Your analytics dashboard tracks clicks, but it doesn’t convey the complete picture.
When a buyer reads an AI answer that mentions your competitor, or scrolls through a Reddit thread where your brand doesn’t appear, that’s lost visibility. And it won’t show up anywhere in your traffic data.
Share of voice (SoV) captures what traffic metrics can’t.
It measures your brand’s visibility against competitors across channels where buyers actually research and make decisions.
While SoV spans social, PR, and paid media, search is where most brands should start. It’s the channel where buyers with the strongest purchase intent show up, and it’s the easiest to measure competitively. That’s what this guide focuses on.
I’ll walk you through four steps to measure your share of voice in organic and AI search. Then, I’ll show you how to turn that data into decisions that move the needle where it matters.
What Is Share of Voice?
Share of voice measures your brand’s visibility relative to competitors across multiple marketing channels.
That includes organic and AI search, social media, review sites, communities, and more.
Traditionally, brands used SoV to track their share of ad spend in a market.
Now it’s evolved into something even more valuable. It can measure your brand’s presence across every touchpoint where buyers research and make decisions.
In simple terms: SoV tells you what percentage of the conversation you own in your category, compared to competitors.
This guide focuses on search SoV — both organic and AI — because that’s where buyer discovery is shifting fastest and where the measurement tools have matured enough to give you actionable data.
I find that search SoV also tends to be the foundation: once you understand your visibility in organic and AI results, layering in other channels becomes much simpler.
What Counts as a “Good” Share of Voice?
While there’s no universal benchmark for SoV, establishing one for your brand comes down to:
Market position: Market leaders have a higher share of voice since they own the conversation. Challengers aim for a mid-range SoV when competing against players with decades of brand equity.
Competitive context: In a fragmented market with 20+ active competitors, 8% SoV could put you in the top five. But in a three-player market, anything below 30% could mean you’re behind the leader.
Beyond these two factors, look at the broader market shifts within your category.
High SoV in a declining market can be a vanity metric. The real win is growing your share as the category grows.
How SoV Works in Traditional vs AI Search
Both SEO and AI SoV answer the same question: What percentage of category demand does your brand own?
You track 100 target keywords. Those keywords generate 50,000 total monthly visits across all ranking sites. You capture 15,000 of those visits.
That’s 30% organic share of voice.
AI SoV measures brand mentions in LLM responses from ChatGPT, Perplexity, Google AI Mode, and similar tools.
For example, you test 100 category-related prompts. Your brand is mentioned in 45 responses and cited in 15. Your competitor shows up in 30 responses with 10 mentions.
An AI visibility tool can calculate your weighted AI SoV based on mentions and citations.
Try now: Curious to know how often your brand shows up in AI responses? Try our free AI visibility checker to find out.
Why Is Share of Voice So Important, Especially Now?
Here are three reasons why share of voice should be your core KPI when visibility is scattered across platforms.
And with zero-click searches on the rise, that half is shrinking fast.
When users get their answers directly from AI Overviews and featured snippets, a huge chunk of your visibility is never captured in Google Analytics.
This makes traffic a lagging indicator of visibility.
Share of voice is a better metric because it measures how visible you are in the consideration set, even when users don’t click your site.
Think of it this way:
A user searches for the “best project management software for remote teams.”
They see an AI Overview listing five tools, including yours. The user reads it, takes no action, and later signs up for a product demo on your site.
Traditional traffic data would show this as “direct traffic” since the person went straight to the website. It wouldn’t capture the discovery that occurred in Google.
But SoV reveals that your brand appeared in the consideration set for this high-intent query.
Work Toward One North Star Metric
Your marketing team might be operating in silos.
The SEO team wants more website visits. PR wants more media mentions. The social team wants better engagement.
Each team tracks its own KPIs and optimizes for different outcomes.
But the long-term power of SoV is that it can become the one metric every team rallies around.
When everyone sees how their work contributes to the same visibility percentage, it changes how teams collaborate.
Here’s what this looks like in practice:
SEO team targets specific keywords to boost traffic and visibility via content
PR secures features in industry publications through expert quotes
Social drives brand conversations on Reddit and LinkedIn
Product wins better reviews on G2 and Capterra
This full picture takes time to build.
Start with the foundation by measuring your SoV in organic and AI search.
Once you have that baseline, you can layer in other channels over time.
Let’s see how you can strategically calculate share of voice in four steps.
I’ll use a fictional project management software example to show how each step translates into business insights.
Step 1: Define Your Industry Landscape
Start by outlining the specific competitors and keywords you’ll track for SoV.
Without clear boundaries, you’ll either miss critical gaps or drown in too much noise.
To map your competitive terrain, pick topic clusters tied to revenue.
For a project management software, I picked these clusters:
Category fundamentals (like “project management 101” and “project management for freelancers”)
Use cases (like “agile project management” and “remote team collaboration”)
Industry-specific (like “construction project management” and “marketing project management”)
Pro tip: Don’t pick these topics solely based on search volume. Choose clusters where gaining visibility directly impacts your bottom line.
One way to assess a topic’s revenue potential is to map it to funnel stages.
Categorize your clusters into three stages:
Awareness: Where people are learning and researching, like how to manage projects
Consideration: Where they’re exploring solutions, like the best project management software
Decision: Where they’re comparing options and ready to buy, like Software A vs Software B
Your SoV at each stage tells you where you’re winning and losing in the buyer journey.
This allows you to allocate resources for maximum business impact.
Let’s say this project management software segments the SoV by funnel stage.
It reveals that most of the brand’s visibility is concentrated at the top with almost none at the decision stage.
That’s a problem.
They’re educating the market, but invisible when prospects are actually comparing options and reaching for their wallets.
Strategic takeaway: They need to prioritize comparison pages and case studies to shift visibility toward the decision stage.
Now, define who you’re measuring against.
In search, you’re competing for visibility against two key players:
Direct competitors: Companies selling similar solutions like Asana, ClickUp, Notion, and Trello
Indirect competitors: Review sites capturing the voice of the customer like G2 and industry publishers ranking for your keywords but not competing for customers like HubSpot and Zoho
Tracking them gives you the complete picture of who controls visibility in your market and where you can break through.
Step 2: Build Your Keyword & Prompt Libraries
Create a library of 200-500 queries that capture how people search in your category.
You need both keywords (what people search) and prompts (what people ask LLMs). Together, they reveal your search visibility spectrum.
Pull SEO Data First
Collect queries where you’re already visible to your audience.
Google Search Console (GSC) is a good starting point for this since it captures actual visibility through impressions.
Impressions show every time your brand appears in results, even when users don’t click.
Go to the “Queries” tab in the “Performance” report.
Click the “Impressions” column header to sort in descending order, and export this list of keywords.
And if you’re running Google Ads, export your PPC keyword list and filter for terms with conversions or high CTR.
You can also repeat this process with tools like Semrush.
Scroll down to the “Top Keywords” section and click the “View all” button.
Adjust the timeline to your preferred range before clicking “Export” to download the full keyword list.
Pro tip: Export all tracked keywords, not just the top money terms. A keyword with 20 monthly searches might seem irrelevant in isolation. But 50 of these collectively represent meaningful category visibility that SoV captures.
Layer in Competitor Intelligence
Besides your own data, track where competitors show up.
This tells you where to compete directly and where to claim ground that they’ve overlooked.
After sourcing keywords, look at how people search for your category in AI tools.
Since AI search queries tend to be more conversational, they often mirror how people talk in community spaces.
Browse Reddit, Facebook groups, and Slack communities to see how your audience phrases their needs and pain points.
For example, this post reveals that agencies want project management tools that aren’t “too corporate or complex for creative teams.”
A question like that can translate directly into an AI prompt: “What’s the most user-friendly project management tool for small creative agencies?”
For decision-stage prompts, review sites G2 and Capterra (or those relevant to your industry) offer a lot of insights.
G2, for instance, lists popular alternatives for every tool.
This is a ready-made list of “[You] vs [Competitor]” and “alternative to [Competitor]” queries your buyers are likely running in AI search.
You can dig deeper with Semrush AI Visibility Toolkit to find prompts where competitors show up in AI answers, but you don’t.
Go to “Prompt Research” and add any of your core topics, like “agile project management.”
Click “Analyze” to get started.
The tool lists real prompts that generate AI responses for your category, such as “best productivity app” and “companies that use agile software development.”
Jot down the prompts relevant to your primary cluster.
Then, repeat for each of your 3-5 clusters.
Document Your Metadata
Finally, organize everything in a master spreadsheet with columns for:
Keyword/Prompt
Topic Cluster
Funnel Stage
Source (SEO/AI)
Once you’re done measuring SoV, this metadata will become your strategic lens.
Use it to decide which clusters to prioritize, which funnel stages are weak, and where SEO and AI visibility diverge.
Here’s what this looks like for the project management software:
Step 3: Calculate Your SoV
Your SoV equals your estimated traffic divided by the total traffic for all tracked brands, multiplied by 100.
Track both SEO and AI SoV to see the full picture of your brand’s visibility.
Calculate SEO Share of Voice
Start by checking your rankings for all the keywords in your tracking list. Track your competitors’ rankings for the same keyword set.
Each ranking position gets an average share of clicks, like position 1 getting roughly 27%.
This will help in estimating the traffic share per keyword.
Note: These benchmarks for organic search CTR shift over time. It’s also crucial to mention that organic CTRs have been declining as AI-generated answers absorb more clicks before users ever reach the results.
Multiply each keyword’s monthly search volume by the click-through rate for your ranking position to estimate your traffic for that duration.
Then, run the same calculation for each competitor.
Use this data to calculate your SoV.
Add up the estimated traffic across all keywords for each brand. Divide your total by the combined total for all tracked brands and multiply by 100.
This manual approach can be time-intensive, especially when tracking hundreds of keywords across multiple competitors.
Semrush handles this math automatically once you set up tracking correctly.
Enter your domain, target search engine, device type, and location.
The location setting matters for SoV tracking because search results vary by location.
If you set the location to the United States, but most of your customers are in New York, your SoV might look different than reality.
Pro tip: Start with country-level tracking to establish your baseline. Only segment by region later if local variations impact your business.
Then, click “Continue to Keywords” to manually add or import your keyword list.
Upload the CSV you made in Step 2 to preserve the data by cluster and funnel-stage categorization.
Then, press “Add keywords to campaign.”
Finally, click “Start Tracking” to begin data collection.
Once this setup is complete, Semrush starts collecting daily ranking data for every target keyword.
