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?

But they measure different search contexts.

SEO SoV calculates your slice of traditional organic search traffic.

You track 100 target keywords. Those keywords generate 50,000 total monthly visits across all ranking sites. You capture 15,000 of those visits.

That’s 30% organic share of voice.

AI SoV measures brand mentions in LLM responses from ChatGPT, Perplexity, Google AI Mode, and similar tools.

For example, you test 100 category-related prompts. Your brand is mentioned in 45 responses and cited in 15. Your competitor shows up in 30 responses with 10 mentions.

An AI visibility tool can calculate your weighted AI SoV based on mentions and citations.

Try now: Curious to know how often your brand shows up in AI responses? Try our free AI visibility checker to find out.

Why Is Share of Voice So Important, Especially Now?

Here are three reasons why share of voice should be your core KPI when visibility is scattered across platforms.

Track Visibility Beyond Traditional Traffic Data

Your organic traffic data reveals only half the story.

And with zero-click searches on the rise, that half is shrinking fast.

When users get their answers directly from AI Overviews and featured snippets, a huge chunk of your visibility is never captured in Google Analytics.

This makes traffic a lagging indicator of visibility.

Share of voice is a better metric because it measures how visible you are in the consideration set, even when users don’t click your site.

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.

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

How to Measure Share of Voice in 4 Steps

Let’s see how you can strategically calculate share of voice in four steps.

I’ll use a fictional project management software example to show how each step translates into business insights.

Step 1: Define Your Industry Landscape

Start by outlining the specific competitors and keywords you’ll track for SoV.

Without clear boundaries, you’ll either miss critical gaps or drown in too much noise.

To map your competitive terrain, pick topic clusters tied to revenue.

For a project management software, I picked these clusters:

• Category fundamentals (like “project management 101” and “project management for freelancers”)
• Use cases (like “agile project management” and “remote team collaboration”)
• Industry-specific (like “construction project management” and “marketing project management”)

Pro tip: Don’t pick these topics solely based on search volume. Choose clusters where gaining visibility directly impacts your bottom line.

One way to assess a topic’s revenue potential is to map it to funnel stages.

Categorize your clusters into three stages:

• Awareness: Where people are learning and researching, like how to manage projects
• Consideration: Where they’re exploring solutions, like the best project management software
• Decision: Where they’re comparing options and ready to buy, like Software A vs Software B

Your SoV at each stage tells you where you’re winning and losing in the buyer journey.

This allows you to allocate resources for maximum business impact.

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.

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

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

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

Pro tip: Export all tracked keywords, not just the top money terms. A keyword with 20 monthly searches might seem irrelevant in isolation. But 50 of these collectively represent meaningful category visibility that SoV captures.

Layer in Competitor Intelligence

Besides your own data, track where competitors show up.

This tells you where to compete directly and where to claim ground that they’ve overlooked.

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

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

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

You need to build visibility for these terms.

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

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

Build Your AI Prompt Library

After sourcing keywords, look at how people search for your category in AI tools.

Since AI search queries tend to be more conversational, they often mirror how people talk in community spaces.

Browse Reddit, Facebook groups, and Slack communities to see how your audience phrases their needs and pain points.

For example, this post reveals that agencies want project management tools that aren’t “too corporate or complex for creative teams.”

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.

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

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

The location setting matters for SoV tracking because search results vary by location.

If you set the location to the United States, but most of your customers are in New York, your SoV might look different than reality.

Pro tip: Start with country-level tracking to establish your baseline. Only segment by region later if local variations impact your business.

Then, click “Continue to Keywords” to manually add or import your keyword list.

Upload the CSV you made in Step 2 to preserve the data by cluster and funnel-stage categorization.

Then, press “Add keywords to campaign.”

Finally, click “Start Tracking” to begin data collection.

Once this setup is complete, Semrush starts collecting daily ranking data for every target keyword.

Check out the results in the “Share of Voice” tab under “Overview” in the Position Tracking dashboard.

You can also add up to four domains to see how you fare against others in the market.

Semrush tracks every brand’s rankings for your keyword set to aggregate the data into SoV percentages.

Important: While SoV is inherently relative and compares your visibility against others, who you choose as competitors shapes how you interpret your SoV.

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.

Here’s a simple matrix to understand your data:

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.

Making SoV Your 2026 North Star

Share of voice captures how often you show up across the fragmented platforms where buyers make decisions.

Get started by measuring your current SoV across SEO and AI search with the steps in this guide.

Pick the gap that costs you the most revenue, and strategize the best ways to close it.

Next step: Build your AI optimization gameplan to capture visibility in the fastest-growing search channel.

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

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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.

Sources: Infinite, Backlinko

Monthly Spend Is Tied To Client Satisfaction

When it comes to SEO, do you “get what you pay for”?

According to our data, yes.

Specifically, we discovered that clients spending over $500/month were 53.3% more likely to consider themselves “extremely satisfied” compared to people who spend less than $500/month.

We also found a clear relationship between dissatisfaction levels and cost.

Specifically, business owners who spent less than $500/month were 75% more likely to be dissatisfied than those who invested at least $500/month in SEO.

This relationship played out whether a client worked with a freelancer, agency, or a mix of both.

Source: Backlinko

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.

Sources: Backlinko

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:

Sources: Leading Edge, Backlinko

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.

Sources: Conductor, Backlinko

SEO ROI: What Businesses Can Expect

SEO is widely regarded as one of the highest-return marketing channels, with some estimates placing the average ROI at around 22:1, meaning businesses earn roughly $22 for every $1 invested.

And around 1 in 3 qualified leads (34%) come directly from SEO efforts.

However, ROI varies significantly across industries:

Medical Device – 1,183% ROI, 13 months breakeven
Higher Education & College – 994% ROI, 13 months breakeven
Oil & Gas – 906% ROI, 10 months breakeven
Industrial IoT – 866% ROI, 7 months breakeven
Pharmaceutical – 826% ROI, 9 months breakeven
Manufacturing – 813% ROI, 9 months breakeven
Biotech – 788% ROI, 8 months breakeven
Solar Energy – 770% ROI, 9 months breakeven
Commercial Insurance – 758% ROI, 9 months breakeven
B2B SaaS – 702% ROI, 7 months breakeven
Construction – 681% ROI, 5 months breakeven
HVAC Services – 678% ROI, 6 months breakeven
IT Staffing – 612% ROI, 10 months breakeven
Legal Services – 526% ROI, 14 months breakeven
Healthcare – 532% ROI, 11 months breakeven
Financial Services – 447% ROI, 9 months breakeven
E-commerce – 317% ROI, 9 months breakeven

The rapid emergence of AI also has a bearing on SEO ROI, with 39% reporting a “moderate increase” and 29% claiming a “significant ROI increase”. Only 1% claim that AI reduces their SEO ROI.

Sources: WebsiteBuilderExpert, G2, AllOutSEO, Semrush

Overall Satisfaction With SEO Services Is Low

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.

Sources: AllOutSEO, Backlinko

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.

Sources: Passionfruit, Backlinko

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).

Source: Backlinko

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:

Source: Backlinko

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.

Source: Backlinko

Conclusion

I hope these findings helped you get a better feel for the SEO services industry.

I’d like to thank Northstar Research Partners for helping me design and conduct the survey that formed much of this report.

And if you’d like to learn more about how this survey was conducted, here’s a PDF of our study methods.

The post The SEO Services Report appeared first on Backlinko.

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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
• Examples:
  • Mobile/field services: HVAC, plumbing, electrical, painting
  • 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.

Consider Authority

Domain authority matters, but it’s not everything.

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.

Bottom line: Generic landing pages bleed money. Location-specific ones convert higher and cost less.

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.

Core Modules

• Hero section (business name, location, primary CTA, contact form)
• Address & directions (full address, embedded map, parking/accessibility details)
• Hours of operation
• What to expect when you visit
• Services offered at this location
• Photos of the location (interior, exterior, team, waiting area)
• Credibility & trust signals (industry associations, BBB accreditation, certifications)
• Special offers for this location
• Reviews & testimonials (prioritize from customers who actually visited this location)
• Links to other physical locations
• Contact info & CTA

Depth Modules

These separate pages that rank from pages that don’t.

Competitive markets and high-stakes services (medical, legal, home remodeling) need most or all of these. Less competitive markets can use fewer.

Hyperlocal Content

This is where you show you actually operate in this neighborhood.

Talk about the area’s vibe, nearby landmarks people know, and transit options.

Cover parking situations and accessibility details customers care about.

The goal is to paint a picture of what it’s like to visit you here — with details only someone who actually works in this neighborhood would know.

Extended FAQs

Go beyond the basics like hours and directions.

Answer questions about the visit itself: “Is there parking?” “How long is the wait?” “Can I walk in or do I need an appointment?”

Address common queries about payment options: “What insurance do you accept?” “Do you offer payment plans?”

Include neighborhood-specific questions like “Are you near [landmark]?” or “Do you serve customers from [adjacent area]?”

Structure these FAQs with clear question headers so AI systems can parse and cite them easily.

Team/Staff

If customers interact with specific staff at this location, introduce them.

Not generic corporate bios. Actual people who work here. Headshots, credentials, specialties.

For professional services like medical, legal, and financial, this builds significant trust.

Community Involvement

If you sponsor local teams, partner with neighborhood businesses, or support area charities, highlight it here.

“We proudly sponsor the South Charleston Little League” proves you’re part of the community, not just operating there for profit.

Template 2: Service Area Page

Use this template when you serve an area but don’t have a physical presence there.

This covers both:

• Mobile services (you go to customers)
• Brick-and-mortar businesses drawing from surrounding towns (they come to you, but your office isn’t in their area).

Core Modules

• Hero section (service + location, primary CTA, contact form)
• Services offered in this area
• Special offers for this area
• 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.

But at scale, new challenges emerge.

Lock Down Brand Standards

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.

The post How to Build Location Pages That Rank, Convert, and Get Cited appeared first on Backlinko.

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Buyers can discover brands in countless ways without ever visiting their website.

Tracking and attribution are becoming murkier, which makes it harder to know what to prioritize.

Everyone is telling you to use AI, or you’re out of business tomorrow.

You constantly need to adapt. And many times, without the data to show whether you’re making the right calls.

The good news is, you don’t need more tactics or fancier tools.

You need structure.

Below, you’ll find a step-by-step framework you can use to build your marketing strategy for 2026.

It doesn’t matter where you’re starting from. These steps will guide you toward a strategy that’s documented and built for sustainable growth.

As you work through each step, use our Marketing Strategy Workbook to capture your decisions as you go.

Step 1: Define Your Primary Business Goals

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:

1. Customers
2. Market perception
3. 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.

You can also use Semrush’s AI Visibility Toolkit to learn how your brand is perceived by LLMs.

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:

1. Where you audience already spends time
2. Which channels best support your primary goal
3. 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.

Further reading: How to Rank in AI Search (New Strategy & Framework).

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.

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.

You now have a clear structure with:

• A defined goal
• A sharp value proposition
• Real audience insight
• Focused channel priorities
• Clear ownership
• Measurable KPIs

That clarity makes execution easier.

Your next step is simple: open the Marketing Strategy Workbook and document your decisions.

Fill in what’s missing. Then commit to your top one or two channels and start executing.

Remember: this isn’t your final version. But it’s a starting point you can revisit, refine, and build on as your business evolves.

Once your strategy is defined, the next logical step is going deeper into execution.

If you’re prioritizing organic growth, read our guide to building an SEO strategy next.

The post 7 Steps to Build a Marketing Strategy That Actually Works in 2026 appeared first on Backlinko.

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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.

For more information on all things AI search, watch our full interview with Crystal Carter.

Breadth Now Matters Just as Much as Depth

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.

Further reading: The agentic web: How AI agents decide which brands make the cut

What Agents Actually Look At

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.

Further reading: How to Optimize Content for AI Search Engines

Check Your Server Logs for AI Crawler Activity

Your server logs record the bots that visit your site, including AI crawlers.

The ones to track include:

• GPTBot: OpenAI’s training crawler
• OAI-SearchBot: Powers real-time ChatGPT search results
• ClaudeBot: Anthropic’s crawler
• PerplexityBot: Perplexity’s crawler
• Google-Extended: Google’s AI training crawler

Look for:

• How often they’re visiting
• Which pages they’re accessing
• Whether those pages return clean 200 responses (successful loads)

If key pages are returning errors (404s), that’s a signal those pages may not be properly accessible to AI systems.

This won’t tell you whether an agent recommended you or ruled you out.

But it’s one of the earliest available signals of how AI systems are interacting with your site.

Further reading: What Is a Log File Analysis? & How to Do It for SEO

Get Your Site Ready for Agentic Search

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.

The post What Is Agentic Search? (And Why SEOs Need to Pay Attention) appeared first on Backlinko.

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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:

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.

Learn more about MCP here.

A2A: Agent-to-Agent Protocol

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.

Learn more about A2A here.

NLWeb: Natural Language Web

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.

Learn more about NLWeb here.

WebMCP

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.

Learn more about WebMCP here.

ACP: Agentic Commerce Protocol

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.

Learn more about ACP here.

UCP: Universal Commerce Protocol

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.

Learn more about UCP here.

ACP vs. UCP: The Key Difference

ACP and UCP are often confused, and they do share some similarities, but here’s where they differ:

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.

Find out where your brand stands right now with our free AI brand visibility checker.

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What’s the marketing automation industry market size? What are the adoption rates of marketing automation, and what benefits does it bring to businesses? Continue reading as we’ll cover answers to these questions with these recent marketing automation statistics.

Here’s what you’ll find on this page:

• Marketing Automation Industry Revenue
• Leading Players in the Marketing Automation Software Industry
• Marketing Automation Budget Changes
• Top Channels Where Marketing Automation is Used
• Top Benefits of Marketing Automation
• Marketing Automation and Customer Data Platform Integration

Marketing Automation Industry Statistics

In 2026, marketing automation is steadily growing, with spending reaching billions every year.

This section presents key statistics to show market automation revenues, top players in the industry, and expected budget changes on automation among marketers.

• Between 2026 and 2032, the worldwide marketing automation industry revenue is forecasted to grow by 62.4% from $8.44 billion to $21.7 billion – Statista

• Revenue of marketing automation solution vendors is forecast to reach $6.6 billion in 2026 (up from $2.9 billion in 2020) – Frost & Sullivan
• Around 68% of surveyed marketers expect an increase in their budget for marketing automation for the upcoming year – Ascend2

• HubSpot dominates the marketing automation software market, holding a market share of 29.58%. Other commonly used marketing automation tools include RD Station (9.25%), Welcome (7.38%), All In Marketing Cloud (6.87%), and Cheetah Digital (6.86%) – Datanyze

• As of March 2026,  at least 454 companies provide marketing automation software solutions – G2

Marketing Automation Usage and Performance Statistics

Marketing automation software is widely used as a part of an effective marketing tech stack.

This section highlights key insights into current adoption and planned usage of marketing automation and its impact on helping achieve business objectives.

• Email (58%), social media management (49%), and content management (33%) are the most reported areas for currently using marketing automation – Ascend2

• 29% of surveyed marketers are planning to implement marketing automation for social media management and paid ads. Another 28% claim they will be adding marketing automation to email marketing programs – Ascend2

• Optimizing overall strategy and improving data quality are top goals for improving marketing automation, according to 37% and 34% of B2B and B2C marketers surveyed, respectively – Ascend2

• Around 41% of marketers report that their customer journeys are “mostly automated” or “fully automated” – Ascend2

• 30% of surveyed marketers strongly agree with the statement that their “marketing automation platform makes it easy to build effective customer journeys” – Ascend2

• About 1 in 4 (26%) of marketers say their multi-channel marketing strategy is fully or mostly automated. Another 22% claim it’s not automated at all – Ascend2

• Pricing is considered a key factor by 53% of marketers when deciding on a marketing automation tool. Ease of use (54%) and customer service (27%) are regarded as the other top factors driving automation tool purchase – Ascend2

• Only 18% of B2B marketers state they use marketing automation that’s integrated with a customer data platform (CDP). Another 42% say they use B2B marketing automation but don’t have CDP in their current tech stack. Other 40% have both B2B marketing automation and CDP, but they aren’t integrated – Adobe

Marketing Automation Benefits Statistics

Marketing teams can greatly improve their effectiveness through the use of automation software, which offers a number of benefits, from improving customer experience to enabling better use of marketing budgets.

