Web Design and Development San Diego

Query intent vs. conversion intent: Why the difference matters

/0 Comments/in , /by
Query intent vs. conversion intent- Why the difference matters

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.

Get the newsletter search marketers rely on.

See terms.

How to optimize for each intent

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.

Read more at Read More

Web Design and Development San Diego

How China’s fragmented search ecosystem is reshaping SEO in 2026

/0 Comments/in , /by
How China’s fragmented search ecosystem is reshaping SEO in 2026

In February 2025, the world watched as a small group of humanoid robots took the stage at the CCTV Chinese New Year show for the very first time. It was a charming performance, even if the steps were shaky and the movements were mostly limited to the arms.

Just one year later, at the Spring Festival Gala, the shaky steps were gone and the humanoid robots were able to actually run and do standing somersaults and full kung fu routines with swords and nunchaku. The message was clear: in just one year, we have witnessed a decade’s worth of advancement.

The 10-year leap in technology is real and not limited to robotics. Which raises a critical question for every digital marketer eyeing the world’s largest web population: How has search in China progressed in recent years?

A parallel in the Chinese search landscape

The answer is that we’re witnessing the first, calculated tremors of a massive shift. AI models have not yet replaced traditional search. The evolution isn’t happening through a single “big bang,” but through a constant, iterative pulse. 

New LLM models are surfacing every few months, each more specialized than the last. Chinese tech giants are increasingly open-sourcing their models, and even industry leaders are hedging their bets. Baidu, for example, is integrating DeepSeek into its search experience, even as its own Ernie (Wenxin) model remains a formidable powerhouse.

Let’s look at how users actually search in China today — and what this nuanced shift from links to reasoning means for your 2026 SEO strategy.

Your customers search everywhere. Make sure your brand shows up.

The SEO toolkit you know, plus the AI visibility data you need.

Start Free Trial
Get started with

Semrush One Logo

The great narrative fallacy: Is web search dead in China?

In many marketing circles, a specific narrative has been repeated so often it has become an article of faith: “Traditional search on Baidu is dead — and has been for years. Websites are obsolete. In China, everything is WeChat.”

This narrative is almost always driven by service providers whose business models depend on WeChat, Douyin, Weibo, or Xiaohongshu marketing. To them, the “open web” is a ghost town. But is this actually true?

The social supremacy argument

There’s a grain of truth in the hype. The Chinese web is a mobile-first multiverse. Users access and explore the web through super-apps:

  • RedNote (Xiaohongshu / Little Red Book): This is the de facto engine for lifestyle research and travel planning.
  • Pinduoduo and Douyin: These are the juggernauts of social commerce and impulse buying.
  • WeChat: The absolute center of daily life, where everything from a quick message to a utility bill payment via QR code happens.

In this environment, social media isn’t just a channel. It’s the air people breathe. For B2C brands, social ads can — and often do — exceed website-driven sales by orders of magnitude.

The B2B reality check

For those of us working with B2B companies that need real visibility in China, the “Baidu is dead” narrative falls apart the moment you look at the analytics. Clients who invest in Baidu SEO and Baidu search engine advertising (SEA) continue to see a steady, high-volume stream of real human visitors — in many cases generating more qualified leads and higher conversion rates than their counterparts in the UK or Germany.

Why? Because when a B2B procurement officer or a technical engineer needs a specific industrial solution, they don’t just scroll until they find it on a social media feed. They search for a verified, authoritative source. In other words, they look for a website.

Is the social media narrative a lie? No. But ignoring a channel that — at least in the B2B sector — remains more effective in China than in many search-first Western countries is simply bad business. The goal isn’t to choose one over the other; it’s to understand how they coexist. 

And just as we’ve settled the debate between web marketing versus app marketing, a new challenger — the LLM — has entered the battleground to disrupt both.

Mapping the 2026 landscape: Intent-based specialization

To a Google-first marketer, the idea of searching anywhere but a search engine feels like a detour. In China, it’s the standard operating procedure. Users don’t just “Google it.” Instead, they choose the tool that fits the intent.

As a Baidu specialist living and working in China, I see this daily. While I might be optimizing a B2B landing page for Baidu, my wife is likely on Pinduoduo, finding household deals, or on Xiaohongshu, planning our next weekend trip. 

The “everything app” exists, but the “right app” always wins the click.