Check out the results in the “Share of Voice” tab under “Overview” in the Position Tracking dashboard.
You can also add up to four domains to see how you fare against others in the market.
Semrush tracks every brand’s rankings for your keyword set to aggregate the data into SoV percentages.
Important: While SoV is inherently relative and compares your visibility against others, who you choose as competitors shapes how you interpret your SoV.
Calculate AI Share of Voice
Your AI SoV shows how often LLMs cite your brand when answering questions in your category.
There’s no standardized way to manually measure AI SoV yet, but this two-step process gets you close:
Step 1: Run each prompt from your library through your AI tools of choice, such as ChatGPT, Claude, Google AI Mode, and any other AI tools your audience uses
Step 2: For each response, document every brand that appears — yours and your tracked competitors. Record whether each brand was mentioned, cited as a source, and whether the sentiment was positive, neutral, or negative.
Once you’ve tested all prompts, count how many times each brand appeared across all responses.
Divide each brand’s total mentions by the total number of prompts tested, and multiply by 100.
Keep in mind: This calculation gives you a directional read instead of a live metric. AI responses vary by session, phrasing, location, and platform. That’s why it’s important to test regularly and track trends over time.
Measuring AI SoV manually for 20 prompts across three platforms is doable. Doing it for hundreds of prompts while tracking how recommendations shift week over week isn’t.
That’s what Semrush’s AI Visibility Toolkit is built for.
Go to the Brand Performance report in Semrush’s AI Visibility Toolkit.
Enter your domain and click “Analyze.”
Pick an AI platform between ChatGPT, Google AI Mode, or Perplexity.
Switch among these tools to identify any significant gaps in platform-specific LLM visibility.
Once the report is generated, you’ll see a pie chart visualizing the distribution of SoV for your competitors.
The tool tests hundreds of prompts related to your category across ChatGPT, Google AI Mode, and Perplexity to measure your AI SoV.
For each prompt, it analyzes AI responses for:
Brand mentions: How often your brand appears in the answer
Citations: Whether the AI links to your content as a source
Context: Whether mentions are positive, neutral, or negative
It aggregates this data across all tested prompts to calculate your percentage of total visibility.
You’ll also find a section comparing each competitor against a set of business drivers specific to your industry.
These drivers are the most frequently mentioned topics for your category.
Use this data to identify clusters where you’re stronger and weaker than your competitors.
Interpreting SEO vs AI Share of Voice
SEO share of voice measures organic traffic while AI share of voice tracks LLM mentions and citations.
These might not always align.
You can have a strong organic share of voice (ranking on top for many keywords) but a weak AI SoV if LLMs don’t find your content credible.
And brands with more credible content can win a bigger slice of AI SoV even without much visibility in organic search.
Maintain content freshness and expand into adjacent topics to defend your position.
You rank well, but LLMs don’t cite you.
Implement content chunking to optimize your content for AI search and create citable assets to create credibility that LLMs value.
Low SEO SoV
AI tools cite your content even though you don’t rank at the top on organic search.
Improve SEO fundamentals, including title tags, internal linking, site speed, and keyword optimization.
Focus on depth over breadth.
Create a definitive, well-researched content resource for every core cluster. This is a good start for building visibility on both traditional and AI search.
Dig deeper: Learn more about building visibility in AI search with LLM seeding.
Step 4: Establish Your Baseline and Track Trends
The final step is turning your SoV numbers into an ongoing tracking system that informs decisions.
Create a baseline dashboard to capture three levels of detail:
Overall metrics: Are you gaining or losing ground overall?
Topic cluster performance: Which topics need more investment?
Funnel stage breakdown: Where in the buyer journey are you least visible?
Here’s what this could look like for the project management software:
Once your baseline is locked in, set your tracking cadence strategically.
A monthly frequency allows you to spot trends without the need for reacting to noise.
With quarterly deep dives, you can:
Analyze cluster-specific performance in detail
Correlate SoV changes with past campaigns
Adjust resource allocation based on what’s working
This rhythm prevents you from chasing short-term variations and missing critical shifts that impact your category.
Pro tip: Set up notifications in Semrush Position Tracking to get real-time alerts. You’re notified when SoV drops more than a certain threshold in any core cluster.
How to Improve Share of Voice
Not every fluctuation in your SoV requires action.
Here’s how to strategically diagnose gaps in your SoV and prioritize the right tactics to fix them.
1. Close Visibility Gaps
Clusters with <10% SoV mean you’re almost invisible.
This is especially damaging in decision-stage queries.
If you have less than 10% visibility when buyers search “best project management software,” you’re not in their consideration set.
At the same time, look for opportunities where competitors dominate, but you can compete.
For example, if your project management tool serves creative agencies but you have zero visibility for “project management for creative teams,” that’s your opening.
Potential Solutions
Diagnose the cause:
Search your weak clusters and compare what ranks against what you have
Check if you lack topic coverage, content depth, or basic optimization
Look at which competitors dominate and what formats they use
Build topical authority for major business themes.
Create one pillar page with multiple supporting articles.
Build backlinks to your pillar content to establish visibility across every query in that cluster.
For example, if we learn that the project management software needs to gain decision-stage visibility, we could prioritize comparison content.
Build pages targeting “[Your Brand] vs [Competitor]” and category buyer’s guides.
2. Solve Efficiency Problems
Compare your SoV to actual traffic.
A cluster like “what is project management” might give you a high SoV.
But if only 1% of that traffic converts, you’re likely burning money on the wrong audience.
You’re winning visibility in areas that don’t drive business outcomes. And competitors are capturing high-intent buyers.
Potential Solutions
Diagnose the cause:
Check if you’re ranking for awareness content when you need decision-stage visibility
Look at your traffic-to-conversion ratio by cluster
Identify if your content attracts the wrong audience (students vs. buyers)
Reallocate resources to high-intent clusters.
Instead of producing more awareness content, shift the budget to bottom-of-funnel content.
This includes comparison pages, case studies, and ROI calculators that target buyers ready to evaluate solutions.
Update existing comparison pages with current data and competitive intelligence.
3. Address Competitive Threats
Keep tabs on competitors gaining ground in your strong clusters.
If a competitor gains over 5% SoV in your strong clusters, it’s an early sign that they’re targeting your territory.
That gap can widen unless you respond to maintain your market share.
Diagnose the cause:
Analyze what new content or tactics they launched
Check if they’re winning on review sites, community platforms, or organic search
Identify if they’re capturing a format you’re missing (video, podcasts, tools)
The fix depends on where your competitors are winning.
If competitors actively feature on review sites, optimize your profiles. Run campaigns to source reviews from happy customers.
If they’re visible on community platforms, proactively engage in communities like Reddit and Slack.
Prioritize Based on Effort vs. Impact
Not all gaps matter equally.
Focus on opportunities that will actually move your revenue pipeline.
Start with high-impact, low-effort wins. Then invest in high-effort moves that compound over time.
High Impact
Low Impact
Low Effort
Optimize content ranking #5-10
Claim existing review site profiles
Update comparison pages with current data
Claim industry directory profiles
Minor content refreshes on supporting pages
Social engagement in established channels
Guest commenting on industry blogs
Newsletter mentions in partner publications
High Effort
Build authority in community spaces (Reddit, forums)
Create comprehensive hub content for weak clusters
Earn citations from AI-referenced sources
Develop thought leadership for industry publications
Content for saturated topics without authority
Channels where your audience isn’t active
Platforms AI tools rarely reference
Keywords outside category relevance
Making SoV Your 2026 North Star
Share of voice captures how often you show up across the fragmented platforms where buyers make decisions.
Get started by measuring your current SoV across SEO and AI search with the steps in this guide.
Pick the gap that costs you the most revenue, and strategize the best ways to close it.
Next step: Build your AI optimization gameplan to capture visibility in the fastest-growing search channel.
SEO services have evolved significantly in recent years, driven by changes in search behavior, AI-powered results, and rising competition across industries.
To give you the most accurate picture of the industry today, we’ve combined data from multiple sources, including our survey of 1,200 business owners.
In this report, you’ll learn:
How much businesses spend on SEO services today
Where companies actually find and hire SEO providers
What factors influence the decision to choose one agency over another
Why clients leave (or stay with) their SEO provider
Key trends shaping the future of SEO services
Let’s dive into the data.
Highlights and Key Statistics:
1. Companies spend $119.4 billion on SEO and digital marketing consulting each year in the US.
2. We found a strong correlation between higher spending and higher client satisfaction in small business SEO. In fact, clients who spent over $500/month were 53.3% more likely to be “extremely satisfied” compared to those who spent less than $500/month.
3. Most small business owners find SEO providers through referrals, Google searches, and online reviews. A small fraction of SEO clients (8%) found their current provider from online advertising.
4. When it comes to choosing a provider, 74% of business owners consider an SEO provider’s reputation “very” or “extremely” important. Monthly cost and the provider’s own Google rankings were also noted as important factors.
5. Overall, SEO client satisfaction is decidedly low. Only 30% would recommend their current SEO provider to a friend or colleague. However, we found that client satisfaction among marketing agencies was higher than that of freelancers.
6. SEO provider turnover is high. 65% of our panel stated that they’ve worked with several different SEO providers. 25% have worked with 3 or more providers.
We have more detailed and expanded findings below.
Average Monthly SEO Spend
In 2025, US organizations spent $119.4 billion on SEO and digital marketing consulting.
Our 2019 research found that small businesses spend $497.16 per month on SEO services.
However, we did discover a large range in SEO spending. Half of our respondents reported spending less than $1,000 per year on SEO. 14% spend $5k+ per year. Only 2% spend over $25k/year.
We also found that agencies tend to get paid significantly more than freelance SEO providers.
Specifically, agencies were 2x more likely to get paid $1k-$2k/month than freelancers, who mostly get paid in the $500-$1k per month range.
Agencies also tend to dominate the high-end pricing range (clients that spend $10k-$25k/year on SEO).
As you can see, 24% of small businesses that work with agencies spend between $10k-$25k/year, compared with 2% that work with a freelance SEO.
When it comes to SEO, do you “get what you pay for”?
According to our data, yes.
Specifically, we discovered that clients spending over $500/month were 53.3% more likely to consider themselves “extremely satisfied” compared to people who spend less than $500/month.
We also found a clear relationship between dissatisfaction levels and cost.
Specifically, business owners who spent less than $500/month were 75% more likely to be dissatisfied than those who invested at least $500/month in SEO.
