How E-Commerce Brands Can Dominate Chat-Based AI Recommendations: A Practical Guide

How E-Commerce Brands Can Dominate Chat-Based AI Recommendations: A Practical Guide

Here’s the deal: across our dataset of 2131 AI prompts, e-commerce brands were mentioned 63.4% of the time on average. Sounds decent, right? But when you break it down by provider, ChatGPT, Google AI Overview, Perplexity, the discrepancies are frankly alarming. ChatGPT shows brands 77% of the time. Google AI? Just 43.5%. And Perplexity? A shocking 36.7%. What this really means is that if you’re relying on AI recommendations to push your products, there’s a very good chance your brand is either being overlooked or completely invisible depending on the platform.

My read on this data: This isn’t just “nice to have” visibility anymore, it’s critical. As generative AI tools become default decision-making assistants for millions (whether that’s choosing a garden shed or finding gluten-free baking mixes), brands that don’t show up will miss out. And that visibility, by the way, is not universal. A brand visible on ChatGPT is far from guaranteed to appear on Google AI Overview, 41% of prompts return different answers. That’s a minefield for any e-commerce business trying to maximise AI-driven sales.

So, the real question: how do you dominate these chat-based systems and ensure your products are part of the conversation? Let’s break it down.

Why Visibility in E-Commerce AI Recommendations Matters

Look, I was chatting with a mate of mine last week who runs an online homeware shop. He’s been using Google’s Bard (experimentally) to test how well his brand comes up in searches like, “best wooden dining chairs under £300.” Turns out his shop wasn’t even mentioned, not once, on the first 10 runs. Meanwhile, the same query on ChatGPT had his shop front-and-centre half the time. What’s going on?

Here’s the kicker: AI doesn’t “browse” like a human user. It pulls data from its trained sources, algorithms, and any live knowledge base it’s accessing. If your e-commerce site isn’t optimised for those systems, it might as well not exist. Across our experiments, 57 tests over 14 businesses, visibility wildly varied based on query type, AI model used, and even wording sensitivity. The same brand could get recommended for “eco-friendly garden furniture” but vanish if the query became “sustainable patio chairs.” That inconsistency means you need to cover all bases. Related reading: this piece about why llm visibility tools are essential for b2b brands in .... Related reading: We Ran 4,500 AI Prompts Across 349 Brands. Two-Thirds Are.... No lazy shortcuts.

Step One: Nail Your Semantic SEO

Before we even talk AI-specific tactics, let’s tackle the bare bones: semantic SEO. AI systems don’t just regurgitate old-school keywords; they interpret context and intent. For example, someone asking ChatGPT for “affordable gym equipment for small spaces” doesn’t want generic value-meal answers. They want specificity. Brands that align their website copy, product descriptions, and metadata with deeper semantic structures tend to perform better.

One finding from our buying-intent tests made this crystal clear: queries with precise intents (e.g., “compact rowing machines for beginners”) matched brands with robust, well-organised product tags and descriptions over free-flowing generic ones. Related reading: our article on the brand invisibility problem: why your business is invi.... If your keywords still read like they were written in 2010, it’s time for an overhaul.

Pro Tip: Build content clusters around common conversational phrases. Think FAQs, blog posts answering niche queries, and even category pages designed for intent-heavy searches like “best-rated camping gear for solo travellers”.