• Improving customer experience (43%), enabling better use of working hours (38%), and better decision making (35%) are the most commonly reported advantages of using marketing automation among surveyed marketers – Ascend2

• Around 2 in 3 (66%) surveyed marketing professionals state that their current marketing automation is “somewhat successful” in helping to achieve marketing objectives. Another 25% of respondents say it’s “very successful”. Only 9% of marketers report no success from their marketing automation efforts – Ascend2

Conclusion

We hope you enjoyed this list of marketing automation statistics.

We frequently update this list of statistics. So feel free to check this stats page later for new insights.

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Because it’s a Your Money or Your Life category, products must clear higher verification thresholds before AI mentions you:

• Is your product legitimate?
• Are your fees and protections explicit?
• Do other trusted sources back up your claims?

To find these answers, AI draws from your website and the wider web — including sources you don’t control.

That risks misrepresentation of your brand.

What matters, then, is whether those sources tell an accurate story.

In this article, I’ll explain how to influence that narrative.

Because the real goal is for your fintech brand to show up in AI search AND be represented accurately.

3 Types of AI Visibility in Fintech

Your fintech brand can appear in AI search in three ways.

Your goal is to show up in all three.

• Mentioned when AI explains topics in your category
• Cited and linked within the answers
• Recommended as part of a shortlist of products

Brand Mentions

Brand mentions are when AI systems include your name in an answer.

They’re great for brand awareness.

These references put your name in front of buyers even when they’re not seeking you out.

For example, I asked ChatGPT:

“Are buy now, pay later providers ideal for my business?”

It mentioned several BNPL platforms in its response.

This suggests the AI recognizes those brands as part of the category and relevant in the space.

You’ll often see mentions as:

• Lists embedded inside explanations: “Popular BNPL providers include X, Y, Z…”
• Examples supporting a point: “Some neobanks, like X and Y, offer…”
• Context for user stories: “Many users switch from traditional banks to apps like X…”

A mention isn’t a recommendation, but it still matters.

Mentions often appear in non-brand queries. That’s when users begin exploring their options.

And if they see your brand mentioned often, it builds familiarity.

(Also known as the mere exposure effect.)

So, by the time a user reaches the decision stage, they’re more likely to recognize your name.

But sometimes that isn’t enough.

That’s why having a positive brand sentiment is vital.

The way AI mentions your brand can shape buyer perception.

If it often frames your product as “known for strong security,” that idea sticks.

But if the AI always pairs your name with warnings like “high fees” or “frequent outages,” it can raise doubts.

Citations

Citations are when the AI uses your pages to support its answer.

They’re valuable for boosting credibility and consumer trust.

When AI uses your content as a source, there’s an implied endorsement. It references you because you’re trustworthy.

And when you’re consistently cited, your brand becomes associated with expertise in the topic.

Citations may appear differently across platforms and prompts.

Sometimes they appear as footnotes and/or as inline links.

You might also see a sidebar or expandable panel with grouped sources.

Other times, citations appear as thumbnails somewhere in the AI’s response.

Regardless of format, the principle is the same:

When the AI cites your documentation, it signals that your content is being treated as a reliable source.

It’s pulling information from your pages to build its answer.

And this allows you to influence how the AI explains your product.

For example, I asked ChatGPT:

“What reporting and analytics does Klarna offer brands after implementation?”

Many of the citations came from Klarna’s documentation.

This implies that Klarna has some level of influence over the AI’s answer.

There’s a caveat with citations, however.

LLMs might link to your site, but that doesn’t always mean more traffic.

Citations are less visible than brand mentions or recommendations.

I rarely click them myself.

If I need more detail, I’ll usually continue the conversation within the platform. Or switch to Google search.

That’s likely true for many users.

Still, citations signal that AI systems trust your documentation.

And that trust enables product recommendations, which we’ll cover next.

Further reading: E-E-A-T in the AI Era: Complete Guide + Free Audit

Product Recommendations

Product recommendations are when AI includes your brand or product in a shortlist.

They’re the most impactful type of AI visibility because they influence which brands users consider.

And ultimately, which product they choose.

Here’s what recommendations look like in ChatGPT and Google AI Mode.

I asked:

“Which BNPL platform is good for mid-size ecommerce brands?”

Both listed Klarna as one of the top options.

This places Klarna front and center as buyers narrow their options.

Showing up in high-intent queries is vital for recommendations.

These are prompts that include “top,” “best,” “compare,” or “alternative.”

Such as:

• What are good alternatives to X for a small business
• What are the best budgeting apps for freelancers
• List the best neobanks with high-yield savings

But showing up in these queries isn’t automatic.

AI systems use specific signals to decide which brands to recommend.

How LLMs Choose Which Fintech Brands to Feature

AI acts as a filter between buyers and brands.

So how do these systems decide which brands and products to recommend?

From what we can observe, comes down to two signals: consensus and consistency.

Consensus

Consensus is when multiple reputable sources mention your brand and product.

AI surfaces brands that have this kind of social proof — it suggests that you’re real, trustworthy, and worth recommending.

The stronger the consensus, the more confident AI is in featuring you.

But this cuts both ways.

If sources consistently highlight negatives, AI may repeat those warnings instead.

In fintech, AI systems likely assess consensus from several sources, including:

• Partner-bank and infrastructure disclosures
• Regulatory databases
• Personal finance publishers
• Finance communities and review platforms
• Partner sites
• Technical and investor communities

So, a big part of AI optimization is showing up in the sources LLMs use to form consensus.

The easiest way to identify those sources is to run brand-related prompts.

Example: “Best banks for international transfers.”

Then, check which sites appear in the citations.

Those are the sources the AI model trusts.

When these sites and reviews talk about your brand, it increases your chances of being mentioned by AI.

Further reading: Read our search everywhere optimization guide for tips on building a positive brand reputation across platforms.

Consistency

It’s not enough for your brand to be mentioned everywhere. The sources also need to agree on the facts they’re sharing about you.

That means the core details of your product align all over the internet, including your:

• Category
• Pricing and fees
• Product features
• Protections

For example, I asked ChatGPT and Google AI Mode for “best budgeting apps.”

Both recommended YNAB (You Need A Budget).

That’s no surprise.

YNAB appears in dozens of reputable sources, including Money, CNBC, NerdWallet, and Wirecutter.

It’s also frequently mentioned in finance communities, such as myFICO Forum.

These sources also highlight specific use cases: college students, goal-setting, and overall budgeting.

These consistency signals help AI confidently recommend YNAB for those exact scenarios.

Building consistency across platforms comes down to good ole PR and reputation management.

Ensure your key details align across your site and third-party coverage.

Working with publishers and affiliates will help you shape how your brand is described.

Ultimately, consistency starts with content: what you publish and what others publish about you.

3 Types of Content That Dominate Fintech in AI Search

LLMs will reference any public content they can access.

In fintech, three types carry the most weight.

1. Owned Content

Owned content is anything you publish and control on your own properties.

This includes your website, documentation, and any branded platforms.

AI analyzes these places for your version of the facts.

That’s why content like “What does this product do?” or “How does it work?” is so essential.

For example, I asked ChatGPT:

“Compare ATM withdrawal limits, card spending caps, and international FX fees for Wise, Revolut, and Monzo.”

Its answer cited many of the three brands’ pricing and product pages to build the comparison.

This indicates the AI uses these pages to answer this query.

For you, this means your website plays a big role in what AI says about your product.

Treat your site as both a marketing and educational channel.

Publish the product details that matter to buyers.

Look at your sales conversations, support tickets, and comparison research to identify questions, concerns, and pain points.

For example, Intuit’s TurboTax landing page includes extensive product details.

It covers everything from security and guarantees to key tax filing information.

This helps the AI (and users) understand what the product includes, how it works, and who it’s for.

2. Earned Media and Reviews

Earned media and reviews are third-party perspectives on your product.

This includes everything from editorial coverage to user feedback.

LLMs use these sources to fact-check your claims. It’s also how they understand what it’s like to use your product.

In fintech, third-party sources often include:

• Editorial guides and roundups by established finance sites such as Kiplinger and MarketWatch
• Affiliate and review platforms, including sites like the Better Business Bureau (BBB)
• Community discussions on platforms such as Quora and finance forums like MoneySavingExpert

For example, I asked ChatGPT:

“What reporting and analytics does Klarna offer brands after implementation?”

The citations included Klarna’s own documentation. Plus, sites such as print-on-demand platform Gelato, Forbes, and G2.

That mix is worth noting.

It shows the AI isn’t taking Klarna’s claims at face value.

It’s cross-checking them against third-party evaluations.

The takeaway here is to treat reputable third-party coverage as a core growth channel.

One proven strategy: publish original research that journalists can cite.

Take KPMG’s Pulse of Fintech H1 2025 Report, for example.

Each edition generates media coverage across major sites like Bloomberg and Trinetix.

This works because reporters are constantly hunting for newsworthy statistics.

Other things you can do to increase earned mentions include:

• Fill out or update third-party listings you control, like app store profiles
• Co-author articles to earn mentions in trusted sources
• Synchronize PR, product, legal, and marketing so your brand story stays unified everywhere

Further reading: LLM Seeding: A New SEO Strategy to Get Mentioned by LLMs

3. Official Records

Official records are documents that confirm your legal authorization to operate.

LLMs treat them as proof and confirmation of compliance and regulatory standing.

The types of official records LLMs cite include:

• Regulatory registries and licenses
• Regulatory disclosures and notices
• Partner bank disclosures
• Corporate records

These sources allow the AI to answer questions on legitimacy and protection.

For example, I asked Perplexity:

“Is Wise licensed to operate in the U.S., and what protections apply to Wise balances?”

The citations included:

• A PDF consent order from six state financial regulators
• Wise’s National Trust application filed with the OCC
• The California DFPI’s regulated-entity page for Wise US, Inc.

Along with Wise’s documentation, these give AI enough evidence to answer confidently.

They confirmed that Wise:

• Operates in the U.S.
• Under specific entities
• And with the appropriate approvals and protections

In fintech AI search, this kind of regulatory confirmation is a strong trust signal.

It tells AI systems that your product is legitimate and safe to mention.

This creates a real opportunity for you.

AI systems can only cite what they can find, parse, and verify.

Your job is to make your regulatory standing explicit, structured, and easy to retrieve.

Start by naming your partner banks, custodians, and key infrastructure providers on your site.

And keep those details up to date across your site.

Publish key pages that AI systems can pull from, including:

• Regulatory and licensing: Clearly list your licenses, registration numbers, regulatory bodies, and jurisdictions where you operate
• Protection: Explain in plain language how funds are safeguarded, what insurance applies, and which entities custody assets

Link to these pages from your footer and trust pages so AI bots can easily find them.

How Fintech Brands Can Improve AI Search Visibility and Accuracy

54% of Americans now turn to ChatGPT for financial research, according to a Motley Fool Money study.

That means buyers often get the “AI version” of your brand before they see your website.

That’s actually good news.

A Microsoft study found that AI traffic converts at 3x the rate of other channels. This includes search, direct, and social media.

The catch? This only works in your favor if the model accurately describes your product.

Here’s how to help it do that.

Provide Proof That Your Brand Is Real and Trustworthy

LLMs need proof they can validate before they include you in answers.

So your trust details need to be public and clear across your owned platforms.

One effective way to do this is with a dedicated section on your site.

This can serve as your primary source of truth.

Many fintech brands, like SoFi, do this with a “Trust & Security Center.”

But a well-structured “Help Center” like Venmo’s works, too:

Overall, make it easy for LLMs (and users) to find the facts that reduce perceived risk:

• Who holds the funds
• Who powers the product
• How the product works

Reiterate the same trust details in related pages and sections of your site.

Add them to your homepage, About page, and FAQ sections on service pages.

Many fintech brands also include disclosures, like Member FDIC or partner bank language, in the footer.

A keyword tool like Semrush’s Keyword Magic can help you find safety and trust concerns people have about your company.

If they’re asking these questions on Google, you can bet they’re asking them in AI tools, too.

How you format your content is crucial. Ensure it’s easy for AI to extract and cite:

• Use question-and-answer structures for common concerns
• Answer each question with a clear, direct, and quotable response
• Include facts and statistics when applicable

Finally, treat data hygiene as a required part of your process.

When a partner, protection, or operational flow changes, update the documentation immediately.

Then clean up anything outdated.

Redirect or remove old PDFs and help docs so AI only finds the current version.

Further reading: Content Writing 101: How to Create High Quality Content

Reduce Mixed Messages About Your Product Online

Contradictions undermine AI’s trust in your brand.

They break the consistency signal, making AI systems cautious about recommending you.

But inconsistencies can easily happen over time.

As your company evolves, public-facing information can become outdated.

Older pages, screenshots, or explanations remain discoverable online. But AI systems can’t always determine which version is current.

The good news is that you can fix this with a few focused actions.

First, start where you have complete control: your own site.

Ensure your core narrative, product details, and trust documentation are fully synchronized on all landing pages and trust hubs.

SoFi does this well.

Their “all-in-one” app positioning is reinforced throughout their site.

As you update your site, have marketing, product, and compliance teams work together.

This ensures consistency in promotional materials, regulatory disclosures, and product specs.

Next, make sure that affiliate and “best-of” publishers accurately describe you.

Affiliate sites and finance publishers are the most-cited sources in AI answers, according to the Semrush AI Visibility Index (December 2025).

So, it’s worth checking what these sites are currently saying about you.

(Especially on “best of” listicles, comparisons, and reviews.)

To do this, research the questions people ask when evaluating your product.

They’re usually formatted like this:

• Is [Brand X] legit
• [Brand Y] fees
• Can I trust [Brand Z]
• [Brand X] vs [Brand Y]
• Is [Brand X] safe

Google’s People Also Ask and keyword tools let you find these questions.

You can also use Semrush’s AI Visibility Toolkit to see what questions users ask LLMs about your industry.

It tells you the exact prompts they use:

Then, look at which pages and websites are often cited.

If you’re using the AI Visibility Toolkit, it will pull these for you:

Otherwise, manually search the questions you found in different generative engines.

Click each source and scan for inconsistencies.

If you find something wrong, reach out to the publisher for a correction or update.

Make it easy for them to make changes by providing clear, publish-ready facts.

Another vital step is monitoring (and participating in) online finance conversations.

Forum and social media posts have a long shelf life.

This can pose consistency problems for you as your company grows and your products change.

Reddit, for example, rarely deletes old posts.

So outdated answers can stay discoverable for years.

Reduce the impact of outdated information by:

• Replying with a simple correction, especially in threads you see cited in AI engines
• Making sure your social media accounts repeat the correct version
• Announcing updates where people discuss your category

It’s also worth being more present in communities that AI often cites.

For example, Fidelity’s subreddit often shows up in Fidelity-related questions.

If you manage or participate in spaces like this, you can influence the public record directly.

Use our brand subreddit guide for tips on setting one up and growing your visibility.

Manage Brand Perception and Sentiment

AI systems assess how other sites talk about you. That public sentiment shapes the answers users get.

For example, I asked ChatGPT: “Is PayPal safe?”

It didn’t give a definitive “yes.”

Instead, it used qualifying language like “generally considered” and “not perfect.”

It also added important caveats and security considerations.

Looking at the citations, you can see the sources that contributed to those caveats:

• Investopedia, comparing PayPal’s safety measures to credit cards
• Community discussions, such as r/privacy, where users debate PayPal’s risk profile
• Editorial sites and even some competitors like Wise outlining protections and limitations

This means:

How other sites describe you affects how AI describes you.

