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.
LinkedIn articles are long-form content published natively on LinkedIn. They live on your profile, get indexed by Google, and surface in LinkedIn search results long after you publish them.
Feed posts drive reach. Articles build the kind of credibility that makes someone want to hire you, work with you, or trust your expertise.
The strongest use case for articles is distribution, not creation. Adapt existing content rather than starting from scratch.
Performance starts with the headline. Specific, opinionated titles outperform vague ones every time.
Articles are increasingly picked up by AI-generated search answers, making them a quiet but growing visibility channel outside LinkedIn itself.
Most brands still treat LinkedIn like a feed-first platform. Post a thought, collect some likes, move on. That works well enough for reach. It does almost nothing for credibility.
The shift worth paying attention to is not about posting frequency. LinkedIn now surfaces content through search and AI-generated answers that reach well beyond your first-degree connections. The professionals and brands showing up in those spaces are not the ones with the most followers. They are the ones publishing LinkedIn articles.
LinkedIn articles are one of the most underused assets in organic LinkedIn marketing right now. I’ll cover what makes them different from feed posts, how to use them as a distribution channel, and how to write them in a way that actually gets read.
LinkedIn Articles Aren’t New, But Their Role Has Changed
LinkedIn launched its publishing platform more than a decade ago under the name Pulse. Most marketers filed it under “things we should probably use” and forgot about it. The format has since been rebranded simply as LinkedIn Articles, and the ones paying attention to what it has become now have a real head start.
What changed is how LinkedIn itself handles content discovery. The platform acts more like a search engine than it used to. Older articles get resurfaced to relevant audiences. Search queries on LinkedIn increasingly pull from published articles, not just profiles. And because LinkedIn articles are public and hosted on a high-authority domain, Google indexes them. A well-written piece can appear in organic search results for months after publication, reaching people who have never heard of your brand.
Most marketers make one of two mistakes here. They either ignore articles entirely, or they copy-paste from their company blog and treat it as done. Neither approach takes advantage of what the format uniquely offers. It is worth noting that articles are available to both individual profiles and company LinkedIn pages, which means the opportunity exists at every level of your presence on the platform.
The deeper issue is that articles require a different strategic mindset than feed content. They are not built for the scroll. Discoverability on LinkedIn works differently than most marketers assume, and that distinction is worth understanding before you publish your first piece.
Why Articles Play a Different Role Than Feed Posts
Feed posts are built for speed. A sharp observation, a quick take. They generate engagement quickly and lose most of it within 48 hours. That is not a flaw, just the format doing what it was designed to do.
Articles operate differently. They are not competing for attention in a scroll. A reader who finds your article through LinkedIn search or a Google result is already in a different mode. They are not skimming a feed but are likely looking for something specific, and are willing to spend time with it.
That behavioral difference is what makes articles valuable for credibility in a way feed posts aren’t. Publishing a well-structured argument on a topic you have real expertise in signals something that likes and comments cannot. It shows you can develop an idea past a single take. The people making decisions about who to hire or work with notice that and they are very much not counting your impressions.
There is also a practical career and business case that rarely gets discussed. The people who evaluate you before a hiring decision or a pitch are not reading your feed. They are searching your name. A LinkedIn profile with published articles on relevant topics sends a different signal than one without. It is the difference between someone who has opinions and someone who has a body of work.
The Real Opportunity: Articles as a Distribution Channel
The framing that kills most LinkedIn article strategies is boiling it down to: “We need to create more content.” More is not the problem. Distribution is.
Most marketing teams are already producing content that never reaches its full potential audience. Blog posts with strong insights get two weeks of traffic and fade. Bylined pieces in trade publications get shared once, then disappear. Presentations from industry events are often never seen again outside the room where they were given.
LinkedIn articles give that content a second life. Take a blog post, extract its central argument, and adapt it for LinkedIn’s format and audience. The original piece stays on your site. The article links back to it and drives qualified traffic from readers who found the piece through LinkedIn search or Google. This extends the shelf life of work you already did without doubling the workload.
The same logic applies at the individual level. An executive’s byline in an industry publication reaches that outlet’s audience once. The same argument published as a LinkedIn article reaches their network, their followers, and anyone searching that topic on the platform for months afterward.
This is the reframe that makes articles sustainable: they are a distribution layer, not a content creation obligation. If your team treats every article as a net-new piece, it will always feel like too much. If they treat it as an adaptation of something that already exists, the lift is manageable and the compounding visibility adds up over time.
How to Write LinkedIn Articles That Actually Perform
Performance starts before the first sentence. Your LinkedIn headline is the only thing most readers will see before deciding whether to click. Vague titles get scrolled past while specific, opinionated ones get clicked. “Thoughts on the Future of B2B Marketing” is invisible. “Why Most B2B Content Strategies Stall at the Awareness Stage” signals a real argument and a reason to keep reading.
Once someone is in, lead with the insight. Most articles lose readers in the first two paragraphs because the writer is still warming up, providing background, explaining what they are about to say. Skip that. Start with the argument. The context can come later (if it is needed at all) structure matters more on LinkedIn than on a traditional blog also, since readers on the platform skim before they commit. Short paragraphs and clear transitions help them orient quickly. The occasional subheading does not hurt either. A reader who skims and grasps the structure is far more likely to slow down and read closely than one who hits a wall of text and bounces.
Tone is the variable most writers underestimate. LinkedIn articles perform better when they sound like a person who has a real position, not a brand running a content calendar. Opinionated works. Specific works better. “Here is what we have seen hold up across dozens of campaigns” lands differently than “Here is what the research suggests.” Readers can tell the difference between lived experience and summarized consensus and respond accordingly.
One practical tip: write the headline last. Draft the piece, find the sharpest sentence in the whole thing, and ask whether it belongs at the top of the article or in the headline. The answer is usually both.
Close with a soft call to action. The article should be valuable on its own, but it can still point somewhere. A forward-looking question to spark discussion, a brief observation that invites a reply, or a link to a related resource all work. Hard sells do not belong here. The goal is to earn the next click, not demand it.
One step most people skip: after publishing, go into the Manage tab and set a custom title and description for your article. These fields are what search engines use in place of your on-page headline, so taking two minutes to optimize them for a target keyword meaningfully improves how the piece gets found off-platform.
Where LinkedIn Articles Fit in a Modern Content Strategy
Most content strategies have a gap between awareness and action. Social content gets attention. Your website converts it. What sits in between is often nothing, and that gap is where brands lose the consideration battle to whoever showed up with more substance.
