Adthena is bringing competitive visibility to ChatGPT ads — launching a new platform designed to track how brands show up across prompts, placements and competitors.
What’s happening. Adthena has unveiled its ChatGPT Intelligence Platform, positioning it as the first tool to offer whole-market visibility into ChatGPT Ads — similar to what it already provides for Google Ads.
The platform monitors more than 300,000 daily prompts, tracking which brands are advertising, where ads appear, and what messaging they use.
Why we care. ChatGPT’s native ads tools currently show advertisers a limited, self-focused view of performance.
Adthena is stepping in to fill that gap — giving advertisers insight into competitors, share of voice and prompt-level activity in a channel that’s still largely opaque.
Zoom in. The platform offers a full view of how ads appear across ChatGPT conversations, alongside competitive intelligence on who is bidding, where and with what creative.
It also includes real-time recommendations to optimise campaigns, helping advertisers act on insights rather than just observe them.
What else. Advertisers can analyse ad copy performance, monitor brand presence and track share of voice — all within a single dashboard that combines ChatGPT and Google Ads data.
That cross-channel view is designed to help teams make smarter budget decisions as search behaviour shifts.
Context. The launch follows Adthena’s earlier AdBridge tool, which helps advertisers migrate Google Ads campaigns into ChatGPT’s Ads Manager.
Together, the tools signal a growing ecosystem forming around AI-driven search advertising.
What they’re saying. CMO Ashley Fletcher said early adopters will shape the competitive landscape — and that the new platform “tells you exactly what to do about it.”
What to watch. Expect to see more third-party tools emerge as advertisers demand better visibility into AI-driven ad environments. Adoption will likely depend on how quickly brands start treating ChatGPT Ads as a core performance channel, while pressure may build on platforms like ChatGPT to improve their own native reporting capabilities.
The TikTok sale is complete. TikTok USDS Joint Venture LLC closed on January 22, 2026, placing majority control in the hands of American investors Oracle, Silver Lake, and MGX. The ad infrastructure and auction mechanics are still running.
User deletions spiked nearly 150 percent post-announcement, but active usage held flat. Sentiment and platform health are two different things.
Governance shifts hit auction dynamics before they touch the product. Watch CPM and conversion rate week over week, not month over month.
Pulling budget reactively during platform transitions destroys learning phase momentum and costs more to rebuild than staying in.
Platform governance is now a media planning variable. The TikTok sale set a precedent that extends to every major platform in your media mix.
On January 22, 2026, TikTok USDS Joint Venture LLC officially purchased TikTok’s U.S. operations from ByteDance, transferring control to an American-led investor group anchored by the tech giant, Oracle, and investment groups Silver Lake and MGX.
What does this mean for advertisers on the platform?
The app isn’t shutting down. This is a governance restructuring, and TikTok’s ad products and auction mechanics are still running for its 170 million U.S. users. That said, regulatory shifts like this create real volatility risks that deserve a structured response.
This guide breaks down what did and didn’t change, and how to protect your performance without abandoning one of the most powerful paid channels in your media mix.
What the TikTok U.S. Sale Actually Changes
After the sale, TikTok USDS Joint Venture LLC now owns the U.S. aspects of the platform. ByteDance still owns a 20 percent stake, but the governing majority is now American.
Here’s what that means in practical terms.
What changed
Data governance is the biggest structural shift. U.S. user data is now stored and managed under American oversight, with Oracle handling cloud infrastructure. The new joint venture is also retraining TikTok’s recommendation algorithm on U.S. user data exclusively, to keep the content feed free from outside manipulation. Users won’t notice that change immediately, but it’s significant.
The American-owned entity now sets content moderation. The transition introduced additional compliance review processes for ad targeting parameters and audience segments, requiring some targeting options to be re-approved as the platform rebuilt its ad infrastructure.
What didn’t change
The TikTok ads infrastructure is intact. TikTok Ads Manager, Smart+, TopView, and In-Feed formats are all still live. At the 2026 NewFronts, TikTok unveiled new ad formats, including Logo Takeovers and Prime Time placements, showing that new ownership isn’t slowing down on advertising anytime soon.
Creator monetization is also unchanged. The TikTok algorithm still powers discovery through the For You Page, so its rules are still critical for anyone trying to make money on the app. Per TikTok CEO Shou Chew’s internal memo, ByteDance’s global entity continues to manage the platform’s e-commerce operations and broader marketing functions on the new U.S. platform.
Early User Signals: Noise or Real Risk?
According to Sensor Tower data shared with CNBC, the daily average of U.S. users deleting TikTok jumped nearly 150 percent in the five days following the joint venture announcement, compared with the previous three months.
A drop that sharp could raise serious concerns for advertisers, but it deserves some context before we decide whether it signals real risk.
Three things fueled the spike, and none of them signal structural collapse:
A data center power outage caused failed uploads and For You feed irregularities, which TikTok publicly acknowledged.
An updated privacy policy prompted in-app backlash, though the flagged language was present in an archived August 2024 version of the same policy.
Uncertainty around the new ownership’s content moderation approach prompted some creators to hedge their distribution across other platforms.
Competing platforms saw temporary bumps. U.S. downloads for UpScrolled increased more than tenfold, and platforms like Skylight Social and Rednote climbed 919 and 53 percent week over week, respectively.
Monitor trends like these. A sustained shift in creator behavior matters far more to your campaigns than a short-term uninstall spike driven by a data center outage and a misread privacy policy.
The Real Paid Media Variable: Auction Volatility
Here’s what most advertisers miss during a major platform transition: governance changes hit auction dynamics before they touch the product.
TikTok operates on an auction system where costs fluctuate based on competition, targeting choices, and ad quality. Your cost per mille (CPM) isn’t a fixed rate. It moves with how many advertisers are competing for the same audience at any given time, which makes the post-sale period worth watching closely.
Two forces are working in opposite directions right now.
The first is upward CPM pressure from the algorithm retraining cycle. The new joint venture is retraining TikTok’s recommendation algorithm on U.S. user data exclusively. As that process plays out, ad delivery patterns can shift mid-campaign. Campaigns optimized against the previous algorithm’s behavior may see performance move before any creative or targeting change explains it.
The second force is a temporary drop in auction competition. Some marketers were already planning to scale back spending heading into the transition. That window won’t stay open long. As advertiser confidence returns and paused budgets resume, CPM pressure will rise again.
Three things to monitor right now:
Watch your week-over-week CPM movement. Any sustained spike signals a shift in auction dynamics, not just creative underperformance.
Monitor conversion rates independently of volume, since algorithm retraining can compress efficiency without changing impression counts.
Track creative fatigue aggressively. TikTok’s auction dynamics and creative decay rates punish advertisers who let assets run too long without refreshing.
Why Overreacting Hurts Performance
Pulling budget in response to platform uncertainty feels like risk management, but it’s often the riskiest move you can make in practice.
TikTok’s algorithm depends on a learning phase to optimize ad delivery. During this window, it tests bidding by evaluating your audience and creative to identify who is most likely to convert. Full optimization stability is generally reached around 50 conversions per ad group.
Any significant change, like pausing campaigns or cutting budgets sharply, pushes an ad group back into the learning phase, resetting the optimization progress already built.
The cost of underfunding is equally concrete. Campaigns that don’t meet effective spending thresholds show CPMs 40 to 60 percent higher than properly funded ones, because the algorithm cannot optimize without sufficient data volume.
The post-sale period sharpens this dynamic considerably. With the algorithm retrained on U.S. data, cost per acquisition may increase 20 to 40 percent before stabilizing. Pausing during this window causes the algorithm to stop learning from your account entirely. Advertisers who read that temporary cost-per-action (CPA) spike as a signal to exit will reset their learning phase mid-cycle, compounding the problem they were trying to solve.
There’s also a competitive angle worth considering. Brands that maintained their presence through the transition period emerged with stronger relative positioning as competitors pulled back. When auction competition drops, CPMs follow. Advertisers who stayed in captured that efficiency. Those who paused paid higher costs to re-enter a recovering auction.
Volatility creates both inefficiency and opportunity. Which one you experience depends on whether you plan for it or react to it.
How to Protect Performance Without Abandoning TikTok
Here’s the operating model to build so you can capitalize on TikTok’s volatility now, or another platform’s in the future.
1. Pre-Approve Budget Flex Scenarios
Making significant budget changes reactively can ruin campaign performance. Deciding your triggers now means you respond with a plan instead of scrambling.
Don’t wait for a performance drop to decide how you’ll respond. Define your thresholds in advance, like a sustained CPM increase of 20 percent or more week-over-week or a conversion rate drop held across two consecutive weeks.
2. Keep Meta and YouTube Shorts Warm
A channel you haven’t run in months is a cold channel. Meta and YouTube Shorts require the same data runway as TikTok to reach full optimization stability, roughly 50 conversion events per ad group. Maintain enough spend on both to keep your audiences warm and your algorithms learning, so you’re never rebuilding from zero.
3. Increase Creative Velocity
On TikTok, creative has a short shelf life. Volatile auctions accelerate that decay further. Volatile auctions accelerate that decay. Have new creative variations ready to deploy before you need them, not after performance has already dropped.
4. Tighten Weekly Reporting Cadence
Temporarily shift from monthly to weekly performance reviews. CPM movement and conversion rate shifts during algorithm retraining happen fast. Catching them early gives you time to adjust bids before small inefficiencies compound.
5. Audit Platform Dependency
You want to ensure you’re spending enough to gain traction, but not so much that one platform can make or break your marketing success. Roughly 13 percent of agencies’ social spend over the past 12 months has gone to TikTok. If TikTok represents more than 30 percent of your paid social budget, you have concentration risk that deserves a contingency plan.
Zooming Out: Governance Is Now a Media Planning Variable
The TikTok case underscores a growing tension between digital privacy and free speech in the government’s approach to technology platforms. As apps collect vast amounts of user data, governments will likely continue scrutinizing foreign-owned platforms.
That scrutiny isn’t going away, and it won’t stay limited to TikTok. If another foreign-owned platform gains popularity, Congress may revisit this model of ownership-based restrictions. The legal and regulatory architecture built around TikTok is now a template.
