Google is broadening what counts as an eligible promotion in Shopping, giving merchants more flexibility heading into next year.
Driving the news. Google is update its Shopping promotion policies to support additional promotion types, including subscription discounts, common promo abbreviations, and — in Brazil — payment-method-based offers.
Why we care. Promotions are a key lever for visibility and conversion in Shopping results. These changes unlock more promotion formats that reflect how consumers actually buy today, especially subscriptions and cashback offers. Greater flexibility in promotion types and language reduces disapprovals and makes Shopping ads more competitive at key decision moments.
For retailers relying on subscriptions or local payment incentives, this update creates new ways to drive visibility and conversion on Google Shopping.
What’s changing. Google will now allow promotions tied to subscription fees, including free trials and percent- or amount-off discounts. Merchants can set these up by selecting “Subscribe and save” in Merchant Center or by using the subscribe_and_save redemption restriction in promotion feeds. Examples include a free first month on a premium subscription or a steep discount for the first few billing cycles.
Google is also loosening restrictions on language. Common promotional abbreviations like BOGO, B1G1, MRP and MSRP are now supported, making it easier for retailers to mirror real-world retail messaging without risking disapproval.
In Brazil only, Google will now support promotions that require a specific payment method, including cashback offers tied to digital wallets. Merchants must select “Forms of payment” in Merchant Center or use the forms_of_payment redemption restriction. Google says there are no immediate plans to expand this change to other markets.
Between the lines. These updates signal Google’s intent to better align Shopping promotions with modern retail models — especially subscriptions and localized payment behaviors — while reducing friction for merchants.
The bottom line. By expanding eligible promotion types, Google is giving advertisers more room to compete on value, not just price, when Shopping policies update in January 2026.
Starting in March 2026, Google Merchant Center will enforce a new system for multi-channel products — items sold both online and in physical stores — requiring advertisers to use separate product IDs when those products differ by channel.
What’s changing. Under the new approach, online product attributes will become the default. If a product’s in-store details differ, advertisers will need to create a second version with a distinct product ID and manage it independently in their feeds.
What advertisers should do. Google has started emailing affected accounts, flagging products that need updates ahead of the March deadline. Retailers should review their product data feeds now to ensure online and in-store items are properly segmented — especially if they rely on Local Inventory Ads or sell across multiple Google surfaces.
Why we care. Many retailers currently manage online and in-store versions of the same product under a single ID. Google’s update changes that assumption, pushing advertisers to explicitly separate products when attributes like price, availability, or condition aren’t identical.
The big picture. This update gives Google cleaner, more consistent product data across channels, but shifts more feed management responsibility onto advertisers — particularly large retailers with complex inventories.
First seen. The update and news of Google’s comms was first mentioned by PPC News Feed founder Hana Kobzová.
Bottom line. If your online and in-store products aren’t truly identical, Google will soon require you to treat them as separate items, or risk issues with visibility and eligibility.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/merchant-multi-channel-products-6mfOLg.jpg?fit=1280%2C720&ssl=17201280http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-06 17:41:142026-01-06 17:41:14Google to require separate product IDs for multi-channel items
Google is updating its advertising policies to allow ads for Prediction Markets in the U.S. starting January 21st — but only for federally regulated entities.
Who qualifies. Eligibility is limited to entities authorized by the Commodity Futures Trading Commission (CFTC) as Designated Contract Markets (DCMs) whose primary business is listing exchange-listed event contracts, or brokerages registered with the National Futures Association (NFA) that offer access to products listed by qualifying DCMs. Advertisers must also apply for Google certification to run ads in the U.S.
Why we care. Prediction markets have long been restricted on Google Ads. This change opens a new advertising channel while keeping tight controls around compliance and regulation. The narrow eligibility and certification requirements mean only compliant, federally regulated players can participate, potentially reducing competition. For qualifying advertisers, this offers earlier access to a high-intent audience within a tightly controlled ad environment.
The fine print. All ads, products, and landing pages must comply with applicable local laws, financial regulations, industry standards, and Google Ads policies. The new policy will appear in the Advertising Policies Help Center, with references in the Financial Services and Gambling and Games sections, and is available now for preview.
The big picture. Google is cautiously expanding access for prediction markets by recognizing them as regulated financial products — while continuing to block unregulated platforms.
SEO now sits at an uncomfortable intersection at many organizations.
Leadership wants visibility in AI-driven search experiences. Product teams want clarity on which narratives, features, and use cases are being surfaced. Sales still depends on pipeline.
Meanwhile, traditional rankings, traffic, and conversions continue to matter. What has changed is the surface area of search.
Pages are now summarized, excerpted, and cited in environments where clicks are optional and attribution is selective.
When a generative AI summary appears on the SERP, users click traditional result links only about 8% of the time.
As a result, SEO teams need a clearer playbook for earning visibility inside generative outputs, not just around them.
This 90-day action plan outlines how to achieve this in a phased, weekly execution, with practical adjustments tailored to the specific purpose of the website.
Phase 1: Foundation (Weeks 1-2)
Define your ‘AI search topics’
Keywords still matter. But AI systems organize information around entities, topics, and questions, not just query strings.
The first step is to decide what you want AI tools to associate your brand with.
Action steps
Identify 5-10 core topics you want to be known for.
For each topic, map:
The questions users ask most often
The comparisons they evaluate
“Best,” “how,” and “why” queries that indicate decision-making intent
Example:
Topic: AI SEO tools
Mapped query types:
Core questions: What are the best AI SEO tools? How does AI improve SEO?
Comparisons: AI SEO tools vs traditional SEO tools.
Intent signals: Best AI SEO tools for content optimization.
Where this shifts by website type
Content hubs (media brands, publishers, research orgs) should prioritize mapping educational breadth – covering a topic comprehensively so AI systems see the site as a reference source, not a transactional endpoint.
Services/lead gen sites (agencies, consultants, local businesses) should map problem-solution queries prospects ask before converting, especially comparison and “how does this work?” questions.
Product and ecommerce sites (DTC brands, marketplaces, subscription ecommerce, retailers) should map topics to use cases, alternatives, and comparisons – not just product names or category terms.
Commercial, long-funnel sites (B2B SaaS, fintech, healthcare) should anchor topics to category leadership – the “what is,” “how it works,” and “why it matters” content buyers research long before demos.
If you can’t clearly articulate what you want AI systems to associate you with, neither can they.
Generative engines consistently surface content that is easy to extract, summarize, and reuse.
In practice, that favors pages where answers are clearly framed, front-loaded, and supported by scannable structure.
High-performing pages tend to follow a predictable pattern.
AI-friendly content structures include:
A short intro (2-3 lines) that establishes scope.
A direct answer placed immediately after the header, written to stand alone if excerpted.
Bulleted lists or numbered steps that break down the explanation.
A concise FAQ section at the bottom that reinforces key queries.
This increases the likelihood your content is:
Quoted in AI Overviews.
Used in ChatGPT or Perplexity answers.
Surfaced for voice and conversational search.
For ecommerce and services sites in particular, this is often where internal resistance shows up. Teams worry that answering questions too directly will reduce conversion opportunities.
In AI-driven search, the opposite is usually true: pages that make answers easy to extract are more likely to be surfaced, cited, and revisited when users move from research to decision-making.
In generative search, content that gets surfaced typically resolves the core question immediately, then provides context and depth.
For many commercial teams, that requires rethinking how early pages prioritize explanation versus persuasion – a shift that’s increasingly necessary to earn visibility at all.
This is where GEO (generative engine optimization) and AEO (answer engine optimization) move from theory into page-level execution.
Add a 1–2 sentence TL;DR under key H2s that can stand on its own if excerpted
Use explicit, question-based headers:
“What is…”
“How does…”
“Why does…”
Include clear, plain-language definitions before introducing nuance or positioning
Example:
What is generative engine optimization?
Generative engine optimization (GEO) helps content get selected as a source in AI-generated answers.
In practice, GEO is the process of structuring and optimizing content so AI tools like ChatGPT and Google AI Overviews can interpret, evaluate, and reference it when responding to user queries.
How does answer-first structure change by site type?
Publishers benefit from definitional clarity because it increases citation frequency.
Lead gen sites see stronger mid-funnel engagement when prospects get clear answers upfront.
Product sites reduce friction by addressing comparison and “is it right for me?” questions early.
B2B platforms establish category authority long before a buyer ever hits a pricing page.
Add structured data (high impact, often underused)
Structured data remains one of the clearest ways to signal meaning and credibility to AI-driven search systems.
It helps generative engines quickly identify the source, scope, and authority behind a piece of content – especially when deciding what to cite.
At a minimum, most sites should implement:
Article schema to clarify content type and topical focus.
Organization schema to establish the publishing entity.
Author or Person schema to surface expertise and accountability.
FAQ schema, where it reflects genuine question-and-answer content, can still reinforce structure and intent – but it should be used selectively, not as a default.
This matters differently by site type:
Content hubs benefit when author and publication signals reinforce editorial credibility and reference value.
Lead gen and services sites use schema to connect expertise to specific problem areas and queries.
Product and ecommerce sites help AI systems distinguish between informational content and transactional pages.
Commercial, long-funnel sites rely on schema to support trust signals alongside relevance in high-stakes categories.
Structured data doesn’t guarantee inclusion – but in generative search environments, its absence makes exclusion more likely.
As generative systems decide which sources to reference, demonstrated experience increasingly outweighs polish alone.
Pages that surface consistently tend to show clear evidence that the content comes from real people with real expertise.
Meaning, signals associated with E-E-A-T – experience, expertise, authoritativeness, and trust – remain central to how generative systems decide which sources to reference.
Key signals to reinforce:
Clear author bios that establish credentials, role, or subject-matter relevance.
First-hand experience statements that indicate direct involvement (“We tested…”, “In our experience…”).
Original visuals, screenshots, data, or case studies that can’t be inferred or synthesized
This is where generic, AI-generated content reliably falls short.
