As part of our broader APAC plan, which includes new Deep Dive events and two local language
editions, we are happy to announce the details for our Chinese language event: Search
Central Live Hong Kong!
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Have you recently noticed AI platforms like ChatGPT or Gemini pulling answers from websites but not always linking back?
Don’t think of it as an unfortunate glitch, but a big shift in how these tools present information.
Large language models (LLMs) change how users see your content. Instead of relying on Google’s ten blue links, people get their answers straight from AI tools in an easy-to-read summary that’s often been condensed and (unfortunately) without any clicks to your site.
If these tools don’t reference your content, you’re missing out on a growing share of visibility. That’s where LLM seeding comes in.
LLM seeding involves publishing content in places and formats that LLMs are more likely to crawl, understand, and cite. It’s not a traditional SEO strategy or “prompt engineering.” Instead, you’ll use this strategy to get your content to appear in AI-generated answers, even if no one clicks.
We’ll cover what LLM seeding is, how it works, and the steps you can take to start showing up in AI responses before your competitors get there first.
LLM seeding is publishing content in formats and locations that LLMs like ChatGPT, Gemini, and Perplexity can access, understand, and cite.
Instead of trying to rank #1 in Google search results, you want to be the source behind AI-generated answers your audience sees. The goal is to show up in summaries, recommendations, or citations without needing a click. The fundamentals overlap with SEO best practices, but the platform you’re optimizing for has changed.
Let’s say you run a productivity software company. Your content marketing team writes a detailed comparison post about the “Best Project Management Tools for Remote Teams.” A month later, someone asks ChatGPT that exact question, and your brand name shows up in the response, even though you don’t rank on page one in Google.
How did the LLM find your information? Here’s what it looks like behind the scenes.
LLMs have been trained on massive datasets pulled from the public web, including blogs, forums, news sites, social platforms, and more. Some also use retrieval systems (like Bing or Google Search) to pull in fresh information. When someone asks a question, the model generates a response based on what it has learned and in some cases, what it retrieves in real time.
Well-structured content, clearly written, and hosted in the right places, is more likely to be referenced in the response: an LLM citation. It’s a huge shift because instead of optimizing almost exclusively for Google’s algorithm, you’re now engineering content for AI-visibility and citations.
Asking ChatGPT for a list of the best laptop backpacks provides several citations and options.
Traditional SEO focuses on ranking high on Google to earn clicks. You optimize for keywords, build backlinks, and improve page speed to attract traffic to your site.
LLM seeding flips that on its head.
You don’t chase rankings. You build content for LLMs to reference, even if your page never breaks into the top 10. The focus shifts from traffic to trust signals: clear formatting, semantic structure, and authoritative insights. You provide unique insights and publish in places AI models scan frequently, like Reddit, Medium, or niche blogs, which increases your chances of being surfaced in AI results.
SEO asks, “How do I get more people to click to my website?”
LLM seeding asks, “How do I become the answer, even if there’s no click?”
The thing is, it’s not an either/or proposition. You still want to do both. But you’re invisible to a constantly growing audience if you’re not thinking about how AI tools interpret and cite your content.
LLM seeding goes beyond vanity metrics to the visibility that actually sticks, even when clicks don’t happen. It can be a real game-changer because it lets you do the following:
If you want LLMs to surface and cite your content, you need to make it easy to find, read, and worth referencing. Here’s how to do that:
LLMs prioritize ranking-style articles and listicles, especially when they match user intent, such as “best tools for freelancers” or “top CRM platforms for startups.” Adding transparent criteria boosts trust.
Semantic chunking breaks your content into clear, focused sections that use subheadings, bullet points, and short paragraphs to make it easier for people to read. This structure also helps LLMs understand and accurately extract details. If you’re having trouble thinking about where to start, think about FAQs, summary boxes, and consistent formatting throughout your content.
LLMs tend to favor authentic, detailed reviews that include pros, cons, and personal takeaways. Explain your testing process or experience to build credibility. Websites like Tom’s Guide and Wirecutter do an excellent job of this.
Wirecutter’s table of contents breaks down why they choose the items they choose and why you, the reader, should trust them.
Side-by-side product or service comparisons (especially Brand A vs. Brand B) are gold to LLMs. You’re more likely to be highlighted if you include verdicts like “Best for Enterprise” or “Best Budget Pick.” An example of a brand that does comparison tables particularly well is Nerdwallet.
Format your FAQs with the question as a subheading and a direct, short answer underneath. LLMs are trained on large amounts of Q&A-style text, so this structure makes it easier for them to parse and reuse your content. FAQ schema is also fundamental to placement in zero-click search elements like featured snippets. The structured format makes your content easier for AI systems to parse and reference.
Almost every article we publish on our site features FAQs that have been properly formatted.
Hot takes, predictions, or contrarian views can stand out in LLM answers, especially when they’re presented clearly and backed by credible expertise. Structure them clearly and provide obvious takeaways.
Use author bios, cite sources, and speak from experience. LLMs use the cues to gauge trust and credibility. If you’ve been focusing on meeting E-E-A-T guidelines, much of your content will already have this baked in.
While ChatGPT may not show users photos inside the chat window, screenshots, graphs, and visuals with descriptive captions and alt text help LLMs (and users who do click through) better understand context. It also breaks up walls of text.
Free calculators, checklists, and templates are highly shareable and are easy for AI systems to parse and extract. Make sure the title and description explain each item’s value upfront.
