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AI search is reshaping how ecommerce brands get discovered.
One week, your products show up in ChatGPT. The next week, they’re replaced by competitors.
For many brands, this uncertainty can feel overwhelming.
Organic visibility now depends less on rankings and keywords, and more on how LLMs gather information, which platforms they rely on, and what signals help them highlight your brand.
In this guide, I’ll explain this crucial shift in detail.
I’ll unpack:
What actually shapes visibility inside AI answers
The business impact of compressed buyer journeys and broken attribution
How you can build lasting relevance in this new search ecosystem
The 3 Types of AI Visibility for Ecommerce Brands
If you’re familiar with SEO, getting AI visibility is similar. It starts with how search systems decide what to display.
But for years, ecommerce SEO was a linear equation: rank = visibility = traffic (and then conversions).
AI search is changing that.
LLMs summarize, compare, and recommend products, all in one place.
In short: Shoppers can discover your products, check alternatives, and make buying decisions within AI chats.
In this new setup, brands compete across three different discovery models.
Type 1: Brand Mentions
Mentions drive product discovery and build top-of-funnel LLM visibility for your brand.
This is where your brand gets featured in AI-generated answers, often without a link to your site.
Mentions often come from reputation signals like:
Reddit posts
Media coverage
User reviews
Social discussions
Put simply, you become part of the conversation.
For new or emerging brands, this is often the first touchpoint to reach shoppers through AI.
Type 2: Citations
Citations are linked references within AI-generated results, like a footnote in an essay.
With citations, LLMs attribute specific information, claims, or data points to your pages.
Your brand becomes a source of truth in AI responses and gains credibility.
How?
When an AI tool cites your brand, it signals to shoppers that you’re an authoritative voice.
Plus, citations can support your positioning. The AI tools can pull your framing and product narrative into their response. Not someone else’s.
Type 3: Product Recommendations
AI platforms actively recommend products for a shopper’s specific needs and concerns.
This is the most impactful layer for ecommerce brands.
Your products can show up with pricing, ratings, and other details.
This type of visibility effectively merges discovery and purchase in one place.
This happens when the LLM reviews the query, compares options, and picks your product as the best fit.
Showing up in the list of recommended products makes your brand a part of the decision interface.
Shoppers can compare specs, prices, and reviews — or even purchase — right in the AI chatbot or search tool itself.
How AI Models Choose Which Ecommerce Brands to Surface
AI visibility as a discipline is still evolving rapidly. But there are clear patterns to which ecommerce brands get seen and which get sidelined.
Two driving forces at play are: consensus and consistency.
Consensus
With traditional search, ecommerce brands could build domain authority through activities like link building and digital PR. Strong pages from an authority perspective tended to perform well in search results.
In AI search, LLMs don’t evaluate your website and product pages in isolation. Authority is built from a consensus across sources.
LLMs ask: “What do credible sources agree on about this product?”
To decide which brands and products deserve visibility, LLMs cross-reference multiple sources, like:
Reddit threads
YouTube videos
Industry reports
Customer reviews
Trusted publishers
Community discussions
So, a glowing review on your PDP might mean little if customers on Amazon consistently leave 1-star ratings.
And a publisher’s feature loses impact if Reddit users repeatedly recommend your competitors instead.
In other words: No single source determines your likelihood of being mentioned or cited. It’s the pattern of consensus across multiple platforms that does this.
For example:
Keychron frequently shows up when you use AI search tools to find mechanical keyboards.
This happens because the brand has earned trust through various sources:
Review sites like PCMag and Tom’s Guide rank Keychron in their top recommendations
Keychron’s Amazon pages are detailed with positive reviews and an average rating of 4.4 stars
Multiple Reddit threads in subreddits like r/MechanicalKeyboards and r/macbook recommend the brand
Several YouTube videos feature Keychron in their roundup of mechanical keyboards
Each trust signal on its own is valuable.
But when taken together, LLMs see a pattern of independent sources validating the same brand/product for a specific use case.
Consistency
LLMs don’t crawl and rank pages the way traditional search engines do.
Instead, when answering a product-related query, an AI model might pull:
Your product name from your Shopify store
Pricing from Google Merchant Center
Key specs from Amazon
Opinions from users on Reddit
If your product title is “stainless steel” on Amazon but “brushed metal” on Walmart, the LLM can’t decide which is correct. This inconsistency could make the AI tool less likely to include any information about your product. Or it could include the wrong information.
This is why data hygiene is crucial for building AI visibility.
You need to maintain a clean, synchronized identity for every product across every channel.
Your product attributes should follow the same pattern across your site, marketplaces, and feeds:
Model numbers
Dimensions
Materials
Weights
Prices
LLMs use these data points to match your products to queries and validate claims across sources.
Your Amazon listing, your Shopify store, your Google Merchant feed — all sources need to tell the same story with the same data.
So, the same SKU name, image, and product description should appear everywhere your product appears.
Finally, outdated data signals decay, and models may deprioritize products with outdated info.
When you change a price or update a key spec, that change should be visible everywhere. Stock availability, pricing, and features should always be up to date.
Types of Content That Dominate Ecommerce AI Search
We’re seeing clear patterns in what gets cited, mentioned, or ignored in AI search for ecommerce.
Understanding these patterns can be the difference between hoping you show up and knowing how to position your brand so that you do show up.
Here’s what’s currently doing well in AI search for ecommerce:
Top Cited Sources
I wanted to see which brands are cited most frequently in LLM responses for ecommerce queries — so I tested it.
I picked nine popular ecommerce niches and searched category-specific queries across ChatGPT, Claude, Perplexity, and AI Mode.
Based on the responses, I made a list of five popular brands showing up frequently for each vertical.
Then, I jumped to the “Competitor Research” tab in Semrush’s AI Visibility Toolkit to run a gap analysis for these five brands in each category.
The “Sources” tab showed which domains LLMs cite most frequently, like this for the “outdoor travel & gear” niche:
This data reveals where LLMs pull product information, and which platforms matter most in your vertical.
Here’s what this data tells you:
Reddit: Reddit is a top-cited source for nearly every industry. If people aren’t discussing your brand in relevant subreddits, invest in Reddit marketing.
YouTube: It’s another universal citation source. Video content from creators and users feeds into AI answers. That means having a YouTube presence can be a huge visibility lever for most ecommerce verticals.
Category-specific platforms: Generic sources like Amazon appear everywhere. But niche platforms (like Petco, Barbend, Sephora) carry weight in their verticals.
Wikipedia: It’s a top source for categories like outdoor gear, healthy drinks, and gadgets. This is where product context and category education matter a lot alongside the likes of specs and pricing.
Going beyond these top-cited platforms, here are the kinds of content LLMs link to most frequently for ecommerce queries:
Publisher Listicles
These are product roundups, buying guides, and comparison posts from established media outlets.
For example, I asked ChatGPT for the best Bluetooth speaker recommendations.
It cites publishers like TechRadar, Rtings.com, and Stereo Guide for this response.
Getting featured in these listicles means you’re part of the source material LLMs use to compile information.
AI models use publisher listicles as sources because they:
Compare multiple products in one place
Refresh their recommendations periodically, providing recency signals
Include specific, comparable details like price ranges, key specs, and pros/cons lists
Fulfill high editorial standards and so may appear more trustworthy than user-generated content
Retailer Product Pages
Retailers like Amazon, Walmart, and Target are among the most frequently cited sources for product queries.
When I asked Perplexity about the NutriBullet Turbo, it cited the product pages from the likes of Walmart and Macy’s.
These PDPs provide structured data points like ratings, pricing, and key specs.
