How we use semrush to enhance the visibility of LLM

What changes with AI SEO and LLM visibility

AI SEO is not just another “fad” on top of traditional SEO. It's the natural evolution of search in an environment where customers don't just type two words into Google, but ask tools like ChatGPT, Perplexity, AI Overviews and gradually Google AI Mode for suggestions, comparisons, product listings, solutions to problems and final purchase decisions. For an e-commerce owner, this change is critical: the question is not only whether the online store appears on the first page of organic results, but whether it is mentioned, suggested or used as a resource within the responses of the major language models. This is exactly what the concept of LLM visibility describes, i.e. the visibility of a brand within responses generated by Large Language Models. See also: Digital Marketing & SEO, business automation & AI, website construction, e-shop construction.

Semrush's article “How We're Using Semrush to Drive LLM Visibility” highlights a practical direction: marketing teams can no longer rely solely on keyword rankings, traffic and backlinks. They need a more complex picture, where they track how their brand appears in AI responses, what topics are related to their entity, which competitors are most frequently cited, which sources models trust, and what content gaps are preventing them from being cited. Simply put, AI SEO shifts the focus from “how do I rank” to “how do I become a credible answer”.

This change is accelerated by user behaviour. Gartner has predicted that traditional search volume could fall by 25% by 2026 due to AI chatbots and virtual agents. At the same time, zero-click searches remain high, meaning that a large proportion of users get a response without necessarily visiting a website. For e-shops, this doesn't mean SEO is over. It means that ecommerce SEO needs to expand: categories, buying guides, product pages, reviews, comparison articles and brand mentions in third-party sources need to be built to be understandable, reliable and usable by search engines and LLMs.

What Semrush's approach shows

What changes in practice on the issue: How we use semrush to enhance the visibility of LLM

The key value of the approach described by Semrush is that it treats LLM visibility as a measurable performance channel rather than an abstract concept. The rationale is to identify the prompts and topic questions that real users use, map brand presence to those responses, compare presence to competitors, and refine assets that affect the likelihood of reporting. This includes owned content, such as blog posts, landing pages and product/category pages, as well as earned media, such as reviews, media mentions, guest posts, “best of” lists, directories, forums and trusted third-party references.

In practice, AI SEO requires a combination of technical SEO, content strategy, entity SEO and digital PR. LLMs don't just “see” a brand through an article. They build insight from multiple signals: consistent brand information, clear subject matter expertise, credible references, clear data structure, quality content, identifiable authors, substantiated claims, and frequent linking of the brand to specific problems it solves. If, for example, an e-shop sells premium cosmetics, it is not enough to have product titles and meta descriptions. It must have guides for skin types, ingredient comparisons, expert content, structured reviews, FAQs, return policies, credibility sources and a presence on third-party websites that AI systems can associate with the brand.

This is where generative engine optimization, also known as GEO, comes in. GEO does not replace SEO, but complements it. Traditional SEO ensures that a page can be crawled, understood and ranked. GEO makes sure that content and brand can be referenced within generative responses. This changes the way we evaluate success. Beyond impressions, clicks and average position, a modern dashboard needs to look at share of voice in AI responses, frequency of brand mentions, quality of sources cited, presence in comparative prompts, sentiment and topical authority by topic cluster.

The data that e-shops need to monitor

The first fact that every marketing team should take seriously is the speed of adoption of AI assistants. The more users get used to getting answers from conversational interfaces, the more important ChatGPT SEO, Perplexity SEO and AI search optimization in general becomes. The illustration below shows the published growth of ChatGPT's weekly active users from 2023 to early 2025, based on announcements and publications referencing OpenAI data.

The second data point concerns the rise of AI Overviews in search results. According to Semrush's analysis for 2025, the occurrence of AI Overviews in the searches studied increased from 6,49% in January to 13,14% in March. For an e-shop, this means that informational and commercial queries around products, categories and solutions can be “filtered” through an AI response before the user reaches an organic result. As shown in the graph below, the change is fast enough that it can't be ignored in SEO planning.

The third data point concerns zero-click searches. SparkToro and Datos recorded that in 2024 58.5% of Google searches in the US and 59.7% in the European Union were completed without a click to another website. This reinforces the need for AI SEO, not because clicks lose their value, but because a brand's influence often begins before a visit. If a user sees your brand within an AI summary, a Perplexity answer or a ChatGPT recommendation, they may later search for you directly, compare prices or return to a branded query. The graph below shows why visibility without a direct click should be treated as a true marketing asset.