It’s telling that many of the best practices for traditional SEO often work well for LLM seeding. At their core, both priorities involve giving people the best possible answers to their questions in a highly readable and simple way to digest. In fact, creating content that works well for all avenues is a cornerstone of search everywhere optimization.
Publishing on your site isn’t enough to excel with LLM seeding. AI models pull from a wide mix of sources across the web. The more places your content shows up, the more likely it is to influence or be cited in AI-generated answers.
LLMs tend to surface structured, public content hubs. Medium, Substack, and LinkedIn articles get crawled often and carry extra weight because of their clean formatting and tied-to-real-author profiles. These sites publish large volumes of content and are widely trusted, so your content benefits from their visibility and is more likely to be surfaced in AI-generated answers.
Contributing to trusted outlets, such as trade blogs, marketing publications, and niche news sites, offers your brand credibility and increases the odds of your content being surfaced or cited in AI-generated answers.
Offering quotes to journalists or bloggers through services like HARO or Featured can land you in articles LLMs surface and cite repeatedly.
Sites like G2, Capterra, or niche review sites are LLM goldmines. Get your customers to leave detailed reviews and provide quotable explanations about why your product or service stands out.
Reddit and Quora are two of the most frequently surfaced sources in AI answers. Niche forums and communities (such as AVS Forum or Contractor Talk) also carry weight because they’re packed with authentic, experience-driven insights. Consider creating a public-facing profile to answer questions about your product or service. In addition, they’re excellent spaces to source user-generated content (UGC) that can provide additional context and support.
Small, research-driven microsites can carry more authority than heavily branded pages. Because they are often well-structured, focused, and treated as independent resources, they are more likely to be picked up by LLMs when generating answers.
Platforms like LinkedIn, YouTube, and even Reddit threads can double as searchable databases for LLMs. Use structured language, captions, and context in every post.
Here’s the bottom line: LLM seeding works best when your content is everywhere AI looks, not just on your blog.
Tracking LLM seeding is different from tracking SEO performance. You won’t always see clicks or referral traffic, but you can measure impact if you know where to look. These KPIs matter the most:
Manual testing: Try running audience-style prompts in ChatGPT, Gemini, Claude, and Perplexity in incognito mode so past queries don’t bias results. As a note here, results can vary from instance to instance, so test multiple times to see consistent patterns.
We’re in pretty good company among the top five resources.
Tracking tools: Perplexity Pro lets you see citation sources, while ChatGPT Advanced Data Analysis can sometimes surface cited domains. Even enterprise tools like Semrush AIO have started to track brand mentions across AI models. There are also dedicated tools like Profound that specifically focus on AI visibility.
Using tools like GA4 can help you determine LLM seeding’s effectiveness, but not via traditional metrics.
With GA4, you’ll want to navigate through Reports > Acquisition > Traffic Acquisition and then filter for your chosen form of traffic. Be sure to review the source/medium dimension for more details about specific LLM platforms. Referral traffic may come from LLMs if they include a clickable link to your website. By contrast, brand mentions without links are more likely to drive users to search for you after using an LLM, which GA4 usually classifies under organic search.
This isn’t super-likely by comparison. Since this is less common, it’s best to look at referral traffic alongside LLM visibility metrics for the full picture of performance.
You have several options for seeking out unlinked mentions. Set up Google Alerts for brand name or product mentions; that can help you surface when your brand is mentioned in the news or other platforms. For example, Semrush’s Brand Monitoring tool lets you look for citations without backlinks.
Semrush touts its brand monitoring tool as one of the best in the business.
No matter which tools you use, building a log to track your monthly tests across AI platforms can provide insights. Document the tool(s) used, prompt asked, and the exact phrasing of the mention. You’ll also want to track your brand sentiment; is your brand being talked about in a positive, neutral, or negative light?
Companies like Serpstat, Similarweb, and Profound have begun to offer AI visibility reporting, and those options will mature fast.
There’s currently no silver bullet to track LLM seeding comprehensively. It’s partly manual work, partly analytics, and partly new tools still in beta. You can create an AI Visibility Dashboard that combines GA4, brand monitoring, and a spreadsheet of monthly AI prompts to get a head start.
LLM seeding is publishing content in formats and locations that large language models (LLMs) are more likely to surface and cite. Instead of optimizing only for search rankings, you’re optimizing for visibility in AI-generated answers.
An LLM citation happens when an AI platform like ChatGPT, Gemini, or Perplexity references your content with a source link in its response.
An LLM mention is when an AI platform references your content but doesn’t provide a clickable source link.
Run audience-style prompts in AI tools (like “best project management software for startups”) and see if your brand shows up. Also, track referral traffic trends in GA4.
Search looks different today because users no longer rely exclusively on Google. Your audience asks questions in ChatGPT, Gemini, and other AI tools. They’re now the ones who decide which brands get mentioned.
LLM seeding matters. You can stay visible even when clicks don’t come and earn credibility by showing up in AI responses. This futureproofs your marketing against zero-click trends and keeps you agile and top of mind.
To win this new landscape, start small: publish in formats LLMs love like listicles, FAQs, and comparisons), seed content across third-party platforms, and track whether your brand shows up in AI outputs.
The companies that adapt today will own the conversation tomorrow.
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Google is no longer the only place people search. Millions now bypass search engines entirely and turn to large language models (LLMs) like ChatGPT, Gemini, and Perplexity for answers.
