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.
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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.
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.
While there’s no universal benchmark for SoV, establishing one for your brand comes down to:
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.
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.
Try now: Curious to know how often your brand shows up in AI responses? Try our free AI visibility checker to find out.
Here are three reasons why share of voice should be your core KPI when visibility is scattered across platforms.
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.
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.
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:
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.
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:
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:
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:
Tracking them gives you the complete picture of who controls visibility in your market and where you can break through.
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.
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.
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.
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.
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.
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.
Finally, organize everything in a master spreadsheet with columns for:
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:
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.
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.
Go to Semrush Position Tracking and click “Create project.”
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.
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:
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:
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.
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.
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:
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:
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.
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.
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.
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.
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.
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.
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.
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 |
|
|
| High Effort |
|
|
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.
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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.
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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:
Let’s dive into the data.
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.
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.
Source: Backlinko
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.
Sources: Backlinko
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:
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:
Sources: Leading Edge, Backlinko
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:
Medical Device – 1,183% ROI, 13 months breakeven
Higher Education & College – 994% ROI, 13 months breakeven
Oil & Gas – 906% ROI, 10 months breakeven
Industrial IoT – 866% ROI, 7 months breakeven
Pharmaceutical – 826% ROI, 9 months breakeven
Manufacturing – 813% ROI, 9 months breakeven
Biotech – 788% ROI, 8 months breakeven
Solar Energy – 770% ROI, 9 months breakeven
Commercial Insurance – 758% ROI, 9 months breakeven
B2B SaaS – 702% ROI, 7 months breakeven
Construction – 681% ROI, 5 months breakeven
HVAC Services – 678% ROI, 6 months breakeven
IT Staffing – 612% ROI, 10 months breakeven
Legal Services – 526% ROI, 14 months breakeven
Healthcare – 532% ROI, 11 months breakeven
Financial Services – 447% ROI, 9 months breakeven
E-commerce – 317% ROI, 9 months breakeven
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.
Sources: WebsiteBuilderExpert, G2, AllOutSEO, Semrush
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.
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.
Sources: Passionfruit, Backlinko
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).
Source: Backlinko
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:
Source: Backlinko
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.
Source: Backlinko
I hope these findings helped you get a better feel for the SEO services industry.
I’d like to thank Northstar Research Partners for helping me design and conduct the survey that formed much of this report.
And if you’d like to learn more about how this survey was conducted, here’s a PDF of our study methods.
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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.
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:
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:
Google added AI bot labels to forum and Q&A structured data, helping distinguish between human and AI-generated contributions. Google also updated its documentation with “read more” deep link best practices.
Why it matters: Search engines are adapting their systems to better interpret and label AI-generated content.
Actionable takeaway:
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:
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:
Google reinforced that commodity content does not perform well, while broader analysis highlights the importance of authority, freshness, and first-party signals.
Why it matters: As AI systems synthesize answers, they rely more heavily on trusted, differentiated sources.
Actionable takeaway:
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:
Several additional developments are worth watching:
The next SEO Update by Yoast is on May 21, 2026, at 4:00 PM CET (10:00 AM EST). Sign up to join the live discussion or get the recording.
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LinkedIn is rolling out Off-Platform Event Ads, giving marketers a new way to promote events without needing a native LinkedIn Event Page.
What’s happening. The new format allows advertisers to run Event Ads that link directly to external destinations — such as webinar platforms, landing pages or livestream sites — instead of keeping traffic on LinkedIn.
This marks a shift from platform-contained experiences to more flexible, marketer-controlled journeys.
How it works. Marketers can create an Event Ad using a third-party URL, add event details like date and format, and choose from objectives including awareness, engagement, traffic or lead generation.
Clicks send users directly to the external event page, while performance metrics remain trackable in Campaign Manager.
Why we care. Until now, promoting events on LinkedIn often meant working within platform constraints, which could fragment the user journey and limit control over registrations.
Off-Platform Event Ads remove that friction by allowing marketers to tap into LinkedIn’s targeting while keeping traffic, data and conversions on their own platforms — making it easier to scale campaigns and maintain a consistent experience.
What to watch:
Availability. Off-Platform Event Ads are currently rolling out globally and are expected to be available to all advertisers by May 6.
