A fan-out query is one of several supporting searches an AI system can generate to answer a shopper's broader question. A request for marathon running shoes, for example, may lead to separate searches about suitability for beginners and experienced runners. For retail SEO, content, and eCommerce teams, this creates opportunities for detailed product information and buying guidance to contribute to an AI-generated answer.

At eTail Palm Springs 2026, Kunal Banerjee, Senior Director, Product Management – Agentic Commerce, Search, Personalization & Shopping at Target, emphasized starting AI initiatives with a clear business objective: "As a clean strategy, you always start with, 'What is the problem to solve?'" He illustrated this with product content creation aimed at increasing SEO and GEO traffic. Applied to fan-out queries, that approach gives retail teams a focused starting point: identify the buying questions their content needs to answer, then assess whether improvements contribute to the intended discovery outcome.

Another 2026 discussion explained how AI assistants can generate related searches to gather information for a response, using marathon running shoes as an example. It also connected broader topic coverage with opportunities for citation, emphasizing the value of interlinked content that addresses several aspects of a purchase decision.

Start with one important buying question in a priority category, then map the considerations a shopper would need resolved, such as suitability, dimensions, compatibility, care, and delivery. Check which existing product pages, category guides, and help resources answer each consideration, and fill gaps with verified information. Link related resources where they help the reader, then review the sources cited in AI answers to assess which parts of the decision your content covers.

Join us at eTail Palm Springs to compare approaches with retail SEO, content, and eCommerce leaders developing more complete answers for AI-assisted product discovery.