Microsoft is now officially releasing a preview of the new AI performance report within Bing Webmaster Tools that now includes Intents, Topics, Citation Share, and Compare. We saw Microsoft demo these features in late April but now it is actually starting to roll out to users.
As a reminder, Bing officially rolled out its AI performance report in February. Google didn’t roll out its AI reporting in Search Console until June, and it seemed forced.
What is new. “These new capabilities build on that foundation by helping publishers better understand why their content is being surfaced, which broader subject areas they are gaining visibility in, how their presence evolves relative to other cited sources, and how citation patterns change over time,” wrote Krishna Madhavan from Microsoft.
Intent: The new Intents feature in Bing Webmaster Tools now classifies the grounding queries in the AI Performance Report in broader categories, such as Informational, Commercial, Navigational, Learn and Solve, Research, Creation, Local, and more. This in a sense helps you understand the intent behind the prompt or query. “This helps publishers move beyond simply seeing which queries triggered citations and begin understanding the broader query context our systems associate with those citation appearances,” Krishna Madhavan wrote.
The example provided was that an e-commerce publisher may discover strong visibility in comparison-oriented or shopping-focused AI experiences, while an educational publisher may find that their content is frequently surfaced in research or learning-oriented interactions. These insights can help publishers better align content structure and depth with the types of experiences where AI systems are surfacing their content.
Topics: The Topics in the AI performance reports group related grounding queries into broader thematic clusters. AI systems reason across concepts and themes rather than isolated keywords, Microsoft explained. So by having topics, it will help publishers understand visibility in the same thematic structure that modern AI systems use to organize information.
So for example, queries such as “solar panels,” “solar energy efficiency,” and “residential solar installation,” for example, may all map into a broader topic cluster like Solar Energy. “This creates a more natural way to analyze AI visibility. Content teams and publishers often think in terms of themes, editorial areas, and audience interests rather than isolated keywords. Topics help bridge that gap by turning grounding query data into a more thematic view of AI engagement,” Microsoft wrote.
One note, “during the preview phase, some labels may still be broad – especially for highly specialized or niche domains – but the system is already beginning to reveal meaningful thematic patterns,” Microsoft wrote.
Citations. Microsoft also added citation share, which shows how much of the citation space your site receives for a specific grounding query. Citation share is calculated as the percentage of citations attributed to your site out of all citations shown across all sites for that same grounding query. “This helps publishers understand not just whether they were cited, but how much visibility they received within the full set of cited sources for that query,” Microsoft explained.
Microsoft added these points:
“This can provide a more directional view into how visibility is evolving over time. Publishers may begin to identify areas where their content has strong and growing representation in AI-generated experiences, as well as areas where visibility may be more fragmented across many sources.”
“Importantly, Citation Share is designed as an observational metric – not a ranking system or a competitive scoreboard. It does not expose competitor domains, represent traffic share, or assign quality scores to content.”
“AI citation ecosystems are inherently dynamic. Citation patterns can shift due to changes in user behavior, evolving models, freshness signals, partner refresh cycles, and broader changes across the web itself.”
Compare. With all of that, you can also compare the changes over time. The compare feature allows you to overlay a previous time period directly onto the current reporting view.
“Compare is designed to help publishers observe changes over time. Citation activity can be influenced by many factors including evolving AI models, competing content, freshness signals, and shifts in user demand,” Microsoft wrote.
Here is what it looks like:
Why we care. While we still do not have click and click-through rate data, Microsoft keeps adding more and more to its AI performance reports.
I am hopful that one day we will get click data, but I am still not expecting to see that from Google or Microsoft any time soon.
Google penalties, also known as manual spam actions, are among the few events in search that can disrupt an otherwise healthy online business overnight.
For companies heavily dependent on organic traffic, the consequences often extend far beyond lost rankings. Revenue drops, customer acquisition costs rise, expansion plans stall, and the effects can linger long after the original policy violations have been remedied.
With a steady 90% market share, Google remains the primary traffic source for many publishers, ecommerce platforms, retailers, travel brands, affiliates, and lead generation businesses.
Direct traffic rarely compensates for a major visibility loss, and Bing seldom offsets the difference. As a result, a manual spam action carries serious operational implications, not merely SEO risks.
Manual actions aren’t algorithm updates
One point still misunderstood throughout the industry deserves clarification. Manual spam actions differ from algorithmic updates. They aren’t fluctuations caused by changes in relevance calculations or ranking system adjustments.
Google’s manual penalties involve direct enforcement after suspected violations against Google Search Essentials, formerly Google Webmaster Guidelines, have been identified and confirmed. The distinction matters because the response required is completely different.
A website affected by changing ranking systems requires analysis, adaptation, and recrawling. A website affected by a manual spam action requires remediation and applying for reconsideration. Those are separate situations entirely.
Google doesn’t issue manual spam actions casually. The process involves internal senior employee review cycles. Suspected violations must be investigated and confirmed first.
Google states clearly that manual actions are the consequence of proven policy transgressions. Despite frequent cries of foul, false positives are exceptionally rare. Once a manual action appears in Google Search Console, the enforcement is already in the production pipeline.
The operational problem is that many businesses fail to recognize how much unresolved policy exposure their web platforms have accumulated over time.
Be the brand customers find first.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
Start your free trial
How penalties develop
The initial steps that ultimately lead to a manual penalty and a website’s drop in search visibility often begin inconspicuously, gradually eroding policy compliance.
An ecommerce business launches an aggressive link acquisition campaign during an early growth phase. Over the years, PageRank-passing spam links accumulate unchecked until eventually nobody remembers where thousands of exact-match backlinks originate.
A publisher enters into commercial partnerships involving sponsored content or affiliate sections, which gradually become structurally integrated into the editorial architecture of the website.
A SaaS company creates large numbers of low-quality location pages while expanding into new markets.
A lead generation business scales supplemental SEO content through low-cost LLM production systems with limited editorial oversight because that appears to be what most competitors are doing.
The underlying patterns are remarkably similar across industries. In many cases, organic search visibility initially improves and may even generate measurable revenue gains attributable to the SEO initiative.
The short-term results reinforce the perception that the approach is working. However, as time passes, nobody revisits whether those earlier decisions remain aligned with evolving search quality standards and webmaster policies.
Why historical violations still matter
One reason manual spam actions create so much disruption is that policy violations often persist quietly for years before review. Many organizations incorrectly assume that questionable SEO tactics of the past lose their relevance over time.
Yet Google Search systems don’t forget historical footprints. Archived URLs remain crawlable. Legacy sections continue contributing content quality signals long after internal ownership was abandoned.
Most persistently, backlink patterns remain visible for decades. Large numbers of websites remain affected by backlinks generated through manipulative campaigns dating back many years.
Paid placements, article syndication networks, private blog networks, commercial keyword-heavy guest posting campaigns, expired domain backlinks, directory spam, and widget distribution schemes that once formed part of mainstream SEO activity are today’s liabilities.
Some of these practices continue to operate more or less openly for years, while enforcement may appear erratic or inconsistent. When left unaddressed, they represent an incalculable risk to the website publisher.
This becomes particularly important during acquisitions. Businesses purchasing established domains frequently inherit unresolved compliance exposure alongside rankings and traffic. Google evaluates the website’s condition, not which employee, agency, or previous owner introduced the violations.
Traffic growth alone doesn’t confirm compliance health. A domain generating millions of clicks may still carry unresolved risks tied to old link schemes, expired sponsorship arrangements, deceptive user-agent cloaking, manipulative redirects, or scaled low-quality content sections. Those issues often go unnoticed until they’re brought to the surface by a Google manual spam action notification.
A common sign of an algorithmic adjustment: Gradual loss of visibility
Reputation abuse and publisher liability
The mechanics behind reputation abuse are straightforward. A trusted brand with an established web platform allows third parties to publish unrelated, often unsupervised content under the same domain name. In many cases, publishers integrated discount coupon sections, casino reviews, affiliate content, or commercially motivated informational pages directly into existing editorial systems.
The problem frequently worsened significantly because the content wasn’t properly segmented. The consequence is that the distinction between trusted editorial work and commercially motivated material became blurred.
Once confronted with a site-wide penalty, affected publishers experience broad visibility declines across the entire platform, not merely within the originally offending sections of the website. The damage to a brand that lends its reputation to a disreputable third party is often substantial.
