seoPublished on July 31, 20266 min read

AI Shopping: Why the Google Merchant Center Feed Matters More Than the Product Page

Recent studies show that ChatGPT pulls products almost exclusively from Google Shopping. Discover why the product feed is now decisive for visibility in AI-driven shopping.

AI ShoppingGoogle Merchant CenterSEOE-commerceGEOMarketing DigitalChatGPTGoogle Shopping

Bitclever AI Research

Autor: Bitclever AI Research ## Executive Summary Recent data reveals that AI-based shopping assistants, such as ChatGPT, build their product recommendations mostly from the Google Merchant Center, rather than from brands' product pages. With 83% of products shown in ChatGPT carousels matching the top results of Google Shopping, the quality of the data feed has become a critical factor for digital visibility. For companies with an e-commerce presence, this represents a structural shift in how they must manage their catalogue data. ## What Happened A study conducted by Tom Wells in March 2026, and cited by Search Engine Land, analysed more than 43,000 products shown in ChatGPT carousels to understand their origin. The results were clear: 83% of the products matched the top 40 organic results on Google Shopping. In the case of Bing, only 11% of products matched that search engine's results, and almost all of these were also present on Google. The research shows that ChatGPT uses a mechanism called "shopping query fan-outs" — specific queries, separate from those that generate the text response — to build its product carousels, typically with eight options. These queries frequently pull from a single page of Google Shopping results, with 60% of the strongest matches coming from the top 10 results. Even more significant: the order of products in the carousel directly reflects their ranking position on Google Shopping. This conclusion was reinforced by an independent study from the company Profound, which analysed more than 1 million shopping offers on ChatGPT during the month of June. The result was even more striking: approximately 99.9% of product citations pulled directly from merchant feeds originated from the Google Merchant Center. In short, the products that consumers see when interacting with AI assistants to shop do not come from the open web, from brand-optimised product pages, or from customer reviews — but from a single technical file that many companies set up years ago and rarely revisit: the Google Merchant Center feed. ## Why This Matters For more than a decade, SEO and e-commerce optimisation strategies have focused heavily on the product page: rich descriptions, quality images, customer reviews, structured data (schema markup) and conversion optimisation. This approach remains relevant for traditional search and for the human user experience. However, the data now available suggests that generative AI engines — which are becoming an increasingly used product discovery channel — do not "read" product pages the way a human user does. Instead, they rely on structured data feeds, previously validated and indexed by platforms such as Google Shopping. This discovery has profound implications for the emerging discipline known as AEO (Answer Engine Optimization) or GEO (Generative Engine Optimization). If the goal is to ensure visibility in AI shopping assistants, optimising on-page content alone is no longer enough. The quality, completeness and freshness of the product feed become the determining factor for a product to even be considered by the recommendation algorithm. The shift in search behaviour — from traditional search engines to conversational assistants — is already underway. Brands that fail to adapt their product data strategy risk becoming invisible in a rapidly growing shopping channel. ## Business Impact For organisations with e-commerce operations, this shift has practical and immediate implications: **1. Urgent audit of the Google Merchant Center** Many companies set up their feed years ago, exclusively for Shopping Ads campaigns, and have never revisited it as a strategic asset for organic discovery. It is necessary to audit attributes such as title, description, category, availability, price, GTIN and images, ensuring they are complete, accurate and up to date. **2. Prioritising structured data over creative content** While product page content remains relevant for conversion and traditional SEO, the feed becomes the priority element for AI visibility. This requires technical and data management resources that, in many companies, had been relegated to the background. **3. Risk of dependency on a single platform** The almost absolute concentration of ChatGPT results on Google Shopping (and the weak match with Bing) highlights a structural dependency on a single data source. Companies that neglect the Google Merchant Center are, in practice, absent from an increasingly relevant discovery channel. **4. Need for continuous monitoring** A product's position on Google Shopping directly influences its position in the AI carousel. This means that feed optimisation, competitive price management and stock availability now have a direct and measurable impact on visibility in conversational assistants. **5. New success metric** Traditional e-commerce KPIs (organic traffic, PDP conversion rate) need to be complemented with indicators related to feed quality and performance, and to presence in AI assistant citations. ## Bitclever Perspective At Bitclever, we closely follow the evolution of search and shopping behaviour driven by generative AI, and we recognise that this shift requires a structured, technical response — not merely cosmetic adjustments. Our approach begins with a diagnostic assessment of the current state of our clients' product feed in the Google Merchant Center — identifying gaps in critical attributes, data inconsistencies and optimisation opportunities that directly impact visibility in generative AI channels. With solid experience in process automation and systems integration, we help companies build automated feed update pipelines, ensuring that information such as stock, price and availability is always synchronised between the catalogue management system and the Google Merchant Center — reducing manual errors and increasing the reliability of the data that feeds AI engines. Additionally, we integrate this analysis into our SEO and Digital Marketing practice, helping clients develop a holistic strategy that covers both traditional product page optimisation and the new discipline of generative engine optimisation (GEO/AEO), ensuring that brands maintain visibility across all relevant consumer touchpoints — whether traditional search engines or conversational AI assistants. More than a reactive response, we believe this is an opportunity for companies to rethink the governance of their product data as a core strategic asset, rather than merely a technical requirement for advertising campaigns. ## Conclusion Product discovery through AI assistants is redefining the rules of the e-commerce game: having a well-built product page is no longer enough — it is essential to ensure that the data feed powering platforms such as the Google Merchant Center is accurate, complete and up to date. Companies that invest now in the quality and governance of their product data will be better positioned to capture the attention — and the sales — generated by this new AI-driven discovery channel.

Sources

https://searchengineland.com/ai-shopping-product-feed-page-484060

← Back to Blog

AI Shopping: Why the Google Merchant Center Feed Matters More Than the Product Page

Sources

Related Articles

AI in SEO: More Speed Doesn't Mean Better Results Without Human Expertise

OpenAI Tests Native Ads in ChatGPT That Launch Conversational AI Agents

AI and Web Accessibility: How Automation Is Creating New Digital Barriers