AI Shopping Visibility

Ordinary AI visibility asks whether AI mentions a brand. Shopping visibility asks the harder, more commercial question: when a buyer asks AI what to buy, does it recommend you? Open a client's AI Visibility project and go to the Shopping tab. It focuses on purchase-intent questions — the "best X for Y", "top X under $Z", "what X should I buy" style of prompt that people ask when they're ready to spend. Shopping visibility is part of AI Visibility, not a separate product, so it uses the same models, the same daily scans, and the same honest measurement (sample size and margin of error on every figure). Nothing is estimated — every number comes from real scan runs of prompts tagged as shopping-intent.

The headline metric is your shopping recommendation rate: across all the purchase-intent runs, how often did AI actually recommend your brand? It's shown as a percentage with the exact sample size ("27/50 shopping runs recommend you", n = 50 runs) and a ± margin of error, with a low-confidence note when the sample is still small (under ten runs). That keeps a single lucky answer from looking like a trend. Right below it, the "You vs competitors — shopping recommendations" table lines your recommendation rate up against each competitor you track, each with its own rate, margin of error and run count. This is the clearest signal of whether AI is steering ready-to-buy shoppers toward you or toward a rival. Set a product type / category on the tab and CrunchJunkie seeds sensible purchase-intent prompt templates for it; you can edit or add your own at any time. At the product level, each tracked product also shows a head-to-head win rate alongside its recommendation rate. Where the recommendation rate is over every shopping run, the win rate is over the "decisive" runs only — the runs where AI recommended at least one tracked product (yours or a competitor's) — and is the share of those that included this one. It answers a sharper question: when AI actually picks something in this category, how often is it you? Because it ignores runs where nothing was recommended, it isn't diluted by prompts that produced no pick, and the decisive-run count is shown so the number always carries its sample size.

Beyond the brand, you can track individual products and see the recommendation rate for each one — so you know exactly which SKUs AI puts in front of buyers, not just whether your brand comes up. Add your own products (and competitors' products) and each becomes a tracked item scanned like any other prompt. CrunchJunkie also runs a discovery view: "Products AI recommended that you're not tracking". When a shopping answer names a specific product you haven't added, it surfaces there with how often it appeared (e.g. "9/12 runs"), and a one-click Track button adds it to your list. It's the fastest way to find the products already winning in AI answers — yours or a competitor's — and start measuring them. Each tracked SKU generates a purchase-intent prompt (one, occasionally two) and is scanned on your project's schedule. Because scanning costs money, tracking is capped per plan: Solo 25, Starter 100, Pro 500, Agency 2,000, and Scale unlimited tracked SKUs. Only SKUs you actually track are scanned, and the tab always shows the projected checks per month before you commit — so cost is transparent. With your own API keys, per-SKU scanning is free; on Managed AI it's metered by check like the rest of AI Visibility.

You don't have to add products by hand. CrunchJunkie imports your catalog from a standard product feed in the Product feed section of the Shopping tab. Three formats are supported: Google Shopping XML (the RSS/Atom feed with the g: namespace that Merchant Center produces), tab-separated (TSV), and comma-separated (CSV). You can either paste a feed URL for CrunchJunkie to fetch, or upload a file (.xml, .csv, .tsv or .txt). CrunchJunkie reads each product's id/SKU, title, brand, product type/category, link and price, mapping common column names automatically (so "sku", "mpn" or "offerid" all resolve to the id, for example). Products without a title are skipped rather than guessed, and very large feeds are capped at 50,000 items. Imported products don't start scanning on their own — importing only populates the catalog. From there you pick which ones to track, filter by product type, brand or price range, and use "Track all matching" to bulk-track a filtered set. The bulk action shows a preview of the projected added checks and new total first, and never tracks past your plan's cap (SKUs beyond it are skipped, not tracked).

If your catalog already lives in Google Merchant Center, connect it directly instead of exporting a feed. Under Data → Integrations (or the Merchant Center card on the Shopping tab), click Connect and authorize with Google. The connection uses the Content API for Shopping and requests Google's Content API scope (auth/content) — you must leave that permission ticked on Google's consent screen, or the connection can't read your catalog. Access is read-only: CrunchJunkie only reads your product list, never writes to your Merchant Center. After connecting, pick the Merchant Center account to import from (sub-accounts under an MCC are listed too), and click Sync now to pull the catalog. Each product is normalised into the same shape as a feed import, so tracking, filtering and bulk actions all work identically. Syncing is user-initiated — click Sync again whenever your catalog changes to refresh it. You can disconnect at any time from the same panel, and revoke access entirely from your Google account.

Good purchase-intent prompts mirror how buyers actually shop. The Shopping tab seeds templates from your product type, and you can also let Crunch write a tailored set: click "Generate with Crunch" and it produces purchase-intent questions written for what you sell, marked with a "Tailored by Crunch" badge. It runs only when you click — nothing is generated automatically on page load — and if no AI model is connected yet, it falls back to sensible static templates and tells you so. Generated prompts are deliberately unbranded category questions (for example "what's the best moisturiser for oily skin under $40?") rather than prompts that name your brand. That's the point: shopping visibility measures whether AI recommends you when the buyer hasn't already picked you — so the prompt must not put your name in the model's mouth. Review, edit and add the generated prompts, then they scan alongside the rest of your shopping prompts. Like the rest of Crunch, generation needs an available AI model (Managed AI or your own key).

Shopping data flows straight into client reports. The AI Visibility report template includes a "Shopping visibility" table and a "Top recommended products" table, both driven by real scan data, so a client sees not just that they're visible in AI but whether AI recommends them and their products for the sale. You can also add either widget to any report by choosing the AI Visibility source and setting the widget's dimension to Shopping or Product. As with every AI-visibility figure, the numbers carry their sample size so the measurement is never over-claimed.