AI models often rely on these product pages because they:
Include structured, machine-readable product data like specs, dimensions, materials, and pricing
Aggregate hundreds or thousands of customer reviews as social proof
Show real-time availability and pricing
Lab Tests and Expert Reviews
In-depth product testing content from experts is another important source for citations.
These websites test products systematically and publish detailed findings.
LLMs can then use this empirical data as the basis for their responses.
For example, I asked Claude to find the best mattress for side sleepers.
The tool references sites like NapLab, Consumer Reports, and Sleep Foundation for data-backed recommendations.
AI models consider lab test or expert review content for citations because they:
Compare products against consistent criteria and benchmarks
Show credibility with independent, systematic evaluation processes
Include measurable data to explain their top-ranked recommendations
Periodically update their recommendations to offer fresh, authoritative data
Reddit Threads and Community Discussions
Conversations on Reddit, Facebook groups, and YouTube comments frequently appear in AI responses.
This is especially true for subjective queries like “Is X worth it?” or “What do people actually think about Y?”
I tested this myself by asking Perplexity whether the Instant Pot Duo is worth buying.
It pulled insights from multiple Reddit threads, a Facebook group, and a YouTube video to respond based on real user input.
Brands that get mentioned positively across multiple Reddit threads build “cultural proof.”
And those organic discussions about your brand feed directly into AI training data and real-time search results.
AI models pull from these communities because they:
Present an aggregated sentiment from community discussions
Contain contrasting opinions and insights to objectively review products
Show different use cases and pain points that a product can tackle
Highlight a product’s pros and cons based on firsthand experience
Comparison Posts
Content that compares two or more products can also help LLMs find the right brands to mention in their response.
When I ask AI Mode for alternatives to the supplement brand Athletic Greens, it mentions five options.
The sources include several comparison articles (alongside some roundups).
Being included in this type of content (even if you’re not the winner) can help build your visibility.
This could be Brand A vs. Brand B blog posts, YouTube videos, review sites, and social media discussions.
AI models refer to these resources because they:
Answer buyers’ questions by comparing two or more products
Focus on decision-making criteria and help people make informed decisions
Let’s now consider the business impact of this AI search setup for your ecommerce brand.
The Compressed Buyer Journey
The traditional ecommerce funnel was built on multiple touchpoints.
A shopper might:
Google a product category
Read reviews on multiple different sites
Check Reddit and YouTube
Visit brand websites to compare prices
Return days later to buy
Each step was an opportunity for your brand to show up, make an impression, and win their trust.
For a lot of purchase decisions, AI search collapses this entire journey into a single interaction.
The same shoppers can now go to AI tools and ask, “What’s the best air fryer for a small kitchen?”
They get a single response with buying criteria, product recommendations, pricing, ratings, and more.
Now, clearly this isn’t going to happen for every purchase decision. These tools are still new for one thing, and it takes a lot to majorly shift buyer behavior. (And of course, SEO is not dead.)
But discovery, evaluation, and consideration CAN all happen in one response now. The AI agent performs the research labor.
That means you have fewer chances to influence buyers.
In the past, if a shopper didn’t discover you in organic search, they might find you through a review site, a Reddit thread, or a retargeting ad.
In other words: You could lose the first touchpoint and still win the sale three touchpoints later.
With AI search, you might only get one shot: the initial response.
For many ecommerce queries, AI tools give you a curated list of options. If you’re not in that initial answer, you don’t exist in the decision process.
Take action: Build an AI search strategy using our Seen & Trusted Brand Framework to increase the probability of your brand getting featured in AI responses.
The Visibility Paradox
Your brand might frequently show up in AI search. But your analytics show flat traffic and zero conversions traced back to AI tools.
Here’s why:
Not all AI visibility is created equal.
Your brand can appear in 10 different AI responses and drive 10 completely different business outcomes.
It all depends on how you’re presented.
Here’s what the visibility spectrum actually looks like for ecommerce brands:
Visibility Type
Example
Business Outcome
Mentioned without context
“Popular air fryer brands include Ninja, Cosori, Instant Pot, and Philips.”
Value: Brand awareness Purchase Likelihood: Low
Mentioned with attributes
“Cosori is known for its large capacity and intuitive controls.”
“The Cosori 5.8-quart model includes 11 presets, uses 85% less oil than deep frying, fits a 3-pound chicken, and costs around $120.”
Value: Active consideration and purchase Purchase Likelihood: High
That means getting mentioned is table stakes, not the end goal.
Building brand awareness without differentiation just makes you a part of the crowd.
To drive real sales, you need to earn citations and product recommendations.
The brands winning in AI search are:
Cited as trustworthy sources
Recommended for specific use cases
Attribution Gets Murky
When shoppers find products through AI but buy elsewhere, analytics tools can’t track the whole journey.
This creates two problems:
You can’t prove the ROI of AI search: Even if AI mentions are driving consideration, you’ll get zero or limited data on that. You won’t see the prompt the user asked or the response from the tool.
You can’t optimize what you can’t measure: When you don’t know how people are discovering you in AI answers, you can’t A/B test your way to better visibility. The feedback loop is broken.
Tools like Semrush’s AI SEO Toolkit are closing this gap by showing how your brand and competitors appear in AI search.
I used the tool to check the AI visibility and search performance for Vuori, an athleisure brand.
The brand has a score of 76 against the industry average of 82, and is frequently mentioned AND cited in AI responses.
The toolkit also identifies specific prompts where your brand is mentioned or missing.
This makes it easy to spot exactly which type of queries are driving visibility and which represent missed opportunities.
For example, here’s a list of prompts where LLMs don’t feature Vuori, but do mention its competitors.
Go to the “Cited Sources” tab to find out the websites that LLMs most commonly refer to for your industry-related queries.
For Vuori, it’s sites like Reddit, Men’s Health, Forbes, and more.
The “Source Opportunities” tab will give you a list of key sites that mention your competitors, but not you. These are sites you should aim to get your brand included on.
Besides tracking your own AI visibility, the AI SEO Toolkit also lets you monitor your competitors’ performance on AI platforms.
The “Competitor Research” report compares you to your biggest competitors in terms of overall AI visibility.
It also highlights topics and prompts where other brands are featured, but you aren’t.
Example of a Brand That’s Winning in AI Search: Caraway
If you want to see what winning in AI search actually looks like, look at the cookware brand, Caraway.
When you ask AI about the “best bakeware set” or the “best ceramic pans,” Caraway almost always makes the shortlist.
Data from Semrush’s AI SEO Toolkit shows that Caraway also outweighs its biggest competitors in AI visibility.
Let’s break down how Caraway built this advantage.
Showing Up Where LLMs Look
Caraway is frequently featured on publishers like Taste of Home, Good Housekeeping, and Food and Wine.
These are the actual sources LLMs cite when constructing answers about cookware-related queries.
For example, here’s a paragraph from the Food and Wine article ChatGPT cited as a source, which mentions the attributes ChatGPT used in its recommendation:
Caraway also earns mentions through organic discussions on Reddit, Quora, and kitchen forums.
Retailer Evidence That AI Can Cite
Caraway’s clean Amazon Brand Store and on-site product pages also make it easily citable.
These product listings and pages give LLMs concrete signals like:
Multiple in-stock SKUs with visible sales velocity (“500+ bought in the past month”)
Product rating and volume
Rich media files
These retailer PDPs become credible sources for verifying pricing, availability, or product specs.
Strong Affiliate Presence
Caraway also runs an affiliate program, and the brand makes it frictionless for publishers to feature its products through:
Affiliate networks: Links are available through major networks like Skimlinks and Sovrn/Commerce
Amazon compatibility: Editors can also use Amazon Associates links for Caraway’s stocked SKUs
Reviewer support: The brand provides an affiliate kit, including link types, banner ads, text links, and email copy
This all makes it easy for Caraway to work with influencers and other publishers to promote its products. And these publishers can then appear as citations when AI tools make their recommendations.
