How to Optimize Your Product Pages for AI Visibility

AI has changed the way people shop. 58% of consumers now use GenAI tools instead of traditional search to find products. Imagine your customer runs a simple query in Google’s AI Mode: “Winter jackets for women.” Instead of a long list of links, they get direc…

58% of consumers now use GenAI tools instead of traditional search to find products. Imagine your customer runs a simple query in Google’s AI Mode: “Winter jackets for women.” But one of the most important — and most controllable — is your product pages. When that information is clear, structured, and specific, your products have a much better chance of appearing in AI results. In this guide, we’ll break down how AI evaluates product pages, and which elements matter most.

Plus, we’ll see how leading ecommerce brands structure their pages to get recommended. Free checklist: To get a head start, download our Product Page AI Optimization Checklist. It includes everything you need to get more product mentions in AI platforms. Ever wondered how large language models (LLMs) choose which products to surface in answers? For LLMs to confidently cite a product page, they need consistent, up-to-date information.

AI models analyze product pages to pull details that help them answer user queries. Prompts are often highly specific requests for products that fit a clear use case or situation. When I searched “best road racing shoes for women” in AI Mode, it recommended Nike’s Alphafly. When AI analyzes reviews, it looks for patterns. This includes repeated mentions of specific use cases, features, or product benefits.

For example, the Nike Alphafly is highly rated with plenty of reviews on the Nike website. Among other benefits, this improves its chances of being recommended by AI platforms. In a similar search for racing shoes, I found that AI Mode cites various third-party sources to support its recommendations. Like this one, that includes a review of Nike shoes, complete with product details. But they create the foundation AI systems need to confidently recommend your products.

Further reading: Learn how LLMs recommend brands in Semrush’s AI Visibility Index. You likely already have some (or all) of the elements below on your product pages. Note: These elements aren’t in any particular order: all are important for AI visibility. A clear product description explains more than what your product is. It spells out what it does, who it’s for, and why someone would choose it.

In other words, AI understands the intent and meaning behind queries. Not just exact-match keywords. For example, when someone searches for “vacuum for pet hair,” AI doesn’t just look for that phrase. It also looks for semantically related terms. Things like “stubborn hair,” “carpets,” “pet odors,” and “allergens.” These terms help AI infer use cases, surface the right features, and decide when your product is a good fit.

Including them on product pages improves your chances of appearing in AI-generated answers. Learn how people talk about the problems they’re facing and the products they’re using. Using our vacuum example, I dove into r/VacuumCleaners. There, I found recurring phrases around weight, clogging, tangles, and flooring-specific concerns. Note: A free Semrush account gives you 10 searches in the Keyword Magic Tool per day.

Or you can use this link to access a 14-day trial on a Semrush Pro subscription. The tool will return a list of “Broad Match” queries, which contain variations of your keyword. In our example, we might use “handheld,” “carpet,” and “hardwood” as semantic keywords. Collect a few key terms, and use them in product descriptions to explain what your product does. I asked AI Mode for the best lightweight vacuum for pet hair.

One of the top recommendations was a Shark vacuum. For one, it has strong consensus signals from third-party reviews and editorial sites. The product name alone — Shark UltraLight PetPro Corded Stick Vacuum — gives a core use case. This strongly suggests AI Mode is pulling this information directly from Shark’s product description for this vacuum. Bottom line: Customer-focused, use-case-driven language helps AI understand when to recommend your product.

Further reading: Need inspiration? Check out some of our favorite ecommerce website examples. If your site has accurate structured data, AI can use that. But crawlers don’t run every minute. That means prices and stock can be stale. This includes Shopify’s Catalog API, OpenAI’s Product Feed Spec, and feeds submitted through Google’s Merchant Center. Pro tip: OpenAI product feed submission is currently available only to approved partners.

Fill out the Merchant Application form for consideration. When you use these, AI search engines can fetch current prices and inventory on demand. That’s the tech that powers real-time recommendations and in-chat shopping in ChatGPT and other AI platforms. This feature brings buy-in-chat functionality to eligible product recommendations in AI Mode and Gemini. LLMs can still find product information on public webpages.

But it may be outdated. Mismatched prices or outdated stock can hurt your AI visibility. In part, because it leads to a poor customer experience. ​​To see how this plays out in practice, I tested ChatGPT’s “Shopping research” mode. I told ChatGPT I was looking for a new couch. I specified both my budget and need for delivery to Massachusetts. ChatGPT returned five options, all of which fit my budget and availability requirements.

The “Best overall” option even highlighted that it was “in stock for fast delivery” to my state. To further test how price affects results, I asked if any of the recommended couches were on sale. To find out why, I reviewed the product pages for each recommendation. But only one clearly highlighted both the original and sale price. Walmart’s product pages boldly showcase the previous price versus the discount.

In its response, ChatGPT specifically mentioned that Walmart displays this info on its product page. When AI systems have access to this data, they can recommend your products when users narrow options by price, availability, or discounts. In AI Mode, you can click a product recommendation and see reviews directly in the sidebar. But LLMs do more than show you reviews. They also weigh reviews and ratings when choosing recommendations.