Recovery efforts frequently prove time-consuming and costly. Removing isolated pages rarely resolves the problem. Many organizations require broader structural changes, including archive cleanup, internal link reviews, crawl management adjustments, sponsorship governance reforms, the removal of spammy redirects, stronger editorial oversight, and stricter technical segmentation.
In short, recovering from such a penalty takes time, costs significant amounts of money, and is often a painful process.
A common sign of a manual spam action: Rapid loss of visibility
The risks of scaled content
Google increasingly scrutinizes large-scale publishing systems that produce repetitive, low-value content without a unique selling proposition.
The issue isn’t maintaining many websites simultaneously. Large website portfolios have thrived in Google Search for years and continue to do so. The underlying problem involves quality control, editorial oversight, originality, and informational value.
Affiliate networks produce near-identical product comparison pages across thousands of long-tail keywords.
Local SEO operations deploy templated service pages across hundreds of regions with minimal differentiation.
AI-assisted workflows publish large numbers of informational pages without factual oversight or genuine expertise to support them.
Most organizations don’t cross into problematic territory intentionally. The transition usually occurs gradually, often unbeknownst to the decision-makers who rely on outdated or misleading recommendations.
The resulting manual spam action in Google Search Console, followed by a sharp decline in rankings, frequently occurs after a prolonged period of spam signal accumulation rather than during the apparent growth phase.
Incomplete remediation prolongs penalties
Many site owners approach reconsideration requests as if they were negotiating with Google. That puts them at a significant disadvantage from the outset.
The reconsideration process exists for one purpose only: to demonstrate that the website has been restored to full compliance with Google’s guidelines. It’s important to note that Google expects complete compliance before lifting a manual spam action.
This means the requirement extends beyond the specific violation highlighted in Google Search Console. A site owner who addresses only one known spam issue while leaving unrelated policy violations unresolved elsewhere on the website will typically face rejection.
A common testing approach, such as a publisher removing some problematic sponsored content while retaining similar affiliate arrangements elsewhere, will fail. Likewise, a business that disavows recent manipulative backlinks while ignoring historical paid link schemes is unlikely to convince Google of its genuine commitment to complying with Google’s policies going forward.
Similarly, a website network that cleans up one property while continuing identical publishing practices across related domains signals incomplete remediation rather than meaningful operational reform. As a result, it stands little chance of regaining Google’s trust.
Why repeated rejections make recovery harder
Effective website recovery requires a comprehensive review rather than selective cleanup. Technical infrastructure, content quality, sponsorship structures, redirect behavior, link acquisition history, indexing patterns, archive sections, and ownership transparency all require examination during serious compliance recovery efforts.
The Google Search team expects compelling documentation detailing what has changed and how future violations will be prevented. Temporary cosmetic adjustments rarely persuade reviewers to lift a manual spam action.
Making matters worse, each rejection typically requires an even more comprehensive review and cleanup effort. At the same time, every reconsideration request that Google deems disingenuous further erodes Google’s trust in the publisher.
The cost of uncertainty
There’s no guaranteed turnaround time for reconsideration processing. Some reviews are completed within days. Others take weeks or months.
At the same time, large websites with extensive SEO legacies accumulated over many years often require longer assessment periods due to the substantial volumes of data that must be crawled and analyzed before changes can be evaluated.
For businesses that rely primarily on Google traffic, this uncertainty creates a potentially existential threat.
An ecommerce business approaching a peak seasonal period with an unresolved manual spam action can face cash flow problems quickly.
Publishers dependent on advertising revenue experience ranking losses that translate directly into declining commercial performance.
Lead generation businesses often encounter immediate pipeline contraction once visibility declines significantly.
The operational risk becomes even greater when companies fail to build a strong brand capable of partially offsetting organic traffic declines through direct navigation or alternative revenue-generating channels. In this context, paid traffic is a poor substitute due to its associated costs.
In short, some online businesses can’t afford to be penalized in the first place.
Penalties can cripple operations
The issue extends beyond SEO performance. Search visibility directly affects commercial expansion, investor confidence, company valuation, partnership negotiations, and revenue stability.
Penalty expiration represents another commonly misunderstood aspect. Google manual spam actions may expire after prolonged periods, often years. However, this is rarely a viable strategy for an affected business.
Waiting passively through an extended period of declining visibility seldom aligns with commercial realities. More importantly, expiration alone doesn’t guarantee recovery or renewed growth, as the penalty could be reapplied not too long after it expired.
Google’s search systems continue evaluating overall site quality independently of manual enforcement status. A website carrying unresolved spam signals across its content, technical infrastructure, or off-page profile may continue to struggle long after the manual action itself has been lifted.
Compliance requires ongoing oversight
Compliance reviews can’t be considered optional or a luxury. Organizations heavily dependent on organic Google visibility require ongoing operational review cycles focused specifically on comprehensive policy compliance.
These reviews shouldn’t be conducted internally. Even the most talented in-house SEO teams are often hard-pressed to diligently identify shortcomings that may reflect on their own work or that of their colleagues. Policy compliance requires external expertise, sufficient authority, and a proven track record.
Purely technical SEO audits, while indispensable, are insufficient if commercial partnerships bypass oversight. Editorial standards alone won’t suffice if historical link manipulation remains unresolved. Planned growth initiatives require evaluation against established compliance frameworks before deployment, not after traffic has become dependent on questionable practices.
Mature organizations increasingly integrate compliance reviews into their operational governance. Sponsorship structures undergo search compliance review before launch. Scaled publishing systems are assessed for quality before expansion. Historical content is evaluated on a recurring basis. Acquisition due diligence includes policy exposure analysis alongside financial review.
Own the conversation before your competitors.
See where your brand appears, where it doesn’t, and exactly how to win more visibility across search, AI, local, social, and every channel that matters.
Start your free trial
Compliance is a business imperative
This level of discipline and vigilance matters because manual spam actions rarely arrive at convenient moments. More often than not, undesirable Google scrutiny coincides with critical periods: just before a long-planned commercial expansion, in the run-up to a migration project, ahead of an acquisition, as the peak retail season begins, or shortly before investor reporting deadlines.
This is hardly intentional. It’s simply a matter of unfortunate timing. Google doesn’t align search quality enforcement with business planning calendars. Google cares primarily about user experience. For every website that loses its top position, there is usually another capable of providing users with a similarly compelling experience.
Businesses that ignore unresolved policy exposure often discover the problem the hard way, only after search visibility has collapsed and online sales have followed suit. At that point, recovery becomes a far more prolonged, expensive, and operationally disruptive undertaking than ongoing compliance reviews would have been prior to penalization.
Nevertheless, the work must be done. The one silver lining is that, in many cases, the process proves cathartic. Once the penalty has been resolved and the website’s SEO signals have become more consistent, the removal of legacy issues often allows rankings not merely to recover, but to exceed their previous highs.
As AI tool usage has become more common, I’ve seen impressive examples of people building tools to automate complex processes that once required significant manual effort. I’ve also seen teams adopt AI simply because it’s available, often with little practical benefit.
My approach is to focus on AI applications that save time and solve real problems.
Recently, I needed to align the SEO architecture for more than a dozen websites across three separate businesses, eight regional domains, and multiple languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.
Historically, mapping thousands of URLs to create cohesive hreflang XML sitemaps would have required specialized software or days of spreadsheet work. Instead, I used Google Gemini to build a custom Python script that handled the heavy lifting.
Here’s how the project evolved from an initial prompt into a highly customized automation tool, and what it taught me about using AI for technical SEO.
Where AI delivers the most value
I use AI primarily for practical, time-saving tasks, including:
Generating regex patterns when I need a quick solution without researching syntax from scratch.
Creating complex spreadsheet formulas for reporting workflows that rely on manual data exports.
Accelerating research and planning for projects that require competitive analysis across multiple business lines.
Building custom automation tools for recurring SEO and data-processing tasks.
The hreflang project discussed here falls into that final category.
Be the brand customers find first.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
Start your free trial
Mapping hreflang at scale
The challenge was clear: map thousands of URLs across more than a dozen multilingual websites into accurate hreflang XML sitemaps.
Rather than tackling the project manually, I used Google Gemini to help build a custom Python solution.