Without visible signals of experience and accountability, AI systems struggle to distinguish authoritative sources from interchangeable ones.
How different site types should demonstrate experience and authority
Media and research sites should reinforce editorial standards, sourcing, and author attribution to support citation trust.
Agencies and consultants benefit from foregrounding lived client experience and specific outcomes, not abstract expertise.
Ecommerce brands earn trust through real-world product usage, testing, and visual proof.
High-ACV B2B companies stand out by showcasing practitioner insight and operational knowledge rather than marketing language alone.
If your content reads like it could belong to anyone, AI systems will treat it that way.
Certain page types are more likely to be cited in AI-generated answers because they organize information in ways that are easy to extract, compare, and reference.
These pages are designed to serve as reference material – resolving common questions clearly and completely, rather than advancing a particular perspective.
Formats that consistently perform well include:
Ultimate guides that consolidate a topic into a single, authoritative resource.
Comparison tables that make differences explicit and scannable.
Statistics pages that centralize data points AI systems can reference.
Glossaries that define terms clearly and consistently.
Pages with titles such as “AI SEO Statistics (2025)” or “Best AI SEO Tools Compared” are frequently surfaced because they signal completeness, recency, and reference value at a glance.
For commercial sites, citation-worthy pages don’t replace conversion-focused assets.
They support them by capturing early-stage, informational demand – and positioning the brand as a credible source long before a buyer enters the funnel.
Generative systems increasingly synthesize signals across text, images, and video when assembling answers.
Content that performs well in AI-driven search is often reinforced across formats, not confined to a single page or medium.
Add descriptive, specific alt text that explains what an image shows and why it’s relevant.
Create short-form videos paired with transcripts that mirror on-page explanations.
Repurpose core content into formats AI systems can encounter and contextualize elsewhere:
YouTube videos.
LinkedIn carousels.
X threads.
How this supports different site goals
Publishers extend the reach and reference value of core reporting and explainers.
Services and B2B sites reinforce expertise by repeating the same answers across multiple surfaces.
Ecommerce brands support discovery by contextualizing products beyond traditional listings and category pages.
Track AI visibility – not just traffic
As generative results absorb more of the discovery layer, traditional click-based metrics capture only part of search performance.
AI visibility increasingly shows up in how often – and where – a brand’s content is referenced, summarized, or surfaced without a click.
With 88% of businesses worried about losing organic visibility in the world of AI-driven search, tracking these signals is essential for demonstrating continued influence and reach.
Signals worth monitoring include:
Featured snippet ownership, which often feeds AI-generated summaries.
Appearances within AI Overviews and similar answer experiences.
Brand mentions inside AI tools during exploratory queries.
Search Console impressions, even when clicks don’t follow.
For long sales cycles in particular, these signals act as early indicators of influence.
AI citations and impressions often precede direct engagement, shaping consideration well before a buyer enters the funnel.
These tools support different parts of an SEO-for-AI workflow, from topic research and content structure to schema implementation and visibility tracking.
Content and AI SEO
Surfer, Clearscope, Frase
Used to identify gaps in topical coverage and evaluate whether content resolves questions clearly enough to be excerpted in AI-generated answers.
Schema and structured data
RankMath, Yoast, Schema App
Useful for implementing and maintaining schema that helps AI systems interpret content, authorship, and organizational credibility.
Visibility and performance tracking
Google Search Console, Ahrefs
Essential for monitoring impressions, query patterns, and how content surfaces in search – including cases where visibility doesn’t result in a click.
AI research and validation
ChatGPT, Perplexity, Gemini
Helpful for testing how topics are summarized, which sources are cited, and where your content appears (or doesn’t) in AI-driven responses.
The rule that matters most
AI systems tend to favor content that provides definitive answers to questions.
If your content can’t answer a question clearly in 30 seconds, it’s unlikely to be selected for AI-generated answers.
What separates teams succeeding in this environment isn’t experimentation with new tactics, but consistency in execution.
Pages built to be understandable, referenceable, and trustworthy are the ones generative systems return to.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/semrush-discover-ai-optimization-031q2A.webp?fit=800%2C440&ssl=1440800http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-05 13:00:002026-01-05 13:00:00A 90-day SEO playbook for AI-driven search visibility
In fact, 74% of shoppers give up because there’s too much choice, according to research by Business of Fashion and McKinsey.
Now?
A shopper submits a query. AI gives one clear answer — often with direct links to products, reviews, and retailers. They can even click straight to purchase.
So, how do you make sure AI recommends your fashion brand?
We analyzed how fashion brands appear in AI search. And why some brands dominate while others disappear.
In this article, you’ll learn how large language models (LLMs) interpret fashion, what drives visibility, and the levers you can pull to get your brand visible in AI searches (plus a free fashion trend calendar to help you plan).
There are three ways people will see your brand in AI search: brand mentions, citations, and recommendations.
Brand mentions are references to your brand within an answer.
Ask AI about the latest fashion trends, and the answer includes a couple of relevant brands.
Citations are the proof that backs up AI answers. Your brand properties get linked as a source. This could be product pages, sizing guides, or care instructions.
Citations also include other sites that talk about your brand, like Wikipedia, Amazon, or review sites.
Product recommendations are the most powerful form of AI visibility. Your brand isn’t just mentioned; it’s actively suggested when someone is ready to buy.
For example, I asked ChatGPT for recommendations of aviator sunglasses:
Ray-Ban doesn’t just show up as a mention — they’re a recommended option with clickable shopping cards.
How AI Models Choose Which Fashion Brands to Surface
If you’ve ever wondered how AI chooses which fashion brands to surface, here are the two basic factors:
By evaluating what other people say about you online
By checking how consistently factual and trustworthy your own information is
Let’s talk about consensus and consistency. Plus, we’ll discuss real fashion brands that are winning at both.
Consensus
If you ask all your friends for their favorite ice cream shop, they’ll probably give different answers.
But if almost everyone coincided in the same answer, you trust that’s probably the best place to go.
AI does something similar.
First, it checks different sources of information online. This includes:
Editorial websites, like articles in Vogue, Who What Wear, InStyle, and others
Community and creator content, including TikTok try-ons, Reddit threads, and YouTube product roundups
Retailer corroboration, like ratings and reviews on Amazon, Nordstrom, Zalando, and more
Sustainability verification from third parties like B Corp, OEKO-TEX, or Good On You
After analyzing this information, it gives you recommendations for what it perceives to be the best option.
Here’s an example of what that consensus looks like for a real brand:
Carhartt is mentioned all over the web. They appear in retail listings, editorial pieces, and in community discussions.
The result?
They get consistent LLM mentions.
Consistency
AI also judges your brand based on the consistency of your product information.
This includes:
Naming & colorways: Identical names/color codes across your own site, retailers, and mentions
Fit & size data: Standardized size charts, fit guides, and model measurements
Materials & care: The same composition and instructions across all channels
Imagery/video parity: The same SKU visuals (like hero, 360, try-on) on your site and retailer sites
Price & availability sync: Real-time updates during drops or restocks to avoid stale or conflicting data
For example, Lululemon does a great job of keeping product availability updated on their website.
If you ask AI where to find a specific product type, it directs you back to the Lululemon website.
This happens because Lululemon’s site provides accurate, up-to-date information.
Plus, it’s consistent across retailer pages.
The Types of Content That Dominate Fashion AI Search
Mentions get you into the conversation. Recommendations make you the answer. Citations build the credibility that supports both.
The brands winning in AI search have all three — here’s how to diagnose where you stand.
Let’s talk about the fashion brands that are consistently showing up in AI search results, and the kind of content that helps them gain AI visibility.
Editorial Shopping Guides and Roundups
Editorial content has a huge impact on results.
Sites like Vogue, Who What Wear, and InStyle are regularly cited by LLMs.
These editorial pieces are key for AI search, since they frame products in context — showing comparison, specific occasions, or trends.
There are two ways to play into this.
First, you can develop relationships with editorial websites relevant to your brand.
Start by researching your top three competitors. Using Google (or a quick AI search), find out which publications have featured those competitors recently.
Then, reach out to the editor or writers at those publications.
If they’re individual creators, you might send sample products for them to review.
Looking for mentions from bigger publications?
You might consider working with a PR team to get your products listed in articles.
To build consistency in that content, provide data sheets with information about material, fit, or care.
Second, you can build your own editorial content.
That’s exactly what Huckberry does:
They regularly produce editorial-style content that answers questions.
Many of these posts include a video as well, giving them more opportunity for discovery in LLMs:
Retailer Product Pages and Brand Stores
Think of your product detail page (PDP) as the source of truth for AI.
If you don’t have all the information there, AI will take its answers from other sources — whether or not they’re accurate.
Product pages (your own website or a retailer’s) need to reflect consistent, accurate information. Then, AI can understand and translate into answers.
Some examples might include:
Structured sizing information
Consistent naming and colorways
Up-to-date prices and availability
Ratings (with pictures)
Fit guides (like sizing guides and images with model measurements and sizing)
Materials and care pages
Transparent sustainability modules
For example,Everlane provides the typical sizing chart on each of its products. But they take it a step further and include a guide to show how a piece is meant to fit on your body.
You can even see instructions to measure yourself and find the right size.
That’s why, when I ask AI to help me pick the right size for a pair of pants, it gives me a clear answer.
And the citations come straight from Everlane’s website.
Everlane’s product pages also include model measurements and sizing.
So when I ask ChatGPT for pictures to help me pick the right size, I get this response:
However you choose to present this information on your product pages, just remember: It needs to be identical on all retailer pages as well.
Otherwise, your brand could confuse the LLMs.
User Generated Video Content
What you say about your own brand is one thing.
But what other people say about you online can have a huge influence on your AI mentions.
Of course, you don’t have full control over what consumers post about you online.
So, proactively build connections with creators. Or, try to join the conversation online when appropriate.
This can help you build a positive sentiment toward your brand, which AI will pick up on.
Not sure which creators to work with?