1. Traditional web search: The authority tier

Despite the “death of the web” narrative, traditional web search remains the primary battleground for B2B and high-authority research. If a user needs a technical whitepaper, a government regulation, or a verified corporate headquarters, they go here.

  • Baidu: Still the mobile heavyweight, with a ~70% mobile market share. Its structural advantage is massive: The Baidu app is installed on over 724 million monthly active devices (as of early 2026). It has evolved into an AI-first portal, but for SEOs, it remains the place where the open web lives and breathes.
  • Microsoft Bing: The professional’s sanctuary. It has claimed a massive chunk of desktop search for those seeking a cleaner, international, or technical experience.
  • Haosou (360 Search): The enterprise default, often pre-installed on corporate PCs and known for its security focus.
  • Sogou: Deeply integrated with WeChat, it’s the bridge between the walled garden and the web.
  • Google: Yes, Google. Despite the firewall, a significant population of tech-savvy professionals and researchers use it via VPN for global technical data and academic resources.

2. Social discovery: The inspiration tier

This is where search becomes discovery. Users don’t always have a keyword, but they do have an interest. In this context, SEO is about social indexing: ensuring your brand appears when a user looks for proof and not just products.

  • WeChat (Weixin): The internal search for official brand news and private traffic.
  • Xiaohongshu (RED): The ultimate product-discovery engine. If you aren’t on RED, you don’t exist in the lifestyle or luxury sectors.
  • Douyin: Visual, video-first search. Users search Douyin to see how something works.
  • Kuaishou: The powerhouse for lower-tier cities and raw, authentic grassroots content.
  • Weibo: Real-time search — what is happening right now in the public eye.
  • Bilibili: Long-form video search for deep dives, tutorials, and Gen Z subcultures.

3. Ecommerce: The transactional tier

In the West, users often start on Google and end on Amazon. In China, the journey frequently starts and ends in the same place.

  • Taobao / Tmall: The grand bazaar. If you want variety and brand stores, this is the first stop.
  • JD.com: The Amazon of China for logistics and high-end electronics.
  • Pinduoduo: The favorite for daily essentials and group-buy deals. Its search logic is entirely driven by value for money.
  • Douyin Mall: The rising star of “impulse search,” merging entertainment with immediate checkout.
  • Xianyu (Goofish): The go-to for the thriving second-hand market and hobbyist niches.

4. Generative AI (LLMs): The reasoning tier

This is the newest layer of the map — the “thinking” search. These AI models don’t just produce lists of links. They are assistants that synthesize the web for the user.

  • Doubao (ByteDance): Currently the most popular consumer AI assistant, used for casual, conversational queries.
  • DeepSeek (Domestic): The choice for developers and those in need of “deep thinking” logic. It’s the engine currently getting tested inside WeChat’s search bar.
  • Kimi (Moonshot AI): The king of long-context. Users use Kimi to search through 50-page PDFs or complex financial reports.
  • Qwen (Alibaba): Powerfully integrated into the Alibaba ecosystem for business and coding tasks.
  • Tencent Yuanbao: The “AI brain” for WeChat content.
  • Wen Xiaoyan (Baidu): The AI-facing evolution of Baidu search.

5. Hyper-local and logistics: The utility tier

For the physical world, search is about “now” and “near me.”

  • Meituan / Dianping: If you’re hungry or want to see a movie, you don’t use Baidu. You use Dianping for reviews and Meituan for transactions.
  • Amap (Gaode) / Baidu Maps: The “search engines of the real world.” SEO on these platforms is purely about point-of-interest (POI) optimization.
  • Ctrip (Trip.com) / Railway 12306: The specialized gates for the massive domestic travel market.

Get the newsletter search marketers rely on.

See terms.

From mapping to maneuvering: The Baidu specialist’s edge

Baidu SEO isn’t dead; your website just isn’t the sole focus of web search anymore.

The ‘walled garden’ SERP: A decade of distraction

If you’re a Google-centric SEO, there are some notable differences when working with Baidu:

  • The ad-heavy layout: It isn’t uncommon to see ads claiming the top, middle, and bottom of a Baidu search engine results page (SERP), occupying nearly 50% of the visible real estate.
  • The Baidu monopoly: The most coveted organic positions are almost always reserved for Baidu’s own properties. Baidu Baike (the encyclopedia), Baidu Zhidao (the Q&A hub), and Baijiahao (the news/blogging arm) are the permanent residents of Page 1.
  • The portal giants: High-authority giants like Zhihu (China’s Quora), Bilibili, and Sohu take up whatever space is left.