From classic keyword tracking to visibility tracking

Keyword tracking is still useful, but it is not enough on its own. An e-shop can be in third place for a keyword and still not be mentioned in an AI Overview that takes up the top of the page. Similarly, it may not rank first organically, but be consistently mentioned as a reliable choice in answers like ’what are the best running shoes for beginners“ or ”what laptop is worthwhile for small business“. Visibility tracking should answer questions such as: does the brand appear in AI responses; in which prompts does it appear; which competitors appear most often; what sources are used; what sentiment accompanies the brand; what product categories are associated with it?;

For this transition to work, careful mapping of the issues is needed. Topical authority is not created from a single 800-word blog post, but from a content system that fully covers a field. For example, an electronics store that wants to gain AI visibility in the “gaming laptops” category needs to feature buying guides, GPU comparisons, refresh rate explanations, cooling tips, product schema, reviews, availability, pricing information, after-sales support and clear warranty policies. The more comprehensive and reliable the information infrastructure, the more likely the brand is to become part of the answer.

Step-by-Step guide for e-commerce owners

Step 1: Map the prompts associated with purchase intention. Don't just start with keywords like “men's sneakers” or “espresso coffee maker”. Create questions that a real customer would ask in an LLM: “which sneakers are best for walking around town”, “which coffee maker is worthy for a small office”, “what to look for before buying a child car seat”. These prompts reveal needs, objections and selection criteria that often don't show up clearly in classic short-tail keywords.

Step 2: Check your current presence in AI Answers. Test systematic prompts in ChatGPT, Perplexity and AI Overviews where available. Record if your brand is mentioned, if competitors are featured, what sources are listed and what the context of the response is. If your brand is absent, don't treat it as a “technical bug”. It is usually an indication that there are not enough strong, consistent and confirmable brands around your expertise.

Step 3: Create content clusters with commercial utility. Each major product category should be supported by content that answers questions before, during and after purchase. A “how to choose a mattress” guide should be linked to mattress categories, individual materials, comparison charts, FAQs, videos, reviews, and trial or return policies. This way, the content is not just written for traffic, but becomes a trust and sales mechanism.

Step 4: Boost entity SEO. LLMs need clarity about who you are, what you sell, where you operate and why you are trusted. Make sure your brand name, branding, contact information, policies, social profiles, Google Business Profile, “About” pages, and third-party platform references are consistent. Add schema markup for Organization, Product, Review, FAQ, Breadcrumb and Article where it makes sense. Schema markup does not guarantee reference to LLM answers, but it helps engines more accurately understand the structure and meaning of your pages.

Step 5: Build credible brand mentions outside of your site. Digital PR takes on new importance in the AI SEO environment. Mentions in industry media, trusted blogs, marketplaces, review platforms, forums, podcasts, and comparison articles can serve as external trust signals. Especially for categories with high market value, such as technology, health, beauty, financial services or B2B equipment, LLMs are more likely to trust brands that appear on multiple trusted sources, not just on their own website.

Step 6: Turn the product pages into real answer sources. Many e-shops have product pages that simply include photos, price and two lines of description. This is insufficient for AI search optimization. Add detailed features, comparison charts, usage instructions, compatibility, limitations, real reviews, customer questions, availability information, return policy and clear shipping details. The more complete the page, the more it can be leveraged as a trusted source by search engines and LLMs.

Step 7: Monitor changes systematically. LLM visibility is not static. Models are updated, sources change, competitors produce new content, and users ask different questions. Create monthly reporting that includes AI mentions, share of voice, prompts where you appear, prompts where you are absent, competitors gaining visibility, new sources mentioned, and improvement actions. This makes AI SEO a process of continuous improvement rather than a one-off project.

How to measure progress without chasing vanity metrics

The biggest pitfall in AI SEO is only measuring what looks easy. Brand showing up once in a prompt doesn't necessarily mean business results. Proper evaluation must combine visibility, quality of presence and impact on demand. Monitor whether branded searches increase, assisted revenue improves, whether users returning via direct or organic have a higher conversion rate, whether categories with a stronger content cluster perform better, and whether references to third-party sources lead to a more reliable perception of the brand.

For an e-commerce team, the most practical approach is to create a small set of “priority prompts” per category. For each product category, select 10 to 20 high-value marketing questions and check in monthly. If, for example, you sell outdoor items, measure prompts such as “best waterproof jacket for hiking”, “what tent to buy for two people” or “which trekking shoes are suitable for beginners”. Success is not just about showing up, but showing up in the right context: as a trusted choice, with a clear link to a category, with positive or neutral sentiment, and next to sources that build trust.

The conclusion is clear: AI SEO doesn't replace the foundation of SEO, but it raises the bar. Technical issues, speed, crawlability, architecture, internal links and content quality remain critical. But the new battle is being fought on credibility, entity clarity, completeness of answers and brand presence in sources that AI systems recognize as useful. Those e-shops that move now will have an advantage because they will train the market, machines and models to connect them to the right needs. Those that wait until organic traffic drops significantly will have to make up distance that isn't built in a matter of weeks.

Frequently Asked Questions