Try searching for your competitors on channels like TikTok or Instagram. See which creators are mentioning their products, and getting engagement.
Search by social channels, and filter by things like follower count, location, and pricing.
Here’s an example: Aritzia has grown a lot on TikTok. They show up in creator videos, fit checks, and unboxing-style videos.
In fact, the hashtag #aritziahaul has a total of 32k posts, racking up 561 million views overall.
Other fashion brands, like Quince, include a reviewing system on their PDPs.
This allows consumers to rate the fit and add pictures of themselves wearing the product.
LLMs also use this information to answer questions.
Creator try-ons, styling videos, and similar content can help increase brand mentions in “best for [body type]” or “best for [occasion]” prompts.
Pro tip: Zero-click shopping is coming. Perplexity’s “Buy with Pro” and ChatGPT’s “Instant Checkout” hint at a future where AI answers lead straight to one-click purchases. The effects are still emerging, but as with social shopping, visibility wins. So, make sure your brand shows up in the chats that drive buying decisions.
Reddit and Community Threads
Reddit is a major source of information for fashion AI queries.
This includes information about real-world fit, durability, comfort, return experiences, and comparisons.
For example, Uniqlo shows up regularly in Reddit threads and questions about style.
You can also find real reviews of durability about the products.
As a result, the brand is getting thousands of mentions in LLMs based on Reddit citations.
Plus, this leads to a ton of organic traffic back to the Uniqlo website.
Obviously, it’s impossible to completely control the conversation around your brand. So for this to work, there’s one key thing you can’t miss:
Your products need to be truly excellent.
A mediocre product that has a lot of negative sentiment online won’t show up in AI search results.
And no amount of marketing tactics can fool the LLMs.
Further reading: Learn how to join the conversation online with our Reddit Marketing guide.
Lab Tests and Fabric Explainers
This kind of content shows the quality of your products.
It gives LLMs a measurable benchmark to quote on things like pilling or color fastness.
This content could include:
“6-month wear” style videos
Pages that explain the fabrics and materials used
Third party tests
Clear care instructions
For example, Quince has an entire page on their website talking about cashmere.
And in Semrush’s AI Visibility dashboard, you can see this page is one of the top cited sources from Quince’s website.
Another option is to create content that shows tests of your products.
Here’s a great example from a brand that makes running soles, Vibram.
They sponsored pro trail runner Robyn Lesh, and teamed up with Huckberry to lab test some of their shoes.
This kind of content is helping Vibram maintain solid AI visibility.
And for smaller brands who don’t have Vibram’s sponsorship budget?
Try doing product testing content with your own team.
For example, have a team member wear a specific product every day for a month, and report back on durability.
Or, bury a piece of clothing underground and watch how long it takes to decompose, like Woolmark did:
Get creative, and you’ll have some fun creating content that can also help your brand be more visible.
Start by checking your AI visibility score. You’ll see how this measures up against the industry benchmarks.
You can prioritize next steps based on the Topic Opportunities tab.
There, you’ll see topics where your competitors are being mentioned, but your brand is missed.
Then, jump to the Brand Perception tab to learn more about your Share of Voice and Sentiment in AI search results.
You’ll also get some clear insights on improvements you can make.
Comparisons and Alternatives Content
AI loves a good comparison post (and honestly, who doesn’t?). So, creating content that compares your products to other brands is a great way to get more mentions.
It helps you get brand exposure without depending on organic traffic dependence. Plus, it helps level the playing field with bigger competitors.
For instance, Quince is often cited online as a cheaper alternative to luxury clothing.
I asked ChatGPT for affordable cashmere options, and Quince was the first recommendation.
So, why is this brand showing up consistently?
One reason is their comparison content.
In each PDP, you’ll see the “Beyond Compare” box, showing specific points of comparison with major competitors.
The right comparisons are handled honestly and tastefully.
Focus on real points of difference (like Quince does with price). Or, show which products are best for certain occasions.
For example: “Our sweaters are great for hiking in the snow. Our competitors’ sweaters are better for indoor activities.”
Comparisons give AI a reason to recommend your fashion brand when someone asks for an alternative.
What This Shift Means for Your Fashion Brand
AI search has changed the way people discover products, and even their path to purchase.
Before, this involved multiple searches, clicking on different websites, or scrolling through forums. Now, you can do this in one simple interface.
So, how is AI changing fashion, and how can your brand adapt?
Editorial, Retailer, and PDP Split
AI search doesn’t treat every source of information equally.
And depending on which model your audience uses, the “default” source of truth can look very different.
ChatGPT leans heavily on editorial and community signals.
It rewards cultural traction — what people are talking about, buying, and loving.
For example, articles like this one from Vogue are a prime source for ChatGPT answers:
Meanwhile, Google’s AI Mode and Perplexity skew toward retailer PDPs.
They look for structured data like price, availability, or fit guides. In other words, they trust whoever has the cleanest, richest product data.
The most visible brands win in both arenas: cultural conversation and PDP completeness.
Here’s What You Can Do
To show up in all major LLMs, you need two parallel pipelines.
Cultural traction: Like press mentions, creator partnerships, and community visibility
Citation-ready proof: For example, complete and accurate PDPs across retailer channels
Here’s an Example: Carhartt
Carhartt is a great example of a brand that’s winning on both sides.
First, they get consistent cultural visibility.
For instance, Vogue reported that the Carhartt WIP Detroit jacket made Lyst’s “hottest product” list. That led to searches for their brand increasing by 410%.
This makes it more likely for LLMs to recommend their products in answers:
This is the kind of loop that works wonders for a fashion brand.
At the same time, Carhartt is also stocked across a huge range of retailers. You can find them in REI, Nordstrom, Amazon, and Dick’s, plus their own direct-to-consumer website.
So, Google AI Mode has an abundance of PDPs, videos, reviews, and Q&A to cite.
This makes Carhartt extremely “citation-friendly” in both models.
No wonder it has such a strong AI visibility score.
Trend Shocks and Seasonal Volatility
Trend cycles aren’t a new challenge in the fashion industry. But it becomes a bigger challenge to maintain visibility when those trends affect which brands appear in AI search.
Micro-trends pop up all the time, triggering quick shifts in how AI answers fashion queries.
When the trend heats up, LLMs pull in brands that appear online in listicles or TikTok roundups.
And when the trend cools? Those same brands disappear just as quickly.
Here’s What You Can Do
To stay present during each trend swing, you need a content and operations pipeline that speaks in real time to the language models are echoing.
Build a proactive trend calendar: Map your content to seasonal moments, like spring tailoring, fall layers, holiday capsules, back-to-school basics, and so on
Refresh imagery and copy to mirror trend language: Update PDPs, on-site copy, and retailer description to match the phrasing used in cultural content
Create rapid-fire listicles and lookbooks: Listicle-style content, creator videos, and other trend-related mentions can help boost visibility. This includes building your own content and working with creators and publications to feature your product in their content.
Anyone who was around for Y2K may have been shocked to see UGG boots come around again.
But the brand was ready to jump onto the trend and make the most of their moment.
Vogue reported that UGG made Lyst’s “hottest products” list in 2024.
Since then, they’ve been regularly featured in seasonal “winter wardrobe essentials” style roundups.
One analyst found that there had been a 280% increase in popularity for the shoes. Funny enough, that trend seems to be a regular occurrence every year once “UGG season” rolls around.
In fact, on TikTok, the hashtag #uggseason has almost 70k videos.
UGG stays visible even as seasons trends shift. That’s because the brand is always present in the content streams that LLMs treat as cultural indicators. By partnering with influencers, UGG amplified its presence so effectively that the boots themselves became a moment — something people wanted to photograph, share, and join in on without being asked.
The result?
They have one of the highest AI Visibility scores I saw while researching this article.
(As a marketer, I find this encouraging. As a Millennial, I find it deeply disturbing.)
Pro tip: Want to measure the results? Track how often your brand or SKUs appear in new listicles per month, plus how they rank in those roundups. Then use Semrush’s AI Visibility Toolkit to track your brand’s visibility using trend-related prompts.
Sustainability and Proof (Not Claims)
Sustainability has become one of the strongest differentiators for fashion brands in AI search.
But only when brands back it up with verifiable proof.
LLMs don’t reward vague eco-friendly language. Instead, they surface brands with certifications, documentation, and third-party validation.
Models also pull heavily from Wikipedia and third-party certification databases. These pages often act as trust anchors for AI search results.
Here’s What You Can Do
You need to build a clear, credible footprint that models can cite.
Centralize pages on materials, care, and impact: Make them brief, structured, and verifiable. Include materials, sourcing, certifications, and repair/resale info.
Maintain third-party profiles: Keep your certifications up-to-date. This includes things like Fair Trade, Bluesign, B-Corp, GOTs, etc.
Standardize sustainability claims across all retailers: If your DTC site says “Fair Trade Certified” but your Nordstrom PDP doesn’t? Models treat that as unreliable.
Here’s an Example: Patagonia
Patagonia is the ruler of AI visibility with a 21.96% share of voice.
In part, this is because of their incredible dedication to sustainability. They basically own this niche category within fashion.
Patagonia’s sustainability claims are backed up by third-party certifications.
And they’re displayed proudly on each PDP.
They’re also transparent about their efforts to help the environment.
They keep pages like this updated regularly.
These sustainable efforts aren’t just big talk.
Review sites and actual consumers speak positively online about these efforts.
They’ve made their claim as a sustainable fashion brand.
So, Patagonia shows up first, almost always, in LLMs when talking about sustainable fashion:
That’s the power of building a sustainable brand.
Make AI Work for Your Fashion Brand
You’ve seen how the top fashion brands earn AI visibility.
The path forward is simple: Consensus + Consistency.
Build consensus by getting people talking: Create shareable content, encourage customer posts, or work with creators and publications.