ChatGPT alone fields over 2.5 billion prompts a day and serves more than 120 million users daily.
This creates a massive opportunity. LLM SEO is how you get your content in front of those systems. The idea is to make your content so clear and credible that a model has no choice but to pull from it.
That means writing in a way machines can process, and people still want to read. Do it right, and you’ll show up where the traffic is already shifting.
This isn’t a future concern. It’s happening now. If you don’t adapt, readers will still get answers—just not from you. You’ll lose the click before you even get the chance to earn it.
LLM SEO is the process of optimizing your content so that large language models can understand, interpret and surface is in their responses. Think of it as preparing your content for systems like ChatGPT, Gemini, and Perplexity just as you prepare content for Google.
Instead of focusing only on rankings, LLM SEO targets being recognized as a credible source. That means:
LLM and traditional SEO share the same goal: to connect your expertise with what people are looking for. What’s changing is where and how those answers show up.
LLM SEO and large language model optimization (LLMO) overlap, but they’re not the same. Think of LLM SEO as a slice of the broader LLMO pie.
LLM SEO specifically targets making your content easy for large language models to parse and cite, often in search engine-related contexts. This includes optimizing for Google’s AI Overviews (AIOs) and ensuring your content is structured so it’s more likely to be surfaced by AI-driven platforms like ChatGPT or Gemini.
LLMO goes further. It’s about increasing your brand’s overall visibility in AI-generated answers across platforms like ChatGPT, Perplexity, Gemini, and Claude. That reach isn’t limited to search. It also means:
In short, LLM SEO helps you show up in AI answers connected to search. LLMO ensures your brand is present across any context where large language models generate responses.
LLM SEO builds on the foundation of traditional SEO but shifts the focus to how large language models process and deliver information.
Traditional SEO is about rankings. You optimize for Google or Bing so your content climbs the results page. Success is measured in keyword positions, clicks, and traffic.
LLM SEO is about citations. Instead of fighting for position one, you make your content easy for LLMs to read, trust, and include in their responses. Success is measured in mentions and visibility inside tools like ChatGPT or Gemini, even if the user doesn’t click through.
The overlap is important. Both require:
The differences matter. Traditional SEO leans on backlinks and click-through optimization. LLM SEO rewards clear language, structured formats like FAQs and lists, and transparent sourcing. Whereas SEO optimizes for crawlers, LLM SEO optimizes for language models.
Marketers who stop at traditional SEO risk losing visibility as more searches end inside AI answers.
Large language models are quickly becoming the go-to source for answers. In fact, 27 percent of people in the U.S. now use AI tools over traditional search engines.
Instead of clicking through search results, people ask AI tools like ChatGPT direct questions and get immediate answers. That shift is changing brand discovery.
You can already see this shift playing out, with some industries showing up in AI Overviews far more often than others.
For businesses, the risk is obvious. If your content isn’t structured for LLMs, your expertise may never surface, even if you rank well in Google. That means losing visibility to competitors optimizing for both.
There’s also the matter of trust. LLMs lean heavily on authoritative, clearly written content with well-cited sources. If your brand is not putting out content that signals credibility, you’re less likely to be included in the answers users see.
Finally, this shift is accelerating. More platforms are rolling out AI-driven responses, and users are adopting them quickly because they save time.
Every month you wait is a month of lost visibility. LLM SEO puts your brand where attention is headed, not where it’s fading.
Visibility in large language models isn’t about hacks. It comes down to making your content easier for these systems to understand, trust, and reuse. The following practices build on what already works in SEO but adapt it for how LLMs process and deliver information.
Large language models are built to handle natural conversation. Content that reads conversationally and adapts to context is more likely to be included in generated answers. Drop the keyword stuffing and rigid phrasing. Instead, write the way people actually ask (and follow up on) questions.
LLMs thrive on clarity. Adding FAQ sections and concise takeaways gives them ready-made snippets they can use to build answers. It helps readers, too, breaking content into scannable, useful chunks while giving AI systems obvious entry points into your page.
Traditional SEO often leaned on exact-match keywords. LLM SEO works better with semantic and contextual phrasing, language that reflects how people naturally ask questions. Build around related terms and long-tail queries so models can recognize intent and surface your content more often.
LLMs look for signals of authority and consistency across multiple platforms. A brand that regularly publishes on its own blog, contributes to third-party sites, and maintains a strong profile across social channels is more likely to be trusted. Consistency reinforces your credibility.
Original insights stand out. Publishing unique research, case studies, or proprietary data makes your content more valuable to LLMs. These models are designed to identify and prioritize information not easily found elsewhere. For example, graphics like the piece below showcase data points that my team sourced on its own.
Optimization does not stop at publishing. Regularly test how your brand appears in ChatGPT, Gemini, or Perplexity. Query these platforms with the same questions your audience might ask. Monitoring performance helps you identify where your content is being cited and where you need to adjust. In the example below, you can see how a brand can be portrayed in AI tools based on different sources. We’ll cover later on how you can go about doing this.
Stale content gets overlooked. Updating old posts with new statistics, recent examples, or revised insights signals that your brand is current.
LLMs draw from a variety of different sources, and this is where Search Everywhere Optimization comes in. LLMs pull from forums, video transcripts, and social media. The more places your brand shows up, the more likely it is to be discovered and cited by AI.
This is the essence of search everywhere optimization: making sure your expertise is visible wherever people (and AI models) go looking for answers.