This relationship played out whether a client worked with a freelancer, agency, or a mix of both.
Referrals and Google Searches Are the Top Ways Businesses Are Finding SEOs
When someone wants to hire an SEO agency, where do they look?
According to our panel, most people find potential SEO service providers through word of mouth, Google searches, and online review platforms (like Yelp).
On the other hand, relatively few find SEO providers through online or offline advertising, or referrals from other vendors (like web designers or writers).
If you’re an agency owner or a freelancer, this is a key finding. If you know where small business owners look to find SEO service providers, you can invest resources to make sure your business has a presence in those places.
Reputation and Cost are Key Factors Involved In Choosing a Provider
Demand for SEO services continues to grow as search becomes more complex and harder to manage in-house.
In fact, recent data shows that 61% of companies hire SEO agencies due to a lack of internal expertise, while others turn to external providers when they don’t have the time, resources, or results needed to scale.
Most common reasons for using SEO services:
Lack expertise – 26.87%
Lack resources – 22.70%
More cost-effective – 21.48%
Poor in-house SEO results – 15.48%
Lack sufficient time – 10.70%
But hiring an agency isn’t just about capability. Once someone finds a list of potential providers, how do they decide which one to go with?
We discovered that reputation, cost, and a provider’s own Google rankings influenced their decision the most.
Small business owners cited client case studies and the provider’s social media presence as significantly less important.
However, even these relatively minor factors played a role in whether or not someone decided to work with a particular SEO provider. For example, 55% of our panel cited “referrals” as an important consideration.
Although the importance of referrals pales in comparison to a provider’s reputation (55% vs. 74%), it’s still something that influenced more than half of the people we spoke to.
Interestingly, we found that a provider’s location mattered quite a bit.
Only 51% knew exactly where their SEO provider was located.
However, 78% of US-based small businesses stated that knowing their provider’s location was “extremely” or “very” important (with 46% stating that a known location was “extremely important”).
If you provide SEO services, making your location clear and obvious may help you land more SEO clients.
Here’s a great example from Siege Media, which actually includes a picture of their office on their about page:
The Vast Majority of Business Owners Expect SEO Services To Increase Customers and Traffic
A recent 2025 survey found that 91% of people who used SEO services reported a positive impact on website performance and marketing goals.
As such, it’s no surprise that expectations are high when working with an SEO provider.
According to our survey, the most important expectations are “accessing new customers”, “increasing traffic”, “increasing brand awareness”, and “building trust” as most important.
“Gaining social media followers”, “increasing number of email subscribers”, and “helping to attract new talent” were cited as relatively unimportant.
In fact, even though this is a common goal set by marketing agencies, only 26% of respondents cited “getting followers on social media sites” as extremely important.
This finding is especially key for SEO providers that are taking on new clients.
For example, a newly-hired SEO provider that says, “Our first step is going to be to get more likes on your Facebook page” isn’t speaking their client’s language.
On the other hand, kicking off the client-provider relationship with: “I look forward to helping you get more targeted traffic and customers” will likely result in a more satisfied client.
Needless to say, for the relationship to last, you need to deliver on those promises (more on that later). But it does help to understand what clients hope to get out of SEO so you can mold your services and reports based on that.
SEO is widely regarded as one of the highest-return marketing channels, with some estimates placing the average ROI at around 22:1, meaning businesses earn roughly $22 for every $1 invested.
And around 1 in 3 qualified leads (34%) come directly from SEO efforts.
However, ROI varies significantly across industries:
The rapid emergence of AI also has a bearing on SEO ROI, with 39% reporting a “moderate increase” and 29% claiming a “significant ROI increase”. Only 1% claim that AI reduces their SEO ROI.
SEO is a long-term investment, and results often take time to materialize; many campaigns require 6–12 months just to break even.
That slower payoff, combined with unclear reporting or misaligned expectations, can leave many businesses frustrated with the SEO services they receive.
In our study, we asked our panelists to rate their current SEO provider (or the last SEO provider they worked with) using the Net Promoter Score.
The results were markedly low.
First off, we found that only 30% of small business owners would recommend their current SEO provider.
Importantly, 30% of our respondents considered themselves “detractors”. Which means they would leave a negative review for their last or current SEO provider.
In fact, the SEO services industry as a whole has an NPS score of 0, which is considered “not likely to recommend”.
When we broke down the NPS scores among agencies, freelancers, and a combination of freelancer and agency, we discovered that agencies had a higher average NPS score than freelancers.
However, all three types of services had fairly low NPS scores.
Clients Cite Lack of Education and Resources as Top Reasons for Low Satisfaction Levels
Delivering effective SEO requires significant investment in talent, tools, and ongoing strategy, something many businesses underestimate when hiring a provider.
Building comparable in-house capabilities can cost $150K–$250K+ for senior talent plus thousands per month in tools, which helps explain why expectations often exceed what lower-cost or under-resourced SEO services can realistically deliver.
NPS is a helpful benchmark. However, NPS can only tell you so much. In other words, it’s difficult to understand why SEO services have such low levels of satisfaction.
That’s why we decided to dig deeper into this finding.
And when we dug a bit deeper to understand more about what’s happening, we uncovered a few surprising insights.
First, many unhappy SEO clients fully or partially blamed themselves.
Specifically, 50% stated that “I feel like I need more training to fully benefit from what SEO offers”, and 28% told us that they “do not have the staff resources to properly benefit from SEO”.
This means that low satisfaction levels aren’t solely due to poor quality work. In fact, many clients are simply not in a position to benefit from SEO due to a lack of resources.
Plus, even clients with resources may not make SEO a priority because they don’t have the training to fully understand how SEO benefits them.
For example, let’s say an SEO provider wants to change a title tag on a client’s site. But it doesn’t happen because their developer is swamped with a website redesign. Also, this client may not understand that this simple change can increase their Google traffic due to a lack of training. So they don’t make that change a priority. And progress stalls.
Which leads us to our second interesting finding, the importance of reporting and transparency.
27% of the clients we spoke with agreed with the statement: “I find SEO to be confusing and unclear about what services they offer.” 25% said that “I am not sure what I am really paying for with SEO.”
In other words, many clients are confused about what their provider is doing for them or what they’re getting out of the arrangement.
These are two points that could be remedied with better reporting and increased transparency.
I should point out that a fair number of clients stated that “I feel like SEO companies are very unreliable” and “I don’t think SEO is worth the money for my business.”
Which means that a simple lack of results and ROI is often the culprit behind low client satisfaction levels.
However, as you just saw, there are usually non-performance-based factors at play as well.
Turnover In the SEO Services Industry Is Extremely High
Likely due to low global satisfaction levels, we found high levels of turnover in the SEO services industry.
Specifically, we found that 65% of small business owners have worked with at least one SEO provider before:
We also found that 1/4th of our panel have worked with 3 or more providers:
However, our data suggests that most clients don’t switch between SEO providers without careful consideration.
In fact, the clients in our panel have been working with their current SEO service for an average of 3 years. And lapsed clients give their service provider an average of 2 years to deliver before moving on.
That said, we did discover a small subset of clients that do rapidly switch between different providers.
These “rapid switchers” tend to hire and fire SEO companies at a fever pitch.
For example, we classified 10% of our panelists as “rapid switchers” (worked with 3 or more SEO providers over the last year).
Most SEO Clients Leave Due to Lack of Results and Cost
We wanted to know why people decide to leave their current SEO provider or switch to another company.
We referred to people who worked with multiple SEO providers as “lapsed clients”. And we asked this subset of lapsed users what went into their decision.
Here were the results:
Not surprisingly, 82% of our respondents cited “Dissatisfaction with business results” as a factor in their decision. 81% reported that cost played a large role as well.
This suggests that clients don’t look at results in a vacuum. They also pay attention to the ROI that they’re getting from SEO. In other words, delivering results for clients is one thing. But it’s also important to demonstrate the ROI that SEO has on their business. Otherwise, they may leave.
Although lack of results and cost were the two largest factors, they weren’t the only reasons that clients decided to stop working with an SEO provider.
In fact, 80% of lapsed clients stated that they found a better option on their own, which suggests that clients are happy to shop around for an alternative to their current SEO provider.
And 34% cited poor “customer service/ responsiveness” as a factor in their decision.
However, relatively few clients cited “pitched by a competitor” as a reason for leaving. In other words, as long as you can keep your clients happy, they’re not likely to leave. This remains true even if a competitor attempts to poach your client with a better offer.
We also asked our “lapsed clients” panelists to describe to us why they decided to stop using an SEO service. Here’s a sample of those responses:
We also asked a group of users who were happy with their SEO service (“existing clients”) what they liked about it. Here’s what they told us:
Existing Clients are 2x More Likely to Be Web Savvy Than Lapsed Clients
We asked our panel to self-report their level of “web savviness”.
Here were the results:
As you can see, 37% of SEO clients consider their web savviness as “somewhat” or “not very”.
The upshot here is that many clients simply don’t have the web savviness to understand key digital marketing terms, like “title tags”, “CSS”, and “backlinks”. This suggests that SEO companies should largely avoid this sort of jargon in favor of terms like “leads”, “sales”, and “first page Google rankings”.
In fact, this is backed up by another finding from our panel: that lapsed clients are significantly more likely to consider themselves not web savvy.
Specifically, we found that existing clients were 2x more likely to consider themselves “extremely web savvy” than lapsed clients.
This suggests that web-savvy users are in a better position to understand how their SEO service is helping them. So they decide to stay. On the other hand, clients who aren’t web savvy may not fully understand what they’re getting from their SEO provider. So they decide to leave.
Most location pages fail for one of two reasons: They’re too thin (just an address and phone number) or too generic (the same template with city names swapped out).
Google sees through both. So does ChatGPT.
But here’s what a good location page can do:
Rank in organic search
Link from your Google Business Profile (GBP)
Get cited in AI answers
Serve as a landing page for ads
Convert visitors into leads
One page, five jobs.
Most location pages do none of this.
They just sit there. Technically live, technically indexed, technically doing nothing.
I’ve built location pages for HVAC companies, electricians, painters, funeral homes, and more across dozens of markets.
The ones that rank fast — sometimes within 48 hours — aren’t longer or stuffed with more keywords.
They’re built for how customers actually interact with that business.
In this guide, I’ll show you exactly how to build location pages that work for your business model. Whether you have 3 locations or 300, physical storefronts or service areas.
You’ll get two plug-and-play templates, ranking tactics, and strategies for showing up when someone asks an AI “best [your service] in [city].”