Build consistency by keeping your product info aligned across your site and retail partners.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-02 15:14:362026-01-02 15:14:36Fashion AI SEO: How to Improve Your Brand’s LLM Visibility
SEO didn’t stand still in 2025. It didn’t reinvent itself either. It clarified what actually matters. If you followed The SEO Update by Yoast monthly webinars this year, you’ll recognize the pattern. Throughout 2025, our Principal SEOs, Carolyn Shelby and Alex Moss, cut through the noise to explain not just what was changing but why it mattered as AI-powered search reshaped visibility, trust, and performance. If you missed some sessions or want the full picture in one place, this wrap-up is for you. We’re looking back at how SEO evolved over the year, what those changes mean in practice, and what they signal going forward.
Key takeaways
In 2025, SEO shifted its focus from rankings to visibility management, as AI-driven search reshaped priorities
Key developments included the rise of AI Overviews, a shift from clicks to citations, and increased importance of clarity and trust
Brands needed to prioritize structured, credible content that AI systems could easily interpret to remain visible
By December, SEO transformed to retrieval-focused strategies, where success rested on clarity, relevance, and E-E-A-T signals
Overall, 2025 clarified that the fundamentals still matter but emphasized the need for precision in content for AI-driven systems
AI-powered, personalized search accelerated. Zero-click results increased. Brand signals, E-E-A-T, performance, and schema shifted from optimizations to requirements.
SEO expanded from ranking pages to representing trusted brands that machines can understand.
February
Massive AI infrastructure investments. AI Overviews pushed organic results down. Traffic dropped while brand influence and revenue held steady.
SEO outcomes can no longer be measured by traffic alone. Authority and influence matter more than raw clicks.
March
AI Overviews expanded as clicks declined. Brand mentions appeared to play a larger role in AI-driven citation and selection behavior than links alone. Search behavior grew despite fewer referrals.
Visibility fractured across systems. Trust and brand recognition became the differentiators for inclusion.
April
Schema and structure proved essential for AI interpretation. Multimodal and personalized search expanded. Zero-click behavior increased further.
SEO shifted from optimization to interpretation. Clarity and structure determine reuse.
May
Discovery spread beyond Google. AI Overviews reached mass adoption. Citations replaced visits as success signals.
SEO outgrew the SERP. Presence across platforms and AI systems became critical.
June – July
AI Mode became core to search. Ads entered AI answers. Indexing alone no longer offers guaranteed visibility. Reporting lagged behind reality.
Traditional SEO remained necessary but insufficient. Resilience and adaptability became essential.
August
Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond the number of sessions.
Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond sessions.
September
AI Mode neared default status. Legal, licensing, and attribution pressures intensified. Persona-based strategies gained relevance.
Control over visibility is no longer guaranteed. Trust and credibility are the only durable advantages.
October
Search Console data reset expectations. AI citations outweighed rankings. AI search became the destination.
SEO success depends on presence inside AI systems, not just SERP positions.
November
AI Mode became core to search. Ads entered AI answers. Indexing alone is no longer a guarantee of visibility. Reporting lagged behind reality.
Clarity and structure beat scale. Authority decides inclusion.
December
SEO fully shifted to retrieval-based logic. AI systems extracted answers, not pages. E-E-A-T acted as a gatekeeper.
SEO evolved into visibility management for AI-driven search. Precision replaced volume.
January: SEO enters the age of representation
January set the tone for the year. Not through a single disruptive update, but through a clear signal that SEO was moving away from pure rankings toward something broader. The search was becoming more personalized, AI-driven, and selective about which sources it chose to surface. Visibility was no longer guaranteed just because you ranked well.
From the start of the year, it was clear that SEO in 2025 would reward brands that were trusted, technically sound, and easy for machines to understand.
What changed in January
Here are a few clear trends that began to shape how SEO worked in practice:
AI-powered search became more personalized: Search results reflected context more clearly, taking into account location, intent, and behavior. The same query no longer produced the same result for every user
Zero-click searches accelerated: More answers appeared directly in search results, reducing the need to click through, especially for informational and local queries
Brand signals and reviews gained weight: Search leaned more heavily on real-world trust indicators like brand mentions, reviews, and overall reputation
E-E-A-T became harder to ignore: Clear expertise, ownership, and credibility increasingly acted as filters, not just quality guidelines
The role of schema started to shift: Structured data mattered less for visual enhancements and more for helping machines understand content and entities
What to take away from January
January wasn’t about tactics. It was about direction.
SEO started rewarding clarity over cleverness. Brands over pages. Trust over volume. Performance over polish. If search engines were going to summarize, compare, and answer on your behalf, you needed to make it easy for them to understand who you are, what you offer, and why you are credible.
That theme did not fade as the year went on. It became the foundation for everything that followed.
February: scale, money, and AI made the shift unavoidable
If January showed where search was heading, February showed how serious the industry was about getting there. This was the month where AI stopped feeling like a layer on top of search and started looking like the foundation underneath it.
Massive investments, changing SERP layouts, and shifting performance metrics all pointed to the same conclusion. Search was being rebuilt for an AI-first world.
What changed in February
As the month unfolded, the signs became increasingly difficult to ignore.
AI Overviews pushed organic results further down: AI Overviews appeared in a large share of problem-solving queries, favoring authoritative sources and summaries over traditional organic listings
Traffic declined while brand value increased: High-profile examples showed sessions dropping even as revenue grew. Visibility, influence, and brand trust started to matter more than raw sessions
AI referrals began to rise: Referral traffic from AI tools increased, while Google’s overall market share showed early signs of pressure. Discovery started spreading across systems, not just search engines
What to take away from February
February made January’s direction feel permanent.
When AI systems operate at this scale, they change how visibility works. Rankings still mattered, but they no longer told the full story. Authority, brand recognition, and trust increasingly influenced whether content was surfaced, summarized, or ignored.
The takeaway was clear. SEO could no longer be measured only by traffic. It had to be understood in terms of influence, representation, and relevance across an expanding search ecosystem.
March: visibility fractured, trust became the differentiator
By March, the effects of AI-driven search were no longer theoretical. The conversation shifted from how search was changing to who was being affected by it, and why.
This was the month where declining clicks, citation gaps, and publisher pushback made one thing clear. Search visibility was fragmenting across systems, and trust became the deciding factor in who stayed visible.
What changed in March
The developments in March added pressure to trends that had already been forming earlier in the year.
AI Overviews expanded while clicks declined: Studies showed that AI Overviews appeared more frequently, while click-through rates continued to decline. Visibility increasingly stopped at the SERP
Brand mentions mattered more than links alone: Citation patterns across AI platforms varied, but one signal stayed consistent. Brands mentioned frequently and clearly were more likely to surface
Search behavior continued to grow despite fewer clicks: Overall search volume increased year over year, showing that users weren’t searching less; they were just clicking less
AI search struggled with attribution and citations: Many AI-powered results failed to cite sources consistently, reinforcing the need for strong brand recognition rather than reliance on direct referrals
Search experiences became more fragmented: New entry points like Circle to Search and premium AI modes introduced additional layers to discovery, especially among younger users
Structured signals evolved for AI retrieval: Updates to robots meta tags, structured data for return policies, and “sufficient context” signals showed search engines refining how content is selected and grounded
March exposed the tension at the heart of modern SEO.
Search demand was growing, but traditional traffic was shrinking. AI systems were answering more questions, but often without clear attribution. In that environment, being a recognizable, trusted brand mattered more than being the best-optimized page.
The implication was simple. SEO was no longer just about earning clicks. It was about earning inclusion, recognition, and trust across systems that don’t always send users back.
April: machines started deciding how content is interpreted
By April, the focus shifted again. The question was no longer whether AI would shape search, but how machines decide what content means and when to surface it.
After March exposed visibility gaps and attribution issues, April zoomed in on interpretation. How AI systems read, classify, and extract information became central to SEO outcomes.
What changed in April
April brought clarity to how modern search systems process content.
Schema has proven its value beyond rankings: Microsoft has confirmed that schema markup helps large language models understand content. Bing Copilot used structured data to generate clearer, more reliable answers, reinforcing the schema’s role in interpretation rather than visual enhancement
AI-driven search became multimodal: Image-based queries expanded through Google Lens and Gemini, allowing users to search using photos and visuals instead of text alone
AI Overviews expanded during core updates: A noticeable surge in AI Overviews appeared during Google’s March core update, especially in travel, entertainment, and local discovery queries
Clicks declined as summaries improved: AI-generated content summaries reduced the need to click through, accelerating zero-click behavior across informational and decision-based searches
Content structure mattered more than special optimizations: Clear headings that boost readability, lists, and semantic cues helped AI systems extract meaning. There were no shortcuts. Standard SEO best practices carried the weight
What to take away from April
April shifted SEO from optimization to interpretation.
Search engines and AI systems didn’t just look for relevance. They looked for clarity. Content that was well-structured, semantically clear, and grounded in real entities was easier to understand, summarize, and reuse.
The lesson was subtle but important. You didn’t need new tricks for AI search. You needed content that was easier for machines to read and harder to misinterpret.
By May, it was no longer sufficient to discuss how search engines interpret content. The bigger question became where discovery was actually happening.
SEO started expanding beyond Google. Visibility fractured across platforms, AI tools, and ecosystems, forcing brands to think about presence rather than placement.
What changed in May
The month highlighted how search and discovery continued to decentralize.
Search behavior expanded beyond traditional search engines: Around 39% of consumers now use Pinterest as a search engine, with Gen Z leading adoption. Discovery increasingly happened inside platforms, not just through search bars
AI Overviews reached mass adoption: AI Overviews reportedly reached around 1.5 billion users per month and appeared in roughly 13% of searches, with informational queries driving most of that growth
Clicks continued to give way to citations: As AI summaries became more common, being referenced or cited mattered more than driving a visit, especially for top-of-funnel queries
AI-powered search diversified across tools: Chat-based search experiences added shopping, comparison, and personalization features, further shifting discovery away from classic result pages
Economic pressure on content ecosystems increased: Industry voices warned that widespread zero-click answers were starting to weaken the incentives for content creation across the web
May reframed SEO as a visibility problem, not a traffic problem.