Riding the Chinese SERP dragon

In this environment, ranking a corporate homepage for a high-volume keyword is a fool’s errand. Instead, we’ve mastered the art of the “long-tail dragon.”

In the West, we talk about the long tail of search as a small, niche opportunity. In China, with its linguistic complexity and massive user base, the long tail is a winding, multi-layered beast that is often more lucrative than the head terms. 

And we don’t just rank a website; we piggyback on the authority of the platforms Baidu already trusts. If you can’t beat Baidu Baike, you become the verified entry inside it.

Interestingly, it is these very platforms — the ones we’ve been using to bypass the “blue link problem” — that have now become the primary focus of the next generation of search.

What is changing in Baidu SEO?

In China, there is no brand loyalty toward particular AI models, as Westerners have toward platforms like ChatGPT and Claude.

The AI-switching reality

Chinese users are restless. They don’t stick with one model. They switch — sometimes because a hyped model hits a downtime wall, and sometimes because a new model claims the throne of the “most intelligent AI.” In this cycle of competition and user preference, an SEO can’t just focus on the “big sources.”

If you’re following the Western playbook, you’re likely chasing Reddit, Quora, and YouTube as your “sources of truth” for AI training. But in China, that focus is dangerously narrow. To win the reasoning battle, you must understand the investor-source connection.

Brainstorming the wisdom platforms

If you want to train AIs to see your brand in China, you have to look at the platforms they were built on:

  • Tencent is invested in Sogou. In 2021, Tencent fully privatized Sogou. This means Sogou Baike is no longer just a Baidu alternative — it is now a core training set for Tencent’s Yuanbao. If you ignore Sogou Baike, you’re invisible to the AI search bar inside WeChat.
  • Bytedance owns Baike.com. Bytedance bought Baike.com (formerly Hudong Baike) specifically to fuel its search ambitions. If you want to get cited by Doubao, your content needs to be mirrored here and not just on Baidu.
  • The neutral giants: Keep an eye on Zhihu. Because both Tencent and Baidu are heavy investors in Zhihu, it remains one of the few neutral high-authority sources that almost every Chinese LLM uses for opinionated or expert reasoning.

The new SEO commandment

We’re no longer just optimizing for a search engine. We’re optimizing for a data pedigree.

If your client is B2B, you might still prioritize the Baidu ecosystem. But if your client is in ecommerce and you aren’t feeding the Qwen engine via Alibaba’s ecosystem, or the Doubao engine via Baike.com, you’re limiting your visibility across key AI systems.

The 2026 China SEO/GEO blueprint: From keywords to semantic saturation

If you’re waiting for a “DeepSeek optimization checklist” or a “Doubao ranking guide,” you’ve already missed the point. Because users switch models as often as they switch takeout apps, you can’t afford to be “Baidu-only” or “WeChat-centric.”

Here is what’s actually working for SEO in China in 2026:

Optimize for citations and not just clicks

While SEO in the West is focused on generative engine optimization (GEO), in China, it’s all about fact density. 

  • The logic: When Kimi or DeepSeek performs a reasoning query, the AI looks for verifiable facts.
  • The tactic: Stop writing marketing fluff. Start using the inverted pyramid writing style. Lead with a direct, data-backed answer in your first paragraph. Use hard statistics, expert quotes, and structured lists. If a model can’t extract a fact from your content in 200 milliseconds, it might hallucinate a competitor’s data instead.

Build an entity moat across wisdom platforms

As we brainstormed earlier, every AI has a “parent” with a preferred data source. But since models are now open-sourcing their weights and distilling each other’s intelligence, your brand must achieve entity consistency.

  • The goal: Your brand name, headquarters, and core product claims must be identical across Baidu Baike (Baidu), Sogou Baike (Tencent), and Baike.com (ByteDance).
  • The result: When these models cross-check their reasoning, they find a consensus. In 2026, consensus is the new authority.

Leverage information gain

Chinese AI models have a well-observed recency bias — they prefer sources that are roughly 25% fresher than traditional search results.

  • The tactic: Don’t just regurgitate what’s already on Zhihu. Provide a “unique data slice.” If everyone says “The best time to post on Douyin is 6 PM,” and you publish a case study proving “11 AM is better for B2B industrial leads,” the AI will cite you as the “nuanced exception.” That citation is worth more than ten #1 rankings.

See the complete picture of your search visibility.