Meanwhile, data sovereignty pressures are intensifying globally. Governments worldwide are restricting cross-border transfers and asserting jurisdiction over data within their borders, possibly touching every major platform operating at scale in the U.S. market.
Platform risk is no longer purely a performance question. Ownership structure and data governance now belong in the same due diligence conversation as CPM benchmarks and audience sizing. A channel that delivers strong return on ad spend (ROAS) today can face structural disruption tomorrow for reasons unrelated to its ad product.
FAQs
Did TikTok Sell?
On January 22, 2026, TikTok closed a deal to divest its U.S. entity to a joint venture controlled by American investors, with Oracle, Silver Lake, and MGX collectively owning 45 percent of the new entity. ByteDance retained nearly 20 percent. The platform continues operating under U.S. majority ownership as TikTok USDS Joint Venture LLC.
How Much Did TikTok Sell For?
The deal valued TikTok U.S. at approximately $14 billion, a figure widely considered low given that TikTok’s U.S. entity generates roughly $14 billion annually in advertising revenue alone.
Analysts have noted that the $14 billion price tag gives the company a price-to-sales ratio comparable to that of mature, low-growth companies, far below the multiples commanded by Meta and Alphabet. Most independent estimates put TikTok U.S.’s true market value significantly higher.
Conclusion
TikTok remains a Tier 1 paid media channel. The U.S. market accounts for roughly 38 percent of TikTok’s entire global advertising income, a concentration that reflects genuine advertiser confidence. That doesn’t change because of a governance restructuring.
What does change is how you should think about it. Tier 1 status doesn’t mean risk-free. The TikTok sale established a precedent for how governments can intervene in platform ownership, and that precedent applies beyond TikTok. Every major platform you rely on now carries some version of this risk.
The smart move is better planning.
Stay active on TikTok while the auction competition is still recovering. Build a paid media strategy that lets you flex budgets quickly when conditions shift. Define your thresholds now so you don’t make reactive decisions under pressure, and keep your creative velocity high. Short-form content gives you a low-cost way to keep creative cycling regardless of what’s happening at the platform level.
The platforms that attract 170 million users don’t disappear overnight. Build your strategy around that reality.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-05 19:00:002026-05-05 19:00:00What Does the TikTok Sale Mean for Advertisers?
OpenAI is taking the next step in building its ChatGPT ads platform — introducing self-serve buying, CPC bidding and improved measurement to bring more advertisers into the ecosystem
What’s happening. Ads in ChatGPT are moving beyond a limited pilot, with new ways for businesses to buy and manage campaigns. Advertisers can now access inventory through agency and tech partners — or directly via a new beta Ads Manager rolling out in the U.S.
This marks a shift from a controlled test environment to a more scalable ad platform.
Why we care. Until now, access to ChatGPT ads has been restricted and expensive, limiting participation to large advertisers. These updates lower the barrier to entry, opening the door for SMBs, startups and a wider range of brands to test the channel.
At the same time, introducing CPC bidding brings ChatGPT closer to established performance platforms, allowing advertisers to optimise for actions — not just impressions.
Self-serve Ads Manager. The new Ads Manager gives advertisers direct control over campaigns, including budgeting, bidding, creative uploads and performance tracking.
While still in beta, it signals OpenAI’s intention to build a full-service ad platform — not just a partner-led ecosystem.
Between the lines. This is a familiar playbook. Platforms typically start with high-touch, partner-led campaigns before moving to self-serve tools that unlock scale. ChatGPT is now entering that second phase.
CPC bidding arrives. Previously, ChatGPT ads were sold on a CPM basis. The addition of CPC means advertisers can now align spend with user actions — a critical step for performance marketers.
Given the nature of ChatGPT queries — often exploratory, comparative and decision-driven — clicks could become a strong proxy for intent.
Measurement catches up. OpenAI is also rolling out pixel-based tracking and a Conversions API, allowing advertisers to measure actions like purchases, sign-ups and leads.
Importantly, this data is aggregated, with no access to individual conversations — reinforcing OpenAI’s emphasis on privacy.
Why this is a big deal. Measurement has been one of the biggest gaps in early ChatGPT ads. Without it, advertisers struggled to justify spend. These updates begin to close that gap and make optimisation more viable.
The ecosystem grows. OpenAI is also expanding its partner network, working with agencies like WPP and Publicis Groupe, as well as tech platforms such as Criteo and Adobe.
This allows advertisers to buy ChatGPT ads through tools and workflows they already use.
What to watch:
How quickly self-serve adoption scales
Whether CPC performance holds as competition increases
How measurement evolves to match advertiser expectations
Initial reports from SimilarWeb indicate ChatGPT ads are outperforming traditional benchmarks on engagement — but with limited inventory and small-scale tests, it’s too early to call this a long-term trend.
What’s happening. According to early analysis, ads appearing in ChatGPT conversations are generating strong click-through rates vs Display and Podcast channels, likely driven by high-intent user queries and the native way ads are integrated into responses.
Unlike traditional search ads, these placements appear directly within conversational answers, making them feel more contextual and less disruptive.
Why we care . If these early CTRs hold at scale, ChatGPT could become a serious performance channel — especially for advertisers looking to reach users at the moment of intent.
But there’s a catch: inventory is still limited, and early performance often looks better before wider rollout introduces more competition and variability.
Between the lines. High CTRs don’t necessarily mean high performance. Conversion quality, cost efficiency and scalability will ultimately determine whether ChatGPT ads can compete with established platforms like Google Ads.
There’s also the novelty factor — users may be more likely to engage simply because the format is new.
Zoom in. Some categories are already showing stronger signals than others.
Mother’s Day-related prompts are far more likely to trigger ads—about three times more than average—because they signal strong purchase intent, with brands like Etsy, Nordstrom and flower retailers already showing strong visibility.
What to watch:
Whether CTRs hold as inventory expands
How conversion rates compare to search and social
If pricing models evolve beyond early testing phases
Bottom line. ChatGPT ads are off to a strong start on engagement — but until scale, cost and conversion data catch up, advertisers should treat this as a promising test channel, not a proven one.
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The AI engine pipeline has 10 gates between your content and a recommendation:
Discovered.
Selected.
Crawled.
Rendered.
Indexed.
Annotated.
Recruited.
Grounded.
Displayed.
Won.
Confidence at each gate multiplies, which means your worst gate sets your ceiling, and a single near-zero anywhere in the chain drags the whole result down with it.
That dynamic leads to a simple rule. The “Straight C” principle: in any multiplicative system, the weakest stage sets the ceiling for the entire system, and the highest-leverage fix is always the near-zero, not the near-perfect.
Brent D. Payne nailed it in Sydney in 2019: “better to be a straight C student than three As and an F.” Gary Illyes had been sketching out Google’s multiplicative ranking model, and I scribbled the lot from memory on split beer mats while everyone else went to the bar for another round. The principle stuck with me even though the beer mats didn’t.
Applied to the 10-gate pipeline, the principle makes the work order obvious: find your F grades, fix them first, then find your D grades, and only then worry about pushing your other gates from C to B to A. Below, I’ll walk you through how to identify the weak gates and prioritize them by scope.
The pipeline runs in two phases with different logic
Phase 1 (discovered through indexed) is infrastructure- and bot-centric. It’s mostly pass or fail: either the system has your content, or it doesn’t. The fixes are technical and well-documented: sitemaps, structured data, rendering, and quality signals.
Phase 2 (annotated through won) is competitive and algorithm-centric. Your content is measured against every alternative the system has for the user’s needs.
Passing all five gates in Phase 1 means the system has your content in stock. Winning Phase 2 end to end means the system chooses you over your competition.
Each stall pattern points to its fix
Fix what’s weak. In DSCRI, the fixes are mechanical, and success is relatively easy to measure.
In ARGDW, the fixes are less obvious, more indirect, and the cause-and-effect relationship is harder to demonstrate. That’s why so many brands and practitioners focus too much on mechanical fixes and not enough on competitive ones.
Each of the 10 gates is a place where the pipeline can stall. These are some suggestions, absolutely not exhaustive: use the strategies you already know, too.
No.
Gate name
Stall
First-party (Entity Home Website)
Second-party (semi-controlled)
Third-party (independent)
1
Discovered
Bots never find the content
Sitemaps, IndexNow, internal linking, and inbound links
Link from your Entity Home Website with clear anchor text
Outbound links from owned properties and second-party content
2
Selected
Found but ignored
Internal links, inbound links, anchor text, content around links, and Publisher and Author N-E-E-A-T-T
Anchor text, content around the link, and link back to your Entity Home for context
Outbound links from owned properties and second-party content, anchor text, and content around the link
3
Crawled
Retrieval fails
Server performance, redirect chains, pruning, and canonicals
Choose reliable platforms; keep URLs clean and stable
Prioritize coverage on sites with strong crawl reputation
4
Rendered
Retrieved, but the system can’t process it
Server-side rendering, reduce external resources, and JavaScript discipline
Use platform-native formatting; avoid embeds that block render
Prioritize coverage on properly rendered sites
5
Indexed
Rendered, but not stored
Site structure, content quality, pruning, and canonicalization
Content quality and original perspectives
Prioritize coverage on fully indexed sites
6
Annotated
Inaccurate, low-confidence annotations
HTML5, structured data, schema markup, site structure, content quality, and unambiguous entity signals
Unambiguous entity signals, and link to your Entity Home for disambiguation
Outreach to clarify entity references, clear anchor text from your owned properties and second-party content
7
Recruited
Missing from one or more layers of the Algorithmic Trinity
Provide what each layer wants: recency, originality, clarity, information gaps, helpful framing, etc.