When discovery happens across platforms, summaries, and AI systems, success depends on how clearly your content communicates meaning, credibility, and relevance. Rankings still mattered, but they were no longer the primary measure of success.
The message was clear. SEO had outgrown the SERP. Brands that focused on authenticity, semantic clarity, and structured information were better positioned to stay visible wherever search happened next.
By early summer, SEO entered a more uncomfortable phase. Visibility still mattered, but control over how and where content appeared became increasingly limited.
June and July were about adjustment. Search moved closer to AI assistants, ads blended into answers, and traditional SEO signals no longer guaranteed exposure across all search surfaces.
What changed in June and July
This period introduced some of the clearest operational shifts of the year.
AI Mode became a first-class search experience: AI Mode was rolled out more broadly, including incognito use, and began to merge into core search experiences. Search was no longer just results. It was conversation, summaries, and follow-ups
Ads entered AI-generated answers: Google introduced ads inside AI Overviews and began testing them in conversational AI Mode. Visibility now competes not only with other pages, but with monetized responses
Measurement lagged behind reality: Search Console confirmed AI Mode data would be included in performance reports, but without separate filters or APIs. Visibility changed more rapidly than reporting tools could keep pace.
Citations followed platform-specific preferences: Different AI systems favored different sources. Some leaned heavily on encyclopedic content, others on community-driven platforms, reinforcing that one SEO strategy would not fit every system
Most AI-linked pages still ranked well organically: Around 97% of URLs referenced in AI Mode ranked in the top 10 organic results, showing that strong traditional SEO remained a prerequisite, even if it was no longer sufficient
Content had to resist summarization: Leaks and tests showed that some AI tools rarely surfaced links unless live search was triggered. Generic, easily summarized modern content became easier to replace
Infrastructure became an SEO concern again: AI agents increased crawl and request volume, pushing performance, caching, and server readiness back into focus
Search moved beyond text: Voice-based interactions, audio summaries, image-driven queries, and AI-first browsers expanded how users searched and consumed information
What to take away from June and July
This period forced a mindset shift.
SEO could no longer assume that ranking, indexing, or even traffic guaranteed visibility. AI systems decided when to summarize, when to cite, and when to bypass pages entirely. Ads, assistants, and alternative interfaces now often sit between users and websites more frequently than before.
The conclusion was pragmatic. Strong fundamentals still mattered, but they weren’t the finish line. SEO now requires resilience: content that carries authority, resists simplification, loads fast, and stays relevant even when clicks don’t follow.
By the end of July, one thing was clear. SEO wasn’t disappearing. It was operating under new constraints, and the rest of the year would test how well teams adapted to them.
August: the gap between visibility and value widened
By August, SEO teams were staring at a growing disconnect. Visibility was increasing, but traditional outcomes were harder to trace back to it.
This was the month when the mechanics of AI-driven search became more transparent and more uncomfortable.
What changed in August
August surfaced the operational realities behind AI-powered discovery.
Impressions rose while clicks continued to decline: AI Overviews dominated the results, driving exposure without generating traffic. In some cases, conversions still improved, but attribution became harder to prove
The “great decoupling” became measurable: Visibility and performance stopped moving in sync. SEO teams saw growth in impressions even as sessions declined
Zero-click searches accelerated further: No-click behavior climbed toward 69%, reinforcing that many user journeys now ended inside search interfaces
AI traffic stayed small but influential: AI-driven referrals still accounted for under 1% of traffic for most sites, yet they shaped expectations around answers, speed, and convenience
Retrieval logic shifted toward context and intent: New retrieval approaches prioritized meaning, relationships, and query context over keyword matching
It reinforced the reality that SEO could no longer rely on traffic as the primary proof of value. Visibility still mattered, but only when paired with outcomes that could survive reduced clicks and blurred attribution.
The lesson was strategic. SEO needed to connect visibility to conversion, brand lift, or long-term trust, not just sessions. Otherwise, its impact would be increasingly hard to defend.
September: control, attribution, and trust were renegotiated
September pushed the conversation further. It wasn’t just about declining clicks anymore. It was about who controlled discovery, attribution, and access to content.
This was the month where legal, technical, and strategic pressures collided.
What changed in September
September reframed SEO around governance and credibility.
AI Mode moved closer to becoming the default: Search experiences shifted toward AI-driven answers with conversational follow-ups and multimodal inputs
The decline of the open web was acknowledged publicly: Court filings and public statements confirmed what many publishers were already feeling. Traditional web traffic was under structural pressure
Legal scrutiny intensified: High-profile settlements and lawsuits highlighted growing challenges around training data, summaries, and lost revenue
Licensing entered the SEO conversation: New machine-readable licensing approaches emerged as early attempts to restore control and consent
Snippet visibility became a gateway signal: AI tools relied heavily on search snippets for real-time answers, making concise, extractable content more critical
Persona-based strategies gained traction: SEO began shifting from keyword targeting to persona-driven content aligned with how AI systems infer intent
Trust eroded around generic, formulaic, AI writing styles: Formulaic, overly polished AI content raised credibility concerns, reinforcing the need for editorial judgment
Measurement tools lost stability again: Changes to search parameters disrupted rank tracking, reminding teams that SEO reporting would remain volatile
What to take away from September
September forced SEO to grow up again.
Control over visibility, attribution, and content use was no longer guaranteed. Trust, clarity, and credibility became the only durable advantages in an ecosystem shaped by AI intermediaries.
The takeaway was sobering but useful. SEO could still drive value, but only when it is aligned with real user needs, strong brand signals, and content that earned its place in AI-driven answers.
October marked a turning point in how SEO performance needed to be interpreted. The data didn’t just shift. It reset expectations entirely.
This was the month when SEO teams had to accept that AI-powered search was no longer a layer on top of results. It was becoming the place where searches ended.
What changed in October
October brought clarity, even if the numbers looked uncomfortable.
AI Mode reshaped user behavior: Around a third of searches now involve AI agents, with most sessions staying inside AI panels. Clicks became the exception, not the default
AI citations increasingly rivalled rankings: Visibility increasingly depended on whether content was selected, summarized, or cited by AI systems, not where it ranked
Search engines optimized for ideas, not pages: Guidance from search platforms reinforced that AI systems extract concepts and answers, not entire URLs
Metadata lost some direct control: Tests of AI-generated meta descriptions suggested that manual optimization would carry less influence over how content appears
Commerce and search continued to merge: AI-driven shopping experiences expanded, signaling that transactional intent would increasingly be handled inside AI interfaces
What to take away from October
October reframed SEO as presence within AI systems.
Traffic still mattered, but it was no longer the primary outcome. The real question became whether your content appeared at all inside AI-driven answers. Clarity, structure, and extractability replaced traditional ranking gains as the most reliable levers.
From this point on, SEO had to treat AI search as a destination, not just a gateway.
November: structure and credibility decided inclusion
If October reset expectations, November showed what actually worked.
This month narrowed the gap between theory and practice. It became clearer why some content consistently surfaced in AI results, while other content disappeared.
What changed in November
November focused on how AI systems select and trust sources.
Structured content outperformed clever content: Clear headings, predictable formats, and direct answers made it easier for AI systems to extract and reuse information
Schema supported understanding, not visibility alone: Structured data remained valuable, but only when paired with clean, readable on-page content
AI-driven shopping and comparisons accelerated: Product data quality, consistency, and accessibility directly influenced whether brands appeared in AI-assisted decision flows
Citation pools stayed selective: AI systems relied on a relatively small set of trusted sources, reinforcing the importance of brand recognition and authority
Search tooling evolved toward themes, not keywords: Grouped queries and topic-based insights replaced one-keyword performance views
What to take away from November
November made one thing clear. SEO wasn’t about producing more content or optimizing harder. It was about making content easier to understand and harder to ignore.
Clarity beats creativity. Structure beat scale. Authority determined whether content was reused at all.
This month quietly reinforced the fundamentals that would define SEO going forward.
Instead of introducing new disruptions, it clarified what 2025 had been building toward all along. SEO was no longer primarily about ranking pages. It was about enabling retrieval.
What changed in December
The year-end review highlighted the new reality of SEO.
Search systems retrieved answers, not pages: AI-driven search experiences pulled snippets, definitions, and summaries instead of directing users to full articles
Literal language still mattered: Despite advances in understanding, AI systems relied heavily on exact phrasing. Terminology choices directly affected retrieval
Content structure became mandatory: Front-loaded answers, short paragraphs, lists, and clear sections made content usable for AI systems
Relevance replaced ranking as the core signal: Being the clearest and most contextually relevant answer mattered more than traditional ranking factors
E-E-A-T acted as a gatekeeper: Recognized expertise, authorship, and trust signals determined whether content was eligible for reuse
Authority reduced AI errors: Strong credibility signals helped AI systems select more reliable sources and reduced hallucinated answers
What to take away from December
December didn’t declare the end of SEO. It defined its next phase.
SEO matured into visibility management for AI-driven systems. Success depended on clarity, credibility, and structure, not shortcuts or volume. The fundamentals still worked, but only when applied with discipline.
By the end of 2025, the direction was clear. SEO didn’t get smaller. It got more precise.
SEO evolved into visibility management for AI-driven search. Precision replaced volume.
2025 didn’t rewrite SEO. It clarified it.
Search moved from ranking pages to retrieving answers. From rewarding volume to rewarding clarity. From clicks to credibility. And from optimization tricks to systems-level understanding.
The fundamentals still matter. Technical health, helpful content, and strong SEO foundations are non-negotiable. But they are no longer the finish line. What separates visible brands from invisible ones now is how clearly their content can be understood, trusted, and reused by AI-driven search systems.
Going into 2026, the goal isn’t to outsmart search engines. It’s to make your expertise unmistakable. Write for humans, structure for machines, and build authority that holds up even when clicks don’t follow.