Here’s how the process unfolded.
Phase 1: Asking for an approach, not just a script
A common pitfall when using generative AI for coding is asking it to sprint before it knows the route. If you simply type, “Write a Python script to create an hreflang sitemap,” you’ll get a generic, fragile piece of code that breaks the moment it encounters real-world data.
Instead, I started by asking for an approach. I explained the scenario: multiple regional domains, organic growth over several years resulting in mismatched URL slugs, translated subfolders, and appended revision years.
Gemini suggested a multi-step, data-driven approach:
Crawl the websites to collect live URLs and their metadata.
Use Python in Google Colab to process the raw data.
Run an exact match cluster first to group identical slugs.
Use an advanced semantic AI model (such as SentenceTransformers) to fuzzy match translated pages based on their titles and normalized URLs.
Phase 2: Crawling and data collection
Following the strategy, I used a crawler to spider all the regional websites. The goal was to generate a unified comma-separated values (CSV) file containing the live URLs, status codes, title tags, and H1s. Screaming Frog worked perfectly for this application.
A critical point: Your AI output is only as good as your crawl data (remember the old saying, “garbage in, garbage out”).
An AI script will fail to map an obvious “exact match” if the target URL is a 404 or a 301 redirect in your source data. You must filter your CSV to include only indexable content before feeding it to the script.
Google Colab provides a free, cloud-based Jupyter notebook environment where you can write, paste, and execute Python code without worrying about local installations or environment variables. You can access it through Google Drive. I found the free version had enough capacity to handle this project.
I uploaded the CSV to Colab, and Gemini provided the initial Python script. The script used a domain-mapping routine to assign language codes, clean the URLs, and generate an XML tree. The initial output was far from perfect.
Phase 4: The iteration (where the real work happens)
If you expect AI to deliver a flawless, edge-case-proof script on the first try, you’ll be disappointed. You’ve probably heard the comparison of AI tools to interns, meaning you need to check their work. That’s very true.
The real value of AI lies in the iteration. As we ran the script, we encountered several unmatched URLs, leaving pages orphaned rather than grouping them with their international counterparts.
Here’s how I iteratively trained the AI to handle the nuances of human-managed websites.
The directory flattening problem
The U.S. site had recently reorganized its blog into topical folders, while the Mexican and Italian sites hadn’t yet been reorganized.
I prompted Gemini with these specific mismatched examples. It responded by adding a URL flattener function to the script, which stripped the topical folders behind the scenes so the translated slugs could align cleanly.
The aggressive semantic trap
To prevent the AI from mixing up different topics, we implemented concept traps. Initially, they were too strict. A UK article about the manufacturing sector wouldn’t match an Italian article because the U.S. title was slightly more generic.
I instructed Gemini to loosen the traps for generic industries while keeping them strictly enforced for critical acronyms (such as “SEO” versus “SEM”). This gave the AI the breathing room it needed to match creative translations.
The translated slug epiphany
The biggest breakthrough came while auditing the Mexican blog orphans. For example, the Spanish URL /detras-de-escenas-historias... is a direct translation of the English /behind-the-scenes-stories... I pointed this out to Gemini.
Instead of forcing me to hard-code hundreds of manual matches, Gemini updated the script to create a “Combined Semantic Signature.” It dynamically translated core operational phrases in the slugs, effectively bridging the language gap for the semantic matching model and connecting dozens of orphaned pages almost instantly.
See where your brand appears, where it doesn’t, and exactly how to win more visibility across search, AI, local, social, and every channel that matters.
Start your free trial
Lessons from building an AI-assisted SEO tool
The project reinforced a simple lesson: AI works best when it’s treated as a collaborator rather than a shortcut.
Be the strategist, let AI be the coder: Don’t just demand a final product. Discuss the architecture, edge cases, and logic first. Treat AI like a junior developer that needs clear architectural direction.
Provide concrete examples: When a script fails, don’t just say, “It’s broken.” For this project, I provided either exact URLs that failed and the URLs they should have matched with, or groups of URLs with mismatches. AI needs concrete patterns to fix its logic.
Embrace the iterative loop: Expect to run the code, identify anomalies, and feed them back into the prompt. Each iteration makes the tool significantly smarter.
Leverage Google Colab: You don’t need to be a Python expert to use Python for SEO. Colab bridges the technical gap, allowing you to run complex data science libraries directly in your browser.
By the end of the project, we had a robust, highly customized Python script that could process a massive CSV and generate a cross-referenced hreflang XML sitemap in minutes.
AI isn’t going to replace technical SEOs anytime soon. However, SEOs who know how to collaborate with AI to build custom, scalable, and useful tools will have a significant advantage.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/How-AI-helped-build-hreflang-XML-sitemaps-at-scale-ONn3vk.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-16 13:00:002026-06-16 13:00:00How AI helped build hreflang XML sitemaps at scale
Much of the GEO conversation focuses on how AI systems discover, extract, cite, and recommend content. That work matters. But visibility also depends on what the content contains once it’s found.
Next-question intent is a way to test whether a page provides enough information to support the user’s next decision, not just the initial query.
The first search is often only the starting point. Real decisions happen in the follow-up questions, comparisons, constraints, and objections that come next.
Content that helps answer those questions gives AI systems more useful material to summarize, compare, cite, and recommend.
From results to narratives: Traditional search vs. AI search
Traditional search was built around a results page: a ranked set of links users could scan, compare, and interpret for themselves. AI search is increasingly built around a synthesized answer drawn from multiple sources.
That changes what content must do. A page can rank, index, and appear technically sound, yet still fail to provide the information needed to support an AI-generated answer. That’s where next-question intent matters.
Search intent asks, “What is this user trying to do?”
Next-question intent asks, “What will the user need to know next before they can trust, compare, choose, buy, book, or move on?”
That question is becoming increasingly important because AI systems don’t simply match queries to pages. They assemble answers, comparisons, qualifications, and recommendations.
In that environment, content must support the full answer path, not just the first query.
Be the brand AI recommends.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
See your AI visibility
The first query is often only the doorway
A user’s first search is often broad, incomplete, or simply exploratory. It signals a direction. Real value appears in what comes next: the follow-up, the objection, the comparison, the constraint, the “practical anxiety,” the “Yes, but what about my very specific situation?” moment.
As the simplest example, someone searches “best CRM software for small business.” The first query becomes a doorway. But the actual buying process begins with the follow-up questions.
Which platform is easiest for a two-person team?
Which integrates best with QuickBooks?
Which one works for a business without a formal sales department?
Which one is best for a local service company rather than a software startup?
Which one won’t make an owner, office manager, or intern quietly resent tech?
These queries aren’t add-on or side questions. They’re the actual decision path.
Otherwise competent content fails at this stage. It answers the query, but doesn’t help complete the conversation. A page can define the category, mention benefits, include a few keywords, and still omit information buyers need to make decisions.
In traditional search, the user might click a few results and assemble context manually. In AI search, the system will assemble it for them. If your content lacks that useful context, it gives the system less to work with and may appear less visible.
Next-question intent is not just a writing exercise
The risk with any new content framework is that it becomes a fresh label for familiar advice. Next-question intent should do more than remind you to “write better content.” It should help you test whether a page contains enough context to support the next step in a user’s decision.
In practical terms, next-question intent means asking whether the content is answer-ready.
Answer-ready content addresses the user’s initial need, anticipates the next layer of decision-making, and provides specific, verifiable, and contextual information to support a synthesized answer.
This distinction matters because AI search visibility isn’t exclusively about rankings. It’s also about citations, mentions, recommendations, and whether a brand is recognized as a trusted answer in a given context.
Those outcomes require something more than volume. They depend on whether the brand’s content provides the system with enough substance to understand what the brand does, who it serves, when it’s useful, why it’s trustworthy, and how it compares to alternatives.
Where good content goes thin
Most brands have decent content that’s accurate, readable, and optimized around a keyword. There may even be an FAQ section, like a useful but decorative basket of afterthoughts.
In AI search, decent may not be enough.
AI systems need extractable clarity, but they also need context. They must understand what something is, who it’s for, when it’s useful (and when it’s not), what evidence supports the claim, and what the user should consider next.
This level of context is where many pages go thin.
As an example, a service page says, “We offer customized marketing strategies.” But what does customized mean?