For example, all the highlighted sources in the ChatGPT conversation below contain Caraway affiliate links:
Part of the Category Narrative
Many style media and mainstream outlets reference Caraway in their content.
Here’s a recent example from an Architectural Digest interview featuring the cookware set as an essential kitchen item.
This creates more authority for the brand in the cookware and kitchen category.
Make AI Work for Your Ecommerce Brand
You now know how the game works and who’s winning. It’s your turn to play it.
But there’s a lot to do.
Making your site readable by LLMs, opmtimizing your structured data, and setting up automated product feeds are just stratching the surface.
Our comprehensive Ecommerce AIO Guide gives you alll of the actionable tactics to consistently show up in AI results.
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SEO never stands still, and neither do we here at Yoast. In our November 2025 edition of the SEO Update by Yoast, our principal SEOs, Carolyn Shelby and Alex Moss, broke down the latest shifts in search, structured data, and AI. Whether you’re running an e-commerce store, managing a content-heavy site, or just keeping up with Google’s ever-changing rules, this edition highlights what actually matters.
Google updates
Google is refining its search results, phasing out certain structured data features, including FAQ snippets and COVID-19 updates. But that doesn’t mean you should strip structured data from your site. It still plays a role behind the scenes, especially for AI retrieval, and could make a comeback later.
For online stores, the message is clearer than ever: product schema is non-negotiable. Search Engine Journal’s Matt Southern explains that Google’s new AI shopping tools, such as agent-based checkout and side-by-side comparisons, require that your product data be complete, consistent, and easily visible. That means no hiding key details behind tabs or toggles. If it’s not easily crawlable, Google’s AI won’t use it.
Search Console updates
Search Console got a few useful upgrades this month. Query Groups now clusters search terms by topic instead of individual keywords, making it easier to spot content gaps and adjust your strategy. Brand Query Filters help distinguish between branded and non-branded searches, which is handy for tracking misspellings or seasonal trends.
Custom Annotations, previously only available in GA4, now allow you to log site changes directly in Search Console. This is great for connecting updates to performance shifts. E-commerce sites also get a small win with shipping and return details, which can now be added without a Merchant Center account. It’s still rolling out, so test it carefully to avoid missteps.
Google and AI
AI continues to reshape search, and Google’s AI Overviews play a significant role in this transformation. Search Engine Roundtable’s Barry Schwartz’s story on Robby Stein from Google emphasizes that these overviews draw from clear, structured content, such as headings, lists, and direct summaries. Word counts don’t matter as much as clarity and extractability.
The downside? According to Danny Goodwin, in Search Engine Land, AI Overviews have slashed organic click-through rates by 61% and paid CTR by 68%. The takeaway isn’t to chase clicks but to optimize for visibility in AI answers. If your content is easy to extract and cite, you’re in a better position.
Beyond Google
Beyond Google, ChatGPT’s new SDK enables developers to build apps within the platform, which could be particularly useful for larger companies seeking to streamline AI integrations. Meanwhile, Adobe’s acquisition of Semrush might push the tool toward enterprise users, so smaller teams should watch for pricing changes.
The next SEO Update by Yoast is scheduled for December 15, 2025, at 4:00 PM CET. Until then, the focus remains on structured data, clear content, and adapting to AI-driven search. For e-commerce sites, this means ensuring that product data is accurate and up-to-date. For content creators, it’s about writing for extractability. And for everyone? Keeping an eye on Search Console’s new tools to stay ahead.
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If you’re serious about visibility in search, you need to start using schema markup. This structured data tells search engines exactly what your content means, not just what it says, so they can display richer, more accurate results.
Schema isn’t just about getting a fancy result in Google’s SERPs anymore. It also increases your chances of being cited in AI-generated summaries Search engines are moving toward generative results, and structured data is now a key signal of authority and clarity.
Key Takeaways
Schema markup is a type of structured data that helps search engines understand the meaning behind your content, not just the text itself.
Sites using SEO schema markup often see improved click-through rates. Users get more context directly in the results, which drives more clicks.
Generative AI search tools now use structured data, which makes schema markup even more valuable for visibility.
Many websites still don’t fully implement schema, so using it correctly gives you an advantage over less-optimized competitors.
What is Schema Markup?
Schema markup is a form of structured data that tells search engines what your content means, not just what it says. It uses a standardized vocabulary from schema.org to label specific pieces of information, like an article’s author, a product’s price, or a recipe’s cooking time.
Here’s an example of the end result of some schema in action, showcasing added details for a recipe:
When you add schema to your HTML, it doesn’t change how your page looks to users, but it helps search engines interpret your content more accurately. That’s how you get things like star ratings, event dates, or FAQ dropdowns in SERPs.
Schema improves categorization by giving structure to information that would otherwise be unstructured or ambiguous. That extra clarity supports more precise indexing and increases your chances of appearing with rich results.
Most content online is considered unstructured data, which means it’s readable by humans but harder for machines to interpret. Schema adds structure that makes meaning explicit, bridging the gap between your content and how search engines understand it.
Types of Schema Markup
There are dozens of schema types, but only a handful consistently drive SEO value. The key is knowing which formats align with your goals and content structure. Here are the high-impact schema types you should focus on:
Commonly Used and SEO-Driven
Article: Use this for blog posts, news, or editorial content. It supports elements like headlines, bylines, and publication dates, helping your content stand out in organic results.
FAQ: Can make your page eligible for expandable Q&A boxes beneath your page title. A strong option for capturing more SERP space. FAQ schema works especially well on service or solution pages.
Product and Review: Must-haves for e-commerce. These display key details like price, availability, and customer ratings.
Local Business: Ideal for brick-and-mortar locations or service areas. It includes address, hours, contact info, and geo coordinates. Event: Showcases information for webinars, conferences, or in-person events like date, time, location, and ticket availability.
Breadcrumb: Enhances your site’s navigational trail in search results. It also helps search engines better understand your site’s structure.
Underutilized but High-Impact Schema Types
Video: Helps search engines surface and display videos with rich details like thumbnails, duration, and key moments.
Course: Designed for online education content. Includes fields for course name, description, provider, and learning outcomes.
Job Posting: If you’re listing open roles on your website, this schema can push them into Google Jobs with structured info like salary, qualifications, and deadlines.
Software Application: Highlights app features, pricing, platform compatibility, and reviews. Ideal for SaaS companies or digital products.
There are also industry-specific schema types for recipes, medical conditions, real estate listings, and more, each designed to help content stand out in competitive niches.
While most websites stick to just one or two schema types, combining them across relevant pages gives Google a clearer picture of your site and can increase eligibility for multiple rich result formats.
Why is Schema Markup Important For SEO?
Schema markup doesn’t directly impact rankings, but it can improve how your pages appear in search by making your content easier for search engines to understand. When used correctly, it clarifies the structure and intent behind your content, which improves how your pages appear in search results.
With SEO schema markup, your listings can include extra context like star ratings, pricing, or FAQs, making them more informative and more likely to be clicked when rich results appear. These enhanced listings improve visibility and help searchers understand your content before visiting your site, which supports better engagement and user satisfaction.
Structured data also improves the user experience by giving searchers helpful, structured details before they even land on your site. This kind of clarity reduces bounce rates and increases engagement, which are both positive behavioral signals.
To be clear, Google has stated that structured data is not a direct ranking factor. But it can improve how your content is understood and discovered in search.