SEO didn’t get smaller this year. It got more precise. Stay with us for our 2026 verdict on where search goes next.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-24 12:52:512025-12-24 12:52:51The 2025 SEO wrap-up: What we learned about search, content, and trust
Most business owners assume that if an ad is approved by Google or Meta, it is safe.
The thinking is simple: trillion-dollar platforms with sophisticated compliance systems would not allow ads that expose advertisers to legal risk.
That assumption is wrong, and it is one of the most dangerous mistakes an advertiser can make.
The digital advertising market operates on a legal double standard.
A federal law known as Section 230 shields platforms from liability for third-party content, while strict liability places responsibility squarely on the advertiser.
Even agencies have a built-in defense. They can argue that they relied on your data or instructions. You can’t.
In this system, you are operating in a hostile environment.
The landlord (the platform) is immune.
Bad tenants (scammers) inflate the cost of participation.
And when something goes wrong, regulators come after you, the responsible advertiser, not the platform, and often not even the agency that built the ad.
Here is what you need to know to protect your business.
Note:This article was sparked by a recent LinkedIn post from Vanessa Otero regarding Meta’s revenue from “high-risk” ads. Her insights and comments in the post about the misalignment between platform profit and user safety prompted this in-depth examination of the legal and economic mechanisms that enable such a system.
The core danger: Strict liability explained
While the strict liability standard is specific to U.S. law (FTC), the economic fallout of this system affects anyone buying ads on U.S.-based platforms.
Before we discuss the platforms, it is essential to understand your own legal standing.
In the eyes of the FTC and state regulators, advertisers are generally held to a standard of strict liability.
What this means: If your ad makes a deceptive claim, you are liable. That’s it.
Intent doesn’t matter: You can’t say, “I didn’t mean to mislead anyone.”
Ignorance doesn’t matter: You can’t say, “I didn’t know the claim was false.”
Delegation doesn’t matter: You can’t say, “My agency wrote it,” or “ChatGPT wrote it.”
The law views the business owner as the “principal” beneficiary of the ad.
You have a non-delegable duty to ensure your advertising is truthful.
Even if an agency writes unauthorized copy that violates the law, regulators often fine the business owner first because you are the one profiting from the sale.
You can try to sue your agency later to get your money back, but that is a separate battle you have to fund yourself.
The unfair shield: Why the platform doesn’t care
If you are strictly liable, why doesn’t the platform help you stay compliant? Because they don’t have to.
Section 230 of the Communications Decency Act declares that “interactive computer services” (platforms) are not treated as the publisher of third-party content.
The original intent: This law was passed in 1996 to allow the internet to scale, ensuring that a website wouldn’t be sued every time a user posted a comment. It was designed to protect free speech and innovation.
The modern reality: Today, that shield protects a business model. Courts have ruled that even if platforms profit from illegal content, they are generally not liable unless they actively contribute to creating the illegality.
The consequence: This creates a “moral hazard.” Because the platform faces no legal risk for the content of your ads, it has no financial incentive to build perfect compliance tools. Their moderation AI is built to protect the platform’s brand safety, not your legal safety.
The liability ladder: Where you stand
To understand how exposed you are, look at the legal hierarchy of the three main players in any ad campaign:
The platform (Google/Meta)
Legal status: Immune.
They accept your money to run the ad. Courts have ruled that providing “neutral tools” like keyword suggestions does not make the platform liable for the fraud that ensues.
If the FTC sues, they point to Section 230 and walk away.
The agency (The creator)
Legal status: Negligence standard.
If your agency writes a false ad, they are typically only liable if regulators prove they “knew or should have known” it was false.
They can argue they relied on your product data in good faith.
You (The business owner)
Legal status: Strict liability.
You are the end of the line.
You can’t pass the buck to the platform (immune) or easily to the agency (negligence defense).
If the ad is false, you pay the fine.
The hostile environment: Paying to bid against ‘ghosts’
The situation gets worse.
Because platforms are immune, they allow “high-risk” actors into the auction that legitimate businesses, like yours, have to compete against.
A recent Reuters investigation revealed that Meta internally projected roughly 10% of its ad revenue (approximately $16 billion) would come from “integrity risks”:
Scams.
Frauds.
Banned goods.
Worse, internal documents reveal that when the platform’s AI suspects an ad is a scam (but isn’t “95% certain”), it often fails to ban the advertiser.
Instead, it charges them a “penalty bid,” a premium price to enter the auction.
You are bidding against scammers who have deep illicit profit margins because they don’t ship real products (zero cost of goods sold).
This allows them to bid higher, artificially inflating the cost per click (CPC) for every legitimate business owner.
You are paying a fraud tax just to get your ad seen.
Because the platform is no longer a neutral host but is vouching for the business (“Guaranteed”), regulators can argue they have stepped out from behind the Section 230 shield.
By clicking “Auto-apply,” you are effectively signing a blank check for a robot to write legal promises on your behalf.
Risk reality check: Who actually gets investigated?
While strict liability is the law, enforcement is not random. The FTC and State Attorneys General have limited resources, so they prioritize based on harm and scale.
If you operate in dietary supplements (i.e., “nutra”), fintech (crypto and loans), or business opportunity offers, your risk is extreme. These industries trigger the most consumer complaints and the swiftest investigations.
If you are an HVAC tech or a local florist, you are unlikely to face an FTC probe unless you are engaging in massive fraud (e.g., fake reviews at scale). However, you are still vulnerable to competitor lawsuits and local consumer protection acts.
Investigations rarely start from a random audit. They start from consumer complaints (to the BBB or attorney generals) or viral attention. If your aggressive ad goes viral for the wrong reasons, the regulators will see it.
International intricacies
It is vital to remember that Section 230 is a U.S. anomaly.
If you advertise globally, you’re playing by a different set of rules.
The European Union (DSA): The Digital Services Act forces platforms to mitigate “systemic risks.” If they fail to police scams, they face fines of up to 6% of global turnover.
The United Kingdom (Online Safety Act): The UK creates a “duty of care.” Senior managers at tech companies can face criminal liability for failing to prevent fraud.
Canada (Competition Bureau): Canadian regulators are increasingly aggressive on “drip pricing” and misleading digital claims, without a Section 230 equivalent to shield the platforms.
The “Brussels Effect”: Because platforms want to avoid EU fines, they often apply their strictest global policies to your U.S. account. You may be getting flagged in Texas because of a law written in Belgium.
The advertiser’s survival guide
Knowing the deck is stacked, how do you protect your business?
Adopt a ‘zero trust’ policy
Never hit “publish” on an auto-generated asset without human eyes on it first.
If you use an agency, require them to send you a “substantiation PDF” once a quarter that links every claim in your top ads to a specific piece of proof (e.g., a lab report, a customer review, or a supply chain document).
The substantiation file
For every claim you make (“Fastest shipping,” “Best rated,” “Loses 10lbs”), keep a PDF folder with the proof dated before the ad went live.
This is your only shield against strict liability.
Audit your ‘auto-apply’ settings
Go into your ad accounts today.
Turn off any setting that allows the platform to automatically rewrite your text or generate new assets without your manual review.
Efficiency is not worth the liability.
Watch the legislation
Lawmakers are actively debating the SAFE TECH Act, which would carve out paid advertising from Section 230.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/12/Why-ad-approval-is-not-legal-protection-Mf1Pt4.webp?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-23 15:00:002025-12-23 15:00:00Why ad approval is not legal protection
Google expanded Demand Gen channel controls to include Google Maps, giving advertisers a new way to reach users with intent-driven placements and far more control over where Demand Gen ads appear.
What’s new. Advertisers can now select Google Maps as a channel within Demand Gen campaigns. The option can be used alongside other channels in a mixed setup or on its own to create Maps-only campaigns.
Why we care. This update unlocks a powerful, location-focused surface inside Demand Gen, allowing advertisers to tailor campaigns to high-intent moments such as local discovery and navigation. It also marks a meaningful step toward finer channel control in what has traditionally been a more automated campaign type.
Response. Advertisers are very excited by this update. CEO of AdSquire Anthony Higman has been waiting for this for decades:
Google Ads Specialist Thomas Eccel, who shared the update on LinkedIn said: “This is very big news and shake up things quite a lot!”
Between the lines. Google continues to respond to advertiser pressure for greater transparency and control, gradually breaking Demand Gen into more modular, selectable distribution channels.
What to watch. How Maps placements perform compared to YouTube, Discover, and Gmail—and whether Google expands reporting or optimization tools specifically for Maps inventory.
First seen. This update was first spotted by Search Marketing Specialist Francesca Poles, when she shared the update on LinkedIn
Bottom line. Adding Google Maps to Demand Gen channel controls is a significant shift that gives advertisers new strategic flexibility and the option to build fully location-centric campaigns.
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Search marketers are starting to build, not just optimize.
Across SEO and PPC teams, vibe coding and AI-powered development tools are shrinking the gap between idea and execution – from weeks of developer queues to hours of hands-on experimentation.
These tools don’t replace developers, but they do let search teams create and test interactive content on their own timelines.
In a zero-click environment, the ability to build unique, useful, conversion-focused tools is becoming one of the most practical ways search marketers can respond.
What is vibe coding?
Vibe coding is a way of building software by directing AI systems through natural language rather than writing most of the code by hand.
Instead of working line by line, the builder focuses on intent – what the tool should do, how it should look, and how it should respond – while the AI handles implementation.
The term was popularized in early 2025 by OpenAI co-founder Andrej Karpathy, who described a loose, exploratory style of building where ideas are tested quickly, and code becomes secondary to outcomes.
His framing captured both the appeal and the risk: AI makes it possible to build functional tools at speed, but it also encourages shortcuts that can lead to fragile or poorly understood systems.
Since then, a growing ecosystem of AI-powered development platforms has made this approach accessible well beyond engineering teams.
Tools like Replit, Lovable, and Cursor allow non-developers to design, deploy, and iterate on web-based tools with minimal setup.
The result is a shift in who gets to build – and how quickly ideas can move from concept to production.
That speed, however, doesn’t remove the need for judgment.
