Google is quietly testing a new way to make Shopping ads feel more local. Select ads using local inventory feeds now display the merchant’s city or town directly above the product title — think “London” or “Tonbridge” — giving shoppers a clearer sense of where the store is based.
Why we care. The new location labels make Shopping ads feel more local and trustworthy, helping nearby retailers stand out in crowded results. Clear city or town indicators can increase click-through rates and drive more in-store visits from shoppers who prefer buying close to home.
It also gives merchants using local inventory feeds a competitive edge by highlighting proximity without needing new ad formats or extra setup.
How it works. The label appears within Shopping ads that already use local inventory data. It joins existing formats like:
In-store
Pickup later
Curbside pickup
But unlike those, this label focuses purely on the store’s location, not fulfillment options.
The catch. Google hasn’t officially announced the feature. Details on rollout, eligibility, and technical requirements remain unknown.
Between the lines. Merchants using local inventory feeds may get a visibility boost if they operate in recognisable or high-trust locations. For users, it’s another nudge to choose nearby retailers over marketplace or long-distance sellers.
First seen. This update was spotted by PPC News Feed founder Hana Kobzová.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/12/location-shopping-ads-EZRhH0.jpg?fit=1280%2C720&ssl=17201280http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-08 18:01:312025-12-08 18:01:31Google Shopping Ads now show merchant location labels
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-08 14:04:382025-12-08 14:04:38Top 16 Best Digital Marketing Agencies in 2025
You can now purchase products directly within ChatGPT.
That’s right, OpenAI recently announced a new feature that turns ChatGPT into a personal shopping assistant. You ask for something, and it doesn’t just recommend it. It finds it, prices it, and even helps you check out all in one chat.
They’re calling it Instant Checkout, and it’s already rolling out with help from e-commerce giants like Stripe and Walmart. The feature enables OpenAI to pull in real-time product listings and personalized suggestions.
It’s still early days, but this is a big deal for e-commerce brands. It opens up an entirely new kind of shopping experience; one where everything from product discovery and research to checkout all happens in a single interface. And with new ChatGPT ads already hitting the ecosystem, it’s clear this is a major market shift.
Key Takeaways
ChatGPT now supports in-chat shopping with real-time product listings and checkout through partners like Walmart.
Users interact with the feature using natural language prompts, making product discovery more conversational than keyword-based.
Product visibility depends on clean data: use schema markup, clear product names, and natural descriptions.
E-commerce brands must adapt fast. AI-driven recommendations are transforming the way customers browse and make purchases.
Optimizing for ChatGPT shopping requires mobile speed, fresh reviews, and structured product content.
What Do We Know About ChatGPT Shopping and How It Works?
Here’s what we know so far: ChatGPT can now help users discover and buy products directly in the chat interface.
The feature is called Instant Checkout, and it’s powered by OpenAI’s integration with tools like Stripe and Shopify, with Walmart also recently partnering for early rollout. The service is available to all U.S. users of ChatGPT, regardless of their tier.
What It Looks Like in Action
Let’s say you ask ChatGPT for “espresso machines under $200.” ChatGPT doesn’t just return a list of brands; it provides:
Curated product suggestions from across major retailers
All of this happens through integrations with online retailers and APIs that deliver live product data behind the scenes. The interesting thing is that brands don’t pay for this visibility in ChatGPT’s shopping function.
Where Google Shopping results are based on brands’ paid ad campaigns or Google’s search algorithm, ChatGPT shopping is more conversational and organic. It focuses on the people (what people are saying bout this product online, what the reviews are, etc.).
Built on Conversational Search
What makes this different is the user experience (UX). You’re not clicking through filters and category pages; you’re chatting. You refine your request like a conversation, asking questions like, “What about ones with arch support?” or “Can you find those in women’s sizes?” That’s a huge shift in how product discovery happens.
So, how does it choose what to show you? The platform analyzes structured metadata and previous model responses. It will look back on how it handled similar queries before it ever touches new search results.
The personalization potential is what makes this even more powerful. ChatGPT will be able to tailor your shopping experience by elevating or demoting various factors of your results based on your needs. For example, if you have a shopping budget of $50, ChatGPT can elevate price as a “signal” and only show you appropriate results. OpenAI is doubling down on the modern customer’s need for personalization.
Is ChatGPT Just Another Shopping Assistant?
Not exactly. Yes, it gives you product recommendations like other AI shopping assistants.
However, ChatGPT takes it a step further by allowing you to shop in a way that feels like texting with a smart, well-informed friend.
Here’s what sets it apart:
Conversational search: You don’t have to use exact filters or keywords. You can talk to it naturally and refine your search.
Live product data: ChatGPT pulls real-time pricing and availability from partner retailers.
Built-in checkout: With select partners, you can complete a purchase directly in the chat.
This changes the experience from “browse and compare” to “ask and buy.”
That kind of frictionless experience makes it especially appealing for time-strapped users, mobile shoppers, and anyone who already uses ChatGPT regularly. It takes online shopping from endless options to making an informed and personalized decision quickly.
How ChatGPT Shopping Will Impact E-Commerce
ChatGPT isn’t just adding shopping features. This will rewrite how people discover and buy products.