“Structured data is not used for ranking purposes, but it can enable search result enhancements and content discovery.” — Google Search Central
If you’re not using schema markup yet, you’re likely leaving visibility and traffic on the table, especially in crowded search spaces.
Schema Markup And AI
As search shifts toward generative results, schema markup becomes increasingly valuable, not as a ranking signal, but as structured clarity that helps machines interpret content consistently at scale. Tools like Google’s AI overviews, ChatGPT, and other large language models increasingly reference or infer structured relationships in your content. While schema markup isn’t directly parsed by every AI tool, it provides a framework that reinforces meaning, credibility, and context.
In Google’s case, schema can increase the chances of being featured or cited in AI-generated summaries by making your content more machine-readable. Clear, structured data helps Google understand which parts of your content are most relevant to a query, and that’s exactly what fuels AI-powered result boxes.
It also supports consistency across platforms, ensuring that search engines, crawlers, and third-party tools are all interpreting your information the same way. That’s critical in a landscape where content can be surfaced in snippets, carousels, voice results, and generative interfaces.
As AI continues to reshape search behavior, structured data plays a critical role in making your content visible and machine-readable across evolving search experiences.
How to Create Schema Markup for SEO
There’s no single way to implement SEO schema markup. The right method depends on your setup, your tools, and how much control you want over the code.
Schema Markup Generators
Schema generators are great to help create your schema type so you don’t have to do it manually. They offer flexibility and control, especially if you want to create cleaner SEO schema markup using JSON-LD.
One great option is Dentsu’s Schema Markup Generator. It supports a wide range of schema types and gives you real-time previews of the structured data output.
Another user-friendly pick is Schema.dev, which offers a visual editor for common schema types like Article, Product, Event, and more. It’s great for marketers who want more polish without touching raw code.
If you’re working on technical SEO at scale, tools like RankRanger’s generator or the Hall Analysis tool can help automate more advanced schema needs.
Most of these tools will output JSON-LD code, which you can copy and paste directly into your website’s head tag or through a CMS plugin.
Build Schema Manually
For developers or SEOs who want full control, manually writing SEO schema markup in JSON-LD is the most flexible option. This approach is ideal when you need to nest data types, customize beyond what’s available in generators, or integrate schema into a templated CMS or headless setup.
The most common format for manual schema is JSON-LD, a lightweight data format that can be placed inside a <script type=”application/ld+json”> tag in your HTML.
Schema.org provides documentation and examples for hundreds of item types, including complex combinations like a Product with reviews, availability, and brand info.
While this method takes more effort, it allows you to fine-tune every field and ensure the markup perfectly matches your content structure.
If you’re confident in your technical skills or already working with structured templates, hand-coding schema can unlock the most advanced use cases.
Use WordPress Plugins
If your site runs on WordPress, adding SEO schema markup is straightforward with the right plugin, with no coding required.
Yoast SEO adds basic structured data out of the box, like Article, WebPage, and Organization schema. You can also set defaults for different post types or override schema per page.
Rank Math offers more flexibility with its built-in Schema Generator. It supports custom fields, nested schema, and additional types like Product, FAQ, and Course. You can add schema site-wide or build it block-by-block using their visual editor.
Another option is the Schema & Structured Data for WP plugin, which offers advanced rule-based schema placement, support for over 30 types, and WooCommerce integration.
Most plugins handle the technical output for you, just select the schema type, fill out the fields, and publish.
Use ChatGPT
ChatGPT is a quick way to generate SEO schema markup without relying on a plugin or tool. It’s especially useful when you want structured data for a specific content type but don’t want to hand-code it from scratch.
To get started, just ask ChatGPT for the schema you need. For example:
“Create JSON-LD schema markup for a Product with name, price, rating, and availability.”
You can also refine the output by adding more context. Want to include an author bio? Just ask. Need multiple FAQs? List them out, and ChatGPT can format them for you.
The results are typically in valid JSON-LD format and can be copied into your site’s HTML or CMS.
It’s not a replacement for technical SEO tools, but it’s a powerful shortcut when used with the right prompts.
Add Schema Markup to Your Site
Once you’ve created your SEO schema markup, you need to place it on your site where search engines can find it. The most common format is JSON-LD, which should be embedded inside a <script type=”application/ld+json”> tag.
If you’re working directly with code, add the schema to the <head> section of your page, or just before the closing </body> tag. This helps ensure it gets picked up by search crawlers.
If you’re using a CMS like WordPress, Shopify, or Wix, many themes or SEO plugins include fields where you can paste your structured data directly. Just copy your JSON-LD and drop it into the appropriate field.
As we mentioned before, plugin-based setups, tools like Rank Math or Yoast will often insert schema automatically based on your settings, with no manual copy-paste needed.
No matter the method, the goal is the same: get valid, clean schema markup live on your site.
Validate Your Schema
Before you publish any SEO schema markup, you need to validate it. Even small formatting issues can break how search engines read your structured data.
You can either paste in your raw JSON-LD code or enter the URL of a published page. The tool will scan your markup and return any errors, warnings, or unsupported types.
Look for a “Valid” result nd ensure the schema type you used is recognized and correctly implemented. If there are issues, revise your code and re-test until everything passes.
You can also use Google’s Rich Results Test to see if your schema is eligible for enhanced SERP features.
Validation is a small step that ensures your markup actually works and gets you the visibility you’re aiming for.
Best Practices For SEO Schema Markup
To get the most out of your SEO schema markup, you need more than valid code. These best practices help ensure your structured data drives real visibility while staying within Google’s guidelines.
Only mark up visible, relevant content:
Don’t tag hidden elements, placeholder content, or anything users can’t actually see.
Schema should reflect what’s on the page. Misleading or hidden markup can get ignored or flagged.
Use the most specific schema type available:
Avoid generic markup. If your content is a recipe, use Recipe schema. If it’s a course, use Course schema. The more specific and accurate, the better.
Keep your structured data up to date:
Prices, dates, product availability, and other time-sensitive data should reflect the live content. Inaccurate schema can confuse search engines and users.
Avoid over-marking or spamming schema types:
Just because a schema exists doesn’t mean it belongs on your page. Only mark up what’s directly relevant and helpful to the user.
Accurate, helpful schema increases your chances of showing up in enhanced results. Misused or sloppy markup reduces trust and visibility.nd not content in hidden div’s or other hidden page elements.”
FAQs
What is schema markup?
Schema markup is a type of structured data that helps search engines understand the meaning of your content. It uses a shared vocabulary defined by schema.org to label key details like titles, authors, ratings, and more. When implemented correctly, it makes your content eligible for rich results, enhanced listings that display extra information directly in search.
What is schema markup SEO?
Schema markup SEO refers to the use of structured data as part of your overall search optimization strategy. While it doesn’t directly impact rankings, schema enhances how your pages appear in the SERPs. By making content easier to interpret and display, it supports better visibility, higher click-through rates, and alignment with user intent.
Does schema markup help SEO?
Yes, but not in the way most people expect. Schema doesn’t give you a direct ranking boost, but it improves how your pages are presented in search. Rich results stand out more, offer better context to users, and tend to earn more clicks. Schema can improve visibility and click-through rates, which can help your content attract more traffic over time.
Conclusion
Schema markup is one of those SEO techniques that helps to improve how your content appears in search results, yet it’s still underused. It helps search engines understand your pages more clearly, which leads to richer results, better visibility, and more clicks.
Whether you’re optimizing blog content, product listings, or service pages, structured data gives your site a clearer presence in search, and that matters in competitive markets.?
You found a digital marketing agency that feels like the one.
The pitch was perfect. They “get” your goals. Their case studies are impressive.
But a few weeks later, reality starts to set in: slow responses, recycled strategies, and reports that don’t show any tangible results.