Fresh perspectives, original content, and regular updates
Outreach for coverage and updates from news, trade, and industry sites
8
Grounded
Not selected as a reference for the topic (not Top of Algorithmic Mind)
Entity identity optimization, Publisher and Author N-E-E-A-T-T, and explicitly connect claims to proof
Consistency of identity, credibility signals, and link claims to proof
Outreach for citations from authoritative sources, and build N-E-E-A-T-T through coverage
9
Displayed
Not chosen as part of relevant answers in the funnel
Close the Framing Gap at each UCD layer, improve brand N-E-E-A-T-T
Frame content to match each UCD layer
Outreach for coverage that closes the Framing Gap, improve N-E-E-A-T-T through external corroboration
10
Won
The page was the recommendation, but didn’t get the click, the citation, or the action
Write copy, titles, and descriptions that are easy for the algorithm to extract intact; frame claims so the algorithm can respect the brand narrative without rewriting it; educate the algorithm on the brand narrative so it doesn’t distort it
Use platform fields the algorithm will lift verbatim (titles, summaries, intros), and keep brand narrative consistent across every property
Brief publishers and partners on your brand narrative so coverage frames claims the way you’d frame them yourself, and correct distorted coverage at source
Reading the table: Across the rows, infrastructure fixes (Gates 1 to 5) are specific, technical, and often binary, while competitive fixes (Gates 6 to 9) point at larger bodies of work (graph presence, proof connection, and framing gap closure) that are strategic rather than technical.
Down the columns, your direct leverage drops as ownership drops:
On first-party, you can fix anything.
On second-party, you control content but not infrastructure.
On third-party, your only real moves are outreach and the links you point at the property.
The further into the pipeline the stall sits, and the further from the entity home website it sits, the more the fix becomes about positioning rather than engineering.
You can buy your way through DSCRI. You have to earn your way through ARGD. Won is its own case. By the time the algorithm reaches won, it has either understood your brand narrative or it hasn’t.
If it has, it respects your titles, your descriptions, and your framing, and the click or citation lands the way you wanted. If it hasn’t understood you fully, it rewrites you, and the rewrite won’t be your framing. Assuming your copywriting is top-notch, that’ll lose clients you should have won.
Educating the algorithm on the brand narrative is the work that decides which of those two outcomes you get, and the work happens across your digital footprint, over time (ongoing), and at every gate.
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Work outside-in, because most of what you need already exists
The pipeline runs at three scopes simultaneously — per item, sitewide, and web wide. Every gate operates at all three. You can’t work on them simultaneously, which means the order you pick is the single biggest decision in the project, and most brands pick the wrong one because they’re watching their competitors instead of the structure.
Here’s a simple fact most brands miss: most of what you need is already in place.
You already have claims (you own a website, you’ve published positioning, you’ve explained who you are and what you do).
You already have proof (clients have written testimonials, journalists have covered you, partners have referenced you, conferences have programmed you).
The two layers exist, they’re just not connected. Joining the dots between existing claims and existing proof is the biggest single piece of leverage available to almost any brand.
Almost nobody is doing it systematically because they’re too busy creating new content from scratch. When I say “join the dots,” that means both bi-directional linking and framing (which I covered in “The framing gap: Why AI can’t position your brand”).
That insight reorders the work. The right sequence is outside-in, and it lines up with claim, prove, and frame at the scope level.
Sitewide first
Get your claims structurally consistent at scale. Templates make it easy for bots to digest your site only if they’re consistent. Get the templates right, and the content taken as a whole reads clearly.
Make sure the categorization is logical, the schema is uniform, the internal linking pattern is predictable, and the HTML5 is built to help bots perform chunking that produces high-confidence, well-bounded representations of every part of every page.
Get the templates wrong, and the algorithms annotate everything with low confidence because the chunking was bad, the categorization was illogical, and the structural signals contradicted each other. That’s a sitewide weakness that the content carries through. This is cascading confidence at scope level.
Content is the input, context is what the templates supply, and confidence is what the system produces when context is consistent enough to make sense of the content. Start at the site level because that’s where the cascade either begins clean or collapses before it starts.
Connect the dots to the existing proof. Once your owned property is making consistent, machine-legible claims, the second- and third-party footprint is where those claims get corroborated.
The work here is mostly auditing, not creating: independent journalists who’ve already covered you, client testimonials sitting on client domains, conference programs that name you, partner mentions, and third-party reviews that already exist.
This is the prove layer, and the leverage is enormous because your competitors are mostly not doing it. They’re watching each other’s websites while the independent layer that actually decides who AI recommends sits unattended on the open web. So, update what you can, and insert bi-directional links strategically to “connect the dots physically.”
Per item last
Frame the connection between claim and proof. Once sitewide claims are clean and web-wide proof is surfaced, it’s time to bring it all together in individual items.
Per-item work builds the relational bridge between specific claims and the evidence. It’s up to you to provide the interpretive frame that tells the algorithms how to read the connection and closes the framing gap one page at a time.
Framing only earns its full return once the two layers underneath are solid, because the frame is the connection between things that already exist, and there’s nothing to connect if the claim is incoherent or the proof hasn’t been surfaced.
Fix the earliest broken gate first, or the fix downstream does nothing
The pipeline is sequential. Each gate’s output is the next gate’s input.
First job: get content flowing through every gate without an absolute fail at any point. If discovery is broken, improving your annotation does nothing because your content never reaches annotation.
The rule is simple: find your earliest failing gate, fix it, then re-measure everything downstream on the improved signal. Fixing gates out of order wastes budget because the bottleneck hasn’t moved. I filed a patent for the technical implementation of this principle, but the principle itself doesn’t need the patent — it’s how any sequential system works.
Once nothing is absolutely failing, start fixing the weakest gates one by one, from weakest to strongest, to maximize the effect of each fix on the signal that flows through everything downstream.
If rendering drops 50% of your useful content, every downstream gate inherits the damage, no matter how strong your competitive positioning is. Push that up to 100%, and you’ve doubled the signal for everything that follows.
Below are potential stalls at each gate (single page) with examples of fixes.
No.
Stall
Problem
Possible fix
1
Not Discovered
Orphaned article about your brand on Poodle Parlours in Paris Monthly
Create a dedicated page on poodleparlour.paris with a TL;DR of the article (use the opportunity to close the Framing Gap), add the publication name, author, date, and an outbound link to the article
2
Not Selected
The 600th episode of your podcast on your website is ignored by bots despite a link from the pagination
Link to it from the homepage, make the anchor text explicit (not “listen here”), and add the link to the YouTube version description
3
Not Crawled
Page load time is slow at peak times
Upgrade hosting and use a CDN
4
Not Rendered
Schema isn’t being ingested by the LLM bots
Move schema inline, or, if that isn’t possible, add the same data to an HTML table on the page
5
Not Indexed
Rendered, but not stored
Site structure, content quality, HTML5, and schema markup
6
Badly Annotated
Inaccurate, low-confidence annotations
HTML5, structured data, schema markup, site structure, content quality, and unambiguous entity signals
7
Not Recruited
Missing from one or more layers of the Algorithmic Trinity
Provide what each layer wants: recency, originality, clarity, information gaps, helpful framing, etc.
8
Not Grounded
Not selected as a reference for the topics (not Top of Algorithmic Mind)
Entity identity optimization, Publisher and Author N-E-E-A-T-T, and explicitly connect claims to proof
9
Not Displayed
Not chosen as part of relevant answers in the funnel
Close the Framing Gap at each funnel layer (Understandability, Credibility, Deliverability), and improve brand N-E-E-A-T-T
10
Not Won
The page was the recommendation, but the algorithm rewrote your title and description
Improve brand Understandability of the brand narrative and framing, tighten the title, description, and intro so the algorithm extracts your version intact rather than rewriting it; these remain the most visible elements at the zero-sum moment in AI
Reading the table: gate-by-gate example issues at item level. I provide some suggested solutions for each. You’ll see that many of the fixes are actions you’d take at sitewide or web-wide scope, which is the point.
Scope determines whether the fix touches one URL or thousands, but the underlying mechanism at each gate is identical. Per-item work is where the fixes get specific, but the patterns repeat.
The authoritative entity advantage compounds across the competitive gates
One strategy will improve your grade at almost every gate in the AI engine pipeline: entity optimization.
When your brand entity is fuzzy across the three graphs (document, concept, and entity), actively optimizing the entity identity improves clarity, focus, and confidence at almost every gate.
But the advantage you’ll gain isn’t uniform: at the infrastructure gates it does little, but from annotation onward, it will make a huge competitive difference.
Here’s the authoritative entity advantage at each pipeline gate.
No.
Stall
The authoritative entity advantage
1
Not discovered
Marginal. A recognized entity in an outbound link from a third party is slightly easier to identify and trace, but discovery itself is infrastructure-driven.
2
Not selected
Significant. A recognized, trusted entity in anchor text (or near the link) increases the probability of selection.
3
Not crawled
None. Crawling is purely server, redirect, and rate-limit mechanics.
4
Not rendered
None. Rendering is purely technical processing.
5
Not indexed
Moderate. Entity clarity helps the system make canonicalization and deduplication calls with confidence; fuzzy entities produce fuzzy storage decisions.
6
Badly annotated
Major. Entity confidence is the foundation of accurate annotation. A fuzzy entity produces low-confidence, often inaccurate annotations across every dimension. A clear entity produces clean, high-confidence annotations.
7
Not recruited
Major. Recruitment into the entity graph, document graph, and concept graph is entity-driven. Clear entities get recruited — fuzzy ones get passed over for clearer alternatives.
8
Not grounded
Major. Top of algorithmic mind is entity-driven: topical ownership, N-E-E-A-T-T, knowledge graph presence, and more. The system grounds in references it trusts.
9
Not displayed
Significant. Entity recognition reduces hedging at display. The system speaks confidently about entities it understands well and hedges on the ones it doesn’t.
10
Not won
Major. Entity confidence decides whether the algorithm respects your brand narrative or rewrites it. High confidence means titles, descriptions, and framings get extracted intact. Low confidence means the algorithm fills in the gaps from training data, and that won’t be the narrative you carefully crafted.
Reading the table: entity advantage is zero or marginal at Gates 1 to 5 (infrastructure), then carries the heaviest load through Gates 6 to 9 (the competitive phase). At won, it’s the mechanism that decides whether the algorithm respects your brand narrative or rewrites it.
This is the most underrated insight in the whole diagnostic. Optimizing any single gate gives you one gate’s worth of improvement. Optimizing the entity gives you compounding improvement across all five gates from annotated through won, which is why entity-led optimization outperforms page-led or keyword-led optimization in AI search.
The authoritative entity advantage names that compounding effect, and it’s the structural reason brands whose entities remain fuzzy pay a confidence tax at every competitive gate.
Before you create anything new, audit what you already have
Once you know which gate is failing, the first question to ask yourself isn’t “what do I need to create?” It’s “what do I already have that would fix this?”