A real strategy?
A lightly personalized template?
A monthly call where everyone nods at a dashboard no one has time to interpret?
The product page says “safe for families.” Safe for whom?
Babies?
Pets?
People with health issues?
A software page says, “built for small businesses.” What business?
A solo bookkeeper?
A nonprofit?
A 40-person heating and cooling company?
A founder doing payroll late at night after working all day?
Broad claims offer humans little to trust and AI systems little to use. Specific, structured, evidence-backed content offers something better.
A next-question audit looks beyond keyword coverage and asks whether a page contains the information needed to support the next step in the user’s journey.
For every important page, you should ask:
What’s the primary question this page answers?
What would a serious buyer, reader, or researcher ask next?
What objection would stop them from acting?
What comparisons would help them understand the category?
What proof would make this answer trustworthy?
What detail would make this financially, technically, locally, or personally relevant?
Where are we applying broad language because we haven’t done the harder thinking?
The best inputs for the audit often come from inside the business, not from keyword tools alone. Customer reviews, comparison queries, demo questions, sales calls, support tickets, chat logs, internal site search, and objection patterns can all reveal the questions real people ask when making decisions.
That information is often closer to the buyer’s actual path than a neat spreadsheet of keywords.
Examples of next-question content across industries
For a local service business, next-question content might involve service areas, prices, appointment windows, insurance, reviews, emergency availability, or what happens after someone books.
B2B software may invest in next-question content that involves integrations, user roles, implementation times, costs for switching, security, support, or whether a lower-tier plan is useful.
For higher-trust categories like medical, financial, and legal, next-question content involves scope, credentials, risk, regulation, evidence, or when to speak with a qualified professional.
The point isn’t to stuff pages with every possible question. It’s to build content around how people actually decide.
AI search rewards content that completes the answer
Next-question intent helps you avoid one of the least useful responses to AI search: publishing more content because visibility feels uncertain. The better move is more specific, decision-ready content.
If your page says, “I/we help small businesses grow,” explain which small businesses, what kind of growth, what constraints, what proof, what trade-offs, and what alternatives.
For example:
“We help local service businesses without in-house marketing teams improve search visibility and generate more qualified appointment requests by clarifying their website content, answering the questions clients actually ask, and building pages that support both traditional and AI-generated search. This is best for businesses looking for durable visibility rather than a quick paid-ad spike.”
In that same line of thought, if a page says “We’re eco-friendly,” explain the materials, sources, use cases, certifications, limitations, disposal issues, and even circumstances where that claim doesn’t apply.
If a page says “This is AI-powered,” explain what that AI tool actually does, what it automates, what remains human-led, what data it uses, and where users will still need judgment.
This isn’t writing for bots. It’s writing for real people whose decisions are increasingly being mediated by AI-generated answers. The goal is to make your expertise, relevance, and trustworthiness easier to understand and use.
If AI can’t find you, customers won’t either.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
See your AI visibility
The new visibility test
Traditional SEO asked whether a page could rank. AI search asks whether a page can contribute to the answer.
Any page can be indexed, optimized, and technically sound, yet still fail if it lacks substance. It might answer the initial query, but ignore the information users need to make a decision.
The opportunity isn’t to chase every new acronym or rebrand every content plan as a new discipline. It’s to build answer-ready content.
That means clearer definitions, stronger examples, honest comparisons, better proof, more precise positioning, and direct answers to the questions customers ask every day.
In traditional search, visibility belonged to the page that best matched the query. In AI search, it increasingly belongs to the content that helps people move forward.
Google is testing “Sponsored Shops,” a format that groups multiple products from a single retailer into one branded unit inside Shopping results.
This moves competition from the product level to the retailer level, changing what it takes to win visibility.
Feed quality, seller ratings, and assortment depth become more critical than ever.
The format introduces multiple click paths within one ad unit, which could complicate attribution and traffic flow.
Performance Max is a likely vehicle through which Sponsored Shops placements will be accessible when the format formally launches, but nobody knows for sure.
Brands that build strong store-level signals now will be better positioned if and when this rolls out broadly.
Google is running a Shopping test that could change how brands compete for visibility in product search. If it scales, the rules shift, and advertisers who see it coming will have a head start.
Here’s what’s happening and what you should be doing about it right now.
What Is Google Actually Testing?
Google’s Sponsored Shops test groups several products from one retailer into a single ad unit inside Shopping results, alongside the store name, ratings, and brand signals. Think of it as a mini storefront sitting directly inside the search results page, rather than a row of individual competing products.
It is still a test. Google has not confirmed a broad rollout. The direction it points toward matters, though, and Shopping advertisers should be paying close attention.
The test does not exist in isolation. It is part of a broader shift Google has been building toward for a while: more brand-centric, discovery-oriented, and AI-mediated shopping experiences. In 2025, Google introduced the Merchant Brand Profile feature, which lets retailers build brand-presence pages in search with lifestyle images, videos, and business descriptions.
Sponsored Shops looks like the logical next step in that direction, bringing brand identity directly into the Shopping ad unit itself.
Why the Format Change Is a Bigger Deal Than It Looks
Right now, Shopping competition is largely a product-level game. Your listing competes against a competitor’s listing. Better feed, stronger bid, you take the placement.
Sponsored Shops changes the terms of that competition. Instead of a single product earning a spot, your entire store is on display at once: assortment, brand presence, and ratings together. A competitor with a stronger catalog and better seller signals will have a structural advantage that no amount of bid optimization can fully offset.
That’s a meaningful shift. Brands that have been winning through finely tuned individual product listings will need to think harder about how their store presents as a whole. Brands that have invested in feed quality, customer experience, and assortment depth will find that investment paying off in ways it didn’t before.
There’s also a measurement angle worth flagging. A single ad unit with multiple clickable elements (store name, individual products, ratings) creates multiple potential click paths. How traffic splits across those paths, and how that maps to your current attribution model, is an open question every Shopping advertiser should be thinking through before this format scales.
What This Signals About Where Google Is Headed
Google has been explicit about where it wants Shopping to go. In its own communications about 2026 priorities, the company described its goal as making search “a more powerful tool for discovery, where ads can inspire and answer all at once.” AI Mode already surfaces organic shopping recommendations based on query relevance, and Google has confirmed it is testing a new ad format inside AI Mode that showcases retailers offering relevant products, clearly marked as sponsored.
Sponsored Shops fits squarely into that roadmap. It moves Shopping slightly up the funnel, making it as much about brand discovery as product comparison. Rather than a format designed purely to capture demand-ready buyers, it is designed to let brands show up with range and identity in front of people who are still forming their consideration set.
For users, the format is intuitive. Browsing several products from the same retailer without leaving the results page is a better experience than clicking in and out of individual listings. Google tends to expand formats that improve user experience. That’s worth taking seriously.
The PMAX Connection
As of right now, we don’t know what vehicle is going to power sponsored shops. Performance Max is a likely bet based on volume and Google’s push for PMax adoption, but nothing is confirmed. PMax already accounts for roughly 62 percent of Google Shopping spend among major advertisers, and it is already designed to surface both store-level and product-level assets dynamically across Google’s ecosystem.
With this said, though, AI Max for shopping is still in beta, so that might impact what plays a role. We also know that Google does tend to favor some of their newer products which likely helps adoption rate (e.g. AI Max, PMax, & Broad being eligible for AIO ad placements).
What to Do Before This Rolls Out
You do not need to wait for a full launch to get ahead of it.
Start with your product feed. Feed quality has always mattered in Shopping, but a storefront format makes weak data much more visible. Every title, description, image, and availability signal is part of how your store presents in that unit. Get it right now. Research consistently shows that product titles, images, and product identifiers are the three highest-impact feed optimizations, and all three will matter even more in a store-level display format.
Take stock of your seller ratings. In a storefront format, ratings are far more prominent than they are in individual listings. If you have not been actively managing reviews and customer experience signals, that needs to change. A store-level placement that leads with a weak rating is a self-defeating ad.
Look at assortment depth. A Sponsored Shops unit showing three products when a competitor shows ten is a losing presentation. Review whether your full catalog is properly represented in your feed and close any gaps.
