Google is rolling out new tools to help advertisers better understand performance across increasingly complex customer journeys.
What’s happening. As AI continues to transform campaigns, creatives and targeting, Google is introducing updates focused on data integration, experimentation and media mix modelling — all aimed at helping marketers turn fragmented signals into actionable insights.
Why we care. Automation has made it easier to run campaigns, but harder to understand what’s actually working. These updates make it easier to connect data, prove what’s actually driving results, and make smarter budget decisions across channels. As AI handles more of the execution, having strong measurement in place becomes the key differentiator for performance and growth.
Data is the starting point. Google is expanding its Data Manager to give advertisers a clearer view of how their data flows across platforms like BigQuery, HubSpot and Shopify.
A new map-based interface will help marketers visualise connections between data sources and identify gaps in tracking or configuration. At the same time, updates to the Google tag aim to simplify setup, allowing advertisers to upgrade existing tags without additional coding.
The goal: make it easier to unify signals and improve data quality — which directly impacts campaign performance.
Between the lines. Google is acknowledging a long-standing issue — advertisers struggle more with data setup and integration than with campaign execution itself.
By simplifying tagging and data flows, Google is trying to remove one of the biggest blockers to effective AI adoption.
Proving what actually works. Google is also introducing Meridian GeoX, a new geo-experimentation tool designed to measure incremental impact across regions.
Built on an open-source framework, GeoX feeds into Google’s broader Marketing Mix Model, Meridian, giving advertisers a more defensible way to validate performance — especially when presenting results to finance teams.
This signals a shift toward causal measurement, not just correlation.
Why it matters. As privacy changes reduce visibility and attribution becomes more complex, marketers are under pressure to prove impact. Tools like GeoX aim to provide that “ground truth” — something many attribution models struggle to deliver.
Simplifying media mix modelling. To address the complexity of Marketing Mix Models (MMMs), Google is launching Meridian Studio — a Google Cloud-powered platform that helps teams build, customise and scale models more easily.
The focus is on operationalising MMMs, making them less resource-intensive and more accessible for enterprise teams managing large datasets.
What to watch:
Whether advertisers adopt MMMs more widely with simplified tools
How effective GeoX is in proving incremental impact
If improved data visibility translates into better campaign performance
Bottom line. Google is making a strategic shift: in an AI-driven world, better measurement — not just better automation — will determine who wins.
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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
Google is trying a new method of bot authentication named Web Bot Auth. Google posted a new help document that explains that Web Bot Auth is a “new cryptographic protocol that helps websites to validate that bots are authentic.”
The goal of Web Bot Auth is to help you automate the process of authenticating which AI Agent bots are authentic and which are fraud.
Limited test. Google said the search compan is “testing the protocol with some AI agents hosted on Google infrastructure.” Not all Google user agents are using Web Bot Auth and Google is not yet signing every request of agents using the protocol.
What is Web Bot Auth. Google defined Web Bot Auth as “Web Bot Auth is an experimental cryptographic protocol used to authenticate requests sent by bots. Instead of relying solely on self-reported headers and IP addresses, Web Bot Auth allows agents to cryptographically sign their requests.”
Web Bot Auth can bring the following benefits according to Google:
Future-proofing: Help establish a web where agent providers and websites can build mutual trust and make informed access decisions.
Cryptographic certainty: Move beyond easily spoofed headers to a verified identity and decouple agent identity from IP addresses.
Better observability: Gain clearer insights into how agents interact with your content.
Why we care. As AI Agents become more and more common across the web, managing which Agents can access your site and web pages may become more and more of a challenge. This new method of authentication may help you allow authentic AI Agents and block the inauthentic AI Agents.
Again, this is an “experimental” feature right now, so keep track of its progress.
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.
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