Instead of browsing categories or scrolling search results, users now get personalized recommendations just by asking a question. That creates a new funnel, one that starts with natural language. This could be new territory for many e-commerce brands.
Discovery Is Getting More Personal
In traditional search, people type product-focused keywords. With ChatGPT, they might say:
“I need a thoughtful gift under $50 for a coworker.” Or “What are some comfy sneakers for walking in Europe this winter?”
These are context-rich prompts that AI can interpret and respond to with curated product suggestions. Brands with clear, structured product data and natural-language copy will excel in this type of environment.
Product Pages Matter More Than Ever
AI pulls data from your listings, descriptions, and reviews. If your content is outdated or poorly structured, you might not even show up to ChatGPT shoppers.
And with impulse buys likely to spike in this kind of frictionless experience, your clarity and trust signals can make or break a sale.
This is the next frontier of AI in e-commerce. The game is constantly evolving, and now it’s about showing up where customers are asking questions and ensuring your brand is one of the first answers shown.
How To Optimize Your E-Commerce Product Pages for ChatGPT Shopping
If you want your products to show up in ChatGPT’s recommendations, your product pages need more than nice images and a sale price. You need structure, clarity, and language that AI understands.
Here’s how to get there:
1. Use Product Schema Markup
Structured data helps AI understand what’s on your page. Add product schema so ChatGPT (and other tools) can pull in your:
Price
Availability
Reviews
Product name and image
This is the foundation. Without it, you’re invisible to most recommendation engines.
2. Write Natural, Benefit-Focused Descriptions
ChatGPT’s main focus here is pulling product info and providing an output that sounds conversational. Rewrite your descriptions to sound like how people talk:
Don’t: “Ergonomic, breathable mesh back with tilt-lock feature”
Do: “Keeps you cool and comfortable during long workdays”
3. Keep Product Names Clear
Avoid overly clever names. “The Cloudstep LX” might sound cool, but no one’s searching for that. Try: “Men’s Waterproof Running Shoes – Cloudstep LX”.
4. Feature Fresh Reviews and Ratings
Recent social proof helps both users and AI understand what’s worth recommending. Keep reviews visible and up-to-date.
5. Speed Up Your Mobile Site
A slow page kills conversions, especially if someone’s trying to buy right in the moment. Optimize images, reduce scripts, and test your load time on mobile to ensure the best user experience.
FAQs
How do you use ChatGPT for shopping?
To use ChatGPT for shopping, start a conversation with a shopping-related prompt like “Find me wireless earbuds under $100.” If you’re using ChatGPT Plus, you’ll get product recommendations that also include links. Some users may also have access to built-in checkout through select partners.
Conclusion
ChatGPT shopping is a new channel, not just a new feature. One where conversation replaces search bars and product discovery happens through real-time, AI-driven recommendations.
If you’re in e-commerce, now’s the time to adapt. That means optimizing your product pages with proper schema markup and making sure your content speaks the way real people do.
Your potential customers are already chatting. The question is: is your brand ready to be part of that conversation?
These days, your audience is every bit as likely to find answers through AI Overviews, generative summaries, and language models powering ChatGPT, Gemini, and Claude as they are traditional search, if not more so. This shift explains why AEO, GEO, and LLMO keep coming up in SEO conversations. Each represents a different way your content gets discovered and surfaced across AI-driven experiences.
With this said, these systems don’t all rank content the same way. Some want clear, direct answers. Others reward depth and authority. A few care most about consistent brand signals. Stick with classic SEO tactics alone, and you’ll miss visibility your competitors are already capturing.
The good news? You don’t need three separate strategies. You need to understand how these approaches connect, so your content performs across search engines, answer engines, and conversational AI. This guide breaks down how they overlap, where they differ, and how to prioritize without duplicating your work.
Key Takeaways
AEO helps your content become the direct answer for specific, question-driven searches.
GEO positions your content as a reliable source that AI systems and generative systems want to summarize and cite.
LLMO improves how language models interpret and reference entities and brands in conversational AI experiences.
These frameworks aren’t SEO replacements; they extend it across new AI-powered discovery surfaces.
Rather than picking a single one, it’s important to understand how AEO, GEO, and LLMO work together so your content earns visibility regardless of where or how people search.
One unified strategy can support all three without creating duplicate content or cannibalizing existing pages.
AEO, GEO, and LLMO: Quick Definitions
Before comparing these frameworks, let’s cover what each one does. This context helps you understand how they interact.
What is AEO?
AEO (answer engine optimization) focuses on making your content easy for search engines to convert into a direct answer. It grew out of featured snippets, voice search, and question-based queries. Instead of optimizing only for rankings, AEO prioritizes structure, clarity, and answer-ready formatting. Think of it as helping search engines extract the “best possible response” from your content so users get fast, accurate information.
What Is GEO?
GEO (generative engine optimization) helps your content become the kind of source generative engines prefer to surface, draw insights from, or align with when producing summaries. It emphasizes depth, expertise, and freshness because generative systems prioritize trustworthy, well-supported content. GEO isn’t about giving short answers. It’s about delivering enough substance that AI systems view your content as authoritative and worth citing.