LinkedIn articles fill that gap. They are where a reader who already knows you exist decides whether your thinking is worth trusting. That is a different job than a feed post or a homepage. It is the consideration stage, and most brands leave it completely unaddressed.
Think about how buying decisions actually get made in B2B. Someone sees a post, looks up the author, skims the profile, and then either moves on or goes deeper. Articles are what “going deeper” looks like. A series of well-argued pieces on a specific topic does more to establish authority than any amount of engagement metrics on short-form content. It is proof of thought, not just presence.
Your owned content still handles the conversion. An article should not be trying to close a deal. It should be building enough confidence that a reader wants to take the next step on their own.
The brands doing this well rarely talk about it as a content strategy. They talk about it as a sales and trust-building motion. That reframe is worth borrowing.
The Missed Opportunity: LinkedIn Articles and AI/Search Visibility
Here is something most LinkedIn content guides do not mention: your articles can show up in Google before your company website does.
LinkedIn’s domain authority is among the highest on the internet. When you publish an article there, you are borrowing that authority. A well-structured piece on a specific professional topic can surface in Google organic results, featured snippets, and AI Overviews faster than a comparable post on a newer company blog that is still building its own search presence.
For brands that are early in their SEO journey, that is a meaningful shortcut. And the opportunity is only growing: LinkedIn is now the second most cited source in AI-generated answers, trailing only Reddit.
Source: Semrush
Most marketers measure LinkedIn articles by what happens on LinkedIn. Reach and engagement matter, but they miss the larger picture. A piece that generates modest engagement on the platform can quietly pull in search traffic for months. The people finding it that way were never in your feed. They were looking for an answer, and your article was there.
AI-generated answers tend to pull from sources that are credible and publicly accessible. LinkedIn articles are both. If you are not publishing them, you are not in that conversation at all.
FAQs
How do you post an article on LinkedIn?
For individual profiles, go to your LinkedIn homepage and click “Write article” in the post creation box. For company pages, click “Create” and then “Publish an article.” Both paths take you to LinkedIn’s native publishing editor. Add a headline, body text, and a cover image, then hit Publish. After publishing, share the article to your feed with a short caption to extend its initial reach.
What do LinkedIn articles look like?
LinkedIn articles have their own URL and display with a headline, a cover image, and a full article body. They live on your profile under the “Articles and Activity” section and can be shared across the platform or externally.
How long should a LinkedIn article be?
Between 600 and 1,200 words tends to work well. That is long enough to develop a real argument, short enough to hold attention. Structure and clarity matter more than hitting a specific word count.
What is the LinkedIn article image size?
The recommended cover image size is 1200 x 627 pixels. Use a clear, high-contrast image that communicates the topic. Skip generic stock photography if you can.
Are LinkedIn articles credible?
They can be. Articles that reflect genuine expertise and specific experience carry strong credibility signals. The format does not make content credible on its own. The thinking does.
Are LinkedIn articles indexed by Google?
Yes. Public LinkedIn articles are indexed by Google and can appear in organic search results. This is one of the most underappreciated benefits of the format, since a well-written article can generate visibility long after it was published.
Conclusion
LinkedIn articles are not a volume play. The teams getting real results from them are not publishing more often. They are being more deliberate, using articles to deepen ideas that already have an audience and extend content that is already doing work elsewhere.
The format rewards a genuine point of view backed by specific experience. If you have content worth publishing, you have content worth adapting. Start with one strong piece, sharpen the argument for a LinkedIn audience, and publish it with a headline that earns the click. The discoverability of that article, both on and off the platform, will depend on how well you understand LinkedIn SEO, so that is worth getting right from the start.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-01 19:00:002026-05-01 19:00:00LinkedIn Articles: What Sets Them Apart & How to Write Them
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)
SEO services have evolved significantly in recent years, driven by changes in search behavior, AI-powered results, and rising competition across industries.
To give you the most accurate picture of the industry today, we’ve combined data from multiple sources, including our survey of 1,200 business owners.
In this report, you’ll learn:
How much businesses spend on SEO services today
Where companies actually find and hire SEO providers
What factors influence the decision to choose one agency over another
Why clients leave (or stay with) their SEO provider
Key trends shaping the future of SEO services
Let’s dive into the data.
Highlights and Key Statistics:
1. Companies spend $119.4 billion on SEO and digital marketing consulting each year in the US.
2. We found a strong correlation between higher spending and higher client satisfaction in small business SEO. In fact, clients who spent over $500/month were 53.3% more likely to be “extremely satisfied” compared to those who spent less than $500/month.
3. Most small business owners find SEO providers through referrals, Google searches, and online reviews. A small fraction of SEO clients (8%) found their current provider from online advertising.
4. When it comes to choosing a provider, 74% of business owners consider an SEO provider’s reputation “very” or “extremely” important. Monthly cost and the provider’s own Google rankings were also noted as important factors.
5. Overall, SEO client satisfaction is decidedly low. Only 30% would recommend their current SEO provider to a friend or colleague. However, we found that client satisfaction among marketing agencies was higher than that of freelancers.
6. SEO provider turnover is high. 65% of our panel stated that they’ve worked with several different SEO providers. 25% have worked with 3 or more providers.
We have more detailed and expanded findings below.
Average Monthly SEO Spend
In 2025, US organizations spent $119.4 billion on SEO and digital marketing consulting.
Our 2019 research found that small businesses spend $497.16 per month on SEO services.
However, we did discover a large range in SEO spending. Half of our respondents reported spending less than $1,000 per year on SEO. 14% spend $5k+ per year. Only 2% spend over $25k/year.
We also found that agencies tend to get paid significantly more than freelance SEO providers.
Specifically, agencies were 2x more likely to get paid $1k-$2k/month than freelancers, who mostly get paid in the $500-$1k per month range.
Agencies also tend to dominate the high-end pricing range (clients that spend $10k-$25k/year on SEO).
As you can see, 24% of small businesses that work with agencies spend between $10k-$25k/year, compared with 2% that work with a freelance SEO.
When it comes to SEO, do you “get what you pay for”?
According to our data, yes.
Specifically, we discovered that clients spending over $500/month were 53.3% more likely to consider themselves “extremely satisfied” compared to people who spend less than $500/month.
We also found a clear relationship between dissatisfaction levels and cost.
Specifically, business owners who spent less than $500/month were 75% more likely to be dissatisfied than those who invested at least $500/month in SEO.
This relationship played out whether a client worked with a freelancer, agency, or a mix of both.
Referrals and Google Searches Are the Top Ways Businesses Are Finding SEOs
When someone wants to hire an SEO agency, where do they look?
According to our panel, most people find potential SEO service providers through word of mouth, Google searches, and online review platforms (like Yelp).
