BacklinkoModify

2026-01-08 01:40:35

With AI tools at everyone’s fingertips, what does “great” content writing mean in 2026?

Content writing is about using words and psychology to deliver value, earn trust, and move readers toward action.

It includes blog posts, social media content, newsletters, and white papers. Or it can be scripts for video, podcasts, and presentations.

Unlike copywriting, which persuades the audience to take an action, content writing builds trust through teaching.

Thanks to AI tools, filling pages is easier and faster than ever.

And as content becomes easier to produce, attention becomes harder to earn — whether readers are scrolling social feeds, skimming search results, or asking AI tools for quick answers.

The best content writers bring a full toolkit: deep research, sharp critical thinking, strategic judgment, and the ability to apply those strengths in ways AI can’t replicate.

In this guide, you’ll learn eight content writing skills that set top performers apart, shaped by my work with leading brands and insights from my colleagues at Backlinko.

Important: Research and editing are learnable skills. But the instinct for what makes content memorable — what makes someone stop scrolling, what creates emotional resonance — that’s the human layer AI can’t recreate.

1. Build and Hone Your Research Skills

Strong research is what separates fluff from content people trust.

Here’s how to build a hands-on research process.

Start with Your Audience

Audience research is the easiest way to understand your readers: their pain points, goals, and hesitations.

Start your research in a few simple but effective ways:

• Mine social media platforms to find emotional drivers behind buying decisions
• Skim product reviews to learn what excites or frustrates your audience
• Talk directly to your audience through polls, surveys, or 1:1 interviews
• Browse community forums to see real conversations around your subject

For example, if you’re writing about the “best SaaS tools,” don’t rely on generic feature lists to inspire your content.

Go where real SaaS buyers are sharing feedback — places like G2 reviews and user-generated forums like Reddit.

These insights help you create genuinely helpful content that connects with readers.

Rosanna Campbell, a senior writer for Backlinko, shares what she looks for when researching an audience:

At a minimum, I like to spend time learning the jargon, current issues, etc., affecting my target reader — usually by lurking on platforms like Reddit, Quora, industry forums, LinkedIn threads, etc. I’ll also find one or two leading voices and read some of their recent content.

But you don’t have to do all the heavy lifting yourself.

AI can speed up much of this process.

Note: AI won’t write great content for you, but it can streamline your research and editing process. Throughout this guide, I’ve included prompts to help you work smarter and faster — not let AI do the thinking for you.

For instance, Michael Ofei, our managing editor, uses a strategic prompt to aggregate audience insights from multiple channels.

Copy/paste this prompt into any AI tool to jumpstart your research (just update your topic description first).

You are a content strategist researching audience pain points for: [TOPIC DESCRIPTION]

RESEARCH SOURCES: Analyze discussions from Reddit, Quora, YouTube comments, LinkedIn posts, and People Also Ask sections from the last 12 months.

PAIN POINT CRITERIA:

• Written as first-person “I” statements
• Specific and actionable (not vague)
• Include emotional context where relevant
• Reflect different sophistication levels (beginner to advanced)

OUTPUT FORMAT: First, suggest 3-5 pain point categories for this topic’s user journey.

Then create a table with:

1. Category (from your suggested categories)
2. Pain Point Statement (first person)
3. User Level (Beginner/Intermediate/Advanced – use one for each pain point)
4. Emotional Intensity (Low/Medium/High)
5. Semantic Queries (related searches)

Aim for 8-12 total pain points that help content rank for both traditional search and LLM responses. Provide only the essential table output, minimize explanatory text.

After using this prompt for the topic “journalist outreach,” Michael received a helpful list of pain points mapped to user level and emotional intensity.

Perform a Search Analysis

Next, it’s time to review organic search results to assess what content already exists and where you can add value.

Chris Shirlow, our senior editor, stresses the importance of looking closely at who’s ranking and how when studying search results:

Analyzing search results gives me a quick pulse on the topic: how people are talking about it, what questions they’re asking, and even what pain points are showing up. From there, I can identify gaps, spot patterns in language and structure, and figure out how to create something that adds value, rather than just echoing what’s already out there.

Pay attention to:

• Content depth: Is the content shallow (short posts) or comprehensive (long guides)?
• Authority: Who’s ranking — big brands, niche experts, or smaller sites?
• Visuals: What kind of visuals can make your content stand out?
• Gaps and missing angles: What’s missing that you could add?

Then, repeat the same process with large language models (LLMs) like ChatGPT, Claude, and Perplexity.

AI has changed how people discover and consume information.

This means it’s no longer enough to rank on Google; your content also needs to surface in AI-generated answers.

Notice the type of insights coming up in AI-generated responses, and find gaps in the results.

Pay attention to the frequently cited brands and content formats to understand what AI considers “trusted.”

Study those articles closely to see how they’re earning citations and mentions.

Map Out Key Topics with Content Tools

Tools like Semrush’s Topic Research also help you learn more about the topics your audience is interested in.

Enter a topic like “lifecycle email marketing” and you’ll get a visual map of related themes like “loyalty program” and “segmenting your audience.”

This gives you insight into the subtopics to cover, questions to answer, and angles that resonate with your audience.

2. Find Fresh Angles to Create Standout Content

Don’t fall into the trap of rehashing what’s already ranking.

Find new angles and content ideas to break through the crowd.

Angles come from tension. This can be a surprising insight, a common mistake, a high-stakes story, or a view that challenges the norm.

Without tension, you’re just adding to the noise. Here’s how to find them.

Find Gaps in Existing Content

Study the top-ranking and frequently cited articles for your topic, and see what’s missing.

It could be:

• Shallow sections that need a deeper analysis
• Topics explained without visuals, examples, or case studies
• Predictable “safe takes” that ignore alternative perspectives and bold advice

Use this framework to document these gaps.

Consider Opportunities for Information Gain

Information gain adds unique value to your content compared to the existing content on the same topic.

Think original data, free templates, and new strategies.

Basically, it helps your content stand out from the crowd. And creates an “aha” moment for your readers.

Use these tips to add information gain to your articles:

• Find concrete proof: Support your claims with original research, case studies, quotes, or real examples from your own experience or industry experts
• Expand on throwaway insights: Take loosely discussed ideas and cover them in detail with additional context, data, and actionable takeaways
• Counter predictable advice: Stand out with contrarian perspectives, exceptions, or overlooked approaches
• Address unanswered questions: Find what confuses readers and fill those gaps with your content

At Backlinko, our writers and editors consider information gain early in outlining to uncover gaps and add value from the start.

Here’s how our senior editor, Shannon Willoby, approaches it:

I try not to default to common industry sources when gathering research. Everyone pulls from these, which is why you’ll often see industry blogs all quoting the same people, statistics, and insights. Instead, I look for lesser-known sources for information gain, like podcasts with industry experts, webinar transcripts, niche newsletters, and conference presentations. AI tools can also help with this task, but you’ll have to thoroughly vet the recommendations.

In my own article on ecommerce SEO audits, I proposed a simplified, goal-based structure for the outline, with an actionable checklist — something missing from existing content.

This approach gave readers a clearer roadmap instead of just another generic audit guide.

Use AI as a Creativity Multiplier

AI content tools make great sparring partners that enhance your thinking.

For instance, Shannon shares her process for using AI to refine her research.

Once I’ve drafted my main points, I’ll ask ChatGPT or Claude a question like, ‘What’s the next question a reader might have after this?’ This helps me spot gaps and add supporting details that make the article more valuable to the audience.

The following prompts can help you find deeper angles and improve your audience alignment:

3. Back Up Your Points with Evidence

Evidence-backed content gives weight to your arguments and makes abstract ideas easier to digest.

It also helps your content stick in readers’ minds long after they’ve clicked away.

This includes firsthand examples, data, case studies, and expert insights.

The key is using reputable, industry-leading sources in your content writing. And backing up claims with verifiable proof.

Pro tip: LLMs favor evidence-backed content when generating responses — boosting both your authority as a writer and your clients’ visibility.

Here’s how different types of evidence can strengthen your content:

• Recent research data: Backs up trends and industry shifts with hard numbers
• Case studies: Proves outcomes are achievable with real-world results
• Expert quotes: Adds credibility when challenging assumptions or introducing new ideas
• Examples: Makes abstract concepts concrete and relatable

4. Structure Your Ideas in a Detailed Outline

An outline organizes your ideas and insights into a clear structure before you start writing.

It maps out the key sections you’ll cover, supporting evidence, and the order in which you’ll present your points.

For example, here’s the outline I created for my Backlinko article on subdomains vs. subdirectories:

I included a working headline, H2s, and main points. I also added my plans for information gain.

This shows clients or employers how you’ll deliver unique value — and keeps you focused on differentiating your content from the start.

To get started with your outline, think of your core argument: what’s the most important takeaway you want readers to leave with?

From there, use the inverted pyramid to create an intuitive structure.

Include the most important details at the start of every section, then layer additional context as you go.

Pro tip: Save time with Semrush’s SEO Brief Generator. Add your topic and keywords, and it generates a solid outline instantly. From there, you can refine it with your own research and insights.

5. Develop Your Unique Writing Voice

Two people can write about the same topic.

But the one with a distinct voice is the one people quote, bookmark, and remember.

Assess Your Writing Personality

To define your writing personality, start by analyzing how you naturally communicate.

Look at your emails, Slack messages, and social posts.

Notice patterns in tone, humor, pacing, analogies, pop-culture references, or how often you use data and stats.

Then, distill these insights into a few adjectives that describe how you want to sound.

Like professional, insightful, and authoritative.

Use these to guide your writing voice.

For example, let’s say your adjectives are conversational, humorous, and authentic.

Here’s how that might look in practice:

• Conversational: Short sentences with casual, relatable language. “Let’s be real — writing your first draft is 90% staring at a blinking cursor.”
• Humorous: Use wit or funny references to engage readers. Instead of “Most introductions are too long,” you might say, “Most intros drag on longer than a Marvel end-credit scene.”
• Authentic: Add stories from your lived experiences to make people feel seen. “When I first launched my blog, my mom was my only reader for six months.”

Get Inspired by Your Favorite Writers

To keep sharpening your voice, study writers you admire.

Pay attention to their rhythm, tone, and structure.

What terms do they use? How do they hold your attention — whether in a long-form blog post or a quick LinkedIn update?

Borrow what works, then put your own spin on it so it still sounds like you.

Adapt to Your Clients’ Voices

As a content writer, clients and employers will often expect you to adapt your writing to their brand voice.

This might mean adjusting your tone, pacing, or word choice to match their brand’s personality.

Study a few of their blog posts or emails to understand their style.

Note patterns in rhythm and vocabulary, and mirror those in your draft — without losing what makes your writing yours.

AI tools can help you check how well your draft matches your client’s voice.

Upload both the brand’s voice guidelines and your draft to an LLM and use this prompt:

I’ve added the brand voice guidelines and my draft for this brand.

Compare my draft against the guidelines and tell me:

• Where my tone, word choice, or style drifts away from the brand voice
• Specific sentences I should rewrite to better match the guidelines
• Suggestions for how to make the overall flow feel more consistent with the brand voice

6. Add Rich Media to Improve Scannability

Even the best ideas lose impact when hidden behind walls of text.

Plus, research shows that most people skim web pages. Their eyes dart to headlines, opening lines, and anything that stands out visually.

That’s why adding visual breaks, such as images, screenshots, and tables, is so important.

Visual content works well when you want to illustrate a point.

It also simplifies or amplifies ideas that are hard to convey with text alone.

As Chris Hanna, our senior editor, puts it:

Often, words alone just won’t make full sense in the reader’s mind, or they won’t have the desired impact on their own. Anytime you’d personally prefer to see a visual explanation, it’s worth thinking about how you can convey it through visuals. If you can imagine watching a video on the topic you’re writing about, use that as your guide for how you could illustrate it with graphics.

Here are a few places where infographics can supplement your writing:

• • Comparisons:

Tables or side-by-side visuals

• Frameworks and models:

Diagrams or matrices

• Workflows and processes:

Flowcharts or timelines

• Abstract concepts:

Layered visuals (like Venn diagrams)

At Backlinko, we track visual break density (VBD) — the ratio of visuals to text.

Our goal is a visual break density of 12% or higher for every article.

That’s about 12 visuals (images, GIFs, callout boxes, or tables) per 1,000 words to keep content easy to scan and engaging.

Here’s how this looks in practice:

We do this to improve the readability, retention, and engagement of our articles, from start to finish.

7. Understand How to Sell Through Your Content

Every piece of content sells something — a product, a signup, a return visit.

But good content doesn’t read like a pitch.