The content on your website already makes most of the claims you need, but they are not presented clearly and consistently. Then, all brands have more existing proof than they’re fully leveraging.
Look at things like conference programs, client case studies, trade publications, podcasts, social media, reviews, and third-party mentions. There might be a lot that you have never explicitly connected back to your brand.
Audit-first beats create-first on every metric that matters. Audit-first is cheap and fast. Create-first is expensive and slow.
The diagnostic tells you which gate needs the work, the audit tells you what you already own that could do the work, and the audit also tells you where the genuine gaps are, so when you do create something new, you’re filling a gap the diagnostic identified rather than guessing.
That principle drives the temporal triad: ROPI, ROI, ROFI.
The temporal triad turns the diagnostic into a working plan: ROPI, ROI, and ROFI
Return on past investment (ROPI) is the audit-first work itself: linking existing claims on your website to existing proof scattered across your digital footprint so the assets you’ve already paid for start paying you back. It’s the cheapest, fastest, and almost always the highest-leverage move available, because the asset has already been built and you’re paying only for the connection.
Return on investment (ROI) is the present-tense work: expanding on content that’s already live, filling the gaps the audit reveals, and creating new pieces in the short term to support what you’re doing today. This is the layer most brands jump to first, and it’s the most expensive of the three when run in isolation, because new creation without ROPI underneath means you’re paying full price to build assets that are already partially in place.
Return on future investment (ROFI) is the planning layer, and it’s where brand strategy and pipeline strategy converge. If you have a clear sense of where the business is going (which categories you’ll own in three years, which positioning you’ll claim, which framings you’ll need supporting evidence for), you can plant seeds today that won’t serve you this quarter but will be load-bearing in 12 or 24 months.
At my company, we plant seeds constantly: claims and framings published now that aren’t doing visible work today but will be the corroborated proof we’ll need when the next phase of our long-term strategy rolls out. The brand that runs ROFI consistently is shaping the frame against which competitors will be measured in the future.
Because you’re educating and training the algorithms, ROFI actually influences the criteria by which the market will judge you in your favor.
Three time horizons for your content (wherever it lives online): ROPI extracts value from what you’ve already built, ROI improves the present, and ROFI engineers the future.
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The same diagnostic works across every AI engine
The 10 gates describe what search engines, assistive engines, and assistive agents actually do, in order, every time they decide whether to recommend you.
Crawl, index, rank was the right model for a 1998 search engine. It hasn’t been the right model for a long time. The brands that are still optimizing for three steps when the systems run on 10 are optimizing for a model that the engines don’t use.
This isn’t my framework. It’s the engines’ framework.
The engines don’t care what you find easy to measure, fun to do, or impressive at the next conference. They care whether your content survives all 10 gates with high confidence at each, and they reward the brands that build for the gates with citations, recommendations, and the actions that follow.
So treat and run it like a system. Fix your F grades first and your D grades next. Work outside-in because that’s where the leverage already lives, and watch the rest compound on top of work you’ve barely had to pay for.
Follow the system, and AI search pays you back, year on year, engine after engine, long past the lifespan of any acronym fashion.
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 Solutions2026-05-05 14:37:242026-05-05 14:37:24The 10-gate AI search pipeline: Find where your content fails
One of the major reasons PPC practitioners hold onto syntax-oriented keyword strategies is the disconnect between “query intent” and “conversion intent.” For years, you’ve likely relied on keywords to show you understand what your customers want and to prequalify traffic using syntax-oriented signals.
As user behavior shifts to more conversational queries and AI becomes an increasingly relevant part of the user journey, the distinction between these two intents becomes even more critical to understand and act on.
Here, we’ll define query and conversion intent and explore strategies to apply them effectively. This isn’t prescriptive. You should make decisions based on what will serve your business well. However, it provides a framework for analyzing your data and optimizing for the right humans.
Disclosure: I’m a Microsoft employee, and I’ll be sharing some examples that pull from Microsoft tooling. However, most of the strategies reflect platform-agnostic approaches.
What are query and conversion intents?
Query intent is the underlying need driving the text put into a search function. This search function can be on a SERP (search engine results page), video/social/gaming/email/site search bar, or AI surface.
Conversion intent is the human need to achieve some outcome, understood through stated and inferred data points. These range from text entered in various search experiences, content consumed, and tracked actions taken.
Different examples of query and conversion intent will have higher or lower rates of confidence based on how explicit text is, as well as patterns in content consumed.
For example, if I search “Microsoft ads login,” both query and conversion intent are clear — I want to log in. It’s easy to match ads and organic content to that query. Videos shown in any video query would have to do with logging in, and emails would be focused around login information.
Google SERP
Bing’s SERP
YouTube results
The query “Microsoft ads” is more nebulous, as such, needs to draw from other signals like previously engaged content and search history. While I might get a login page, I’d likely also see blog/sales content, third-party advice on Microsoft ads, and potentially competitor info trying to capitalize on the general nature of the query.
Google SERP
Bing SERP
YouTube results
Let’s look at a non-branded example as well. “Purple hair dye” has a clear transactional intent. While the user might not have a brand in mind, they know they want a specific color.
We don’t know if the user is looking for a semi-permanent or permanent color. We also don’t know the user’s pronouns, so matching them to a specific demographic to entice a purchase is a gamble.
Google SERP
Bing SERP
YouTube results
In the query “purple hair dye for long wavy hair,” the transactional intent is maintained. However, the query focuses more on the core needs of the person behind the text. Long, wavy hair means there needs to be enough dye to cover long hair.
Additionally, while some men have long wavy hair, the person behind the query is more likely to identify as female.
Wavy hair has a different composition than straight or curly hair, so products specifically for wavy hair will be more relevant than those without hair type identifiers.
Google SERP
Bing SERP
YouTube results
In all of these examples, there was clear conversion intent. The human behind the query clearly wanted to achieve something. However, if we relied only on the text (i.e., query intent), we might miss a meaningful opportunity to connect with customers.
This is why close variants (which have been available on both Google and Microsoft for ~10 years) represent a useful way to unshackle ourselves from syntax alone.
Additionally, by limiting our understanding of queries to SERPs, we ignore critical insights from where our customers connect, work, and play. Microsoft’s internal data from March 2024 shows that brands that use both Audience ads (display, native, and video) and Search see a 6x conversion rate. Part of this is brand recognition, and the power of brand media buys influencing performance.
Yet there’s also the pragmatic piece that some marketers refuse to engage with video and social. By being where your competitors refuse to be, you can shape and capture desire while they fight over a shrinking share of voice.
Once you understand the difference between query and conversion intent, you can begin mapping out the actions needed to capitalize on both.
Conversion intent is much easier to understand than query intent. This is why AI systems typically run queries in the background to understand human input and get at the conversion intent behind the query.
To succeed at shaping queries and capturing conversions, it’s critical to understand the input points for humans and the AI systems that will be serving them results.
Let’s revisit the “purple hair dye for long wavy hair” query:
Copilot surfaces how it arrived at the output by looking up information and finding the best matches. This is similar to the SEO concept of E-E-A-T.
Yet you’ll notice that the results for my personal Copilot are different than the traditional SERP (chiefly that ads aren’t the dominant result — ads serve at the bottom of clearly transactional conversations after organic listings).
This is where the “Details” function comes into play and can help you know where to focus content, feed, and messaging functions:
This product is pretty flat on price, save for some deep summer dips. If I’m desperate for color, I might buy now, or I might wait for what seems like a regular summer sale. I’m also getting insights into why this product is wonderful (hair conditioning, cruelty-free, vibrant, and customizable color, etc.).
These are things I’ve shown interest in through past purchases, conversations with Copilot, and other signals it has access to.
Brands that want to optimize for query intent need to make sure the following are in good order:
Feed/landing page clarity
It should be incredibly easy to map what the product/service is to the query. While there is value in some 1:1 matching of language, it’s much more important that the core offering be understood as aligned with what the human is looking for.
For example, DUI and DWI are technically two different charges and have geo implications. However, DUI tends to be the universal legal charge and service.
Images adding context
Visual content is critical to engage humans. However, if the image isn’t clear or is duplicative of another service/product page, you might confuse the user and the machine attempting to understand and position you for queries. This is why it’s critical to add alt text (even on paid landing pages) for images and videos.
A good way to test whether your visuals are serving you well is to put the landing page into a PMax campaign creator. If you see the images and they match the correct service text, you’ve done a good job.
Invest time in understanding how humans and AI are querying
Free tools like Google Trends, Microsoft Clarity, and Bing Webmaster offer insights into search trends, citations, grounding queries, and which AI systems and humans are successfully engaging with your content.
Conversion intent is more straightforward, though debatably harder because it requires more creative and critical thinking:
Matching messages to personas
The reason one person says yes to you might be completely different from the reason someone else does. Locking in conversion intent includes being mindful of how you’re selling yourself. If you ignore what matters to your customers in reviews, intake from customer success or sales, and other signals, you risk selling yourself badly and losing the customer.
This is where AI-powered creative and audience mapping can be helpful, since platforms have access to more insights than a brand does during the auction.
Honor the impulse nature of visual content
Someone coming to you from a display spot or short video is very different than someone coming from a text-laden SERP. They were inspired to act and need frictionless paths to conversion.
One-click checkout (including solutions like Copilot Checkout) ensures humans don’t need to think to do business with you.
Ultimately, both query and conversion intent need brand and performance marketing to be successful, and it’s critical to understand how the success metrics manifest.
The converging roles of brand and performance
For a long time, brand and performance marketing were treated as separate motions, with separate owners, budgets, and success metrics.
Brand was about reach, recall, and long-term connection.
Performance was about efficiency, conversion rate, and immediate return.
That separation made sense when channels, measurement, and user journeys were cleaner than they are today. It’s much harder to maintain in an environment where AI systems infer intent continuously and across surfaces.
A user doesn’t experience brand and performance as separate. They experience confidence, familiarity, relevance, and ease. Those signals are created over time through exposure, engagement, and trust, and they often determine whether conversion intent ever materializes, regardless of how “high intent” a query might appear on its own.