Bottom line. By opening Event Ads to off-platform destinations, LinkedIn is making it easier for marketers to scale event promotion — without forcing them to build inside its walls.
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Every brand holds its claims, and somewhere in the archive of its digital life, there’s proof to back them up.
The AI assistive engine (the systems behind ChatGPT, Perplexity, and Google AI Overviews) holds that proof too, scattered across its training data and retrieval index, alongside competitors’ claims.
The audience has a need but no vocabulary to bridge the gap between what they want and what the brand or the engine already knows.
All three lack the same thing: a frame, the interpretive context that turns scattered information into a narrative worth transmitting (for the brand), citing (for AI), and acting on (for the user).
This is where the claim-frame-prove (CFP) process comes in. Claim and proof are mechanical, and frame is strategic.
Claim and prove are mechanical work the engine can verify. Frame is the strategic move only the brand can make.
CFP runs claim by claim, fact by fact. The brand’s full picture is built from many CFP cycles compounding: each claim framed and proven becomes a fact in the corpus, and the cumulative weight of those facts is what positions the brand to dominate.
AI can join known facts, but it can’t leap to a new one that benefits your brand. Given Facts A and B, AI can derive Conclusion C that follows logically. That’s standard inference, and the engines do it well.
What they can’t do reliably is the move a creative human makes constantly: looking at A and B, reaching to a non-obvious J that’s commercially beneficial, and then constructing the logical bridge so the engine can use it.
From here, I’ll use the alphabet as the scale: C is the obvious commercial reach, J is the non-obvious, and Q is the most ambitious leap a brand can sensibly make.
Two examples from my own positioning make the difference concrete.
A + B → C
A + B → J
That second move — from A and B to J — is strategic claim bridging: selecting which non-obvious J benefits the brand from the space of derivable conclusions, and then constructing the logical connection from accepted facts to that chosen J so the engine transmits it as fact rather than as the brand’s opinion of itself.
Two operations packed into one move: the strategic part is choosing J, and the bridging part is making the inference watertight.
AI doesn’t choose the J that’s good for your brand. You do. That choice, and the bridge that proves it, is the work AI has no commercial stake in, and a future (more capable) AI without your stake just produces a more sophisticated version of the same problem.
Whether AI can be creative is contested ground. The narrower claim holds regardless: even when AI produces a novel-looking output, it has no commercial intent guiding which J to derive. From the same A and B, an AI could just as easily produce a damaging J as a beneficial J. It has no skin in your commercial game.
A creative marketer does both things at once: reaches imaginatively to a non-obvious J, and chooses the J that serves the brand. That’s the move AI engines can’t reach, and it’s why the frame has to come from someone placing the information online (the brand, a client, or an independent source).
The disposition that lets you see this work is what I’ve been calling “empathy for the machine,” a phrase I started using in client consulting around 2011-2012 (originally as “empathy for the beast,” retired once I got more serious about the business side of digital marketing), and first published formally in 2019.
It’s the discipline of stepping outside your own perspective to see what the machine actually struggles with. That advice applies to anything in SEO/AAO — in this case, specifically to when it grounds, attributes, and synthesizes claims about your brand.
Unfortunately, brands all too often produce material aimed at human readers and assume the machine will figure out the rest. With a little empathy for the machine, brands design material the machine can use as its own interpretation (feed the beast).
This produces three different levels of brand-AI communication, each one building on the previous.
Levels 1 and 2 are the foundations every brand needs in place, and Level 3 is where framing enters, and what this article is designed to change your thinking.
The SEO toolkit you know, plus the AI visibility data you need.
Proof exists, but there’s nothing linking it to the claim. This is where most brands sit, and it leaves the engine to perform inference over whatever it can find.
The brand publishes Claim A on its website. Proof Z exists somewhere else: a conference program, an industry database, a Wikipedia citation, and a trade publication from four years ago. The brand assumes the engine will connect the two.
To connect them, the engine has to perform inference. Can it derive the conclusion that this brand is credible for this claim, given scattered premises across different domains, formats, and varying source authority?
There’s no copy stating the connection, no hyperlinks pointing from claim to proof, and no schema encoding the relationship.
That depends almost entirely on how confidently the machine already understands the entity, and that runs on three sub-levels.
If the machine has no confident understanding of the brand, and the proof isn’t explicitly linked, no connection happens. The proof might as well not exist.