Track, optimize, and win in Google and AI search from one platform.

Start Free Trial
Get started with

Semrush One Logo

The era of the entity architect

We’ve come a long way from the shaky steps of the 2025 CCTV Gala.

In 2026, China’s search ecosystem is no longer a directory of links. It’s a living, reasoning entity.

For the Western search specialist, the lesson is clear: The “super app” was a distraction. The real story is the fragmentation of intent.

My wife still goes to Pinduoduo for the best price. My colleagues still go to Bing for technical sanctuary. And the “I, Robot” enthusiasts of 2026 are using a rotating door of LLMs to find their answers.

As a Baidu specialist, my job has shifted from “ranking a website” to “architecting an entity.” We no longer build for the bot; we build for the source. If you’re the undeniable source of truth across the platforms that shape China’s information ecosystem, it doesn’t matter which model delivers the answer.

You’ll be the one they’re cheering for.

Read more at Read More

Web Design and Development San Diego

Unifying the search experience for real growth in 2026 by Level Agency

/0 Comments/in , /by

In February 2024, Gartner predicted that traditional search volume would drop 25% by 2026. It didn’t. Google’s search revenue accelerated to 17% year-over-year growth, crossing $63 billion in Q4 2025 alone. But clicks per search are falling while query volume explodes. The pie got bigger. The slices got redistributed. And most search teams are still optimizing for the old pie.

Are you still poring over spreadsheets full of organic keyword rankings like it’s 2003? Your customers don’t care where they’re getting their answers. They’re just looking for answers they can trust. And they’re finding those answers across more surfaces than your rank tracker knows exist.

If your organic strategy lives in one spreadsheet, your paid strategy in another, and your AI search strategy in a third (or nowhere), you’re optimizing for a search experience that no longer exists.

What “search” actually looks like now

Google “best tax software” right now. Go ahead, I’ll wait.

Count the surfaces on that single results page. Sponsored ads across the top. An AI Overview with its own recommendations and citations. A Reddit thread (because Google knows people trust other people more than brands). Organic listings from CNET, H&R Block, and others. A video carousel. Discussion forum links. A product carousel with images and prices. More sponsored results at the bottom. And a “People also search for” section feeding the next query.

That is one search. One keyword. And nobody owns it.

Now think about how different people actually use that page. I scroll past everything to find the Reddit thread, because I want to know what real humans recommend. My dad clicks the first sponsored ad because he doesn’t understand paid advertising (sorry, dad!) and just trusts Google to surface the best option up top. Someone else reads the AI Overview, gets a good-enough answer, and never clicks anything at all. A fourth person watches the Smart Family Money video and leaves.

Same query. Four completely different paths. Four different “winners.” And if you’re the brand celebrating a number-three organic ranking on this page, you may be missing that most of the real estate, and most of the user attention, lives somewhere other than those blue links.

This is what I mean by the total SERP experience. Your customer sees the whole page. You should too.

The AI layer changes the math

AI Overviews now appear on roughly 25% to 48% of Google queries, depending on the study. ChatGPT processes 2.5 billion prompts a day. Perplexity is up 239% year over year. These are real numbers from real platforms where real buyers are forming opinions about your brand, or not forming opinions because you’re nowhere to be found.

But before the panic sets in: AI tools still account for less than 1% of U.S. web traffic. Google sends 300x more referral traffic than all AI platforms combined. The sky isn’t falling, but the ground is shifting.

The shift that matters most is behavioral. Wynter’s 2026 research found 68% of B2B buyers now start their research in AI tools before they ever open Google. They ask ChatGPT to narrow the field, then Google the shortlist to validate. AI evaluates, Google verifies, and your website converts. If your brand is missing from that first AI conversation, you’re not even on the shortlist when the Googling starts.

Why the click data is more interesting than scary

A Search Engine Land analysis of 25 million organic impressions across 42 clients found organic CTR drops 61% when an AI Overview appears. In addition, paid CTR drops 68%.

EVERYBODY FREAK OUT!!! Right? Not quite.

Here’s what the panicked LinkedIn posts leave out: brands cited inside AI Overviews see 35% more organic clicks and 91% more paid clicks. Being in the AI Overview doesn’t cannibalize your traffic. If anything, it amplifies it. The AI Overview functions like a trust signal, a stamp of “this brand is relevant to your question” that makes people more likely to click your listing below.