From a metrics perspective, this convergence is clear. Brand-oriented activity influences performance outcomes even when it isn’t the final touch. Exposure to display, native, or video doesn’t always produce an immediate click, but it changes how humans and systems interpret future behavior.
When someone later performs a search, engages with an AI assistant, or compares options on a marketplace, prior brand interactions act as accelerators. They reduce hesitation, shorten decision cycles, and increase the likelihood that a conversion signal will be credited downstream.
From a strategy standpoint, this means brand work should no longer be evaluated solely on isolated upper-funnel KPIs, and Performance work can’t be evaluated purely on last-click efficiency.
Audience-based formats, contextual placements, and visual storytelling directly shape conversion intent by shaping preferences and expectations before a query even occurs. Search and shopping formats then serve as capture mechanisms, translating that latent intent into action.
This is particularly relevant in AI-assisted experiences, where systems synthesize multiple inputs before presenting options or recommendations. Content, feeds, reviews, images, and historical engagement all influence how brands are represented and when they appear.
In these environments, strong brand signals don’t compete with performance outcomes. They enable them by making the brand easier to understand, trust, and choose.
Brand and performance don’t need to use the same tactics, but they must be planned together. Measurement frameworks should account for assistive value, not just final interactions.
Creative strategies should recognize that inspiration and conversion often happen at different moments. Optimization should focus less on forcing intent into rigid buckets and more on supporting the full decision journey.
When we recognize that query intent and conversion intent are related but not identical, the convergence of brand and performance becomes less a philosophical debate and more an operational necessity.
Success comes from designing systems that reflect how humans actually decide, not just how they type.
Key takeaways
Query intent describes what is said; conversion intent reflects what the human needs to accomplish. They overlap, but they aren’t interchangeable.
Brand activity shapes conversion intent long before a query is expressed and influences how future interactions are interpreted.
Performance outcomes improve when Brand signals reduce friction, uncertainty, and choice overload.
AI-driven experiences amplify this convergence by relying on cumulative signals rather than single actions.
Sustainable optimization requires aligning brand and performance strategies, metrics, and expectations around the same human outcomes.
In February 2025, the world watched as a small group of humanoid robots took the stage at the CCTV Chinese New Year show for the very first time. It was a charming performance, even if the steps were shaky and the movements were mostly limited to the arms.
Just one year later, at the Spring Festival Gala, the shaky steps were gone and the humanoid robots were able to actually run and do standing somersaults and full kung fu routines with swords and nunchaku. The message was clear: in just one year, we have witnessed a decade’s worth of advancement.
The 10-year leap in technology is real and not limited to robotics. Which raises a critical question for every digital marketer eyeing the world’s largest web population: How has search in China progressed in recent years?
A parallel in the Chinese search landscape
The answer is that we’re witnessing the first, calculated tremors of a massive shift. AI models have not yet replaced traditional search. The evolution isn’t happening through a single “big bang,” but through a constant, iterative pulse.
New LLM models are surfacing every few months, each more specialized than the last. Chinese tech giants are increasingly open-sourcing their models, and even industry leaders are hedging their bets. Baidu, for example, is integrating DeepSeek into its search experience, even as its own Ernie (Wenxin) model remains a formidable powerhouse.
Let’s look at how users actually search in China today — and what this nuanced shift from links to reasoning means for your 2026 SEO strategy.
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The great narrative fallacy: Is web search dead in China?
In many marketing circles, a specific narrative has been repeated so often it has become an article of faith: “Traditional search on Baidu is dead — and has been for years. Websites are obsolete. In China, everything is WeChat.”
This narrative is almost always driven by service providers whose business models depend on WeChat, Douyin, Weibo, or Xiaohongshu marketing. To them, the “open web” is a ghost town. But is this actually true?
The social supremacy argument
There’s a grain of truth in the hype. The Chinese web is a mobile-first multiverse. Users access and explore the web through super-apps:
RedNote (Xiaohongshu / Little Red Book): This is the de facto engine for lifestyle research and travel planning.
Pinduoduo and Douyin: These are the juggernauts of social commerce and impulse buying.
WeChat: The absolute center of daily life, where everything from a quick message to a utility bill payment via QR code happens.
In this environment, social media isn’t just a channel. It’s the air people breathe. For B2C brands, social ads can — and often do — exceed website-driven sales by orders of magnitude.
The B2B reality check
For those of us working with B2B companies that need real visibility in China, the “Baidu is dead” narrative falls apart the moment you look at the analytics. Clients who invest in Baidu SEO and Baidu search engine advertising (SEA) continue to see a steady, high-volume stream of real human visitors — in many cases generating more qualified leads and higher conversion rates than their counterparts in the UK or Germany.
Why? Because when a B2B procurement officer or a technical engineer needs a specific industrial solution, they don’t just scroll until they find it on a social media feed. They search for a verified, authoritative source. In other words, they look for a website.
Is the social media narrative a lie? No. But ignoring a channel that — at least in the B2B sector — remains more effective in China than in many search-first Western countries is simply bad business. The goal isn’t to choose one over the other; it’s to understand how they coexist.
And just as we’ve settled the debate between web marketing versus app marketing, a new challenger — the LLM — has entered the battleground to disrupt both.
Mapping the 2026 landscape: Intent-based specialization
To a Google-first marketer, the idea of searching anywhere but a search engine feels like a detour. In China, it’s the standard operating procedure. Users don’t just “Google it.” Instead, they choose the tool that fits the intent.
As a Baidu specialist living and working in China, I see this daily. While I might be optimizing a B2B landing page for Baidu, my wife is likely on Pinduoduo, finding household deals, or on Xiaohongshu, planning our next weekend trip.
The “everything app” exists, but the “right app” always wins the click.
1. Traditional web search: The authority tier
Despite the “death of the web” narrative, traditional web search remains the primary battleground for B2B and high-authority research. If a user needs a technical whitepaper, a government regulation, or a verified corporate headquarters, they go here.
Baidu: Still the mobile heavyweight, with a ~70% mobile market share. Its structural advantage is massive: The Baidu app is installed on over 724 million monthly active devices (as of early 2026). It has evolved into an AI-first portal, but for SEOs, it remains the place where the open web lives and breathes.
Microsoft Bing: The professional’s sanctuary. It has claimed a massive chunk of desktop search for those seeking a cleaner, international, or technical experience.
Haosou (360 Search): The enterprise default, often pre-installed on corporate PCs and known for its security focus.
Sogou: Deeply integrated with WeChat, it’s the bridge between the walled garden and the web.
Google: Yes, Google. Despite the firewall, a significant population of tech-savvy professionals and researchers use it via VPN for global technical data and academic resources.
2. Social discovery: The inspiration tier
This is where search becomes discovery. Users don’t always have a keyword, but they do have an interest. In this context, SEO is about social indexing: ensuring your brand appears when a user looks for proof and not just products.
WeChat (Weixin): The internal search for official brand news and private traffic.
Xiaohongshu (RED): The ultimate product-discovery engine. If you aren’t on RED, you don’t exist in the lifestyle or luxury sectors.
Douyin: Visual, video-first search. Users search Douyin to see how something works.
Kuaishou: The powerhouse for lower-tier cities and raw, authentic grassroots content.
Weibo: Real-time search — what is happening right now in the public eye.
Bilibili: Long-form video search for deep dives, tutorials, and Gen Z subcultures.
3. Ecommerce: The transactional tier
In the West, users often start on Google and end on Amazon. In China, the journey frequently starts and ends in the same place.
Taobao / Tmall: The grand bazaar. If you want variety and brand stores, this is the first stop.
JD.com: The Amazon of China for logistics and high-end electronics.
Pinduoduo: The favorite for daily essentials and group-buy deals. Its search logic is entirely driven by value for money.
Douyin Mall: The rising star of “impulse search,” merging entertainment with immediate checkout.
Xianyu (Goofish): The go-to for the thriving second-hand market and hobbyist niches.
4. Generative AI (LLMs): The reasoning tier
This is the newest layer of the map — the “thinking” search. These AI models don’t just produce lists of links. They are assistants that synthesize the web for the user.
Doubao (ByteDance): Currently the most popular consumer AI assistant, used for casual, conversational queries.
DeepSeek (Domestic): The choice for developers and those in need of “deep thinking” logic. It’s the engine currently getting tested inside WeChat’s search bar.
Kimi (Moonshot AI): The king of long-context. Users use Kimi to search through 50-page PDFs or complex financial reports.
Qwen (Alibaba): Powerfully integrated into the Alibaba ecosystem for business and coding tasks.
Tencent Yuanbao: The “AI brain” for WeChat content.
Wen Xiaoyan (Baidu): The AI-facing evolution of Baidu search.
5. Hyper-local and logistics: The utility tier
For the physical world, search is about “now” and “near me.”
Meituan / Dianping: If you’re hungry or want to see a movie, you don’t use Baidu. You use Dianping for reviews and Meituan for transactions.
Amap (Gaode) / Baidu Maps: The “search engines of the real world.” SEO on these platforms is purely about point-of-interest (POI) optimization.
Ctrip (Trip.com) / Railway 12306: The specialized gates for the massive domestic travel market.
From mapping to maneuvering: The Baidu specialist’s edge
Baidu SEO isn’t dead; your website just isn’t the sole focus of web search anymore.
The ‘walled garden’ SERP: A decade of distraction
If you’re a Google-centric SEO, there are some notable differences when working with Baidu:
The ad-heavy layout: It isn’t uncommon to see ads claiming the top, middle, and bottom of a Baidu search engine results page (SERP), occupying nearly 50% of the visible real estate.
The Baidu monopoly: The most coveted organic positions are almost always reserved for Baidu’s own properties. Baidu Baike (the encyclopedia), Baidu Zhidao (the Q&A hub), and Baijiahao (the news/blogging arm) are the permanent residents of Page 1.
The portal giants: High-authority giants like Zhihu (China’s Quora), Bilibili, and Sohu take up whatever space is left.
Riding the Chinese SERP dragon
In this environment, ranking a corporate homepage for a high-volume keyword is a fool’s errand. Instead, we’ve mastered the art of the “long-tail dragon.”
In the West, we talk about the long tail of search as a small, niche opportunity. In China, with its linguistic complexity and massive user base, the long tail is a winding, multi-layered beast that is often more lucrative than the head terms.