Vibe coding works best when it’s treated as a craft, not a shortcut.
Blindly accepting AI-generated changes, skipping review, or treating tools as disposable experiments creates technical debt just as quickly as it creates momentum.
Mastering vibe coding means learning how to guide, question, and refine what the AI produces – not just “see stuff, say stuff, run stuff.”
This balance between speed and discipline is what makes vibe coding relevant for search marketers, and why it demands more than curiosity to use well.
Vibe coding vs. vibe marketing
Vibe coding should not be confused with vibe marketing.
AI no-code tools used for vibe coding are designed to build things – applications, tools, and interactive experiences.
AI automation platforms used for vibe marketing, such as N8N, Gumloop, and Make, are built to connect tools and systems together.
For example, N8N can be used to automate workflows between products, content, or agents created with Replit.
These automation platforms extend the value of vibe-coded tools by connecting them to systems like WordPress, Slack, HubSpot, and Meta.
Used together, vibe coding and AI automation allow search teams to both build and operationalize what they create.
Why vibe coding matters for search marketing
In the future, AI-powered coding platforms will likely become a default part of the marketing skill set, much like knowing how to use Microsoft Excel is today.
AI won’t take your job – but someone who knows how to use AI might.
We recently interviewed candidates for a director of SEO and AI optimization role.
None of the people we spoke with were actively vibe coding or had used AI-powered development software for SEO or marketing.
That gap was notable.
As more companies add these tools to their technology stacks and ways of working, hands-on experience with them is likely to become increasingly relevant.
Vibe coding lets search marketers quickly build interactive tools that are useful, conversion-focused, and difficult for Google to replicate through AI Overviews or other SERP features.
For paid search, this means teams can rapidly test interactive content ideas and drive traffic to them to evaluate whether they increase leads or sales.
These platforms can also be used to build or enhance scripts, improve workflows, and support other operational needs.
For SEO, vibe coding makes it possible to add meaningful utility to pages and websites, which can increase engagement and encourage users to return.
Returning visitors matter because, according to Google’s AI Mode patent, user state – which includes engagement – plays a significant role in how results are generated in AI Overviews and AI Mode.
For agency founders, CEOs, CFOs, and other group leaders, these tools also make it possible to build custom internal systems to support how their businesses actually operate.
For example, I used Replit to build an internal growth forecasting and management tool.
It allows me to create annual forecasts with assumptions, margins, and P&L modeling to manage the SEO and AI optimization group.
There isn’t off-the-shelf software that fully supports those needs.
Vibe coding tools can also be cost-effective.
In one case, I was quoted $55,000 and a three-month timeline to build an interactive calculator for a client.
Using Replit, I built a more robust version in under a week on a $20-per-month plan.
Beyond efficiency, the most important reason to develop these skills is the ability to teach them.
Helping clients learn how to build and adapt alongside you is increasingly part of the value agencies provide.
In a widely shared LinkedIn post about how agencies should approach AI, Chime CMO Vinneet Mehra argued that agencies and holding companies need to move from “we’ll do it for you” to “we’ll build it with you.”
In-house teams aren’t going away, he wrote, so agencies need to partner with them by offering copilots, playbooks, and embedded pods that help brands become AI-native marketers.
Being early to adopt and understand vibe coding can become a competitive advantage.
Used well, it allows teams to navigate a zero-click search environment while empowering clients and strengthening long-term working relationships – the kind that make agencies harder to replace.
Top vibe coding platforms for search marketers
There are many vibe coding platforms on the market, with new ones continuing to launch as interest grows. Below are several leading options worth exploring.
AI development tool and experience level
Pros
Cons
Google AI Studio (Intermediate)
• Direct access to Google’s latest Gemini models. • Seamless integration with Google ecosystem (Maps, Sheets, etc.). • Free tier available for experimentation.
• Locked into Google’s ecosystem and Gemini models. • Limited flexibility compared to open platforms. • Smaller community/resources compared to established tools.
• Relatively new platform with less maturity. • Limited customization for complex applications. • Generated code may need refinement for production.
Figma Make (Intermediate)
• Seamless design to code workflow within. • Ideal for teams already using Figma. • Bridges gap between designers and developers.
• Requires Figma subscription and ecosystem. • Newer tool, still evolving features. • Code output may need developer review for production.
Replit (Intermediate)
• All-in-one platform (code, deploy, host). • Strong integration capabilities with third-party tools. • No local setup required.
• Performance can lag compared to local development. • Free tier has significant limitations. • Fees can add up based on usage.
Cursor (Advanced)
• Powerful AI assistance for experienced developers. • Works locally with your existing workflow. • Advanced code understanding and generation.
• Steeper learning curve, requires coding knowledge. • Need to download the software GitHub dependency for some features.
For beginners:
Lovable is the most user-friendly option for those with little coding experience.
Figma Make is also intuitive and works well for teams already using Figma.
Replit is also relatively easy to use and does not require prior coding experience.
For developers, Replit and Cursor offer deeper tooling and are better suited for integrations with other systems, such as CRMs and CMS platforms.
Google AI Studio is broader in scope and offers direct connections to Google products, including Google Maps and Gemini, making it useful for teams working within Google’s ecosystem.
You should test several of these tools to find the one that best fits your needs.
I prefer Replit, but I will be using Figma Make because our creative teams already work in Figma.
Bubble is also worth exploring if you are new to coding, while Windsurf may be a better fit for more advanced users.
Practical SEO and PPC applications: What you can build today
There is no shortage of things you can build with vibe coding platforms.
The more important question is what interactive content you should build – tools that do not already exist, solve a real problem, and give users a reason to return.
Conversion focus matters, but usefulness comes first.
Common use cases include:
Lead generation tools
Interactive calculators, such as ROI estimators and cost analyzers.
Quiz funnels with email capture.
Free tools, including word counters and SEO analyzers
Content optimization tools
Keyword density checkers.
Readability analyzers.
Meta title and description generators
Conversion rate optimization
Product recommenders.
Personalization engines.
Data analysis and reporting
Custom analytics dashboards.
Rank tracking visualizations.
Competitor analysis scrapers, with appropriate ethical considerations.
Articles can only take you so far in a zero-click environment, where AI Overviews increasingly provide direct answers and absorb traffic.
Interactive content should be an integral part of a modern search and content strategy, particularly for brands seeking to enhance visibility in both traditional and generative search engines, including ChatGPT.
Well-designed tools can earn backlinks, increase time on site, drive repeat visits, and improve engagement signals that are associated with stronger search performance.
For example, we use AI development software as part of the SEO and content strategy for a client serving accounting firms and bookkeeping professionals.
Our research led to the development of an AI-powered accounting ROI calculator designed to help accountants and bookkeeping firms understand the potential return on investment from using AI across different parts of their businesses.
The calculator addresses several core questions:
Why AI adoption matters for their firm.
Where AI can deliver the most impact.
What the expected ROI could be.
It fills a gap where clear answers did not previously exist and represents the kind of experience Google AI Overviews cannot easily replace.
The tool is educational by design.
It explains which tasks can be automated with AI, displays results directly on screen, forecasts a break-even point, and allows users to download a PDF summary of their results.
AI development software has also enabled us to design additional calculators that deliver practical value to the client’s target audience by addressing problems they cannot easily solve elsewhere.
Vibe coding works best when it follows a structured workflow.
The steps below outline a practical process search marketers can use to plan, build, test, and launch interactive tools using AI-powered development platforms.
Step 1: Research and ideation
Run SERP analysis, competitor research, and customer surveys, and use audience research tools such as SparkToro to identify gaps where AI Overviews leave room for interactive tools.
Include sales, PR, legal, compliance, and cybersecurity teams early in the process.
That collaboration is especially important when building tools for clients.
When possible, involve customers or target audiences during research, ideation, and testing.
Step 2: Create your content specification document
Create a content specification document to define what you want to build before you start.
This document should outline functionality, inputs, outputs, and constraints to help guide the vibe coding software and reduce errors.
Include as much training context as possible, such as brand colors, tone of voice, links, PDFs, and reference materials.
The more detail provided upfront, the better the results.
Begin with wireframes and front-end design before building functionality.
Replit prompts for this approach during setup, and it helps reduce rework later.
Getting the design close to final before moving into logic makes it easier to evaluate usability.
Design changes can always be made later.
Step 4: Prompt like a product manager
After submitting the specification document, continue prompting to refine the build.
Ask the AI why it made specific decisions and how changes affect the system.
In practice, targeted questions lead to fewer errors and more predictable outcomes.
Step 5: Deploy and test
Deploy the tool to a test URL to confirm it behaves as expected.
If the tool will be embedded on other sites, test it in those environments as well.
Security configurations can block API calls or integrations depending on the host site.
I encountered this when integrating a Replit build with Klaviyo.
After reviewing the deployment context, the issue was resolved.
Step 6: Update the content specification document
Have the AI update the content specification document to reflect the final version of what was built.
This creates a record of decisions, changes, and requirements and makes future updates or rebuilds easier.
Save this document for reference.
Step 7: Launch
Push the interactive content live using a custom domain or by embedding it on your site.
Plan distribution and promotion alongside the launch.
This is why involving PR, sales, and marketing teams from the beginning of the project matters.
They play a role in ensuring the content reaches the right audience.
The dark side of vibe coding and important watchouts
Vibe coding tools are powerful, but understanding their limitations is just as important as understanding their strengths.
The main risks fall into three areas:
Security and compliance.
Price creep.
Technical debt.
Security and compliance
While impressive, vibe coding tools can introduce security gaps.
AI-generated code does not always follow best practices for API usage, data encryption, authentication, or regulatory requirements such as GDPR or ADA compliance.
Any vibe-coded tool should be reviewed by security, legal, and compliance professionals before launch, especially if it collects user data.
Privacy-by-design principles should also be documented upfront in the content specification document.
These platforms are improving.
For example, some tools now offer automated security scans that flag issues before deployment and suggest fixes.
Even so, human review remains essential.