This scenario is painfully common, but it’s not inevitable.
Choosing an agency that performs as well as they sell is possible — if you know what to look for.
In this guide, I’ll cover:
Red flags that signal an agency might overpromise and underdeliver
Green flags that separate the great partners from the mediocre ones
Must-ask questions to help you spot these flags before you sign the contract
You’ll also get real-world advice from experienced marketing leaders who’ve seen both dream partnerships and nightmare contracts.
By the end, you’ll know exactly how to choose a digital marketing agency in 2026. One that drives results instead of draining your budget.
First up: Vital questions to ask before jumping into a partnership.
Before You Hire a Digital Marketing Agency, Ask These Questions
Finding the right agency starts with understanding what you need and why.
Do You Have Product-Market Fit and a Clear Target Audience?
Even the best agency can’t sell a product that doesn’t solve a real problem for a defined audience.
If product-market fit isn’t there, your results will stall.
Ask yourself:
What pain points do we solve?
Who’s willing to pay for this?
Who else is competing for this audience?
Use a market analysis tool like Semrush’s Market Overview to confirm there’s real, sustainable demand.
For example, a quick search for Purina pet food shows strong growth and evenly distributed traffic — a clear sign of opportunity.
That’s the kind of demand signal you want before investing in outside help.
Do You Have a Clear Goal for Your Marketing Strategy?
A marketing agency can help you refine your goals.
But you’ll get better results when you already know what success looks like.
Vague goals like “increase website traffic” sound good, but they’re too broad to measure. Instead, set SMART goals — specific, measurable, achievable, relevant, and time-bound.
Here’s what a SMART goal looks like in action:
“Generate 120 qualified demo requests per month within four months by improving landing page copy and optimizing Google Ads.”
Clear goals like this help you find the right agency. And give them a focus to rally around and drive results.
Do You Have the Bandwidth to Manage an Agency?
Working with an agency isn’t a set-it-and-forget-it kind of task.
Regular, consistent communication with your agency is part of this process.
Sure, the level of autonomy will depend on the agency and the work.
But generally, the best agencies keep the door to conversation open.
Here’s what you can expect:
Provide materials and align on strategy and deliverables up front
Join weekly or biweekly check-ins (typically about an hour)
Review work and share feedback monthly
Pro tip: Assign one internal “agency owner.” Their job will be to keep decisions moving, share context fast, and unblock workflows.
Do You Know What Marketing Services You Need?
“Full-service marketing” sounds great. Until you realize you’re paying for tactics that get you nowhere.
There are many types of digital marketing agencies:
SEO and content: Drive organic growth through optimized content
Branding and design: Shape your visual identity and messaging
Video: Create video content that converts
Consultant: Help define priorities before execution
But before you pick one, identify what’s already working (and what’s not).
The more specific you are about your needs, the easier it is to find a partner whose strengths align with your goals.
Start by looking at your top-performing channels, campaigns, and content in analytics tools.
If content and partnerships drive results for you, that’s a hint about where to invest.
Next, check what’s working for your competitors.
For example, Semrush’s Organic Social tool reveals how your competitors generate traffic from social media.
And tells you exactly which platforms send the most traffic to their websites.
If others in your space are thriving on social while you’re not, that’s a clue to where you could expand.
Pro Tip: Before looking for an agency, ask yourself: Do I need strategy, execution, or both?
Is Your Internal Team Aligned on What You Need?
Clear goals mean nothing if your team isn’t aligned.
Without internal buy-in, even the best agency partnership can derail fast.
Marketing leader Eric Doty learned this the hard way.
After hiring an agency for a logo redesign (and spending weeks on revisions), leadership revealed they wanted to keep the full company name.
“In the end, we wasted around $15,000 on these iterations when all the company really wanted was to change the font.”
Avoid this by:
Defining who owns the agency relationship
Deciding who signs off on deliverables
Getting stakeholder input before work gets started
Once you’re aligned internally, you’re ready to align externally with your agency.
6 Red Flags That a Marketing Agency Will Waste Your Time (and Budget)
The sales call sounds great.
But how do you know whether the relationship will work long-term?
Don’t go in blind. Here are six warning signs and how to spot them.
1. They’re Not Willing to Invest Time in You
This isn’t something an agency will just come out and say directly. But there may be indications that they’ve currently got too much on their plate.
(And you’re about to be thrown onto the back burner.)
For one, look for a high amount of employee turnover. Employees leave when stress is high.
Check LinkedIn to learn about their employees and watch for downward growth trends.
You’ll also want to pay close attention to the discovery call.
If it’s all about them and nothing about you, that’s a sign they’re not taking the time to understand your business.
An agency that “yeses” you to death without adding ideas or offering pushback is another red flag.
They’re likely more focused on producing work as fast as possible than on providing a sustainable strategy.
Pro tip: Ask for a sample strategic recommendation on the call. Something lightweight like: “How would you improve our blog content?” The right agency will share high-level insights — not just a sales script.
And it’s never a good sign if they get defensive when you ask questions.
This can be an indicator that they’re not willing to invest time in the relationship.
I once hired an agency to help run paid social ads, and they did the absolute bare minimum. I had to point this out to get any attention, and by then, our three-month trial engagement was practically over, and we saw no results. While I don’t know for a fact it’s because we were on the lower end of their engagement value, it seems likely.
Looking at recent testimonials or mentions of the agency can help.
But sometimes, asking pointed questions is the best way to get an answer.
For example:
What’s your typical engagement type?
How long are your typical engagements?
How many clients does your team normally work with at once?
By asking these questions, you’ll get a better sense of the agency’s bandwidth.
2. Their Offerings Haven’t Evolved (or Have Evolved Too Much)
It’s no secret that marketing has evolved over the past few years.
And AI has only accelerated those changes.
So, if an agency hasn’t evolved its strategy to match the industry, it’s a sign they’re coasting on an outdated approach.
Want to find this out before the discovery call?
First, check the age of their case studies. Older case studies indicate a strategy that hasn’t changed.
Next, look at the wording on their services page.
If it sounds generic or dated, that’s a red flag.
In the example below, wording like “Taking over Google” is no longer fully relevant.
Plus, there’s no mention of local search or AI results.
(Which is odd, since they target local businesses.)
Pro tip: Trend chasing is another huge red flag. If you see a digital marketing agency that’s majorly pivoted without the data or case studies to back up those decisions, then you may want to steer clear.
Make sure they’re thinking ahead — not clinging to old playbooks — by asking:
How have your offerings changed in the past year?
How has your process changed since AI came on the scene?
How much does your team use AI when creating deliverables?
What’s your perspective on marketing in the AI era?
But you don’t want to get stuck in a relationship that’s not working.
Shorter contracts may not have an out clause. But if you’re getting ready to sign a contract for a year or more, and there’s no way out of that relationship, that could be a red flag.
For longer contracts, a 30-day out clause is typical. That means you both can leave the contract if things aren’t working out.
If you ask for this clause and the agency is pushing back hard, that’s a warning sign.
Amanda agrees:
No failsafe means the agency knows retention is a problem. And they may be more focused on cash flow than results.
Again, communicating clearly is important here.
When in doubt, ask the digital marketing agency these questions:
How have you handled failed campaigns in the past? Did you course-correct mid-campaign, or offer free revisions?
What barriers to success do you see with our engagement?
What’s your policy for a 30-day out in the contract?
4. Communication Isn’t Clear or Easy
The way your agency communicates during the discovery phase is a key indicator of how they’ll communicate once that contract is signed.
Here are some key warning signs you could see early in the process:
You have to chase them for updates or next steps: If getting in contact with the agency is hard before you sign the contract, don’t expect it to improve later on.