What Is LLMO?
LLMO (large language model optimization) focuses on how large language models understand, interpret, and surface information about entities. Instead of optimizing for traditional SERPs, you optimize for conversational responses from tools like ChatGPT, Gemini, Claude, and Perplexity. LLMO emphasizes entity clarity, consistent terminology, strong brand signals, and original insights that models can incorporate into long-form answers.
AEO vs GEO vs LLMO: The Comparisons
AEO, GEO, and LLMO all fall under modern SEO, but they optimize for different AI-driven experiences. Here’s how they compare.
AEO: Formatting and structure so engines can extract a precise answer.
GEO: Trustworthiness, depth, citations, and topical authority.
LLMO: Brand clarity, entity consistency, and unique perspectives AI can reuse.
The Role They Play in Your Strategy
AEO: Captures quick answers and action-based queries.
GEO: Positions your content as source material for generative systems.
LLMO: Shapes how AI tools talk about, reference, and summarize your brand.
How AEO, GEO, and LLMO Work Together
AEO, GEO, and LLMO aren’t separate marketing channels. They form a layered system that helps your content perform everywhere people search or ask questions. Treat them as connected instead of competing, and it gets easier to build one strategy that supports all three.
AEO Sets the Structure
AEO gives your content the clarity and formatting models need to extract direct answers. It helps you win question-based queries in search, and it makes generative engines more likely to pull accurate, well-structured information. Clean headers, short definitions, and precise formatting start the chain.
GEO Adds the Depth and Authority
Once structure is in place, GEO strengthens your content with research, topical depth, and context. Generative engines favor content that demonstrates expertise and provides more than a simple answer. Your deeper sections—examples, sources, statistics, analysis—give AI tools something credible to cite.
LLMO Adds Context and Brand Understanding
LLMO builds on both layers by helping large language models understand entities, brands, terminology, and expertise. Repeat key entities consistently and appear across credible sources, and models become more likely to reference your business in conversational responses.
What Do You Prioritize First?
Not every business needs the same optimization approach. AEO, GEO, and LLMO support different goals, so your starting point depends on your business model, audience, and growth targets.
AEO should lead when your content relies on capturing direct, question-based searches. It’s the strongest fit for:
Local and service businesses answering specific queries
Product-led brands solving practical “how to” or “what is” searches
Companies optimizing for featured snippets or quick-answer visibility
Pages driving conversions from intent-heavy traffic
If immediate clarity drives results, start with AEO.
GEO plays a bigger role when your strategy depends on depth and credibility. Choose GEO first if you:
Publish long-form content or educational resources
Compete in broad, research-oriented verticals
Need visibility in AI Overviews and other generative results at the top of search
Want to strengthen your brand’s expertise through content
Businesses in SaaS, B2B, and thought leadership-heavy industries benefit most.
LLMO matters when your goal is influencing how models interpret and reference entities and brands. Prioritize LLMO first if you:
Want AI tools to mention your brand in long-form responses
Invest heavily in original research, frameworks, or analysis
Need consistency in how your brand and expertise are described
Care about unlinked mentions and semantic authority
If brand equity and expert positioning drive your strategy, LLMO should take priority.
How To Optimize for All Three
You don’t need three playbooks to optimize for AEO, GEO, and LLMO. The most efficient approach is building one content system that naturally supports all three. Structure your pages well, go deep on topics, and keep your entities consistent. That makes them easier for search engines, generative systems, and large language models to understand and reuse.
1. Start With Strong SEO Fundamentals
A fast site, clear navigation, clean URLs, and solid internal linking are still the backbone of modern visibility. These basics ensure your content is discoverable no matter which AI-driven system tries to interpret it.
2. Use Structure That Supports AEO
Place short definitions, question-based headers, and scannable sections near the top of your content. This makes your page extraction-friendly for answer boxes and helps generative engines pull accurate information. Key Takeaways sections are a great starting point:
3. Expand Depth to Support GEO
After the quick answers, build out deeper explanations, examples, research-backed analysis, and supporting context. This gives AI systems something substantial to cite and increases your authority on broader topics. The inverted pyramid method is a great way to structure content with this in mind.
4. Strengthen Entities to Support LLMO
Reinforce consistent terminology, expert bios, brand descriptions, and niche-specific language. The clearer your entities are, the easier it is for AI models to recognize and reuse your content accurately.
5. Use Layouts That Work Across AI Formats
Pages should be readable by both humans and machines:
Short intros
Quick definitions
Logical headers and subheads
Lists and steps
Deep sections with context
Supporting data or examples
This format helps your content perform across search engines, answer engines, and conversational AI.
FAQs
Are AEO, GEO, and LLMO the same?
No. AEO, GEO, and LLMO all build on SEO, but they focus on different things. AEO is about making your content easy for search engines to turn into direct answers. GEO is about creating deep, trustworthy content that generative systems can summarize and cite. LLMO is about helping large language models understand entities, terminology and expertise.