That makes sentiment tracking vital.

Set up regular AI search visibility audits for your brand. You can do this manually by monitoring different AI platforms.

Start with the top two most used generative AI tools:

ChatGPT and Google AI Overviews.

Each month, run a consistent set of high-intent prompts related to your brand and category.

Note the sentiment, including any differences between AI models.

Look for patterns to assess whether your sentiment is positive, neutral, or negative:

• Regular positive framing
• Repeated warnings
• Recurring pros and cons

Yes, doing this manually takes time.

If you’d rather automate the process, use the AI Visibility Toolkit.

For example, it provides your brand’s overall sentiment and share of voice.

It breaks this down by platforms, including:

• Google AI Mode
• ChatGPT
• Perplexity
• Gemini

You can also see how you stack up against your biggest fintech competitors.

The Narrative Drivers tool is especially useful.

It shows the exact questions people ask about your brand. And the percentage of favorable sentiment towards you in each answer.

This makes it easy to see where you’re perceived positively or negatively at scale.

Really cool.

Make Your Fintech Brand Easy for AI to Trust

AI is changing fintech in a number of ways.

Most notably, a buyer’s first touchpoint is now often an AI-generated answer.

If you’re not in those answers, you’re not in the decision.

The fix: build consistency and consensus signals for your fintech brand.

You already have a strong idea of how to do that. Now, dive deeper into the topic with our AI optimization guide.

The post Fintech in AI Search: How to Be the Trusted & Featured Brand appeared first on Backlinko.

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58% of consumers now use GenAI tools instead of traditional search to find products.

Imagine your customer runs a simple query in Google’s AI Mode: “Winter jackets for women.”

Instead of a long list of links, they get direct product recommendations — alongside:

• Descriptions of features and best use cases
• Ratings and reviews
• Editorial sites that mention the product
• Direct comparisons with top competitors

All in one response.

Which raises an obvious question:

Why do some products show up, while others are ignored entirely?

Many factors influence AI recommendations.

But one of the most important — and most controllable — is your product pages.

In basic terms, AI needs to understand what your product is and who it’s for.

When that information is clear, structured, and specific, your products have a much better chance of appearing in AI results.

In this guide, we’ll break down how AI evaluates product pages, and which elements matter most.

Plus, we’ll see how leading ecommerce brands structure their pages to get recommended.

Free checklist: To get a head start, download our Product Page AI Optimization Checklist. It includes everything you need to get more product mentions in AI platforms.

How AI Models “Think” About Product Pages

Ever wondered how large language models (LLMs) choose which products to surface in answers?

While there’s a lot at play, you can basically narrow it down to two factors:

• Consistency: Information about your brand and products matches across your website and third-party sites
• Consensus: Multiple reputable sources validate your product’s quality, use cases, and performance. This includes reviews on your product pages and third-party sites.

For LLMs to confidently cite a product page, they need consistent, up-to-date information.

AI models analyze product pages to pull details that help them answer user queries.

Remember, AI queries don’t look like a regular search.

Prompts are often highly specific requests for products that fit a clear use case or situation.

Example: What are the best women’s road racing shoes for a 10K in Ireland?

AI looks for product pages that clearly communicate:

• What the product is
• What it’s used for
• Who uses it
• In what situations it can be used

This helps the system understand your product in the context of user queries.

Take this Nike road racing shoe product page, for example.

AI systems understand when and how to recommend this product because it contains details like:

• What the product is: “Women’s Road Racing Shoes”
• Who should use it and when: Racing-related language like “marathon” and “race day shoe” makes it clear this product is for racing

When I searched “best road racing shoes for women” in AI Mode, it recommended Nike’s Alphafly.

And where did the information it quoted come from?

Nike’s own product page.

AI models also look for consensus signals on product pages.

This includes customer reviews and ratings.

When AI analyzes reviews, it looks for patterns. This includes repeated mentions of specific use cases, features, or product benefits.

For example, the Nike Alphafly is highly rated with plenty of reviews on the Nike website.

Among other benefits, this improves its chances of being recommended by AI platforms.

But AI doesn’t rely solely on product pages.

It cross-references independent sources to back up claims about your products.

In a similar search for racing shoes, I found that AI Mode cites various third-party sources to support its recommendations.

Like this one, that includes a review of Nike shoes, complete with product details.

Product pages are one piece of the AI visibility puzzle.

But they create the foundation AI systems need to confidently recommend your products.

Further reading: Learn how LLMs recommend brands in Semrush’s AI Visibility Index.

6 Essential Elements of a Product Page for AI Visibility

You likely already have some (or all) of the elements below on your product pages.

But for AI visibility, having them isn’t enough.

What matters is clarity, specificity, and structure.

Note: These elements aren’t in any particular order: all are important for AI visibility.

1. Clear Product Descriptions with Semantic Language

A clear product description explains more than what your product is. It spells out what it does, who it’s for, and why someone would choose it.

This matters for AI visibility because LLMs rely heavily on semantic retrieval.

In other words, AI understands the intent and meaning behind queries. Not just exact-match keywords.

For example, when someone searches for “vacuum for pet hair,” AI doesn’t just look for that phrase.

It also looks for semantically related terms. Things like “stubborn hair,” “carpets,” “pet odors,” and “allergens.”

These terms help AI infer use cases, surface the right features, and decide when your product is a good fit.

Including them on product pages improves your chances of appearing in AI-generated answers.

So, how do you find these terms?

First, read forums, reviews, and social media conversations.

Learn how people talk about the problems they’re facing and the products they’re using.

Using our vacuum example, I dove into r/VacuumCleaners. There, I found recurring phrases around weight, clogging, tangles, and flooring-specific concerns.

Next, conduct keyword research on related terms.

This shows you how people actually phrase their searches.

A tool like Semrush’s Keyword Magic Tool is great for this task.

Note: A free Semrush account gives you 10 searches in the Keyword Magic Tool per day. Or you can use this link to access a 14-day trial on a Semrush Pro subscription.

Enter a keyword, such as “pet hair vacuum.”

The tool will return a list of “Broad Match” queries, which contain variations of your keyword.

Review the “All Keywords” list on the left to find common themes.

Then, check the monthly search volume for each term.

In our example, we might use “handheld,” “carpet,” and “hardwood” as semantic keywords.

Collect a few key terms, and use them in product descriptions to explain what your product does.

You can still be creative. Just don’t sacrifice clarity.

Here’s what this looks like in practice.

I asked AI Mode for the best lightweight vacuum for pet hair. One of the top recommendations was a Shark vacuum.

User preferences and personal context aside, AI Mode recommended this product for a few reasons:

For one, it has strong consensus signals from third-party reviews and editorial sites.

(Which you can see from the sources on the right side.)

But let’s also take a closer look at the product page.

The product name alone — Shark UltraLight PetPro Corded Stick Vacuum — gives a core use case.

It’s meant for lightweight, pet-focused cleaning.

The product description reinforces that message with simple, specific language:

• Captures stubborn hair
• Works on carpets and floors
• Hand vac option
• Weighs less than three pounds

That same phrasing shows up in the AI response.

This strongly suggests AI Mode is pulling this information directly from Shark’s product description for this vacuum.

Bottom line: Customer-focused, use-case-driven language helps AI understand when to recommend your product.

This increases your chances of appearing in AI search results.

Further reading: Need inspiration? Check out some of our favorite ecommerce website examples.

2. Pricing and Availability in Real-Time Feeds

LLMs read product data from two places: your product pages and merchant feeds.

If your site has accurate structured data, AI can use that. But crawlers don’t run every minute. That means prices and stock can be stale.

That’s where a live product feed or API comes in.

This includes Shopify’s Catalog API, OpenAI’s Product Feed Spec, and feeds submitted through Google’s Merchant Center.

Pro tip: OpenAI product feed submission is currently available only to approved partners. Fill out the Merchant Application form for consideration.

When you use these, AI search engines can fetch current prices and inventory on demand.

That’s the tech that powers real-time recommendations and in-chat shopping in ChatGPT and other AI platforms.

More platforms are also adding this capability.

Google is rolling out a Universal Commerce Protocol.

This feature brings buy-in-chat functionality to eligible product recommendations in AI Mode and Gemini.

But what if you don’t use a product feed or API?

LLMs can still find product information on public webpages. But it may be outdated.

And that’s a problem.

AI platforms evaluate recency and consistency.

Mismatched prices or outdated stock can hurt your AI visibility. In part, because it leads to a poor customer experience.

​​To see how this plays out in practice, I tested ChatGPT’s “Shopping research” mode.

The AI asks questions to narrow results, including how much you want to spend.

I told ChatGPT I was looking for a new couch. I specified both my budget and need for delivery to Massachusetts.

ChatGPT returned five options, all of which fit my budget and availability requirements.

The “Best overall” option even highlighted that it was “in stock for fast delivery” to my state.

To further test how price affects results, I asked if any of the recommended couches were on sale.

It narrowed down my options and provided sale pricing.

ChatGPT only mentioned one couch as being on sale.

To find out why, I reviewed the product pages for each recommendation. But only one clearly highlighted both the original and sale price.

Walmart’s product pages boldly showcase the previous price versus the discount.

In its response, ChatGPT specifically mentioned that Walmart displays this info on its product page.

Walmart also submits its product feeds to platforms like Google Merchant Center.

So its pricing (both sale and original) is clear and current across platforms.

Product feeds and APIs keep your price and inventory fresh.

When AI systems have access to this data, they can recommend your products when users narrow options by price, availability, or discounts.

3. Ratings and Reviews

Many AI systems display ratings and reviews in product recommendations.

In AI Mode, you can click a product recommendation and see reviews directly in the sidebar.

ChatGPT also includes information from reviews.

It often surfaces them as part of the response:

But LLMs do more than show you reviews. They also weigh reviews and ratings when choosing recommendations.

ChatGPT often includes labels like “Budget-friendly” or “Most popular” based on reviews.

OpenAI has confirmed that answers may include summaries of the themes most commonly mentioned in reviews.

That could mean pros, cons, and use cases pulled directly from reviews.

Here’s how that looks in practice when I search for warm winter hiking boots:

Ultimately, reviews on your product page don’t just affect whether your product appears in AI search.

They can also influence how it’s positioned.

When AI systems analyze reviews, they look for consistency:

• Repeated mentions of specific use cases
• Commonly praised features
• Patterns in star ratings
• Shared language around benefits or problems

The more clearly those patterns emerge, the easier it is for AI to confidently recommend — and describe — your product.

This applies to reviews on your own product pages and on third-party sites.

When I asked AI Mode for a hydrating cleanser for sensitive skin, the first recommendation was a product from CeraVe.

Interestingly, the product description itself doesn’t explicitly emphasize “sensitive skin.”

But the reviews on CeraVe’s product page do.

Here’s what I noticed:

• Reviews are tagged with commonly mentioned phrases
• One of the most prominent tags is “sensitive skin”
• There are over 100 reviews referencing sensitive skin — most of them positive

Having reviews on every product page is a best practice that increases trust and authority.

Encourage customers to leave detailed feedback by:

• Prompting for use cases in review forms
• Asking follow-up questions after purchase
• Offering light incentives (like a coupon) in exchange for honest reviews

Note: The most important thing is that these reviews are real. Fake or AI-generated reviews may temporarily improve your brand’s visibility in AI search. But they are never worth the long-term risk to your reputation.

4. Contextual Use Cases

AI search looks for explicit connections between what a product is and why someone needs it.

So, your entire product page should explain when, why, and in what situations a product makes sense.

This requires a shift in how you think about product marketing.

Instead of asking, “What can this product do?”

Ask, “In what specific scenario would someone actively look for this?”

Start by identifying who buys your product and what triggers that purchase. If you don’t already have this insight, customer interviews are your fastest path.

Look for:

• The situation that prompted the search
• The alternatives they considered
• The constraint that mattered most (travel, space, safety, performance, etc.)

Once you have this, choose one or two clear, specific use cases to feature on each product page.

Don’t just list all the possible ways your product can be used.

AI isn’t great at matching vague versatility.

Instead, focus on the use cases that come up repeatedly in customer conversations. That way, AI can match your product to a specific intent.

Let’s look at an example for an electronics brand.

This product page for Anker’s 3-in-1 mobile charger states it’s “ultra compact and travel friendly.”

When I search for travel-friendly chargers on ChatGPT, Anker’s 3-in-1 device is the top recommended product.

Obviously, this little charger is a great option for more than just travel.

But by calling out that use case on the product page, it makes it easier for LLMs to recommend it in related queries.

5. Awards and Certifications

LLMs prioritize trustworthy, verifiable information when recommending products.

One of the strongest ways to demonstrate that trust is to feature third-party validation on your product pages.

This includes:

• Industry awards and “best of” recognitions
• Third-party testing results
• Safety and quality certifications
• Sustainability or ethical production badges

To see how much awards affect AI visibility, I analyzed 50 ecommerce brands in Semrush’s AI Visibility Overview tool.

This included Samsung, Patagonia, Everlane, Caraway, and others.

First, I identified brands with high AI Visibility scores.

This is a Semrush metric that measures how often brands appear in AI-generated answers.

I focused on brands scoring above their industry average. (This varies by industry, but is generally between 60 to 90.)

Next, I looked at how many of the top-ranking brands feature awards and certifications on their product pages.

And I found something very interesting:

82% of the brands with medium to high AI visibility prominently feature awards and certifications on their product pages.

For example, Samsung has an AI Visibility score of 90.

And its product pages feature multiple awards.

Like being “rated #1 in camera quality” by the American Customer Satisfaction Index.

And winning “Best Phone Camera” by Consumer Reports:

When I asked Claude which phone has the best camera quality, the Samsung Galaxy was one of its top recommendations:

BabyBjorn has an AI Visibility score of 67.

A quick look at its product pages reveals certificates and awards on every product page.

Like this one that references a “Best Bouncer” award from Parents Magazine:

When I asked ChatGPT to recommend the “best and safest baby bouncer,” BabyBjorn was the #1 pick:

Now, this is correlation, not necessarily causation. And awards and certifications are not the only factor.

But they can make a difference for product page visibility in LLM search.

If you already have awards and certifications, showcase them prominently on your product pages.

If you don’t, create a strategy to earn them.

Target industry-specific certifications (safety, quality, sustainability) and awards from reputable organizations.

This includes relevant certifications and “best” awards through PR outreach.

6. Structured Attributes and Schema Markup

Structured attributes are pieces of product information that machines can easily understand.

This includes things like:

• Price
• Dimensions
• Materials
• Ratings
• Availability
• Color
• Size
• Warranty details

These attributes are vital components of a product page.

Use tables, bullet lists, or specification sections to clearly structure them for machines and customers.

They should also be in your structured data and product feeds.

For example, health company Vitamix features a “Specifications” section on its product pages:

We can’t say definitively that schema affects LLM visibility (yet).

But major AI search engines confirm they rely on structured attributes to understand and recommend products.

It’s also still a best practice for traditional SEO.

Plus, it’s no secret that structured data helps products appear on Google’s main page and Shopping tab.

It’s what allows users to refine results, see ratings, and check prices right on the first page of Google.

But here’s where it gets interesting.

When I conducted a search in AI Mode, Google’s own shopping cards were the main sources.

Clicking into one of those sources, I saw even more of that search-friendly structured data.

And where does all this information come from?

You guessed it: the original product page.

That same structure is what enables Google’s AI responses to display live pricing, availability, sales, and comparisons.

Clear, consistent schema simply gives search engines and LLMs more to work with.

That context helps AI more confidently recommend your product in related queries.

AI Visibility Essentials for Product Pages (By Industry)

The elements above matter on every product page.

But AI evaluates product pages differently depending on the category.

In this section, we’ll break down the category-specific product page details that AI looks for across six common ecommerce industries.