And we don’t just rank a website; we piggyback on the authority of the platforms Baidu already trusts. If you can’t beat Baidu Baike, you become the verified entry inside it.
Interestingly, it is these very platforms — the ones we’ve been using to bypass the “blue link problem” — that have now become the primary focus of the next generation of search.
What is changing in Baidu SEO?
In China, there is no brand loyalty toward particular AI models, as Westerners have toward platforms like ChatGPT and Claude.
The AI-switching reality
Chinese users are restless. They don’t stick with one model. They switch — sometimes because a hyped model hits a downtime wall, and sometimes because a new model claims the throne of the “most intelligent AI.” In this cycle of competition and user preference, an SEO can’t just focus on the “big sources.”
If you’re following the Western playbook, you’re likely chasing Reddit, Quora, and YouTube as your “sources of truth” for AI training. But in China, that focus is dangerously narrow. To win the reasoning battle, you must understand the investor-source connection.
Brainstorming the wisdom platforms
If you want to train AIs to see your brand in China, you have to look at the platforms they were built on:
Tencent is invested in Sogou. In 2021, Tencent fully privatized Sogou. This means Sogou Baike is no longer just a Baidu alternative — it is now a core training set for Tencent’s Yuanbao. If you ignore Sogou Baike, you’re invisible to the AI search bar inside WeChat.
Bytedance owns Baike.com. Bytedance bought Baike.com (formerly Hudong Baike) specifically to fuel its search ambitions. If you want to get cited by Doubao, your content needs to be mirrored here and not just on Baidu.
The neutral giants: Keep an eye on Zhihu. Because both Tencent and Baidu are heavy investors in Zhihu, it remains one of the few neutral high-authority sources that almost every Chinese LLM uses for opinionated or expert reasoning.
The new SEO commandment
We’re no longer just optimizing for a search engine. We’re optimizing for a data pedigree.
If your client is B2B, you might still prioritize the Baidu ecosystem. But if your client is in ecommerce and you aren’t feeding the Qwen engine via Alibaba’s ecosystem, or the Doubao engine via Baike.com, you’re limiting your visibility across key AI systems.
The 2026 China SEO/GEO blueprint: From keywords to semantic saturation
If you’re waiting for a “DeepSeek optimization checklist” or a “Doubao ranking guide,” you’ve already missed the point. Because users switch models as often as they switch takeout apps, you can’t afford to be “Baidu-only” or “WeChat-centric.”
Here is what’s actually working for SEO in China in 2026:
Optimize for citations and not just clicks
While SEO in the West is focused on generative engine optimization (GEO), in China, it’s all about fact density.
The logic: When Kimi or DeepSeek performs a reasoning query, the AI looks for verifiable facts.
The tactic: Stop writing marketing fluff. Start using the inverted pyramid writing style. Lead with a direct, data-backed answer in your first paragraph. Use hard statistics, expert quotes, and structured lists. If a model can’t extract a fact from your content in 200 milliseconds, it might hallucinate a competitor’s data instead.
Build an entity moat across wisdom platforms
As we brainstormed earlier, every AI has a “parent” with a preferred data source. But since models are now open-sourcing their weights and distilling each other’s intelligence, your brand must achieve entity consistency.
The goal: Your brand name, headquarters, and core product claims must be identical across Baidu Baike (Baidu), Sogou Baike (Tencent), and Baike.com (ByteDance).
The result: When these models cross-check their reasoning, they find a consensus. In 2026, consensus is the new authority.
Leverage information gain
Chinese AI models have a well-observed recency bias — they prefer sources that are roughly 25% fresher than traditional search results.
The tactic: Don’t just regurgitate what’s already on Zhihu. Provide a “unique data slice.” If everyone says “The best time to post on Douyin is 6 PM,” and you publish a case study proving “11 AM is better for B2B industrial leads,” the AI will cite you as the “nuanced exception.” That citation is worth more than ten #1 rankings.
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The era of the entity architect
We’ve come a long way from the shaky steps of the 2025 CCTV Gala.
In 2026, China’s search ecosystem is no longer a directory of links. It’s a living, reasoning entity.
For the Western search specialist, the lesson is clear: The “super app” was a distraction. The real story is the fragmentation of intent.
My wife still goes to Pinduoduo for the best price. My colleagues still go to Bing for technical sanctuary. And the “I, Robot” enthusiasts of 2026 are using a rotating door of LLMs to find their answers.
As a Baidu specialist, my job has shifted from “ranking a website” to “architecting an entity.” We no longer build for the bot; we build for the source. If you’re the undeniable source of truth across the platforms that shape China’s information ecosystem, it doesn’t matter which model delivers the answer.
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 Solutions2026-05-05 12:00:002026-05-05 12:00:00How China’s fragmented search ecosystem is reshaping SEO in 2026
In February 2024, Gartner predicted that traditional search volume would drop 25% by 2026. It didn’t. Google’s search revenue accelerated to 17% year-over-year growth, crossing $63 billion in Q4 2025 alone. But clicks per search are falling while query volume explodes. The pie got bigger. The slices got redistributed. And most search teams are still optimizing for the old pie.
Are you still poring over spreadsheets full of organic keyword rankings like it’s 2003? Your customers don’t care where they’re getting their answers. They’re just looking for answers they can trust. And they’re finding those answers across more surfaces than your rank tracker knows exist.
If your organic strategy lives in one spreadsheet, your paid strategy in another, and your AI search strategy in a third (or nowhere), you’re optimizing for a search experience that no longer exists.
What “search” actually looks like now
Google “best tax software” right now. Go ahead, I’ll wait.
Count the surfaces on that single results page. Sponsored ads across the top. An AI Overview with its own recommendations and citations. A Reddit thread (because Google knows people trust other people more than brands). Organic listings from CNET, H&R Block, and others. A video carousel. Discussion forum links. A product carousel with images and prices. More sponsored results at the bottom. And a “People also search for” section feeding the next query.
That is one search. One keyword. And nobody owns it.
Now think about how different people actually use that page. I scroll past everything to find the Reddit thread, because I want to know what real humans recommend. My dad clicks the first sponsored ad because he doesn’t understand paid advertising (sorry, dad!) and just trusts Google to surface the best option up top. Someone else reads the AI Overview, gets a good-enough answer, and never clicks anything at all. A fourth person watches the Smart Family Money video and leaves.
Same query. Four completely different paths. Four different “winners.” And if you’re the brand celebrating a number-three organic ranking on this page, you may be missing that most of the real estate, and most of the user attention, lives somewhere other than those blue links.
This is what I mean by the total SERP experience. Your customer sees the whole page. You should too.
But before the panic sets in: AI tools still account for less than 1% of U.S. web traffic. Google sends 300x more referral traffic than all AI platforms combined. The sky isn’t falling, but the ground is shifting.
The shift that matters most is behavioral. Wynter’s 2026 research found 68% of B2B buyers now start their research in AI tools before they ever open Google. They ask ChatGPT to narrow the field, then Google the shortlist to validate. AI evaluates, Google verifies, and your website converts. If your brand is missing from that first AI conversation, you’re not even on the shortlist when the Googling starts.
Why the click data is more interesting than scary
A Search Engine Land analysis of 25 million organic impressions across 42 clients found organic CTR drops 61% when an AI Overview appears. In addition, paid CTR drops 68%.
EVERYBODY FREAK OUT!!! Right? Not quite.
Here’s what the panicked LinkedIn posts leave out: brands cited inside AI Overviews see 35% more organic clicks and 91% more paid clicks. Being in the AI Overview doesn’t cannibalize your traffic. If anything, it amplifies it. The AI Overview functions like a trust signal, a stamp of “this brand is relevant to your question” that makes people more likely to click your listing below.
The real twist, though, is that ranking well in organic doesn’t guarantee you show up in AI. Tom Capper’s research at Moz found 88% of AI Mode citations are NOT in the organic SERP for the same query. Organic and AI are pulling from different source pools. You can be number one in Google and completely invisible in ChatGPT’s answer to the same question.
And the small amount of traffic that does come from AI? It converts at more than quadruple the rate of organic, according to Semrush. These visitors arrive more informed, more intentional, and more ready to buy. Which makes sense, because they’ve already done the evaluation inside the AI interface. By the time they click, they’re just confirming and often converting.
The org chart is the problem
Most companies have SEO reporting to content, PPC reporting to demand gen, and AI search reporting to nobody. BrightEdge found 54% of organizations have handed AI search to the SEO team alone, which is a little like asking your plumber to also handle the electrical work because, hey, it’s all in the same house.
The waste from this setup is real. One branded Performance Max campaign paid roughly $500,000 for clicks that would have come through organic anyway. Google’s own research confirms: when you rank number one organically, only half your paid clicks are truly incremental. The other half? You bought what you already owned.
Meanwhile, McKinsey found that a brand’s own website makes up only 5% to 10% of the sources AI references. AI pulls from Reddit, review sites, affiliates, publishers, and user-generated content. You can have the best SEO program in your category and be completely absent from AI search results because AI is reading what other people say about you, not what you say about yourself.
The unified approach works. Level cut acquisition costs 18% and boosted SEO leads 22% by merging paid and organic for a B2B SaaS client. And we can use tools in our Level Intelligence Suite to connect performance signals across search surfaces. The channels compound each other. Treating them as separate line items on separate P&Ls leaves that compounding on the table.
Three audits you can run Monday morning
You don’t need a six-month transformation to start seeing the gaps. Three lenses, applied to your top 20 keywords, will show you where the opportunities and the waste are hiding.
Lens 1: Where do you actually appear? Check your organic rankings, paid ad coverage, and AI visibility across ChatGPT, Perplexity, and Gemini for the same set of keywords. Semrush has a free AI visibility checker. Most teams have never looked at all three surfaces side by side, and the gaps are almost always larger than they expect.
Lens 2: Where are you paying for traffic you already own? Cross-reference your number-one organic rankings with active PPC bids on the same terms. Start with branded keywords, where the waste is usually largest and the test is cleanest. If you rank first and you’re still bidding, you’re probably buying your own clicks.