If the machine has no confident understanding of the brand, but the proof is explicitly linked, the connection happens because the link does the work that the entity resolution couldn’t.
If the machine has a strong, confident understanding of the brand, the connection happens even without the link, because a well-resolved entity shortens the logical distance the machine has to traverse (linkless links, as I’ve called them).
The link still adds confidence (more than one path always does), but it’s no longer load-bearing as the entity carries the work.
The implication runs through the rest of the pipeline. Entity clarity in the knowledge graph isn’t a nice-to-have sitting alongside content work. It’s the variable that decides whether your content work has to carry all the weight or almost none of it.
Any proof that isn’t explicitly linked is missed at sub-level one, caught at sub-level two, and confidently embedded at sub-level three.
When entity understanding is weak, the result is familiar to anyone tracking AI visibility: a meritorious brand appears occasionally, and when it does, the wording is hedged, and the brand sits mid-to-low-pack. The engine did the best inference it could, and, being a responsible probability engine, it hedged.
Worse, opportunities for inclusion are throttled across adjacent queries the fact should have pulled the brand into, because the fact was never connected to the proof that would have warranted the inclusion in the first place.
What happens when Level 1, scattered proof of claims, is done well? Brand X is infrequently mentioned, unconvincingly, as a provider of Y.
Here, the brand explicitly connects claim to proof through a combination of copy, hyperlinks, and schema. It also closes the inference gap by providing what the engine would otherwise have to figure out.
The brand publishes Claim A and explicitly connects it to Proof Z, with the logical thread stated in copy, anchored by hyperlinks to the proof, and encoded in schema: a fact with a significant number of supporting pieces of evidence joined to it three ways, leaving nothing for the engine to infer.
Connected proof of claims is a spectrum, not a switch. At the low end, you’ve connected some of your proof, which already beats Level 1 because the engine no longer has to figure out the connections you’ve made, but it’s still figuring out the ones you haven’t.
If your competition has connected more of theirs, you’re still losing the comparison on the proof you left scattered. At the high end, you’ve connected all of it: every claim joined to every piece of supporting evidence, nothing scattered, and nothing left for the engine to guess at.
Most brands sit somewhere between scattered and connected simply because they’ve connected only the most obvious proof, and the AI may well have already figured the obvious ones out for itself: the links don’t teach it anything it didn’t already know.
With connected proof of claims done comprehensively for a given claim, the engine has enough corroboration to back the brand confidently, and the claim becomes fact in the corpus. Confidence transfers cleanly because there’s nothing to guess at.
Connected proof of claims is also a great weapon for a smaller brand competing with a bigger one: a specialist accounting firm with 50 pieces of proof, all explicitly connected to a specific positioning, beats a Big 4 with thousands of unconnected pieces on that specific positioning, because connection is what turns proof into substance that the engine can transmit.
What happens when Level 2, connected proof of claims, is done well? Brand X is frequently mentioned convincingly as a provider of Y.
This is where framing enters, and where strategic claim bridging earns its name.
For each claim that matters, the brand publishes Claim A, connects the proof, and then does the thing the engine can’t do (and the audience is unlikely to do either, for that matter).
It reaches the non-obvious J that benefits the brand, and constructs the bridge from A and B to J in language the engine can transmit. Not merely “we are the leader in X, demonstrated by Y,” but the frame:
A frame is a logical inference from corroborated facts, where the brand chose where the inference would land. For example:
Every component is verifiable independently, and every connection between components is logical. The J the bridge reaches to is the one I chose, not the J the engine would have generated unprompted.
One well-constructed frame makes one claim into fact in the AI’s voice. Run that across the claims that matter, and the cumulative weight is what shifts a brand from “frequently mentioned convincingly” to “almost always mentioned as the leading provider”: dominance is a stack of well-framed facts, not a single masterstroke.
The result: the AI doesn’t merely confirm, it enthuses. “Brand X leads in Y, and here is why that matters for your situation.”
The engine transmits the frame wholesale, in the language you chose, to the audience you specified, with a reason to keep coming back. The machine didn’t generate the narrative; it relayed it warmly.
What happens when Level 3, framed proof of claims, is done well across the claims that matter? Brand X is almost always mentioned as the leading provider of Y, and dominates the space.