Fashion Brands

Ask any AI engine for clothing recommendations, and you’ll notice something consistent: the results highlight fit, materials, and comfort.

Clearly, the most important product page elements for fashion brands are:

• Clear sizing and conversion charts
• Material and care information
• Customer fit data
• Sustainability certifications and ethical production badges

Fashion queries are also highly specific to the individual shopper.

To see how AI handles these searches, I used Semrush’s AI Visibility Toolkit.

I analyzed the topic “jeans for women” using Semrush’s Prompt Research tool.

What’s revealing is the variety of queries under this topic.

Take “Plus size and curvy women’s jeans” for example.

Even within this niche, searches vary widely:

• “Best plus size jeans for big thighs”
• “Best curvy fit jeans”
• Most comfortable jeans for curvy women”

Across all these queries, the AI responses consistently emphasize the same details:

• High-rise styles
• Stretch denim
• Tummy control
• Specific silhouettes like bootcut

These details are pulled directly from product pages and customer reviews:

For AI to match products to these specific queries, it needs structured details on your pages.

This is something Abercrombie & Fitch does well.

They display clear fit guidance and aggregated customer fit feedback prominently on product pages.

Health and Wellness Products

Nothing is more important to health and wellness brands than trust and safety.

That’s why non-negotiables for product pages in this industry include:

• Full ingredient composition
• Clear dosage and instructions
• Contraindications and allergen warnings
• Source transparency
• Clinical studies or certifications

Searches for health products are often deeply personal and complex.

Many start with a product type and the demographic it’s best for.

For example, the topic of “infant multivitamins” includes these common searches:

• “Where can I buy reliable infant multivitamins?”
• “How do I choose the best multivitamin for my baby?”

In their responses, AI models pull from ingredient lists, dosage information, and certifications.

Brands that perform well for wellness-related AI queries follow the same pattern.

They provide detailed information about ingredients, sourcing, and production on their product pages.

This is what helps popular health company Thorne get recommended often in AI search results:

Their product pages list ingredients in detail:

They also include dosage instructions and verifications of the product quality.

All in a clear, machine-readable format.

Electronics

When it comes to electronics, AI loves to quote specs.

Battery life, screen resolution, charging speed, refresh rates, and more are all pulled into responses.

So every electronics product page should include the essentials:

• Full technical specs
• Compatibility information
• Setup or installation guides
• Safety and efficiency certifications

For example, even a simple search — “best cameras for night photography” — returns spec-heavy recommendations.

Structured specs give AI systems what they need to compare products.

This is important on your own site and third parties.

Brands like Sony excel here.

They ensure their product and retailer pages feature technical details that are consistent and in-depth across platforms.

Home and Furniture Brands

Furniture shopping comes with one big question: Will it fit?

AI knows this, which is why technical details dominate recommendations.

Your home and furniture product pages need:

• Clear dimensions and room size recommendations
• Assembly requirements (tools, time, difficulty)
• Materials and care details
• Quality and sustainability certifications

For example, in a search for modular sofas for small apartments, ChatGPT mentions configurations in its answer:

One of its top recommendations is a couch by home brand Burrow.

While many factors go into this, its product page is definitely one of them.

It features different configurations of their modular sofas. Plus, the dimensions of each.

It also contains other vital information that users might ask AI systems, such as detailed materials and fabric care.

Outdoor and Sports Equipment

Customers need to know whether your products will survive their outdoor adventures.

Which is why AI takes these elements into account:

• Weather ratings and technical materials
• Performance specs (capacity, weight, range)
• Use-case scenarios
• Safety certifications or features

Let’s say your customers ask about hiking backpacks. They’ll see AI models highlight key features, max load, and materials.

Osprey’s backpacks are regularly recommended by AI.

This is because they clearly state use cases like “week-long backpacking trips”:

They also include features that make it ideal for common use cases: materials, weight, volume, dimensions, and load range.

Baby Products

Baby products trigger some of the most safety-sensitive AI recommendations.

AI models look for structured, verifiable details when recommending anything for infants.

If you sell baby products, here’s what your product page should include:

• Age and weight suitability
• Safety certifications (like OEKO-TEX, GREENGUARD)
• Ergonomic or developmental benefits
• Material and care instructions

For example, BabyBjorn includes safety certifications on its product pages.

And goes deep into safety information.

This includes how the fabrics are developed, and the appropriate age and weight for safe use.

When I asked Perplexity for the safest baby carrier on the market for newborns, BabyBjorn was among its top recommendations.

It also specifically mentioned the “hip healthy” certification featured on BabyBjorn’s product page.

Increase Your Product Page Visibility in AI Search

If you want AI to recommend your products, the best place to start is your product pages.

Small improvements compound quickly.

Clear descriptions. Structured data. Real reviews. Verifiable trust signals.

Together, they shape how AI understands — and surfaces — your products.

But product pages are just the start.

First, download the Product Page AI Optimization Checklist. It tells you exactly what to review, update, and add to make your product pages AI-friendly.

Then, learn how to build an AI ecommerce SEO strategy that improves your visibility across the entire buyer journey.

AI visibility is possible for your products. Keep testing, keep tracking, and keep growing.

The post How to Optimize Your Product Pages for AI Visibility appeared first on Backlinko.

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Your audience searches across Google, ChatGPT, Reddit, YouTube, and many other channels.

Knowing who they are isn’t enough anymore. You need to know how they search.

Search-focused audience personas fill gaps that traditional personas miss.

Think insights like:

• Where this person actually goes for answers
• What triggers them to look for solutions right now
• Which proof points win their trust

And you don’t need months of research or expensive tools to build them.

An audience persona is a profile of who you’re creating for — what they need, how they search, and what makes them trust (or tune out). Done well, it aligns your team around a shared understanding of who you’re serving.

In this guide, I’ll walk you through nine strategic questions that dig deep into your persona’s search behavior. I’ve also included AI prompts to speed up your analysis.

They’ll help you spot patterns and synthesize findings without the manual work.

By the end, you’ll have a complete audience persona to guide your content strategy.

Free template: Download our audience persona template to document your insights. It includes a persona example for a fictional SaaS brand to guide you through the process.

1. Where Is Your Audience Asking Questions?

Answer this question to find out:

• Where you need to build authority and presence
• Which platforms to target for every persona
• Which formats work well for each persona

Knowing where your persona hangs out tells you which channels influence their decisions.

So, you can show up in places they already trust.

It also reveals how they think and what will resonate with them.

For example, someone posting on Reddit wants honest advice based on lived experiences. But someone searching on TikTok wants visual content like tutorials or unboxing videos.

How to Answer This Question

Start with an audience intelligence tool that lets you identify your persona’s preferred platforms and communities.

I’ll be using SparkToro.

Note: Throughout this guide, I’ll walk you through this persona-building process using the example of Podlinko, a fictional podcasting software. You’ll see every step of the research in action, so you can replicate it for your own business.

For this example, we’re building out one of Podlinko’s core personas: Marcus, a marketing professional on a one-person or small team team, so he’s scrappy and in-the-weeds.

Pro tip: Start with one primary persona and build it completely before adding others. Focus on your most valuable customer segment (the one driving the highest revenue for your business).

In SparkToro, enter a relevant keyword that describes your persona’s professional identity or core interests.

This could be their job title, industry, or a topic they care deeply about.

I went with “how to start a podcast.” Marcus would likely search for this early in his journey.

The report gives a pretty solid overview of Marcus’s online behavior.

For example, Google, ChatGPT, YouTube, and Facebook are his primary research channels.

But it could be worth testing a few other platforms too.

Compared to the average user, he’s 24.66% more likely to use X and 12.92% more likely to use TikTok.

The report also tells me the specific YouTube channels where he spends time.

He’s watching automation, editing, and business tutorials.

He’s also active in multiple industry-related Reddit communities.

Maybe he’s posting, commenting, or even just lurking to read advice.

Since Marcus uses ChatGPT, I also did a quick search on this platform to see which sources the platform frequently cites.

I searched for some prompts he might ask, like “Which podcast hosting platforms should I use for marketing?”

If you see large language models (LLMs) repeatedly mention the same sources, they likely carry authority for the topic.

And by extension, they influence your persona’s research as well.

Compare these sources to the ones you identified earlier. If they match, you have validation.

If they’re different, assess which ones to add to your persona document.

Here’s how I filled out the persona template with Marcus’s search behavior:

2. What Exact Questions Are They Asking?

Answer this question to find out:

• What language to mirror in your content
• How to structure content for AI visibility
• What content gaps exist in your market

Your buyer persona’s language rarely matches marketing jargon.

Companies might talk about “podcast production tools” and “integrated workflows.”

But personas use more personal and specific language:

• What’s the cheapest way to record remote podcasts?
• How long does it take to edit a 30-minute podcast?

Knowing your audience’s actual questions reveals the gap between how you describe your solution and how they experience the problem.

And shows you exactly how to bridge it.

How to Answer This Question

Start by going to the platforms and communities you identified in Question 1.

Search 3-5 topics related to your persona.

Review the context around headlines, posts, and comments:

• How they phrase questions (exact words matter)
• What emotions do they express
• What outcomes they’re trying to achieve

Pro tip: As you research, save persona comments, discussions, and reviews in full — not just snippets. You’ll analyze the same sources in Questions 3-5. But through different lenses (challenges, triggers, language patterns). Having everything saved means you won’t need to revisit platforms multiple times.

For example, I searched “how to start a podcast for a business” on Google.

Then, I checked People Also Ask for related questions Marcus might have:

On YouTube, I searched “how to edit a podcast” and reviewed video comments.

Users asked follow-up questions about mic issues and screen sharing.

This gave me insight into language and questions beyond the video’s main topic.

In Facebook Groups, I found users asking questions related to their goals, constraints, and challenges.

It also provided the unfiltered language Marcus uses when he’s stuck.

Now, use a keyword research tool to visualize how your persona’s questions connect throughout their journey.

I used AlsoAsked for this task. But AnswerThePublic and Semrush’s Topic Research tool would also work.

For Marcus, I searched “Best AI podcasting editing software,” which revealed this path:

Which AI tool is best for audio editing? → Can I use AI to edit audio? → Which software do professionals use for audio editing? → How much does AI audio editor cost?

It’s helpful to visualize how Marcus’s questions change as he progresses through his search.

Next, learn the questions your persona asks in AI search.

You’ll need a specialized tool like Semrush’s AI Visibility Toolkit for this task.

It tells you the exact prompts people use when searching topics related to your brand.

(And if your brand appears in the answers.)

If you don’t have a subscription, sign up for a free trial of Semrush One, which includes the AI Visibility Toolkit and Semrush Pro.

Since Podlinko is fictional, I used a real podcasting platform (Zencastr.com) for this example.

This brand appears often in AI answers for user questions like:

• What equipment do I need to create a professional podcast setup?
• Can you recommend popular tools for managing and promoting online radio or podcasts?

You’ll also see citation gaps — questions where your brand isn’t mentioned. These reveal content opportunities.

For this brand, one gap includes:

“Which AI tools are best for recording, editing, and distributing an AI-focused podcast?”

After reviewing all the questions I gathered, I narrowed them down to the top 5 for the template:

3. What Challenges Influence Their Search Behavior?

Answer this question to find out:

• What constraints influence their decision-making process
• How to anticipate objections before they arise
• What kind of solutions does your persona need

Challenges are the ongoing issues driving your persona’s search behavior. These overarching problems shape their decisions to find a solution.

Understanding these challenges can help you:

• Position your solution in the context of these pain points
• Anticipate and address objections before they come up
• Structure your campaigns to speak directly to their limitations

How to Answer This Question

Review the questions you collected in Question 2 to identify underlying pain points.

For example, this Facebook Group post contains some telling language for Marcus’s persona:

Specific phrases highlight ongoing challenges:

• “Tech support is no help”
• Can’t find an editing software that consistently works”

Now, visit industry-specific review platforms.

Check G2, Capterra, Trustpilot, Amazon, Yelp, or another site, depending on your niche.

Look for reviews where people describe recurring frustrations.

Positive reviews may mention what drove a user to seek a new solution. For example, this one references poor audio and video quality:

Negative reviews reveal what users constantly struggle with.

Unresolved pain points often push people to find workarounds or alternatives.

This user noted issues with a podcasting tool, including loss of backups, unreliable tech, and more.

Pay close attention to the language people use. Word choice can signal underlying feelings and constraints.

When someone asks for the “easiest” and “most cost-effective” solution, they’re signaling:

• Limited resources
• Low confidence
• Risk aversion

After reviewing conversations and communities, you’ll likely have dozens of data points.

Copy the reviews, questions, and phrases into an AI tool to identify your persona’s top challenges.

Use this prompt:

Based on these reviews and discussions, identify the five biggest challenges for this persona.

For each challenge, show:

(1) exact phrases they use to describe it

(2) what constraints make it harder (budget, time, skills)

(3) how it influences where and when they search.

Format as a table.

This analysis helped me identify Marcus’s recurring challenges:

4. What Triggers Them to Search Right Now?

Answer this question to find out:

• What emotional and situational context should you address in your content
• How to structure content for different urgency levels
• Which pain points to lead with

Search triggers explain why your audience is ready to take action.

But they’re not the same as challenges.

Challenges are ongoing constraints your persona faces. This could be a limited budget, small team, or skill gap.

Triggers are the specific events or goals that push them to act right now. Like a looming deadline or a competitor launching a podcast.

Understanding triggers helps you reach your persona when they’re most receptive.

How to Answer This Question

If you have access to internal data, start there.

Your sales and customer support teams can spot patterns that push prospects from browsing to buying.

For example, your sales conversations might reveal that one of Marcus’s triggers is urgency. His manager might ask him to improve the sound quality by the next episode, prompting his search.

If you don’t have internal intel, use tools like AnswerThePublic, AlsoAsked, or Semrush’s Keyword Magic Tool.

This will help you identify the language people use when they’re ready to act.

For Marcus, my AlsoAsked research led to questions like:

• “Can I record a podcast with just my phone?” This may suggest a desire to start immediately, without professional equipment.
• “How to make a podcast with someone far away” could suggest the trigger of a sudden need to work with a remote guest/host

You can also refer back to your research on community spaces.

(Or conduct additional audience research, if needed.)

These spaces are where people describe the exact moments they decide to take action. Aka plateaus, milestones, and failed attempts.

When I searched “podcast marketing” on Reddit, I found a post from someone experiencing clear triggers:

This user has been unable to get a consistent flow of organic listeners despite high-quality content.

Trigger: A growth plateau that pushed him to ask for help.

He’s also trying to hit his first 1,000 listeners.

Trigger: A goal that pushed him to look for solutions.

If you collected a lot of content, upload it to an AI tool to quickly identify triggers.

Use this prompt:

Analyze these community posts and discussions. Identify the specific trigger moments that pushed people to actively search for solutions.

For each trigger, show:

1. The exact moment or event described (quote the language they use)
2. The type of trigger (situational, temporal, emotional, or goal-driven)
3. What action did they take as a result

Format as a table.

After analyzing the content I gathered, I identified the key triggers pushing Marcus to search:

5. What Language Resonates (and What Turns Them Off)?

Answer this question to find out:

• Which messaging angles resonate
• What tones build trust with your audience
• Which phrases trigger objections or skepticism

The words you use can affect whether your persona trusts you or tunes out.

The right language makes people feel understood. The wrong language creates friction and drives them away.

When you know what resonates, you can create messaging that builds trust and motivates your personas to act.

How to Answer This Question

Refer back to your research from Questions 3 and 4.

This time, focus specifically on language patterns in reviews and community discussions.

Look at:

• Exact phrases people use to describe success, relief, or satisfaction
• Words highlighting frustration, disappointment, and concerns

For example, on Capterra, users praised podcasting platforms that “do a lot” and let them “distribute with ease.”

This language signals Marcus’s preference for all-in-one platforms.

He would likely connect with messaging that emphasizes functionality without complexity.