It gently nudges people to take action by building trust and solving real problems.

Lead with Value

This is what Klaviyo, an email marketing platform, does through its blog content.

They include helpful examples, original data, and actionable tips in their content writing.

But they also weave in product mentions that feel helpful, not salesy.

There are case studies, screenshots, and examples that show how real clients used their platform to increase revenue.

This is smart for a few reasons.

It proves their expertise, reinforces how their product solves real problems, and delivers value — even if the reader never becomes a customer.

Focus on Outcomes, Not Features

People don’t care what a company offers — they care what it helps them achieve.

Features talk about what you offer. Outcomes show people how they can benefit.

Here’s what this looks like in practice:

Show people you understand their frustrations by baking their pain points into your content writing.

When readers sense you’ve been in their shoes, they’re more open to your advice.

Take this HubSpot CRM product page, for example.

​​It highlights real frustrations — setup hassles, messy migrations, lost data — the exact headaches their audience feels.

Then, it shifts to outcomes with copy like “unified data” and higher productivity from “day one.”

That’s outcome-driven content writing. It connects with the audience immediately and makes the benefits crystal clear.

Share Your Firsthand Struggles

Authority matters, but so does humility.

Be honest about your wins and failures. It makes your content feel real.

Here’s an example from one of my Backlinko articles where I shared my struggles with creating a social media calendar:

I relate to the audience with language like “too many tabs” and “overwhelming categorization.”

And provide a free calendar template so readers can apply what they learn.

Pro tip: Free resources, such as tools, frameworks, and templates, make your content more actionable. Even a simple checklist or worksheet can help readers take the next step, and make your work far more memorable.

8. Finalize Your Work

Here’s the truth: your first draft is never your best draft.

Editing is where your content truly comes alive.

Step Away from Your Draft

One of the simplest editing tricks in the book? Give your draft some breathing room.

Chris Shirlow, our senior content editor, explains why:

Spend too much time in an article and you lose all perspective. Take a walk, sleep on it, or do something totally unrelated. When you come back, you’ll see what’s working — and what’s not — much more clearly.

It may take a few rounds of editing and refining before you get everything just right:

• Round 1 (quick wins): Go through the article. Does it flow logically? Is it easy to understand? Do your examples clearly illustrate the core ideas?
• Round 2 (structure): Ask AI for editing feedback. What are you missing? Does the structure/writing flow naturally? Is there any room to add more value?
• Round 3 (polish): Tighten sentences, transitions, audience alignment, and examples

Here’s a prompt you can use for Round 2:

You are an expert editor specializing in long-form content writing. Please analyze my draft on the topic [ADD TOPIC] for its structure, flow, and reader experience.

Specifically, give feedback and suggestions on:

1. Structure: Are the sections ordered logically? Does each section build on the previous one?
2. Depth and focus: Which parts feel under-explained or too detailed? How can I tighten or expand them to improve the flow?
3. Reader journey: Where might readers drop off or lose context?

Summarize your feedback into 3–5 actionable editing priorities.

Pro tip: AI suggestions feel generic? Train the tool on your style first. Both Claude and ChatGPT let you upload writing samples and guidelines so their suggestions align with your voice.

Prioritize Clarity Over Cleverness

If your audience has to re-read a sentence to understand it, you’ve lost them.

As Yongi Barnard, our senior content writer, says:

A clever turn of phrase is nice, but the goal is for readers to understand your point immediately. Edit out any language that makes them pause to figure out what you mean.

Take a quick litmus test: Is this sentence/phrase/word here because it helps my audience, or because I like how it sounds?

You’ll know a sentence/phrase needs to be cut if it…

• Slows down the flow
• Makes the point harder to understand
• Is redundant

Common issues in content writing (and how to fix them) include:

Build Your Personal Editing Checklist

Every writer has blind spots: repeated grammar errors, overused words, or formatting mistakes.

That’s why Yongi suggests creating a personal editing checklist that includes common errors and recurring feedback from editors.

Chris Hanna suggests going through the checklist before submitting your draft:

Run a cmd+F (Mac) or CTRL+F (Windows) search in the doc each time. It’ll help you catch the most important but easy-to-fix errors.

Over time, you’ll naturally make fewer mistakes.

Here’s an editing checklist to get you started:

The Self-Editing Checklist

Big picture

• Does the piece serve the reader (not me)?
• Is the main takeaway crystal clear from the start?
• Does the flow make sense, with each section leading naturally to the next?

Clarity and value

• Is every section genuinely useful, not filler?
• Did I back up claims with examples, data, or stories?
• Did I explain the ideas simply enough that my target readers would get it?

Language and style

• Am I prioritizing clarity over cleverness?
• Are any sentences too long or clunky — could I cut or split them?
• Did I cut filler words (actually, very, really, in order to, due to the fact that)?

Engagement

• Did I vary sentence lengths?
• Does the tone feel human — not robotic, not overly formal?
• Is there at least a touch of personality (humor, storytelling, relatability)?

Polish

• Are transitions smooth between sections?
• Did I run a spell-check and grammar-check?
• Did I read it out loud (or edit bottom-up) to catch awkward phrasing?
• Did I run through my personal “repeat offender” list (words/phrases I overuse)?

Final Pass

• Did I add relevant internal links?
• Does the article end with a clear, valuable takeaway?
• Did I include a natural next step (CTA, resource, or link) without sounding pushy?

Pro tip: Use a free tool like Hemingway Editor to tighten your writing. It gives you a readability grade and highlights long sentences, passive voice, and other clarity issues.

How to Become a Content Writer: A Quick Roadmap

If you’re starting from scratch, don’t worry — every great content writer began exactly where you are.

Here’s how to build momentum and get noticed.

Find a Niche You’re Passionate About

The fastest way to level up as a writer? Specialize.

Niching down builds authority — and makes clients trust you faster.

So, pick a niche (or two) and become an expert.

A good niche checks three boxes:

• Passion: You care enough to keep learning and writing when it gets tough
• Potential: There’s growing demand for this information
• Profitability: Businesses invest in content on this topic

Pro tip: Validate before you commit. Check job boards, freelance platforms, and brand blogs to see who’s hiring and publishing in that niche. If both interest and demand line up, you’ve found a winner.

Build Expertise and Authority in Your Niche

Once you pick a niche, become a trusted voice.

This gives you multiple advantages:

• Traditional and AI search engines see your content as authoritative
• Readers are more likely to trust what you say
• Your content is more likely to be shared and quoted

Start with what you know. Draw from your own experiences to add depth and credibility.

For example, the travel writer India Amos built her authority by writing firsthand reviews.

Her Business Insider piece about a ferry ride is grounded in real experience, making the content trustworthy and relatable.

But don’t limit yourself to content writing for clients. Get your name out there.

• Contribute guest posts and expert quotes to reputable sites
• Speak at conferences or webinars
• Share insights on social media

The more you’re cited as an expert, the stronger your credibility.

Learn SEO Fundamentals

SEO basics remain essential to content writing: keyword research, competitive analysis, and on-page optimization.

But you’ll also want to know how to write and structure LLM-friendly content.

YouTube videos, blogs, and courses can help you understand these topics quickly.

It’s also helpful to familiarize yourself with popular SEO tools.

Clients often expect you to know platforms like:

• Clearscope, Surfer SEO, MarketMuse: Content optimization and readability scoring
• Semrush, Ahrefs, Moz: Keyword research and competitive analysis
• Perplexity, ChatGPT, Gemini: AI search insight and prompt-based content discovery

Pro tip: Consider pursuing niche-specific certifications to stand out. This is especially helpful in “Your Money or Your Life” (YMYL) fields like finance, health, or law, where expertise and trust matter most.

Show Proof of Work with a Portfolio

A portfolio showcases what you bring to the table and provides proof of your accomplishments as a writer.

But you don’t have to spend weeks (or months) building one.

What matters most is what’s inside your portfolio, such as:

• A short intro about who you are and what you offer
• Writing samples that showcase your expertise
• Testimonials or references
• Contact information

Tools like Notion, Contra, Authory, and Bento let you design a portfolio in minutes.

For instance, here’s my Authory portfolio:

I like this platform because it automatically adds all articles credited to my name.

You can also invest in a website for more control and search visibility.

I did both — having a portfolio and website helps me improve my online visibility:

LinkedIn can also double as your portfolio.

Add details about each client and link to your articles in the “Experience” section of your profile.

Share your on-the-job insights, feature testimonials, and engage in relevant conversations.

And don’t forget to post your favorite work, from blog posts to copywriting.

Unlike a static site, LinkedIn keeps you visible in real time.

Future-Proof Your Content Writing Skills

Use what you’ve learned here to create content that builds your reputation and lands clients.

Because great content writing doesn’t just fill pages. It opens doors.

And as AI continues to reshape the content world, the best writers don’t resist it — they evolve with it.

So, don’t fear artificial intelligence as a writer. Use it to your advantage.

Read our guide: How to Use AI to Create Exceptional Content. It’s packed with practical workflows, expert insights, and handy prompts that will help you work smarter and stay ahead.

The post Content Writing 101: 8 Skills That Set Top Writers Apart appeared first on Backlinko.

2026-01-02 23:14:36

AI chat is changing how people shop for fashion — fast.

Before AI, buying something as simple as casual leggings meant typing keywords into Google. Then, sifting through pages of results.

Comparing prices. Reading reviews. Getting overwhelmed.

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).

Note: The data in this article comes from Semrush’s AI Visibility Index, August 2025.

The 3 Types of AI Visibility in Fashion

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.

You can also use tools like Semrush’s Influencer Analytics app to discover influencers.

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.

Want to check your brand’s AI visibility?

Try the AI Visibility Toolkit from Semrush to see where your brand stands in AI search, and learn how to optimize.

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.

This is part of LLM seeding.

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.

1. Cultural traction: Like press mentions, creator partnerships, and community visibility
2. 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.

1. 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
2. Refresh imagery and copy to mirror trend language: Update PDPs, on-site copy, and retailer description to match the phrasing used in cultural content
3. 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.

Download our Trend Calendar for Fashion Brands to plan ahead for upcoming trends and create content that matches.

Here’s an Example: UGG

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.

1. Centralize pages on materials, care, and impact: Make them brief, structured, and verifiable. Include materials, sourcing, certifications, and repair/resale info.
2. Maintain third-party profiles: Keep your certifications up-to-date. This includes things like Fair Trade, Bluesign, B-Corp, GOTs, etc.
3. 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.

To get started, download our Fashion Trend Content Calendar to plan your strategy around seasonal trends.

Want to go deeper? Check out our complete guide to AI Optimization.

The post Fashion AI SEO: How to Improve Your Brand’s LLM Visibility appeared first on Backlinko.

2025-12-17 22:53:02

Creating an official brand subreddit is one of the hardest marketing spaces to get right.

Why?

Because it’s hostile ground for traditional brand messaging, with some of the internet’s snarkiest users watching every move.

But if you strike the right balance, the payoff is huge.

Just look at r/fidelityinvestments.

This brand subreddit has grown into a self-sustaining community.

Members trade tips, ask questions, and help resolve issues before the company’s reps even show up.

Fidelity’s still there, keeping things on track.

But because the users trust the brand and the space, conversations flow without much hand-holding.

So how do you get there?

This guide breaks it down.

With details on when to create a brand subreddit, when to skip it, and what separates vibrant communities from ghost towns.

(Plus, tons of examples from standout company-owned subreddits.)

Shoutout to Olena Bomko, go-to-market strategist, for sharing her insights from building a brand subreddit.

Free Resource: Get the Brand Subreddit Launch Kit. It’s everything you need to confidently plan, launch, and manage your community.

Do You Need a Brand Subreddit?

For most brands, the answer is no.

But for a select few, it can become a long-term asset.

You know you’re one of them if:

• Your customers already talk about you in at least three subreddits
• You can engage daily without pitching
• You’ve got a Reddit native to run your subreddit
• You see complaints as free market research, not an insult

Miss one, and you’ll struggle to keep your subreddit alive.

So, let’s look at each in detail.

Is Your Audience Already on Reddit?

Start by confirming whether your audience already talks about you on Reddit.

Run a quick check and look for:

• Multiple subreddits covering your category
• Regular mentions of your brand or competitors
• Questions or troubleshooting threads about your product space

Use Reddit’s search bar as your starting point.

Type in your brand name, your competitors, or keywords related to your industry. Then, see what comes up.

For example, when I search “Semrush enterprise,” I find frequent mentions of Semrush in SEO subreddits.

Users debate its pros and cons, compare it with competitors, and discuss its product features.

That’s organic energy already in motion. So, a brand subreddit just gives it a home.