Measuring success in LLM SEO is not as straightforward as checking keyword rankings, but there are now tools and methods that make it possible.
Specialized platforms like Profound are built to track how often brands and websites appear in AI-generated answers across platforms. See below for a look at the Profound interface and how it helps showcase AI visibility.
Established SEO platforms, including Semrush, have also rolled out features that measure AI visibility alongside traditional search metrics. In the screenshot below, you can see how Semrush showcases AIO presence for a given page.
These tools give you a clearer picture of whether your content is surfacing where people are asking questions.
In addition to platforms, hands-on monitoring still matters. Query the models directly using the same questions your audience would ask. Document when your content is cited and watch for changes over time. This kind of manual testing tracks progress beyond what analytics alone can show.
You should also monitor referral traffic. Some AI tools now include links to sources, and those clicks show up in analytics as traffic. Another thing to keep an eye out for is brand mentions. Even if an AI result doesn’t give a link, brand mentions inside AI outputs are valuable, as they reinforce awareness and authority.
Finally, fold LLM SEO tracking into your broader SEO reporting. Look at engagement signals like time on page, repeat visits, and social shares for optimized content. If people find your content more useful, LLMs are more likely to treat it as a trusted source.
The bottom line is that measurement is evolving. You now have tools, data, and direct testing methods that show whether your LLM SEO efforts are paying off.
LLM SEO is the process of optimizing content so large language models such as ChatGPT, Gemini, and Perplexity can understand, interpret, and surface it in their responses.
Traditional SEO focuses on ranking in search engine results. LLM SEO focuses on being cited inside AI-generated answers. Both rely on quality content, authority, and structure, but the measurement of success is different.
No. LLM SEO is a subset of LLM optimization (LLMO). LLM SEO focuses on search-related visibility in LLM outputs, while LLMO covers the broader goal of increasing brand presence across all AI-generated answers.
Tracking visibility in LLMs involves querying the models directly, monitoring referral traffic from AI tools in places like GA4, and using platforms such as Profound or Semrush that offer AI visibility tracking.
Adoption of LLMs is growing rapidly. Users are increasingly asking questions on these platforms instead of traditional search engines. Brands that optimize early gain visibility where attention is shifting, while others risk losing ground.
Large language models are already changing how people search and discover brands. More users are asking questions in ChatGPT, Gemini, and Perplexity instead of clicking through a list of Google results. That shift is real, and it’s growing.
LLM SEO is how to meet that change head-on. The same fundamentals still matter: quality content, structure, and authority. But they need to be applied in ways LLMs can understand and reuse. That means writing conversationally, answering questions directly, and keeping your content current and credible.
This shift also fits into the bigger picture of search. The rise of zero-click searches shows how often users get the information they need without visiting a website at all. At the same time, semantic search highlights how engines and now LLMs look at meaning and context instead of just exact keywords.
If you want a practical first step, update one or two of your top-performing pages. Add FAQs, refresh the data, and shape answers around the questions your audience is actually asking. Then watch how often those pages begin showing up in both search engines and AI outputs.
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Product variations are more than just an ecommerce feature. They give your customers choices, whether it’s size, color, style, or material, while helping your store stand out in competitive search results. When optimized correctly, product variations do more than display available options. They improve the customer experience by making shopping easier. At the same time, they boost conversions by catering to diverse needs and support your SEO strategy by targeting more keywords.
This guide will explain the best practices for product variations and show you how to optimize them for search engines and customers so your ecommerce site can grow in traffic, rankings, and sales.
Product variations or product variants are different versions of the same product designed to give customers options. These variations can be based on attributes like size, color, material, style, or capacity. Instead of creating multiple product listings, variations group all options under a single product, making it easier for customers to browse and purchase.
For example, when you search for an iPhone on Amazon, you’ll see options for different colors and storage capacities, all available on a single page. This setup lets customers explore multiple choices without leaving the main product page.
Managing product variations depends on the platform you use:
In WooCommerce, product variations are created using attributes such as size or color, and then assigning values to those attributes. Store owners can upload unique images, set prices, and adjust stock for each variation
In Shopify, variations are managed under the ‘Variants’ section of a product. You can add options like size, color, or material, and then assign values. Each variant can have its own price, SKU, and image, making it simple to customize how the variations appear in your store
Read more: Shopify Help Center – Adding variants
Okay, now let’s see why you need product variants and not upload each option as a completely separate product. Think of it this way: customers don’t want to scroll through endless listings just to compare a black t-shirt with a white one or a 64GB phone with a 128GB version. Variations keep everything in one place, making shopping smoother and more intuitive.
Here’s why product variations are so important for your customers:
Product variations are not just about creating a better shopping experience; they also bring direct ecommerce SEO benefits that can help your store attract more qualified traffic. When optimized correctly, variants can make your product pages richer, more discoverable, and more engaging.
Variants allow you to target a wider range of long-tail keywords that reflect real customer search behavior. For example, instead of only competing for ‘men’s wallet,’ you can rank for ‘men’s black leather wallet’ or ‘slim men’s brown wallet.’ These specific keywords usually carry higher purchase intent and face less competition
Each variation allows you to add unique attributes, descriptions, and specifications. This creates a more detailed and content-rich product page that search engines and AI-driven engines (like ChatGPT or Google’s AI Overviews) value when surfacing answers and shaping brand perception.