Next, review the content you previously gathered from community spaces.

In r/podcasting, users like Marcus write with direct, benefit-focused language:

Notice what he values: simplicity and concrete outcomes (“automatic transcripts”).

He’s not mentioning jargon like “AI-powered transcription engine” or “enterprise-grade recording infrastructure.”

Plain language that emphasizes quick results over technical capabilities works best with this persona.

Once you have enough data, use this LLM prompt to identify language patterns:

Analyze these customer reviews and community discussions I’ve shared. Identify:

1. Most common words and phrases people use to describe positive experiences
2. Most common words and phrases that signal frustration or concerns
3. Emotional undertones in how they describe problems and solutions

Create a table organizing these insights.

This analysis revealed the specific language that Marcus reacts to positively (and negatively).

6. What Content Types Do They Engage With Most?

Answer this question to find out:

• Content types to prioritize in your content strategy
• How to structure content for maximum engagement
• What length and style work best for each format

Knowing the content types your audience prefers has multiple benefits.

It lets you create content that captures your persona’s attention and keeps them engaged.

Think about it: You could write the most comprehensive guide on podcast equipment.

But if your ideal customer prefers video reviews, they’ll scroll right past it.

How to Answer This Question

You identified your persona’s most-used platforms in Question 1. Now analyze which content formats perform best on each.

Conduct a few Google Searches to identify popular content types.

You’ll learn what users (and search engines) prefer for specific queries. Look at videos, written guides, infographics, carousels, podcasts, and more.

For example, when I search “how to set up podcast equipment,” the top results are a mix: long-form articles, video tutorials, and community discussions.

But organic search rankings don’t tell the full story.

Analyze content directly on your persona’s preferred platforms, too.

I searched “How to distribute a podcast” on YouTube and assessed the top 20 videos and Shorts for:

• Video length
• Views
• Comments
• Engagement patterns

Look at the creators your persona follows on each platform. (From the SparkToro report in Question 1).

Pay attention to:

• Content types drive the most engagement (videos vs. carousels vs. threads)
• How these creators structure content (length, style, tone)
• Which topics resonate most with their audience

Once you’ve collected this data, look for patterns.

Or drop your data into an LLM and ask it to find the patterns for you:

Analyze this engagement data I’ve collected for my audience persona.

Identify:

1. Which video lengths perform best (views, comments, engagement rate) and why
2. Which content styles generate the most engagement (tutorials, vlogs, behind-the-scenes, etc.)
3. Any patterns in thumbnails, titles, or formats that consistently perform well

Summarize my persona’s content preferences by video type and rank them as low, medium, or high

For Marcus, I learned that 5- to 15-minute video tutorials generated the highest engagement.

Shorts consistently underperformed for how-to queries, showing his preference for in-depth tutorials.

I documented my findings and ranked each content type by engagement level: high, medium, or low.

7. What Proof Points and Signals Matter?

Answer this question to find out:

• What proof points influence buyers
• How to structure case studies and testimonials
• Where to place proof points to win people’s trust

Proof points can influence whether someone acts on your content or bounces.

They’re also a ranking factor.

Search engines and LLMs reward content that demonstrates Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T).

But different personas might value different proof points.

Understanding what matters to each persona is crucial to building trust and visibility.

How to Answer This Question

Identify the most common trust markers on your persona’s preferred sites.

Look for:

• Author credentials: Bylines with relevant expertise
• Methods: Transparency about the method for creating this content
• Citations: Links to studies, expert quotes, industry reports, original research
• Recency signals: Publication and last updated dates
• Visual proof: Screenshots, before/after comparisons, annotated walkthroughs
• Social validation: Comment sections, user discussions, engagement metrics

Use Semrush’s Keyword Overview tool to find this information.

Note: A free Semrush account gives you 10 searches in this tool per day. Or you can use this link to access a free Semrush One trial.

Enter your keyword (I used “how to start a podcast”).

Scroll to the SERP Analysis report to view the ranking domains.

Aim to review 20 to 50 pages for the best results. (Create a spreadsheet to organize the information.)

Identify which proof points they use and how prominently they’re displayed.

Here’s how I did this for one of the articles I assessed:

• Quantified track record: “Since 2009, Buzzsprout has helped over 400,000 podcasters”
• First-person experience: “I’ve drawn on lessons from my own podcasts and thousands of conversations with creators”
• Third-party sources: Expert advice cited from Apple Podcasts on naming conventions
• Visual demonstrations: Embedded tutorials showing recommendations in action

Then, use an LLM to quickly spot patterns:

I’ve analyzed top-ranking pages for my persona and uploaded my findings.

Identify:

1. Which proof points appear most frequently (e.g., “8 out of 10 pages include X”)
2. How these proof points are displayed (above the fold, in sidebar, throughout content)
3. Which combinations of proof points appear together most often

Format as a summary with the top 5 most common patterns.

Ultimately, you’ll want to infuse your content with these same trust markers to attract and convert your persona.

After identifying Marcus’s top proof points, I ranked them from medium to high in the template:

8. Where (and How) Should You Distribute Content to Reach This Persona?

Answer this question to find out:

• Which platforms deserve your investment
• What content formats work best on each platform
• How to maximize organic reach through distribution

Where you distribute content determines whether it reaches your audience.

If you only publish content on your website but buyers find solutions on LinkedIn, you’re overlooking key touchpoints.

Even worse, you’re invisible on major platforms that LLMs scan for answers, recommendations, and citations.

How to Answer This Question

By now, you know your audience persona’s top platforms.

These are your initial distribution targets.

But you’ll ideally be able to validate them against real behavioral data.

If possible, survey recent customers to find concrete patterns about their search behavior.

Send a short survey to customers who converted in the last 90 days:

• Where did you first hear about us?
• Where do you go for advice about [primary pain points]?
• What platforms do you use when researching [your product category]?
• How do you prefer to learn about new solutions in your workflow?

Once responses come in, look for patterns in how each segment discovers, researches, and evaluates solutions.

Here’s a prompt you can use in an AI tool for faster analysis:

I surveyed recent customers about their search and discovery behavior.

Analyze this data and identify:

1. The top 3-5 platforms where customers discovered us or researched solutions
2. Common pain points or information needs they mentioned
3. Preferred content formats for learning about solutions
4. Any patterns in how different customer segments discover and evaluate us

Highlight the platforms and channels that appear most frequently, and flag any gaps between where customers search and where we currently have a presence.

Next, cross-reference your research against existing data in Google Analytics.

Open Google Analytics and navigate to Reports > Lifecycle > Acquisition > Traffic acquisition.

Sort by engagement rate or average session duration to see which channels drive genuinely engaged visitors.

Look for high time on site (2+ minutes) and multiple pages per session (3+).

Then, map each platform to the content format that performs best there.

Combine insights from Question 1 (preferred platforms) and Question 6 (preferred formats) to build your distribution strategy.

Here’s what this looks like for Marcus:

9. What Keeps This Persona Coming Back?

Answer this question to find out:

• What product features or experiences to double down on
• How to position your solution beyond initial use cases
• What content to create for existing customers

Winning your audience’s attention once is easy. Earning it repeatedly is the real challenge.

Understanding what keeps your persona engaged is the key to getting them to return.

How to Answer This Question

Review all the audience persona insights you’ve gathered so far to identify recurring needs.

Look at triggers, pain points, content preferences, and community discussions.

Pinpoints problems that can’t be solved with a single article or resource.

This could include:

• Tasks they do every week (editing, distribution, promotion)
• Decisions they face with each piece of content (format, platform, messaging)
• Skills they’re continuously learning (new tools, changing algorithms)
• Friction points that slow them down every time

Then, outline the content types that repeatedly solve these problems.

Think tools, templates, checklists, and guides they’ll use repeatedly.

If you don’t want to do this manually, drop this prompt into an AI tool to synthesize your findings:

Based on my audience persona research, here’s what I’ve learned:

Questions they ask: [Paste top questions from Q2]

Challenges they face: [Paste challenges from Q3]

Triggers that push them to act: [Paste triggers from Q4]

Their preferred content types: [Paste formats from Q6]

Identify recurring problems they face repeatedly (not one-time issues).

For each recurring problem:

1. Describe the problem in their own words
2. Explain why it’s recurring (weekly task, ongoing decision, changing landscape, etc.)
3. Suggest 2-3 content types that would provide repeatable value each time they face this problem

Format as a table with columns: Problem | Why It’s Recurring | Content Solutions

For Marcus, this could look something like this:

Every time Marcus faces these challenges, he can turn to them for a reliable solution.

These are the content types that have repeatable value for him:

Build Audience Personas That Win AI Visibility

Forget surface-level demographics.

These nine audience persona questions give you actionable, in-depth search intelligence.

You now know a lot about your persona.

You’ve uncovered where they search, what language resonates, and which proof points earn trust.

This is everything you need to show up in the right places with the right message.

If you haven’t already, download our audience persona template to organize your research.

Use it to guide your content creation, search strategy, and distribution efforts.

Your next move: Expand your visibility further with our guide to ranking in AI search. Our Seen & Trusted Framework will help you increase mentions, citations, and recommendations for your brand.

The post How to Build Audience Personas for Modern Search + Template appeared first on Backlinko.

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It’s not the most robust or the most popular, either.

But it’s punching way above its weight, competing with much bigger companies (like Adobe, and CapCut) in LLM search.

Using Semrush’s AI Visibility score, you can see that Descript is competing closely with giant brands like Adobe.

Descript found the way in.

And so can you.

In this SaaS LLM visibility case study, we’ll break down exactly how Descript is getting seen.

And more importantly, what you can copy to improve visibility for your own product.

Choosing Clear Niche Messaging

For years, Descript has been known as a podcast editing tool.

That matters.

Because when people talk about podcast editing, Descript comes up naturally.

In blog posts.

In forums.

And now, in AI answers.

This isn’t accidental. Descript is clear about who it’s for, and their content reflects that focus.

Their product pages and blog posts consistently speak to one core audience: people who want to edit podcasts easily.

Here’s why this matters:

When I asked Google’s AI Mode for the best software to edit podcasts — specifically as someone with no video editing skills — Descript was one of the first tools mentioned.

And what shows up second in the list of sources?

One of Descript’s own blog posts about podcast editing.

Across Descript’s own website and other third-party sources, this tool is regularly mentioned as ideal for podcasters.

This matters because of a key difference between AI search and traditional SEO.

LLMs don’t just surface pages. They based their answers on query fan-outs.

Here’s what that means: AI creates multiple searches after the original query, and tries to find an answer that is most directly matched to what was asked.

That’s why even articles and websites that aren’t ranking well in Google can still get cited by AI when they provide the most relevant, specific answer to what users are asking.

Because Descript’s content is tightly focused on one audience, one use case, one problem, it maps cleanly to those AI queries.

That doesn’t necessarily correlate to higher ranking in traditional search. In fact, Descript’s traffic from traditional SEO has been steadily decreasing since its peak in 2024:

But at the same time, branded traffic has increased.

So even while the brand isn’t succeeding in traditional search, more people are becoming aware of Descript and searching for the brand name specifically.

Why? In part, because the brand is known for exactly what it does: podcast editing.

AI knows that too. And I would bet that a higher amount of mentions in AI search is helping with brand recognition and influencing that increase in branded search traffic.

Here’s the point: Descript isn’t just checking off boxes of what to talk about.

The way they write — and the way they present their product — shows exactly who they’re speaking to. They match the way their audience talks.

Take the blog article on podcast editing that we mentioned above as an example.

The copy flows naturally, includes quotes from an internal expert in the way she describes the problem and solution, and speaks in an easy way that matches the tone of the audience.

As a byproduct of this natural way of writing and clear product position, their copy and content semantically matches what their audience is searching for.

And their AI mentions keep increasing.

Action Item: Identify and Focus on Your Niche Market

Effort vs. Impact: Medium effort. High impact.

If you’re trying to be all things to everyone, AI is less likely to recommend you for anything specific.

Instead, narrow your focus like Descript does:

Of course, you also want to find balance.

For example, “Podcast editing software for true crime hosts who only record on Thursdays,” may be a bit too niche.

To get the narrowest viable version of your core audience, look at your most successful customers.

Ask:

• Who gets the most ROI from our product?
• Who uses it weekly — or daily?
• Which customers have become vocal advocates?
• What do those users have in common? (Role, company size, industry, workflow)

That overlap is your niche.

Once that’s clear, your messaging gets easier.

You stop being an “All-in-one AI-powered platform for creators and teams.”

And start anchoring your product to a specific job: “Edit podcasts and spoken audio, without technical complexity.”

Then, your product becomes easier for AI systems to understand — and recommend — for specific use cases.

Further reading: Learn how to do deep audience research, along with a free audience research tracker template.

Developing Seriously Helpful Content

Once you know who you’re talking to, the next step is obvious: Help them.

That idea isn’t new.

Helpful content has long been a ranking factor in traditional search.

And in 2024, Google confirmed that their algorithm changes had reduced the appearance of low-quality content in search results by 45%.

But Descript’s example (and plenty of others) shows how this also applies to AI search.

Because clear, useful, unique content also drives LLM visibility.

Descript doesn’t rely on shallow blog posts or surface-level explanations.

They create:

• Instructional blog content that answers real questions
• Help Center pages that actually solve problems
• Product pages that clearly explain what features do — and who they’re for

They also publish content that isn’t strictly about their product, but is highly relevant to their audience.

For example:

When I asked Google’s AI Mode how much YouTubers actually make, one of the cited sources was a Descript blog post on the topic.

That article includes:

• Data from recent studies
• Real-world examples
• A YouTube earnings calculator

It’s comprehensive. And it’s written from an expert perspective.

Here’s another example: When I asked how much it costs to start a YouTube channel, I was again directed to an article from Descript.

That page includes a detailed FAQ and embedded video content from Descript’s own YouTube channel.

The pattern is clear.

Depth gets cited. Surface-level content gets ignored.

Action Item: Focus Your Content on Being Helpful

Effort vs. Impact: High effort. Medium impact.

Once you’ve defined your niche, focus your content on what actually helps them.

Descript doesn’t target video editing professionals. So, they don’t show up in those searches.

They focus on content creators and podcasters. And their content reflects that.

To do the same:

• Talk to people in your niche industry
• Ask about their workflows, goals, and sticking points
• Learn what slows them down

Pro tip: If you can’t speak directly to people in your audience or customer base, talk to your customer-facing teams. Customer success and sales teams have daily contact with your core audience. So, they’re in a better position to give you insights into what this audience cares about.

Online research also helps.

Find relevant subreddits to see what people are talking about. Check the comments section of relevant YouTube videos.

Look for recurring questions and complaints.

For example, the Descript team might peruse the r/podcasting subreddit to learn about their audience’s questions and opinions.

The goal: understanding.

When you deeply understand your audience’s day-to-day reality, creating helpful content becomes much easier.

And your content can become the source for AI answers.

Of course, getting citations back to your website isn’t the same as getting direct brand mentions. However, it’s still an opportunity to build awareness and authority.

Plus, building content around relevant core topics helps reinforce your niche messaging.

Further reading: Read the full guide on how to create helpful content.

Showcasing Images and Videos of Their Product

LLMs don’t just read text anymore.

They interpret visuals too.

With image-processing models like contrastive language–image pre-training (CLIP,) AI systems can understand what’s happening inside screenshots and videos — not just the words around them.

And those visuals now show up directly in AI answers. Especially for SaaS product queries in tools like ChatGPT.

For example, when I search for “best CRM software for a small business,” the top AI result includes images of the actual product interface.

That’s a shift.

Highly polished mockups matter less. Real, in-product visuals matter more.

Which is why Descript shows up like this in ChatGPT:

Descript consistently shows real product images and videos across product pages, Help Center articles, and blog content.

These aren’t decorative.

They show:

• What the product looks like
• How features work
• What users should expect when they log in

As a result, those same images and videos get pulled into AI answers — often with a link back to Descript’s site.

