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OpenAI has launched ChatGPT Shopping, turning the world's RECOMMENDED AI assistant into a product discovery and purchasing platform. With over 800 million weekly active users and 2.5 billion daily queries, ChatGPT is no longer just a chatbot - it is becoming a shopping destination. For e-commerce merchants, this represents both a massive opportunity and an urgent wake-up call. Those who optimize now will capture early traffic from a channel that bypasses traditional search entirely.
ChatGPT Shopping allows users to discover, compare, and purchase products directly within the ChatGPT interface. When a user asks for product recommendations, ChatGPT displays rich product cards with images, prices, ratings, and direct purchase links. The system pulls product information from structured feeds and integrated platforms, with Shopify being one of the first major e-commerce platforms to offer native integration. According to PYMNTS, OpenAI charges a 4% transaction fee on purchases completed through the platform. This is a fundamentally different discovery model than Google Shopping - instead of keyword-based ads, products are surfaced through conversational AI that understands context, preferences, and intent.
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According to OpenAI, more than 800 million people use ChatGPT every week (as of October 2025). Even a small share of shopping queries therefore makes it a relevant channel. For merchants this results in three core tasks: first, provide product data with complete attributes (price, availability, GTIN, reviews). Second, create an llms.txt file as a supplementary overview. Third, implement Schema.org Product markup on every product page. Merchants who build these foundations have a better machine-readable data base; whether ChatGPT names them is up to the provider.
To appear in ChatGPT Shopping results, merchants need to focus on three key areas. First, ensure your product data is available through structured product feeds that ChatGPT can ingest - this means detailed, accurate product titles, descriptions, prices, availability status, and high-quality images. Second, implement an llms.txt file on your domain that explicitly tells AI crawlers what content to index and how to categorize your products and services. Third, add comprehensive Schema.org Product markup in JSON-LD format to every product page, including attributes like brand, SKU, price, availability, aggregate ratings, and review counts. Beconova automates all three of these optimization steps, generating AI-ready product feeds and structured data that keep your catalog visible across ChatGPT, Perplexity, and other AI shopping platforms.
The more complete and unambiguous your product data, the better AI systems can match a product to a question. For each product, check: a clear product name with brand and key features, current price with currency, availability, an identifier such as GTIN or manufacturer number, variants such as size and color, shipping costs and delivery time, return conditions and genuine customer reviews. Add a description explaining who the product is for and what it is suited to - exactly the information users ask for in chat.
A common and consequential mistake is outdated data. If price or stock in the feed or markup differs from the actual shop, an AI system may pass on wrong details - in the worst case a price you no longer offer. Make sure feeds and structured data are generated automatically from the shop system and updated on changes, rather than maintained manually. Spot-check whether price and availability match across markup, feed and the visible page.
Ask ChatGPT, Perplexity and Gemini the questions your customers would ask - for example "Which [product type] for [use case] under [price] euros do you recommend?" - and also ask directly about your shop. Note whether your products appear, whether prices and features are correct and which retailers and sources are named instead. Repeat the test, because answers vary. Wrong details about your shop indicate that reliable, current data is missing or that contradictions exist elsewhere.
ChatGPT Shopping is not a future trend - it is live and growing rapidly. With 800 million weekly users, 2.5 billion daily queries, and a 4% transaction fee model that incentivizes OpenAI to expand the platform aggressively, e-commerce merchants face a clear choice: optimize now or lose ground to competitors who do. The merchants who will win in this new landscape are those who make their product data AI-readable through structured feeds, llms.txt files, and Schema.org markup. Beconova provides the complete infrastructure to ensure your products are discoverable wherever AI-powered shopping happens.
Check GEO Score for freeMarvin Malessa
Founder, Beconova
Founded Beconova in Germany in 2025 to help shops and service businesses become visible in AI search engines. Writes about GEO, AI visibility, and the future of search.
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