On the other hand, relatively few find SEO providers through online or offline advertising, or referrals from other vendors (like web designers or writers).
If you’re an agency owner or a freelancer, this is a key finding. If you know where small business owners look to find SEO service providers, you can invest resources to make sure your business has a presence in those places.
Reputation and Cost are Key Factors Involved In Choosing a Provider
Demand for SEO services continues to grow as search becomes more complex and harder to manage in-house.
In fact, recent data shows that 61% of companies hire SEO agencies due to a lack of internal expertise, while others turn to external providers when they don’t have the time, resources, or results needed to scale.
Most common reasons for using SEO services:
Lack expertise – 26.87%
Lack resources – 22.70%
More cost-effective – 21.48%
Poor in-house SEO results – 15.48%
Lack sufficient time – 10.70%
But hiring an agency isn’t just about capability. Once someone finds a list of potential providers, how do they decide which one to go with?
We discovered that reputation, cost, and a provider’s own Google rankings influenced their decision the most.
Small business owners cited client case studies and the provider’s social media presence as significantly less important.
However, even these relatively minor factors played a role in whether or not someone decided to work with a particular SEO provider. For example, 55% of our panel cited “referrals” as an important consideration.
Although the importance of referrals pales in comparison to a provider’s reputation (55% vs. 74%), it’s still something that influenced more than half of the people we spoke to.
Interestingly, we found that a provider’s location mattered quite a bit.
Only 51% knew exactly where their SEO provider was located.
However, 78% of US-based small businesses stated that knowing their provider’s location was “extremely” or “very” important (with 46% stating that a known location was “extremely important”).
If you provide SEO services, making your location clear and obvious may help you land more SEO clients.
Here’s a great example from Siege Media, which actually includes a picture of their office on their about page:
The Vast Majority of Business Owners Expect SEO Services To Increase Customers and Traffic
A recent 2025 survey found that 91% of people who used SEO services reported a positive impact on website performance and marketing goals.
As such, it’s no surprise that expectations are high when working with an SEO provider.
According to our survey, the most important expectations are “accessing new customers”, “increasing traffic”, “increasing brand awareness”, and “building trust” as most important.
“Gaining social media followers”, “increasing number of email subscribers”, and “helping to attract new talent” were cited as relatively unimportant.
In fact, even though this is a common goal set by marketing agencies, only 26% of respondents cited “getting followers on social media sites” as extremely important.
This finding is especially key for SEO providers that are taking on new clients.
For example, a newly-hired SEO provider that says, “Our first step is going to be to get more likes on your Facebook page” isn’t speaking their client’s language.
On the other hand, kicking off the client-provider relationship with: “I look forward to helping you get more targeted traffic and customers” will likely result in a more satisfied client.
Needless to say, for the relationship to last, you need to deliver on those promises (more on that later). But it does help to understand what clients hope to get out of SEO so you can mold your services and reports based on that.
SEO is widely regarded as one of the highest-return marketing channels, with some estimates placing the average ROI at around 22:1, meaning businesses earn roughly $22 for every $1 invested.
And around 1 in 3 qualified leads (34%) come directly from SEO efforts.
However, ROI varies significantly across industries:
The rapid emergence of AI also has a bearing on SEO ROI, with 39% reporting a “moderate increase” and 29% claiming a “significant ROI increase”. Only 1% claim that AI reduces their SEO ROI.
SEO is a long-term investment, and results often take time to materialize; many campaigns require 6–12 months just to break even.
That slower payoff, combined with unclear reporting or misaligned expectations, can leave many businesses frustrated with the SEO services they receive.
In our study, we asked our panelists to rate their current SEO provider (or the last SEO provider they worked with) using the Net Promoter Score.
The results were markedly low.
First off, we found that only 30% of small business owners would recommend their current SEO provider.
Importantly, 30% of our respondents considered themselves “detractors”. Which means they would leave a negative review for their last or current SEO provider.
In fact, the SEO services industry as a whole has an NPS score of 0, which is considered “not likely to recommend”.
When we broke down the NPS scores among agencies, freelancers, and a combination of freelancer and agency, we discovered that agencies had a higher average NPS score than freelancers.
However, all three types of services had fairly low NPS scores.
Clients Cite Lack of Education and Resources as Top Reasons for Low Satisfaction Levels
Delivering effective SEO requires significant investment in talent, tools, and ongoing strategy, something many businesses underestimate when hiring a provider.
Building comparable in-house capabilities can cost $150K–$250K+ for senior talent plus thousands per month in tools, which helps explain why expectations often exceed what lower-cost or under-resourced SEO services can realistically deliver.
NPS is a helpful benchmark. However, NPS can only tell you so much. In other words, it’s difficult to understand why SEO services have such low levels of satisfaction.
That’s why we decided to dig deeper into this finding.
And when we dug a bit deeper to understand more about what’s happening, we uncovered a few surprising insights.
First, many unhappy SEO clients fully or partially blamed themselves.
Specifically, 50% stated that “I feel like I need more training to fully benefit from what SEO offers”, and 28% told us that they “do not have the staff resources to properly benefit from SEO”.
This means that low satisfaction levels aren’t solely due to poor quality work. In fact, many clients are simply not in a position to benefit from SEO due to a lack of resources.
Plus, even clients with resources may not make SEO a priority because they don’t have the training to fully understand how SEO benefits them.
For example, let’s say an SEO provider wants to change a title tag on a client’s site. But it doesn’t happen because their developer is swamped with a website redesign. Also, this client may not understand that this simple change can increase their Google traffic due to a lack of training. So they don’t make that change a priority. And progress stalls.
Which leads us to our second interesting finding, the importance of reporting and transparency.
27% of the clients we spoke with agreed with the statement: “I find SEO to be confusing and unclear about what services they offer.” 25% said that “I am not sure what I am really paying for with SEO.”
In other words, many clients are confused about what their provider is doing for them or what they’re getting out of the arrangement.
These are two points that could be remedied with better reporting and increased transparency.
I should point out that a fair number of clients stated that “I feel like SEO companies are very unreliable” and “I don’t think SEO is worth the money for my business.”
Which means that a simple lack of results and ROI is often the culprit behind low client satisfaction levels.
However, as you just saw, there are usually non-performance-based factors at play as well.
Turnover In the SEO Services Industry Is Extremely High
Likely due to low global satisfaction levels, we found high levels of turnover in the SEO services industry.
Specifically, we found that 65% of small business owners have worked with at least one SEO provider before:
We also found that 1/4th of our panel have worked with 3 or more providers:
However, our data suggests that most clients don’t switch between SEO providers without careful consideration.