A well-structured page that clearly displays variations keeps users from bouncing to competitor sites when they don’t immediately find their preferred option. Instead, they spend more time exploring, comparing, and interacting with your store, which indirectly supports SEO through stronger engagement signals.
When product variants are properly marked up with structured data, search engines can display rich snippets that include price ranges, availability, color options, and reviews. This not only makes your listings stand out but also boosts click-through rates (CTRs) from search results.
Yoast SEO’s Structure data feature describes your product content as a single interconnected schema graph that search engines can easily understand. This helps them interpret your product variations more accurately and increases your chances of getting rich results, from product details to FAQs.
In short, optimized product variants make your product pages more keyword-diverse, content-rich, and engaging while also improving how your store is presented in search results and generative AI chat replies.
Here’s the part you’ve been waiting for: how to optimize your product variations for SEO, conversions, and user experience. In this section, we’ll cover the right technical implementation, smart SEO tactics, and the common mistakes you’ll want to avoid.
Getting the technical setup right is the foundation for optimizing your product variations for both ecommerce SEO and user experience. Poor implementation can lead to crawl inefficiencies, duplicate content, and a confusing buyer journey.
Here’s how to approach it effectively:
One of the biggest decisions you’ll make is how to structure URLs for your product variations:
?color=red&size=12): Good for filtering and faceted navigation, but they can create crawl bloat if not managed properly. Always define URL parameters in Google Search Console and use canonical tags to consolidate signals/red-dress-size-12): This can be useful when specific variations have significant search demand (like ‘iPhone 15 Pro Max 512GB Blue’). However, it requires careful duplication management and unique, optimized content for each pageTakeaway: Use a hybrid approach. Keep a single master product page, but only create dedicated variation URLs for high-demand search queries (with unique descriptions, images, and structured data).
Note: only create dedicated variation URLs if you can add unique value (content/images), otherwise, it risks duplication
Internal linking is crucial in helping search engines understand the relationships between your main product page and its variations.
The Internal linking suggestions feature in Yoast SEO Premium is a real time-saver. As you write, it recommends relevant pages and posts so you can easily connect variations, parent products, and related content. This not only strengthens your site structure and boosts SEO but also ensures visitors enjoy a seamless browsing experience.
Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!
Takeaway: Build a clean hierarchy where category pages → main product pages → variations, ensuring both users and crawlers can navigate easily.
Filters (like size, color, brand, or price) enhance user experience but can create SEO challenges if every filter combination generates a new crawlable URL.
<nofollow or noindex for low-value filter pages (like ‘price under $20’ if it doesn’t add SEO value)Takeaway: Conduct a filter audit in Google Search Console. Identify which filtered URLs actually drive impressions and clicks, and only allow those to be indexable.
When it comes to product variations, visuals and supporting media play a critical role in both SEO and conversions. Shoppers often make purchase decisions based on how well they can visualize a specific variation. In fact, 75% of online shoppers rely on product images when making purchasing decisions.
Also read: Image SEO: Optimizing images for search engines
Here’s how you can optimize media content for ecommerce product variations:
Avoid using the same generic image across all variations. Display each color, size, material, or feature with its own high-quality image set. For example, if you sell a t-shirt in six colors, show each color separately to help customers make confident choices.
Showcase variations with interactive media like 360° spins or short product videos. For example, a ‘black leather recliner’ video demonstrates texture and function more effectively than a static image, leading to higher engagement and conversions.
Every image should have descriptive, keyword-rich alt text that specifies the variation. Instead of writing ‘red shoe,’ use ‘women’s red running shoe size 8.’ File names (e.g., womens-red-running-shoe-size8.webp) and captions should also reinforce the variation for better indexing.
Use the Product schema to explicitly define images and videos for each variation. Including structured data ensures that Google and AI-driven engines like ChatGPT can clearly interpret your variation visuals and display them in rich results or AI summaries.
For instance, assigning images to specific SKUs (via image markup) makes it easier for search engines to show the correct variation in shopping results.
Optimizing product variations for SEO requires more than attractive visuals and solid descriptions. You need to apply some proven SEO techniques to ensure search engines correctly interpret your product pages and users get the best possible experience.
Here are a few key practices every ecommerce store owner should follow:
Product variations often generate multiple URLs, which can cause duplicate content problems. Canonical tags help solve this by pointing to the primary version of a page, consolidating ranking signals, and avoiding internal competition.
Yoast simplifies this process by automatically inserting canonical URL tags on your product pages. This ensures search engines know which version to prioritize, prevents diluted link equity, and even consolidates social shares under the original page. For store owners, this means less technical overhead and stronger, cleaner rankings.
Global product identifiers like GTINs, MPNs, and ISBNs act as unique fingerprints for your products. They help Google and other search engines correctly match your items in their vast index, which improves the accuracy of search listings and reduces confusion with similar products. They also add credibility, since customers can cross-check these identifiers before purchase.
With Yoast WooCommerce SEO, adding these identifiers becomes much easier. The plugin reminds you to fill in missing SKUs, GTINs, or EANs for each product variation and automatically outputs them in structured data. This not only helps your products qualify for rich results but also ensures that no variant is left incomplete from an SEO standpoint.
Unlock powerful features and much more for your online store with Yoast WooCommerce SEO!
Google Search Console is a goldmine for understanding how product variations are performing. By monitoring which variant pages are driving impressions, clicks, and conversions, you can refine your SEO strategy.
For example, if certain variants attract little traffic but consume crawl budget, it might be better to consolidate them under canonical tags.