Two Types of Location Pages (and When You Need Each)
Before you build anything, you need to know which type of location page you’re creating.
Get this wrong, and you’ll confuse users, Google, and AI systems.
For example: A bank branch in Philadelphia needs a completely different page than an HVAC company serving Philadelphia from 30 miles away.
Take Bank of America’s Philadelphia branch.
The page shows exactly what someone needs to visit: full address, hours, parking, what to expect when they walk in.
Now compare that to Sila, an HVAC company serving Southeastern Pennsylvania.
They don’t have an office in Philadelphia. But their page proves they cover the area and gives customers confidence to call them.
Physical Location Pages
Creating location-specific pages is how you convert local searches to foot traffic.
What it is: A page for a place customers actually visit
Examples: Bank branches, retail stores, medical offices, restaurants, walk-in clinics
User intent: Directions, hours, parking, what to expect when they arrive
Key signal: You have a real address where customers walk in
Merit Dental’s Sandusky location shows exactly what visitors need: address, hours, map, and a photo of the actual building.
Everything invites you to visit.
Service Area Pages
Service area pages are how you dominate search in 50 towns without opening 50 offices.
What it is: A page for a geographic area you serve, but don’t have a physical presence in
Brick-and-mortar with regional draw: Chiropractors, dentists, urgent care
User intent: Proof you serve their area, credibility, why they should choose you
Key signal: You want visibility in this area but have no physical address there
Infinity Roofer travels to customers across the Denver metro, so their service area page focuses on building credibility through local expertise (mentioning “Denver’s infamous hailstorms”).
Note: Neighborhood pages (e.g., “Electrician in South Philadelphia”) are a more granular version of service area pages. Same approach, tighter geographic focus.
But service area pages aren’t just for businesses that come to you.
Brick-and-mortar locations should use them too when they draw customers from surrounding towns.
For example, Centre for Healing Arts is based in Limerick, Pennsylvania. But they created this service area page for Pottstown, just 7 miles away.
How They Work Together
Many businesses need both.
For example:
McCafferty Funeral & Cremation Inc. has two physical offices: Philadelphia and New Hope, Pennsylvania.
They also serve families in surrounding communities like Lambertville, New Jersey (just 2 miles from their New Hope location).
They need physical location pages for their two offices and service area pages for nearby towns like Lambertville where they don’t have a physical presence.
To make this structure work, link them strategically.
Service area pages link to your nearest physical location. Physical location pages link out to the service areas they cover.
This creates a clear hierarchy for users and search engines.
How to Make Your Location Pages Perform
Whether you’re optimizing for organic rankings, AI citations, paid traffic, or conversions, the same core principles apply.
Match Searcher Intent
Does your page match what someone searching “[service] in [city]” actually wants?
Physical location searchers are often looking for logistics. Hours, directions, parking, what to expect when they visit.
Service area searchers want proof you serve their region and reasons to choose you.
Mismatch = bounce.
Add Real Local Value (Not Just City Name Swaps)
This is where most location landing pages fail.
Swapping city names isn’t unique. Google knows.
Check out these near-identical pages from an HVAC company in Tucson, Arizona.
Real local value means neighborhood-specific details, regional challenges, and local expertise you can’t copy-paste.
For example, Wade Paint Co’s Sullivan’s Island house painting page includes FAQs about historic preservation requirements.
These are concerns unique to this barrier island’s homes.
Or Bill Joplin’s Plano HVAC page, which discusses how Plano’s climate and types of homes affect system sizing.
Details only someone actually working in that market would know.
Right-Size Your Content Depth
Not every location page needs 2,000 words.
But major purchases like home remodeling, medical procedures, or legal services typically require extensive information.
Why?
Because customers are investing significant time and money.
Check out this service area page from Assembly Squad Remodeling, a bathroom contractor.
It addresses different Chicago building types, the specific challenges of each, even pricing ranges for various project scopes.
Low-consideration pages can be leaner. Like this laundromat location page in Indianapolis, Indiana.
Consideration isn’t the only factor in determining page depth.
Competitive markets require more content to differentiate. Less competitive markets can get away with less.
So, match your page’s depth to what the decision actually requires.
I’ve ranked service area pages on domains with Authority Scores (AS) of 20-30 in as little as 48 hours.
Sometimes, in even less time.
With even lower Authority Scores.
How?
I focused on building pages around searcher intent.
In my experience, a well-built location page on a smaller site can beat a thin page on a high-authority domain.
Like how this local painting company is outranking CertaPro Painters, a national franchise. As well as Yelp.
Structure for AI and Search Engines
Schema markup is table stakes. You need LocalBusiness, FAQPage, and Review at minimum.
Scannable sections with descriptive headings help crawlers, AI systems, and humans find what they need fast.
Optimize for Each Channel
The core factors above apply everywhere. But each channel rewards certain elements more than others.
Organic Rankings
The more comprehensive your content, the better it ranks.
Answer questions competitors ignore. Address objections before users have to ask.
You still need keywords, too. Naturally integrated in your title, headings, and body content.
Just don’t stuff “[city] [service]” in every sentence:
Local backlinks to that specific location page signal you’re actually relevant to that area.
Get mentioned by local chambers, news sites, neighborhood blogs, industry directories.
Real images make a difference, too.
Photos of your actual location, your team, or projects you’ve completed.
Stock photos just won’t cut it.
AI Citations
When someone asks Google AI Mode, ChatGPT, or Perplexity for local recommendations, will your business show up?
Third-party “best of” features increase your citation chances significantly.
When you’re mentioned on local roundups, listicles, or “top 10” posts, AI systems are more likely to reference you. They trust these aggregated sources.
Comparison tables also make it easy for AI to pull and cite your content.
Format your information so it’s scannable. Pricing breakdowns, service comparisons, coverage areas. AI loves data it can parse quickly.
FAQ sections with clear question headers work because large language models (LLMs) are trained in part on Q&A content.
Write your questions the way people actually ask them. Then, answer them directly.
Structure your content the way these systems are trained to consume information, and you’re more likely to get cited.
Experiment: What AI actually cites for local queries
I tested 30 “best [service] in [city]” queries across Google AI Mode, ChatGPT, and Perplexity. Then, cataloged every source in their citation panels — 725 citations total.
Each platform told a completely different story.
Google AI Mode leaned heavily on Yelp listings (32%) and Reddit threads (30%). Community discussions and review platforms drove the majority of its citations.
ChatGPT favored editorial “best of” lists more than any other platform — 22% of its citations came from third-party roundups. Getting featured in a local magazine’s “Top 10” list matters here.
Perplexity was the outlier. It cited business websites directly 73% of the time — including location pages. Strong site content gets found.
The takeaway: each platform pulls from a different layer of the web.
Yelp profiles and Reddit mentions for Google AI Mode. Editorial roundups for ChatGPT. Your own site for Perplexity.[/largequote]
Paid Landing Pages
If you’re running Google Ads for local services, your location pages make perfect landing pages.
But only if you get the messaging right.
Your ad says “24/7 Emergency Plumber in Orange County”?
That exact promise needs to be the first thing someone sees when they land on the page.
Not buried in the third paragraph. Not implied. Right there in the headline.
When your landing page headline matches your ad copy, Google sees a better user experience.
That improves your Quality Score and lowers your cost per click (CPC).
Specificity matters too.
If your ad targets “Landscaping Denver,” don’t send them to a service area page for all of Colorado.
Send them to your Denver-specific page with Denver details, Denver reviews, Denver project photos.
Pro tip: The goal here isn’t just to rank — it’s to take up as much search engine results page (SERP) real estate as possible.
With the right setup, your brand can appear three times in a single SERP: your GBP in the map pack, your location page in organic results, and your PPC ad at the top (all using that same location page).
When someone sees your brand multiple times on the same SERP, you get instant credibility. And, it can boost your click-through rates (CTRs).
Most businesses treat these as separate channels. Smart ones use location pages to connect them all.
Template 1: Physical Location Page
Use this template when customers come to you: a storefront, office, branch, restaurant, or clinic they physically visit.
Photos showing your work in this area (or team working in similar neighborhoods)
Credibility & trust signals (industry associations, certifications, years serving this area)
Reviews & testimonials (prioritize reviews from customers in this specific area)
Link to nearest physical location (if applicable “We’re based 15 minutes away in [city]”)
Contact info & CTA
Depth Modules
Here’s how you build a service area page that actually competes. The more competitive your market, the more of these modules you’ll need.
Hyperlocal Content
Show you actually understand this area’s unique challenges.
Maybe you’re a pest control company that can speak intelligently about termite pressure zones in the Southeast.
Or, a pool service that addresses the hard water issues Arizona homeowners deal with constantly.
The more specific you get about problems only someone working in this market would recognize, the harder you are to compete with.
Previous Work in Area
Prove you actually serve this geography with specifics.
We’ve completed 180+ pool installations in Scottsdale over the last 4 years.”
Then, add examples. “Last summer we built three saltwater pools in the DC Ranch community during that record-breaking heat wave.”
Before/after photos from local projects work here, too. Real numbers and real examples beat vague claims every time.
Extended FAQs
Service area pages need to answer two types of questions: Can you actually help me, and do you understand what makes my area different?
Answer service logistics questions like “Do you service [specific neighborhood]?” or “How quickly can you get here?”
Address technical questions tied to local conditions like “Do I need a permit for AC replacement in [city]?” or “What foundation issues are common in this area?”
Write your questions the way people actually ask them. Then, answer them directly.
Scaling for Enterprise
Everything above works whether you have 5 locations or 500.
Centralized templates prevent local teams from going rogue.
Define what’s editable (local details, testimonials, staff bios) versus what’s locked (brand messaging, legal disclaimers, core service descriptions).
Create a style guide specifically for location pages.
Build approval workflows for new pages or major edits so you catch problems before they go live.
Avoid the Duplicate Content Trap
The biggest risk at scale is 50 pages that look identical with city names swapped.
Each page needs genuinely unique content — not just find-and-replace.
Like these examples from Public Storage.
They stay unique by tying each page to real places and explaining the specific storage needs that come with living there.
Audit regularly for pages that are too similar. Remember that thin pages hurt your entire domain, not just that one page.
Choose Your Content Team Structure
Centralized teams give you more control and consistency but less local flavor.
Local teams create more authentic, hyperlocal content but are harder to manage for quality.
The hybrid approach usually works best: The central team owns templates and core messaging; local teams add hyperlocal details and testimonials.