In fact, the clients in our panel have been working with their current SEO service for an average of 3 years. And lapsed clients give their service provider an average of 2 years to deliver before moving on.
That said, we did discover a small subset of clients that do rapidly switch between different providers.
These “rapid switchers” tend to hire and fire SEO companies at a fever pitch.
For example, we classified 10% of our panelists as “rapid switchers” (worked with 3 or more SEO providers over the last year).
Most SEO Clients Leave Due to Lack of Results and Cost
We wanted to know why people decide to leave their current SEO provider or switch to another company.
We referred to people who worked with multiple SEO providers as “lapsed clients”. And we asked this subset of lapsed users what went into their decision.
Here were the results:
Not surprisingly, 82% of our respondents cited “Dissatisfaction with business results” as a factor in their decision. 81% reported that cost played a large role as well.
This suggests that clients don’t look at results in a vacuum. They also pay attention to the ROI that they’re getting from SEO. In other words, delivering results for clients is one thing. But it’s also important to demonstrate the ROI that SEO has on their business. Otherwise, they may leave.
Although lack of results and cost were the two largest factors, they weren’t the only reasons that clients decided to stop working with an SEO provider.
In fact, 80% of lapsed clients stated that they found a better option on their own, which suggests that clients are happy to shop around for an alternative to their current SEO provider.
And 34% cited poor “customer service/ responsiveness” as a factor in their decision.
However, relatively few clients cited “pitched by a competitor” as a reason for leaving. In other words, as long as you can keep your clients happy, they’re not likely to leave. This remains true even if a competitor attempts to poach your client with a better offer.
We also asked our “lapsed clients” panelists to describe to us why they decided to stop using an SEO service. Here’s a sample of those responses:
We also asked a group of users who were happy with their SEO service (“existing clients”) what they liked about it. Here’s what they told us:
Existing Clients are 2x More Likely to Be Web Savvy Than Lapsed Clients
We asked our panel to self-report their level of “web savviness”.
Here were the results:
As you can see, 37% of SEO clients consider their web savviness as “somewhat” or “not very”.
The upshot here is that many clients simply don’t have the web savviness to understand key digital marketing terms, like “title tags”, “CSS”, and “backlinks”. This suggests that SEO companies should largely avoid this sort of jargon in favor of terms like “leads”, “sales”, and “first page Google rankings”.
In fact, this is backed up by another finding from our panel: that lapsed clients are significantly more likely to consider themselves not web savvy.
Specifically, we found that existing clients were 2x more likely to consider themselves “extremely web savvy” than lapsed clients.
This suggests that web-savvy users are in a better position to understand how their SEO service is helping them. So they decide to stay. On the other hand, clients who aren’t web savvy may not fully understand what they’re getting from their SEO provider. So they decide to leave.
Each month, we host an SEO update covering the latest in search and AI. During this month’s edition, our SEO experts Carolyn Shelby and Alex Moss, cover everything from the latest advances in Agentic AI to Google’s spam and core updates and why simply publishing more content is no longer enough and in many cases actively works against you. Read this recap for the highlights or watch the April 2026 SEO Update by Yoast to delve into the latest news.
Watch the full recap on YouTube to dive deeper into these topics, hear some examples and hear the answer to audience questions.
SEO and AI news from April 2026
Google introduces new AI agent signals and infrastructure
Google added a new Google-agent user agent, signaling more explicit support for AI-driven crawling and interaction. At the same time, proposals like WebMCP aim to standardize how AI agents interact with websites, while Google leadership suggests search is evolving into an AI agent manager.
Why it matters: The web is being restructured around agent access, not just human browsing.
Actionable takeaway:
Ensure your content is accessible and understandable for both traditional crawlers and emerging AI agents.
Google continues expanding AI capabilities and efficiency
Google introduced TurboQuant, a new approach to AI model compression that significantly improves efficiency. At the same time, Google is expanding task-based features in AI Mode and refining how users interact with AI-driven search experiences.
Why it matters: As AI becomes faster and more integrated, user expectations and search behavior will continue to shift.
Actionable takeaway:
Focus on making content easy to extract and act on within AI-driven workflows.
Structured data and documentation evolve for AI-first search
Why it matters: Search engines are adapting their systems to better interpret and label AI-generated content.
Actionable takeaway:
Use structured data and clear linking practices to improve how your content is interpreted and displayed.
Core updates, spam policies, and enforcement continue to tighten
Google completed its March 2026 spam update and core update, while also introducing updates to spam policies addressing tactics like back button hijacking and improving spam reporting tools.
Why it matters: Enforcement is becoming more granular, targeting both technical manipulation and low-value content.
Actionable takeaway:
Review your site for outdated or risky tactics and ensure a strong focus on quality and user experience.
Platforms and tools expand AI-driven workflows
Elementor launched Angie, an agentic AI for WordPress, while Cloudflare introduced EmDash as a WordPress alternative and continued work on agent readiness standards.
Anthropic released Claude Design and previewed Mythos, while OpenAI tested an AdsBot and introduced a ChatGPT ad manager interface.
Why it matters: AI is increasingly embedded directly into content creation, workflows, and monetization systems.
Actionable takeaway:
Evaluate how AI tools fit into your content and operational workflows, not just your marketing strategy.
Authority, trust, and content quality remain central
Why it matters: As AI systems synthesize answers, they rely more heavily on trusted, differentiated sources.
Actionable takeaway:
Invest in original, high-quality content and consistent brand signals across channels.
Measurement and reporting begin shifting toward AI visibility
Bing previewed AI Citation Share, and new dashboards are emerging that map how AI systems ground answers in source content. A temporary Google Search Console glitch also highlighted how dependent SEOs still are on traditional metrics.
Why it matters: Visibility is moving beyond rankings into citation, inclusion, and influence within AI-generated responses.
Actionable takeaway:
Start paying attention to how your content appears in AI systems, not just where it ranks.
Also in the news…
Several additional developments are worth watching:
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-29 11:42:422026-04-29 11:42:42The April 2026 SEO Update by Yoast recap
Content agencies often specialize in certain industries or subsets of content marketing, such as technical SEO or conversion-focused content.
Our list of some of the best content marketing brands in the business covers a range of services and specialties.
Check out their client lists and portfolios to see if their work aligns with your expectations and preferences.
Knowing what to look for in a content marketing company and the right questions to ask can help you identify the ones with the abilities and capacity to help you expand and improve your content strategy and reach your marketing goals.
The content world is changing, but people still know its value. A 2023 survey from the Content Marketing Institute showed that over three-fourths of marketers indicated that content marketing generates demand and leads.