Price creep
Another common risk is what could be described as the “vibe coding hangover.”
A tool that starts as a quick experiment can quietly become business-critical, while costs scale alongside usage.
Monthly subscriptions that appear inexpensive at first can grow rapidly as traffic increases, databases expand, or additional API calls are required.
In some cases, self-hosting a vibe-coded project makes more sense than relying on platform-hosted infrastructure.
Hosting independently can help control costs by avoiding per-use or per-visit charges.
Technical debt
Vibe coding can also create technical debt.
Tools can break unexpectedly, leaving teams staring at code they no longer fully understand – a risk Karpathy highlighted in his original description of the approach.
This is why “Accept all” should never be the default.
Reviewing AI explanations, asking why changes were made, and understanding tradeoffs are critical habits.
Most platforms provide detailed change logs, version history, and rollback options, which makes it possible to recover when something breaks.
Updating the content specification document at major milestones also helps maintain clarity as projects evolve.
Vibe coding is your competitive edge
AI Overviews and zero-click search are changing how value is created in search.
Traffic is not returning to past norms, and competing on content alone is becoming less reliable.
The advantage increasingly goes to teams that build interactive experiences Google cannot easily replicate – tools that require user input and deliver specific, useful outcomes.
Vibe coding makes that possible.
The approach matters: start with research and a clear specification, design before functionality, prompt with intent, and iterate with discipline.
Speed without structure creates risk, which is why understanding what the AI builds is as important as shipping quickly.
The tools are accessible. Lovable lowers the barrier to entry, Cursor supports advanced workflows, and Replit offers flexibility across use cases.
Many platforms are free to start. The real cost is not testing what’s possible.
More importantly, vibe coding shifts how teams work together.
Agencies and in-house teams are moving from “we’ll do it for you” to “we’ll build it with you.”
Teams that develop this capability can adapt to a zero-click search environment while building stronger, more durable partnerships.
Build something. Learn from it. The competitive advantage is often one prompt away.
Large language models (LLMs) like ChatGPT, Perplexity, and Google’s AI Overviews are changing how people find local businesses. These systems don’t just crawl your website the way search engines do. They interpret language, infer meaning, and piece together your brand’s identity across the entire web. If your local visibility feels unstable, this shift is one of the biggest reasons.
Traditional local SEO like Google Business Profile optimization, NAP consistency, and review generation still matter. But now you’re also optimizing for models that need better context and more structured information. If those elements aren’t in place, you fade from LLM-generated answers even if your rankings look fine. When you’re focusing on a smaller local audience, it’s essential that you know what you have to do.
Key Takeaways
LLMs reshape how local results appear by pulling from entities, schema, and high-trust signals, not just rankings.
Consistent information across the web gives AI models confidence when choosing which businesses to include in their answers.
Reviews, citations, structured data, and natural-language content help LLMs understand what you do and who you serve.
Traditional local SEO still drives visibility, but AI requires deeper clarity and stronger contextual signals.
Improving your entity strength helps you appear more often in both organic search and AI-generated summaries.
How LLMs Impact Local Search
Traditional local search results present options: maps, listings, and organic rankings.
LLMs don’t simply list choices. They generate an answer based on the clearest, strongest signals available. If your business isn’t sending those signals consistently, you don’t get included.
If your business information is inconsistent and your content is vague, the model is less likely to confidently associate you with a given search. That hurts visibility, even if your traditional rankings haven’t changed. As you can see above, these LLM responses are the first thing that someone can see in Google, not an organic listing. This doesn’t even account for the growing number of users turning to LLMs like ChatGPT directly to answer their queries, never using Google at all.
How LLMs Process Local Intent
LLMs don’t use the same proximity-driven weighting as Google’s local algorithm. They infer local relevance from patterns in language and structured signals.
They look for:
Reviews that mention service areas, neighborhoods, and staff names
Schema markup that defines your business type and location
Local mentions across directories, social platforms, and news sites
Content that addresses questions in a city-specific or neighborhood-specific way
If customers mention that you serve a specific district, region, or neighborhood, LLMs absorb that. If your structured data includes service areas or specific location attributes, LLMs factor that in. If your content references local problems or conditions tied to your field, LLMs use those cues to understand where you fit.
This is important because LLMs don’t use GPS or IP address at the time of search like Google does. They are reliant on explicit mentions and pull conversational context, IP-derived from the app to get a general idea, so it’s not as proximity-exact relevant to the searcher.
These systems treat structured data as a source of truth. When it’s missing or incomplete, the model fills the gaps and often chooses competitors with stronger signals.
Why Local SEO Still Matters in an AI-Driven World of Search
Local SEO is still foundational. LLMs still need data from Google Business Profiles, reviews, NAP citations, and on-site content to understand your business.
These elements supply the contextual foundation that AI relies on.
The biggest difference is the level of consistency required. If your business description changes across platforms or your NAP details don’t match, AI models sense uncertainty. And uncertainty keeps you out of high-value generative answers. If a user has a more specific branded query for you in an LLM, a lack of detail may mean outdated/incorrect info is provided about your business.
Local SEO gives you structure and stability. AI gives you new visibility opportunities. Both matter now, and both improve each other when done right.
Best Practices for Localized SEO for LLMs
To strengthen your visibility in both search engines and AI-generated results, your strategy has to support clarity, context, and entity-level consistency. These best practices help LLMs understand who you are and where you belong in local conversations.
Focus on Specific Audience Needs For Your Target Areas
Generic local pages aren’t as effective as they used to be. LLMs prefer businesses that demonstrate real understanding of the communities they serve.
Write content that reflects:
Neighborhood-specific issues
Local climate or seasonal challenges
Regulations or processes unique to your region
Cultural or demographic details
If you’re a roofing company in Phoenix, talk about extreme heat and tile-roof repair. If you’re a dentist in Chicago, reference neighborhood landmarks and common questions patients in that area ask.
The more local and grounded your content feels, the easier it is for AI models to match your business to real local intent.
Phrase and Structure Content In Ways Easy For LLMs to Parse
LLMs work best with content that is structured clearly. That includes:
Straightforward headers
Short sections
Natural-language FAQs
Sentences that mirror how people ask questions
Consumers type full questions, so answer full questions.
Instead of writing “Austin HVAC services,” address: “What’s the fastest way to fix an AC unit that stops working in Austin’s summer heat?”
LLMs understand and reuse content that leans into conversational patterns. The more your structure supports extraction, the more likely the model is to include your business in summaries.
Emphasize Your Localized E-E-A-T Markers
LLMs evaluate credibility through experience, expertise, authority, and trust signals, just as humans do.
Strengthen your E-E-A-T through:
Case details tied to real neighborhoods
Expert commentary from team members
Author bios that reflect credentials
Community involvement or partnerships
Reviews that speak to specific outcomes
LLMs treat these details as proof you know what you’re talking about. When they appear consistently across your web presence, your business feels more trustworthy to AI and more likely to be recommended.
Use Entity-Based Markup
Schema markup is one of the clearest ways to communicate your identity to AI. LocalBusiness schema, service area definitions, department structures, product or service attributes—all of it helps LLMs recognize your entity as distinct and legitimate.
The more complete your markup is, the stronger your entity becomes. And strong entities show up more often in AI answers.
Spread and Standardize Your Brand Presence Online
LLMs analyze your entire digital footprint, not just your site. They compare how consistently your brand appears across:
Social platforms
Industry directories
Local organizations
Review sites
News or community publications
If your name, address, phone number, hours, or business description differ between platforms, AI detects inconsistency and becomes less confident referencing you. It’s also important to make sure more subjective factors like your brand voice and value propositions are also consistent across all these different platforms.
One thing that you may not be aware of is that ChatGPT uses Bing’s index, so Bing Places is one area to prioritize building your presence. While it’s not necessarily going to mirror how Bing will display in the search engine, it uses the data. Things like Apple Maps, Google Mps, and Waze are also priorities to get your NAP info.
Use Localized Content Styles Like Comparison Guides and FAQs
LLMs excel at interpreting content formats that break complex ideas into digestible pieces.
Comparison guides, cost breakdowns, neighborhood-specific FAQs, and troubleshooting explainers all translate extremely well into AI-generated answers. These formats help the model understand your business with precision.
If your content mirrors the structure of how people search, AI can more easily extract, reuse, and reference your insights.
Internal Linking Still Matters
Internal linking builds clarity, something AI depends on. It shows which concepts relate to each other and which topics matter most.
Connect:
Service pages to related location pages
Blog posts to the services they support
Local FAQs to broader category content
Strong internal linking helps LLMs follow the path of your expertise and understand your authority in context.
Tracking Results in the LLM Era
Rankings matter, but they no longer tell the full story. To understand your AI visibility, track:
Branded search growth
Google Search Console impressions
Referral traffic from AI tools
Increases in unlinked brand mentions
Review volume and review language trends
This is easier with the advent of dedicated AI visibility tools like Profound.
The goal here is to have a method to reveal whether LLMs are pulling your business into their summaries, even when clicks don’t occur.
As zero-click results grow, these new metrics become essential.
FAQs
What is local SEO for LLMs?
It’s the process of optimizing your business so LLMs can recognize and surface you for local queries.
How do I optimize my listings for AI-generated results?
Start with accurate NAP data, strong schema, and content written in natural language that reflects how locals ask questions.
What signals do LLMs use to determine local relevance?
Entities, schema markup, citations, review language, and contextual signals such as landmarks or neighborhoods.
Do reviews impact LLM-driven searches?
Yes. The language inside reviews helps AI understand your services and your location.
Conclusion
LLMs are rewriting the rules of local discovery, but strong local SEO still supplies the signals these models depend on. When your entity is clear, your citations are consistent, and your content reflects the real needs of your community, AI systems can understand your business with confidence.
These same principles sit at the core of both effective LLM SEO and modern local SEO strategy. When you strengthen your entity, refine your citations, and create content grounded in real local intent, you improve your visibility everywhere—organic rankings, map results, and AI-generated answers alike.
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