</strong></h3>



<p>Here&#8217;s where it gets frustrating. And we&#8217;ll be straight with you about it.</p>



<p>Every serious search practitioner lives and dies by competitive intelligence: auction Insights, competitor ad copy, impression share trends. You know this stuff because running campaigns without it isn&#8217;t a strategy. It&#8217;s guesswork.</p>



<p>None of those tools exist for ChatGPT Ads.</p>



<p>OpenAI&#8217;s native Ads Manager shows you your own data. Your spend, and basic performance metrics such as impressions, clicks, CPC, and CTR, and that&#8217;s it. What you can&#8217;t see: which competitors are showing up alongside you, which prompts are triggering their ads, what creative they&#8217;re running, or how your share of voice stacks up across the market.</p>



<p>You&#8217;re spending real budget on a channel where you can only see yourself. In a format where there&#8217;s one winner per response. That&#8217;s a problem worth taking seriously.</p>



<h2 class="wp-block-heading" id="h-what-does-full-visibility-actually-look-like">What does full visibility actually look like?</h2>



<p>That&#8217;s the gap we built <a href="https://www.adthena.com/chat-gpt-ads/?utm_campaign=38650020-FY26.SELarticles&amp;utm_source=SEL&amp;utm_medium=article&amp;utm_term=ChatGPT%20June" target="_blank" rel="noopener sponsored nofollow">ChatGPT Ads Intelligence</a> to close.</p>



<p>We monitor 300,000+ prompts daily across the markets where ChatGPT Ads are live, giving you the whole-market view that&#8217;s been missing from this channel since day one. It&#8217;s the same competitive intelligence approach that&#8217;s made Adthena essential for Google paid search, now fully applied to ChatGPT.</p>



<p>You can see which competitors are bidding on your prompts, track share of voice week on week, and find the greenfield prompts (the high-intent queries where no one&#8217;s advertising yet) before someone else does.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="652" height="231" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-4.png" alt="Adthena ChatGPT Ads Intelligence dashboard showing a brand's ad presence compared to five competitors, with a donut chart showing 88% top 10 visibility and a trend line graph tracking competitor ad impressions over time." class="wp-image-479310"/><figcaption class="wp-element-caption"><em>Share of voice vs top competitors</em></figcaption></figure>
</div>


<p>That last part matters more than it might sound. In a new channel where positions aren&#8217;t entrenched yet, first-mover advantage is still genuinely available. It won&#8217;t be forever.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="652" height="317" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-5.png" alt="Adthena ChatGPT Ads Intelligence showing a table of prompts where ads have been detected, including each prompt's ad detection rate, competitor count, competitive rate, and top competitor, with a Greenfield filter applied." class="wp-image-479311"/><figcaption class="wp-element-caption"><em>Prompt-level ad detection and competitive data</em></figcaption></figure>
</div>


<p><a href="https://www.adthena.com/chat-gpt-ads/?utm_campaign=38650020-FY26.SELarticles&amp;utm_source=SEL&amp;utm_medium=article&amp;utm_term=ChatGPT%20June#row-329640-5" target="_blank" rel="noopener sponsored nofollow">Try ChatGPT Ads Intelligence free for 21 days</a> and see the full picture.</p>



<h2 class="wp-block-heading" id="h-why-does-this-matter-beyond-chatgpt"><strong>Why does this matter beyond ChatGPT?</strong></h2>



<p>Let&#8217;s zoom out for a second.</p>



<p>Search isn&#8217;t one place anymore. It hasn&#8217;t been for a while. Users are turning to AI-native interfaces like ChatGPT and Perplexity for exactly the kind of high-intent queries paid search was built around: product recommendations, service comparisons, purchase decisions. The intent is there. The ads are starting to follow.</p>



<p>For search practitioners, the job description is expanding whether you signed up for it or not. The same rigor you apply to Google Ads (competitive monitoring and share of voice tracking) needs to extend to wherever high-intent search is actually happening.</p>



<p>The brands paying attention to ChatGPT Ads now are the ones who&#8217;ll be hardest to displace when the channel fully matures. We&#8217;ve seen this movie before. The early Google Ads practitioners who understood the platform first built advantages that took years to close.</p>



<p>That window is open right now. Our data shows it clearly.</p>



<p>Ready to see what&#8217;s happening on ChatGPT Ads? <a href="https://www.adthena.com/chat-gpt-ads/?utm_campaign=38650020-FY26.SELarticles&amp;utm_source=SEL&amp;utm_medium=article&amp;utm_term=ChatGPT%20June#row-329640-5" target="_blank" rel="noopener sponsored nofollow">Start your free 21-day trial</a> of Adthena&#8217;s ChatGPT Ads Intelligence.</p>