Conclusion
AEO, GEO, and LLMO aren’t replacements for SEO. They’re extensions of it, shaped by how AI systems now interpret and deliver information. Structure your content for clear answers, go deep enough to be cited in generative summaries, and stay consistent so language models understand you. Do that, and you earn visibility across the entire search ecosystem.
You don’t need three separate strategies. A single, unified approach helps your content perform everywhere your audience looks for answers—on search engines, inside AI Overviews, and across conversational tools. The real opportunity isn’t choosing between AEO, GEO, and LLMO. It’s creating content that works across all of them.
If you want help implementing these strategies or need a deeper analysis of how your content currently performs across these channels, check out my SEO consulting services.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-05 20:00:002025-12-05 20:00:00AEO vs GEO vs LLMO: Are They All SEO?
If you’ve been paying attention to SEO, you’ve seen these acronyms everywhere: AEO and GEO. They sound interchangeable. They’re not.
AEO (answer engine optimization) helps your content show up as a direct answer. Think featured snippets or voice search responses. GEO (generative engine optimization) is built for AI-powered results like Google’s AI Overviews and ChatGPT. GEO creates content that AI models can summarize, cite, and serve to users.
Most marketers treat these strategies like they’re the same thing. That’s a mistake.
This post breaks down the real difference between AEO and GEO, when to use each, and how to build a strategy that works with the way people (and machines) search in 2026.
Key Takeaways
AEO and GEO are both modern extensions for your current SEO strategies
AEO helps your content appear as a direct answer in featured snippets and search features.
GEO creates in-depth content that generative AI can summarize and cite.
They serve different purposes. GEO works better for comprehensive topics; AEO targets short, answerable questions.
A smart SEO strategy in 2026 includes both, depending on your goals and content types.
What is AEO?
AEO stands for Answer Engine Optimization. It’s a content strategy designed to help your site appear as a direct answer in search results. You’ve seen it. Google snippets, People Also Ask boxes, voice assistant responses. That’s AEO.
Search engines shifted from listing links to answering questions directly. AEO helps your content align with that shift by making it easy for search engines to understand and serve.
How it works:
Write content around specific, searchable questions.
Use headers that mirror the way people search.
Follow with short, clear answers.
Add schema markup like FAQ or HowTo to improve eligibility for rich results.
AEO focuses on creating content that’s clean, relevant, and easy to parse. Businesses answering high-intent queries (like “how much does X cost” or “what is the best Y for Z”) see fast results with AEO.
AEO helps you meet users in the moment they need answers and gives your site a shot at showing up before competitors even get a click.
What is GEO?
GEO stands for generative engine optimization. GEO addresses how AI-powered search engines now generate answers. Instead of listing links or pulling quotes, AI models summarize information from multiple sources, often without sending a single click your way.
With GEO, you position your content to become a trusted source that AI systems cite, summarize, or build from. You’re not just trying to rank.
What matters most for GEO:
Longform, helpful content that answers complex topics completely.
Demonstrated expertise (author bios, credentials, original insights).
Fresh data, sources, and citations that AI models trust.
Clear formatting that machines can parse but humans still find useful.
GEO matters more as tools like Google’s AI Overviews and Bing’s Copilot shape the SERP experience. If your content lacks depth or clarity, it won’t get featured.
As AI-generated search results become standard, GEO helps you stay visible even when there’s no traditional snippet or blue link.
GEO vs AEO: The Core Differences
GEO and AEO serve different purposes in modern SEO. One helps you show up as an answer. The other helps you become the source.
AEO is best for:
Appearing in featured snippets, answer boxes, or “People Also Ask”
Answering short, direct questions with structured content
Using headers that match common search phrases
Adding schema markup like FAQ or HowTo
Targeting high-intent keywords like product comparisons or service pricing
Improving visibility in traditional search results
GEO is best for:
Being cited in Google’s AI Overviews or Bing’s Copilot summaries
Publishing detailed content with original data and strong expertise
Including author bios, credentials, and experience indicators
Citing reputable sources and updating content regularly
Writing guides or thought leadership that solve complex questions
Staying visible as search engines shift toward generative answers
You don’t need to choose one or the other. AEO helps you win high-visibility spots for quick answers. GEO helps you earn trust and long-term visibility. The best strategies use both.
When Should You Prioritize One Over the Other?
Use AEO when: You want quick visibility for specific, question-based queries. This works well for:
Service businesses targeting local search
Product comparisons or cost-related questions
Short-form content like FAQs or support articles
Use GEO when: You’re building authority or competing on informational depth. Best for:
Longform guides and evergreen content
Thought leadership or expert breakdowns
Topics that benefit from original data or multiple perspectives
Most businesses benefit from a mix. AEO captures search features quickly. GEO builds lasting trust and relevance as search evolves.
Think of them as complementary tools. The right strategy depends on who you’re targeting and what content you’re creating.
How to Optimize for AEO
To succeed with answer engine optimization, you need to structure your content the way search engines expect it.
Here’s where to start:
Write headers as clear, direct questions.
Follow each question with a short, to-the-point answer. Aim for two to four sentences.
Use bullet points, numbered lists, or short paragraphs to improve scanability.
Add like FAQ or HowTo schema to help search engines understand the format.