Audit your PMax asset groups. Given that PMax is the likely vehicle for Sponsored Shops placements, your asset groups should be fully built out with all image formats, high-quality lifestyle images alongside product images, accurate brand descriptions, and audience signals that represent your full customer base rather than just buyers of individual products.
Revisit your attribution setup. Multiple click paths inside a single unit means your current reporting may not capture traffic flow accurately. Think about how you will measure this before the format exists in your account at scale.
FAQs
What exactly is a Sponsored Shops unit?
A Sponsored Shops unit groups multiple products from a single retailer into one ad block inside Google Shopping results, displayed alongside the store name, ratings, and brand signals. Rather than individual product listings competing side by side, the format presents a mini storefront for a single brand.
Is Sponsored Shops live now?
As of now, Sponsored Shops is still in testing. Google has not confirmed a broad rollout timeline. The format is worth preparing for regardless, since the steps that improve your eligibility for it also strengthen your existing Shopping performance.
Which campaign type will Sponsored Shops use?
Performance Max is the most likely vehicle, given that it already accounts for the majority of Shopping spend and dynamically surfaces store-level and product-level assets across Google’s ecosystem. Making sure your PMax asset groups are fully built out is the right preparation move.
Will smaller retailers be disadvantaged?
Formats that reward assortment breadth, seller ratings, and feed quality tend to favor established retailers with larger catalogs and more customer reviews. That said, a well-optimized feed and a strong seller rating matter more than raw catalog size. Smaller retailers with tight assortments and excellent customer experience signals are not automatically excluded.
What should I do right now?
Focus on feed quality, seller ratings, and PMax asset completeness. These are the fundamentals that will determine Sponsored Shops eligibility and performance when the format expands, and they are also the fundamentals that determine your current Shopping performance.
Conclusion
Sponsored Shops is still in testing. Google Shopping is clearly moving toward a model where brands compete as storefronts, not just as individual products. The shift fits a broader pattern: more AI-mediated discovery, more brand-level visibility signals, more emphasis on the full store experience rather than the individual listing.
The time to build those store-level signals is before the competition catches up, not after. The good news is that everything you do to prepare for Sponsored Shops makes your existing Shopping campaigns stronger right now. There’s no downside to starting.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-09 18:13:492026-06-09 18:13:49Google Is Testing Sponsored Shops in SERPs: What This Means for Advertisers
Google searches ended without a click 68.01% of the time in the U.S. during the first four months of 2026, according to new SparkToro research based on Similarweb clickstream data. That’s up from 60.45% in 2024, a 7.56-point increase in two years.
Fewer searches result in clicks. The share of searches generating at least one click fell 9.51 percentage points between 2024 and 2026 (a 22.9% decline), according to SparkToro. This includes clicks to organic results, paid ads, and Google-owned properties such as Maps and YouTube, but excludes follow-up searches within Google.
Over the same period, the share of searches that led to another Google search rose 7.2 percentage points.
This trend reflects Google’s growing ability to answer questions directly in search results while encouraging users to refine or continue their searches within Google, according to SparkToro.
AI Overviews and zero click. SparkToro believes AI Overviews are likely contributing to the increase in zero-click searches, though the study doesn’t isolate the extent to which the overall rise between 2024 and 2026 can be attributed specifically to AI Overviews.
AI Overviews now appear on more than 20% of Google searches, according to the research. When they do, click-through rates drop by nearly 60%.
AI Mode and zero click. It appears to have played only a limited role during the January to April study period. SparkToro found that just 0.34% of searches transitioned into AI Mode during that time.
However, Google said at I/O 2026 that AI Mode had surpassed 1 billion monthly users and that query volume was more than doubling each quarter, suggesting its impact on search behavior could grow significantly.
Zero click history. SparkToro has tracked zero-click search behavior for years, though its underlying data sources have changed over time. Because the studies rely on different providers, panels, and methodologies, long-term comparisons are not directly equivalent. Still, the available data consistently points to a rise in zero-click behavior over time, according to SparkToro.
2024:58.5% of Google searches in the U.S. (and 59.7% in the EU) ended with no clicks, based on Datos data.
Why we care. The findings suggest Google is increasingly satisfying user needs without sending users to external websites. However, you should interpret direct comparisons across years cautiously because SparkToro’s historical analyses rely on different clickstream data providers and panels.
SEO still matters, but… SEO alone may be insufficient for many publishers seeking to regain historical levels of Google-referred traffic. SparkToro co-founder Rand Fishkin recommended investing in brand awareness and influence on the platforms where your audience already spends time, regardless of whether those efforts drive direct website visits.
Some categories continue to benefit significantly from SEO, including branded searches, local business queries, and high-intent transactional searches, Fishkin said.
About the data. The study used Similarweb desktop and mobile web panel data covering U.S. Google searches from January through April 2026. SparkToro assumed that two-thirds of searches occurred on mobile devices and one-third on desktops. The analysis excludes searches conducted in Google’s mobile search app, where SparkToro said zero-click behavior may be even higher.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/google-zero-stNSya.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-09 15:01:422026-06-09 15:01:42Google zero-click searches hit 68% in early 2026: Study
For large websites, server logs often reveal technical SEO problems long before rankings decline. They show how search engines crawl your site, where crawl budget gets wasted, how quickly servers respond, and whether important pages remain accessible.
Unlike Google Search Console, analytics platforms, and third-party crawlers, server logs capture every request search engines make to your infrastructure.
Yet many organizations never analyze them — missing one of the most valuable sources of technical SEO data available.
Why server logs reveal what other SEO tools miss
Many SEO teams rely on Google Search Console, Bing Webmaster Tools, third-party crawlers, and analytics platforms. Those tools help, but they all rely on data samples, delayed reporting, or simulated crawls.
Server logs capture direct interactions between crawlers and infrastructure. That distinction matters on websites with hundreds of thousands or millions of URLs.
A log file records every request processed by a server. For SEO purposes, the most useful entries come from crawlers such as Googlebot, Bingbot, GPTBot, Applebot, and other verified search engine bots.
Each request generates operational data, including the requested URL, response code, timestamp, user agent, and response timing. Over time, those records form a detailed crawl history.
Your customers search everywhere. Make sure your brand shows up.
The SEO toolkit you know, plus the AI visibility data you need.
Start Free Trial
Get started with
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.
Soft 404s remain one of the most overlooked yet highly consequential SEO issues for large online brands. Unlike a standard 404 page, which correctly returns an HTTP 404 status code, a soft 404 returns a 200 OK response while serving thin, empty, or functionally useless content.
To search engines, these pages appear crawlable and indexable despite offering little or no value, which can quietly waste crawl budget and dilute overall site quality signals.
Common soft 404 examples include:
Out-of-stock product pages that remain live without meaningful replacement content.
Empty category templates created through faceted navigation.
Broken internal search result pages.
Placeholder inventory URLs with little usable information.
Expired listings that still return a 200 OK status code.
Failed rendering can create similar issues when JavaScript content doesn’t fully load for crawlers. On large web platforms, these low-value pages often accumulate quickly and consume significant crawl activity without contributing meaningful search visibility.
Search engines eventually classify many of these pages as low quality. The issue becomes operational when crawlers continue revisiting those URLs repeatedly. Document size analysis within logs provides one way to identify potential soft 404 patterns at scale.
Landing pages with nearly identical response sizes can sometimes indicate templated low-value responses. A group of 60,000 product URLs all returning responses smaller than 100 bytes after inventory expiration usually points toward placeholder templates rather than meaningful content.
Internal search systems create another common example. Empty search result pages often generate highly consistent response sizes because the template loads correctly while no actual content appears.
Response codes alone rarely expose the full pattern of crawl behavior. A clearer operational picture emerges when HTTP status codes are analyzed alongside response sizes, crawl frequency, and URL patterns. Together, these signals reveal how search engines interact with different sections of a web platform and where crawl inefficiencies begin to accumulate.
Large publishers, such as news websites, also encounter soft 404 issues through broken pagination systems or empty archive states.
SaaS platforms sometimes expose onboarding placeholders through crawlable public URLs.
Marketplace websites frequently generate thin pages for inactive listings while still returning successful responses. Document size analysis helps identify these patterns quickly across large datasets.
The case for log retention
Short log retention periods limit the quality of server log analysis. Many crawl patterns develop gradually, with search engines adjusting crawl allocation over weeks or months rather than days.