Clear ownership prevents pages from going stale.
Connecting Physical Locations to Service Areas
If you have three offices serving 50 towns, your structure matters.
This avoids confusion for users and search engines while signaling which pages matter most.
Build Neighborhood Pages That Don’t Suck
Don’t create neighborhood pages for every ZIP code.
Prioritize competitive markets, areas with genuine search volume, and places where you have real hyperlocal expertise.
Thin neighborhood pages hurt more than they help. Ten strong neighborhood pages beat 100 weak ones.
Audit and Fix Underperformers
Monitor your location pages’ SEO performance to spot underperformers.
Run regular audits for thin content, outdated information, and broken links.
Set a refresh cadence: quarterly reviews at minimum. Kill pages that aren’t earning traffic or conversions.
Set Up Your Production System
Use CMS templates that enforce your structure.
At my agency, we use WordPress with custom templates to ensure consistency across all location pages.
You also want to track all location pages in a spreadsheet or database with URLs, last updated dates, and performance metrics.
Set automated alerts for pages that haven’t been touched in 6+ months.
Programmatic approaches can work if you have genuinely unique data for each page. For example, a brand like Expedia pulling real hotels, prices, and reviews.
But if you’re just swapping city names, you’re creating thin content at scale. In that case, build fewer pages manually with real depth.
Start Small, Scale Smart
Start with one page.
Pick your highest-priority location or service area and build it using the templates above.
Don’t try to launch 50 pages at once.
Get that first page ranking, converting, and getting cited by AI. Then, use it as your model for the rest.
Remember: One well-built location page can do the work of five different marketing assets.
But most businesses will never build pages this detailed. That’s your advantage.
Need help managing location pages at scale?
Our guide to multi-location SEO shows you how to optimize GBPs, track citations, and coordinate review strategies across every location without losing your mind.
You can only choose channels, messaging, and KPIs once you know what goal you’re supporting.
Of course, revenue growth is almost always the overarching goal.
But most marketing strategies ladder up to that larger goal by supporting things like:
Demand generation
Brand awareness
Retention or expansion
To define your own primary marketing goal, work through these steps:
Name the real problem marketing needs to solve right now: Is the issue volume? Lead quality? Retention? CAC? Awareness? Be specific.
Choose one primary goal: Over the next 6-12 months, what single outcome should marketing influence most?
Identify 1-2 secondary goals (optional): You can support these goals, but not at the expense of the primary goal.
Turn this into a SMART goal and pressure-test it: Pick a goal that you can measure and achieve in a set amount of time.
Of course, the “right” marketing goal depends on your situation.
Early-stage companies need momentum. Growth-stage teams focus on scalable demand. More mature companies might focus on efficiency, retention, or expansion.
The goal you choose sets the direction for every decision that follows.
Here’s an example:
In 2020, Fireflies.ai launched with a small team and limited marketing budget. They needed to drive user adoption and growth, fast.
So, they chose a strategy that focused on product-led, word-of-mouth growth. One of the best drivers: make it easy and worthwhile to refer new users.
They skipped popular tactics like paid acquisition, brand campaigns, and traditional demand gen funnels.
Why?
Because their resources, product design, and business stage made product-led growth the highest-impact path.
Their goal dictated everything else, including how they tracked success. Fireflies.ai co-founder and CEO Krish Ramineni talked about this. He said success was measured with:
Increased product usage
More users inviting Fireflies’ AI notetaker into their meetings
Organic mentions across the web
With this strategy, they were able to grow to over 10 million users, without ever using paid ads.
Before you choose channels or tactics, you need the same clarity Fireflies had. What outcome does marketing actually need to drive right now?
To go even deeper, answer the questions in Step 1 of our Marketing Strategy Workbook.
Step 2: Pinpoint Your Unique Value Proposition (UVP)
A strong unique value proposition (UVP) answers one question:
Why should someone choose you over the best alternative?
In other words, what makes your business meaningfully different from your competitors?
Here’s how to figure it out:
First, identify and analyze your best customers.
The most obvious candidates are the customers who renew subscriptions or keep purchasing from your brand.
But don’t forget your brand evangelists. Who is out there recommending your products regularly?
Once you’ve built that list, ask yourself:
What do these customers have in common?
Your UVP usually lives where you deliver the most consistent, measurable results.
Tip: Our marketing workbook walks you through more questions to help you identify your UVP.
Next, identify the core outcome. What real-world result do those customers get?
Go beyond the surface-level benefits. Think about what changes in your customers’ daily routine. How does your product affect their daily life? How does it impact their business?
Is it smoother communication? Fewer mistakes? Less stress? Better data? Stronger performance?
Anchor your UVP to a real outcome.
Then, define your defensible difference.
Now ask: what allows you to deliver that outcome better or differently than alternatives?
That could be:
Proprietary data
A specific process
Product architecture
Speed
Category specialization
Pricing structure
Brand trust
Community
Be specific. “Easy to use” and “innovative” don’t count unless you can prove why.
Finally, pressure test your analysis.
Ask yourself: If we disappeared tomorrow, what would our best customers struggle to replace?
That point of friction is your real differentiation. It means your UVP isn’t something interchangeable with any other brand in your industry.
Once you have this, your UVP becomes the baseline for the rest of your marketing strategy. It’s a foundation for your message that shows up over and over again.
Doordash is a great example of this. Their tagline is: “Everything you crave, delivered.”
This simple UVP defines:
The audience state (craving)
Breadth (everything)
Outcome (delivery convenience)
The same story shows up everywhere.
Homepage messaging. App story copy. Email newsletters.
The result of having that solid UVP?
DoorDash reinforces one idea: we’re the easiest way to get what you want, when you want it.
That’s the kind of core benefit you want your audience to remember.
Step 3: Perform Audience Research
Your UVP is your hypothesis.
Now, it’s time to validate it.
We have a full guide to audience research, so save that for later. In the meantime, here are three places to gather information:
Customers
Market perception
Competitors
First, let’s start with customer research.
Your goal: understand what your customers actually care about.
Start with a segment of your customers, ideally the high-value customers you identified in Step 2.
Then, answer these four questions:
What problem consistently pushes them to look for a solution?
What triggers that search?
What objections slow down decisions?
What words do they use to describe the problem?
You don’t need months of research.
Start with even just two or three customer conversations to understand how buyers describe their challenges. Talk to your sales or customer success teams to learn about top objections, misunderstandings, or decision blockers.
Next, dig into the market perception of your brand and industry.
Start with social media research. Search on relevant Reddit threads, skim through YouTube comments, or read reviews on third-party sites.
As real people describe the problems they’re facing, pay attention to the emotional language and repeated frustrations. Learn from the criteria they use to compare similar products.
Conversely, when someone recommends your brand specially, what’s the context?
For example: I searched for mentions of Omnisend in an email marketing subreddit. And I learned that the brand is often brought up in conversations about email marketing for ecommerce brands.
Given Omnisend brands itself as email marketing software for ecommerce, this lines up.
Essentially, Semrush runs AI searches for prompts related to your business and gathers a crowdsourced opinion of your brand.
Because LLMs are informed by how your brand appears across the web, this serves as a useful way to gauge both how your brand is perceived online and what the LLMs specifically are telling your target audience about your brand.
Head to the “Brand Performance” dashboard, then scroll to see “Key Business Drivers” to see the topics your brand is associated with in AI answers.
When I analyzed this data for Omnisend, I found that one of their top drivers is deep ecommerce store integration. Which aligns perfectly with what I saw earlier on Reddit.
When you’ve gathered this data, you can use it to pressure test your UVP from Step 2.
Do customers mention the differentiator you identified?
Do they value the outcome you thought was most important?
Are they choosing you for the reason you expected?
Pro tip: If everything feels perfectly aligned, you probably didn’t dig deep enough. This step should create clarity by surfacing the disconnect between what you want people to know, and what they actually know about your brand. The gap is what you aim to solve with your marketing strategy.
Lastly, competitor research can add another layer to this by telling you what’s already being said in the market.
For example, content marketing agency Animalz paid attention to competitors. They noticed that other agencies were competing for the same SEO-driven keywords.
Meanwhile, their ideal clients — CMOs and founders — cared more about experience-driven insight than traffic volume.
So Animalz leaned into what only they could offer: insights from hundreds of content programs.
They focused on original research, experience-driven frameworks, and thought leadership — not search volume.
The result? Fewer generic visitors, more high-quality leads. According to their homepage, their client list includes the likes of Google, Amazon, Airtable, and Atlassian.
That’s the goal here. Understand the audience. Study the landscape. Then, position yourself where you’re both relevant and differentiated.
By the end of this step, you should be able to clearly state:
The core problem your audience is trying to solve
The trigger that pushes them to act
The language they use
The top objection(s) you must address
That’s enough to inform channel decisions and messaging — without drowning in data.
Step 4: Choose Your Marketing Channels
You can’t reasonably “be everywhere.”
Every channel has different mechanics, expectations, and resource demands. So, choose a small number of channels based on:
Where you audience already spends time
Which channels best support your primary goal
What you can execute consistently with your current resources
Here’s what major channels can look like in practice:
Email marketing: High-ROI channel for nurturing, retention, and revenue expansion. It’s one of the most accessible channels to start with. And data shows consistently high conversion rates (2.8% for B2C and 2.4% for B2B).
HubSpot uses educational newsletters to deliver value first. Then, they naturally route engaged readers toward tools and upgrades.
Search (SEO + AI Optimization): When done well, long-form, evergreen content can drive results that compound over time. The key is to optimize for both traditional SEO ranking and AI summaries. Structure content clearly so it’s understood and surfaced — even in zero-click environments.
NerdWallet does this by publishing structured, comparison-driven guides. These rank in search and appear in AI answers. That builds visibility even when users don’t click.
Social media marketing: Platform-native content is built for discovery and engagement. It requires knowing your audience deeply, and playing into the right trends.
One of the most well-known examples of a brand that does this well is Duolingo. Their TikTok and Instagram content leads with humor. Over the years, it’s built massive awareness without traditional selling.
Affiliate and influencer marketing: Leverage trusted voices to expand reach and credibility.
Glossier does this by partnering with creators. This builds authentic recommendations into growth.
Paid advertising: Best for speed and high-intent capture. Requires budget discipline and clear measurement.
Shopify uses paid search to capture intent from searches like “how to start dropshipping for free”
And this likely pays off, considering Shopify has been bidding on the keyword (and ranking as the top ad) for the past year:
Customer and community marketing: Build owned spaces that compound trust and advocacy. It’s a big time lift, but it can pay off in the long run.