Related reading: Audience Research: Stop Guessing What Your Buyers Care About

Another thing:

Some product types just naturally fit Reddit’s community culture, including:

• Technical or complex tools: SaaS, software, or tools where users want support and feature breakdowns
• Niche ecommerce brands: Mattresses, supplements, and other high-consideration DTC products people love to compare and review
• Finance and service tools: Banks, brokers, and budgeting apps where transparency matters
• Gaming and entertainment: Games or media with built-in fandoms
• Consumer tech: Gadgets and devices that need troubleshooting and setup discussions
• News and media brands: Outlets and publishers where audiences already debate coverage and breaking stories

Are You Committed to Building a Community?

If your only goal is to “control the narrative,” stop right here.

(I can already hear the Reddit mob sharpening their pitchforks.)

Yes, a brand subreddit can absolutely strengthen your reputation. But only as a byproduct of serving your community first.

Your reason for being should be to create a space where users can connect and feel heard.

For example, r/fidelityinvestments is a customer care channel with official Fidelity associates.

But it’s also a community.

Where members troubleshoot for each other, share feedback, and even defend the brand when criticisms pop up.

Do You Have an Assigned Moderator?

Someone has to own your brand subreddit.

And they need to be there every day:

Sparking conversations and posting prompts. Plus, modelling the tone you want until the community naturally mirrors it.

That takes a rare mix of skills:

• Technical familiarity with your product
• Context across marketing, support, and PR
• Sharp community instinct and tone awareness

Without that person, keeping your subreddit healthy will always feel like a grind.

Are You Cool with Public Scrutiny?

Even the best teams take hits on Reddit.

The question is: Can you handle it?

Because you will get complaints, and you will get called out.

Sometimes, it’s a full-blown PR storm. Like when REI’s CEO hosted an AMA and got flooded with employee complaints about wages, hours, and sales quotas.

Other times, it’s smaller.

Like when a Sonos marketing email revealed someone’s password.

Big or small, the spotlight’s the same.

And the internet expects one thing:

That you stand there, take it, and handle it in stride.

(To their credit, both the REI CEO and u/keithfromSonos did just that.)

So, ask yourself:

“Do we have a team that can handle that pressure and keep the tone steady?”

If not, skip the brand subreddit rather than lose your cool in public for everyone to screenshot.

Alternatives to a Brand Subreddit

If you don’t meet the above conditions, it doesn’t mean you can’t be on Reddit.

You can still build visibility without launching an official community.

Start by getting active in existing unofficial brand-related subreddits.

GoPro, for example, doesn’t run r/gopro.

Yet, it’s one of the most vibrant product spaces on the platform.

Another option is to create a non-branded subreddit around your niche.

For example, if you sell hiking gear, launch r/TrailTips or r/UltralightKit.

You still get visibility without the pressure of running an official branded space.

Another alternative is using your user account as your brand’s central presence.

Many media companies do this well. Like The Washington Post at u/washingtonpost/ and Drop.com at u/drop_official/.

How to Create a Company Subreddit (5 Steps)

Think a company subreddit fits your brand?

Perfect! When done right, it can deliver real results, including:

• Deeper customer insights
• A self-sustaining community
• More visibility in SEO and large language models (LLMs)

“Our share of voice has definitely improved. Two months ago, Reddit Answers didn’t even mention Favikon when I searched for the best influencer marketing platforms. Now, it’s up there in Reddit’s search results.”

– Olena Bomko

Ready to build yours? Let’s get into it.

Step 0: Meet the Minimum Requirements

Before creating a subreddit, become a Redditor first.

Spend time on the platform and learn the culture.

Observe how conversations flow, how moderators maintain order, and what earns trust.

Further reading: Reddit Marketing in 3 Hours/Week: From 0 Karma to Real Cred

Reddit also has minimum requirements before you can create a community:

• Your account must be at least 30 days old
• You need positive karma (the exact threshold isn’t public)

Translation:

You have to earn your place before you can build one.

Step 1. Get Clear on Your Goals

Before you create a brand subreddit, pick one primary goal for your community.

This dictates how you run the subreddit:

• What you post
• How you show up
• How you measure success

(We’ll talk about cadence, staffing, and moderation in later steps.)

Plus, when everyone knows the “why,” every post naturally lines up with it.

Side note: Your community can support other goals. But your primary goal should define how you measure success. That’s what makes it easier to see whether it’s actually working.

For example, a support-first subreddit focuses on speed, accuracy, and trust.

It needs moderators who know the product and can solve problems publicly.

r/fidelityinvestments is an example of this.

Verified associates answer customer questions, while pinned announcements guide users through service updates.

And, if they were tracking key performance indicators (KPIs), they’d likely focus on response time and resolution rate.

Now, compare that to a community-first subreddit.

It usually thrives on curation, conversation, and peer support.

Moderators act more like hosts, encouraging user-generated content (UGC) and keeping discussions flowing.

r/LifeOnPurple runs this way.

The mattress brand posts lightly, shares occasional updates, and lets UGC drive momentum.

Their key metrics probably include:

• Percentage of UGC
• Active users
• Returning posters

Common Brand Subreddit Goals

Here are the top three core goals most brand subreddits serve.

Choose one, commit to it, and let the rest orbit naturally to keep your subreddit focused.

Step 2: Put People (and Rules) in Place

Once you’ve set your goals, decide who’ll run the subreddit. And how.

The right person (or team) makes sure that:

• Questions get answered quickly
• Moderation feels fair
• Brand messaging stays consistent

Start by assigning one primary moderator.

They’ll be accountable for growth, moderation quality, and reporting insights.

In most teams, that’s your community manager, social media lead, or support head.

Preferably, someone who knows the product and understands community dynamics.

But a great subreddit is rarely a one-person show.

So, make sure your moderator has access to others in the company.

Here’s how that can look depending on your subreddit type:

• Support-heavy subreddits: Include a product specialist or customer service rep who can jump in fast
• Community-first spaces: Bring in someone from marketing or content to spark conversations or highlight great posts
• Developer or technical subs: Involve a product manager or engineer who can step in when discussions get technical

For example, r/SEMrush is run by Semrush employees who actively join conversations and clarify product questions when needed.

In contrast, r/hubspot’s moderators are a combination of members from the HubSpot support team and a power user.

Bring Key People to Your Subreddit

You should also have a few “guest stars” lined up.

These are your execs, product managers (PMs), or team leads.

They don’t need to be available all the time.

But, having them join conversations signals two things: access and accountability.

For example, as Favikon builds its company subreddit in its early stages, the team regularly runs AMAs with leaders and associates.

Define Your Ground Rules

Everyone who represents your brand on Reddit should know exactly how to show up.

So, create an internal guide — like a company subreddit playbook — outlining how your brand speaks and behaves on Reddit.

At a minimum, cover these areas:

• Brand tone: How your company sounds when it speaks
• Disclosure: Make it clear you’re speaking for the brand. Use verified handles or flairs like “Official Response” or “From the CEO.”
• Confidentiality: Define what can be shared publicly vs. what stays internal
• Escalation: Outline how moderators flag issues to support, PR, or product teams
• Response guidelines: When to jump in, when to step back, and when to let the community self-resolve
• Moderation scenarios: How to handle misinformation, conflict, or spam consistently and fairly
• Crisis protocols: Who leads if a post goes viral, a complaint snowballs, or a product issue surfaces

Reality check: You don’t need an extensive playbook on day one. Start with the essentials that help moderators act confidently. Then, evolve it as your subreddit — and your instincts — mature.

Step 3: Set Up Your Subreddit

With your moderators and rules ready, it’s time to build the actual space.

To set it up, use a desktop. It’s much smoother than mobile.

Start by clicking “Start a ccommunity” in the left-hand sidebar.

You’ll see a pop-up window that walks you through setup.

Here’s what matters most in each step.

Pick the Right 3 Topics

First, you’ll be asked to choose three topics your community belongs to.

These help Reddit’s discovery algorithm surface your subreddit to the right users.

So, your topic choice could affect who finds you.

In other words:

Treat topic selection like SEO for community discovery.

Choose Your Community Type

Next, decide how open your subreddit will be:

• Public: Best for most brand launches
• Restricted: Useful for soft launches
• Private: Good for internal pilots or early betas
• Mature (18+): Only if your content genuinely requires age restriction.

Most brands should go “Public” for organic reach.

But there are also situations where “Private” or “Restricted” makes sense.

For example, if you want to keep everything hidden while you build, set it to “Private.”

And, if you’re not launching yet — but you want to own the URL before someone else grabs it — go “Restricted.”

Just remember, switching later requires Reddit’s approval.

Name Your Subreddit

Next comes naming your community.

This one’s permanent. So, check spelling and capitalization.

Stick with r/YourBrand or r/yourbrand when possible.

If it’s taken, use a clear variant such as r/YourBrandOfficial, r/YourProduct, or r/YourBrandSupport.

Here are a few examples:

• r/0xPolygon (Polygon Labs)
• r/SEMrush (Semrush)
• r/LifeOnPurple (Purple Mattress)

Next, add a short description in the field below the subreddit name.

You can update this anytime.

So, keep it simple for now. (Unless you’ve already got a strong one.)

An effective subreddit description should:

• Say who it’s for
• Say what members can do
• Set expectations

For example, Favikon’s description clearly states what the community is for and what the brand will provide.

It’s obvious that the space serves both the community (creators) and the brand’s updates.

Fidelity’s description, on the other hand, is clear that it’s a customer care channel. With Fidelity associates answering product-related questions.

It also clarifies that they don’t handle account-specific issues:

A small but crucial detail that manages expectations early.

Add Visuals to Make It Look Official

After writing your description, it’s time to add visuals:

Specifically, your icon and banner.

For your icon, upload a recognizable asset, such as your logo.

This helps users instantly see that the subreddit is official.

Next, add your banner.

A 1920 x 384 pixel image works best, though Reddit also allows slimmer options like 1920 x 256 or 1920 x 128.

Your banner should reflect your brand identity without feeling like an ad.

The r/LifeOnPurple subreddit, for example, uses the Purple Mattress logo and a clean purple banner consistent with its brand design.

But r/MobileLegendsGame uses detailed artwork that fits its gaming audience.

Once you’ve uploaded your logo and banner, click “Create Community.”

And voila! That’s your subreddit live.

Step 4: Personalize and Prepare for Launch

Once your subreddit exists, the next step is to make it feel alive.

Do these four things to make it feel welcoming:

1. Add clear community rules
2. Write and pin a welcome post
3. Add a few starter threads
4. Set up sticky highlights

Let’s walk through each.

Define Community Rules

Every subreddit needs community rules.

They define the kind of space you’re building.

You don’t need a long list, especially at the start. Four to six guidelines are enough to set expectations and boundaries.

Cover the basics first:

• No spam
• Be respectful
• Don’t share personal information

Then, add one or two brand-specific rules.

For example, r/mintmobile, a community with heavy customer engagement, adds a rule against spreading false information.

Plus, a reminder not to post personal details.

While r/hubspot, a fairly new subreddit, has only three rules.

To add rules, click “Mod Tools” at the top right sidebar of your subreddit page.

Then, scroll down to the “MODERATION” section in the left sidebar.

Click “Rules” > “Create Rule.”

Pro tip: Spend time exploring Mod Tools. That’s where you customize your subreddit’s look, rules, and automation. The more familiar you are with that panel, the smoother your moderation as the community grows.

Write the Welcome Post

A welcome post helps new visitors understand what the subreddit’s for and how to participate.

There’s no single right format.

Just make it clear and approachable.

r/reolinkcam, for example, uses a pinned “Please Read This Before Posting” thread.

It starts with short, practical guidance, followed by a quick intro, links to product setup guides, and an FAQ section.

r/Comcast_Xfinity takes a different approach.

Its welcome post lays out the community code of conduct, explains how to use flairs, and summarizes key rules.

To create your first post, click “Create Post.”

It’s at the top right corner of your subreddit page.

Post Conversation Starters

Once your welcome post is live, add a few early posts to make your community feel active.

Some threads you can write include:

• FAQ: Answer common support or sales questions your team already gets
• Product updates or announcements: Share new releases to keep people in the loop
• Community guidelines: Restate the rules and add context, like where to report bugs or how to tag posts
• How to/tutorial: Solve a top recurring problem. It reduces tickets and becomes a reusable resource.

Pin Community Highlights

Sticky posts are the first thing visitors see when they land on your subreddit.

They’re pinned to the top of your feed.

When used well, they double as trust signals. A kind of proof that your brand is active and organized.

Start by pinning your “Welcome Post,” then layer in others as your community grows.