This is no surprise when you realize 70 percent of people would prefer to learn about a company through an article rather than advertising.
Content marketing can generate huge amounts of traffic, leads, and sales for your business. If you’re a company looking to get started with content marketing, it can be tough to find the resources and expertise you need.
What kind of content do your customers want from you? Is that the same kind of content that creates revenue for your business? Today we’ll take a look at the best content marketing companies in the industry to help you answer those questions and more.
Agency
Best For
Ideal For
Notable Clients
Standout Approach
NP Digital
Immediate and consistent revenue growth
Broad (B2B, e-commerce, SaaS, finance)
CNN, Adobe, Western Union, SoFi
Revenue-focused content with technical SEO built into every campaign from the start
Seer Interactive
Big data search and content
Competitive industries like finance, banking, and mortgages
Asos, Intuit, SendGrid, Terminix
12,000GB proprietary data warehouse surfaces hidden customer trends competitors can’t see
Brainlabs
Technical SEO
Not specified
Formula 1, Estée Lauder, Capital One, Polaroid
Built on a team of mathematicians, scientists, and programmers driving data-backed automation and testing
Fractl
In-depth, research-heavy content
Research-intensive industries
Porch, Fanatics, Superdrug, Healthline
Research published in Harvard Business Review, The Economist, and the NYT, with a dedicated client growth division
Entrepreneurial approach to flipping underperforming businesses through aggressive conversion optimization
The Content Bureau
B2B content marketing
Technology, venture capital, and financial sectors; global corporations
American Express, PayPal, Microsoft, Cisco
Woman-owned agency with 80 percent of staff at 10+ years tenure, offering premium, high-attention client service
Webprofits
Growing challenger brands
E-commerce, consumer, and retail brands scaling fast
Logitech, Philips, Nespresso, HP
“Fluid marketing” methodology blends digital strategy and performance marketing to find hidden growth opportunities
Siege Media
Scalable SEO content
Fortune 500 companies down to small startups
Zillow, Airbnb, TripAdvisor, Asana
Passive link generation through content, backed by a proprietary link management tool maintained monthly
Directive
Performance marketing for tech companies
Tech companies of all sizes
Amazon, Bill.com, Matillion, SentinelOne
Generated $10B+ in client revenue by acting as an embedded extension of in-house marketing teams
1. NP Digital – Best for Immediate and Consistent Revenue Growth
NP Digital is my content marketing company. We created NP Digital in 2017 to serve the millions of people who needed help with their content marketing to grow revenue.
Rankings are important, but many marketers still focus obsessively on keywords and content that doesn’t lead to revenue. I’ve always focused on helping readers build a business that generates traffic, leads, and, most importantly, revenue. So we have a big focus on developing high-quality content that ranks high and converts visitors into customers by aligning with user intent.
Today, we’re one of the top content marketing brands in the business. a powerhouse global agency with one of the top 100 blog destinations in the world.
Another thing that’s different about NP Digital is the fact that we incorporate technical SEO into our content marketing planning. SEO — technical, on-page, off-page, local, etc.— it’s always a package deal with content marketing. Our status as one of the top SEO agencies means you get the best of both worlds.
We stay on top of Google’s updates and algorithms and adjust our strategies accordingly. This means the content we create for our clients automatically performs well with Google. here’s no extra work required.
NP Digital is my way of helping everyone achieve the revenue and growth they deserve in their business.
2. Seer Interactive – Best for Big Data Search and Content
Wil Reynolds founded Seer Interactive, which got its start as a search engine optimization company. What makes Seer one of the best content marketing companies on our list is its focus and emphasis on big data.
Using a combination of in-house and third-party tools, they’ve built a massive data warehouse with almost 12,000 gigabytes of data they can analyze to identify new, hidden, and unexpected customer trends.
If you’re in a competitive or cutthroat industry (e.g., finance, banking, or mortgages), this data is what you need to stay ahead of your competitors.
With Seer Interactive, their approach is SEO-heavy. That should be an important priority for every company, whether you’re big or small, but not every company is ready for Big Data.
Brainlabs was founded by Daniel Gilbert in 2012. Understanding that marketing was becoming all about data, he took the unusual tactic of hiring mathematicians, scientists and programmers to support automation and data-driven insights.
His approach paid off: Since 2020, the agency has expanded its services by acquiring other marketing companies, including the SEO-focused Distilled, a leader in the space.
Today Brainlabs is known as one of the top content marketing agencies for technical SEO and helping companies evolve in an increasingly competitive SEO landscape. They are constantly experimenting and testing to improve conversion rates.
4. Fractl – Best for In-Depth, Research-Heavy Content
Fractl is a research-heavy, data-driven content marketing company. They’re focused on rapid, organic growth that’s driven by content marketing, data journalism, digital PR, and search engine optimization.
Research makes Fractl unique.
They’re always researching industry-related topics, and they share their understanding of the art and science behind newsworthy content. They share their research in top publications, leading market resources, scientific journals, and authoritative conferences around the world.
Their research has been published in MarketingProfs, TNW, The Economist, Time, the Harvard Business Review, the New York Times, Pub Con, and many other publications and journals.
If you’re in a research-heavy industry and you’re looking for a high-growth content marketing company, Fractl is a good choice. Aside from being one of the best content marketing brands, they’re one of the few companies that have a division dedicated to client growth.
5. Column Five – Best for Data and Content Visualization
Column Five describes itself as a creative content agency. They’re primarily focused on the visual side of content marketing — storytelling, design, data visualization, video, interactive motion graphics, even exhibition design.
They are most known for their “child of the 90s” viral video on behalf of Internet Explorer, which launched their reputation as one of the best content marketing brands out there.
As a content creation company, Column Five is focused primarily on content strategy, content creation, and content distribution. They rely on a simultaneous mix of organic and paid distribution channels to draw attention to client content.
The company mantra is “the best story wins,” showing their commitment to developing great content that delivers big results. It specializes in content that is “inherently newsworthy,” making it more likely to get traffic, links, and media attention.
6. Single Grain – Best for Conversion-Driven Content Marketing
In 2014, entrepreneur and leading marketing expert Eric Siu made a big gamble. He bought a failing SEO agency for less than the cost of a cappuccino — $2. This wasn’t the first time he’d made a seemingly risky bet — in the past he led the growth strategy for an online education company when it had just a few months of cash left in the bank.
“A month into it, the CEO pulls me aside,” Siu recalls, “and he’s like, ‘Eric, you know, 48 people, their families, they’re riding on your shoulders right now, and if you can’t hit numbers in the next month, we’re gonna have to let you go.’”