Here’s something worth sitting with for a moment.
Your competitors are running ads on ChatGPT. You can’t see them. You don’t know which prompts they’re bidding on, what creative they’re running, or how their presence compares to yours. And unlike Google Ads (where Auction Insights at least gives you a rear-view mirror), there’s currently no native way to get any of that picture on ChatGPT.
That’s the blind spot. And it’s bigger than most search teams realize.
OpenAI launched advertising inside AI-generated responses earlier this year. Brands moved fast, all in within weeks. The minimum spend dropped, the Ads Manager launched, and a genuinely new ad channel was born. With ChatGPT advertising expected to expand to U.K. markets soon, the window for early-mover advantage is closing faster than it looks.
We’ve been watching since day one. Here’s what we’ve found.
What does ChatGPT Ads actually look like right now?
We analyzed nearly 1 million query indexes across 20 industries and five markets (the U.S., U.K., Australia, New Zealand, and Canada) between March 2026 and May 2026. The data tells a clear story.
It’s a U.S.-first channel. Everywhere else is still warming up.
In the U.S., ChatGPT served ads on 4.5% of queries. Across roughly 170,000 U.K. indexes in the same period? We found zero ads. The U.S. accounts for around 90% of all ChatGPT ad placements in our dataset. Canada and New Zealand are active. Australia’s at 1.6%. The U.K. hasn’t flipped yet, but it will.
ChatGPT ad frequency by market



For U.K. search teams, that’s a two-sided finding. The channel isn’t live here yet. But your U.S. competitors have had months to figure out which prompts convert, what creative works, and where the real opportunity sits. When U.K. advertising opens up, they won’t be starting from scratch. You might be.
There’s only one ad per response the majority of cases
In the U.S., ChatGPT averages just 1.06 ad items per ad-bearing answer. That means in the vast majority of responses, there’s a single sponsored slot. Not three. Not a carousel. One.
That changes the stakes completely. In Google Ads you can hold position two or three and still get clicks. On ChatGPT, you’re either in the answer or you’re not. Share of voice here is binary in a way paid search has never quite been before.
Some industries are blocked for now
Four categories returned zero ChatGPT ads across the entire dataset: Legal, Pharma, Banking, and Nonprofit. Healthcare was near-zero at 0.45%. This looks like deliberate OpenAI policy rather than lack of demand. These restrictions will evolve. When they do, the teams that are already watching will have a head start.
The hottest categories aren’t the ones you’d expect
Logistics tops the chart at 12.4% ad frequency, followed by Home & Garden at 12% and Beauty & Cosmetics at 10%. These are categories building serious ground on ChatGPT right now, well above a platform average of around 3.3%. Media & Entertainment (8%), Insurance (7.2%), and Energy & Utilities (6.4%) aren’t far behind.
ChatGPT ad frequency by industry, all markets



Retail is where the real money is flowing
Retail & Fashion makes up 24% of U.S. query volume but accounts for 39% of all U.S. ad items. That’s not a scrape artifact. It’s genuine advertiser demand. With an ad frequency of 6.55% against a U.S. average of 4.5%, retail brands are competing hard for ChatGPT presence. The category accounts for more than a third of all ad placements in our full dataset.
Retail & Fashion: share of queries vs share of ad items, U.S.



So what’s the actual problem?
Here’s where it gets frustrating. And we’ll be straight with you about it.
Every serious search practitioner lives and dies by competitive intelligence: auction Insights, competitor ad copy, impression share trends. You know this stuff because running campaigns without it isn’t a strategy. It’s guesswork.
None of those tools exist for ChatGPT Ads.
OpenAI’s native Ads Manager shows you your own data. Your spend, and basic performance metrics such as impressions, clicks, CPC, and CTR, and that’s it. What you can’t see: which competitors are showing up alongside you, which prompts are triggering their ads, what creative they’re running, or how your share of voice stacks up across the market.
You’re spending real budget on a channel where you can only see yourself. In a format where there’s one winner per response. That’s a problem worth taking seriously.
What does full visibility actually look like?
That’s the gap we built ChatGPT Ads Intelligence to close.
We monitor 300,000+ prompts daily across the markets where ChatGPT Ads are live, giving you the whole-market view that’s been missing from this channel since day one. It’s the same competitive intelligence approach that’s made Adthena essential for Google paid search, now fully applied to ChatGPT.
You can see which competitors are bidding on your prompts, track share of voice week on week, and find the greenfield prompts (the high-intent queries where no one’s advertising yet) before someone else does.
Share of voice vs top competitors



That last part matters more than it might sound. In a new channel where positions aren’t entrenched yet, first-mover advantage is still genuinely available. It won’t be forever.
Prompt-level ad detection and competitive data



Try ChatGPT Ads Intelligence free for 21 days and see the full picture.
Why does this matter beyond ChatGPT?
Let’s zoom out for a second.
Search isn’t one place anymore. It hasn’t been for a while. Users are turning to AI-native interfaces like ChatGPT and Perplexity for exactly the kind of high-intent queries paid search was built around: product recommendations, service comparisons, purchase decisions. The intent is there. The ads are starting to follow.
For search practitioners, the job description is expanding whether you signed up for it or not. The same rigor you apply to Google Ads (competitive monitoring and share of voice tracking) needs to extend to wherever high-intent search is actually happening.
The brands paying attention to ChatGPT Ads now are the ones who’ll be hardest to displace when the channel fully matures. We’ve seen this movie before. The early Google Ads practitioners who understood the platform first built advantages that took years to close.
That window is open right now. Our data shows it clearly.
Ready to see what’s happening on ChatGPT Ads? Start your free 21-day trial of Adthena’s ChatGPT Ads Intelligence.

We've analyzed nearly 1 million queries to show you what's really happening. Most search teams are missing it completely.

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