Notion supports user-led communities and templates. They’ve built a marketing engine that turns customers into educators and evangelists.
With these channels in mind, it’s time to narrow your focus.
Ask:
Does my audience actively use this channel?
Does this channel support my primary goal directly?
Do we have the skills and resources to execute this well?
Can we sustain this for at least 6-12 months?
Once you’ve committed to 1-2 primary channels, define what success looks like for each one. List the resources you’ll need, and be honest about constraints.
You can use the Marketing Strategy Workbook’s impact vs. effort scoring model to pressure-test your decisions before moving forward.
Step 5: Solidify Your Messaging and Differentiation by Channel
If you just copy-paste your messaging across platforms, it’ll feel out of place. But if you reinvent your story on each channel, your brand will feel fragmented.
This step is about finding the right balance.
For each channel, define:
Which problem you’re emphasizing
What format fits that channel
How your tone and depth should adjust
But your core promise stays intact.
This matters more now than ever because people encounter brands across platforms before they visit your website. On top of that, AI systems look for consistent messaging to help inform their responses to user prompts.
So, how do you build your own channel messaging playbook?
Use our Marketing Strategy Workbook to walk through the main audience problems, content formats, and how your brand should show up on each channel.
If you do this step well, you’ll end up with the right balance of consistency and adaptation.
Duolingo does this really well. Their core story is consistent: learning a language should feel fun, not intimidating.
What changes is how the brand shows up depending on the channel:
On TikTok they’re chaotic, with trend-driven, mascot-heavy humor. That entertainment-first strategy has earned them 17 million followers.
Their Instagram features similar humor, but slightly more polished and adapted to Reels culture.
Their Facebook uses toned-down humor for an older demographic.
And on LinkedIn, the brand keeps a professional tone, but still recognizably Duolingo.
Same brand. Same core message. Different execution.
That’s what you’re aiming for.
By the end of this step, you should be able to say:
What problem each channel focuses on
What format you’ll use
How your tone and depth will adapt — without changing your core message
Step 6: Assign Project Owners and Resources
A marketing strategy only works if someone owns it.
For every primary channel, there should be one person responsible for results. Otherwise, it’s easy for momentum to slide.
Before assigning that owner, do a quick reality check:
How much budget is actually available?
How many hours per week can realistically go toward this?
What skills are missing?
Will you need outside help?
You can use the Marketing Strategy Workbook to keep track of team capacity and resources:
Once you understand the constraints you’re working with, clarify roles using a RACI structure:
Responsible: Who executes the work?
Accountable: Who owns performance?
Consulted: Who provides input?
Informed: Who needs visibility?
Lastly, don’t let channels operate in silos. SEO should inform paid. Sales objections should shape content. Customer success insights should influence customer marketing tactics. All of these teams would fall into the “consulted” category in our RACI framework.
Cross-team collaboration gives your digital marketing strategy the right foundation to build on.
By the end of this step, your strategy should feel operational, not theoretical.
Step 7: Establish KPIs and a Reporting Plan
KPIs let you get feedback on your marketing strategy’s performance over time. And feedback allows you to improve (without guessing).
The problem is, it’s harder than ever to measure what’s working. Marketing channels don’t always tie back directly to revenue. Some channels influence things that are harder to quantify, like brand awareness, AI visibility, or trust.
Instead of forcing attribution into a neat checklist, track metrics in three layers:
Visibility: Are we being seen?
Engagement: Are people responding (positively)?
Trust and intent: Are signals improving?
For email, you could report on open rates (visibility), clicks (engagement), and conversions (intent).
For social media marketing, you might track metrics like reach (visibility), comments (engagement), or saves (trust).
Of course, most marketers still need to answer one uncomfortable question:
How does this tie back to revenue?
It won’t always be perfect. But you can create stronger connections with a few simple systems.
Use UTM parameters on every campaign link. That way, you can trace traffic and conversions back to specific channels, campaigns, or posts.
Set up goal tracking or conversion events in Google Analytics. See which channels drive form fills, purchases, demo requests, or trials.
Review user paths to understand how people move through your site before converting. Just remember: many buyers interact with multiple channels before taking action, so treat these as a guide, not as a definitive start-to-finish buying journey.
For B2B teams, align with sales on pipeline influence. Even if marketing isn’t the final touchpoint, it often plays an early role in deal creation.
Multi-touch attribution may not be possible from day one. But these steps will give you directional clarity.
If a channel consistently drives qualified traffic, assisted conversions, or branded search growth, it’s contributing to revenue — even if it’s not the last click.
Reporting should tell a story, not just hand out numbers. The idea is to show progress, but also know when you need to pivot.
So, take a deep breath, start small, and scale over time.
If fancy dashboards and complex reporting tools feel like too much, just pick 2-3 metrics per channel. Then, assign a clear reporting owner, and set up a review cadence (probably monthly or quarterly).
This is enough to get started.
Start with small tests to see what actually works in your industry, with your audience. Don’t get distracted by the noise of new tools and trends.
Focus on what’s actually working, and then improve and scale the ideas that work best.
Start Small, Scale Up
Important reminder: You don’t need to track everything perfectly from day one. Here’s a plan to scale reporting over time.
Month 1: Establish baselines
Set up tracking
Collect initial data
Identify what’s easiest to measure vs. what requires more setup
Months 2-3: Validate what matters
Test small initiatives
See what moves the needle
Adjust metrics if needed
Months 4+: Optimize and scale
Double down on what’s working
Cut or pivot what’s not
Refine your reporting process
Every quarter, revisit things like channel performance, KPI relevance, and execution quality.
When this is in place, build a simple feedback loop:
Analyze performance
Dig deeper to understand the patterns
Reprioritize channels and actions
Update your strategy and goals
Use the Marketing Strategy Workbook to run through this feedback loop, and document your insights and decisions. As your data improves, so will your strategy.
Evolve Your Marketing Strategy as You Grow
A marketing strategy is a living thing. That means you can revisit, refine, and strengthen the system over time.
At one end: a human asks an AI a question and gets a fast, generated response.
At the other: an AI receives a goal and browses the web on a human’s behalf. It evaluates your brand, makes a decision, and leaves no trace in your analytics.
That’s agentic search.
And it’s already emerging.
ChatGPT’s deep research, Gemini’s agentic mode, and Perplexity’s research features are early expressions of it. Shopping within ChatGPT and booking tables without ever visiting a website are where it’s heading.
AI systems are already running multi-step evaluations with less human direction at each step.
The brands that show up in those evaluations aren’t waiting to see how this develops.
They’re optimizing for it now.
By the end of this guide, you’ll know what agentic search is, how it differs from typical AI search, and how you can prepare your brand for it.
What Agentic Search Actually Is
Agentic search is AI that searches and acts on your behalf — not just composing an answer from its training data, but going out to find information, use tools, and complete tasks.
At the simpler end of the agentic search spectrum, the AI retrieves sources and synthesizes a response.
At the more complex end, the AI agent receives a search goal, breaks it into sub-tasks, searches across multiple sources, cross-references what it finds, and takes action, without waiting for your input at each stage.
Examples of Agentic Search in Action
At the simpler end of the agentic search spectrum, you give an AI tool a prompt like “Which project management software is best for a remote team of ten?”
It won’t just produce an answer from its training data. It’ll go online, search for comparison articles, pull pricing and feature information from review platforms, and synthesize a recommendation.
Move further along the AI search spectrum and the behavior gets more complex.
For instance, imagine you ask the AI to research the competitive landscape in your market. It formulates a plan, then runs multiple searches across different source types — news coverage, review platforms, company pages, industry analysis.
It cross-references what it finds, and you get a structured report.
You’re still the one taking action based on this report, but this is a step up from the fairly simple, synthesized response we’re now used to.
Further still: some agents don’t need a prompt at all. Configured with a recurring search task, like monitoring competitor pricing, flagging new entrants, or summarizing industry news weekly, they run on a schedule.
And at the furthest end of the agentic search spectrum, the AI finds the right option, evaluates it against alternatives, and completes a transaction on your behalf. You asked for a recommendation. It booked the table.
Both OpenAI and Google have published open protocols specifically designed to make this possible (more on them soon).
Why This Is Different from What SEOs Already Know
Agentic search challenges some of the core assumptions SEO has operated on for years.
Here are the three that matter most.
Rankings Matter Less Than Before for Overall Visibility
AI tools are built to pull from a deliberately diverse range of sources, not just the highest-ranking pages.
A single search query triggers retrieval across multiple source types: editorial content, review platforms, community forums, company pages. No single ranking position dominates that process.
AI tools also heavily weigh up content and brand relevance when forming responses, versus factors like website authority, which is more important for SEO.
That doesn’t mean backlinks don’t matter — they do. But topical depth and relevance to the searcher’s intent are the focus in these tools.
Finally, when an AI tool processes a search, it generates multiple related sub-queries, pulling from the results of each. This is called query fan-out.
Your ranking for the original keyword is just one input into a much wider retrieval process. This makes broader topical coverage a key component of AI search in general. This is how you show AI agents that you’re worth citing, recommending, and taking action on.
Your Content Depth Is Now a Competitive Advantage
As Crystal Carter, Head of AI Search & SEO at Wix, puts it: “LLMs don’t get tired of reading 45 pages about your business.”
The average user won’t read countless pages of product documentation. But an agent will — and it’ll use what it finds to make a recommendation.
FAQs, knowledge base articles, documentation, case studies — content that might rarely surface in a standard browsing session becomes evidence in an agentic evaluation.
Crystal gives Levi’s sustainability documentation as an example.
A human visitor might not find it. If you were wondering if Levi’s were sustainable, you’d probably look them up on a single trusted site.
Compare that with what Perplexity AI does to answer the question “Are Levi’s sustainable?”
It conducts a deep dive into Levi’s site.
It evaluates evidence from 15 different sources.
It reads multiple pages from Levi’s own site, including their sustainability report, details on the sustainability of their fibers, their stance on human rights, and a page on slavery from a domain in a separate geography (Levi’s UK).
To succeed in agentic search, you need to make sure agents can answer any questions about your brand your users may have.
AI systems don’t simply retrieve results. They actively research, compare, and filter brands before a human ever sees a recommendation.
Your brand isn’t being ranked once. It’s being audited across sources.
If we take the Levi’s example again, ChatGPT doesn’t just look at Levi’s own content to answer the sustainability question.