Regular audits also help you detect indexing issues, thin content problems, or underperforming structured data. This keeps your product catalogue lean, crawl-efficient, and focused on driving meaningful organic traffic.
Also read: How to check the performance of rich results in Google Search Console
Even if you’ve implemented the right technical setup, added structured data, and optimized your media content, a few small mistakes can undo all that effort. To make sure your product variations support SEO and conversions instead of hurting them, here are some common pitfalls to avoid:
By keeping these errors in check, you’ll ensure your product variation strategy strengthens your SEO and user experience instead of working against them.
Product variations are not just small details hidden in your catalogue. They play a major role in how both search engines and shoppers experience your store. When done right, they prevent duplicate content issues, improve crawl efficiency, deliver richer search results, and create a seamless journey for your customers.
The key is to treat product variations as part of your overall SEO strategy, not as an afterthought. Every unique image, structured snippet, and clear variation option makes your store more visible, more reliable, and more profitable.
This is where Yoast SEO becomes a game-changer. With automatic structured data, smart handling of canonical URLs, and advanced content optimization tools, Yoast helps you get product variations right the first time.
The post A detailed guide to optimizing ecommerce product variations for SEO and conversions appeared first on Yoast.
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“Good SEO is good GEO.” That’s according to Google’s Danny Sullivan, a director within Google Search, and former search liaison
Why we care. You can believe Google if you want. But we’ve tried to consistently say that we believe GEO is an emerging practice. That doesn’t mean it replaces SEO today or tomorrow – because SEO fundamentals matter and SEO is still not dead. But I also agree with Michael King’s assessment that SEO is deprecated. The future of Google and conversational AI search will be answers, not ranking, regardless of what Googlers say publicly today.
What he’s saying. Here’s some of what Sullivan said about SEO/GEO during his keynote at WordCamp US on Aug. 28:
CTR question. During the audience Q&A, blogger Angie Drake said her organic search click-through rate has plummeted since AI Overviews launched, even though impressions are up (known as the great decouoling of search). She asked Sullivan what Google will do to compensate publishers who are losing clicks. Sullivan’s response:
Other takeaways. Some other data Sullivan shared:
The keynote. Here is the full video. I’ve linked to the takeaways portion of Sullivan’s presentation, where he discusses GEO. Drake asks her CTR question starting at 45:06.
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Google Ads now lets you select your business(es) by searching Google Maps for location assets / extensions. This addition should make it easier to manage existing and new location assets for your Google Ads campaigns.
More details. This change was spotted by Greg Kohler who posted a screenshot of the change on X and wrote:
“New (easier) way to add location assets (extensions) to your Google Ads campaigns – now you can search and select your business using Google Map.”
Joe Youngblood praised this change on X, saying:
“One of the single most agonizing parts of building out a new campaign or taking over an old account. This looks like it will fix it!”
Screenshot. Here is that screenshot:
More details. Google Ads has a help document that explains how to use it. It says:
If neither Google Business Profile nor Chain stores work for you, you can select up to 10 locations from Google Maps to link with your Ads account. These Google Maps locations must be yours, or they may be disapproved.
No matter which location source you use when creating location assets, you can customize your locations further at the campaign or ad group level. You can choose to add all account-level locations, use just a subset of account-level locations using Location groups, or choose “No location asset” to keep the asset from showing for specific campaigns or ad groups.
Why we care. This can help you manage location assets for both existing and new campaigns. This seems like a big time saver for many advertisers who use Google Ads.
Read more at Read More
Google has a new subtle but powerful feature in the Google Ads advertiser console to help you manage your campaigns. New checkboxes are available to let you select the campaigns you want and filter the view to only show those campaigns.
Previously, you were only able to select one campaign at a time, but now you can select multiple campaigns.
What it looks like. Here is the full screenshot from Thomas Eccel who posted the screenshot on LinkedIn:
Why we care. This new checkbox allows you to manually filter by more than one campaign at a time, allowing you to apply and manage your campaigns more efficiently. You can compare multiple campaign performance at the same time and save a huge amount of time when reporting, comparing, or managing these campaigns.
Read more at Read More
Google AI Mode got an upgrade to its large language models that enhances its ability to answer complex STEM questions. Plus, the responses should be “tighter, easier to scan and get to the point up front before elaborating,” Robby Stein, Google’s VP of Product at Google Search wrote.
This comes in time for the upcoming school year, with many kids starting school this week and some already starting a few weeks ago.
What Google said. Robby Stein posted on X:
Very excited about this week’s AI Mode model update. We’re seeing big improvements for complex STEM questions– great for students heading back to school. Overall responses should also be tighter, easier to scan and get to the point up front before elaborating.
Srini Venkatachary, VP of Engineering at Google DeepMind, responded:
Really excited with this strong update in time for school year. Please send us your feedback.
Nick Fox, SVP, Knowledge & Information at Google, wrote on X:
AI Mode continues to ship & ship fast! This past week, we released a big under-the-hood upgrade to the model capabilities leading to much improved responses. Excited for you to see and feel the difference
Why we care. Google recently expanded AI Mode to 180 countries and territories and now more and more searchers have access to it. Google will continue to improve its models, with the aim of making AI Mode and its AI responses better.
Here is the latest improvement for AI Mode that Google has announced.
Read more at Read More
Generative engine optimization (GEO) is the new kid on the block, the hot topic.
