### OpenAI Launches Shopping Research in ChatGPTOpenAI has introduced shopping research, a new feature in ChatGPT that transforms the AI into an interactive product researcher. Users describe their needs—such as a quiet cordless vacuum for a small apartment or a gift for a child who loves art—and the system responds with clarifying questions on budget, size, preferences, and priorities like performance or price. It then conducts multi-step web searches, pulling structured data on prices, specs, reviews, and availability from quality sources to deliver a personalized buyer's guide with ranked options, comparisons, and trade-offs[4][1][2].The feature rolled out on November 24, 2025, for logged-in users across free, Plus, Pro, and other plans on mobile and web, with nearly unlimited usage through the holidays to aid gift shopping. Powered by a specialized GPT-5 mini variant trained via reinforcement learning for shopping tasks, it takes several minutes per query, achieving 52% accuracy on multi-constraint requests (like specific price ranges, colors, and features) versus 37% for standard ChatGPT Search. OpenAI notes potential errors in pricing or availability, urging verification on retailer sites[2][3][4].### Implications for E-Commerce Product FeedsShopping research pulls real-time data from across the web, synthesizing it into structured guides rather than raw lists. This demands e-commerce platforms maintain dynamic, high-quality <a href="/blog/product_feed/">product feeds</a> with up-to-date specs, prices, and reviews to surface accurately in AI-driven searches. Incomplete or stale feeds risk exclusion from recommendations, as the AI prioritizes reliable sources[1][4].For cataloging standards, the feature enforces a shift toward semantic richness: products must include detailed attributes (dimensions, materials, user ratings) that align with natural language queries. Categories like electronics, beauty, home goods, kitchen appliances, and outdoor gear perform best due to their spec-heavy nature, while apparel struggles with subjective factors like fit[2][3][4].### Elevating Product Card Quality and CompletenessBuyer guides highlight trade-offs and personalization—drawing on ChatGPT memory for context like past gamer preferences or style dislikes—exposing gaps in basic product cards. E-commerce must enhance cards with comprehensive details, images, and user-generated content to match the depth AI synthesizes. Interactive refinements, such as marking options "not interested" or "more like this," further pressure platforms to enable real-time filtering[1][2][6].This raises the bar for content completeness: partial specs or outdated reviews lead to suboptimal rankings, as the AI cross-references multiple sites. Platforms with robust, standardized cards gain visibility in these conversational flows[1][5].### Accelerating Assortment DeploymentTraditional e-commerce relies on manual curation for new assortments, but shopping research speeds discovery by indexing web data instantly. Merchants can output inventory faster via AI-optimized feeds, reducing time-to-market for seasonal or niche items. The feature's deep research mode—handling complex decisions in minutes—bypasses exhaustive browsing, funneling traffic to well-indexed catalogs[4][6].Holiday boosts like unlimited queries underscore this velocity: high-traffic periods amplify exposure for agile feed managers, potentially reshaping assortment velocity from weeks to days[4]. Learn more about the topic in our article about <a href="/blog/common-mistakes-in-product-feed-uploads/">Common Mistakes in Product Feed Uploads</a>.### No-Code and AI Integration in Content WorkflowsNo-code tools now integrate seamlessly with AI researchers, automating feed generation and card enrichment without dev teams. Shopping research's reliance on structured web data incentivizes low-code platforms to embed AI for dynamic cataloging, such as auto-tagging specs or generating comparison tables. You can find out how to structure your product data in CSV format in our article about <a href="/blog/csv-format-how-to-structure-product-data-for-smooth-integration/">CSV Format</a>.Future Instant Checkout—already live for select merchants—hints at closed-loop journeys, blending research with frictionless buys. This no-code/AI synergy streamlines content infrastructure, turning static catalogs into adaptive, query-responsive systems[2][3]. Also, find out more about <a href="/blog/artificial-intelligence-for-business/">Artificial Intelligence for Business</a>.*Retail Dive.* *OpenAI Blog.*---The evolution of AI-powered shopping research highlights a crucial shift in e-commerce: the emphasis on data quality and completeness within product feeds. As AI tools become more sophisticated, they rely on rich, structured product information for optimal performance. This trend underscores the importance of solutions like NotPIM, which provide the tools and capabilities to standardize, enrich, and optimize product data, ensuring that e-commerce businesses can thrive in an increasingly AI-driven landscape by being accurately and comprehensively represented in relevant buyer journeys. For more information, check out our article about <a href="/blog/data-integration-challenges-whats-holding-your-online-store-back/">Data Integration Challenges</a>.