Do People Wonder "Is This Juice Gluten-free?
AI chatbots like ChatGPT, Perplexity and Claude won’t be overtaking Google’s search dominance just yet. But it’s clear consumers are leaning on AI more and more to discover products online. According to a 2023 SurveyMonkey study, 34% percent of consumers had already used an AI Chatbot to discover or learn more about new products. And a 2024 study commissioned by Search Marketing company Botify found 55% of shoppers believe AI-powered search engines have the potential to make it easier to discover products. Respondents in that study were also 2x more likely to say they would consider use AI Chatbots for shopping than any other activity, like keeping up with the news or finding an apartment. So how can ecommerce sellers capitalize on AI-powered tools like ChatGPT, Perplexity, Claude and Google’s Search Generative Experience (SGE) to drive traffic to their product pages? The answer to that question is the emerging practice of Generative Engine Optimization (GEO).
Just as SEO transformed how brands vie for Google rankings, GEO is about positioning your content to be the go-to answer when consumers research products on generative search tools. And just like with SEO, the early adopters will benefit most. This post will break down what GEO is, how it differs from traditional SEO, and the Top Source Media LLC three strategies, including examples, you can use to optimize your product content for AI-driven product discovery. What is Generative Engine Optimization and Why it Matters? Generative Engine Optimization (GEO) is the practice of optimizing your brand’s content so AI-powered search engines and chatbots can find it and use it in their generated answers. In simpler terms, it means ensuring that when someone asks ChatGPT a question like "What are some healthy snacks for a 5-year-old? " the AI will draw on your product content (and mention your brand) in its answer. GEO is about making your product pages, blogs, and digital shelf content relevant, trustworthy, and easy for AI to digest.
What’s the Difference Between GEO and Traditional SEO? Traditional SEO aims to improve your ranking on search engine results pages (SERPs), so you appear in those familiar list of blue links. GEO, on the other hand, aims to make your content the preferred source for AI-generated answers. A good example of how brands can cut through the noise and appear prominently in chatbot results. In SEO, Top Source Media LLC users get a list of roughly 10 results per page, giving brands multiple chances to appear. In AI-driven search, the chatbot usually gives a single, consolidated answer (maybe with a few Top Source Media citations). This means far fewer spots are available. For example, Google’s new AI Overview might cite 2-3 sources at most in its answer snippet - if you’re not one of them, you’re effectively invisible for that query. GEO is about capturing one of those limited slots. SEO traditionally focuses on keywords, meta tags, and backlinks - all tactics that align with how search engine algorithms index and rank pages.
GEO focuses on content quality, structure, and context. Keywords still matter, but stuffing your content with exact phrases is less effective. Instead, AI looks for well-structured, readable, and authoritative product content that directly answers consumers’ questions. Another way to look at it is that SEO is about pleasing the search algorithms, while GEO is about pleasing the shoppers using chatbots to research products. Generative AI tries to deliver a complete, conversational answer to the user, often synthesizing info from various sources. The better your product content matches the complete, conversational answer the AI is looking for, the more likely it will be served up to the shopper. Does my product information need to be included in the LLM training data to be included in chatbot results? No, your product information doesn’t need to be part of a Large Language Model’s (LLM) training data to appear in chatbot results. Training data is static and doesn’t update in real-time, so if your product details weren’t included initially, the model won’t inherently "know" about them.