Did I mention he was just 25 years old at the time?
Eric leveraged his marketing know-how and entrepreneurial outlook to turn Single Grain around and take it to where it is today: solidly among the ranks of the best content marketing brands out there.
Eric Siu and the Single Grain team can do for your business what they do best: turn it around. They know how to turn a faltering business into a successful one with an approach of optimizing for conversions and focusing on rapid growth.
7. The Content Bureau – Best for B2B Content Marketing
The Content Bureau bills itself as a premier B2B content marketing company. This agency is woman-owned, 100 percent virtual, and their team is 90 percent female, of which a third are women of color. The Content Bureau focuses its attention on the technology, venture capital, and financial sectors, working almost exclusively with global corporations that rely on them year-round.
Many of their clients are long-term, stable clients who prefer their premium approach, exclusive attention, and veteran workforce; 80 percent of their team have been with The Content Bureau for 10+ years.
As an organization, they give their clients lots of handholding; they’re open and transparent with each of their clients, and they deliver amazing service with their extraordinary content.
Webprofits is the content marketing and advertising company that was co-founded by Sujan Patel and Alex Cleanthous. Their company focuses on challenger brands in the e-commerce, consumer, and retail space that want to grow their business fast. They’ve refined their process based on real-life, in-the-trenches experience.
In fact, Patel doesn’t think of Web Profits as an agency. He calls it a marketing “hit squad,” a team of specialists who understand your business inside and out.
What makes Web Profits one of the top content marketing companies? They use a unique “fluid marketing” approach, which combines digital strategy with performance marketing. This enables its team of experts to identify hidden correlations and connections that can point to exciting opportunities for content marketing.
This makes the Web Profits team uniquely qualified to serve challenger brands that want to make a big impact.
Siege Media prides itself on taking a “scientific approach” to scaling SEO-focused content. The agency works with a wide range of companies, from established Fortune 500 businesses to small startups.
The focus of the business is on link-building. Siege Media creates content that serves as passive link generators, a tactic they say is more effective than manual outreach. Their formula results in high-impact content that produces instant results—and it’s a cost-efficient tactic, too.
Siege’s superpower is a proprietary solution for link management. Siege maintains the tool for its clients on a monthly basis, ensuring that websites are always aligned with overall goals and updates.
This commitment to innovation and leveraging technology for content marketing makes Siege one of the best content marketing companies for the future.
10. Directive – Best for Performance Marketing for Tech Companies
CEO Garrett Mehrguth founded Directive when he was just 21, focusing on SEO. Today it works with some of the world’s most prominent tech companies, helping them become more discoverable in a dynamic and often challenging industry. Since its founding, it’s generated more than $10 billion in revenue.
The agency uses a unique data-driven methodology to generate quality leads organically across the marketing funnel. The team prefers to act as a partner rather than a vendor, serving as an extension of its clients’ in-house marketing teams.
4 Characteristics that Make a Great Content Marketing Company
A good content marketing company will have no problem demonstrating that they have the expertise and the resources they need to make your campaign a success. These are some qualities to expect in a high-quality content marketing agency.
1: A Stable Team of Content Creators
Content mills produce poorly written filler content that’s mainly written for search engines. Not only is that a short-sighted approach, but Google’s algorithm is more likely to ding sites that use it—especially now that it is incorporating AI.
The best content marketing companies have a roster of regular and consistent writers on their team. Stable writers are skilled at writing, grammar, logical consistency, and storytelling. These writers can draw your readers in, creating content that moves people towards a specific goal or objective that you have in mind.
These writers don’t need a lot of babysitting, and they’re able to figure things out, to a certain extent, on their own. They’re dependable, and they’re able to match your brand voice.
When you contact a content marketing company, you’ll want to ask them questions about how they run their business.
How many writers do you have on staff?
Are they freelance or W-2? Do you use a mix of both?
How many of your writers are full-time? Part-time?
How do you manage your team of writers?
How many years of experience does the average writer on your team have?
When you ask companies these questions, listen to their answers carefully. Look for any inconsistencies or red flags. If you spot any, bring them up immediately and ask for an answer.
2: Access to Publishers and Influencers
According to Derek Halpern, founder of Social Triggers, you should be spending 20 percent of your time on content creation and 80 percent of your time on content promotion. The content marketing companies you work with are no different. If you’re investing a significant amount of time and money in creating an amazing piece of content, you should be spending 4x as much time on promotion to make sure your target audience sees it.
When you’re working with a content marketing company, they should already have a list of influencers and publishers in their address book. They should also have strong connections and relationships with the right people, so they’re reasonably sure they can drive traffic to your content.
3: Specialized Knowledge About Your Industry
In an ideal world, your content marketing provider has a significant amount of experience in your space, or the ability to connect with experts who do. At a minimum, you’ll want to ensure that the content marketing company you choose can write credibly about the topics that are relevant to your business.
The more specialized the content, the more important these criteria are for your business.
Industries like healthcare, engineering, or finance require large amounts of specialized experience. It’s unrealistic to expect an inexperienced company to write credibly about a highly technical topic.
Specialization requires specialists. The more technical your business, the more important it is to hire a content marketing company with experience and expertise in your field.
4: Content Analysis and Measurement
When you’re investing in the services of a content marketing company, you’ll want to see the numbers. The agency should be able to provide you with a detailed breakdown that includes data outlining your performance as well as the KPIs, metrics, and sentiment surrounding your content.
This information should give you the answers to the following questions:
Does this content move us closer to our campaign goals?
Does this piece of content (e.g., blog post, whitepaper, e-book, infographic) lead to enough conversions?
How far are people reading into your content?
Where in our flywheel are we losing customers?
What do we need to change/optimize to improve our conversion rates?
Which content marketing opportunities are we missing, and where?
Creating content isn’t enough. The content marketing company you choose should provide you with the actionable data you need and a comprehensive strategy to create profitable content for your business.
What To Expect From a Great Content Marketing Company
Top content marketing agencies are able to get you up to speed on their processes and provide you with a consistent and comprehensive set of deliverables. These deliverables ensure that your content marketing campaigns stay on track and that you’re able to achieve the consistent results you need.