For example, r/SEMrush keeps its biggest updates (like the AI Visibility Toolkit launch) and company news pinned.

This way, new visitors instantly see what’s new.

Meanwhile, r/fidelityinvestments often features

• Engagement prompts
• Weekly Q&As
• Official announcements

To make any post sticky, open the post, scroll down, and click the shield icon.

Then, select “Add to highlights.”

That post will now appear at the top of your subreddit.

Step 5: Launch Your Subreddit

Now that everything’s in place, it’s time to spark the first lights of community.

Invite Founding Members

Founding members help set the tone and the tempo of your brand subreddit.

Ideally, they’re your superfans. People who already share your enthusiasm.

They’re usually:

• Power users who love your product
• Loyal customers who actively engage
• Industry peers who enjoy sharing what they know

These voices bring authenticity and fill your first threads with real conversation.

They’ll also help define your culture.

So, treat them like subreddit co-founders, not just early users.

How do you get them?

Start with a simple, genuine invitation.

A one-on-one message always beats a mass announcement.

Announce It Publicly (But Frame It Right)

Once you’ve got a few active members and threads, announce your subreddit in your owned channels, including:

• Email
• Slack community
• Social media
• Company website

But don’t pitch it as “a place to follow us.”

Frame it as a shared space where your team and users exchange insights, solve problems, and showcase projects.

You can also invite followers from other platforms when there’s something happening — like an AMA or live discussion.

The way Olena does it on X, for example.

This approach builds awareness and attracts people who genuinely want to be part of your community.

Cross-Promote in Related Subreddits (Carefully)

If you or your team already participates in related subreddits, mention your new community when it genuinely adds value to a discussion.

Side note: Always check each subreddit’s rules first. Many ban self-promotion.

This tactic works best when your user account already has credibility in that subreddit.

If people recognize your username from your past helpful comments, the subreddit mention feels natural, not sneaky.

Pro tip: NEVER ask employees to pose as independent users to promote your brand. That’s called astroturfing — and it’s one of the fastest ways to destroy credibility on Reddit.

How to Keep Your Brand Subreddit Alive

Once your founding members are active, the real work begins:

Keeping your subreddit alive and thriving.

You don’t need dozens of posts a day, but you do need steady participation.

Moderate and Engage Consistently

How often you show up depends on your subreddit’s purpose, but the principle stays the same:

Be present.

• Respond quickly: Aim to reply within 24 hours
• Enforce rules fairly: Remove spam and toxic behavior, but don’t over-police
• Check in daily (or at least on weekdays): Even 15–20 minutes a day keeps threads from going unanswered

For example, moderators in r/Comcast_Xfinity regularly pin troubleshooting threads and reply to outage questions.

From their flairs alone, you can tell they’re listening and available.

Side note: A flair is a small label that appears next to a username or post title. It adds instant context to every interaction. You can customize flairs in Mod Tools.

Start Meaningful Rituals and Events

Rituals keep communities alive and give people a reason to come back.

Some easy ones to start include:

• Weekly or monthly megathreads for support or feedback
• Recurring posts like “Feedback Friday” or “Tutorial Tuesday”
• Regular AMAs with your CEO or product team
• Community contests or creative prompts

Keep these rituals going long enough, and people start showing up out of habit.

It becomes a place where regulars connect through shared threads and interests.

And that’s how your subreddit turns from just another space into a familiar home.

Not sure where to start?

Look at non-brand subreddits for inspiration.

For example, r/bullcity — Durham, North Carolina’s official subreddit — has a biweekly anything goes thread.

This is where people can add any posts that “would otherwise be considered spam” into the thread.

It’s pinned in the community highlights and keeps local conversations active and open.

Encourage User Contributions

Invite members to share their own tips, advice, and projects.

Then, amplify their participation:

• Make a special flair for “Top Contributor”
• Highlight the most useful tips
• Feature a “Member of the Month”

These small bits of recognition let people know their voice matters. And can turn a casual user into a loyal regular.

Pro tip: Reddit’s spam filter can be overzealous. Keep an eye on auto-removed posts so real users don’t lose motivation.

Handle Criticism Transparently (and With Grace)

Negative posts are inevitable, and deleting them is the worst move you can make.

Instead, respond honestly. Acknowledge the issue, and explain what’s being done about it.

Even if your answer isn’t perfect, that transparency helps build credibility.

To see how it’s done well, look at how other brands handle criticism or answer tough questions.

For example, Beardbrand owner, u/bandholz, once replied to the question:

“Is Beardbrand just not great anymore?” in a calm and factual way.

This turned a critical post into a constructive discussion.

Track Your Subreddit Engagement and Growth

To grow your subreddit, think less about control and more about connection.

And always watch the engagement:

Are members helping each other? Are discussions happening without you prompting them?

When activity dips, nudge it with a new prompt or AMA.

When it grows, resist the urge to overmanage.

Then, use Reddit Analytics to see whether the community is growing or slowing.

This helps you quickly gauge what’s working.

“I spend time in Reddit’s native analytics tools. They’re not super detailed, but I can track member growth and weekly contributions. I can also see daily numbers for posts, comments, and unique users. For what I do — and what I need to track right now — that’s more than enough.”

Make Your Brand Subreddit the Hub

Your brand subreddit works best as part of a complete Reddit presence, not in isolation.

Once it’s well-established, blend it with smart Reddit marketing, including ads, partnerships, and organic participation.

That’s when Reddit stops being just another forum and becomes an ecosystem that grows your visibility and your credibility at the same time.

The post How to Build a Brand Subreddit: Full Setup Guide (+ Examples) appeared first on Backlinko.

2025-12-13 01:58:08

Modern SEO teams aren’t just optimizing for rankings in traditional search anymore.

They’re also optimizing for visibility in AI-powered search and answer engines.

And that shift is showing up in job listings.

I recently came across this position:

This isn’t an outlier.

Dozens of companies are now posting similar roles, and the shift runs deeper than new job titles.

I reviewed 100+ general SEO job postings.

96% mentioned AI somewhere in the description.

AI is creating entirely new positions, but it’s also changing what existing roles require.

Why?

Because AI search works differently from traditional Google ranking.

It extracts passages, synthesizes information, and presents instant answers from multiple sources.

This shift opens up new visibility opportunities beyond ranking in traditional search engines.

SEO teams that expand their skills now can ensure their brands are visible in AI search.

In this guide, you’ll learn:

• Why traditional SEO skills are no longer enough to cover what AI search requires
• Which AI-era skills your SEO team needs
• How to evolve your existing team (without adding unnecessary new roles)

Want a faster way to apply what you’re about to learn?

Download the AI SEO Team Building Assistant.

Upload it to your preferred AI platform (like ChatGPT or Gemini). Type “START” and follow the conversation.

Once complete, you’ll get a custom one-page plan, a checklist, and more, showing exactly how to evolve your SEO team for AI-first search.

The Skills Gap Between Traditional and AI SEO

The current SEO skill set still matters.

Keyword analysis. Technical optimization. Link building. None of that goes away.

But AI search adds a new layer your team needs to master.

Here’s what I mean:

Traditional SEO gets your pages ranking in top search positions.

AI SEO gets your brand visible in AI-generated answers — through brand mentions, citations, or both.

You’re expanding what SEO covers. Not replacing it.

Let me break down what’s changed and what it means for your team.

What’s Changed

Search behavior itself has evolved a lot over recent years.

A growing number of people don’t just “Google” anymore. They discover, compare, and decide across multiple platforms. (And this has been the case since long before ChatGPT came along.)

Someone might start on TikTok, check Reddit reviews, search on Google, and ask ChatGPT for a summary before taking action. And they might revisit these platforms at various stages of the journey.

That journey looks less like a straight line and more like a network.

Here are five other changes reshaping how search works today:

• Whole-web signals: AI pulls from your website and everywhere else your brand appears online. Your entire digital footprint influences your AI visibility.
• Entity recognition: AI understands your brand as a concept it can connect to products, industries, and related topics, not just keywords to match (learn more in our guide to entity SEO)
• Passage-level retrieval: AI extracts specific sections from your content to use in its answers, not entire pages. This means it needs to be clear what each section of your content is about.
• Conversational search behavior: AI search queries tend to be longer and more specific. People describe problems in detail rather than typing short keywords, which means the AI often cites highly specific content rather than generic guides.
• Zero-click reality: Users can now get complete answers without visiting websites. Traffic from search is no longer guaranteed, even with strong visibility.

What This Means for Your Team

These changes don’t require you to rebuild your team from scratch.

But they do require expanding what your team focuses on:

• Your content team still writes. But now they also need to structure content so AI can easily understand it and extract sections for its answers.
• Your technical SEO team still optimizes site architecture. But priorities shift toward AI crawlability, performance, and schema implementation.
• Your strategist still tracks performance. But now they also need to measure citations and brand mentions across AI platforms.

Most of these skills build on what your team already knows. Again, they’re extensions, not replacements.

4-12 months is a typical timeline to get your team comfortable with AI SEO fundamentals.

You’ll need some combination of internal training, external guidance, and selective hiring — depending on your current gaps. I’ll talk more about this later.

First, let’s break down the specific skills your AI SEO team needs.

Essential AI SEO Skills Your Team Needs

Not everyone needs to be an AI SEO expert in all areas.

One person (typically a lead or strategist) needs strategic understanding. They understand how AI search works and can adapt when platforms change.

The rest of your team needs execution capability. They can follow guidelines and apply best practices.

It’s helpful if they show interest in understanding AI SEO, but it’s not required.

Here are the key skills that bridge traditional SEO and AI search.

Understanding AI Retrieval

AI platforms find and reference content differently from Google’s traditional ranking systems.

Some platforms, like Perplexity, search the web in real-time.

Others, like ChatGPT, can search the web or pull from their training data.

And AI Overviews use Google’s existing index and Gemini’s training data.

To optimize for and appear in these places, your team needs to understand how these systems select what to cite and mention.

When someone asks a question, these platforms look for content that directly answers the query. They prioritize sources that are clearly structured and contextually relevant.

Note: AI systems also use a process called query fan-out. This involves expanding one user prompt into multiple related sub-queries behind the scenes.

That means your content can surface even if it doesn’t match the original question exactly. If it covers a related angle or entity that the AI connects to the topic, it can be cited or mentioned.

Learn more about this in Semrush’s guide to query fan-out optimization.

Who Can Own It?

Your SEO lead or strategist typically owns this skill.

They already understand search intent and ranking logic — the same foundations that AI retrieval builds on.

In smaller teams, a content strategist can also take this on with a shallow learning curve.

Typically, they’ll spend 2-3 hours monthly testing how your brand appears across AI platforms. Document patterns in what gets cited. And adjust content strategy based on what’s working.

Writing for AI Extraction

AI search tools don’t respond to user queries with entire articles. Instead, the AI pulls specific passages that answer those queries.

If a passage requires a lot of surrounding context to make sense, AI may be less likely to understand its relevance and therefore be less likely to use it.

This means each section of your content needs to still make sense even when taken out of the context of the rest of the article.

Each section should answer a specific question on its own, without relying on references to other parts of the article.

This is generally just good writing practice. If you find yourself making too many unique points in one section, it’s probably best to split it into subsections.

But clarity here is also key.

For example, avoid: “As we mentioned earlier, this approach works well…”

Instead, write: “Structuring content into self-contained passages helps AI extract and cite your information more effectively.”

Here’s another example of effective writing for AI extraction:

The second version makes sense whether someone reads your full article or sees just that paragraph in an AI response.

This doesn’t mean every sentence needs a complete context. It means key passages should stand alone.

Who Can Own It?

Your content or editorial team can handle this.

SEO provides the framework and guidelines. Writers implement it in their daily work.

For example, editorial reviews the article structure before publishing, ensuring each section has a clear, standalone takeaway.

Sometimes that means breaking a 500-word section into three shorter subsections with specific headers.

By the way: As a content marketer myself, I don’t think this shift is dramatic.

Most great content teams already write clearly and structure information logically. This just prioritizes ensuring key passages work independently.

Building AI-Readable Structure

AI needs clear signals to understand your site’s structure and how content relates to other pages on your site.

Things like schema markup, internal linking, and clear site hierarchy provide those signals.

For example, schema markup makes your data more structured by defining what your content represents.

This can make it easier for AI systems to interpret and cite your content accurately.

While the full impact is still unclear, structured data makes your content easier to parse, which is helpful for search engines anyway. And since Gemini can lean on Google’s search infrastructure, it’s not all that unreasonable to expect that schema could at least indirectly affect your visibility in places like AI Overviews and AI Mode, now or in the future.

Similarly, internal linking shows how topics connect.

And a clear site hierarchy indicates which pages are most important.