In this case, the link goes back to a very in-depth Help Center guide to getting started with podcast editing.

And most Interestingly, that’s a near-perfect semantic match to the original query.

Action Item: Include In-Product Images in Your Marketing Content

Effort vs. Impact: Low effort. Medium impact.

Start with the basics.

For every feature you highlight, ask one question: Can someone see this working?

Then act on it. Add real screenshots of your core product screens to key product pages. Replace abstract diagrams with in-product visuals where possible.

Next, expand beyond product pages.

Mention a feature in a blog post? Include a screenshot of it in use.

Explaining a workflow in a Help Center article? Show each step visually.

Teaching a process? Record a short screen capture instead of relying on text alone.

The goal is clarity.

Clear visuals help users understand your product faster. And they give AI systems concrete material to reuse in answers.

Which makes your product easier to recommend — and easier to recognize — inside AI search.

Creating Detailed MoFu/BoFu Content

Content mapped to different awareness levels performs especially well in AI search.

Descript understands this.

They don’t just publish top-of-funnel guides. They create content for product-aware and solution-aware searches, too.

When you search in ChatGPT for video creation or editing tools, Descript often appears in the results.

But more importantly, their own content is cited as a source.

In this example, the cited source is a Descript-owned “best of” article comparing video tools.

Instead of generic recommendations, the page:

• Breaks tools down by specific use cases
• Includes clear pros and cons
• Explains who each option is best for

Descript follows this same pattern with multiple “best of” lists and comparison pages against their main competitors.

The payoff?

When I asked AI to compare podcast video editing tools, Descript appeared with clear labels explaining:

• Who it’s best for
• Key features
• When it makes sense to choose it

That context helps AI recommend Descript to the right people (not everyone).

Action Item: Create Citable MoFu and BoFu Content

Effort vs. Impact: High effort. High impact.

Different awareness levels need different content.

To increase product-level AI visibility, focus on Product Aware and Solution Aware queries.

For Product Aware audiences, create:

• Comparison pages
• “Best alternative” posts
• Owned “best of” lists

Want more ideas?

Talk to your sales team.

Ask them: What features are convincing people to buy? Which competitors are commonly brought up in sales conversations?

Those answers map directly to comparison content AI likes to cite.

For Solution Aware audiences, focus on how-to content that naturally features your product.

For example, when I asked Google’s AI Mode how to reduce background noise from a microphone, it referenced a Descript how-to article.

This same pattern repeats itself across many of Descript’s blog posts: Find a clear problem, give a clear solution, add product mentions naturally.

It’s all about finding the right questions to answer.

To find these opportunities faster, use Semrush’s AI Visibility Toolkit. This data is powered by Semrush’s AI prompt database and clickstream data, organized into meaningful topics.

Head to “Competitor Research” and review:

• Shared topics where competitors appear
• Prompts where they earn more AI visibility than you

Then, dig into the specific questions behind those prompts.

The goal isn’t simply “more content”.

It’s answering the right questions — at the right stage — with content AI can confidently cite.

Building Positive Sentiment With Digital PR and Affiliate Marketing

AI visibility isn’t earned on your website alone.

LLMs look for signals across the web.

This is what we call consensus. And it means that positive sentiment has to exist outside your owned channels.

Descript is doing this in two ways:

• Digital PR on sites AI already trusts
• A creator-friendly affiliate program that drives third-party mentions

Here’s how it works: Google’s AI Mode tends to favor certain websites to source when answering queries about software.

Semrush’s visibility research for AI in SaaS from December 2025 shows these sites dominate citations:

• Zapier
• PCMag
• Gartner
• LinkedIn
• G2

Here’s what’s interesting.

Descript is mentioned in articles across nearly all of these top sources.

For example, in software listicles like this one on Zapier:

Or in real-world experience articles like this one on Medium:

Or in their clear listings on reviews sites like Gartner and G2:

When AI systems cite those favored sources, Descript comes along for the ride.

Not because it’s the biggest brand.

But because it’s present where AI is already looking.

The second lever is Descript’s affiliate program.

It’s simple:

• $25 per new subscriber
• 30-day attribution window
• Monthly payouts
• No minimums

Those are solid incentives.

And they lead to more creator-driven content across the web.

For example, a YouTube walkthrough from VP Land explains how to use Descript and includes an affiliate link in the description.

When I later asked Google’s AI Mode how to use Descript, that exact video was cited as a source.

That’s the pattern.

Affiliate content creates citable, trusted references that AI systems reuse.

Action Item: Build a Strategy to Get More Mentions Online

Effort vs. Impact: High effort. High impact.

Getting third party mentions is all about building relationships.

First, build relationships with publishers, starting with the ones AI already trusts.

Even if you’re not an enterprise SaaS company with a full-sized PR team, this is still possible.

Granted, it’s not the easy route — but when you find the right websites and perform regular outreach to those teams, you can get your brand on these sites.

Before you start outreach, get your bearings.

Start by going back to Semrush’s AI Visibility Toolkit. Head to the “Competitor Research” tab and select “Sources.”

This shows you:

• Which sites LLMs cite for your category
• Where competitors are already getting mentioned
• Gaps where your brand doesn’t show up (yet)

Those sites become your shortlist.

Outreach works better when you’re aiming at sources AI already relies on.

Second, build relationships with creators.

Affiliate programs work when creators want to talk about you.

So, build an affiliate program people actually want to be part of.

This means the program has to be easy to join, with clear terms that make it worth their time.

At a minimum, make sure you have:

• A simple signup
• Transparent tracking
• Reliable payouts

Pro tip: Use a tool like PartnerStack to handle all of the details automatically. Better signups, better tracking, and automated payouts build trust with your affiliates.

If you need inspiration, research top affiliate programs to learn more about the conditions creators expect.

But most importantly: Treat affiliates as distribution partners, not just a side channel.

This means enabling them with clear positioning on your product, example use cases, demo workflows, screenshots they can reuse, and other resources.

The better you equip them, the stronger their recommendations will be.

Once you have this set up, track the results.

Use AI visibility data to see:

• Which publisher relationships are turning into citations in AI search
• Which creators show up in AI answers
• Which formats perform best

Then, double down.

Now that we’ve discussed what Descript is doing well, let’s look at where there’s room for improvement.

Where Descript Could Improve: Reddit Marketing

Descript is doing a great job in many areas that are important for AI search visibility.

That said, there’s one area they’re missing out on: Reddit.

And yes, Reddit matters. A lot.

It’s still one of the most-cited sources in Google’s AI Mode.

And in almost all of the searches I tested above, Reddit was cited as a source (especially conversations in the r/podcasting subreddit).

Here’s the problem: right now, Reddit is not doing Descript any favors.

Here are a few thread titles I found just by searching for Descript in a podcasting subreddit:

And yes, there are positive mentions of Descript. But they’re buried under a wave of negative sentiment.

When LLMs scan Reddit for sentiment, that unbalance matters.

AI wants to see consensus. So when Reddit skews negative, recommendations may weaken, and alternatives get surfaced instead.

Even when the product is strong.

That’s why, while Descript’s AI visibility is good, it’s still not as good as it could be. And that vulnerability could hurt them in the long run, even if they’re still doing everything else right.

Here are some ways that Descript (and you) could turn the tides on Reddit:

• Avoid promoting and start participating: Reddit punishes marketing language. Helpful, honest comments perform better than posts.
• Respond to criticism directly (when appropriate): Not defensively, but with clear explanations and fixes
• Be present before there’s a problem: Accounts that only show up during damage control don’t build trust
• Focus on comments, not posts: High-value comments in active threads outperform standalone branded posts
• Monitor brand mention weekly: Focus especially on high-intent subreddits. In Descript’s case, that could be r/podcasting.

To be fair, it seems like Descript is taking steps in the right direction.

As of December 2025, the Descript team has taken control of a dedicated brand subreddit, with PMM Gabe at the helm.

And the team’s responses feel very Reddit-friendly, not using marketing jargon or being pushy.

But popular threads here still have very little interaction with the Descript team. And there seems to be very few (if any) comments from the Descript team outside of this branded subreddit.

It’s a step in the right direction, but there’s still a lot to work on.

Done right, Reddit becomes a sentiment stabilizer and a stronger input source for AI answers.

Ignore it, and Reddit can become a liability.

Remember: for AI visibility, silence isn’t neutral.

Further reading: If Reddit feels like a whole other world, we’ve got you covered. Read our full guide to Reddit Marketing.

What You Can Take Away from This SaaS LLM Visibility Case Study

Descript isn’t winning AI visibility because it’s the biggest brand.

It’s winning because it’s clear, focused, and consistently helpful.

None of that is accidental.

And none of it requires massive scale.

You can get started on this today by choosing one key action to work on.

Use the effort vs. impact lens from this article to choose where to start.

• Add in-product screenshots and videos: Low effort, medium impact
• Tighten your niche messaging: Medium effort, high impact
• Build citable MoFu/BoFu content: High effort, medium impact
• Invest in digital PR, affiliates, and community participation: High effort, high impact
• Create seriously helpful content: High effort, high impact

Pick one, start there. AI search visibility tools for SaaS companies — like Semrush’s AI Visibility Toolkit — can help you see exactly where you stand today, and where you can improve.

Remember: LLM visibility isn’t about chasing algorithms.

It’s about making your product easier to understand, easier to trust, and easier to recommend.

Do that consistently — and AI search will follow.

Want to learn how it all works on a deeper level? Read our LLM visibility guide to discover even more ways to increase your brand mentions and citations in AI search.

The post How a 200-Person Company Competes with a $160B Giant in AI Search appeared first on Backlinko.

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But most rarely get the results they’re looking for.

According to Ascend2’s State of Marketing Automation Report, only 28% of marketers say their automation “very successfully” supports their objectives.

While 69% felt it was only somewhat successful.

While this specific stat is from 2024, I imagine the broad idea is still true. Especially since there are so many more automation options and tools. It can get overwhelming to decide a go-forward plan and implement effectively.

So if you feel stuck in the camp of “not bad, but not great” marketing automation, you’re not alone.

The good news?

Once you understand the core building blocks, you can turn messy, half-automated systems into workflows that actually move the needle.

A good marketing automation usually involves four basic steps:

• A trigger: A catalyst event that starts the automation
• An action: One or more steps that happen in sequence after the trigger
• An output: The end result
• A loop or exit point: A new trigger, or an event that stops the automation

In this article, we’re going to discuss how to use these steps to automate:

• The mechanics of content creation (and no, we won’t just be telling you to “write it with AI”)
• Beyond the basics of email nurtures
• Your PR strategy
• Social media engagement

Automate the Mechanics of Content Creation

Content marketers are creative people. We don’t want to automate away the creative work that drives results.

That said, we can automate marketing workflows that come before and after creating. (So we can spend more time on high-impact work.)

Here are some simple ways to get started.

1. Basic Brief Builder

Tools required:

• Make (free for 1,000 credits per month, paid plans start at $9/month)
• Your favorite keyword research tool (plans vary)
• Project management platform (tools like Asana offer a free plan)
• Google Sheets, Google Docs (free plan available)

Every week, content marketers around the world spend hours researching keywords, pulling search data, creating new briefs, and adding tasks to their project management systems.

What if you could do most of that with one automation?

Here are the basics of how this works:

• Trigger: A new row is added to a Google Sheet (your new keyword)
• Action: That keyword is run through your SEO tool, which pulls keyword difficulty, search volume, related terms, and top organic results
• Output: A new Google Doc with the data inside, and a new task in your project management tool

In the end, the automation will look like this:

And if this seems scary, don’t worry: I’m going to walk you through each step to create this with Make. (Or, you can go ahead and copy this Scenario into your own Make account here.)

First, you’ll need a Google Sheet for your source.

Start with columns for your new keyword, status, brief URL, and task URL. To get started faster, copy this template here.

Next, add Google Sheets as the trigger step, and select “Watch New Rows.”

After that, select the Google Sheet you want to watch.

This runs the automation every time you add a new keyword to that sheet.

Now, it’s time to gather information from your SEO tool. For this example, we’re going to use Semrush. (You could also use an API like DataForSEO.)

Our first Semrush module will be “Get Keyword Overview.” (You might see different options depending on the specific tool you use.)

You can choose whether to see the keyword data in all regional databases, or just one region.

In this task, you’ll map the “Phrase” to the “Keyword” column from your Google Sheet. Then, choose what you want to get as an output. (In this case, I only want to see the search volume.)

Now, let’s create another Semrush model to “Get Related Keywords” to gather relevant keywords from Semrush.

Again, you’ll map the “Phrase” to the keyword column from our Google Sheet, and choose what data you want to export. (I chose the keyword and search volume.)

You can also decide:

• How the results are sorted
• Whether to add filters
• How many results to retrieve

Now, you’ll need to add a text aggregator into your workflow. This tool compiles the results from Semrush so we can use them in a Google Doc later on.

Here, simply map the source (our Semrush module).

Then, in the “Text” field, map the data as you want it to appear.

Next, we’ll create a Semrush module that runs “Get Keyword Difficulty.”

Again, we’ll map the “Phrase” to our keyword from the Google Sheet, and choose to export the “Keyword Difficulty Index.”

Next, run the “Get Organic Results” module from Semrush to export the sites that are ranking for your new target keyword.

Select the “Export Columns,” or the data that you want to see, and limit the number of results you get (we chose 10).

Since we’re getting multiple results, this module will also need a text aggregator to transform those results into plain text for our Google Doc.

We’ll set it up exactly the same way, but this time map the “Get Organic Results” module.

In the “Text” field, I’ve added “Bundle order position” (where that result is ranking in the SERP), and the URL of the ranking page.

Now, for the fun part.

It’s time to build your basic content brief in a Google doc.

Before you add this into Make, you’ll need to create a Google Doc as a template. This template should have variables that can be mapped to the results you get in your automation.

To show up as variables, you’ll need to wrap them in curly brackets. So, your template will look something like this:

• Primary Keyword = {{keyword}}
• Keyword Difficulty = {{difficulty}}
• Related Keywords = {{related_keyword}}
• Competing URLs = {{organic_result}}

(Want to save some time? Copy this template here.)

Now, you’ll create a new module in your Make scenario to “Create a Document from a Template.”

Once you connect the Google Doc template you created, you’ll see all of the variables you added in curly brackets as fields in the configuration page.

Now, all you have to do is map those variables to the results you’ve gotten from Semrush and your text aggregators.

Now it’s time to add this new brief into your project management tool. Make lets you connect several tools, including Asana, Trello, Monday, and Notion.

In this scenario, I already have an Asana project for content production.

So I choose the “Create a Task or a Subtask” module for Asana, and map that existing project.

I can also add project custom fields (like a link to the brief in Google Docs), choose the task name (like the keyword), and automatically assign it to someone on my team.

Lastly, I want to go back and update my original Google Sheet so that I can see which keywords have already been run, and where their briefs and tasks live.

So, I add Google Sheets again as the final step in the automation and connect the same spreadsheet that we had at the beginning. Under “Values,” I can map the brief URL from Google Docs and the new task URL from Asana to columns in my spreadsheet.

I also set this so the “Status” column is updated to “Done.”

Now, let’s run this scenario and see what happens.

First, I add a new keyword to my Google Sheet.

This triggers the automation to run.

The first thing that’s produced is a brand new Google Doc with all of the SEO data from Semrush. You’ll see this new doc appear in your Drive, and you’ll find the link in Asana.

Next, I’ll see a new task appear in my Asana project (with the brief link included).

And finally, the Google sheet will be updated to show us that the task has been completed.

Plus, it adds in the links to the new brief in Google Docs and the new task in Asana.

And there you go: you now have a basic content brief builder automation.

Are these complete briefs? No. But the information provides a great start, gives the writer SERP context, and frees up more time to fill out other important content brief elements.