Each level builds on the previous: connected proof of claims requires scattered proof of claims connected, and framed proof of claims requires connected proof of claims bridged strategically.
The brands that think they’re at framed proof of claims are usually at framed proof of claims for humans, and scattered proof of claims for machines. Marketing and narrative work supplies frames to humans all the time, and plenty of brands do it well.
What almost no brand does is supply frames the machine can use, and the gap between the two is where framed proof of claims is most powerful.
Some brands operate below even that and are effectively standing still: published facts at the surface, few proof connections, and no interpretive content the machine can use for any purpose.
The signature objection from a standing still brand is the same in every consulting room: “We already do this, our website explains who we are.” The website does that. The website is doing zero work to help the machine with framing.
The cost of standing still isn’t visible until a model update or two down the line. Brands that think they’re at framed proof of claims are usually investing harder in the wrong layer (content), while the layer that matters (framing and, ideally, joining the dots) compounds for someone else.
The gap widens every year. If you have content that doesn’t frame effectively or join the dots with links to proof, you’re leaking huge value, and pushing through connection and framing is the best return on past investment you can make right now: you’re doing the heavy lifting for the machines, and they’ll reward you for giving them this extremely valuable context on a plate.
Three structural conditions separate framed proof of claims from marketing-and-narrative-as-usual, and missing any one collapses the brand back to connected proof of claims or lower.
The entity has to be well-established, well-resolved, and trusted, because a frame can’t anchor to a vague brand. The underlying proof has to be connected, because most brands have fluent marketing prose on top of scattered proof, which is scattered proof of claims with prettier wallpaper.
The bridge itself has to be strictly logical, because machines read logic first and tone second, and a logically broken bridge fails, however well it’s written.
Smarter AI rewards better framing rather than replacing it, and the reason is the same selection pressure SEO practitioners have been operating under since the early 2000s.
There’s a seductive and entirely wrong conclusion to draw from rapid improvement in AI reasoning: that engines will eventually figure out how to frame brands correctly without help. The opposite is true. The engine rewards the brand whose assets reduce its own workload for the same or better result.
Search engines reward sites that are easy to crawl, render, and classify. Knowledge Graphs reward entities that are easy to resolve. AI assistive engines reward content that is easy to ground, verify, and transmit confidently. Where the engine has to choose between two roughly equivalent candidates, the candidate that demands less computation, less inference, and less guesswork wins.
Framed proof of claims is that principle operating at the bridging layer. A more capable engine encountering this level has the bridge handed to it ready-made. It doesn’t have to figure out the frame, it transmits the bridge the brand supplied, fluently and confidently, with the engine’s full reasoning capability now amplifying rather than substituting for the framing work.
A more capable engine without a frame falls back to inference over scattered evidence, which is expensive, ambiguous, and produces hedged output. Every improvement in reasoning capability makes the hedging more detailed and the noncommittal language more sophisticated, but the underlying problem isn’t capability, it’s the absence of a frame to amplify. The engine is doing more work for a worse result, and that’s the exact failure mode the engine’s selection pressure is designed to penalize.
The gap between those two outcomes is the framing gap, and it widens with every generation. Brands implementing only connected proof of claims don’t lose ground in absolute terms, they lose ground relative to brands implementing Framed Proof of claims faster every year, because the engine increasingly rewards assets that let it deploy its growing capability productively rather than waste it on guessing and hedging.
The selection pressure that rewarded fast websites in 1998, clean HTML in 2003, and structured data in 2015 rewards framed proof of claims now. The mechanism of gaining a competitive advantage by reducing costs for the AI for the same or better results hasn’t changed — and probably never will.
The framed proof of claims trajectory rises steeply and continues climbing. The connected proof of claims trajectory rises gently and flattens. The shaded area between the two lines is labeled the framing gap and visibly widens with each generation.
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The bridge is human territory, and it stays human because it requires commercial intent specific to the brand that the engine doesn’t have.
Everything the machine does well will get better: retrieval, connection, pattern extraction, and synthesis. None of that helps the brand whose evidence the machine can see but can’t bridge meaningfully to a beneficial conclusion.
Whether AI confirms your brand, overlooks it, or champions it comes down to one discipline: strategic claim bridging, claim by claim, fact by fact. It’s the last layer of brand-AI communication that won’t yield to automation, if it yields at all.
This is the 11th piece in my AI authority series.
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