Shopping Modes

Documentation Index

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AI shopping results do not respond the same way to every query. Depending on buyer intent, they usually fall into two broad modes: Carousel and Research. Understanding shopping modes helps you interpret prompt-level data and understand what kind of content is helping competitors win.

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Carousel mode

In carousel mode, the AI returns a visual grid of product cards — typically 3–6 products with names, images, prices, and a brief description. This is the AI equivalent of a search results page. When it happens: Purchase-intent queries where the buyer wants options fast. Example query: “best trail running shoes under $150” What it means for ranking: Position matters enormously here. Position 1 gets the most attention, and being absent is a meaningful miss. How to optimize: Strong product listings, structured data, accurate pricing, and review counts are the primary ranking signals in carousel responses.

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Research mode

In research mode, the AI returns a long-form buyer guide — a detailed text response that compares options, explains trade-offs, and makes a recommendation. Sometimes called “long-form” or “deep research” mode. When it happens: Comparison, feature-specific, and research queries where the buyer is still evaluating. Example query: “what should I look for in trail running shoes for technical terrain?” What it means for ranking: Presence matters more than exact order. Being cited in a trusted research response builds authority earlier in the funnel. How to optimize: Authority signals matter most — third-party reviews, editorial coverage, and expert citations. Brands that are well-covered by authoritative sources rank well in research responses.

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Why it matters

A prompt’s shopping mode tells you what kind of influence your ranking has. Your prompt table shows the mode for each query. Filter by mode to understand where your visibility gaps are most costly.