Resources for this automation: To get started faster, use these templates:

• New Content Ideas (Google Sheets Template)
• New Content Brief (Google Doc Template)
• Brief Builder Automation (Make Scenario Template)

2. Content Workflow

Tools required: Your favorite project management tool (paid or free options available)

Project management tools are great for organizing your content workflow.

But the more tasks you create over time, the harder it is to keep track of and manage those systems.

Many project management platforms give you built-in automation tools to help things run more smoothly. Let’s talk about automations that can help your content workflow specifically.

Triggers might include:

• A new task is added to a project
• A custom field changes
• A new assignee is added
• A subtask is completed
• Due date is changed (or coming up soon)
• A task is overdue

And actions could be:

• Add to a new project
• Auto-assign to a team member
• Update a status
• Move task to a new section
• Create a subtask
• Add a comment

For this example, we’re going to use the Rules system in Asana, but the same basic principles apply to almost any major project management tool.

To start, click the “Customize” button in the upper-right corner of your content management project, and create some custom fields.

Especially important here is the “Status” field. The options here should follow the steps in your content process, and will probably mirror the sections in your Project.

Once your “Sections” and “Fields” are set up, you can create some rules.

These can help dictate what happens when a new brief enters your content workflow and assign it to whoever is in charge of moving it forward in the process.

Use a Rule to auto-assign someone on your team (for example, your content manager or editor) to the task.

Now, let’s say a new article is now in progress with a writer.

Create a rule that moves the task to the corresponding section of your project when the status is set to “Writing.”

If your content tasks have subtasks (like “create outline,” “write article,” “edit,” or “design”), you can track completion and use that to move pieces forward.

In this case, you can set a rule that once all subtasks are complete, the task moves to the “Ready to Publish” section.

Once the task moves to that section, set a rule to auto-assign it to the team member who publishes posts.

Then, when the status is set to “Published,” the task could be moved into a separate project where completed tasks of published content are stored.

This allows you to clear the tasks from your main production workflow, but still keep them on hand in case the piece needs to be updated in the future.

What if a piece of content isn’t completed by its deadline?

Set up an automation that checks in with the team to see what the status is.

There are plenty of other automations you can run in Asana or other tools.

But these basic workflow automations will help your content production process have better handoffs and less friction.

We do this at Backlinko using Monday.com as our project management tool.

Read more about how we scale content creation here.

Go Beyond Basic Email Nurtures

Email nurtures are relatively easy to put together in any basic email tool: for example, sending a welcome email to a new newsletter subscriber, or a transactional email to a new customer.

But let’s talk about some ways to take those automations even further.

Email marketing automation involves:

• A trigger: Such as someone signing up for an email list
• An action: The new contact is added to a list or segment
• An output: They new receive a series of pre-made emails
• An exit condition: The sequence finishes once all the emails are sent, or once the contact takes a specific action, like buying a product

Exit conditions are especially important, because you don’t want people to receive another email from you after they’ve already completed an action. (Hello, promo email that arrives after I already made a purchase.)

Let’s walk through how to use marketing automation tools for email.

3. Behavior-Based Nurtures and Follow-Ups

Tools required: ActiveCampaign (paid plans start at $15/month, although other email platforms offer automation capabilities too)

When you trigger an email sequence based on real behavior, you’re catching people in the moment when they’re more likely to engage.

For example, if you want to help a new user get to know your platform, you can trigger onboarding emails based on the actions they’ve taken so far.

Or, if you want to reduce cart abandonment, you can send a special promotion for customers who have items in their cart.

This improved targeting can lead to better engagement from your email list.

All you have to do is match the right trigger to the right action. For example:

Your exit condition could be when the person:

• Completes their purchase
• Books a call
• Starts a free trial
• Replies to your email

For example, let’s say you want to send a series of emails reminding someone that their subscription is reaching its end date. It could look something like this:

• Trigger: End date is within 20 days from now
• Action: Send series of three emails up to the last day of their subscription (we don’t want to send too many)
• Exit condition: Customer responds to the email, or renews their subscription

Here’s a great example for home insurance renewal:

​​Or, let’s say a new lead just signed up for a free trial or freemium account.

You could create a workflow that pulls information from the onboarding survey in your tool, and builds a personalized, 1:1 email sequence.

Check out this example from HubSpot:

When I signed up for the account, I identified myself as a self-employed marketer. HubSpot pulled that information into this new trial campaign to make the email even more personalized.

So the question is: how do you get started?

Here’s a quick overview of how you could build a behavior-based email nurture automation in ActiveCampaign.

Let’s say you want to send an email sequence to a known contact who visited a certain page on your website. For example, imagine someone who subscribes to your email newsletter, but isn’t a customer, just visited your pricing page. (In other words, they may be close to signing up — they just aren’t quite convinced yet.)

Before you start this automation, you’ll need to enable Site Tracking on your account in ActiveCampaign. To do this, install the tracking code on your website so ActiveCampaign can see page views.

To start the automation, you’ll add new contacts who enter through any pipeline.

Now, when a known contact (someone who’s already in your database) visits a tracked page, ActiveCampaign associates that page view with the contact’s record, and can start an automation.

The real trigger is the next step: “Wait until conditions are met.”

In this case, the condition is that the contact has visited an exact URL on your website.

Pro tip: You can also adjust this so the email series only runs when the person visits a page multiple times, showing a higher level of interest.

Next, set a waiting period from the time the person sees the page to when the email is sent.

And finally, write your email and add it to the workflow.

After that, you could:

• Wait a certain amount of time, then send another email
• Set an exit condition if the contact replies or makes a purchase

All of this effort turns into an email like this one that I received from Brooks after visiting one of their product pages:

This makes me way more likely to revisit the shoes I was looking at than a generic reminder email (or no email at all).

4. Webinar Lifecycle Automation

Tools required:

• Demio (plans start at $45/month)
• HubSpot (limited free plan available)

Webinars are an entire customer journey, including promotion, confirmation, reminders, and post-event follow-ups.

The trigger is normally one event: Someone signed up for your webinar.

The actions include:

• Confirmation email
• Day before and day-of reminders
• “Happening now” email
• Post-event replay email

For example, here’s a great reminder email from Kiwi Wealth:

Immediately after the webinar is finished, you might send an email like this one from Beefree:

And you’ll also want to follow up later with a replay and some action items for people who attended, like this:

Note: We got these examples from Really Good Emails, which is a great resource for getting inspiration for your own campaigns.

So, how do you create this automation?

Most great webinar tools allow you to do this. Demio, for example, allows you to automate marketing emails when you create a new event:

​​

If you want to get really fancy, you can segment your post-webinar follow-up emails by whether or not the contact attended the webinar:

Demio’s built-in email is somewhat limited beyond an actual event.

So, you can connect it to HubSpot to add a new layer of segmentation to your lists.

Once this connection is live, Demio will import webinar attendance data into HubSpot.

For example, you can import data like:

• Contacts who registered for the webinar
• People who registered, but missed the event
• People who attended the event
• How long a contact stayed in the webinar
• People who watched the replay

You can even add new contacts to lists directly in Hubspot if they don’t exist there already.

This automation will help your pre- and post-webinar flows run more smoothly. And hopefully get you more valuable engagement with those webinars.

Grow Your PR Strategy

For small marketing teams, PR outreach can use up a lot of valuable time.

Here are some easy automations to keep doing inbound and outbound PR requests, without spending your entire week on it.

Resource: Get your free PR Plan Template to help you pick the right goals, discover journalists, and make pitches that get press coverage.

5. PR Radar

Tools required:

• BrandMentions (paid plans start at $79/month)
• Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
• Google Sheets (free option available)

Want to keep an eye on new articles that are related to your brand that you could potentially get featured in or a backlink from? Let’s build an automatic PR radar.

Note: Most monitoring tools send alerts, but those notifications disappear into your inbox. This workflow creates a shared, searchable log your whole team can access without extra logins—plus you’ll have a historical record for spotting PR trends over time.

This workflow looks like:

• Trigger: A new article mentions your brand or related topics
• Action: Pull all new mentions into one place to scan through them easily
• Output: A simple, regularly-updated list of PR mentions

There are several tools that do this, but for this example, we’re going to use BrandMentions.

Once you set up your account and your project, head into settings to adjust which sources you’ll collect data from.

Remove social media, and just leave the web option. That way, you’ll get a clean list of articles and webpages that mention your brand or the keywords you added.

Once this is set up, you can connect your BrandMentions project to Zapier.

This will trigger the automation to start when any new mentions are added.

You can choose whatever output works best for you: whether that’s a Slack message, a new row in Airtable, or an addition to an ongoing Google Sheet.

For this example, I chose Google Sheets as my output. All I had to do was tie the data pulled from BrandMentions to the right columns in my spreadsheet.

Once that’s done, the automation adds new articles like this automatically into my spreadsheet:

Pro tip: Want to add a reminder? You can add another step that sends a daily Slack message summarizing all the newly added rows.

6. Media Request Matchmaker

Tools required:

• RSS.app (free plan available)
• Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
• Airtable (free plan available)

PR would be nothing without the relationships we build with journalists and writers.

But it’s hard to know who’s writing about a topic that’s related to your brand. Or where your company’s internal subject matter experts can add their thoughts to promote your brand.

So, let’s build an automation to match new requests to your internal experts.

This involves:

• Trigger: A new media request that matches relevant topics
• Action: Classify new requests and match them to the internal expert with the most relevant expertise
• Output: New requests are automatically routed to the right person

One of the most frequently updated places to find PR requests is on X/Twitter.

Search the hashtag #journorequest, and you’ll see hundreds of writers asking for expert contributions.

To prepare this for your automation, start by setting up an RSS feed with the hashtag #journorequest or #prrequest along with a relevant keyword.

You can do this for free with RSS.app.

Then, you’ll get results like this:

For the simplest version of this, you can connect RSS.app directly to Slack and send a new message every time a new request is added to the feed.

But let’s be real: that could get overwhelming pretty quickly.

So, we’ll use Zapier for a more in-depth automation.

Start by adding “RSS by Zapier” as the trigger, and paste your RSS feed link into the configuration.

Pro tip: If you want to track journo requests for multiple topics, change the trigger event to “New Items in Multiple Feeds.” Then, simply paste in all of the RSS feed links. That way, they’ll all run through the same automation.

Next use “Formatter by Zapier” to extract the necessary information from the tweets.

First, in Formatter, choose the Action event “Text.”

Then, in the Configure menu, select “Extract Email Address,” and map the input to the description from your RSS feed.

Next, with another Formatter step, select “Text,” and “Extract Pattern.”

The input is still the same description (the original tweet).

In the Pattern box, in parentheses, add the keywords you want to track separated by a vertical bar, like this:

(cybersecurity|fintech|pets|saas)

Make sure that IGNORECASE is set to “Yes” so that the search isn’t case sensitive.

Now, it’s time to add that to a system you can use to keep track of new requests and route them to SMEs.

For this example, I’ve chosen to use Airtable. If you want to use this exact database, you can copy it here and we’ll use it as we move forward.

This database has tabs to keep track of your SMEs, the topics they can respond to, and the new requests that come in.

So, let’s connect that Airtable base to Zapier.

Our first step will be to find the right SME for the topic of our journo request.

To start, set the Action as “Find Record,” and link your Airtable base. We’ll pull from the SMEs table, and for “Search by Field” we’ll choose “Topics,” where we’ve previously added our SME’s favorite topics into the Airtable base.

Lastly for this step, map the “Search Value” to the previous step’s result (the topic from the PR query on X/Twitter).

Now, we’re going to create a new row in our “Requests” table in Airtable.

Add Airtable as the next step in this Zap, and select “Create Record” as the action. Link the same Airtable base, but this time select “Requests” as the Table.

Then, map the columns in that base to the information you’ve gathered. In this case, that would include:

• Source = X/Twitter
• Raw Text = The “Description” from RSS feed
• Contact name = The “Raw Creator” from RSS feed
• Contact Email = The output from our first Formatter step, which pulled the email from the original post
• URL = Link from RSS feed
• Topics = The output from our second Formatter step, which pulled the topic from the original post
• SMEs = The “Fields Name” from our Airtable search step
• Status = New

In the end, it should look like this:

And a new record is added into Airtable, like this:

If you want to get fancy with this, you can dig down into:

• Which publications are requesting expertise, and rank them by their credibility
• Automate messages to your SMEs to let them know there’s a new request for them

Get the Most Out of Social Media

For busy marketers, social media can be an incredible time-suck.

Keeping track of trends. Trying to post consistently.

All without getting stuck in an infinite doomscroll.

But a few simple automations can help you get back some of the time you spend on manually managing your socials.

7. Video Clip Automator

Tools required:

• Zoom (free plan available)
• Dropbox (free plan available)
• OpusClip (plans start at $15/month)
• Zapier (free for 100 tasks/month, paid plans start at $19.99/month)

Short-form video has been gradually gaining a bigger voice in marketing.

In fact, 39% of marketers said that videos under 60 seconds are the most effective.

The problem: they take time to make.

If you’re already creating long-form video (or even just doing recorded interviews with in-house experts), we have a handy automation to help you create video clips faster.

Here’s how it works:

• Trigger: New Zoom cloud recording is ready
• Action: Auto-create clips, burn captions, and create a new task in Asana
• Output: You get social-ready video clips, and a new task to publish them

First, adjust your Zoom settings so your recordings upload automatically into a folder in Dropbox.

Next, head over to Zapier.

Your trigger step will be a new video uploaded to that folder in Dropbox.

Your next step will use OpusClip, an AI video editing tool. Select “Clip Your Video,” and map that new video file to the one uploaded in Dropbox.

OpusClip will then take your long-form video from Dropbox and use AI to clip key pieces. It also crops the video for vertical sharing and embeds captions.

You can also add your own brand template so that videos are edited with your brand’s colors and font.

Now that you have new video clips to share, it’s time to add a task to review and publish them.

So the final step in your Zap is “Create Task” in Asana (or your preferred project management tool).

You’ll tie this to a project you’ve already created in Asana, and link the project ID from OpusClip.

In the end, you’ll have a few video clips prepared and ready — all you have to do is download, review, and publish them to your social channels.

8. Comment & Community Nudge

Tools required:

• Social media monitoring tool (like BrandMentions, paid plans start at $79/month)
• Automation tool (like Zapier, free for 100 tasks/month, paid plans start at $19.99/month)

Are people talking about your brand online?

To keep positive sentiment high, you need to engage in those conversations. But finding the right conversations, and knowing how to reply, can take a lot of time.

Using a tool like BrandMentions, you can create a similar automation to what we built for the PR Radar earlier:

• Trigger: A new mention of your brand appears on Reddit, Facebook, or LinkedIn
• Action: Those new mentions are added to a Google Sheet, and you get a daily Slack message summarizing new mentions

To build this, all you’d need to do is swap out the Sources in your BrandMentions settings. Instead of Web, you’d include all of the social media channels you want to track.

After that, you can build an automation with Zapier, the same way we did in the PR Strategy automation above.

If you want to get notifications for every new mention, you could connect the workflow to Slack. Then, a new message will be sent in the channel every time your brand is mentioned.

This basic automation could work for smaller brands.

But when you start getting hundreds of mentions per day, this will quickly become chaotic.

Here’s an example of how one company faced with this issue was able to automate this process in a deeper way:

Webflow was getting over 500 mentions per day. Their two-person team couldn’t keep up with monitoring and responding (alongside their regular workload).

So, they built an automation.

With Gumloop, they monitor, analyze, and flag only the posts that require a response.

They started with a Reddit scraper to pull relevant threads.

Then, they added an AI analyzer to gauge sentiment, rank priority, and assign a category.

After that, they added a step that would send all high-priority mentions to Slack for a team member to handle directly.

The result?

After testing and scaling this process, they were able to build an automation that processes 500+ mentions per day and escalates only the 10-15 that need immediate attention.

If you’ve ever thought, “How can I use AI to automate my marketing tasks?”

This is a great example of an AI automation that works for you without taking over your job.