Target keywords that show featured snippets or “People Also Ask” boxes in the results.
This kind of content works best when it gives the reader a fast, helpful answer and signals to Google that it’s ready to be used in search features.
If you’re not sure where to begin, look at keywords already showing rich results. That’s where answer engine optimization gives you the best shot at quick visibility.
How To Optimize for GEO
Generative engine optimization focuses on making your content useful to AI. That means going beyond surface-level advice and creating content that’s reliable, comprehensive, and trustworthy.
Here’s what to prioritize:
Write longer, in-depth content that covers the full context of a topic.
Use original insights, quotes, or proprietary data whenever possible.
Include clear author bios that show subject matter expertise.
Add reputable outbound links to support your claims.
Keep your content updated and show a visible “last modified” date.
AI-powered search features pull from sources that demonstrate experience and authority. If your content looks like it was written for real people and backed by real experts, it’s more likely to be cited.
AI-powered features are changing how content gets discovered, which is why it’s important to keep pace with ongoing search engine trends. When you understand how engines choose and surface content, you can create pages that are more likely to be summarized or cited.
Common Mistakes When Implementing AEO and GEO
I see businesses make the same mistakes with AEO and GEO. Here’s what to avoid.
Treating them as mutually exclusive. You don’t pick one and ignore the other. Your FAQ page needs AEO. Your comprehensive guide needs GEO. Most content benefits from both approaches applied strategically.
Optimizing for machines at the expense of humans. If your content reads like it was written for an algorithm, you’ve gone too far. AI models favor content that serves real people. Write for humans first, then add the technical elements that help machines understand.
Ignoring content freshness. This kills GEO. AI models prioritize current information. If your comprehensive guide hasn’t been updated in two years, it won’t get cited. Set a schedule to review and refresh your GEO content.
Skipping schema markup for AEO.Schema is the difference between hoping for a featured snippet and actually getting one. FAQ and HowTo schema takes minutes to implement. Use it.
Not tracking results separately. You need to know which strategy drives which outcomes. Track featured snippet appearances for AEO content. Monitor AI Overview citations for GEO pieces. Without separate tracking, you’re flying blind.
The biggest mistake? Doing nothing because you’re overwhelmed. Start small. Pick one piece of content for AEO optimization and one for GEO. Learn what works for your audience, then scale from there.
FAQs
What is the difference between AEO and GEO?
AEO is focused on structuring content for direct answers in search results, like featured snippets or “People Also Ask” boxes. GEO is about creating trustworthy, in-depth content that AI tools can summarize or cite.
Is AEO just a new name for SEO?
No. AEO is a specific part of SEO that targets how search engines deliver answers, especially for short-form, question-based content. It works alongside your broader SEO efforts, including technical, on-page and content optimization, not in place of them.
How is GEO changing SEO strategies?
GEO requires marketers to prioritize quality, authority, and freshness. It’s shifting the focus from simply ranking on page one to being used as a source in generative AI experiences.
Conclusion
AEO and GEO are core parts of how search works today.
AEO helps you win visibility in high-intent, answer-focused moments. GEO positions your content to be referenced and repurposed by AI tools that are reshaping how people get information.
The smartest now combine both. You target quick wins with AEO while building long-term authority through GEO.
As search continues to evolve, your content should too. Keep it helpful. Keep it credible. Make sure it’s built to show up, whether a human or an algorithm is doing the reading.
Want help optimizing for both AEO and GEO? Check out my SEO consulting services for hands-on support with building your strategy.
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Link building is the practice of earning links from other websites to your own. These links act as signals of trust and authority for search engines, helping your pages rank higher in search results. Quality matters more than quantity. A few relevant, high-authority links are far more valuable than many low-quality ones. Modern link building focuses on creating genuinely useful content, building genuine relationships, and earning links naturally, rather than manipulating rankings.
Link building helps establish content credibility through acquiring backlinks from other websites.
It focuses on quality over quantity, emphasizing trust and relevance in search engine rankings.
Effective link building involves engaging with digital PR and fostering genuine relationships with sources.
Producing valuable content and fostering connections leads to high-quality links and improved online visibility.
Today, AI-driven search evaluates authority based on context, relevance, and structured data, not just backlinks.
What is link building?
Link building means earning hyperlinks from other sites to show search engines your content is trustworthy and valuable. Now, it’s more like digital PR, focusing on relationships, credibility, and reputation, not just quantity. AI-powered search also considers citations, structured data, and context alongside backlinks. By prioritizing quality, precision, and authority, you build lasting online visibility. Ethical link building remains one of the most effective ways to enhance your brand’s search presence and reputation.
Link building is a core SEO tactic. It helps search engines find, understand, and rank your pages. Even great content may stay hidden if search engines can’t reach it through at least one link.
To get indexed by Google, you need links from other sites. The more relevant and trusted those links are, the stronger your reputation becomes. This guide covers the basics of link building, its connection to digital PR, and how AI-driven search evaluates trust and authority.
A link, or hyperlink, connects one page on the internet to another. It helps users and search engines move between pages.