Think of it as creating a map.

Instead of making AI infer relationships, you’re explicitly defining them.

Beyond your site: Entity databases

Once you have the basics down, consider registering your brand and products in databases like Wikipedia, Wikidata, or Crunchbase.

These knowledge bases help AI systems understand entity relationships and how your brand fits into broader industry contexts.

This bridges on-site structure (like schema markup) with off-site presence. You’re helping AI systems recognize your brand across the web, not just on your site.

You don’t need this starting out. But it’s worth exploring once your core AI SEO structure is in place.

Who Can Own It?

Your technical SEO can take ownership of this skill.

They already handle the fundamentals like implementing schema markup, managing site architecture, and optimizing internal linking structures.

The approach doesn’t change much. They’re just applying the same technical skills with AI systems in mind.

Tracking AI Performance

Traditional SEO metrics (like rankings, organic traffic, and click-through rates) still matter.

But they don’t say anything about your brand’s AI search visibility.

You need different metrics now, including:

• Platform breakdown: Where you’re showing up (ChatGPT, Perplexity, Google AI Overviews, etc.)
• Citation frequency: How often your content gets cited as a source in AI responses
• Mention rate: How often your brand appears in AI-generated answers or recommendations
• Mention sentiment: Whether those mentions are positive, neutral, or negative

These numbers indicate whether your AI SEO strategy is working.

Semrush’s AI Visibility Toolkit can help you track these key AI search metrics.

Without specialized tools, you’ll need to manually search key queries across platforms and track when your brand appears.

Who Can Own It?

Your SEO analyst or whoever handles performance reporting can own this.

They’re already tracking traditional metrics. AI performance metrics become an addition to that dashboard.

If using AI visibility tools, they’ll monitor your visibility score and citation trends monthly.

Without specialized tools, they’ll need to manually search key queries across platforms, document when and how your brand appears, and track changes over time.

Further reading: 5 LLM Visibility Tools to Track Your Brand in AI Search

Optimizing Off-Site Signals

AI tools go beyond just looking at your website and pull from everywhere your brand is mentioned online. Including:

• G2 reviews comparing tools
• Reddit threads discussing your product
• Forum conversations about your industry
• News articles mentioning your company

If those mentions are sparse or outdated, AI has less information to pull from when someone searches for your brand specifically or asks about your product category.

This is where AI search extends beyond your domain.

Who Can Own It?

No single person can own this entirely.

PR, community management, and customer success each control different pieces of the puzzle.

Someone from SEO can take the coordination role, ensuring these teams understand how their work affects AI visibility.

In practice, this often means your SEO lead or director works cross-functionally to align off-site efforts with AI discoverability goals.

For example, they work with customer success to encourage reviews on platforms like G2 or Trustpilot.

They also monitor where your brand gets mentioned across forums, social platforms, and community discussions.

Further reading: Search Everywhere Optimization Guide (+ Free Checklist)

Platform-Specific Optimization

Different AI platforms retrieve and display information in their own ways.

For example:

• Perplexity searches the web in real-time and shows numbered citations
• ChatGPT can search the web or pull from its training data
• Google’s AI Overviews draw from Google’s search index and Gemini’s training data

What gets you cited on one platform won’t automatically work on another because each platform follows patterns in what it mentions and cites.

For instance, I searched “which is the best camera phone of 2025” across three platforms.

ChatGPT cited multiple YouTube videos, a Reddit thread, Tom’s Guide, Yahoo, and Tech Advisor.

Google’s AI Mode cited one YouTube video along with a bunch of other websites — no Tom’s Guide, Yahoo, or Tech Advisor.

Claude cited Quora and Android Authority twice. No Reddit threads, YouTube, or Tom’s Guide.

Same query, completely different sources and mentions.

Your team needs to understand these differences when optimizing for AI visibility.

You don’t need separate strategies for each platform. But knowing how different platforms prioritize sources helps you structure your entire approach, from content to technical implementation to off-site presence.

Who Can Own It?

Your SEO lead or strategist can typically own this.

They can track how your brand appears across platforms and identify what’s working where.

They’ll spot gaps in coverage on LLMs that matter to the brand. For example, strong presence in ChatGPT but weak in Perplexity.

Then they work with content, technical, and other teams to adjust the overall strategy.

Query Intent Mapping

People search differently in AI platforms than they do in Google.

Traditional Google: “best CRM software”

ChatGPT: “I need a CRM for a 50-person sales team, budget around $10K annually, must integrate with Salesforce”

The queries are longer. More conversational. More specific.

I checked my own most recent 100 prompts to ChatGPT. They averaged 13 words each.

Compare that to traditional Google searches, which typically run 3-4 words.

Understanding these prompt patterns helps you create content that answers the actual questions people ask AI.

You need to think beyond traditional keywords.

What detailed questions are the people in your audience asking? What context are they providing? What outcome do they want?

Who Can Own It?

Whoever leads keyword research or content planning can take this on, usually your SEO strategist or content planner.

This builds directly on existing keyword research skills.

You’re expanding from “what keywords do people use?” to “what problems are people trying to solve?”

(Which you should have been doing all along, but now with a stronger focus.)

This person will analyze how people search in AI platforms and document the longer, conversational queries they use.

Then they’ll build content briefs that address those specific questions and scenarios.

The Build, Buy, or Borrow Decision: Getting AI SEO Skills on Your Team

You know which skills your team needs.

Now comes the practical question: how do you actually get them?

You have three options:

• Build internally
• Hire new talent
• Bring in outside expertise

Here’s a snapshot of the pros and cons of all three:

Most teams end up doing some combination of all three. The key is knowing which approach works best for specific skills.

Let’s look at each one in detail.

1. When to Build (Develop Internally)

Upskilling your current team is almost always the smartest first move.

They already know your brand, your workflows, and your audience. That context shortens the learning curve dramatically.

Focus on developing skills that evolve naturally from what your team already does.

For example:

• Train writers to structure content for AI extraction
• Help your SEO lead understand AI retrieval patterns and how citations work
• Encourage your analyst to track AI visibility metrics alongside rankings

These are logical extensions of existing expertise. Not entirely new disciplines.

Now, training doesn’t have to mean building a full internal curriculum.

Start small. For example:

• Run short internal workshops to explain how AI search retrieves and cites content
• Review recent AI-generated answers for your top keywords and note which competitors get mentioned
• Compare their cited passages to yours, and update one or two articles using those patterns

To make internal training effective, use this quick checklist:

Upskilling may not be the fastest route to output. It can take a few months before you see real traction.

But it is the most sustainable.

Once your team starts applying AI-first thinking, you’ll see compounding returns with every new SEO campaign.

2. When to Buy (Hire New Talent)

When you need expertise faster than you can build it internally, it’s time to hire.

Bringing in new talent makes sense when the skill is both specialized and strategic.

Something that gives your brand a long-term edge, not just a short-term fix.

For example:

• Hiring a data or visibility analyst who understands how to measure citations and brand mentions across AI platforms
• Bringing in a technical SEO who can model entities and implement structured data at scale
• Adding an AI content strategist who can guide how your content aligns with AI retrieval patterns

These hires extend the capabilities of your existing SEO team. They don’t replace it.

The key to finding the right people?

Clarity before you post the job. Decide what outcome you’re hiring for.

Do you need faster technical execution, deeper analytics, or dedicated AI visibility leadership?

Before you start recruiting, here’s a quick checklist to work through:

With clear hiring criteria, you’ll know which expertise to prioritize and what title makes sense for your organization.

3. When to Borrow (Outsource or Consult)

Not every skill is worth building or hiring for.

Some are highly specialized. Others you only need for a short period.

That’s where borrowing expertise makes sense — through consultants, freelancers, or agencies.

Outsourcing works best when you need to move fast on projects that require niche expertise.

For example:

• Hiring a consultant to set up AI visibility tracking before your analyst takes over
• Partnering with a content firm to scale passage optimization across hundreds of pages
• Bringing in a Reddit marketing expert to boost your brand’s presence in relevant subreddits

This approach gives you access to deep expertise without expanding headcount.

You can bring in specialists to handle complex projects, fill capability gaps, or run pilot programs that would slow your internal team down.

Sometimes that means a one-off engagement.

Other times, it’s a recurring partnership that supports your strategy long-term.

The goal isn’t to offload responsibility. It’s to fill gaps your team can’t cover yet and to get critical work done without slowing down larger projects.

When evaluating potential partners, here’s a quick checklist to follow:

Further reading: SEO Consultants Guide: When to Hire and What to Expect

The Hybrid Reality

In practice, it’s rare that a team is fully built, bought, or borrowed.

You’ll probably use all three, often at the same time.

How much you lean on each one depends on factors like:

• Your current team’s strengths and bandwidth
• Budget flexibility for hiring or contracting
• The urgency of upcoming SEO goals
• How quickly AI search is evolving in your industry
• Leadership’s appetite for experimentation

In my experience, many teams land somewhere near a 70-20-10 split. Which is roughly 70% built internally, 20% borrowed through outside experts, and 10% bought as new hires.

The exact ratio matters less than how deliberately you manage it.

Here’s how to keep that balance right:

• Prioritize by impact: Build skills that sustain long-term visibility. Borrow when you need speed or experimentation. Buy only when a role becomes essential to your strategy.
• Keep ownership internal: Even if outside partners execute the work, ensure someone on your team owns the outcome and applies the learnings.
• Plan for rotation: As new AI SEO trends emerge, your mix will likely shift. What starts as a borrowed skill may become core within six months.
• Audit regularly: Review your mix every quarter to see which skills rely too heavily on outside help. If a borrowed skill becomes recurring, start building it internally.

Follow this quick team review checklist to keep stock of your built, bought, and borrowed setup.

The key is flexibility and adaptability.

As priorities shift, don’t hesitate to rebalance how your team works.

That might mean promoting someone internally to take ownership of AI visibility, bringing in a freelancer to handle off-site optimization, or hiring a new analyst to deepen your data capability.

Adjust your structure based on what delivers the most impact, not what’s written on the org chart.

Your AI SEO Adoption Roadmap

You don’t need a massive reorg to evolve your SEO team for AI search.

You need a plan that helps your team build capability, test what works, and scale what proves effective.

This roadmap gives you that plan.

It breaks down:

• What to focus on in each phase
• How to build momentum
• What progress should look like along the way

By the end, your team will know how to apply AI SEO principles consistently.

Note: This timeline is a starting point, not a rule.

Startups with smaller teams might compress this into 6 months. Enterprises coordinating across departments might need 15-18 months.

The timeline matters less than starting now and making steady progress.

Phase 1: Foundation

Start by taking stock of where your team stands.

Before diving into new tactics, align everyone around what AI SEO means for your brand and how your current approach fits into it.

This stage sets direction and gives your team the confidence to move with purpose.

Here’s what to focus on in the first three months:

1. Assess current capabilities: Review your team’s strengths across content, technical, and analytical areas. Identify which AI-era skills exist internally and which ones you’ll need to hire for or outsource.
2. Establish your visibility baseline: Search your most important topics in tools like ChatGPT, Perplexity, and Google AI Overviews. Track if (and how) your brand shows up.
3. Pick 2-3 priorities to act on: Choose the areas with the clearest opportunity to improve. That might mean tightening content clarity, mapping entities, or aligning off-site mentions.
4. Run a small pilot: Select a few representative pages and update them based on what you’ve learned. Then recheck whether those updates help your brand appear more often in AI answers.
5. Document key learnings: Capture what worked and what didn’t in a short internal memo. This becomes the foundation for next quarter’s priorities.

Goal: Build clarity, alignment, and a shared understanding of how AI search changes what your team prioritizes.

By the end of this phase, your team should understand what makes content discoverable in AI search, have a documented baseline to track progress, and have at least one small win that proves the approach works.

Phase 2: Acceleration

Once you’ve built your baseline, it’s time to turn insights into action.

The second phase focuses on building capability and momentum. This involves scaling what worked in your pilot, closing skill gaps, and introducing systems that help your team move faster together.

Here’s what to focus on over the next few months:

• Strengthen capability: Run short training sessions to deepen AI SEO understanding across functions. If a skill gap exists, bring in a freelancer, consultant, or new hire to fill it quickly.
• Encourage cross-functional collaboration: Bring content, SEO, analytics, product, and brand together under one shared visibility goal. Clarify ownership so responsibilities don’t overlap.
• Expand your pilot: Apply what worked from Phase 1 to more pages or campaigns
• Build repeatable workflows: Turn early learnings into working systems. Standardize how technical, analytical, and content tasks are executed for AI-driven discovery. Each function should know what “AI-ready” means in its area.
• Use shared dashboards: Track AI visibility metrics in one place and review them as a team so everyone sees how their work contributes to results
• Run monthly reviews: Check how well your team is adapting to new systems and responsibilities. Identify where people need support, additional training, or outsourced help.