To do this, your content marketing provider should provide you with onboarding guidance and specific deliverables throughout the pre-launch, launch, and post-launch phases of your campaign. These should include:
Content samples demonstrating your knowledge and expertise
The information and materials (e.g., credentials, existing content) they need from you to get started
A statement of work and a list of deliverables (e.g., 14 2,500-word articles each month, edits included)
Their process (if they’re not working with you and yours)
Projected campaign milestones, timelines, and calendars
Your point-of-contact, including their name, and contact information
Hours of availability
The best way to communicate (e.g., Slack, email, phone, chat, or text)
Expectations from you
Their process, policies, and procedures
Analysis and reports, including business goals, objectives, KPIs, metrics, strategy, tactics, and risks
Content audits
Consistent updates on your campaign performance
Regular (weekly or monthly) calls to discuss performance
Consistently updated due dates and delivery timelines
Monthly debrief to discuss successes and failures
Here are some additional details you should also expect from your content marketing providers:
Good boundaries (including the ability to say no)
Prompt and clear feedback
Accurate information on various parts of your campaign, including financial, campaign, and performance data
The best content marketing companies ask a lot of questions. They make sure to provide you with the upfront information you need to vet their company and make an informed decision. Once you’ve decided to move forward, they ask you for all of the information and materials they’ll need to produce the results you want.
FAQs
What makes good content marketing?
Good content marketing is different for every business, but in general, it involves creating well-written content that provides valuable information for your target market. It also draws in qualified leads and converts them into customers at a rate that justifies your investment.
How do you track content marketing results?
Tracking content marketing results involves setting clear goals, identifying key performance indicators (KPIs) such as website traffic, inquiries, and conversion rates to use as metrics, and monitoring the results. Most content marketing agencies use analytics tools to track and measure results.
How do you optimize for content marketing?
Optimizing for content marketing involves several steps. First, research who your target audience is and their needs. This will guide you toward topics for content development that can answer their questions and provide valuable information. Incorporate SEO to ensure your content ranks high on search engine results pages and brings in organic traffic. Finally, analyze the results to refine content topics, formats, and overall strategy.
Which content marketing agency is best for B2B companies?
B2B companies should look for agencies that focus on long-form content, SEO, and lead generation. The best partners understand how to create content that nurtures prospects over time, not just drives traffic. Agencies with strong experience in SaaS or professional services tend to perform best here.
Which content marketing company is best for small businesses?
Small businesses need agencies that balance quality with cost. Look for teams that offer flexible packages or project-based work instead of large retainers. The goal is to get consistent, high-quality content without overcommitting your budget early on.
Which agency is best for SEO-driven content?
You want an agency that combines content creation with keyword research and technical SEO. Firms that focus heavily on search performance will build content designed to rank, not just read well. Check for proven results in organic traffic growth and rankings.
Should you hire a specialized content agency or a full-service marketing agency?
Specialized agencies go deeper into content strategy and production. Full-service agencies connect content to SEO, paid media, and conversion optimization, which can drive better overall results. If content is your main bottleneck, go specialized. If growth is the goal, full-service often wins.
How do you choose the right content marketing agency?
Start with their results. Look for case studies showing traffic growth, lead generation, or revenue impact. Then review their content quality and process. The best agencies have a clear system for research, creation, and optimization.
Conclusion
Content marketing produces more leads and revenue than traditional marketing methods. If you’re looking for a good content marketing company to help you get started, it can be tough. Use this list to identify the companies that are a good fit for your business.
With this post, you should have a pretty good idea of the questions to ask, what to expect, and how to select the right content marketing provider.
Invest the right amount of effort with the right company, and your content marketing will grow faster than you expect. It’s tough in the beginning, but it will take effort, push through, and keep creating really helpful content, even if it’s hard.
You’ll see consistent revenue growth once customers realize that you’re serious about helping them solve their problems. Content marketing is the best way to show them that you understand, and you can help. With this said, combining with other disciplines is the best way to unlock your content’s true potential. Check out my lists of the best CRO agencies and top social media agencies for more information.
One of the most dependable ways to grow organic visibility was to publish more content. Expanding into the long tail and creating pages around different variations of a topic often led to steady traffic growth.
Many SEO teams still operate with this mindset. Content calendars are built around search volume targets, and growth is often equated with how much new content is produced. The problem is the results no longer reflect the effort.
In many cases, adding more pages doesn’t lead to increased visibility and can even dilute overall performance. Large content libraries are harder to maintain, compete internally, and often result in fewer pages surfacing in search results.
The challenge is no longer producing more content, but understanding why much of it fails to contribute to visibility.
Why content volume worked for SEO
For a long time, increasing content volume was a rational and effective strategy. Search engines relied heavily on keyword matching and topical coverage, which meant expanding into the long tail created more opportunities to capture demand.
Competition was also significantly lower, and many queries had limited high-quality results, so publishing across a wide range of keyword variations often led to quick visibility gains. In this environment, covering more topics translated directly into increased traffic.
Publishing frequency also helped strengthen domain authority. Sites that consistently added new content signaled freshness and relevance, which improved their ability to compete in search results.
This approach was further amplified by programmatic SEO. By creating scalable templates and targeting large keyword sets, companies generated thousands of pages and captured traffic at scale.
Most importantly, this strategy worked because it aligned with how search engines evaluated content at the time. Expanding coverage increased the likelihood of ranking, and more pages meant more opportunities to be discovered.
However, the conditions that made this approach effective have changed. As search ecosystems have evolved and competition has increased, the relationship between content volume and visibility has become less predictable.
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Why this model is breaking down
Content saturation
Most commercially relevant topics now have dozens of established pages competing for the same queries, many with years of accumulated links and behavioral data.
A new page enters this environment at a disadvantage because the keyword spaces it targets are already consolidated around results with existing authority and signal history.
Diminishing returns
As sites expand into adjacent keyword variations, search engines increasingly route similar queries to the same URL rather than distributing traffic across multiple pages.
This shows up in Google Search Console as two or three URLs splitting impressions on identical queries — neither ranking strongly because neither has consolidated authority. The intent overlap that content teams treat as coverage, Google treats as redundancy.
Changes in search experience
AI Overviews now appear across a significant and growing share of informational queries. Google has confirmed continued expansion of the feature across search types and markets. Informational content is the most affected by this shift, and it’s also the type most volume strategies produce.
A site with a large number of blog articles is therefore more exposed than one focused on a smaller set of transactional pages. More ranked pages don’t produce proportional traffic when an increasing share of visible positions no longer generate a click.
Indexing limits
Google’s budget documentation states directly that low-value URLs drain crawl activity away from pages that matter. At scale, thin or redundant content is deprioritized — meaning a significant percentage of a site’s published pages may never meaningfully enter search competition regardless of how much continues to be added.
What’s less understood is how content libraries behave at scale. These are system-level problems that compound over time and are difficult to reverse.