Historical log data reveals long-term shifts in crawl behavior, including:
Changes in crawl frequency.
Legacy URL activity.
Migration effects.
Infrastructure instability.
Seasonal crawl patterns.
Redirect persistence.
Broader crawl budget fluctuations.
For large websites, six to 36 months of logs often provide meaningful operational history.
Historical data is especially valuable during migrations. Teams compare crawler behavior before and after structural changes to determine whether important sections gained or lost crawl visibility. Without retained logs, those comparisons disappear permanently.
Many organizations still overwrite logs quickly or don’t retain them at all. Once lost, historical crawl data can’t be reconstructed later.
Separating search crawlers from bot noise
Raw server logs contain large volumes of automated traffic unrelated to SEO. Many bots impersonate Googlebot or Bingbot, making accurate filtering essential before meaningful analysis can begin. Effective validation typically combines user agent analysis, reverse DNS checks, and trusted IP verification to separate legitimate crawlers from scrapers, monitoring systems, and malicious automation.
Once filtered correctly, server logs reveal clear behavioral differences between crawler types, including Googlebot Smartphone, Googlebot Image, Bingbot, Applebot, AdsBot, and newer AI-oriented crawlers. Each interacts with web platforms differently, creating distinct crawl patterns, resource demands, and indexing behavior.
Image crawlers place heavier demands on media infrastructure. Mobile crawlers focus more heavily on rendering consistency. AI-focused crawlers often revisit large archive sections repeatedly.
Crawler segmentation helps technical teams prioritize infrastructure improvements based on actual crawl demand rather than assumptions.
Monitoring migrations with log data
Migrations are one of the highest-risk periods in technical SEO, as even well-tested launches can introduce crawl instability.
Server logs provide direct visibility into how search engines respond after deployment, including which redirects crawlers continue to follow, whether redirect chains form, which legacy URLs remain active, and where 404 spikes occur.
Logs also reveal how crawl allocation shifts across the platform, whether response times begin to deteriorate, and which sections search engines continue to prioritize after the migration goes live.
A migration may appear successful during browser testing while crawlers encounter entirely different behavior through caching systems, CDN routing, or redirect logic.
Large ecommerce migrations often reveal persistent crawl activity on old URL structures weeks or months after launch. International platforms sometimes discover regional redirect inconsistencies affecting only certain crawlers. Logs expose those failures early enough to correct them.
Collecting the right log data
Useful log analysis depends on complete records. At a minimum, logs should include:
Remote IP address, including originating IP and optional (X-)Forwarded-For information.
User agent string.
Request protocol, such as HTTP, HTTPS, or WSS.
Request hostname.
Request path.
Request parameters.
Request time, including date, time, and time zone.
Request method.
Response HTTP status code.
Response timings.
These fields create the operational baseline required for meaningful crawl analysis.
Hostname and protocol fields often receive less attention than they deserve. Missing these values creates blind spots on multilingual websites, subdomain-heavy platforms, and CDN-driven architectures.
Many organizations simplify analysis by storing the full request URL as a normalized field containing protocol, hostname, path, and parameters.
Additional fields can further improve analysis quality:
Response byte size.
Cache status.
Referrer.
CDN edge location.
Upstream timing.
Compression type.
Response size data becomes especially valuable during soft 404 investigations and duplicate content analysis.
Why logs remain underused
Server logs often fall between departments. Infrastructure teams view them as operational data. Security teams use them for threat monitoring. SEO teams focus on crawling and indexing. Analytics teams prioritize user behavior reporting.
As a result, one of the most valuable technical SEO datasets within an organization often remains completely unused. Yet server logs answer operational questions that few other systems can.
They reveal which pages absorb the largest share of crawl resources, which sections return unstable responses, and which deprecated URLs continue receiving heavy crawler activity years later.
Logs also expose latency issues affecting specific crawler groups and low-value pages that dilute crawl efficiency. These insights directly influence rankings, crawl allocation, and search visibility.
Technical SEO and GEO increasingly overlap with infrastructure engineering because search engines continuously evaluate operational quality. Server logs expose those operational realities in detail.
For large websites, log analysis stops being optional once crawl scale reaches enterprise complexity. The data already exists. The advantage comes from retaining it, structuring it properly, and using it consistently.
See the complete picture of your search visibility.
Track, optimize, and win in Google and AI search from one platform.
Start Free Trial
Get started with
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.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-09 13:00:002026-06-09 13:00:00What server logs reveal that SEO tools miss
You can be a strong brand, publish high-quality content, and still not have topical authority.
Just look at Great Jones, a kitchenware company.
Their Dutch oven (called The Dutchess) is beautiful, well-reviewed, and featured in industry-leading sites like Vogue, the New York Times, Bon Appétit, and The Kitchn.
But search “best Dutch ovens” on Google or ask an LLM for recommendations, and the brand rarely appears.
It’s not that Great Jones lacks content or press.
What’s missing is the pattern — a consistent, positive framing that ties the brand to Dutch ovens across its own site and third parties.
Without this, search engines and large language models (LLMs) can’t confidently connect the brand to the topic, so they default to the names with stronger signals.
Many brands have some version of this gap. And AI search has only made it more visible.
The good news: You can build this pattern.
In this guide, I’ll show you how using the Topical Authority Pyramid, a framework I created to turn your brand into the go-to name in your niche.
This framework builds on conversations with Amanda Milligan, Content and Growth Manager at Semrush, and my work in brand positioning across ecommerce, SaaS, and finance.
What Is Topical Authority?
Topical authority is your site’s earned reputation for expertise on a specific subject. It forms when your brand and topic appear together repeatedly across the sources that buyers, search engines, and LLMs trust.
Think about the brands you automatically connect with certain topics.
Like these:
You didn’t consciously decide to make those associations.
They formed because those brands kept showing up with the same message, in the same spaces, around the same topic.
That’s topical authority — and it’s also how search engines and LLMs learn which brands are most strongly associated with a topic.
The Topical Authority Pyramid Framework
Topical authority has traditionally been defined by content volume and breadth of coverage.
Publish comprehensively on a subject, and you’d own it.
That’s no longer enough.
As Amanda explains:
The phrase “topical authority” has been around for a long time, but the thinking around it has evolved significantly. At its core, it’s always been about your brand becoming associated with specific topics. What’s changed is how we try to build that association.
Today, search engines and LLMs look for more than coverage. They look for a clear position on the topic and external evidence that supports it.
To address this, I created the Topical Authority Pyramid:
The Pyramid breaks topical authority into three layers:
Foundational authority: On-site content and credibility signals that demonstrate experience, expertise, authoritativeness, and trustworthiness (E-E-A-T), and category fit. (Think category pages, about pages, author bios, comparison content, FAQs, customer reviews, case studies, and more.) Still important, but not enough on its own.
Point of view (POV-led authority): A specific, consistent angle that separates you from every other brand covering the same ground. It gives buyers a reason to choose you and AI systems the confidence to recommend you over competitors.
Proof-backed authority: Third-party signals (mentions, reviews, citations, and data) that back up your POV across the wider web. It turns your POV from self-declared to independently verified.
Each layer works alongside the others to establish your brand as the expert in your niche and earn more visibility in search engines and LLMs.
Many brands, including Great Jones, have strong foundational authority and scattered proof, but no consistent POV tying it all together.
Here’s how to build all three.
Free resource: Download our free Topical Authority Audit template to audit your topics, score competitor authority, and track your progress. Fill it out as you work through each step below or at your own pace.
Step 1: Audit Your Topic Reputation
Your brand likely already has a topical reputation, whether you’ve shaped it intentionally or not.
Audit it before deciding what to build.
Research Your Current On-Site Associations
The gap between what you publish and what you want to be known for may be wider than you expect.
This is something Amanda has experienced firsthand:
When I did content audits, I’d inventory every piece of content by topic. You might find you have dozens of pieces on something that isn’t even your priority, and only five on the topic you actually want to own. That mismatch is exactly what a topic audit is designed to surface because what you’ve published is what you’re telling Google and buyers your priorities are.
The fastest way to assess this is with Semrush’s Organic Rankings tool.
Enter your domain to automatically see your brand’s strongest topic associations, organized by the topics getting visibility.
When I did this for Great Jones, their strongest topical associations were “recipes” and “celebrity chefs.”