For readers, links make it easy to explore related topics. For search engines, links act like roads, guiding crawlers to discover and index new content. Without inbound links, a website can be challenging for search engines to discover or assess.
You can learn more about how search engines navigate websites in our article on site structure and SEO.
A link in HTML
In HTML, a link looks like this:
<a href="https://yoast.com/product/yoast-seo-wordpress/">Yoast SEO plugin for WordPress</a>
The first part contains the URL, and the second part is the clickable text, called the anchor text. Both parts matter for SEO and user experience, as they inform both people and search engines about what to expect when they click.
Internal and external links
There are two main types of links that affect SEO. Internal links connect pages within your own website, while external links come from other websites and point to your pages. External links are often called backlinks.
Both types of links matter, but external links carry more authority because they act as endorsements from independent sources. Internal linking, however, plays a crucial role in helping search engines understand how your content fits together and which pages are most important.
To learn more about structuring your site effectively, refer to our guide on internal linking for SEO.
Anchor text
The anchor text describes the linked page. Clear, descriptive anchor text helps users understand where a link will direct them and provides search engines with more context about the topic.
For example, “SEO copywriting guide” is much more useful and meaningful than “click here.” The right anchor text improves usability, accessibility, and search relevance. You can optimize your own internal linking by using logical, topic-based anchors.
Link building is the process of earning backlinks from other websites. These links serve as a vote of confidence, signaling to search engines that your content is valuable and trustworthy.
Search engines like Google still use backlinks as a key ranking signal; however, the focus has shifted away from quantity to quality and context. A single link from an authoritative, relevant site can be worth far more than dozens from unrelated or low-quality sources.
Effective link building is about establishing genuine connections, rather than accumulating as many links as possible. When people share your content because they find it useful, you gain visibility, credibility, and referral traffic. These benefits reinforce one another, helping your brand stand out in both traditional search and AI-driven environments, where authority and reputation are most crucial.
Link quality over quantity
Not all links are created equal. A high-quality backlink from a well-respected, topic-relevant website has far more impact than multiple links from small or unrelated sites.
Consider a restaurant owner who earns a link from The Guardian’s food section. That single editorial mention is far more valuable than a dozen random directory links. Google recognizes that editorial links earned for merit are strong signals of expertise, while low-effort links from unrelated pages carry little or no value.
High-quality backlinks typically originate from websites with established reputations, clear editorial guidelines, and active audiences. They fit naturally within the content and make sense to readers. Low-quality links, on the other hand, can make your site appear manipulative or untrustworthy. Building authority takes time, but the reward is a reputation that search engines and users can rely on.
Read more about this long-term approach in our post on holistic SEO.
Shady techniques
Because earning high-quality links can take time, some site owners resort to shortcuts, such as buying backlinks, using link farms, or participating in private blog networks. These tactics may yield quick results, but they violate Google’s spam policies and can result in severe penalties.
When a site’s link profile looks unnatural or manipulative, Google may reduce its visibility or remove it from results altogether. Recovering from such penalties can take months. It is far safer to focus on ethical, transparent methods. In short, you’re better off avoiding these risky link building tricks, as quality always lasts longer than trickery.
How to earn high-quality links
The most effective way to earn strong backlinks is to create content that others genuinely want to reference and link to. Start by understanding your audience and their challenges. Once you know what they are looking for, create content that provides clear answers, unique insights, or helpful tools.
For example, publishing original data or research can attract links from journalists and educators. Creating detailed how-to guides or case studies can help establish connections with blogs and businesses that want to cite your expertise. You can also build relationships with people in your industry by commenting on their content, sharing their work, and offering collaboration ideas.
Newsworthy content is another proven approach. Announce a product launch, partnership, or study that has real value for your audience. When you provide something genuinely useful, you will find that links and citations follow naturally.
Structured data also plays an important role. By using Schema markup, you help search engines understand your brand, authors, and topics, making it easier for them to connect mentions of your business across the web.
Search is evolving quickly. Systems like Google Gemini, ChatGPT, and Perplexity no longer rely solely on backlinks to determine authority. They analyze the meaning and connections behind content, paying attention to context, reputation, and consistency.
Links still matter, but they are part of a wider ecosystem of trust signals. Mentions, structured data, and author profiles all contribute to how search and AI systems understand your expertise. This means that link building is now about being both findable and credible.
To stay ahead, make sure your brand and authors are clearly represented across your site. Use structured data to connect your organization, people, and content. Keep your messaging consistent across all channels where your brand appears. When machines and humans can both understand who you are and what you offer, your chances of visibility increase.
You can read more about how structured data supports this process in our guide to Schema and structured data.
Examples of effective link building
There are many ways to put link building into action. A company might publish a research study that earns coverage from major industry blogs and online magazines. A small business might collaborate with local influencers or community organizations that naturally reference its website, thereby increasing its online presence. Another might produce in-depth educational content that other professionals use as a trusted resource.
Each of these examples shares the same principle: links are earned because the content has genuine value. That is the foundation of successful link building. When people trust what you create and see it as worth sharing, search engines take notice, too.
In conclusion
Link building remains one of the most effective ways to establish visibility and authority. Today, success depends on more than collecting backlinks. It depends on trust, consistency, and reputation.