You can’t get clear answers to your questions: Asking about timeline, resources, and processes is normal. If they can’t give you straight answers to basic questions, beware.
You have no idea who you’ll be working with: It’s typical to talk to a salesperson or account manager in the early stages. But if you get pushback when asking to speak to the people you’ll be working with, that’s a red flag.
Chelsea Castle, head of brand and content at Close, experienced this firsthand.
Here’s her agency horror story:
One of my biggest career mistakes was not speaking up sooner and louder about yellow flags with an agency. From the initial meeting, something felt off in our communication. There were bumps and issues throughout the entire nine-month engagement. We didn’t love the output, and they weren’t doing things we suspected they should be doing.
Collaboration and communication were messy. We ended up firing this agency and losing the five figures spent on them, which left us with no completed work. Talk about a challenging conversation with your CEO!
To know more about communication before signing the contract, ask questions like:
Who’s my main point of contact with your agency?
Who’s going to be working on the project with me?
Who will be included in the check-in meetings?
At what points in the process do you track metrics to assess if we’re on the right track?
5. They Promise More Than They Can Reasonably Deliver
Overselling can lead to disaster down the road. But, how do you know if an agency is selling something they can’t deliver?
First, look at the language they use to describe their services or results.
If they make exaggerated claims or promises, it’s worth pausing.
For example, this agency’s website has red flags written all over it:
(I wish this were a made-up website, but it’s not.)
Claims like this sound great, but it’s important to take a step back and look at the facts.
Can they actually back up their claims with real examples?
Can they reasonably guarantee results without knowing anything about the potential client?
Danni Roseman, a brand manager at a SaaS company, hired an agency that promised the world but didn’t live up to expectations.
I assumed a team would handle our project. We later found out that only one person had the expertise we needed. It wasn’t enough. Deadlines slipped, quality dropped, and “edits” turned into full rewrites on our end. Hand-holding your agency isn’t part of the deal.
An agency that’s focused on revenue may sell more than the team is capable of doing, and you’re left with the aftermath.
Another side to this is whether the team has experience using or integrating with your tech stack.
Eric once worked with an email marketing agency that promised big things.
But ended up having no experience integrating with Microsoft Teams (a must-have for his company).
They decided to lead a procurement process for us to find a tool that integrated with Teams. This turned into a massively bloated project, when, really, they should’ve just told me from the get-go that they had no experience with this tool.
So, how do you make sure that what the sales team is offering can actually be delivered down the road?
First, ask pointed questions like:
Who on your team has experience working with the tools in our tech stack?
How much experience does your team have with these tools?
How many years of experience does the team have in this type of project?
What’s the project (within the type of service you’re looking for) that you enjoyed working on the most?
Can you give me some names of people I can talk to about your work?
Lastly, get references.
The sales team is going to say everything right. You need something solid to back up those claims.
Most agency websites say some version of “We do X for Y.” But can they explain how?
This is something you can check for on their website.
For example, what do their case studies look like? Are they just screenshots, or do they explain the process behind the work?
Here’s an example:
What looks impressive at first glance melts away when you realize these are just screenshots.
No discussion of the work, no explanation.
Here are some other warning signs to look out for:
Their process isn’t up for discussion: If an agency tells you anything along the lines of, “Trust us, we’ll handle it,” beware
They’re using the same templated strategies for every client: On the discovery call, are they bringing ideas to the table? Do they take your unique situation into account?
Their reporting is focused on big-number vanity metrics: Case studies with numbers are great. But do those numbers tell you a story of real impact?
They can’t explain why something worked: This could mean the team has little understanding of the mechanics behind the results
If you’re not sure about their process, ask questions like:
How do you approach new engagements?
How much time do you spend determining strategy?
How is the strategy adjusted as time goes on?
How often will we meet for check-ins?
Can you tell me about a project you worked on (in this vertical/type) that didn’t go well? How did your team handle that situation?
When you’re evaluating an agency, Chelsea’s advice rings true:
Ultimately, I think the biggest flag cannot be said; it can only be felt. Intuition and how you connect with someone are crucial in selecting and building long-lasting external relationships.
6 Green Flags You’ve Found a High-Performing Marketing Agency
Despite the horror stories we’ve discussed, great agencies do exist.
Here are the most common green flags — and tips for choosing a digital marketing agency that will actually deliver on its promises.
1. They Start with Questions, Not Tactics
The right agency feels like a partner.
They’re curious about your business and invested in your success.
On the discovery call, look for all of these green flags:
They start by asking deep questions about your business model, ICP, positioning, and goals
They’re comfortable pushing back respectfully if a strategy doesn’t align with best practices
They focus on how their work ties to your business outcomes, not vanity metrics
For example, KlientBoost, a PPC agency, doesn’t just offer standard strategy packages.
They ask questions about what the client needs, their goals, and their situation.
This information lets them tailor quotes to each client’s needs.
2. You Get Good Feedback From Third Parties
Good feedback, testimonials, and reviews are always a green flag.
First, check vetted, third-party review sites like Clutch.
Look for reviews that mention:
Quality of the digital marketing agency’s work
Communication style
Costs
Timing
Some reviews even include specific numbers and results.
Another way to get feedback is to ask your network.
Ask around in your favorite Slack communities and check on Reddit or LinkedIn.
You’ll learn who’s worked with this agency and what their impressions are.
Chelsea swears by using your network to find good agencies.
The best hires for me have almost always come through network referrals. When a trusted friend or colleague makes a recommendation, they’re risking their reputation to vouch for them. So you can be confident they’re worth your time.
What should you do if you don’t have any network recommendations?
Check out industry award winners, says Chelsea:
When I needed to hire a web design agency, I looked at Webflow’s Webby winners. While many great agencies don’t get awards like this, it was a sure bet to start my search by looking at those recognized in this credible, trustworthy way. I ended up finding a fantastic partner who was great to work with.
Within awards like Webby, you’ll find some incredible projects (and the agencies that made them happen).
3. The Full Team Will Be Involved in Communication
Knowing who’s involved in your project can help you have more confidence in the work being done.
Plus, if it’s easy to talk to the team before the project gets started, it’s a good sign that communication will be top-notch after the contract is signed as well.
Ask early on who will be on calls with your team.
If you find out it’s more than just one account manager, that means multiple people are invested in your engagement.
For example, check out this about page from content agency Beam:
You see the founders of this team.
But you also see the content producers and their social profiles. This level of transparency is a green flag.
4. They’re Transparent About Scope, Pricing, Timing, and How Work Gets Done
Your agency should be very clear about vital details upfront.
This includes:
The scope of the projects they do
Timing they can commit to
Any processes they use
For example, KlientBoost creates marketing plans for clients.
But even before you give them any information or sign up for a call, they show you a sneak peek of what a marketing plan looks like for their clients.
Another aspect of transparency is pricing.
Knowing what you’ll pay (and exactly what that cost includes) is essential to the project’s success.
That’s why some agencies, like A2Media, show their pricing right on their homepage:
Of course, not every agency lists its pricing publicly.
And there are plenty of different pricing structures, each with its pros and cons.
When talking about rates, ask the agency why they take the approach they do.
Get estimates for what each type of project entails.
If you’re comfortable with those ranges and estimates, include those in the contract.
When you can get clear answers to these questions, it’s a good sign they’ll live up to their promises.
When you find an agency you like, check out their marketing.
Most of the time, it’s a good indicator of the quality of their work.
In the past year, I’ve had two fantastic experiences with marketing agencies.
And both of them had one key aspect that was a huge green flag for me: their brand marketing was on point.
Take A2Media, for example.