Dutch ovens barely registered.
Yet, the Dutchess is their primary product.
And “Dutch oven” alone gets over 200,000 monthly Google searches.
Great Jones has a big opportunity to increase their topical authority for Dutch ovens and convert some of this search interest into sales.
These are the kind of topical association gaps you want to surface in this step.
Two more places to look:
Google Search Console: Go to “Performance” > Queries and sort by clicks or impressions. You’ll see the topics that attract users to your site.
Branded queries on Google and LLMs: Search “[your brand] + your topic” and “what is [your brand] known for” to see how search engines and LLMs describe you
Audit Your Off-Site Presence
Next, review your third-party coverage: mentions, reviews, roundups, and editorial press.
This is where many brands have the biggest gap, and it’s the one AI systems appear to weigh most heavily.
Run these checks:
Search “[your brand] + [topic]” and look beyond your own site: What’s showing? Industry blogs? Reddit? Editorial coverage? Or nothing?
Ask an LLM: “What are the best [topic] brands?” and “Where would you recommend buying [topic]?” See whether your brand surfaces and what it’s associated with.
Check “best of” lists, roundups, and comparison articles for your topic: Are you in them? If so, where do you rank and how are you described? If not, who is?
A quick off-site audit for Great Jones showed me they’ve earned coverage any kitchenware brand would envy: features in major lifestyle publications and partnerships with prominent chefs and influencers.
But when you look specifically at Dutch oven coverage, the off-site gap is obvious.
Most of the top-ranking articles are a few years old (or older):
And the overall sentiment is inconsistent.
For example, in Food & Wine’s Dutch oven roundup, the Dutchess appears under the “Other” section (rather than “Top Picks”) with a caveat about heating issues.
In this Bon Appétit roundup of the best Dutch ovens, Great Jones is categorized under “Dutch ovens we don’t recommend.”
They’re also notably missing from some use-case roundups, like this one from Serious Eats:
In Reddit threads where buyers are actively looking for Dutch oven recommendations, Great Jones rarely comes up.
When it does, many of the threads are from years ago:
Great Jones has real brand equity to build on.
But it’s just not adding up to a solid reputation in Dutch ovens — yet.
Step 2: Choose the Topic You’ll Build Authority Around
You can’t build authority on everything at once.
This step narrows your focus to one topic worth owning based on a few crucial factors:
What drives revenue
Where competitors are weak
Where your brand has room to claim a position
Build and Prioritize Your Topic List
Start by listing the topics you want buyers, search engines, and LLMs to associate with your brand.
Begin with the obvious ones: the products, categories, use cases, and problems you want to be known for.
Then expand with adjacent topics buyers already care about.
For Great Jones, that might include slow cooking, one-pot meals, kitchen gifting, or cookware care.
Look especially for topics where you already have traction, competitors are weak, or your brand should be associated but currently isn’t.
Once you’ve identified 10 to 15 topics, add them to the “Topic Audit & Scoring” tab in your spreadsheet.
Next, narrow the list down.
Not every topic on your list is worth building a reputation around right now.
For each one, ask two questions:
Do you want to own it? Does it drive revenue, support a product you sell, or build a reputation that brings buyers to you?
How urgent is it?
High: Directly tied to revenue and an opportunity you can act on now
Medium: Tied to revenue, but the opportunity or timing isn’t right yet
Low: Worth tracking but not acting on yet, or no direct business connection
You should end up with three to five high-priority topics to investigate next.
Run a Query Audit
Now test each shortlisted topic to see who already owns the space and where there’s room for your brand to carve out a position.
For each topic, run four queries on Google and LLMs:
Query type
What to search
What it tells you
Head term
The topic as-is (“Dutch ovens”)
Who owns the broad topic; what AI defaults to
Best query
Add “best” or a qualifier (“best Dutch ovens under $200”)
Where buyer intent lives; which brands AI recommends
Brand query
Your brand + the topic (“Great Jones Dutch oven”)
Where you specifically stand; how AI currently describes you
Specific angle
A query tied to an association you might want to own (“Dutch oven for gifting”)
Whether that territory is already claimed or still open
As you run each query, note:
Which formats show up most: editorial lists, reviews, Reddit threads, brand pages
Whether AI systems name specific brands without being asked (unprompted)
Whether community results show buyers asking for recommendations or comparing options
Record this in the “Query Audit” tab of your spreadsheet.
If a query shows buying intent but the top results barely address it, that’s a topical authority opportunity.
For example, when I search “Dutch ovens” and “best Dutch ovens,” the same brands consistently come up: Le Creuset, Staub, Lodge, and Caraway.
But rarely Great Jones.
And for “Dutch oven for gifting,” ChatGPT didn’t mention Great Jones at all.
Great Jones only appears when buyers already know to look for them.
More importantly, some topics, such as gifting, aesthetics, and non-toxic coating, are not clearly owned by any brand.
That’s where the opportunity is.
Score by Association Strength
After the Query Audit, score your presence on each topic against three competitors on a 0 to 3-point scale.
The score reflects your overall standing across the Topical Authority Pyramid: foundational, POV, and proof combined:
Score
What it means
0
Not present anywhere for this topic
1
Present but weak or negative
2
Present and positive but inconsistent
3
Consistently prominent across high-authority sources and AI
Note: This isn’t a precise measurement. Use your observations, priorities, and market knowledge to guide the score.
Score your brand first, then each competitor.
After your scoring is complete, look for high-priority topics where you scored a 1 or 2 and at least one competitor scored a 0 or 1.
Those are topics where buyer demand is real, you have some footing, and no competitor has locked it down — the conditions for a winnable position.
For Great Jones, “Dutch ovens for gifting” fits the pattern: high priority, room to claim it, and no clear leader.
By the end, you should have one topic to focus on.
Have more than one? Choose the one closest to revenue or where the gap between your current and desired reputation is smallest.
Have none? Go niche. Instead of “Dutch ovens,” try “enameled cast iron Dutch ovens.” A narrower topic is easier to own and still builds toward the bigger one.
Step 3: Identify Your Topic POV
You’ve identified one viable topic. Next, decide what reputation to build around it.
Your POV is the specific angle you own inside that space.
It’s what makes your brand distinct to buyers, search engines, and AI systems.
Like these brands — same topic, completely different associations:
Research What’s Already Owned
Before identifying your POV, map what dominant brands in your space are already known for.
These are the POVs to avoid. Going after any of them directly means competing for territory another brand has spent years building.
Start with your notes from the Query Audit. The patterns there tell you a lot about which competitors own what.
To run your audit, go through each belief in the table and identify which proof assets you already have and which are missing.
Use the POV Proof Planner in your template to record your findings:
For Great Jones’s gifting POV, a quick proof audit surfaces:
Consideration proof exists: The brand has features in the New York Times, Good Housekeeping, and many others, but most aren’t connected to gifting or were published years ago
Comparison proof is sparse: Some decision-stage proof tied to gifting exists for Great Jones, but it’s not consistent enough to increase AI recommendations
Step 5: Build Your On-Site Foundation
Before search engines and LLMs can associate your brand with your POV, you need to establish it on your site.
This step is about building that foundation: the hub and supporting pages where your topic, POV, and early proof signals all come together.
Create a Hub Page for Your POV
Your hub page is the central authority document for your POV.
It defines the topic, explains why it matters, and routes buyers to supporting pages that go deeper.
Side note: If you’ve built pillar pages and topic clusters before, this will feel familiar. The structure is similar, but the organizing principle is proof and belief, not coverage and keywords.
For Great Jones, that could be a “Dutch oven gifting guide.”
It would link to the Dutch oven product page and explain why Dutch ovens make exceptional gifts.
Supporting pages, such as gift basket ideas, a gifting FAQ, and a report on cookware gifting would also be linked.
If you’ve been publishing for a while, you may already have a page that can serve as the hub: a category page, a subcategory page, or an industry-specific landing page.
Build Supporting Pages
Supporting pages go deeper than the hub.
Each one proves a specific aspect of your POV at a specific stage of the buyer journey.
Go back to the proof assets you mapped in Step 4 — they tell you what you need to prove and at which stage.
Your supporting pages are how you do it.
For Great Jones, the comparison stage is a clear gap.