Consider link building as an integral part of your digital PR strategy. Focus on creating content that deserves attention, build relationships with credible sources, and communicate your expertise clearly and effectively. The combination of valuable content, ethical outreach, and structured data will help you stand out across both Google Search and AI-driven platforms.
When you build content for people first, the right links will follow.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-04 12:47:092025-12-04 12:47:09What is link building in SEO?
If your marketing still treats everyone the same, you’re falling behind.
Audience segmentation is what turns generic campaigns into personalized, high-performing ones. Segmented email campaigns can generate a 760 percent increase in revenue compared to non-segmented ones.
That same principle applies across paid ads, social content, product messaging, and just about any other marketing channel you can think of.
Without segmentation, you’re guessing what your audience wants. That leads to wasted ad spend, and low engagement.
Segmentation gives you an edge. It helps you deliver the right message, to the right people, at the right time.
In this guide, you’ll learn what audience segmentation is, how the different types work, and how to apply them to drive better results across your funnel.
Key Takeaways
Audience segmentation is the process of dividing your broader audience into smaller, more specific groups.
Segmentation helps improve engagement, click-through rates, and conversions across every channel.
There are five core types: demographic, geographic, psychographic, behavioral, and firmographic (which is specifically for B2B).
Good segmentation starts with real data, not assumptions, and improves over time.
The most effective marketing strategies use segmentation to deliver more personalized and relevant messaging.
What Is Audience Segmentation?
Audience segmentation is the process of dividing your broader audience into smaller, more specific groups based on shared characteristics. These characteristics can be demographic, geographic, behavioral, or even psychographic.
The goal is simple: understand your audience better so you can speak to them more effectively.
Think of it like this: you wouldn’t send the same message to a first-time visitor and a loyal customer. And you wouldn’t talk to a 23-year-old in the same way you’d market to a 65-year-old. Segmentation helps you avoid that one-size-fits-none approach.
This isn’t just a tactic for email marketers, either. It’s a core part of building relevant campaigns across paid ads, landing pages, SMS, product marketing, and more.
Here’s what segmentation unlocks:
More personalized content and offers
Smarter ad targeting
Higher engagement rates
Better alignment across your marketing funnel
Audience segmentation often gets confused with defining your target audience. But while defining a target audience helps you understand who you’re going after at a high level, segmentation helps you break that audience down into actionable groups for more precise messaging.
Most marketers aren’t struggling with a lack of data. The challenge is turning that data into action.
That’s where customer and audience segmentation creates real value. When you group your audience based on shared traits or behaviors, you can tailor your messaging, timing, and channels to what actually resonates.
Brands that use segmentation typically see:
Higher open and click-through rates
Increased customer lifetime value
Lower cost per acquisition (CPA)
More efficient use of ad budgets
65 percent of consumers expect personalization in their customer experience. And it’s not limited to email. Whether you’re running Google Ads, building a product launch campaign, or personalizing a homepage—segmentation improves performance across the board.
It also allows you to meet customers where they are in their journey. Someone new to your brand might need education. A returning customer may be ready for an upsell. With segmentation, you can deliver the right message at the right moment.
Types of Audience Segmentation
There are several ways to segment your audience. Each type gives you a different lens into what drives your customers’ behavior. The best strategies use a mix of these, depending on your goals, product, and data.
Here are the five most common types of audience segmentation:
Demographic Segmentation
This is the most straightforward method. You segment based on traits like:
Age
Gender
Income level
Education
Marital status
Example: A clothing brand might promote its premium line to high-income professionals while marketing basics to students or entry-level workers.
Geographic Segmentation
Here, you group users by physical location:
Country or region
Climate
City size
Urban vs. rural
Example: A food delivery app might market lunch deals to users in busy cities while promoting family meals in suburban areas.
Psychographic Segmentation
This method looks at the “why” behind your customer’s actions:
Personality traits
Interests and hobbies
Lifestyle choices
Core values
Example: A fitness brand might market high-performance gear to athletes and eco-friendly materials to sustainability-minded shoppers.
Behavioral Segmentation
Segment based on how people interact with your brand:
Purchase history
Engagement level
Brand loyalty
Product usage
Example: A SaaS company might send upgrade offers to heavy users and reactivation emails to inactive accounts.
Firmographic Segmentation (B2B Only)
This is the B2B version of demographic segmentation:
Company size
Industry
Revenue
Location
Decision-maker role
Example: A software vendor might offer enterprise features to large corporations and budget-friendly plans to startups.
Real-World Segmentation Examples Across Channels
Segmentation works across every channel you’re using. The tactics change, but the principle stays the same: send the right message to the right person.
Email Marketing: New subscribers get your welcome series. Inactive customers (90+ days) get a win-back offer with a discount. Same list, different messages based on engagement level.
Paid Advertising:Cart abandoners see retargeting ads featuring the exact product they left behind. Cold audiences see brand awareness content and educational posts. Match the ad creative to where they are in the funnel.
Content Personalization: SaaS visitors see automation guides and workflow content. E-commerce brands see conversion optimization and retention posts. Your CMS can handle this with simple behavioral tags based on past visits.