Lens 3: Where is AI ignoring you? Compare your organic rankings with your AI citation presence. Only 11% of domains get cited by both ChatGPT and Perplexity, so strength in one guarantees nothing in the other. And check your robots.txt while you’re at it. If you’re blocking AI crawlers like OAI-SearchBot or PerplexityBot, you’ve pulled yourself off those shelves entirely.
This diagnostic shows you the full picture. What to do about it, the actual unification framework, is what I’m laying out at SMX Advanced.
The window won’t stay open
Generative Engine Optimization (GEO) keyword difficulty currently averages 15 to 20, compared to 45 to 60 for equivalent SEO terms. That gap will close. Once an LLM selects a trusted source, it reinforces that choice across related prompts. The brands getting cited now are training the models to keep citing them. Winner-takes-most dynamics are being baked into the weights.
Many companies are seeing search traffic drop significantly. Those same brands, the ones that get it right, are seeing the inverse when it comes to business growth. Rankings and revenue have decoupled. The brands that win from here are the ones that stopped measuring channels in isolation and started measuring the search experience their customers actually have.
We’re presenting a search unification framework at SMX Advanced in our session, “Organic, paid, and AI search: one strategy to rule them all.” If you want to stop optimizing for three separate channels and start compounding performance across every search surface, join us for the session or come find the Level team at Booth #9.
Remember: The search experience that existed in 2023 is gone. The strategy should be too.
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Automation doesn’t fail on its own — it does exactly what it’s trained to do. The problem is that when Google Ads is fed incomplete, misaligned, or overly broad signals, it can optimize toward the wrong outcome faster than most advertisers realize.
In our second installment of SMX Now, our new monthly series, Ameet Khabra of Hop Skip Media will break down a real account where a 417% jump in conversions turned out to be the wrong kind of success. She’ll use that case study to explain the four key ways automation drift enters an account: signal drift, query drift, inventory drift, and creative drift.
You’ll leave with a practical framework for diagnosing drift early, understanding where human oversight matters most, and managing automation more deliberately so it works toward real business goals — not just platform-reported wins.
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 Solutions2026-05-04 17:19:262026-05-04 17:19:26SMX Now: The automation drift and how to correct course
Your analytics dashboard tracks clicks, but it doesn’t convey the complete picture.
When a buyer reads an AI answer that mentions your competitor, or scrolls through a Reddit thread where your brand doesn’t appear, that’s lost visibility. And it won’t show up anywhere in your traffic data.
Share of voice (SoV) captures what traffic metrics can’t.
It measures your brand’s visibility against competitors across channels where buyers actually research and make decisions.
While SoV spans social, PR, and paid media, search is where most brands should start. It’s the channel where buyers with the strongest purchase intent show up, and it’s the easiest to measure competitively. That’s what this guide focuses on.
I’ll walk you through four steps to measure your share of voice in organic and AI search. Then, I’ll show you how to turn that data into decisions that move the needle where it matters.
What Is Share of Voice?
Share of voice measures your brand’s visibility relative to competitors across multiple marketing channels.
That includes organic and AI search, social media, review sites, communities, and more.
Traditionally, brands used SoV to track their share of ad spend in a market.
Now it’s evolved into something even more valuable. It can measure your brand’s presence across every touchpoint where buyers research and make decisions.
In simple terms: SoV tells you what percentage of the conversation you own in your category, compared to competitors.
This guide focuses on search SoV — both organic and AI — because that’s where buyer discovery is shifting fastest and where the measurement tools have matured enough to give you actionable data.
I find that search SoV also tends to be the foundation: once you understand your visibility in organic and AI results, layering in other channels becomes much simpler.
What Counts as a “Good” Share of Voice?
While there’s no universal benchmark for SoV, establishing one for your brand comes down to:
Market position: Market leaders have a higher share of voice since they own the conversation. Challengers aim for a mid-range SoV when competing against players with decades of brand equity.
Competitive context: In a fragmented market with 20+ active competitors, 8% SoV could put you in the top five. But in a three-player market, anything below 30% could mean you’re behind the leader.
Beyond these two factors, look at the broader market shifts within your category.
High SoV in a declining market can be a vanity metric. The real win is growing your share as the category grows.
How SoV Works in Traditional vs AI Search
Both SEO and AI SoV answer the same question: What percentage of category demand does your brand own?
You track 100 target keywords. Those keywords generate 50,000 total monthly visits across all ranking sites. You capture 15,000 of those visits.
That’s 30% organic share of voice.
AI SoV measures brand mentions in LLM responses from ChatGPT, Perplexity, Google AI Mode, and similar tools.
For example, you test 100 category-related prompts. Your brand is mentioned in 45 responses and cited in 15. Your competitor shows up in 30 responses with 10 mentions.
An AI visibility tool can calculate your weighted AI SoV based on mentions and citations.
Try now: Curious to know how often your brand shows up in AI responses? Try our free AI visibility checker to find out.
Why Is Share of Voice So Important, Especially Now?
Here are three reasons why share of voice should be your core KPI when visibility is scattered across platforms.
And with zero-click searches on the rise, that half is shrinking fast.
When users get their answers directly from AI Overviews and featured snippets, a huge chunk of your visibility is never captured in Google Analytics.
This makes traffic a lagging indicator of visibility.
Share of voice is a better metric because it measures how visible you are in the consideration set, even when users don’t click your site.
Think of it this way:
A user searches for the “best project management software for remote teams.”
They see an AI Overview listing five tools, including yours. The user reads it, takes no action, and later signs up for a product demo on your site.
Traditional traffic data would show this as “direct traffic” since the person went straight to the website. It wouldn’t capture the discovery that occurred in Google.
But SoV reveals that your brand appeared in the consideration set for this high-intent query.
Work Toward One North Star Metric
Your marketing team might be operating in silos.
The SEO team wants more website visits. PR wants more media mentions. The social team wants better engagement.
Each team tracks its own KPIs and optimizes for different outcomes.
But the long-term power of SoV is that it can become the one metric every team rallies around.
When everyone sees how their work contributes to the same visibility percentage, it changes how teams collaborate.
Here’s what this looks like in practice:
SEO team targets specific keywords to boost traffic and visibility via content
PR secures features in industry publications through expert quotes
Social drives brand conversations on Reddit and LinkedIn
Product wins better reviews on G2 and Capterra
This full picture takes time to build.
Start with the foundation by measuring your SoV in organic and AI search.
Once you have that baseline, you can layer in other channels over time.
Let’s see how you can strategically calculate share of voice in four steps.
I’ll use a fictional project management software example to show how each step translates into business insights.
Step 1: Define Your Industry Landscape
Start by outlining the specific competitors and keywords you’ll track for SoV.
Without clear boundaries, you’ll either miss critical gaps or drown in too much noise.
To map your competitive terrain, pick topic clusters tied to revenue.
For a project management software, I picked these clusters:
Category fundamentals (like “project management 101” and “project management for freelancers”)
Use cases (like “agile project management” and “remote team collaboration”)
Industry-specific (like “construction project management” and “marketing project management”)
Pro tip: Don’t pick these topics solely based on search volume. Choose clusters where gaining visibility directly impacts your bottom line.
One way to assess a topic’s revenue potential is to map it to funnel stages.
Categorize your clusters into three stages:
Awareness: Where people are learning and researching, like how to manage projects
Consideration: Where they’re exploring solutions, like the best project management software
Decision: Where they’re comparing options and ready to buy, like Software A vs Software B
Your SoV at each stage tells you where you’re winning and losing in the buyer journey.
This allows you to allocate resources for maximum business impact.
Let’s say this project management software segments the SoV by funnel stage.
It reveals that most of the brand’s visibility is concentrated at the top with almost none at the decision stage.
That’s a problem.
They’re educating the market, but invisible when prospects are actually comparing options and reaching for their wallets.
Strategic takeaway: They need to prioritize comparison pages and case studies to shift visibility toward the decision stage.
Now, define who you’re measuring against.
In search, you’re competing for visibility against two key players:
Direct competitors: Companies selling similar solutions like Asana, ClickUp, Notion, and Trello
Indirect competitors: Review sites capturing the voice of the customer like G2 and industry publishers ranking for your keywords but not competing for customers like HubSpot and Zoho
Tracking them gives you the complete picture of who controls visibility in your market and where you can break through.
Step 2: Build Your Keyword & Prompt Libraries
Create a library of 200-500 queries that capture how people search in your category.
You need both keywords (what people search) and prompts (what people ask LLMs). Together, they reveal your search visibility spectrum.
Pull SEO Data First
Collect queries where you’re already visible to your audience.
Google Search Console (GSC) is a good starting point for this since it captures actual visibility through impressions.
Impressions show every time your brand appears in results, even when users don’t click.
Go to the “Queries” tab in the “Performance” report.
Click the “Impressions” column header to sort in descending order, and export this list of keywords.
And if you’re running Google Ads, export your PPC keyword list and filter for terms with conversions or high CTR.
You can also repeat this process with tools like Semrush.
Scroll down to the “Top Keywords” section and click the “View all” button.
Adjust the timeline to your preferred range before clicking “Export” to download the full keyword list.
Pro tip: Export all tracked keywords, not just the top money terms. A keyword with 20 monthly searches might seem irrelevant in isolation. But 50 of these collectively represent meaningful category visibility that SoV captures.
Layer in Competitor Intelligence
Besides your own data, track where competitors show up.
This tells you where to compete directly and where to claim ground that they’ve overlooked.
After sourcing keywords, look at how people search for your category in AI tools.
Since AI search queries tend to be more conversational, they often mirror how people talk in community spaces.
Browse Reddit, Facebook groups, and Slack communities to see how your audience phrases their needs and pain points.
For example, this post reveals that agencies want project management tools that aren’t “too corporate or complex for creative teams.”
A question like that can translate directly into an AI prompt: “What’s the most user-friendly project management tool for small creative agencies?”
For decision-stage prompts, review sites G2 and Capterra (or those relevant to your industry) offer a lot of insights.
G2, for instance, lists popular alternatives for every tool.
This is a ready-made list of “[You] vs [Competitor]” and “alternative to [Competitor]” queries your buyers are likely running in AI search.
You can dig deeper with Semrush AI Visibility Toolkit to find prompts where competitors show up in AI answers, but you don’t.
Go to “Prompt Research” and add any of your core topics, like “agile project management.”
