About Shopping

Overview

Shopping Analysis gives retail and e-commerce brands a clear view of how their products appear inside AI-powered shopping experiences. Profound captures the full AI response: product tiles, images, prices, merchant links, and buy buttons. This lets you see exactly which products are featured, which retailers own the checkout, and how your brand compares to competitors.

Shopping Analysis is currently focused on ChatGPT Shopping. Coverage of additional AI commerce surfaces, including Perplexity and Gemini, is evolving. Shopping Analysis is most relevant for retail, e-commerce, and CPG brands with physical or digital products.

AI shopping is a blind spot for brands 

More customers are starting their purchase journey by asking an AI rather than visiting a website. When they do, the AI recommends specific products, describes them, and links to merchants, all without any input from brands.

Brands have no visibility into which products are recommended, how they're described, or which competitors appear. When a product does appear, the checkout link may route to a third-party retailer rather than the brand's own store. Because AI responses surface individual SKUs, a brand can appear to have strong category visibility while quietly losing product slots to competitors.

How Profound fills the visibility gap

Profound Shopping Analysis treats AI shopping surfaces the same way behavioral analytics tools treat a website: as a measurable, improvable channel. The platform runs your tracked prompts through an LLM, monitors which ones trigger a shopping mode response, and captures the full result when they do. You get product-level data on placement, merchant attribution, attribute accuracy, and competitive positioning. This is the same depth of insight you previously got from web analytics, now applied to conversational commerce.

Key features

Shopping Mode Rate

Prompts that reliably trigger AI shopping mode typically combine a baseline commercial intent with one or two buyer profile factors, such as price range, gender, or preference. For example:

• Best running shoes under $150

• What refrigerator should I buy with an in-door ice machine

• Affordable standing desk for small apartments

Shopping Mode Rate shows what percentage of your tracked prompts trigger the shopping mode answer, rather than a standard text answer. Check it in Shopping > Brands > Shopping Mode Rate to understand how much signal you’re working with before reading any other data in the module.

Shopping Visibility Score

The Visibility Score tracks how consistently your brand and products appear across shopping mode responses. A score of 100% means your brand appears every time AI triggers shopping mode for that prompt set. The score breaks down by topic, and visibility can swing significantly between topics for the same brand, so topic-level review is more revealing than the overall figure.

Merchant layer

When your product appears in an AI Shopping tile, it typically includes multiple checkout links: some from third-party retailers, some from your own store. The merchant layer shows who those retailers are, how often they appear, and what share of their recommended products belong to your brand. Use this to understand whether your direct-to-consumer store is competitive at checkout or whether third-party retailers are capturing most of the traffic.

Product layer (SKU-level analysis)

The product layer tracks individual SKUs, not just brand mentions. You can see which specific products appear most in shopping results, which prompts surface them, and how AI describes their attributes. Competitors or unrelated products may be winning product placements in your category, and this view surfaces exactly where those gaps exist.