LLM visibility for e-commerce: why you're being ignored

Your potential customers aren't browsing your Shopify store anymore. They're asking ChatGPT "what's the best sustainable activewear brand?" or telling Claude "find me a leather laptop bag under $200."

And unless you've optimised for it, your brand isn't part of that conversation.

This is the reality of Large Language Model (LLM) visibility. While you've spent years perfecting your product pages for Google Shopping, the game has fundamentally shifted. AI assistants are becoming the new product discovery layer, and most e-commerce brands are completely invisible.

What LLM visibility actually means for e-commerce

LLM visibility is whether AI models like ChatGPT, Claude, or Perplexity mention and recommend your brand when users ask shopping-related questions. Unlike search rankings where you compete for position 1-10, AI recommendations are binary: you're either mentioned or you don't exist.

Consider the customer journey now. Someone planning a home office upgrade doesn't google "best ergonomic chairs" and click through comparison sites anymore. They ask ChatGPT: "What's the best ergonomic chair for someone with lower back pain, budget around $800, and I'm 6'2"?"

The AI provides 2-3 specific brand recommendations with reasoning. Those brands just captured consideration. Everyone else lost the sale before the customer even visited a website.

Why product pages aren't enough

Traditional e-commerce optimisation focused on product pages: clean images, detailed specs, customer reviews, conversion-optimised copy. These still matter for closing sales, but they're increasingly irrelevant for discovery.

AI models don't browse your product pages the way Google crawls them. They synthesise information from multiple sources to make recommendations. Your perfectly optimised product listing might never get seen if the AI doesn't know to recommend your brand in the first place.

Here's what's actually happening: AI models pull from editorial content, comparison sites, review platforms, industry publications, and trusted sources. If your brand isn't mentioned in these contexts with clear value propositions and differentiators, you're invisible regardless of how good your products are.

The sources that actually drive LLM recommendations

Editorial content and reviews AI models heavily weight editorial sources. Getting featured in publication reviews, buying guides, and "best of" lists dramatically increases LLM visibility. A mention in Wirecutter or similar trusted publications carries more weight than a thousand optimised product descriptions.

Comparison and resource sites Comprehensive comparison content that positions your brand against alternatives helps AI models understand your positioning. If you're never included in category comparisons, AI can't recommend you appropriately.

User-generated content at scale Reddit threads, community discussions, and authentic user reviews provide context AI models use to understand real-world product performance and customer satisfaction. Brands with active community discussion get recommended more often.

Structured brand informationClear, authoritative information about your brand story, product categories, differentiators, and use cases helps AI models understand when to recommend you. Vague or promotional content gets ignored.

What leading e-commerce brands are doing differently

Building citation-worthy content Smart brands are publishing comprehensive buying guides, category education content, and genuinely useful resources that position them as authorities. Not promotional content—actual educational material that other sites reference and AI models cite.

Earning external mentions Actively pursuing features in industry publications, securing product reviews from trusted sources, and building relationships with editors who cover their categories. Every external mention builds the authority AI models recognise.

Creating clear product positioning Developing specific, memorable frameworks for describing their products and differentiators. "The first carbon-neutral activewear brand" or "ergonomic chairs designed specifically for tall users" gives AI models clear hooks for recommendations.

Optimising for question-based discovery Understanding the specific questions customers ask AI assistants and ensuring their brand appears in answers to those questions. This requires different content than keyword-focused SEO.

The ChatGPT shopping reality

ChatGPT recently launched shopping features that directly recommend products within conversations. This isn't hypothetical future technology—it's live now and changing purchase behaviour.

When users ask shopping questions, ChatGPT can display product tiles with images, prices, and direct purchase links. The brands appearing in these tiles are capturing sales. Everyone else is losing traffic they used to get from traditional search.

The criteria for appearing in ChatGPT Shopping aren't public, but early data suggests it prioritises brands with strong external validation, clear positioning, verified product information, and positive review signals across multiple platforms.

Practical steps for e-commerce brands

Start by understanding what AI currently says about your category. Ask ChatGPT, Claude, and Perplexity the questions your customers would ask. Which brands get recommended? What sources get cited? Where are the obvious gaps?

Audit your external presence. Where is your brand mentioned outside your own site? Do trusted publications cover your products? Are you included in category comparisons and buying guides? If not, that's your priority.

Develop substantive content around your category, not just your products. Publish buying guides, usage tips, industry education. Make yourself the authority AI models cite when discussing your category.

Build relationships with publications and reviewers in your space. Getting featured in trusted editorial sources matters more for LLM visibility than any amount of paid advertising.

Track your presence in AI responses systematically. Tools like Profound (and now Yotpo's upcoming platform) let you monitor how often your brand appears and in what context. You can't optimise what you don't measure.

Why this matters more than you think

E-commerce discovery is shifting faster than most brands realise. Traditional search traffic is declining as AI-assisted search grows. Google's own AI overviews are reducing click-through rates to product pages. ChatGPT's shopping features are capturing purchase intent before users ever visit comparison sites.

The brands investing in LLM visibility now will establish themselves as AI-recommended authorities in their categories. The brands ignoring this shift will find their customer acquisition costs increasing as traditional channels become less effective.

This isn't about replacing your existing marketing. It's about ensuring your brand remains visible as the discovery layer changes. Your product quality, pricing, and customer experience still matter. But they only matter if customers discover you in the first place.

The window is still open

Most e-commerce brands haven't heard of LLM visibility yet. They're still optimising exclusively for traditional search while their customers increasingly use AI assistants for product research.

This creates a genuine first-mover advantage. The brands building LLM visibility now will capture disproportionate recommendation share before the market catches up.

Your customers have already changed how they discover products. They're asking AI assistants for recommendations, trusting those recommendations, and making purchases based on them. The only question is whether your brand will be part of those conversations.

Traditional SEO helped you rank for keywords. LLM visibility ensures you get recommended when it matters. One optimised for search engines. The other optimises for being genuinely worth recommending.

Which strategy positions your e-commerce brand for how customers actually shop today?

‍