SEO professionals and stakeholders want to know: How much should I invest in it in a world where the people writing the checks are a bit skeptical?
In the world of large language models (LLMs) in 2025, that’s a complicated question.
This article breaks down why by covering:
One of the fundamental challenges for the creators of LLMs (LLMs) like OpenAI or Claude is timeliness.
Their training data is static, locked to a specific cutoff date.
For example, the GPT-5 model’s knowledge cutoff is Sept. 30, 2024.
It’s more recent than GPT-4o’s cutoff of Oct. 1, 2023, but still not up to the present day.
Updating that training data is extremely costly, and it’s increasingly under public scrutiny – both for the resource-intensive nature of the process and the potential copyright issues it raises.
In my view, these large-scale training updates are becoming less and less likely over time.
So how do OpenAI, Claude, or Gemini keep their answers current?
They use retrieval-augmented generation (RAG), where the model enriches its responses by effectively “browsing” the web. ChatGPT relies on Bing, while Gemini draws from Google.
(There are signs Gemini doesn’t always use live results, but rather cached ones – that’s a whole other article, and one Dan Petrovic has already written smartly about.)
Grounding is a similar concept here, so for this article, we’ll treat it as the same “timely” method, even though there are important nuances in implementation.
What does this mean for SEOs and digital marketers deciding how much to invest in GEO?
Quite simply: we still need to prioritize traditional SEO first. RAG is a limited resource, and research shows:
It’s also important to note: when ChatGPT cites your brand, it doesn’t just pull from your website. It pulls from sources across the web.
The bottom line: you still need to master traditional SEO fundamentals to rank in LLM-driven search.
If you don’t have the authority to break into the Top 20 results, plus a diversified outreach strategy for press mentions and brand visibility, it will be much harder to surface in generative search.
Dig deeper: SEO vs. GEO: What’s different? What’s the same?
As a low-risk, forward-looking, brand-focused SEO, you must plan for a future where generative search dominates, driving most traffic and revenue.
At that point, we must assume our websites and digital properties function primarily as enriched data feeds for LLMs.
It’s also critical to clearly define our brand for both Google and Bing, as strong, unambiguous entity signals will only grow in importance.
Optimizing your data infrastructure and strengthening brand signals – through consistent press mentions, directory listings, and owned media – are essential but resource-intensive tasks.
They demand coordination across departments that rarely collaborate and often require dismantling entrenched processes.
Because many businesses hesitate to make these foundational changes, you’ll need to account for the time required to execute the work and the time required to gain stakeholder alignment.
The work required to make your website as LLM-friendly as possible falls into two main buckets: technical and brand.
Implementing thorough schema markup
This is a contentious topic.
LLMs don’t directly use schema markup in their training data (it’s stripped), and in their RAG process, everything is tokenized and likely broken into n-grams.
I’m not suggesting schema markup is a direct way to influence visibility in LLMs.
It’s a vehicle for helping Google and Bing understand:
This builds your brand and search engines’ recognition of it, which should improve your visibility in results.
Technical copywriting
On navigational pages – like product collection pages or company listing pages if you’re a marketplace – create technical copy (done via AI with smart prompting if you’re working at scale) that summarizes the available resources.
For example:
Notice how direct and technical this is. The clear formatting ties back to dependency hops in natural language processing.
XML sitemaps
A spring cleaning task.
Your XML feeds should be 100% to spec:
I’m calling it out specifically because it’s one of the most direct ways for search engines and other bots to see and navigate the full scope of your website.
JavaScript fallbacks
This has always been important but has fallen by the wayside in recent years.
Training data for LLMs is static HTML. For the most part, they don’t render JavaScript.
Make sure to have functional JavaScript fallbacks.
Address technical debt
This will depend on your organization. It could mean:
All of these impact performance.
The technical strength and response time of your website will only grow more important.
Every piece of tech debt is an opportunity to improve.
Dig deeper: A technical SEO blueprint for GEO: Optimize for AI-powered search
Brand tasks would include things like:
Dig deeper: In GEO, brand mentions do what links alone can’t
If there’s only one takeaway, it’s this: keep investing the majority of your time and budget in traditional SEO, while dedicating a smaller portion to technical and brand tasks like those outlined above.
Look closely at the 1-5% improvements you’ve been putting off – things like:
This “spring cleaning” and tech debt cleanup should be a priority.
Add in the brand work as well, since it strengthens traditional search today and also lays the groundwork for an LLM-led search future.
If you don’t already have regular reporting in place for stakeholders and leadership, create it now.
There’s a perception that large language models are evolving rapidly and changing everything at once.
That isn’t entirely true – but we do need to plan.
Establishing a cadence of reporting and education means that when real shifts do happen, your stakeholders will already be aligned and ready to support the work.
Finally, treat GEO/AI optimization as roughly 20% work.
This means building systemic schema layers across your organization and creating structured connections in the machine’s native language – code.
Start with:
Done properly, this work should have no negative impact on your business metrics, and it builds support for more holistic optimization over time.
Going all in on LLM-specific tactics isn’t the best use of your resources today.
Instead, treat it as complementary work – something that strengthens your technical and brand foundation while preparing you for a future where generative search plays a central role.
Read more at Read More
AI has fundamentally changed how people search and engage with information online.
Features like AI Overviews may boost visibility, but they’ve also reduced clicks, leaving many websites with less traffic despite stronger rankings.
Discovery no longer happens in just one place.