Goal: Build capability, consistency, and accountability across your team’s AI SEO initiatives.

By the end of this phase, your team should operate with clear workflows and defined ownership across technical, analytical, and content areas.

You should also have unified dashboards that let all stakeholders track progress and collaborate without duplicated work.

Phase 3: Scale

This final phase turns AI-first thinking into how your team operates by default.

The goal now is to make the new skills, workflows, and decision habits permanent. This way, your AI SEO capability grows without needing constant resets.

Here’s what to focus on in the next six months:

• Integrate what works: Expand the proven approaches from earlier pilots across your full SEO and content programs. Keep the frameworks that consistently improve visibility; drop the ones that don’t.
• Solidify roles and ownership: Define who leads AI-related strategy, measurement, and experimentation. Clarify responsibilities so the team stays agile even as you scale.
• Strengthen internal training: Turn what your team learned into short onboarding sessions, playbooks, or process docs. This keeps new hires aligned and prevents knowledge loss.
• Plan for selective specialization: As your AI SEO programs mature, assign ownership where consistent work is required. That could mean promoting a team member to lead AI visibility reporting, assigning an SEO specialist to oversee off-site signals, or partnering long-term with a proven external expert.
• Create leadership visibility: Share quarterly reports on AI-driven results and learnings with senior stakeholders. This keeps support (and budgets) growing with your progress.

Goal: Make AI-first execution routine and scalable across your team.

By the end of this phase, your team should operate with defined roles and responsibilities. You should have internal systems for training, reporting, and process consistency.

Leadership should have visibility into AI performance outcomes so the team treats AI SEO as an integrated function, not an experiment.

Measuring AI SEO Team Success

You can measure your AI SEO team’s success by tracking how often your brand appears in AI-powered answers.

Here are important AI SEO metrics to track:

• Citation frequency: How often AI platforms cite your content as a source
• Brand mention rate: How often your brand appears in AI responses
• Platform coverage: Which AI platforms reference you (ChatGPT, Perplexity, Google AI Overviews, etc.)
• Sentiment: Whether those mentions align with your brand positioning

Semrush’s AI Visibility Toolkit makes tracking these metrics simple.

It shows your AI Visibility Score and how many times your brand is mentioned across different AI platforms.

It also shows which prompts your brand appears for, revealing which topics your team’s content strategy is successfully targeting.

In your Brand Performance report, you can compare your brand’s visibility against multiple competitors.

The report includes insights like your Share of Voice (percentage of mentions compared to competitors) and sentiment analysis. This tells you whether AI platforms present your brand positively or negatively.

For larger organizations, Semrush offers Enterprise AIO, with team collaboration features and advanced analytics.

Specifically, your AI Visibility Score is a good overall indicator of your AI SEO team’s performance.

If it has improved over 3-12 months, it means your team is executing well. The skills are translating into real visibility.

If results aren’t showing after two quarters, revisit your priorities. You might be focusing on the wrong skills first or need to adjust your build/buy/borrow mix.

Pro tip: When you start building your team’s AI SEO skills, benchmark your brand’s AI Visibility Score alongside five competitors.

After 3-12 months, compare growth rates, not just final scores.

Your score might increase from 30 to 40 (+10 points). But if competitors jumped from 40 to 60 (+20 points), not only are they more visible — they’re also outpacing you.

Track relative growth to understand your true competitive position.

Get a Custom AI SEO Team Plan in 20-30 Minutes

AI SEO is built on traditional SEO. But there are more layers to it.

Your SEO team needs updated systems and upgraded skills so your brand gets mentioned (and your website cited) in AI search results.

We created the free AI SEO Team Building Assistant to turn everything you just read into a custom action plan for your team.

Download the file, upload it into your AI platform of choice (Claude, ChatGPT, Gemini), and follow the conversation.

This is an interactive session that adapts to your specific team, budget, and constraints. It’s not just a cookie-cutter report after a basic prompt.

It takes around 20 minutes to work through (but you should take your time with it). At the end, you’ll walk away with a complete implementation plan.

Here’s an example of the output, starting with the one-page plan:

You’ll also get a “Skills Ownership Map” showing which team member owns which skill. And which skills to build, borrow, or buy.

Plus a Phased Roadmap, KPI Tracking Framework, Leadership Brief, and 30-day checklist.

Everything is tailored to the specific inputs you provide in the interactive conversation.

Here are some tips for getting the most out of this assistant:

• Block 30 uninterrupted minutes so you can really engage with the conversation
• Have your current team structure in mind
• Be specific in your answers (vague input = generic output)
• Be honest about constraints (like budget, time, and capabilities)

Download the AI SEO Team Building Assistant and start building your AI-ready team.

The post How to Build an AI-Ready SEO Team: A Complete Guide appeared first on Backlinko.

2025-12-04 01:28:22

AI chat is the number one source B2B buyers use to shortlist software.

Not review sites. Not vendor websites. Not salespeople. AI chat.

G2’s 2025 survey of 1,000+ decision makers found that 87% say AI tools like ChatGPT, Perplexity, and Gemini are changing how they research software.

Half of SaaS buyers now start in AI chat instead of Google Search.

They’re “one-shotting” their research with prompts like “Give me CRM solutions for a large gym that work on iPads.”

What used to take hours of “Google —> right-click —> open new tab” is condensed to minutes.

If your product doesn’t show up when buyers ask AI to recommend solutions in your category, you’re losing deals before they begin.

This guide shows you exactly how to change that.

I’ll walk you through:

• How AI visibility works for SaaS
• Why some brands dominate AI answers
• What you can do to make sure AI recommends you

Side note: The data in this article comes from Semrush’s AI Visibility Index (August 2025), focusing on the Digital Tech and Software category.

The 3 Types of AI Visibility for SaaS Brands

There are three ways your brand can show up in AI search:

1. Brand mentions
2. Citations
3. Recommendations

Type 1: Brand Mentions

Brand mentions mean your brand appears in the AI’s answer.

It’s not always an endorsement. It’s simply the AI recognizing your brand as relevant to the topic.

For example, I asked ChatGPT:

“How can remote teams stay aligned on projects?”

ChatGPT outlined a few tactics and listed several tools, naming specific brands as examples with no extra context about any of them.

At this level, how AI talks about your brand is super important. AKA: brand sentiment.

A positive tone builds early trust while a negative one sets bad expectations.

Let me show you what I mean.

I asked ChatGPT:

“What do marketers on Reddit say about top reporting dashboards.”

ChatGPT summarized Reddit’s discussions, listed a few tools, and included criticisms about some products.

If I were evaluating dashboards, the negative details about AgencyAnalytics and Looker Studio would create a subtle bias that would follow me as I continued my research.

That’s no bueno.

So make sure sentiment around your mentions leans positive.

How do you keep an eye on brand sentiment?

Easy. Use Semrush AI Visibility Toolkit.

Just go to “AI Visibility” > “Perception” and you’ll see key sentiment drivers surrounding your brand. The tool will show you Brand Strength Factors (positive influence on sentiment) and Areas for Improvement (negative sentiment factors).

Type 2: Citations

Citations are instances of AI using your content as a source.

It’s a strong signal that the AI trusts your brand and is using your content to build its answer.

In Google AI Mode, citations show up as clickable links on the right-hand side of the response.

In ChatGPT, they appear as footnotes or small inline links.

Citations come with two complications.

First, they’re not as visible as brand mentions.

The footnote-style links are easy to miss, which means you probably won’t get meaningful traffic from these citations.

Second, citations don’t always create brand awareness.

The AI can use your content, but not mention your brand.

Semrush’s AI Visibility Index report calls this the “Zapier Paradox.”

In the Google AI Mode dataset, Zapier was the most-cited domain in the entire software category. It appeared in around 21% of the analyzed prompts.

Yet it ranked only #44 for brand mentions.

That means the AI trusts Zapier’s content enough to use it constantly.

But that trust hasn’t translated into more visibility for the brand itself.

That doesn’t mean citations are useless. Far from it, since they’re still the only method of sending users directly from AI search to your website.

But if you’re cited for an answer that recommends other brands (like Zapier often is), the citation isn’t super useful for your brand.

That’s why you want the third type of AI visibility.

Type 3: Product Recommendations

Product recommendations are where the AI moves from “here are some options” to “here’s what you should choose.”

To get recommended, your brand typically needs two things working in your favor:

• Positive sentiment
• Enough verified facts for the AI to feel confident putting your name forward

For example, when I asked:

“Which CRM is best for small businesses?”

ChatGPT recommended six CRM platforms.

Each one came with a breakdown of strengths.

That’s the AI directly influencing my consideration set.

And when the AI wraps up the answer with the top three CRMs, these three brands stay top of mind.

As the reader, I’m thinking:

“Alrighty. These are the tools I should probably compare.”

That’s the power of SaaS product recommendations in AI search.

The AI isn’t just helping me explore the category. It’s shaping the shortlist I walk away with.

How AI Models Choose Which SaaS Brands to Surface

When AI answers a query, it cross-checks sources.

It compares what you say about your product with its training data. Along with what the rest of the internet says about you.

If the details line up, you’ve got consensus and consistency: two forces that drive visibility in AI search.

Consensus

Consensus happens when many credible places describe your product in the same way.

In practice, the AI is looking for alignment across sources like:

• Review sites (G2, Capterra, TrustRadius)
• Industry blogs and SaaS publishers
• Expert posts on LinkedIn or in public newsletters
• User communities like Reddit and Quora
• Your own website and documentation

Basically: anywhere your product is being talked about in a credible context.

Take Asana, for example.

It routinely appears in AI answers about project management tools.

And you can see why when you look at its footprint online.

Across multiple places, you’ll find the same core description repeated from their website to Capterra to Reddit.

All of these brand mentions alone help boost Asana’s potential visibility.

But when they also all point to the same story, that’s consensus. This helps AI feel confident surfacing the brand repeatedly.

Consistency

Consistency means the details match everywhere they appear.

When AI scans the web, it’s looking for verifiable facts. If those specifics line up, it trusts them.

But, if those signals don’t match, the model becomes unsure.

(Just like you would if five people gave you five different versions of the same “fact.”)

For example, let’s say:

• Your pricing page says your Standard plan includes unlimited reports
• Your help center says Standard users get 50 reports a month
• Recent reviews say they hit limits after a week

Now you’ve got three conflicting stories.

When the AI sees that, it can’t tell which one is true. It might use the right one, or it might use the wrong one. Or it might not use any of them.

That’s why data hygiene matters in AI search.

The key facts about your brand should be consistent everywhere your brand is described.

The Content That Dominates SaaS AI Search

Not all content carries the same weight in SaaS AI search.

Some formats show up repeatedly because they help models verify what’s true about a product.

Review Platforms

Review platforms are some of the most heavily cited sources in SaaS AI search.

These sites, including G2, Capterra, and TrustRadius, give AI unfiltered, third-party proof about your product.

The platforms help the model validate:

• Who you are
• What your product actually does
• How reliable it is
• How users feel about it

In other words, this is where AI goes to separate your claims from real user experience.

And the data shows how influential they are.

According to Semrush’s AI Visibility Index, G2 is the 4th most-cited source for ChatGPT and 6th for Google AI Mode across the entire tech and SaaS category.

That tells us that:

• Review platforms aren’t just buyer research hubs
• They’re part of an AI’s verification layer

What people say about you in these places becomes part of the material the AI uses when describing your brand.

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

Community & User-Generated Content (UGC)

Community conversations are another major source LLMs lean on in SaaS AI search.

They cite from places like:

• Reddit
• Quora
• Industry forum threads
• Product community boards
• Niche groups where users compare experiences

For example, I asked ChatGPT:

“Why do people switch from ActiveCampaign to Klaviyo?”

ChatGPT cited two Reddit threads in its answer.

That’s why your presence in these community spaces matters.

Not in a “go spam Reddit” way.

But in a “be part of the conversations that shape how people talk about your product” way.

Because those public, unscripted conversations can become part of your brand’s source of truth inside LLMs.

Further reading: Reddit Marketing in 3 Hours/Week: From 0 Karma to Real Cred

Best-Of Listicles & Tool Roundups

Best-of listicles and tool roundups give LLMs structured, pre-sorted information that they can easily digest.

These articles hand the AI a ready-made map of a category, including:

• Who the key players are
• How the tools differ
• Which products consistently show up together

The AI regularly pulls from a mix of major publishers, niche SaaS blogs, and established industry media.

