Shoppers who don’t know what they want represent an opportunity for marketers to showcase products. The concept of “intent” has long existed in marketing, with terms like “purchase intent” and “informational intent” describing targeting prospects based on an apparent need. AI’s growth in search and shopping adds precision to these behaviors and opportunities. The differences in query length and complexity between traditional search and chat with Claude, Gemini, or ChatGPT are stark. Conventional searches average about four words, while ChatGPT queries are typically 23, according to a 2026 Semrush post.
An apartment dweller who wants a compact, simple coffee grinder might ask a search engine for just that: a “small simple coffee grinder.” The same query in an AI interface could have more context: “a quiet coffee grinder for a small apartment that works for pour-over and does not make a mess.” The best answer to both queries might be a conical burr grinder, but the second form exposes the opportunity. Product intent clusters have a familiar hub-and-spoke structure. Each cluster targets a specific shopper scenario.
Related: New GA4 Channel Tracks AI Traffic
Ecommerce marketers could combine content, merchandising, and product detail pages to influence AI responses, much like long-tail posts helped optimize organic search listings. Such product intent clusters resemble traditional topic clusters but focus on information, use cases, and specific customer scenarios that collectively point to a single product. The product detail page remains the source of truth for a purchase decision: specifications, pricing, and details. It drives conversions and is rankable, extractable, and understandable as an entity.
The best approach does not mean turning every product page into a massive buying guide. The page should remain focused. It can link to supporting content, organize related questions, and clarify the product’s use cases. The supporting pages in a product intent cluster are funnel-focused around a specific shopping scenario. For example, “best coffee grinders for pour-over” is too broad. A better intent page addresses a consumer’s situation: “best pour-over coffee grinders for tiny kitchens.” The shopper wants pour-over quality, has limited counter space, seeks little noise, and wants easy cleanup.
Related: Ecommerce platforms roll out fresh tools
An intent page should guide the shopper toward a purchase by identifying the product features that matter and showing why the product fits the need. The page should also follow traditional search engine optimization, with Schema.org structured data markup and the use of entities. The page should be useful and readable for humans, but its primary purpose is to communicate with AI bots. Marketers should aim for dozens (or more) of these intent pages per product. Before generative AI, few ecommerce marketing teams could justify researching, outlining, writing, optimizing, and maintaining a page for a specific scenario like “the best pour-over coffee grinders for tiny kitchens.” The use case was too narrow, the labor cost too high, and the potential benefit too uncertain.
Automation and genAI can produce and maintain an endless number of quality intent pages, carefully and precisely prompt-engineered. The process can even identify the topics by feeding structured customer feedback, such as support tickets and product reviews, into a genAI platform. The shift toward detailed prompts means shoppers will likely turn to genAI when they don’t know what they want. Intent pages give those systems a reason to connect a product with a shopper’s need and willingness to buy. As these tools become more common, the structure of product discovery will likely change. The focus moves from matching a keyword to solving a specific problem. Marketers must build the infrastructure to answer those problems, or they risk falling behind as the search experience evolves.