The founder, Ademola, regularly produces video content on LinkedIn that generates strong engagement with his niche audience.
Another example is Beam.
They offer great content services to clients.
But they also produce fantastic content on their own website that’s both interesting and fun to read.
This pattern repeats itself over and over again.
KlientBoost’s LinkedIn video ads aren’t only hilarious but also deeply relatable.
Juice, a brand and web agency, has an incredibly stylish and fun website.
If they do great work for themselves, it’s a positive sign they’ll do great work for you.
6. Your Personalities Match
Yes, personality is subjective. And judging a marketing agency on “vibes” might sound a bit woo-woo.
But remember, this is a relationship. Hopefully, a long-term one.
So, the right agency should also match your style and get your vision.
Here are some green flags when it comes to personality match:
Their team seems genuinely excited about your product and mission
They treat your team members with respect, regardless of title
Their company culture aligns with yours
You enjoy working with them
They make collaboration energizing, not draining
Chelsea saw a personality match early on with a video agency, which gave her the confidence to move forward.
From the very first call, it just felt right. The agency owner and I instantly clicked and saw eye to eye on many things. He asked thoughtful, intentional questions that signaled respect, expertise, and a desire to find the best way to work together that prioritized me and my team. We’ve been working with this partner for more than a year, and have every intention of holding onto them for as long as we can.
Bonus: They Have Proven Expertise in Your Vertical
We’ve covered the most vital factors to evaluate when choosing a marketing agency partner.
But niche experience is worth considering, too.
While it’s not a necessity, it can be a really great bonus when combined with what we’ve discussed above.
For example, this agency focuses on dental practices:
While this agency focuses on marketing for law firms:
From just those two websites, it’s clear that their approach, strategy, and personality are very different.
And they’re each uniquely qualified to help clients in their chosen industry.
Other agencies may not have experience in your specific vertical. But they can demonstrate proven experience in the services you need.
For example, let’s say you want an agency that can help you show up in AI responses.
Then, you come across a case study like this:
Obviously, this agency has adapted its services to include AI search.
And has proven expertise in exactly what you need.
Ready to Choose a Digital Marketing Agency? Trust the Patterns (and Your Gut)
Choosing the right marketing agency comes down to spotting patterns.
Red flags: Overpromising, poor communication, and teams that won’t invest time in your success
Green flags: Thoughtful questions, killer third-party reviews, and teams that practice what they preach
But don’t forget the value of your gut reaction.
If something feels off during discovery, it won’t magically disappear once the contract is signed.
The best agency relationships start with a genuine connection.
As Chelsea says, “In any kind of creative work, sometimes you really do just have to go off vibes.”
When you find a team that gets your vision, respects your goals, and makes collaboration energizing, that’s your signal to move forward.
Understanding what’s happening in SEO will help you ask better questions. And spot whether agencies are using outdated tactics or staying ahead of the curve.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-26 16:01:522025-11-26 16:01:52How to Choose a Digital Marketing Agency That Actually Delivers
Marketers are making bold statements about AI SEO every day.
The problem?
Most of them are half-right at best.
“SEO is dead.”
“Long-form content is pointless.”
“AI SEO is just good SEO.”
Here’s the truth:
When it comes to AI, the answer is rarely that simple.
Are you trying to show up in ChatGPT or Google’s AI Overviews?
Do you want the AI to recommend your brand or cite your content?
Is the model pulling from training data or live web results?
Each of those questions has a different approach.
Trying to generalize only causes confusion.
So, let’s skip the hype and get specific.
This guide tests today’s biggest AI myths in SEO to uncover what’s true, what’s false, what’s complicated, and what all of it really means for your marketing strategy.
Semantic HTML (clean heading hierarchy, proper use of <p> and <section>)
Schema markup
Side note:Google has confirmed that schema markup can help with AI visibility in its own products. It’s not a guarantee, but it’s smart technical hygiene. And it’s likely to become even more important as AI evolves.
That means your ranking foundation still matters, but it’s no longer enough.
Off-site credibility: Brand associations built through mentions, citations, and expert recognition
Takeaway: SEO fundamentals get you indexed. Off-site authority gets you cited. AI SEO is about expanding what “optimization” means beyond your own site.
3. True or False: All AI SEO Works the Same
False.
Marketers talk about “showing up in AI answers” like it’s one game.
It’s not.
Google dominates the search landscape so much that traditional SEO is pretty unified — one platform, one algorithm, one analytics dashboard.
But there’s no single kind of AI visibility and no single playbook for earning it.
What’s Actually Happening
Every AI platform behaves slightly differently.
They draw from unique data pipelines, weigh off-site signals differently, and credit sources in their own ways.
For example, Google’s AI tools still echo its ranking system.
Originality.AI found that many Google AI Overviews come from the top 10 ranking pages.
But for brand mentions (answers that refer to your company), ranking seems to have more of an impact on ChatGPT.
Brands that rank on page one of Google show up more often in ChatGPT answers. Seer Interactive found a 0.65 correlation between high rankings and brand mentions.
In other words, if HubSpot ranks on page one for “CRM software,” ChatGPT is more likely to name it when users ask for the best CRMs.
Takeaway: Each platform plays by slightly different rules. Treat AI SEO like an ecosystem, not a checklist.
4. True or False: If You’re Cited by AI, You’ll Also Get Mentioned
Mostly false.
Mentions and citations aren’t the same thing — and one doesn’t guarantee the other.
Mentions = when your brand appears in the answer
Citations = when your content is trusted as a source
You need both to stay visible long term.
What’s Actually Happening
If you had to choose, being mentioned matters more in the short term.
When someone asks ChatGPT for “the best CRM for small businesses,” you want your brand to show up, even without a link.
But long-term visibility compounds when you’re both seen and trusted.
Brands that are both mentioned and cited appear 40% more often in repeat AI searches, AirOps found.
And that’s harder than you might think.
According to Semrush’s AI Visibility Index, fewer than 1 in 10 brands appear in AI answers as both mentioned and cited.
Most only get one: they’re either mentioned without a link or cited without being named.
For instance, if I look up “What’s the best HR software for small businesses?” I get the following response from ChatGPT:
Of all the responses, only Rippling was mentioned as a good choice of software and cited as a source.
Getting mentioned and cited consistently means playing a longer, smarter game.
To win both, you need to shape the way AI systems talk about your brand.
Earn mentions through off-site authority — PR, reviews, credible partnerships — and citations through trustworthy, reference-worthy content.
Takeaway: Mentions get you visibility. Citations earn you trust. You need both to last.
5. True or False: AI Engines Don’t Care About E-E-A-T
It’s complicated.
AI engines tend to cite pages that look trustworthy: clear sourcing, visible citations, and credible domains.
When AI engines use query fan-out, they break one question into many.
If a short page or definition answers a single sub-question directly, it might get pulled into that specific part of an AI answer.
Still, those are situational wins, not a replacement for authority.
And there’s more nuance here:
The Muck Rack study found that when questions got subjective — like asking for advice or step-by-step guidance — AI models pulled more from corporate blogs than authoritative news sources.
But, whether the LLMs are looking at official news sites, corporate blogs, or community sources, they consistently preferred credible content.
Credibility takes different forms. But AI systems pull from sources people trust most, whether institutional or experiential.
Clarity and organization make you easier to cite, but credibility will keep you there.
Plus, E-E-A-T keeps your content people-friendly as well as AI-friendly.
Takeaway: E-E-A-T still matters. It just needs to be paired with structured, clearly scoped content that AI systems can read and reuse.
6. True or False: Content Recency Matters Even More for AI Visibility
Mostly true.
Keeping content up to date has always been best-practice SEO.
And it’s also important for AI visibility on most of the public platforms.