To convince buyers the Dutchess is a better gift than the alternatives, they need dedicated comparison pages, backed by awards, endorsements from leading industry sites and public figures, and head-to-head data.
Other supporting pages might include:
Dutch oven gift basket ideas: What to pair it with and how to present it, backed by customer photos and a relevant publication feature
Gifting FAQ: Sizing, monogramming, return policies, with real customer questions pulled from reviews
The Gift-Worthy Dutch Oven Report: Proprietary survey data on how customers buy, give, and display the product
Pro tip: Strengthen your hub and cluster pages with on-site trust signals. Include author bios that show real niche experience in the topic, named expert sources or contributors, and an About or editorial page that clearly ties your brand and contributors to the category.
Identify what pages you need, and fill out the rest of the “On-Site Foundation Planner” tab in your template.
Structure Each Page for Readers and Machines
Lead with the most important information first — also known as the inverted pyramid.
It makes your pages easier for readers to scan and for machines to interpret.
Then, make sure each page has:
Clear section headings: Labeled so readers and machines immediately understand what each section covers
POV language: Reuse the same phrases and framing tied to your angle throughout
Schema markup: Structured data that helps search engines and AI systems understand your content and context
Semantic HTML: Proper use of HTML tags so machines can correctly interpret your page structure
Link Your Pages
Each hub and supporting page proves something on its own.
Link them together, and you create a proof system.
Link from the hub to your 5–10 most important supporting pages in the body. Not just in the nav, breadcrumbs, or footer.
Link every supporting page back to the hub. Keep key pages within 2–3 clicks of each other.
Use descriptive, relevant anchor text to help people and machines understand what the linked page is about
Step 6: Create an Off-Site Proof System
A strong POV and foundation won’t get you into AI answers if the association exists only on your site.
This is one of the biggest shifts in how topical authority works, as Amanda explains:
Topical authority isn’t just about what’s on your site anymore. You need third-party sources — coverage, mentions, appearances, even reviews — independently reinforcing the same association. If the only place your brand is tied to a topic is your own content, that’s often not enough to build the pattern that AI systems and search engines need to trust you on it.
This step reinforces your POV in the places buyers and AI systems already trust.
Start with One Signature Proof Point
A signature proof point is an original, specific story or finding about your topic.
Something others outside your brand would want to reference, share, or build on.
That could be:
Proprietary data from your own sales, customer behavior, or research
A trend you’ve spotted and named before anyone else
A contrarian observation backed by evidence
For Great Jones and the gifting POV, the insight has to tie Dutch ovens to gifting.
They might pull data from their own sales — say, a 4x spike in Dutch oven purchases in the two weeks before Mother’s Day — and turn it into a “State of Mother’s Day Gift-Giving” report.
That report becomes a press pitch to lifestyle publications, a video on their YouTube channel, and a thread on Reddit’s r/gifts.
One insight, multiple placements, all reinforcing the same association: Great Jones = gifting.
To find yours, start with your proof assets from Step 4.
Look for patterns in your data, reviews, industry trends, or customer behavior.
Distribute Your Proof Point
Once you have a signature insight, decide where and how to distribute it.
There are four main buckets:
Brand channels: Content you publish directly to audiences you’ve built: email newsletters, marketplaces, review sites, podcasts, social media, SMS or loyalty messaging, local profiles
Community: Discussions in spaces your buyers already trust, such as Reddit, niche forums and industry groups, social media comments and communities
Partners: Others who extend your reach into new audiences, including affiliates, influencers, retail partners, and integrations
Earned: Third-party coverage you pitch but don’t control, such as media mentions, press features, user-generated content, and editorial placements
For each bucket, identify the specific publications, platforms, or communities where your insight is most relevant.
Not sure where to start?
Run a search on Google or an LLM related to your proof point and look at the sites that rank and the sources that get cited.
Those are the places worth showing up in. List them in the “Off-Site Proof Planner” tab of your template.
For Great Jones, some of that infrastructure is already in place.
They already have the social media following, media clout, and collaborations with names like cookbook author Molly Baz.
What they need is a focused distribution of insights around their gifting POV.
That might look like:
Briefing partner creators on a gifting-specific collaboration, like pitching fresh coverage that ties the Molly Baz collab to gifting
Pitching their Mother’s Day gifting sales data to lifestyle publications already covering Dutch ovens
Reframing existing social content around the gifting angle
Now check whether it’s starting to influence how search engines and LLMs describe your brand.
Use the “Progress Tracker” tab in your spreadsheet to record what you find at 30, 60, and 90-day intervals.
Foundational Layer: Are You Showing Up More?
Coverage tracking tells you whether your topical footprint is growing:
Go back to your Step 2 notes. How many of your four query types surfaced your brand unprompted? Run them again and compare.
Also monitor pages ranking for queries you didn’t directly target, and rising impressions for queries related to your topic.
For Great Jones, the baseline visibility was weak for many non-brand Dutch oven queries.
Showing up in two or three queries at 90 days — especially “Dutch ovens for gifting” — would be a real sign of progress.
Tools that help:
Semrush’s Organic Rankings tool (the Topics report) for association trends
Semrush AI Visibility Toolkit: The Visibility Overview tool to see whether your AI Visibility score and mention count are climbing, and Prompt Tracking to re-run your query set on a set cadence
Google Search Console for impressions and queries by page
The POV layer tracks language. Specifically, whether mentions of your brand are increasingly paired with your POV.
Run POV-specific prompts monthly and check the wording.
For Great Jones, that’s searches like “Dutch oven wedding gift” or “best Dutch oven to give as a gift.”
And when the Dutchess shows up in reviews, comparisons, and “best of” listicles, watch for the language around it.
Is it being called “a great house-warming gift,” “splurge-worthy,” or “the kind of gift that gets displayed”?
That’s the POV landing.
Tools that help:
Brand24 to track web and social mentions
Semrush’s Perception tool for sentiment trends, and Narrative Drivers for the attributes and phrases AI ties to your brand
Proof Layer: Are Others Confirming Your POV?
The proof layer tracks third-party confirmation.
Are media mentions, third-party pages, and niche communities backing up the POV you want to own?
Start with your proof point.
Are others citing or referencing it? That’s a signal your off-site distribution is working.
Then, go broader.
Run [Your Brand] + [POV] queries on Google and an LLM.
Check whether you’re appearing in more third-party sources associated with your POV.
Are buyers recommending you unprompted in Reddit or niche communities? Are your hub pages attracting links from relevant sites?
When your brand appears, is it being described in relation to your POV?
For Great Jones, that might be a gift guide naming the Dutchess as the go-to Dutch oven for wedding gifts.
Tools that help:
Google Alerts for basic brand mention tracking, or Meltwater for a more robust option
Semrush’s Competitor Research tool to surface sites citing competitors but not you, and Narrative Drivers for the Top Cited Domains shaping your topic
Build the Pattern That Wins in AI Search
Great Jones proves that great press and a great product aren’t enough for topical authority.
If search engines and LLMs don’t have clear associations attached to your brand, showing up online will be a struggle — no matter what Vogue thinks of you.
But that’s fixable.
The Topical Authority Pyramid gives you the framework:
A strong foundation that proves you belong in the category
A POV that makes you distinct
Proof that backs it up across the web
Once your first topic takes shape, expand.
Follow the Topical Authority Pyramid for your next topic, claim more territory, and deepen your authority in adjacent spaces.
Do this well, and search engines and LLMs may just start recommending you by default.
Want a repeatable way to monitor your AI visibility over time? Our AI visibility audit guide walks you through it step by step.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-08 21:32:122026-06-08 21:32:12How to Build Topical Authority in the AI Search Era (7 Steps)
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.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-08 15:42:582026-06-08 15:42:58Google to update Local Services Ads policies in July
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.
Your customers search everywhere. Make sure your brand shows up.
The SEO toolkit you know, plus the AI visibility data you need.
Start Free Trial
Get started with
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.
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.
Narrative
What it tracks
The core strategic question
The “best” narrative
How 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” narrative
How 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” narrative
How 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.
See the complete picture of your search visibility.
Track, optimize, and win in Google and AI search from one platform.
Start Free Trial
Get started with
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.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-08 14:00:002026-06-08 14:00:00The problem with AI share of voice and 3 metrics that matter more