The real twist, though, is that ranking well in organic doesn’t guarantee you show up in AI. Tom Capper’s research at Moz found 88% of AI Mode citations are NOT in the organic SERP for the same query. Organic and AI are pulling from different source pools. You can be number one in Google and completely invisible in ChatGPT’s answer to the same question.

And the small amount of traffic that does come from AI? It converts at more than quadruple the rate of organic, according to Semrush. These visitors arrive more informed, more intentional, and more ready to buy. Which makes sense, because they’ve already done the evaluation inside the AI interface. By the time they click, they’re just confirming and often converting.

The org chart is the problem

Most companies have SEO reporting to content, PPC reporting to demand gen, and AI search reporting to nobody. BrightEdge found 54% of organizations have handed AI search to the SEO team alone, which is a little like asking your plumber to also handle the electrical work because, hey, it’s all in the same house.

The waste from this setup is real. One branded Performance Max campaign paid roughly $500,000 for clicks that would have come through organic anyway. Google’s own research confirms: when you rank number one organically, only half your paid clicks are truly incremental. The other half? You bought what you already owned.

Meanwhile, McKinsey found that a brand’s own website makes up only 5% to 10% of the sources AI references. AI pulls from Reddit, review sites, affiliates, publishers, and user-generated content. You can have the best SEO program in your category and be completely absent from AI search results because AI is reading what other people say about you, not what you say about yourself.

The unified approach works. Level cut acquisition costs 18% and boosted SEO leads 22% by merging paid and organic for a B2B SaaS client. And we can use tools in our Level Intelligence Suite to connect performance signals across search surfaces. The channels compound each other. Treating them as separate line items on separate P&Ls leaves that compounding on the table.

Three audits you can run Monday morning

You don’t need a six-month transformation to start seeing the gaps. Three lenses, applied to your top 20 keywords, will show you where the opportunities and the waste are hiding.

Lens 1: Where do you actually appear? Check your organic rankings, paid ad coverage, and AI visibility across ChatGPT, Perplexity, and Gemini for the same set of keywords. Semrush has a free AI visibility checker. Most teams have never looked at all three surfaces side by side, and the gaps are almost always larger than they expect.

Lens 2: Where are you paying for traffic you already own? Cross-reference your number-one organic rankings with active PPC bids on the same terms. Start with branded keywords, where the waste is usually largest and the test is cleanest. If you rank first and you’re still bidding, you’re probably buying your own clicks.

Lens 3: Where is AI ignoring you? Compare your organic rankings with your AI citation presence. Only 11% of domains get cited by both ChatGPT and Perplexity, so strength in one guarantees nothing in the other. And check your robots.txt while you’re at it. If you’re blocking AI crawlers like OAI-SearchBot or PerplexityBot, you’ve pulled yourself off those shelves entirely.

This diagnostic shows you the full picture. What to do about it, the actual unification framework, is what I’m laying out at SMX Advanced.

The window won’t stay open

Generative Engine Optimization (GEO) keyword difficulty currently averages 15 to 20, compared to 45 to 60 for equivalent SEO terms. That gap will close. Once an LLM selects a trusted source, it reinforces that choice across related prompts. The brands getting cited now are training the models to keep citing them. Winner-takes-most dynamics are being baked into the weights.

Many companies are seeing search traffic drop significantly. Those same brands, the ones that get it right, are seeing the inverse when it comes to business growth. Rankings and revenue have decoupled. The brands that win from here are the ones that stopped measuring channels in isolation and started measuring the search experience their customers actually have.

We’re presenting a search unification framework at SMX Advanced in our session, “Organic, paid, and AI search: one strategy to rule them all.” If you want to stop optimizing for three separate channels and start compounding performance across every search surface, join us for the session or come find the Level team at Booth #9.

Remember: The search experience that existed in 2023 is gone. The strategy should be too.

Read more at Read More

Web Design and Development San Diego

SMX Now: The automation drift and how to correct course

/0 Comments/in , /by

Automation doesn’t fail on its own — it does exactly what it’s trained to do. The problem is that when Google Ads is fed incomplete, misaligned, or overly broad signals, it can optimize toward the wrong outcome faster than most advertisers realize.

In our second installment of SMX Now, our new monthly series, Ameet Khabra of Hop Skip Media will break down a real account where a 417% jump in conversions turned out to be the wrong kind of success. She’ll use that case study to explain the four key ways automation drift enters an account: signal drift, query drift, inventory drift, and creative drift.