For example, when I asked for the top AI SEO tools, Google AI Mode’s citations included a bunch of best lists.

Every roundup, comparison post, or “best tools for X” mention becomes one more anchor AI tools can grab when they’re trying to answer a question about your category.

Pro tip: Don’t ignore your own media. AI models also use company-owned content as reference material. So create your own well-structured roundups and comparison pages in the niches where your product plays.

For example, when I asked ChatGPT whether Omnisend or Mailchimp is better for ecommerce, one of the citations was Omnisend’s own blog post comparing the two tools.

In other words: their own content helped shape the AI’s narrative.

Documentation & Product Knowledge Bases

AI also uses your product documentation to understand how your product works: what it does, who it’s for, and what its technical capabilities are.

For example, when I asked Google AI Mode, “Is Semrush good for enterprise?” the model pulled from several Semrush-owned pages:

• The Enterprise landing page
• A press release on the enterprise platform
• A blog on “What Is Enterprise SEO”
• An enterprise client case study

Together, those pages gave the model context to understand Semrush’s enterprise offering.

One more thing:

Make sure your content is well-structured, clear, and complete.

If it’s vague or lacks key details, the AI might look elsewhere to fill the gaps.

The Semrush study shows this clearly with pricing.

When SaaS brands don’t publish transparent pricing, AI models fill the blanks using community speculation. This speculation is often tied to negative sentiment.

So, how do you structure your content for better AI visibility?

Use:

• Clear, explicit content using conversational language
• Clean formatting that makes details easy to extract
• Tables, charts, and Q&A blocks that package information neatly
• Headings that signal hierarchy

Want the full breakdown? Our article on how to rank in AI search walks you through the full process.

Video Content

Text may fuel most AI answers, but video (especially YouTube) has become a meaningful signal, too.

In fact, YouTube is the 10th most-cited source in Google AI Mode for SaaS-related prompts.

This means AI isn’t just reading the web. It’s also learning from what people show and say on camera.

For SaaS brands, that’s a real visibility lever.

If your product appears in YouTube reviews, tutorials, comparisons, or walkthroughs, the AI can pull those details straight into its explanations.

For example, when I asked Google AI Mode whether the paid version of HubSpot is worth it, one of the citations was a YouTube review.

If you don’t have a YouTube presence yet, it’s worth planning for.

Start by getting your product included in other creators’ reviews and tutorials.

Then build out your own YouTube channel to control the narrative long-term.

What This Shift Means for Your SaaS Brand

If you’ve already put in the work on your SaaS SEO basics, you’re already in a good position.

But AI search adds a new layer, and it requires a few more steps to stay visible.

Make AI Visibility a Company-Wide Effort

AI search visibility isn’t something marketing can brute-force on its own since consensus and consistency play such a major part.

Multiple teams should keep their corners of the internet aligned in your brand story.

This means:

• Marketing keeps claims factual and up to date
• Product Marketing ensures documentation, changelogs, and feature pages match what’s actually live
• Customer Success helps maintain accurate review-site profiles
• PR/Comms monitors media mentions so nothing drifts off-message

To make that doable, create a simple internal “source of truth” every team can follow.

This doesn’t need to be a 100-page brand bible.

Start with:

• Exact product names, tier names, and feature labels
• The approved value props and phrasing you want repeated everywhere
• Performance claims or metrics that should stay consistent across your site, docs, and press
• Integration names and technical terms written the same way across all surfaces

We do this at Semrush.

And it makes a huge difference in making sure everyone is speaking the same language.

Start With Your Website

Your website is the part of your presence you fully control, so this is where to start making optimizations.

Make sure your content is clear, crawlable, and structured so AI can easily parse it.

Here’s where to focus first:

• Put all content in HTML: AI reads HTML far more reliably than JavaScript
• Use clear headings and hierarchy: They help both users and models navigate the page
• Add schema markup: It gives AI models a structured way to understand your data exactly the way you want

(We don’t know how heavily AI tools lean on schema right now. But given it’s an SEO best practice, it’s still worth doing anyway.)

Next, create content that covers the full customer journey.

The more touchpoints you cover, the more chances you have to show up as users explore your category.

Your goal isn’t just to appear when someone searches for your brand — it’s to appear whenever they search for anything related to your category.

For example, Semrush publishes content for every stage:

• Top of Funnel (Awareness): Guides like “AI SEO Tips: How to Earn Citations & Mentions in AI Search”
• Mid-Funnel (Consideration): A full FAQ category answering the most common SEO questions people search before choosing a tool
• Bottom of Funnel (Decision): A fully crawlable knowledge base explaining product features, workflows, and how the platform actually works

Expand Beyond Your Site

Once your website is solid, the next step is to build credibility in the external sources AI cross-references.

The same core facts — your features, use cases, pricing signals, customer proof — should show up consistently on sites like:

• G2, Capterra, TrustRadius (user validation)
• Niche media and publisher sites (authority)
• Partner blogs and integrations (ecosystem relevance)
• Community spaces like Reddit or LinkedIn (real-world use and sentiment)

When all of these places tell the same story about what you do and who you’re for, you build consensus.

And once that consensus forms, AI can surface and confidently recommend your brand.

Getting your brand into all these places takes time. So, stack your efforts in layers:

• Lock down key review sites first
• Join conversations already happening in communities like Reddit
• Pitch niche SaaS sites, journalists, and publishers

Track the Signals That Show You’re Gaining Ground

It’s not as easy to track AI visibility right now as it is to track SEO visibility.

But there are a few indicators that reveal whether you’re becoming part of the model’s go-to answers.

These are worth checking monthly or quarterly:

• Share of voice: How often your brand appears in AI-generated results for your category
• Brand sentiment: The tone of the mentions
• Citation frequency: How often your domain is used as a source in AI answers

Use Semrush’s AI Visibility Toolkit to track these metrics.

Example of a Brand That’s Winning in AI Search (Slack)

Slack ranks ninth overall in the Digital Technology/Software category for AI visibility.

That visibility isn’t tied to one use case or category, as Slack shows up everywhere for various queries.

From prompts about remote work to team communication and the best tools for small businesses.

Here’s what they’re doing that you can steal:

Slack Owns Their Category (Not Just Brand-Specific Prompts)

Slack doesn’t only show up when someone searches for “Slack.”

They show up for everything inside their category, in prompts about:

• Use cases: “team chat for remote work”
• Features: “tools with shared channels”
• Problems: “how to align remote teams”
• Price: “team communication tools”

Showing up in these various category prompts builds early recognition.

This then affects what happens next as the user goes deeper into their buying journey.

For example, a user might start an AI conversation with:

“Which is better, Slack or Teams?”

Slack shows up in the citations because they’ve published content that answers that question.

Now, let’s say the user sees a drawback in the AI’s answer.

The user might follow up with:

“What are Slack’s security concerns?”

And Slack again shows up in the citations, this time through their own blog content.

Slack is actively shaping the conversation.

As the user moves from comparison to evaluation to decision, Slack’s content keeps appearing in the AI’s reasoning.

In short: Slack gets to influence the story at every step of the buyer journey.

Slack’s Messaging Is Clear

One thing Slack absolutely nails is message consistency.

Everywhere you look — their website, their docs, their review profiles, their blog — you get the same story about what Slack does and who it’s for.

Go to their site and you’ll see pages laying out features, use cases, and integrations. All in plain, straightforward language.

Even their blog posts break down new features in that same accessible tone.

That clarity matters because it makes it incredibly easy for AI to learn what’s what.

When your content follows a simple structure of “Here’s the feature, here’s what it does, here’s how it works,” the model can easily classify information.

But Slack doesn’t just do this on their site.

Jump over to their review profiles and you’ll find the exact same messaging — the same features, same categories, same positioning.

That consistency is a big plus.

When your messaging stays the same across every channel, you give the AI reliable information to work with.

Slack Is Present Everywhere LLMs Go for Answers

Slack has a footprint across every layer that large language models pull from.

The community layer: Reddit threads, Quora discussions, and YouTube reviews:

The expert layer: SaaS tutorials, niche SaaS blogs, and trusted industry publishers:

The verification layer: G2, Capterra, and TrustRadius:

This breadth matters because it helps LLMs find patterns.

When Slack’s value prop, features, and positioning appear the same way across all three layers, the AI treats that agreement as “high-confidence” information.

This gives the AI zero doubts about what Slack does and what it offers — and therefore what kinds of queries the AI should recommend Slack for.

Help AI Find and Feature Your SaaS Brand

For SaaS AI search, the game is simple:

Show up everywhere the AI looks.

For software companies, that means being intentional about what you publish, how you structure it, and where you show up across the web.

You don’t just need to “write more content.”

You need to create the right content, in the right places, in the right formats that AI models rely on.

It’s a big shift, for sure.

But you can make the whole thing far easier by following our search everywhere optimization guide.

The post SaaS in AI Search: Who’s Ranking (+ How to Steal Their Spot) appeared first on Backlinko.

2025-11-28 23:00:21

AI search is reshaping how ecommerce brands get discovered.

One week, your products show up in ChatGPT. The next week, they’re replaced by competitors.

For many brands, this uncertainty can feel overwhelming.

Organic visibility now depends less on rankings and keywords, and more on how LLMs gather information, which platforms they rely on, and what signals help them highlight your brand.

In this guide, I’ll explain this crucial shift in detail.

I’ll unpack:

• What actually shapes visibility inside AI answers
• The business impact of compressed buyer journeys and broken attribution
• How you can build lasting relevance in this new search ecosystem

The 3 Types of AI Visibility for Ecommerce Brands

If you’re familiar with SEO, getting AI visibility is similar. It starts with how search systems decide what to display.

But for years, ecommerce SEO was a linear equation: rank = visibility = traffic (and then conversions).

AI search is changing that.

LLMs summarize, compare, and recommend products, all in one place.

In short: Shoppers can discover your products, check alternatives, and make buying decisions within AI chats.

In this new setup, brands compete across three different discovery models.

Type 1: Brand Mentions

Mentions drive product discovery and build top-of-funnel LLM visibility for your brand.

This is where your brand gets featured in AI-generated answers, often without a link to your site.

Mentions often come from reputation signals like:

• Reddit posts
• Media coverage
• User reviews
• Social discussions

Put simply, you become part of the conversation.

For new or emerging brands, this is often the first touchpoint to reach shoppers through AI.

Type 2: Citations

Citations are linked references within AI-generated results, like a footnote in an essay.

With citations, LLMs attribute specific information, claims, or data points to your pages.

Your brand becomes a source of truth in AI responses and gains credibility.

How?

When an AI tool cites your brand, it signals to shoppers that you’re an authoritative voice.

Plus, citations can support your positioning. The AI tools can pull your framing and product narrative into their response. Not someone else’s.

Type 3: Product Recommendations

AI platforms actively recommend products for a shopper’s specific needs and concerns.

This is the most impactful layer for ecommerce brands.

Your products can show up with pricing, ratings, and other details.

This type of visibility effectively merges discovery and purchase in one place.

This happens when the LLM reviews the query, compares options, and picks your product as the best fit.

Showing up in the list of recommended products makes your brand a part of the decision interface.

Shoppers can compare specs, prices, and reviews — or even purchase — right in the AI chatbot or search tool itself.

Dig deeper: 5 LLM Visibility Tools to Track Your Brand in AI Search (2025)

How AI Models Choose Which Ecommerce Brands to Surface

AI visibility as a discipline is still evolving rapidly. But there are clear patterns to which ecommerce brands get seen and which get sidelined.

Two driving forces at play are: consensus and consistency.

Consensus

With traditional search, ecommerce brands could build domain authority through activities like link building and digital PR. Strong pages from an authority perspective tended to perform well in search results.

In AI search, LLMs don’t evaluate your website and product pages in isolation. Authority is built from a consensus across sources.

LLMs ask: “What do credible sources agree on about this product?”

To decide which brands and products deserve visibility, LLMs cross-reference multiple sources, like:

• Reddit threads
• YouTube videos
• Industry reports
• Customer reviews
• Trusted publishers
• Community discussions

So, a glowing review on your PDP might mean little if customers on Amazon consistently leave 1-star ratings.

And a publisher’s feature loses impact if Reddit users repeatedly recommend your competitors instead.

In other words: No single source determines your likelihood of being mentioned or cited. It’s the pattern of consensus across multiple platforms that does this.

For example:

Keychron frequently shows up when you use AI search tools to find mechanical keyboards.