It also looks at official rating bodies, third-party research, and media publications. It acts more like a professional researcher than a human conducting a low-stakes product search question.
An agentic system evaluates brands through layered filters like:
Can it find you clearly?
Does it understand you correctly?
Are you validated elsewhere?
Does it trust you enough to recommend you?
If you fail any of those layers, you can disappear entirely from the final answer.
Your Site Needs to Be Usable By Agents, Not Just People
Increasingly, AI agents interact with businesses through structured agentic protocols designed for machine-to-machine communication.
Instead of just relying on what’s in a page’s HTML, AI agents are moving toward standardized protocols, like the Agentic Commerce Protocol (ACP) and Natural Language Web (NLWeb).
This changes what “being accessible” actually means.
Content that only exists inside a visual interface — FAQs that expand on click, pricing tables rendered dynamically, product comparisons loaded via JavaScript — may never exist in the structured layer agents rely on to retrieve and execute actions.
And if they can’t access it, they can’t use it.
That matters because AI agents are increasingly the ones deciding what to include in their recommendations and what to ignore. The human only sees your site if you’re in those recommendations.
So the question is no longer just: “Can people find my website?”
It’s: “Can AI systems clearly understand and use my business information without friction?”
Because in this new system, if your business isn’t easy for AI to access and act on, you may not show up at all.
An agent evaluating your brand might find everything it needs on a single page of your website.
But when it does go looking further, it’s not just gathering information. It’s also checking whether the rest of its sources agree.
An agent corroborates, actively checking whether the picture is consistent across everything it finds.
Here are some of the key places agents look:
Your Website
Agents are likely to prioritize sites that are easy to parse and extract from. They look for:
Clear, up-to-date pricing in plain HTML (not hidden behind interactions).
Feature descriptions that explain capabilities — not just marketing claims.
Positioning that makes it obvious who the product is for (and who it isn’t).
Review Platforms (G2, Capterra, Trustpilot)
Agents read review content for specificity, covering things like use case, company size, outcomes, and integrations.
Community Signals (Reddit & Other Forums)
Agents look at user sentiment on community platforms to cross-check vendor claims.
A brand that talks about itself one way and gets discussed differently in communities creates a consistency gap that leaves agents hesitant to recommend your brand (at least without caveats).
Third-Party Editorial
Agents also look at comparison articles, analyst coverage, and industry endorsements.
Appearing consistently in credible “best X for Y” content is a positive signal.
6 Things to Do Before Agentic Search Goes Mainstream
Agentic search isn’t fully mainstream yet, but the infrastructure is being built now.
The brands that will be well-positioned are the ones that start taking action before their rivals are even aware of what agentic search is.
Here’s how to make sure you’re one of those brands.
1. Run a Cross-Source Consistency Audit
Check your pricing, features, and positioning across your own site, your G2 and Capterra profiles (or any other platforms your target audience users), and comparison articles where your brand appears.
Flag and correct every contradiction.
Make this a recurring part of your workflow. Old positioning language lingers in third-party content long after you’ve updated your own pages.
2. Build Hub Pages for Your Highest-Value Queries
If you don’t have them already, create new standalone pages that fully answer the key questions: what you do, who it’s for, how it compares to other solutions, what it costs, and what customers say.
3. Pressure-Test Your Declared Audience
Pull up your homepage, pricing page, and top comparison content.
Ask: can an agent clearly extract who this is for, what problem it solves, and what makes it right for a specific profile?
To make this concrete, paste the content into an AI tool and use this prompt:
“You are an AI agent evaluating this company. Based only on the content provided, extract: (1) who this product is for, (2) what problem it solves, (3) key use cases, and (4) what differentiates it from alternatives. Then highlight any ambiguity or contradictions.”
If the output is vague or generic, your positioning is too.
4. Ask Customers for More Detailed Reviews
Most reviews are vague: “Great product, really helpful team.”
That doesn’t help AI systems understand when your product is actually a good fit.
Instead, ask customers to be more specific about how they use it and what changed.
For example, in your review requests, you can say:
“If you’re happy to leave a review, it would be really helpful if you could include:
What you use the product for
Your company size or team type
The problem you were trying to solve
The outcome or result you saw
Any tools you integrate with”
5. Check Your Accessibility
Make sure your pricing, FAQs, and feature comparisons are in plain HTML.
Also check your forms and CTAs. If an agent needs to book, enquire, or transact on a user’s behalf, it needs to be able to find and use the form. So don’t hide them behind JavaScript.
6. Implement Agentic Search Protocols
While agentic search protocols are still new and being actively developed, understanding how they work and implementing them on your site can help you prepare for wider rollouts.
For more information on which protocols matter and what they do, read our guide to agentic search protocols.
7. Monitor Your AI Footprint
Right now, here are two things you can actually track to monitor your AI footprint:
Run Regular Brand Queries
Open ChatGPT, Perplexity, and Google AI Mode, and search for your brand by name.
Then search for the category queries a buyer would use — “best [product type] for [your target audience].”
In both cases, document what comes back. Is your brand mentioned? Is what’s being said accurate? Is it consistent with your current positioning?
Do this monthly and track how things change over time.
If your positioning is wrong or outdated, update your homepage, pricing, and comparison pages first (these are usually the sources AI systems rely on most).
If competitors are being favoured, strengthen your comparison content and aim to get more third-party reviews.
If you’re missing entirely, check whether your key pages are crawlable, indexable, and clearly describe your use case.
Agentic search is already here. And as time goes on, complex agentic tasks — like signing up for a tool or buying on behalf of the user — will only become more common. That’s why it’s worth preparing for full agentic search right now.
Start by figuring out where you stand currently.
Tools like Semrush’s AI Visibility Toolkit show you how AI systems currently perceive your brand across platforms. That’s your baseline before you tackle anything else. Learn how to use it in our Semrush AI visibility guide.
A user asks Gemini: “Find me a task chair under $400 with lumbar support and free shipping. Order the best one.”
The AI doesn’t open a new tab. It doesn’t ask the user to click anything. Instead, it queries product databases, cross-references reviews, checks real-time inventory, compares shipping policies, and initiates a checkout — all without a human touching a single page.
These are all things the user would have done themselves, but now in a fraction of the time, with as much effort as it took to write the initial prompt.
Okay, we might not be quite at the stage where everyone is letting AI agents make all their purchases for them. But it’s no longer an unrealistic future.
What made that possible isn’t the AI models themselves. It’s the infrastructure we’re seeing become an increasingly important part of how modern websites are built. This infrastructure consists of a stack of protocols that tells AI agents how to find each retailer’s site, understand their catalog, verify their claims, and take action.
These protocols define how AI agents interact with your brand. And most SEOs have no idea they exist.
By the end of this article, you’ll understand what each protocol does, how they differ from one another, and why you need to pay attention to what’s going on underneath the hood of AI search if you want to stay visible going forward.
Why Protocols Matter for SEOs
Protocols determine whether an AI agent can interact with your brand programmatically, or whether it has to guess. Brands that can speak the agent’s language are more likely to not just be surfaced, but also recommended and, ultimately, interacted with to make purchases.
Think of how robots.txt and XML sitemaps became table stakes for search crawlers. Agentic protocols are shaping up to be that for AI agents.
Put simply: if you want agents to be able to take action on your site — whether that’s making a purchase, booking a table, or completing a form — you need to understand these protocols.
Note: We’re not suggesting that without these protocols AI agents and users will never access your site or buy from them. Agentic commerce is still pretty new, and even the protocols themselves are still evolving. But we believe that agents will increasingly act on behalf of users, and that the easier you make it for them to do that on your website, the better positioned you’ll be as agentic commerce becomes the norm.
The Protocol Stack: A Quick Map
These protocols aren’t competing standards fighting for dominance. They operate at different layers of the same stack, and most are designed to work together.
Here’s a quick breakdown of what these protocols do:
Layer
What It Does
Key Protocols
Agent / Tool
Connects agents to external data, APIs, and tools
MCP
Agent / Agent
Lets agents hand off tasks to other agents
A2A
Agent / Website
Lets websites become directly queryable by agents
NLWeb, WebMCP
Agent / Commerce
Enables agents to discover products and complete purchases
ACP, UCP
Note: As with everything AI, the agentic protocols we’ll give more details on below are constantly evolving. This means some platforms are yet to adopt some of the protocols, and the specifics of each protocol could also change over time.
MCP: Model Context Protocol
MCP is the universal connector between AI agents and external tools, data sources, and APIs.
How It Works
Before MCP, every AI tool needed a custom integration for every data source it wanted to access. If you wanted a chatbot to pull live pricing from your database and cross-reference it with your CMS, someone had to build a bespoke connection between those systems. Then rebuild it whenever either one changed.
MCP standardizes that connection. Think of it as USB-C for AI: one protocol that lets any agent plug into any tool, database, or website that supports it.
An agent using MCP can pull live pricing data, check inventory, read structured content from a site, or execute a workflow, all through the same interface.
The website or tool publishes an MCP server, and the agent connects to it. There’s much less need for custom integration work on either side.
Who’s Behind It
MCP was launched by Anthropic in November 2024. It has since been adopted by OpenAI, Google, and Microsoft. MCP is now governed by an open-source community under the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation.
As of early 2026, there are more than 10K MCP servers out there, making it the de facto standard for agent-to-tool connectivity.
What It Means for Your Brand
Structured data, clean APIs, and accessible HTML have always been good technical SEO. Now they’re also agent compatibility requirements. Brands with MCP-compatible data give agents something to work with. Brands without it force agents to scrape pages and infer meaning, which creates friction and can affect whether they recommend you.
A2A is the standard that lets AI agents from different vendors communicate, delegate tasks, and hand off work to one another.
How It Works
MCP lets an agent talk to tools. A2A lets agents talk to each other.
When a task is complex enough to need multiple specialist agents — like one for research, one for comparison, and one for completing a transaction — A2A is the protocol that coordinates them.
Each A2A-compliant agent publishes an “Agent Card” at a standardized URL (that looks like “/.well-known/agent-card.json”). This card advertises what the agent can do, what inputs it accepts, and how to authenticate with it. Other agents discover these cards and route tasks accordingly.
The result: agents from entirely different companies, built on different frameworks, running on different servers, can collaborate on a single user request. No custom-built connections required.
Who’s Behind It
Google launched A2A in April 2025 with 50+ technology partners, including Salesforce, PayPal, SAP, Workday, and ServiceNow. The Linux Foundation now maintains it under the Apache 2.0 license.
