Your product feed is the ceiling on paid performance and AI shopping visibility. Enter a product's details below to score its ad and AI feed-readiness 0–100, see exactly which attributes pass or fail, and get specific fixes for each gap.
Google Shopping, Performance Max, and Meta Advantage+ catalogs match, rank, and approve products from feed attributes. A missing GTIN, thin title, or absent category suppresses impressions and triggers disapprovals — you can't bid past a poor feed. Data quality sets the reach ceiling before spend enters the auction.
ChatGPT, Perplexity, and Gemini shopping answers read the same structured attributes and long descriptions to decide what to cite. Products with detailed copy, clear brand and category, and explicit color/size/material are easy to recommend. Sparse listings are invisible to queries like "navy merino sweater under $100."
This product is under-fed. Expect disapprovals, low match rates, and near-zero pickup in AI shopping answers until the essentials are filled in.
FIX: Use the pattern Brand + Product + Key Attribute + Type (e.g. "Acme Merino Wool Crew-Neck Sweater"). Keep it 30–150 characters so it survives truncation in Shopping and AI results.
FIX: Write 500+ characters of unique, specific copy: use cases, materials, dimensions, care, and who it's for. Long, factual descriptions are what LLMs quote when recommending products.
FIX: Add at least 3 high-resolution images on clean backgrounds showing angles, scale, and in-use context. Feeds with a single image lose in Shopping ad ranking and visual AI results.
FIX: Populate the Barcode (GTIN/UPC/EAN) field on each variant. Google matches products to its catalog by GTIN — missing it suppresses impressions and blocks unique-product identifiers AI shopping relies on.
FIX: Set the Vendor field in Shopify (it maps to the feed "brand" attribute). Brand is required for most Shopping categories and is a primary signal AI models use to attribute and rank products.
FIX: Fill Shopify's Product Type and map a Google Product Category. Precise categorization drives correct auction placement and lets AI engines slot the item into the right comparison set.
FIX: Add structured color, size, and material attributes (as metafields/variants and in copy). These attributes are what shoppers and AI assistants filter on — without them the product is invisible to attribute-based queries.
Directional estimate. The score models the attributes Google Merchant Center, Meta catalogs, and AI shopping engines weight most — always validate against your Merchant Center diagnostics before launching.
Feed-readiness is how complete and structured your product data is for the systems that consume it: Google Shopping and Performance Max, Meta Advantage+ catalogs, and AI shopping engines like ChatGPT and Perplexity. A feed-ready product has a descriptive title, a long unique description, multiple images, a GTIN/barcode, a brand, a product category, and structured attributes (color, size, material). Missing fields don't just look incomplete — they cap how often the product can be shown and how confidently an AI can recommend it.
Ad platforms match, rank, and approve products using the attributes in your feed. A missing GTIN can suppress impressions or trigger disapprovals; a thin title loses relevance in the auction; no product category means the item lands in the wrong comparison set. You can't bid your way out of a poor feed — the data quality sets the ceiling on reach before spend even enters the equation.
LLM-based shopping assistants read the same structured attributes and long-form descriptions to decide what to cite. Products with detailed descriptions, clear brand and category, and explicit color/size/material attributes are far easier for a model to match to a query and recommend with confidence. Sparse listings are effectively invisible to attribute-based AI queries like "navy merino sweater under $100."
Start with the fields that gate eligibility: GTIN/barcode, brand/vendor, and product category. Those unblock Shopping and PMax approval. Then invest in title structure and a 500+ character description, which drive both auction relevance and AI citation quality. Images and structured attributes round out the score.
The score models the attributes Google Merchant Center, Meta catalogs, and AI shopping engines weight most heavily, so it's a reliable directional read on feed health. Exact requirements vary by category and destination — treat a high score as necessary, not sufficient, and always validate against your Merchant Center diagnostics.
Your catalog data — titles, GTINs, images, and attributes — sets the ceiling on Google Shopping, Meta catalog ads, and AI shopping visibility.
A 40-point audit for Google Shopping feeds: titles, descriptions, attributes, GTIN coverage, and the policy gaps most Shopify stores miss.
Step-by-step guide to connecting Shopify with Google Merchant Center, optimizing your product feed, running Shopping ads, and leveraging free listings.
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