This happens because the brand has earned trust through various sources:

• Review sites like PCMag and Tom’s Guide rank Keychron in their top recommendations
• Keychron’s Amazon pages are detailed with positive reviews and an average rating of 4.4 stars
• Multiple Reddit threads in subreddits like r/MechanicalKeyboards and r/macbook recommend the brand
• Several YouTube videos feature Keychron in their roundup of mechanical keyboards

Each trust signal on its own is valuable.

But when taken together, LLMs see a pattern of independent sources validating the same brand/product for a specific use case.

Consistency

LLMs don’t crawl and rank pages the way traditional search engines do.

Instead, when answering a product-related query, an AI model might pull:

• Your product name from your Shopify store
• Pricing from Google Merchant Center
• Key specs from Amazon
• Opinions from users on Reddit

If your product title is “stainless steel” on Amazon but “brushed metal” on Walmart, the LLM can’t decide which is correct. This inconsistency could make the AI tool less likely to include any information about your product. Or it could include the wrong information.

This is why data hygiene is crucial for building AI visibility.

You need to maintain a clean, synchronized identity for every product across every channel.

Your product attributes should follow the same pattern across your site, marketplaces, and feeds:

• Model numbers
• Dimensions
• Materials
• Weights
• Prices

LLMs use these data points to match your products to queries and validate claims across sources.

Your Amazon listing, your Shopify store, your Google Merchant feed — all sources need to tell the same story with the same data.

So, the same SKU name, image, and product description should appear everywhere your product appears.

Finally, outdated data signals decay, and models may deprioritize products with outdated info.

When you change a price or update a key spec, that change should be visible everywhere. Stock availability, pricing, and features should always be up to date.

Types of Content That Dominate Ecommerce AI Search

We’re seeing clear patterns in what gets cited, mentioned, or ignored in AI search for ecommerce.

Understanding these patterns can be the difference between hoping you show up and knowing how to position your brand so that you do show up.

Here’s what’s currently doing well in AI search for ecommerce:

Top Cited Sources

I wanted to see which brands are cited most frequently in LLM responses for ecommerce queries — so I tested it.

I picked nine popular ecommerce niches and searched category-specific queries across ChatGPT, Claude, Perplexity, and AI Mode. 

Based on the responses, I made a list of five popular brands showing up frequently for each vertical.

Then, I jumped to the “Competitor Research” tab in Semrush’s AI Visibility Toolkit to run a gap analysis for these five brands in each category. 

The “Sources” tab showed which domains LLMs cite most frequently, like this for the “outdoor travel & gear” niche:

This data reveals where LLMs pull product information, and which platforms matter most in your vertical.

Here’s what this data tells you:

• Reddit: Reddit is a top-cited source for nearly every industry. If people aren’t discussing your brand in relevant subreddits, invest in Reddit marketing.
• YouTube: It’s another universal citation source. Video content from creators and users feeds into AI answers. That means having a YouTube presence can be a huge visibility lever for most ecommerce verticals.
• Category-specific platforms: Generic sources like Amazon appear everywhere. But niche platforms (like Petco, Barbend, Sephora) carry weight in their verticals.
• Wikipedia: It’s a top source for categories like outdoor gear, healthy drinks, and gadgets. This is where product context and category education matter a lot alongside the likes of specs and pricing.

Going beyond these top-cited platforms, here are the kinds of content LLMs link to most frequently for ecommerce queries:

Publisher Listicles

These are product roundups, buying guides, and comparison posts from established media outlets.

For example, I asked ChatGPT for the best Bluetooth speaker recommendations.

It cites publishers like TechRadar, Rtings.com, and Stereo Guide for this response.

Getting featured in these listicles means you’re part of the source material LLMs use to compile information.

AI models use publisher listicles as sources because they:

• Compare multiple products in one place
• Refresh their recommendations periodically, providing recency signals
• Include specific, comparable details like price ranges, key specs, and pros/cons lists
• Fulfill high editorial standards and so may appear more trustworthy than user-generated content

Retailer Product Pages

Retailers like Amazon, Walmart, and Target are among the most frequently cited sources for product queries.

When I asked Perplexity about the NutriBullet Turbo, it cited the product pages from the likes of Walmart and Macy’s.

These PDPs provide structured data points like ratings, pricing, and key specs.

AI models often rely on these product pages because they:

• Include structured, machine-readable product data like specs, dimensions, materials, and pricing
• Aggregate hundreds or thousands of customer reviews as social proof
• Show real-time availability and pricing

Lab Tests and Expert Reviews

In-depth product testing content from experts is another important source for citations.

These websites test products systematically and publish detailed findings.

LLMs can then use this empirical data as the basis for their responses.

For example, I asked Claude to find the best mattress for side sleepers.

The tool references sites like NapLab, Consumer Reports, and Sleep Foundation for data-backed recommendations.

AI models consider lab test or expert review content for citations because they:

• Compare products against consistent criteria and benchmarks
• Show credibility with independent, systematic evaluation processes
• Include measurable data to explain their top-ranked recommendations
• Periodically update their recommendations to offer fresh, authoritative data

Reddit Threads and Community Discussions

Conversations on Reddit, Facebook groups, and YouTube comments frequently appear in AI responses.

This is especially true for subjective queries like “Is X worth it?” or “What do people actually think about Y?”

I tested this myself by asking Perplexity whether the Instant Pot Duo is worth buying.

It pulled insights from multiple Reddit threads, a Facebook group, and a YouTube video to respond based on real user input.

Brands that get mentioned positively across multiple Reddit threads build “cultural proof.”

And those organic discussions about your brand feed directly into AI training data and real-time search results.

AI models pull from these communities because they:

• Present an aggregated sentiment from community discussions
• Contain contrasting opinions and insights to objectively review products
• Show different use cases and pain points that a product can tackle
• Highlight a product’s pros and cons based on firsthand experience

Comparison Posts

Content that compares two or more products can also help LLMs find the right brands to mention in their response.

When I ask AI Mode for alternatives to the supplement brand Athletic Greens, it mentions five options.

The sources include several comparison articles (alongside some roundups).

Being included in this type of content (even if you’re not the winner) can help build your visibility.

This could be Brand A vs. Brand B blog posts, YouTube videos, review sites, and social media discussions.

AI models refer to these resources because they:

• Answer buyers’ questions by comparing two or more products
• Focus on decision-making criteria and help people make informed decisions

Learn more: LLM Seeding: A New Strategy to Get Mentioned and Cited by LLMs

What This Shift Means for Your Ecommerce Brand

Let’s now consider the business impact of this AI search setup for your ecommerce brand.

The Compressed Buyer Journey

The traditional ecommerce funnel was built on multiple touchpoints.

A shopper might:

• Google a product category
• Read reviews on multiple different sites
• Check Reddit and YouTube
• Visit brand websites to compare prices
• Return days later to buy

Each step was an opportunity for your brand to show up, make an impression, and win their trust.

For a lot of purchase decisions, AI search collapses this entire journey into a single interaction.

The same shoppers can now go to AI tools and ask, “What’s the best air fryer for a small kitchen?”

They get a single response with buying criteria, product recommendations, pricing, ratings, and more.

Now, clearly this isn’t going to happen for every purchase decision. These tools are still new for one thing, and it takes a lot to majorly shift buyer behavior. (And of course, SEO is not dead.)

But discovery, evaluation, and consideration CAN all happen in one response now. The AI agent performs the research labor.

That means you have fewer chances to influence buyers.

In the past, if a shopper didn’t discover you in organic search, they might find you through a review site, a Reddit thread, or a retargeting ad.

In other words: You could lose the first touchpoint and still win the sale three touchpoints later.

With AI search, you might only get one shot: the initial response.

For many ecommerce queries, AI tools give you a curated list of options. If you’re not in that initial answer, you don’t exist in the decision process.

As AI platforms make it easy for shoppers to buy directly within the chat, you often won’t get a second chance.

Take action: Build an AI search strategy using our Seen & Trusted Brand Framework to increase the probability of your brand getting featured in AI responses.

The Visibility Paradox

Your brand might frequently show up in AI search. But your analytics show flat traffic and zero conversions traced back to AI tools.

Here’s why:

Not all AI visibility is created equal.

Your brand can appear in 10 different AI responses and drive 10 completely different business outcomes.

It all depends on how you’re presented.

Here’s what the visibility spectrum actually looks like for ecommerce brands:

That means getting mentioned is table stakes, not the end goal.

Building brand awareness without differentiation just makes you a part of the crowd.

To drive real sales, you need to earn citations and product recommendations.

The brands winning in AI search are:

• Cited as trustworthy sources
• Recommended for specific use cases

Attribution Gets Murky

When shoppers find products through AI but buy elsewhere, analytics tools can’t track the whole journey.

This creates two problems:

• You can’t prove the ROI of AI search: Even if AI mentions are driving consideration, you’ll get zero or limited data on that. You won’t see the prompt the user asked or the response from the tool.
• You can’t optimize what you can’t measure: When you don’t know how people are discovering you in AI answers, you can’t A/B test your way to better visibility. The feedback loop is broken.

Tools like Semrush’s AI SEO Toolkit are closing this gap by showing how your brand and competitors appear in AI search.

I used the tool to check the AI visibility and search performance for Vuori, an athleisure brand.

The brand has a score of 76 against the industry average of 82, and is frequently mentioned AND cited in AI responses.

The toolkit also identifies specific prompts where your brand is mentioned or missing.

This makes it easy to spot exactly which type of queries are driving visibility and which represent missed opportunities.

For example, here’s a list of prompts where LLMs don’t feature Vuori, but do mention its competitors.

Go to the “Cited Sources” tab to find out the websites that LLMs most commonly refer to for your industry-related queries.

For Vuori, it’s sites like Reddit, Men’s Health, Forbes, and more.

The “Source Opportunities” tab will give you a list of key sites that mention your competitors, but not you. These are sites you should aim to get your brand included on.

Besides tracking your own AI visibility, the AI SEO Toolkit also lets you monitor your competitors’ performance on AI platforms.

The “Competitor Research” report compares you to your biggest competitors in terms of overall AI visibility.

It also highlights topics and prompts where other brands are featured, but you aren’t.

Learn more about how these tools can help you boost your visibility with our full Semrush AI SEO Toolkit guide.

Example of a Brand That’s Winning in AI Search: Caraway

If you want to see what winning in AI search actually looks like, look at the cookware brand, Caraway.

When you ask AI about the “best bakeware set” or the “best ceramic pans,” Caraway almost always makes the shortlist.

Data from Semrush’s AI SEO Toolkit shows that Caraway also outweighs its biggest competitors in AI visibility.

Let’s break down how Caraway built this advantage.

Showing Up Where LLMs Look

Caraway is frequently featured on publishers like Taste of Home, Good Housekeeping, and Food and Wine.

These are the actual sources LLMs cite when constructing answers about cookware-related queries.

For example, here’s a paragraph from the Food and Wine article ChatGPT cited as a source, which mentions the attributes ChatGPT used in its recommendation:

Caraway also earns mentions through organic discussions on Reddit, Quora, and kitchen forums.

Retailer Evidence That AI Can Cite

Caraway’s clean Amazon Brand Store and on-site product pages also make it easily citable.

These product listings and pages give LLMs concrete signals like:

• Multiple in-stock SKUs with visible sales velocity (“500+ bought in the past month”)
• Product rating and volume
• Rich media files

These retailer PDPs become credible sources for verifying pricing, availability, or product specs.

Strong Affiliate Presence

Caraway also runs an affiliate program, and the brand makes it frictionless for publishers to feature its products through:

• Affiliate networks: Links are available through major networks like Skimlinks and Sovrn/Commerce
• Amazon compatibility: Editors can also use Amazon Associates links for Caraway’s stocked SKUs
• Affiliate-safe pages: Product detail pages feature clean URLs, consistent pricing, and stock availability
• Reviewer support: The brand provides an affiliate kit, including link types, banner ads, text links, and email copy

This all makes it easy for Caraway to work with influencers and other publishers to promote its products. And these publishers can then appear as citations when AI tools make their recommendations.

For example, all the highlighted sources in the ChatGPT conversation below contain Caraway affiliate links:

Part of the Category Narrative

Many style media and mainstream outlets reference Caraway in their content.

Here’s a recent example from an Architectural Digest interview featuring the cookware set as an essential kitchen item.

This creates more authority for the brand in the cookware and kitchen category.

Make AI Work for Your Ecommerce Brand

You now know how the game works and who’s winning. It’s your turn to play it.

But there’s a lot to do.

Making your site readable by LLMs, opmtimizing your structured data, and setting up automated product feeds are just stratching the surface.

Our comprehensive Ecommerce AIO Guide gives you alll of the actionable tactics to consistently show up in AI results.

The post How Ecommerce Brands Actually Get Discovered In AI Search appeared first on Backlinko.