It’s fragmented across search engines, social platforms, paid ads, and AI tools, creating a complex user journey that’s harder than ever to track.
As behavior shifts with these new technologies, search marketing is evolving in response.
Yet while the platforms, tools, and touchpoints keep changing, the core principles of effective marketing remain the same.
Marketers who stay grounded in these fundamentals will be best equipped to adapt and grow.
Here are nine timeless marketing principles that will hold steady – no matter how search evolves.
Where people search and find information will continue to change over time as preferences for LLMs, social media, or video content shape where people go for answers.
However, what remains constant is search intent.
Users are always looking to:
Focus on the why behind a search – the intent driving it.
The key question is whether your content aligns with that intent.
If it doesn’t, you’re overlooking a critical driver of user behavior.
When content matches search intent, users immediately recognize its value and engage, which is why intent should remain central to your marketing strategy.
Even as AI continues to drive change in how companies reach their audiences, brand recognition and loyalty remain important pillars of long-term engagement and growth.
Discovery channels are shifting as people find brands through social media, search engines, paid ads, email, and more.
That’s why it’s important to continually reassess the customer journey and understand where your audience is finding you.
After discovery, your job is to highlight your unique value – what sets you apart from competitors and how you provide real value to your audience.
Keep asking yourself:
The clearer and more consistently you communicate this in the spaces that matter, the more you’ll earn trust, recognition, and reliability – all of which shape how people respond to your brand.
Brand loyalty isn’t automatic. It’s something you earn by building real relationships with your customers and consistently providing value.
Loyalty creates long-term stability and growth, even as platforms and algorithms continue to shift.
While search intent and brand recognition can attract new visitors, loyalty turns impressions into conversions and builds lasting customer lifetime value.
Beyond search intent and branding, truly knowing your audience is essential for long-term marketing success.
Without that insight, you risk falling into “spray and pray” campaigns that waste resources and fail to connect.
Building clear audience personas helps you decide not just what campaigns and content to create, but how to present them in ways that resonate.
That means understanding who your audience is, what motivates them, their pain points, their values, and where they spend their time.
These insights form the foundation of a strategy built to genuinely connect with your audience.
Dig deeper: SEO personas for AI search: How to go beyond static profiles
Even if your content matches search intent and your brand is well recognized, audiences won’t engage without credibility.
This matters even more today, as AI tools summarize information and highlight only the most trustworthy sources.
Both search engines and AI prioritize trust signals. But for users, those signals are everything.
Expertise, consistency, transparency, and reliability are what build that trust.
People want to feel confident that your brand will deliver on its promises and provide lasting value.
When they believe you’re dependable, they’re more likely to engage, return, and recommend your brand to others.
Today, customer service is inseparable from brand experience.
Every interaction – whether answering a support ticket or replying to a social media comment – shapes how people perceive your credibility and value.
Testimonials and reviews create a powerful feedback loop: one story sparks another, influencing how others view your brand.
Campaigns can drive visibility, but audiences still turn to peer reviews on platforms like Reddit to validate those impressions and decide whether to trust you.
Audience sentiment has become its own form of publicity.
With user-generated content (UGC) shaping perception and AI systems relying on reviews and sentiment signals to recommend brands, customer experience is now a direct driver of both reputation and visibility.
Dig deeper: How to use SEO and CX for better organic performance
A core principle that hasn’t changed is the need for an optimized user experience.
When someone lands on your site, the page should minimize friction in the buying journey.
Whether visitors arrive through ads or organic search, they need clear conversion paths that guide them smoothly forward.
Audiences expect ease and clarity when looking for information or taking action.
Slow load times, unnecessary clicks, or confusing layouts increase drop-offs, abandoned forms, and carts – leaving users frustrated.
A good user experience makes the journey to conversion as effortless as possible.
Done well, it not only boosts conversions but also builds satisfaction and trust.
AI may be transforming how people search, but mobile devices remain the primary way users access and engage with brands.
For many, the first interaction with your brand happens on a phone.
That’s why user experience must extend beyond conversion paths.
It also has to be fully optimized for mobile. Otherwise, you risk frustration, lost trust, and missed conversions.
Mobile users abandon sites that load slowly, require pinching and zooming, use hard-to-tap buttons, or rely on clunky forms.
Even a few seconds of delay or disruptive layout shifts can cause drop-offs.
And because search engines prioritize mobile-friendliness, optimizing for mobile isn’t just about usability. It also directly impacts rankings and visibility.
Accessibility is a core part of creating inclusive experiences for your entire audience.
In the U.S., it’s also a legal responsibility.
Making your site accessible means adding features like:
If accessibility is overlooked, you risk excluding parts of your audience and facing ADA lawsuits.
But when you design with accessibility in mind, you reach more people, strengthen trust, and ensure everyone can engage with your brand.
No matter how search evolves, quality content and authority remain the foundation of visibility and trust.
Algorithms may shift and discovery channels may change, but users will always value content that is accurate, relevant, and genuinely helpful.
Authority is earned over time by:
The more credibility your brand builds, the more likely users (and search engines) are to consider you a worthwhile recommendation.
Dig deeper: Mastering content quality: The ultimate guide
AI is transforming how people search and how brands reach them, but the fundamentals of marketing haven’t changed.
What still matters is:
Technology and platforms will keep evolving.
But the brands that stay grounded in these timeless principles will be the ones that adapt, grow, and thrive in the future.
Read more at Read More