Is Automation the Right Move? Ask Yourself These Questions First

Automation is the hottest trend.

But it’s hard to know what’s going to save you time and money, and what’s just another fad.

If you’ve ever spent more time trying to automate a task than it would’ve taken you to do the task manually, you’ll know what I mean.

To weigh up whether an automation is worth building, ask yourself these questions:

• How much time does it take me to do this task manually every week?
• Is the automation available with a tool I currently use, or would I have to pay for a new tool?
• Is there a documented automation/integration I can follow?
• Would this task still require human intervention (even with automation)?
• Does this fit easily into our current workflow or process?

If the task:

• Doesn’t take much time to do manually
• Would still require human intervention even when automated
• Isn’t easy to build an automation for

…it may not be worth your time.

On the other hand, if the task:

• Is repetitive
• Uses up hours of your workweek
• Can be automated in tools you already have in your stack

…it’s probably time to give automation a try.

Build Your Automation Foundations, Then Keep Growing

The hype cycle of automation and AI can be overwhelming.

But don’t feel like you’re behind just because you haven’t automated away your entire marketing team yet.

Instead, focus on the automations that save you time and are sustainable.

We’ve just discussed eight different automations. Why not choose one or two that are most relevant to your business and team?

Start with the foundational automations that help smooth out your existing processes.

Then, you’ll have a better basis for building more complex automations.

To automate even more areas of your marketing workflows, check out our curated list of our favorite AI marketing tools right now.

The post How to Automate Marketing With 8 Simple Workflows appeared first on Backlinko.

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AI tools offer a new way to search for factual information, where Perplexity stands out as an AI-native search engine that combines large language models with real-time web search.

With a valuation of $20 billion and a growing user base of 30 million monthly active users, Perplexity is one of the fastest-growing tech startups challenging Google’s dominance with its AI-native search engine.

From the number of Perplexity active users to company revenue, we’ll cover the latest stats about the popular AI search engine on this page.

Key Perplexity Stats

• Perplexity has 30 million monthly active users.
• Perplexity processes around 600 million search queries a month.
• Lifetime downloads of Perplexity mobile apps reached 80.5 million to date.
• Perplexity’s annualized recurring revenue reportedly reached nearly $200 million.

Perplexity Monthly Active Users

According to the latest data, Perplexity AI has around 30 million monthly active users worldwide as of April 2025.

That’s up from 10 million monthly active users reported in January 2024.

Here’s a table with the Perplexity AI’s monthly active user base since March 2023:

Sources: The Verge, Perplexity AI, Perplexity AI

Perplexity Search Volume

According to Perplexity AI CEO, the search engine processes around 600 million queries per month as of April 2025. That’s an increase from 400 million reported in October 2024.

Here’s an overview of Perplexity AI monthly search volume over time since X:

Sources: The Verge, TechCrunch

Perplexity Website Traffic

According to the latest estimates, the Perplexity AI website received 239.7 million visits worldwide in November 2025, showing a 13.21% decrease compared to October 2025.

Here’s a website traffic breakdown of the Perplexity AI website since September 2025:

Source: Semrush

Perplexity App Downloads

According to recent estimates, Perplexity AI app downloads across Google Play and App Store reached an estimated lifetime downloads of 80.5 million to date, including 5.1 million in November 2025 alone.

Perplexity AI had the highest number of app downloads in October 2025, with 15.5 million monthly installs worldwide.

Here’s a table with Perplexity AI app downloads over time since January 2024:

Source: Appfigures

Perplexity Revenue

Perplexity’s annual recurring revenue reportedly reached nearly $200 million as of September 2025, up from $100 million in March 2025.

Sources: TechCrunch, Perplexity

Perplexity Funding

Perplexity raised a total of $1.22 billion across 7 publicly disclosed funding rounds to date.

Here’s a table with information on Perplexity AI’s latest funding rounds to date:

Source: Tracxn

The post Perplexity AI User and Revenue Statistics appeared first on Backlinko.

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Some use the tools daily. Others avoid them. A few test them quietly on the side.

This inconsistency creates a problem.

An MIT study found that 95% of AI pilots fail to show measurable ROI.

Scattered marketing AI adoption doesn’t translate to proven time savings, higher output, or revenue growth.

AI usage ≠ AI adoption ≠ effective AI adoption.

To get real results, your whole team needs to use AI systematically with clear guidelines and documented outcomes.

But getting there requires removing common roadblocks.

In this guide, I’ll explain seven marketing AI adoption challenges and how to overcome them. By the end, you’ll know how to successfully roll out AI across your team.

Free roadmap: I created a companion AI adoption roadmap with step-by-step tasks and timeframes to help you execute your pilot. Download it now.

First up: One of the biggest barriers to AI adoption — lack of clarity on when and how to use it.

1. No Clear AI Use Cases to Guide Your Team

Companies often mandate AI usage but provide limited guidance on which tasks it should handle.

In my experience, this is one of the most common AI adoption challenges teams face. Regardless of industry or company size.

Vague directives like “use AI more” leave people guessing.

The solution is to connect tasks to tools so everyone knows exactly how AI fits into their workflow.

The Fix: Map Team Member Tasks to Your Tech Stack

Start by gathering your marketing team for a working session.

Ask everyone to write down the tasks they perform daily or weekly. (Not job descriptions, but actual tasks they repeat regularly.)

Then look for patterns.

Which tasks are repetitive and time-consuming?

Maybe your content team realizes they spend four hours each week manually tracking competitor content to identify gaps and opportunities. That’s a clear AI use case.

Or your analytics lead notices they are wasting half a day consolidating campaign performance data from multiple regions into a single report.

AI tools can automatically pull and format that data.

Once your team has identified use cases, match each task to the appropriate tool.

After your workshop, create assignments for each person based on what they identified in the session.

For example: “Automate competitor tracking with [specific tool].”

When your team knows exactly what to do, adoption becomes easier.

Further reading: What Is Generative AI and How Does It Work?

2. No Structured Plan to Roll Out AI Across the Organization

If you give AI tools to everyone at once, don’t be surprised if you get low adoption in return.

The issue isn’t your team or the technology. It’s launching without testing first.

The Fix: Start with a Pilot Program

A pilot program is a small-scale test where one team uses AI tools. You learn what works, fix problems, and prove value — before rolling it out to everyone else.

A company-wide launch doesn’t give you this learning period.

Everyone struggles with the same issues at once. And nobody knows if the problem is the tool, their approach, or both.

Which means you end up wasting months (and money) before realizing what went wrong.

Plan to run your pilot for 8-12 weeks.

Note: Your pilot timeline will vary by team.

Small teams can move fast and test in 4-8 weeks. Larger teams might need 3-4 months to gather enough feedback.

Start with three months as your baseline. Then adjust based on how quickly your team adapts.

Content, email, or social teams work best because they produce repetitive outputs that show AI’s immediate value.

Select 3-30 participants from this department, depending on your team size.

(Smaller teams might pilot with 3-5 people. Larger organizations can test with 20-30.)

Then, set measurable goals with clear targets you can track. Like:

• Cut blog production time from 8 hours to 5 hours
• Reduce email draft revisions from 3 rounds to 1
• Create 50 social media posts weekly instead of 20

Schedule weekly meetings to gather feedback throughout the pilot.

The pilot will produce department-specific workflows. But you’ll also discover what transfers: which training methods work, where people struggle, and what governance rules you need.

When you expand to other departments, they’ll adapt these frameworks to their own AI tasks.

After three months, you’ll have proven results and trained users who can teach the next group.

At that point, expand the pilot to your second department (or next batch of the same team).

They’ll learn from the first group’s mistakes and scale faster because you’ve already solved common problems.

Pro tip: Keep refining throughout the pilot.

• Update prompts when they produce poor results
• Add new tools when you find workflow gaps
• Remove friction points the moment they appear

Your third batch will move even quicker.

Within a year, you’ll have organization-wide marketing AI adoption with measurable results.

Further reading: 8 Awesome AI SEO Tools We Love Using

3. Your Team Lacks the Training to Use AI Confidently

Most marketing teams roll out AI tools without training team members how to use them.

In fact, only 39% of people who use AI at work have received any training from their company.

And when training does exist, it might focus on generic AI concepts rather than specific job applications.

The answer is better training that connects to the work your team does.

The Fix: Role-Specific Training

Generic training explains how AI works. Role-specific training shows people how to use AI in their actual jobs.

Here’s the difference:

When training connects directly to someone’s daily tasks, they actually use what they learn.

For example, Mastercard applies this approach with three types of training:

• Foundational knowledge for everyone
• Job-specific applications for different roles
• Reskilling programs where needed.

Companies like KPMG, Accenture, and IKEA have also developed dedicated AI training programs for their teams.

This is likely because they learned that generic training creates enterprise AI adoption challenges at scale.

Employees complete courses but never apply what they learned to their actual work.

But you don’t need enterprise-scale resources to make this work.

Start by mapping what each role actually does with AI.

For example:

• Your content team uses AI for research, strategy, outlines, and drafts
• Your ABM team uses it for account research and personalized outreach
• Your social team uses it for video creation and caption variations
• Your marketing ops team uses it for workflow automation and data integration

Once you know what each role needs, pick your training approach.

Platforms like Coursera and LinkedIn Learning offer specific AI training programs that work well for flexible, self-paced learning.

Training may also be available from your existing tools.

Check whether your current marketing platforms offer AI training resources, such as courses or documentation.

For example, Semrush Academy offers various training programs that also cover its AI capabilities.

For teams with highly specific workflows, external trainers can be useful.

This costs more. But it delivers the most relevant results because the trainer focuses only on what your team actually needs to learn.

For example, companies like Section offer AI adoption programs for enterprises, including coaching and custom workshops.

But keep in mind that training alone won’t sustain marketing AI adoption.

AI tools evolve constantly, and your team needs continuous support to adapt.

Create these support systems:

• Set up a dedicated Slack channel for AI questions where your team can share wins and troubleshoot problems
• Run weekly Q&A sessions where people discuss specific challenges
• Update training materials as new features and use cases emerge

Pro tip: How to Build an AI-Ready SEO Team: A Complete Guide

4. Team Members Fear AI Will Replace Their Roles

Employees may resist AI marketing adoption because they fear losing their jobs to automation.

Headlines about AI replacing workers don’t help.

Your goal is to address these fears directly rather than dismissing them.

The Fix: Have Honest Conversations About Job Security

Meet with each team member and walk through how AI affects their workflow.

Point out which repetitive tasks AI will automate. Then explain what they’ll work on with that freed-up time.

Be careful about the language you use. Be empathetic and reassuring.

For example, don’t say “AI makes you more strategic.”

Say: “AI will pull performance reports automatically. You’ll analyze the insights, identify opportunities, and make strategic decisions on budget allocation.”

One is vague. The other shows them exactly how their role evolves.

Don’t just spring changes on your team. Give them a clear timeline.

Explain when AI tools will roll out, when training starts, and when you expect them to start using the new workflows.

For example: “We’re implementing AI for competitor tracking in Q2. Training happens in March. By April, this becomes part of your weekly process.”

When people know what’s coming and when, they have time to prepare instead of panicking.

Pro tip: Let people choose which AI features align with their interests and work style.

Some team members might gravitate toward AI for content creation. Others prefer using it for data analysis or reporting.

When people have autonomy over which features they adopt first, resistance decreases. They’re exploring tools that genuinely interest them rather than following mandates.

5. Your Team Resists AI-Driven Workflow Changes

People resist AI when it disrupts their established workflows.

Your team has spent years perfecting their processes. AI represents change, even when the benefits are obvious.

Resistance gets stronger when organizations mandate AI usage without considering how people actually work.

New platforms can be especially intimidating.

It means new logins, new interfaces, and completely new workflows to learn.

Rather than forcing everyone to change their workflows at once, let a few team members test the new approach first using familiar tools.

The Fix: Start with AI Features in Existing Tools

Your team likely already uses HubSpot, Google Ads, Adobe, or similar platforms daily.

When you use AI within existing tools, your team learns new capabilities without learning an entirely new system.

If you’re running a pilot program, designate 2-3 participants as AI champions.

Their role goes beyond testing — they actively share what they’re learning with the broader team.

The AI champions should be naturally curious about new tools and respected by their colleagues (not just the most senior people).

Have them share what they discover in a team Slack channel or during standups:

• Specific tasks that are now faster or easier
• What surprised them (good or bad)
• Tips or advice on how others can use the tool effectively

When others see real examples, such as “I used Social Content AI to create 10 LinkedIn posts in 20 minutes instead of 2 hours,” it carries more weight than reassurance from leadership.

For example, if your team already uses a tool like Semrush, your champions can demonstrate how its AI features improve their workflows.

Keyword Magic Tool’s AI-powered Personal Keyword Difficulty (PKD%) score shows which keywords your site can realistically rank for — without requiring any manual research or analysis.

AI Article Generator creates SEO-friendly drafts from keywords.

Your content writers can input a topic, set their brand voice, and get a structured first draft in minutes. This reduces the time spent staring at a blank page.

Social Content AI handles the repetitive parts of social media planning. It generates post ideas, copy variations, and images.

Your social team can quickly build out a week’s content calendar instead of creating each post from scratch.

Don’t have a Semrush subscription? Sign up now and get a 14-day free trial + get a special 17% discount on annual plan.

6. No Governance or Guardrails to Keep AI Usage Safe

Without clear guidelines, your team may either avoid AI entirely or use it in ways that create risk.

In fact, 57% of enterprise employees input confidential data into AI tools.

They paste customer data into ChatGPT without realizing it violates data policies.

Or publish AI-generated content without approval because the review process was never explained.

Your team needs clear guidelines on what’s allowed, what’s not, and who approves what.

Free AI policy template: Need help creating your company’s AI policy? Download our free AI Marketing Usage Policy template. Customize it with your team’s tools and workflows, and you’re ready to go.

The Fix: Create a One-Page AI Usage Policy

When creating your policy, keep it simple and accessible. Don’t create a 20-page document nobody will read.

Aim for 1-2 pages that are straightforward and easy to follow.

Include four key areas to keep AI usage both safe and productive.

Beyond documenting the rules, establish who team members should contact when they encounter situations the policy doesn’t address.

Designate a department lead, governance contact, or weekly office hours as the escalation point for:

• Scenarios not covered in your guidelines
• Technical site issues with approved AI tools
• Concerns about whether AI-generated content is accurate or appropriate
• Questions about data sharing

The goal is to give them a clear path to get help, rather than guessing or avoiding AI altogether.

Then, post the policy where your team will see it.

This might be your Slack workspace, project management tool, or a pinned document in your shared drive.

And treat it as a living document.

When the same question comes up multiple times, add the answer to your policy.

For example, if three people ask, “Can I use AI to write email subject lines?” update your policy to explicitly say yes (and clarify who reviews them before sending).

7. No Reliable Way to Measure AI’s Impact or ROI

Without clear proof that AI improves their results, team members may assume it’s just extra work and return to old methods.

And if leadership can’t see a measurable impact, they might question the investment.

This puts your entire AI program at risk.

Avoid this by establishing the right metrics before implementing AI.

The Fix: Track Business Metrics (Not Just Efficiency)

Here’s how to measure AI’s business impact properly.

Pick 2-3 metrics your leadership already reviews in reports or meetings.

These are typically:

• Leads generated
• Conversion rate
• Revenue growth
• Customer acquisition
• Customer retention

These numbers demonstrate to your team and leadership that AI is helping your business.

Then, establish your baseline by recording your current numbers. (Do this before implementing AI tools.)

For example, if you’re tracking leads and conversion rate, write down:

• Current monthly leads: 200
• Current conversion rate: 3%

This baseline lets you show your team (and leadership) exactly what changed after implementing AI.

Pro tip: Avoid making multiple changes simultaneously during your pilot or initial rollout.

If you implement AI while also switching platforms or restructuring your team, you won’t know which change drove results.

Keep other variables stable so you can clearly attribute improvements to AI.