Content debt
Every page published creates an ongoing obligation. It needs to be monitored for ranking decay, updated when information changes, evaluated periodically for pruning or consolidation, and factored into crawl allocation. These costs are rarely accounted for at the point of creation.
At low volumes, this is manageable. At scale, it becomes a compounding liability. A site with 2,000 articles isn’t sitting on 2,000 assets, it’s managing 2,000 maintenance commitments that depreciate at different rates.
Editorial resources that could strengthen existing high-performing pages are instead absorbed by keeping a growing library from becoming a liability.
The true cost of a volume-driven content strategy only becomes visible 18 to 24 months after the investment, when maintenance demands begin to outpace the capacity to meet them.
Crawl inefficiency and cannibalization
Google allocates a finite crawl budget to each domain. When a site scales content volume without proportional gains in quality or authority, Googlebot distributes that budget across a larger number of pages, many of which offer limited signal value. The result is that high-value pages are crawled less frequently, indexed less reliably, and are slower to reflect updates.
This creates a compounding problem for sites with important transactional or evergreen pages that depend on frequent re-crawling to stay current and competitive. Beyond crawl distribution, similar pages targeting overlapping intent compete for the same ranking positions internally.
Search engines consolidate these signals rather than rewarding each page individually, meaning two pages targeting near-identical queries often perform worse combined than one authoritative page targeting both would perform alone.
Topical authority dilution
Search engines evaluate whether a site is a genuinely deep and trustworthy resource within a defined topic space. Expanding into a wide range of loosely related subtopics can erode this signal rather than strengthen it.
A site with 40 tightly interconnected, substantive pieces on a specific topic will consistently outperform one with 400 surface-level articles spread across adjacent themes. The depth and coherence of coverage within a defined area are what build the authority signal that drives durable rankings.
Pursuing breadth at the expense of depth fragments that signal, making it harder for search engines to assign clear expertise to the domain on any individual topic, even the ones the site knows best.
Weak content and behavioral signals
Search engines use behavioral data such as dwell time, return-to-search rates, and click-through rates as quality signals at both the page and domain levels.
When a site publishes high volumes of content that users engage with poorly, those signals accumulate and begin to affect how search engines evaluate the domain as a whole. This creates a negative reinforcement loop that’s difficult to detect and slow to reverse.
Weak pages actively contribute to lower domain-level quality assessments, affecting the performance of pages that would otherwise rank well. More mediocre content compounds. Each low-engagement publish incrementally reduces the baseline trust that search engines extend to the domain’s better work.
The goal of SEO has traditionally been to rank. Increasingly, the more valuable outcome is to be cited or referenced in AI-generated summaries, pulled into knowledge panels, or sourced by other publishers as a primary reference. These two outcomes require fundamentally different content strategies.
LLMs and AI Overviews are selective about which sources they draw from. The selection is weighted toward pages with strong E-E-A-T signals, high specificity, and clear authoritativeness within a defined domain.
A site that has published hundreds of generic articles covering a topic broadly is less likely to be treated as a primary source than a site that has published fewer, more definitive pieces with clear depth and original perspective.
Volume doesn’t increase citation probability — it may actively reduce it by signaling that the domain is a generalist content producer rather than a reliable primary reference.
The long tail is saturated
The accessible long tail that drove content volume strategies for the better part of a decade no longer exists in the same form. Between 2010 and 2020, there were genuinely underserved keyword opportunities across most industries.
Today, in most commercial verticals, every remotely valuable query has multiple established pages competing for it, especially from high-authority domains with years of accumulated signals.
New content entering this environment doesn’t find open space. It enters a war of attrition against incumbents with advantages it can’t easily overcome. The marginal SEO return on a new article targeting a long-tail keyword is a fraction of what it was five years ago.
The economics only justify creation when there’s a genuinely differentiated angle, a proprietary data point, or a perspective that exists on your page that other pages can’t offer. A keyword existing is no longer a sufficient reason to publish.
At scale, these factors turn content growth into diminishing returns rather than compounding gains. The library becomes harder to maintain, harder for search engines to evaluate clearly, and harder to extract meaningful visibility from — regardless of how much is added to it.
The implication is to change what publishing is for.
Volume targets made sense when more pages meant more opportunities. In the current environment, they measure the wrong thing. The more useful question isn’t how much content a team is producing, but how much of what already exists is actively contributing to visibility, and what is quietly working against it.
For most sites, that audit reveals the same pattern. A relatively small number of pages generate the majority of organic traffic. A larger number generates little to none, and a significant portion actively drains crawl allocation, fragments topical authority, or dilutes the behavioral signals that stronger pages depend on.
You need to move from expansion to consolidation. Existing pages that cover overlapping intent are stronger merged than competing. Thin pages that rank for nothing and engage no one are more valuable removed than retained.
The energy going into producing new content at volume is often better spent deepening the pages that already have authority and signal history behind them.
New content earns its place when it:
Addresses something genuinely unaddressed.
Offers a perspective that existing pages can’t.
Targets an intent the site currently lacks.
In practice, this means retiring a few default assumptions:
That publishing for every keyword variation is coverage.
That indexing is the same as performance.
That output volume is a proxy for strategic progress.
None of these were ever true measures of content effectiveness. They were convenient ones.
The replacement for volume isn’t simply better content. It’s a different definition of what content is trying to achieve.
Depth over breadth
Focus coverage on a smaller number of topics and develop them thoroughly. A single piece that addresses a topic with specificity, original perspective, and clear authorial expertise will outperform multiple pieces covering adjacent variations of the same theme.
Depth is what builds authority signals, drives engagement, and increases citation potential. Prioritize what the site can say with the most credibility.
Distribution as a multiplier
Allocate more effort to distribution. Publishing less creates capacity to deliver strong content to the right audiences. Distribution is a core part of SEO performance in a citation-driven environment.
Being citation-worthy
Create content that can serve as a primary source. Focus on clear points of view, verifiable expertise, and specific insights that other pages can’t replicate.
The goal is to be referenced in AI-generated summaries, cited by other publishers, and included in the knowledge systems search engines rely on.
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The uncomfortable truth
Sites that rely on frequency and broad coverage are being outperformed by sites that are clearly authoritative on a defined topic, consistently useful to a specific audience, and structured in a way that search systems can evaluate with confidence.
Prioritize depth, clarity of expertise, and consistency within a focused topic area. Treat each published page as a long-term asset that requires ongoing maintenance, evaluation, and improvement.
The content factory model is no longer effective. The approach that replaces it requires more effort, stronger editorial standards, and a higher bar for what gets published.
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 Solutions2026-04-28 14:00:002026-04-28 14:00:00Why more content is no longer a reliable way to grow SEO