What It Means for Your Brand
As multi-agent workflows become more common, agents may evaluate your brand across multiple checkpoints before a human sees the result.
That chain might look something like this:
A research agent surfaces your product from a broad category query
An evaluation agent reads your reviews and checks the sentiment
A pricing agent verifies your costs against third-party sources
A trust agent cross-references your claims for consistency
A2A orchestrates that entire chain. If your data is inconsistent across sources, like if your pricing page says one thing and your G2 profile says another, the AI agent might filter your brand out as a contender. All before the user even sees you as an option.
NLWeb is Microsoft’s open protocol that turns any website into a natural language interface, queryable by both humans and AI agents.
How It Works
Right now, when an AI agent visits your website, it might have to make a lot of guesses. It scrapes your HTML, infers meaning from your content, and relies on your page being structured properly to be able to parse it effectively. There’s a lot of room for error.
Once a site implements NLWeb, any agent can send a natural language query to a standard “/ask” endpoint and receive a structured JSON response. Your site then answers the agent’s question directly, rather than the agent interpreting your HTML.
Every NLWeb instance is also an MCP server. A site implementing NLWeb automatically becomes discoverable within the broader MCP agent ecosystem without any additional configuration.
Who’s Behind It
NLWeb was created by R.V. Guha, the same person behind RSS, RDF, and Schema.org. (That’s no coincidence.) NLWeb deliberately builds on web standards that already exist, which means a lot of websites are close to NLWeb-ready right now.
Microsoft announced NLWeb at Build 2025 in May 2025. It’s open-source on GitHub. Early adopters include TripAdvisor, Shopify, Eventbrite, O’Reilly Media, and Hearst.
What It Means for Your Brand
For SEOs, NLWeb is a natural extension of work you may already be doing.
Schema markup, clean RSS feeds, and well-structured content are the foundation NLWeb builds on. Sites that have invested in structured data have a head start. Sites that haven’t are harder for agents to work with, but they can easily catch back up by implementing schema markup now.
Structured data already helps search engines, and it can make it easier for agents to understand and interact with your site too. That increases the value of technical SEO work you may have been putting off.
WebMCP is a proposed W3C standard that lets websites declare their capabilities directly to AI agents through the browser.
How It Works
NLWeb makes your content queryable. WebMCP goes one step further: it lets websites declare what actions they support. These actions could include “add to cart,” “book a demo,” “check availability,” and “start a trial.”
These capabilities are declared in a structured, machine-readable format. Instead of an agent scraping your UI and guessing how your checkout works, WebMCP gives it an explicit map, straight from the source (you).
Who’s Behind It
Google and Microsoft proposed WebMCP, and the W3C Community Group is currently incubating it. Chrome’s early preview shipped in February 2026, with broader browser support expected by mid-to-late 2026.
What It Means for Your Brand
WebMCP is the clearest preview of where agent-website interaction is heading.
Imagine you have two brands with similar products, similar pricing, and similar reviews. The one whose site declares clear, structured capabilities is easier for an agent to act on. The other requires guesswork.
Agents are likely to take the path of least friction, and WebMCP helps you reduce friction to a minimum.
ACP is OpenAI and Stripe’s open standard for enabling AI agents to initiate purchases.
How It Works
ACP focuses specifically on the checkout moment. It creates a standardized way for an AI agent to complete a purchase on a merchant’s behalf, handling payment credentials, authorization, and security through the protocol itself.
Before ACP, an agent that wanted to complete a purchase had to navigate each merchant’s unique checkout flow. A different form, a different payment process, and a different confirmation step for every retailer. ACP standardizes this process.
Merchants integrate with ACP through their commerce platform, and once live, checkout becomes agent-executable. The user doesn’t have to do anything except approve.
ACP originally powered ChatGPT’s instant checkout functionality, but that has since been removed by OpenAI in favor of dedicated merchant apps. ACP may still power product discovery within ChatGPT, and may be used within these apps, but things are evolving fast.
Who’s Behind It
OpenAI and Stripe launched ACP in September 2025. It’s open-sourced under Apache 2.0, with platform support still expanding.
What It Means for Your Brand
If an agent has shortlisted your product and the user tells it to go ahead and pay, ACP is what allows the agent to complete the transaction. If your brand isn’t integrated with this workflow, you risk the AI agent getting stuck or being unable to complete that purchase.
The agent can recommend you, but it can’t buy from you. That gap will matter more as agentic commerce becomes the norm.
UCP is Google and Shopify’s open standard for the full agentic commerce journey, from product discovery through checkout and post-purchase.
How It Works
ACP focuses on the checkout moment, while UCP covers the entire shopping lifecycle.
An agent using UCP can discover a merchant’s capabilities, understand what products are available, check real-time inventory, initiate a checkout with the appropriate payment method, and manage post-purchase events like order tracking and returns. All through a single protocol.
UCP is built to work alongside MCP, A2A, and AP2 (Agent Payments Protocol), meaning it plugs into the broader agent infrastructure rather than replacing it.
Merchants publish a machine-readable capability profile. Agents then discover it, negotiate which capabilities both sides support, and proceed.
Who’s Behind It
Google and Shopify co-developed UCP, with Google CEO Sundar Pichai announcing it at NRF 2026. More than 20 launch partners signed on, including Target, Walmart, Wayfair, Etsy, Mastercard, Visa, and Stripe.
What It Means for Your Brand
When a user asks Google AI Mode to find and buy something, UCP determines whether your brand is in the conversation, and whether the agent can actually complete the transaction.
The machine-readability of your product data, the consistency of your pricing across sources, the clarity of your inventory signals: all of it feeds directly into whether an agent can successfully transact with you.
ACP and UCP are often confused, and they do share some similarities, but here’s where they differ:
ACP
UCP
Built by
OpenAI + Stripe
Google + Shopify
Scope
Discovery and checkout layers
Full journey: discovery, checkout, and post-purchase
Powers
ChatGPT instant checkout and product discovery
Google AI Mode, Gemini
Architecture
Centralized merchant onboarding
Decentralized: merchants publish capabilities at /.well-known/ucp
Status (early 2026)
Live, wider rollout in progress
Live, wider rollout in progress
ACP and UCP are complementary, not competing. A brand may eventually support both — one for ChatGPT’s ecosystem, one for Google’s.
For now, the practical question is: which platforms matter most to your customers, and where does your commerce infrastructure make integration easiest? Choose the protocol that aligns with your answer, or use both.
Example of Agentic Search Protocols in Action
These protocols don’t operate in isolation. Here’s what they might look like working together (note that this isn’t necessarily exactly what’s going on at each stage, and is just for illustrative purposes):
Scenario: A user asks Gemini: “Find me a comfortable task chair under $400 with lumbar support and free shipping. Order the best option.”
Step 1: MCP Activates
The agent uses MCP to connect to external tools: product databases, review platforms, retailer inventory feeds. It can query live data rather than relying on cached or trained knowledge.
Step 2: A2A Coordinates
The agent then coordinates with specialist agents published by brands and review platforms via A2A. One evaluates ergonomics reviews. One checks pricing consistency across sources. One verifies free shipping claims against each retailer’s actual policy page.
Step 3: NLWeb Answers Queries Directly
The agents query each retailer’s site. Brands with NLWeb implemented respond to the agent’s /ask query with structured data. This includes things like accurate inventory, real-time pricing, and product attributes. Brands without it force the agent to scrape and infer, slowing it down and potentially leading to them being skipped altogether.
Step 4: WebMCP Declares Available Actions
The “winning” retailer’s site has declared its checkout capabilities via WebMCP. The agent knows exactly what actions are available and how to initiate them without any guesswork.
Step 5: UCP Completes the Transaction
The purchase is executed via UCP, entirely within Google’s AI experience. The merchant’s backend communicates through the standardized API. The user gets an order confirmation, and they never visited a single product page.
Obviously this is the fully agentic scenario. In reality, not every purchase is going to be left entirely to an AI agent.
But even when a human wants to evaluate options before clicking buy, making it as easy as possible for the agent to make recommendations is still good practice. That’s why these protocols are worth paying attention to.
What SEOs Should Do Now
Understanding the protocol layer is step one. Here’s where to focus next:
1. Prioritize Machine-Readable Content Over Volume
Before adding more pages, make sure your existing pages can be parsed cleanly by an agent. That means:
Having your pricing in plain text, not locked behind JavaScript drop-downs
Using feature lists that don’t require interaction to reveal
Including FAQ content that renders server-side
Using schema markup on product and organization pages
An agent that can’t read your page can’t recommend or buy your products.
2. Audit Your Structured Data
NLWeb builds on Schema.org, RSS, and structured content that sites already publish. If you’ve invested in schema markup, you have a head start on NLWeb compatibility.
If you haven’t, this is now a double reason to prioritize it: it improves your search visibility and makes your site more easily queryable by agents.
3. Check Your Consistency Across Sources
Agents verify claims by cross-referencing your site, review platforms, and third-party content. If your pricing page says one thing and your Capterra profile says another, agents can flag the discrepancy and lose confidence in your brand, making the recommendation or purchase less likely.
Audit for cross-source consistency the same way you’d audit NAP consistency in local SEO. It’s the same underlying principle, just for a different kind of crawler.
4. Get on the ACP and UCP Waitlists Now
These protocols are in active rollout. Early adopters benefit from lower competition in agent-mediated commerce while the rest of the ecosystem catches up. Join Stripe’s waitlist for ACP access. And join Google’s UCP waitlist too.
For other protocols like MCP, talk to your dev team about making sure your site supports them.
5. Monitor Your AI Footprint as a Regular Practice
Search your brand in ChatGPT, Perplexity, and Google AI Mode. Are agents describing your product accurately? Is your pricing consistent with what they’re surfacing? Are competitors appearing where you aren’t?
This is the new version of checking your SERP presence, and it needs to become a recurring part of your workflow, not a one-time audit.
Understand how your brand is appearing in AI search right now with Semrush’s AI Visibility Toolkit. It shows you where you’re showing up, where you’re behind your rivals, and exactly what AI tools are saying about your brand.
What’s Next for Agentic Search Protocols?
The protocols we’ve discussed here are already live, but they’re still evolving.
WebMCP is still in early preview. ACP and UCP are mid-rollout. New protocols — for agent payments, agent identity, agent-to-user interaction — are still being drafted and debated.
But the SEOs that understand and implement these protocols correctly are the ones most likely to see success.