ChatGPT often includes labels like “Budget-friendly” or “Most popular” based on reviews. OpenAI has confirmed that answers may include summaries of the themes most commonly mentioned in reviews. Ultimately, reviews on your product page don’t just affect whether your product appears in AI search. The more clearly those patterns emerge, the easier it is for AI to confidently recommend — and describe — your product.

When I asked AI Mode for a hydrating cleanser for sensitive skin, the first recommendation was a product from CeraVe. Interestingly, the product description itself doesn’t explicitly emphasize “sensitive skin.” Having reviews on every product page is a best practice that increases trust and authority. Note: The most important thing is that these reviews are real. Fake or AI-generated reviews may temporarily improve your brand’s visibility in AI search.

But they are never worth the long-term risk to your reputation. AI search looks for explicit connections between what a product is and why someone needs it. So, your entire product page should explain when, why, and in what situations a product makes sense. Start by identifying who buys your product and what triggers that purchase. If you don’t already have this insight, customer interviews are your fastest path.

Once you have this, choose one or two clear, specific use cases to feature on each product page. Instead, focus on the use cases that come up repeatedly in customer conversations. That way, AI can match your product to a specific intent. This product page for Anker’s 3-in-1 mobile charger states it’s “ultra compact and travel friendly.” When I search for travel-friendly chargers on ChatGPT, Anker’s 3-in-1 device is the top recommended product.

But by calling out that use case on the product page, it makes it easier for LLMs to recommend it in related queries. One of the strongest ways to demonstrate that trust is to feature third-party validation on your product pages. To see how much awards affect AI visibility, I analyzed 50 ecommerce brands in Semrush’s AI Visibility Overview tool. This is a Semrush metric that measures how often brands appear in AI-generated answers.

I focused on brands scoring above their industry average. (This varies by industry, but is generally between 60 to 90.) Next, I looked at how many of the top-ranking brands feature awards and certifications on their product pages. 82% of the brands with medium to high AI visibility prominently feature awards and certifications on their product pages. Like being “rated #1 in camera quality” by the American Customer Satisfaction Index.

A quick look at its product pages reveals certificates and awards on every product page. Now, this is correlation, not necessarily causation. And awards and certifications are not the only factor. If you already have awards and certifications, showcase them prominently on your product pages. Target industry-specific certifications (safety, quality, sustainability) and awards from reputable organizations.

Structured attributes are pieces of product information that machines can easily understand. Use tables, bullet lists, or specification sections to clearly structure them for machines and customers. But major AI search engines confirm they rely on structured attributes to understand and recommend products. What OpenAI says: “When determining which products to surface, ChatGPT considers structured metadata from first-party and third-party providers (e.g., price, product description).

Depending on your needs, some of these factors will be more relevant than others. For example, if you specify a budget of $30, ChatGPT will focus more on price, whereas if price isn’t mentioned, it may focus on other aspects instead.” Plus, it’s no secret that structured data helps products appear on Google’s main page and Shopping tab. It’s what allows users to refine results, see ratings, and check prices right on the first page of Google.

When I conducted a search in AI Mode, Google’s own shopping cards were the main sources. Clicking into one of those sources, I saw even more of that search-friendly structured data. That same structure is what enables Google’s AI responses to display live pricing, availability, sales, and comparisons. That context helps AI more confidently recommend your product in related queries. In this section, we’ll break down the category-specific product page details that AI looks for across six common ecommerce industries.

Ask any AI engine for clothing recommendations, and you’ll notice something consistent: the results highlight fit, materials, and comfort. I analyzed the topic “jeans for women” using Semrush’s Prompt Research tool. For AI to match products to these specific queries, it needs structured details on your pages. They display clear fit guidance and aggregated customer fit feedback prominently on product pages.

In their responses, AI models pull from ingredient lists, dosage information, and certifications. Brands that perform well for wellness-related AI queries follow the same pattern. They provide detailed information about ingredients, sourcing, and production on their product pages. Battery life, screen resolution, charging speed, refresh rates, and more are all pulled into responses. For example, even a simple search — “best cameras for night photography” — returns spec-heavy recommendations.

They ensure their product and retailer pages feature technical details that are consistent and in-depth across platforms. It features different configurations of their modular sofas. Plus, the dimensions of each. It also contains other vital information that users might ask AI systems, such as detailed materials and fabric care. Customers need to know whether your products will survive their outdoor adventures.

Let’s say your customers ask about hiking backpacks. They’ll see AI models highlight key features, max load, and materials. They also include features that make it ideal for common use cases: materials, weight, volume, dimensions, and load range. AI models look for structured, verifiable details when recommending anything for infants. This includes how the fabrics are developed, and the appropriate age and weight for safe use.

When I asked Perplexity for the safest baby carrier on the market for newborns, BabyBjorn was among its top recommendations. It also specifically mentioned the “hip healthy” certification featured on BabyBjorn’s product page. If you want AI to recommend your products, the best place to start is your product pages. First, download the Product Page AI Optimization Checklist. It tells you exactly what to review, update, and add to make your product pages AI-friendly.

Then, learn how to build an AI ecommerce SEO strategy that improves your visibility across the entire buyer journey. AI visibility is possible for your products. Keep testing, keep tracking, and keep growing. Backlinko is owned by Semrush. We’re still obsessed with bringing you world-class SEO insights, backed by hands-on experience. Unless otherwise noted, this content was written by either an employee or paid contractor of Semrush Inc.

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