But the relationship between freshness and visibility isn’t one-size-fits-all.
What’s Actually Happening
Seer Interactive found that nearly 65% of AI bot visits go to content published in the last 12 months.
I checked this out for myself using ChatGPT. I asked the query:
How do I create an AI-optimized content strategy?
Then, I asked:
Can you show me the sources you used for that answer?
And it returned:
The earliest resource was from 2023.
(It didn’t find a date for the Airtable and RevvGrowth articles because they weren’t “visible in the header.”)
Finally, I asked why it chose those sources to answer the question.
It returned:
Note: It listed recency as its top criteria.
But there’s some variation in how important recency is.
Seer Interactive found that freshness matters most in fields like finance, HR, and tax, where outdated data loses credibility fast.
In travel, the window is broader.
Evergreen guides (“best destinations for weekend city breaks”) still perform, but regular updates help maintain visibility.
And in energy, for example, relevance often beats recency. Educational, evergreen pages (“green vs. renewable energy”) continue attracting AI hits years after publication.
Even instructional content in slow-moving niches can perform long after it’s published.
Seer found AI bots still visiting decking tutorials written 10–15 years ago — proof that quality evergreen content can still hold its ground.
Takeaway: Fresh content gets more bot activity. But credible, well-maintained evergreen pages still win trust. Especially when they’re the best answer for the human behind the query.
7. True Or False: Long-Form Content Is Pointless to Create Now
False.
Many marketers are making a simple mistake:
They hear “AI prefers short answers” and conclude “AI prefers short content.”
AI is more likely to use or cite content that is structured so it’s easy to understand.
But that’s not about length. That’s about structure.
What’s Actually Happening
AI systems don’t skip long pieces.
They skip messy pieces.
Content passages with clear headings helps models scan, interpret, and extract the right snippets.
There’s nothing to say your content needs to be short.
Example: Ask ChatGPT for “the best resources to learn SEO,” and you’ll often see Backlinko mentioned.
Those guides are deep, not brief.
They’re cited because they give a complete answer in a format both humans and models can follow.
Long-form content also compounds your odds of being mentioned.
AI visibility is a probability game.
The more your content earns human discussion, the more likely it is to appear when AI answers a question.
And humans don’t rave about shallow content.
People share and reference the pieces that teach them something new: frameworks, research, comparisons, stories.
Cutting them down for AI only strips out the context that makes your brand trustworthy.
Takeaway: Long-form isn’t outdated. It’s still a way to build authority, trust, and the kind of signal both readers and AI models rely on.
8. True or False: You Should Skip the ToFu Content Now
False.
This is one of the most persistent AI myths in content marketing.
“If AI answers everything, why bother with top-of-funnel (ToFu)?”
But ToFu content still matters. It just has a new job.
In the past, you could publish a big guide like “What Is SEO?” and watch it climb the rankings.
Those broad, educational posts drove traffic because people had to click through to learn.
Now, AI Overviews and large language models answer those same questions right on the results page.
But that doesn’t mean top-of-funnel content is dead.
It just means it’s working differently.
What’s Actually Happening
ToFu content isn’t the traffic engine it once was.
But it still powers two things your marketing ecosystem depends on: awareness and authority.
ToFu Builds Awareness
ToFu content helps new audiences discover your brand, even if they don’t click.
When someone searches “What is the best time to send marketing emails?” and sees your brand name in a featured snippet or short summary, that’s still visibility.
It’s like a digital billboard.
People might not visit your site right away, but they’ll start to recognize your name the next time they see it.
The more consistently your brand shows up around key industry topics, the more familiar it feels to your future buyers.
That awareness pays off later when they’re comparing vendors or deciding who to trust.
ToFu Earns Credibility
Google and AI systems both reward depth of coverage.
They look for brands that explain an entire topic — not just their own product.
A Search Engine Land analysis of 8,000 AI citations found that AI systems repeatedly pull from in-depth, trusted sources, not surface-level articles.
If your site only has bottom-of-funnel pages like “Why Choose [Your Product],” algorithms see a narrow view.
But when you also publish foundational explainers and educational content, it shows that your brand understands the full landscape.
That matters for AI visibility too.
Takeaway: ToFU content strengthens your overall site signals. Even if ToFu posts don’t drive conversions, they reinforce your brand’s expertise across the funnel.
9. True Or False: You Should Publish 10x More Content with AI
False.
In theory, more content should mean more visibility.
In practice, that’s not what’s happening.
Teams feel pressure to publish faster because AI makes production easier.
But volume isn’t the same as reach.
Most scaled AI content dies in search before it ever earns authority.
AI platforms seem to be taking the same approach. They reward original insight and authority, not sheer output.
Takeaway: If you want visibility in both Google and AI search, slow down and build credibility.
10. True or False: High-Quality Content Is All You Need to Appear in LLMs
It’s more complicated than that.
Many marketers assume that if they simply create great content, AI tools like ChatGPT, Perplexity, or Gemini will automatically surface it.
But “great” isn’t enough.
High-quality content is a requirement. It’s what gets your pages seen, crawled, and trusted in the first place.
But visibility in AI search depends on something bigger: how consistently your brand is referenced and recognized across the web.
What’s Actually Happening
LLMs generate responses using two data sources:
Training data: The static dataset the model was trained on months (or years) ago
The live web: Real-time crawling and retrieval from indexed pages, like Google AI Overviews or Perplexity
Each system rewards a different kind of visibility, and each treats “quality” in its own way.
Training-data systems reward brand association.
When a model relies on its training data, it draws on patterns it has already learned.
That includes which brands are consistently associated with which topics.
If your brand’s name and theme appear together across thousands of credible pages, that association becomes part of the model’s long-term memory.
For example, Canva is strongly associated with “simple design.” So, if you ask ChatGPT “What is the simplest design program?” it’s probably going to answer Canva.
That’s how brands build “semantic ownership” of an idea.
Over time, those associations become the model’s defaults, a durable moat that competitors can’t easily displace.
Quality still matters here.
It determines whether people read, share, and cite your work — the human behaviors that create the signals AI later learns from.
Meanwhile, web-indexed systems reward structure and authority.
When an AI system relies on live web data, the process looks more like search.
Models retrieve pages in real time, parse structure, and extract concise, factual snippets.
In this environment, “quality” means clarity, structure, and credibility.
For example, if someone asks an AI tool “best CRM software for small business,” the model pulls from pages that look like strong search results.
In this case, that would probably be list posts with clear headings, comparison tables, and trustworthy sources.
A messy blog without structure or citations wouldn’t make the cut.
Takeaway: High-quality content is your ticket in, not your winning hand. Authority, structure, relevancy, and consistent brand signals are what actually get you cited in LLM answers.
How to Level Up Your SEO Strategy for AI Visibility
You’ve seen the myths. You understand the reality.
Now, here’s what to actually do about it.
The good news? You don’t need to blow up your entire SEO strategy.
Most of what you’re already doing still works.
You just need to expand where you’re looking and what you’re measuring.
Start Measuring What You Can’t See
Your analytics are lying to you by omission.
When someone discovers your brand through ChatGPT and visits you three days later, it shows up as direct traffic or a branded search. Zero attribution to the AI mention that started the journey.
So you’ll need to:
Track the indirect signals.
Rising branded searches while organic clicks decline? That could be LLM discovery.
Direct traffic holding steady despite fewer Google clicks? Same thing.
Sales calls where prospects say “found you through AI”? You’re getting cited.
Use dedicated AI tracking tools.
Options include Peek.ai and ZipTie.Dev. For more comprehensive features, Semrush Enterprise AIO is a good option, especially if you need full-funnel visibility and advanced reporting.
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