Product Rollouts: Power users get early beta access to new features. Light users get the stable release later with more documentation. This reduces your support burden and makes heavy users feel valued.
SMS Marketing: Previous buyers in specific zip codes get flash sale alerts for local stores. First-time visitors get a welcome discount. High intent plus geographic relevance equals higher conversion rates.
The channel doesn’t matter. What matters is matching the message to the person and where they are in their journey.
How To Segment Your Audience, Step-By-Step
Getting started with segmentation doesn’t have to be complex. Here’s a simple process you can use to organize your audience into actionable groups.
1. Start With Data You Already Have
Look at what’s in your CRM, email platform, or analytics tool. Useful data often includes location, purchase history, on-site behavior, and sign-up source.
2. Define Your Most Important Attributes
Based on your goals, decide which traits matter most. For an e-commerce brand, it could be past purchase behavior. For a SaaS company, it might be usage level or company size.
3. Build Initial Segments
Group your audience using filters like:
“Has purchased in last 30 days”
“Visited pricing page but didn’t convert”
“Signed up from Facebook campaign”
Start simple. You can get more granular later.
4. Map Each Segment to the Customer Journey
Think about where each group is in their decision-making process. Someone early in the funnel needs education. A returning visitor might need an incentive.
If you haven’t done this yet, use customer journey mapping to connect segments to meaningful actions.
5. Test, Learn, and Refine
Segmentation isn’t one-and-done. Use A/B testing to refine your messaging, offers, and timing by segment. Drop what doesn’t work. Scale what does.
Best Practices for Audience Segmentation (That Actually Work)
Anyone can slice up an email list but effective segmentation goes beyond basic filters. Here are a few proven tips to get better results without overcomplicating your strategy.
Use Real Data, Not Assumptions
Avoid guessing what people care about. Use actual behavior, survey responses, or analytics to guide how you group your audience.
Keep Segments Useful, Not Just Accurate
A perfect audience profile is useless if it’s too small to act on. Prioritize segments that tie directly to your business goals—like conversions, upsells, or retention.
Don’t Over-Personalize
Over-segmentation can create unnecessary complexity. You don’t need 30 different versions of the same email. Focus on meaningful variations that actually move metrics.
Update Your Segments Regularly
Customer behavior changes. Segments should too. Review and refresh your data often to avoid targeting stale or irrelevant groups.
Align Segments With Personas
Your audience groups should reflect the same needs and motivations as your core buyer personas. If you don’t have a clear set, start with this guide to building an accurate customer persona.
I see the same mistakes over and over. Avoid these pitfalls to get better results from your segmentation strategy.
Segmenting too early. You need data before you can segment effectively. If you’re working with a brand-new list or product, focus on collecting behavioral data first. Premature segmentation based on assumptions will waste time and money.
Creating too many micro-segments. A segment with 47 people isn’t actionable. Keep your segments large enough to matter. If a group is too small to justify custom creative or messaging, fold it into a larger segment.
Using outdated data. Someone who bought six months ago isn’t in the same segment as someone who bought yesterday. Refresh your segments quarterly at minimum. Monthly is better for fast-moving businesses.
Segmenting but not personalizing. Building segments means nothing if you send the same message to everyone. Each segment should get tailored copy, offers, or creative. Otherwise, you’re just organizing your list for no reason.
Ignoring overlap between segments. People can belong to multiple groups. A high-value customer might also be geographically close to your store. Think about how segments intersect and prioritize which message matters most.
Not testing segment performance. Track metrics by segment. If one group consistently underperforms, either refine the segment definition or adjust your messaging. Segmentation without measurement is guesswork.
FAQs
What is audience segmentation?
Audience segmentation is the process of dividing your broader audience into smaller groups based on traits like behavior, interests, demographics, or location. It helps you deliver more targeted and relevant marketing.
What are the types of audience segmentation?
The most common types include demographic, geographic, psychographic, behavioral, and firmographic segmentation. Each one gives you a different way to understand and connect with your audience.
How do you segment your audience effectively?
Start with data you already have—like purchase history or engagement. Then group users based on shared traits, align segments to the customer journey, and continuously refine based on performance.
Conclusion
Audience segmentation isn’t a tactic you add later. It’s where effective marketing starts.
By breaking your audience into meaningful groups, you gain the ability to tailor messages, prioritize the right channels, and improve your results across the board. Whether you’re building email campaigns, running paid ads, or planning content, segmentation keeps your strategy focused and relevant.
Start with the data you already have. Pick one or two segments that align with your goals. Then test, learn, and scale.
The more precise your segmentation, the more personal your marketing will feel and the better it will perform.
Need help building a segmentation strategy that actually drives results? Check out my consulting services for hands-on support.
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:
Brand mentions
Citations
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.
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.
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.
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
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.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-03 17:28:222025-12-03 17:28:22SaaS in AI Search: Who’s Ranking (+ How to Steal Their Spot)
AI shopping assistants like ChatGPT, Perplexity, and Google’s generative search are influencing purchase decisions before customers ever reach your product […]
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