You’ll leave with a practical framework for diagnosing drift early, understanding where human oversight matters most, and managing automation more deliberately so it works toward real business goals — not just platform-reported wins.

Join us May 6 at noon ET.

Save your spot

Read more at Read More

Best B2C SEO Agencies in 2026

/0 Comments/in , /by

If your product and category pages are not ranking for high-intent buying queries, and your brand is missing from AI-generated […]

The post Best B2C SEO Agencies in 2026 appeared first on Onely.

Read more at Read More

Best AI SEO Agencies for Home Services (2026)

/0 Comments/in , /by

You have already noticed the problem. When a homeowner asks Google AI Overview “who is the best HVAC company near […]

The post Best AI SEO Agencies for Home Services (2026) appeared first on Onely.

Read more at Read More

LinkedIn Articles: What Sets Them Apart & How to Write Them

/0 Comments/in /by

Key Takeaways:

  • 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.

An example Linkedin article.

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.

Content on Neil Patel's Linkedin profile.

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.

A Linkedin article based on on-site content.

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.

A Linkedin article appearing in Google AI Overviews.

 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.

A chart showing the top cited domains on LLMs.

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.

Read more at Read More

Google AI mode & SEO: What is it & how it works?

/0 Comments/in , /by

Google Search is changing fast. First, we got AI Overviews. Then Google introduced AI Mode – a much more conversational, exploratory version of Search that feels a lot closer…

The post Google AI mode & SEO: What is it & how it works? appeared first on Mangools.

Read more at Read More

How to Calculate Share of Voice (+ Why it Matters for SEO)

/0 Comments/in /by

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.

Share of Voice

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.

What counts as good share of voice

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?

But they measure different search contexts.

SEO SoV calculates your slice of traditional organic search traffic.

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.

Share of voice: Two different games

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.

Track Visibility Beyond Traditional Traffic Data

Your organic traffic data reveals only half the story.

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.

Traffic vs share of voice iceberg

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

Share of voice as a north star metric

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.

Read more: How to Rank Higher (& Get More Visibility) on Google in 2026

How to Measure Share of Voice in 4 Steps

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.

Map share of voice to buyer journey

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.

GSC – Performance – Queries – Impressions

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.

Open your Semrush Position Tracking project (or create one for your domain).

Scroll down to the “Top Keywords” section and click the “View all” button.

Position Tracking – Overview – Top keywords

Adjust the timeline to your preferred range before clicking “Export” to download the full keyword list.

Position Tracking – Export keywords

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.

You can use Semrush’s Keyword Gap tool to find these opportunities.

Add your domain along with up to four competitors, then hit “Compare.”

Filter to the “Missing” section to find keywords with proven search demand that competitors have validated.

You need to build visibility for these terms.

For example, this project management tool could target keywords like “Gantt chart” and “project management software” to boost its SoV.

Keyword Gap – Trello – Missing keywords

Dig deeper: How to Run a Competitor Traffic Analysis (9 Steps)


Build Your AI Prompt Library

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.”

Reddit – Project management tools

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.

G2 – Asana – Top alternatives

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.

AI SEO – Prompt Research

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.

Prompt Research – Agile project management – Prompts

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:

Keyword Funnel Analysis

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.

How to calculate SEO share of voice

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.

Go to Semrush Position Tracking and click “Create project.”

Enter your domain, target search engine, device type, and location.

Position Tracking – Targeting

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.

Position Tracking – Keywords

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.

Position Tracking – Backlinko – Share of Voice

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.

Position Tracking – Backlinko – Share of Voice

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.

How to calculate AI share of voice

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.”

AI SEO – Brand Performance

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.

Brand Performance – Paypal – Select platform

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.

Semrush – Brand Performance – Sentiment & Share of Voice

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.

Brand Performance – Backlinko – Key Business Drivers

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.

Here’s a simple matrix to understand your data:

High AI SoV Low AI SoV
High SEO SoV You dominate both traditional and AI 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:

Share of voice baseline dashboard

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.

The post How to Calculate Share of Voice (+ Why it Matters for SEO) appeared first on Backlinko.

Read more at Read More

10 Best AI SEO Agencies for Global Brands in 2026: An Engineering-Grade Evaluation

/0 Comments/in , /by

For global brands, AI citation is binary: your brand is either mentioned when a buyer asks “what is the best […]

The post 10 Best AI SEO Agencies for Global Brands in 2026: An Engineering-Grade Evaluation appeared first on Onely.

Read more at Read More