Click “Analyze” to get started.
The tool lists real prompts that generate AI responses for your category, such as “best productivity app” and “companies that use agile software development.”
Jot down the prompts relevant to your primary cluster.
Then, repeat for each of your 3-5 clusters.
Document Your Metadata
Finally, organize everything in a master spreadsheet with columns for:
Keyword/Prompt
Topic Cluster
Funnel Stage
Source (SEO/AI)
Once you’re done measuring SoV, this metadata will become your strategic lens.
Use it to decide which clusters to prioritize, which funnel stages are weak, and where SEO and AI visibility diverge.
Here’s what this looks like for the project management software:
Step 3: Calculate Your SoV
Your SoV equals your estimated traffic divided by the total traffic for all tracked brands, multiplied by 100.
Track both SEO and AI SoV to see the full picture of your brand’s visibility.
Calculate SEO Share of Voice
Start by checking your rankings for all the keywords in your tracking list. Track your competitors’ rankings for the same keyword set.
Each ranking position gets an average share of clicks, like position 1 getting roughly 27%.
This will help in estimating the traffic share per keyword.
Note: These benchmarks for organic search CTR shift over time. It’s also crucial to mention that organic CTRs have been declining as AI-generated answers absorb more clicks before users ever reach the results.
Multiply each keyword’s monthly search volume by the click-through rate for your ranking position to estimate your traffic for that duration.
Then, run the same calculation for each competitor.
Use this data to calculate your SoV.
Add up the estimated traffic across all keywords for each brand. Divide your total by the combined total for all tracked brands and multiply by 100.
This manual approach can be time-intensive, especially when tracking hundreds of keywords across multiple competitors.
Semrush handles this math automatically once you set up tracking correctly.
Enter your domain, target search engine, device type, and location.
The location setting matters for SoV tracking because search results vary by location.
If you set the location to the United States, but most of your customers are in New York, your SoV might look different than reality.
Pro tip: Start with country-level tracking to establish your baseline. Only segment by region later if local variations impact your business.
Then, click “Continue to Keywords” to manually add or import your keyword list.
Upload the CSV you made in Step 2 to preserve the data by cluster and funnel-stage categorization.
Then, press “Add keywords to campaign.”
Finally, click “Start Tracking” to begin data collection.
Once this setup is complete, Semrush starts collecting daily ranking data for every target keyword.
Check out the results in the “Share of Voice” tab under “Overview” in the Position Tracking dashboard.
You can also add up to four domains to see how you fare against others in the market.
Semrush tracks every brand’s rankings for your keyword set to aggregate the data into SoV percentages.
Important: While SoV is inherently relative and compares your visibility against others, who you choose as competitors shapes how you interpret your SoV.
Calculate AI Share of Voice
Your AI SoV shows how often LLMs cite your brand when answering questions in your category.
There’s no standardized way to manually measure AI SoV yet, but this two-step process gets you close:
Step 1: Run each prompt from your library through your AI tools of choice, such as ChatGPT, Claude, Google AI Mode, and any other AI tools your audience uses
Step 2: For each response, document every brand that appears — yours and your tracked competitors. Record whether each brand was mentioned, cited as a source, and whether the sentiment was positive, neutral, or negative.
Once you’ve tested all prompts, count how many times each brand appeared across all responses.
Divide each brand’s total mentions by the total number of prompts tested, and multiply by 100.
Keep in mind: This calculation gives you a directional read instead of a live metric. AI responses vary by session, phrasing, location, and platform. That’s why it’s important to test regularly and track trends over time.
Measuring AI SoV manually for 20 prompts across three platforms is doable. Doing it for hundreds of prompts while tracking how recommendations shift week over week isn’t.
That’s what Semrush’s AI Visibility Toolkit is built for.
Go to the Brand Performance report in Semrush’s AI Visibility Toolkit.
Enter your domain and click “Analyze.”
Pick an AI platform between ChatGPT, Google AI Mode, or Perplexity.
Switch among these tools to identify any significant gaps in platform-specific LLM visibility.
Once the report is generated, you’ll see a pie chart visualizing the distribution of SoV for your competitors.
The tool tests hundreds of prompts related to your category across ChatGPT, Google AI Mode, and Perplexity to measure your AI SoV.
For each prompt, it analyzes AI responses for:
Brand mentions: How often your brand appears in the answer
Citations: Whether the AI links to your content as a source
Context: Whether mentions are positive, neutral, or negative
It aggregates this data across all tested prompts to calculate your percentage of total visibility.
You’ll also find a section comparing each competitor against a set of business drivers specific to your industry.
These drivers are the most frequently mentioned topics for your category.
Use this data to identify clusters where you’re stronger and weaker than your competitors.
Interpreting SEO vs AI Share of Voice
SEO share of voice measures organic traffic while AI share of voice tracks LLM mentions and citations.
These might not always align.
You can have a strong organic share of voice (ranking on top for many keywords) but a weak AI SoV if LLMs don’t find your content credible.
And brands with more credible content can win a bigger slice of AI SoV even without much visibility in organic search.
Maintain content freshness and expand into adjacent topics to defend your position.
You rank well, but LLMs don’t cite you.
Implement content chunking to optimize your content for AI search and create citable assets to create credibility that LLMs value.
Low SEO SoV
AI tools cite your content even though you don’t rank at the top on organic search.
Improve SEO fundamentals, including title tags, internal linking, site speed, and keyword optimization.
Focus on depth over breadth.
Create a definitive, well-researched content resource for every core cluster. This is a good start for building visibility on both traditional and AI search.
Dig deeper: Learn more about building visibility in AI search with LLM seeding.
Step 4: Establish Your Baseline and Track Trends
The final step is turning your SoV numbers into an ongoing tracking system that informs decisions.
Create a baseline dashboard to capture three levels of detail:
Overall metrics: Are you gaining or losing ground overall?
Topic cluster performance: Which topics need more investment?
Funnel stage breakdown: Where in the buyer journey are you least visible?
Here’s what this could look like for the project management software:
Once your baseline is locked in, set your tracking cadence strategically.
A monthly frequency allows you to spot trends without the need for reacting to noise.
With quarterly deep dives, you can:
Analyze cluster-specific performance in detail
Correlate SoV changes with past campaigns
Adjust resource allocation based on what’s working
This rhythm prevents you from chasing short-term variations and missing critical shifts that impact your category.
Pro tip: Set up notifications in Semrush Position Tracking to get real-time alerts. You’re notified when SoV drops more than a certain threshold in any core cluster.
How to Improve Share of Voice
Not every fluctuation in your SoV requires action.
Here’s how to strategically diagnose gaps in your SoV and prioritize the right tactics to fix them.
1. Close Visibility Gaps
Clusters with <10% SoV mean you’re almost invisible.
This is especially damaging in decision-stage queries.
If you have less than 10% visibility when buyers search “best project management software,” you’re not in their consideration set.
At the same time, look for opportunities where competitors dominate, but you can compete.
For example, if your project management tool serves creative agencies but you have zero visibility for “project management for creative teams,” that’s your opening.
Potential Solutions
Diagnose the cause:
Search your weak clusters and compare what ranks against what you have
Check if you lack topic coverage, content depth, or basic optimization
Look at which competitors dominate and what formats they use
Build topical authority for major business themes.
Create one pillar page with multiple supporting articles.
Build backlinks to your pillar content to establish visibility across every query in that cluster.
For example, if we learn that the project management software needs to gain decision-stage visibility, we could prioritize comparison content.
Build pages targeting “[Your Brand] vs [Competitor]” and category buyer’s guides.
2. Solve Efficiency Problems
Compare your SoV to actual traffic.
A cluster like “what is project management” might give you a high SoV.
But if only 1% of that traffic converts, you’re likely burning money on the wrong audience.
You’re winning visibility in areas that don’t drive business outcomes. And competitors are capturing high-intent buyers.
Potential Solutions
Diagnose the cause:
Check if you’re ranking for awareness content when you need decision-stage visibility
Look at your traffic-to-conversion ratio by cluster
Identify if your content attracts the wrong audience (students vs. buyers)
Reallocate resources to high-intent clusters.
Instead of producing more awareness content, shift the budget to bottom-of-funnel content.
This includes comparison pages, case studies, and ROI calculators that target buyers ready to evaluate solutions.
Update existing comparison pages with current data and competitive intelligence.
3. Address Competitive Threats
Keep tabs on competitors gaining ground in your strong clusters.
If a competitor gains over 5% SoV in your strong clusters, it’s an early sign that they’re targeting your territory.
That gap can widen unless you respond to maintain your market share.
Diagnose the cause:
Analyze what new content or tactics they launched
Check if they’re winning on review sites, community platforms, or organic search
Identify if they’re capturing a format you’re missing (video, podcasts, tools)
The fix depends on where your competitors are winning.
If competitors actively feature on review sites, optimize your profiles. Run campaigns to source reviews from happy customers.
If they’re visible on community platforms, proactively engage in communities like Reddit and Slack.
Prioritize Based on Effort vs. Impact
Not all gaps matter equally.
Focus on opportunities that will actually move your revenue pipeline.
Start with high-impact, low-effort wins. Then invest in high-effort moves that compound over time.
High Impact
Low Impact
Low Effort
Optimize content ranking #5-10
Claim existing review site profiles
Update comparison pages with current data
Claim industry directory profiles
Minor content refreshes on supporting pages
Social engagement in established channels
Guest commenting on industry blogs
Newsletter mentions in partner publications
High Effort
Build authority in community spaces (Reddit, forums)
Create comprehensive hub content for weak clusters
Earn citations from AI-referenced sources
Develop thought leadership for industry publications
Content for saturated topics without authority
Channels where your audience isn’t active
Platforms AI tools rarely reference
Keywords outside category relevance
Making SoV Your 2026 North Star
Share of voice captures how often you show up across the fragmented platforms where buyers make decisions.
Get started by measuring your current SoV across SEO and AI search with the steps in this guide.
Pick the gap that costs you the most revenue, and strategize the best ways to close it.
Next step: Build your AI optimization gameplan to capture visibility in the fastest-growing search channel.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-30 19:59:142026-04-30 19:59:14How to Calculate Share of Voice (+ Why it Matters for SEO)
