{"id":1683,"date":"2026-07-27T08:09:09","date_gmt":"2026-07-27T08:09:09","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/product-feed-ai-shopping\/"},"modified":"2026-07-27T08:09:09","modified_gmt":"2026-07-27T08:09:09","slug":"product-feed-ai-shopping","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/product-feed-ai-shopping\/","title":{"rendered":"Product Feed for AI Shopping: Which Fields Actually Get Quoted"},"content":{"rendered":"

A product feed for AI shopping is the structured catalogue file \u2014 the same Google Shopping spec, plus platform-specific eligibility flags \u2014 that assistants read to name, price and describe your products inside a generated answer.<\/strong> It is not a new file format. It is the same feed, judged by a different consumer.<\/p>\n

Most merchants build one the way they built for Google Shopping in 2018: fill every column, chase 100% attribute completeness, celebrate a clean diagnostics dashboard. Then an assistant writes a four-sentence recommendation that repeats a price, half a title, one spec, and a star rating \u2014 and ignores the other fifty fields.<\/p>\n

That gap between what merchants populate<\/em> and what assistants repeat<\/em> is measurable. Over six weeks we captured 4,480 AI shopping answers and diffed every product mention against the merchant's underlying feed record. Nine fields carried 93% of everything the models actually quoted. Several fields that feed vendors sell as "AI-critical" were echoed zero times.<\/p>\n

Here is the field-by-field evidence, the framework we now use to read a feed, and the fixes that follow from it.<\/p>\n

What "getting quoted" means in an AI shopping answer<\/h2>\n

A field is quoted<\/em> when its value appears in the generated prose the shopper reads \u2014 not in a card, not in a filter, not in a link.<\/strong> "Runs $189 and ships free in two days" quotes price and shipping. A product card showing an image quotes nothing; it renders.<\/p>\n

The distinction matters because the three behaviours have different owners. Rendered fields are controlled by the surface's UI. Filter fields decide whether you enter the candidate set at all. Quoted fields are the only ones that shape how a shopper hears about you \u2014 the sentence that makes them click or skip.<\/p>\n

Merchants optimise all three as if they were one job. They aren't.<\/p>\n

How we tested: 4,480 answers across four surfaces<\/h2>\n

We ran 1,120 shopping-intent prompts between 22 April and 2 June 2026 across four surfaces: ChatGPT with shopping enabled, Google AI Mode, Perplexity, and Microsoft Copilot. Prompts spanned eight categories chosen for different attribute density \u2014 running shoes, standing desks, espresso machines, dog food, video-editing laptops, mattresses, vitamin C serums, and cordless drills.<\/p>\n

That produced 4,480 captured answers containing 27,400 product mentions from 1,860 merchant domains<\/strong>. For 812 of those domains we could read the underlying feed record (public Google Shopping feed or client-supplied file), which left 11,318 mentions where a field-level comparison was possible<\/strong>. Every rate below is calculated on those 11,318.<\/p>\n

Our matching rule: a field counts as echoed when a normalised version of its value appears in the answer text as a span of three or more consecutive tokens. We excluded matches where the value was trivially derivable from the brand name or the URL slug.<\/p>\n

Two honest limits.<\/strong> First, we cannot always prove provenance<\/em> \u2014 a price in an answer may have come from the feed, the page, or a retailer aggregator, and for shared values we can only observe co-occurrence. Second, this is one sample in one quarter; surface behaviour changes with model releases, and we have already seen Copilot's answer length shift twice. Treat the ranking of fields as robust and the exact percentages as a snapshot.<\/p>\n

\"Field<\/figure>\n

The field echo table: what actually surfaced in answer text<\/h2>\n

Fields are sorted by echo rate. The "job" column is our classification, explained in the next section.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Feed field<\/th>\nEcho rate<\/th>\nPrimary job<\/th>\n<\/tr>\n<\/thead>\n
price<\/code> \/ sale_price<\/code><\/td>\n88%<\/td>\nQuote<\/td>\n<\/tr>\n
title<\/code> (3+ token span)<\/td>\n84%<\/td>\nQuote<\/td>\n<\/tr>\n
brand<\/code><\/td>\n79%<\/td>\nQuote<\/td>\n<\/tr>\n
description<\/code> (any span)<\/td>\n61%<\/td>\nQuote<\/td>\n<\/tr>\n
star_rating<\/code> + review_count<\/code><\/td>\n44%<\/td>\nQuote<\/td>\n<\/tr>\n
availability<\/code><\/td>\n33%<\/td>\nQuote<\/td>\n<\/tr>\n
shipping<\/code> \/ delivery window<\/td>\n27%<\/td>\nQuote<\/td>\n<\/tr>\n
color<\/code> \/ size<\/code> variant attributes<\/td>\n22%<\/td>\nQuote<\/td>\n<\/tr>\n
material<\/code><\/td>\n16%<\/td>\nQuote<\/td>\n<\/tr>\n
dimensions<\/code> \/ weight<\/code><\/td>\n13%<\/td>\nQuote<\/td>\n<\/tr>\n
return_policy<\/code> \/ return_deadline_in_days<\/code><\/td>\n11%<\/td>\nQuote<\/td>\n<\/tr>\n
q_and_a<\/code><\/td>\n9% overall \u2014 31% where populated<\/td>\nQuote<\/td>\n<\/tr>\n
unit_pricing_measure<\/code><\/td>\n6% overall \u2014 38% in consumables<\/td>\nQuote<\/td>\n<\/tr>\n
condition<\/code><\/td>\n3%<\/td>\nFilter<\/td>\n<\/tr>\n
age_group<\/code> \/ gender<\/code><\/td>\n2%<\/td>\nFilter<\/td>\n<\/tr>\n
video_link<\/code><\/td>\n0.4%<\/td>\nRender<\/td>\n<\/tr>\n
gtin<\/code> \/ mpn<\/code><\/td>\n0.2%<\/td>\nGate<\/td>\n<\/tr>\n
image_link<\/code><\/td>\n0% quoted, rendered in 51%<\/td>\nRender<\/td>\n<\/tr>\n
product_category<\/code> (taxonomy)<\/td>\n0%<\/td>\nFilter<\/td>\n<\/tr>\n
item_group_id<\/code><\/td>\n0%<\/td>\nFilter<\/td>\n<\/tr>\n
promotion_id<\/code><\/td>\n0%<\/td>\nFilter<\/td>\n<\/tr>\n
custom_label_0<\/code>\u20134<\/code><\/td>\n0%<\/td>\nNeither<\/td>\n<\/tr>\n
model_3d_link<\/code><\/td>\n0%<\/td>\nNeither<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Nine fields \u2014 price, title, brand, description, rating, review count, availability, shipping and variant attributes \u2014 accounted for 93% of every echoed span<\/strong> in the sample. If your feed roadmap this quarter does not touch those nine, it is not a plan for AI shopping visibility.<\/p>\n

Gate, Filter, Quote: a framework for reading your own feed<\/h2>\n

Every field in a product feed for AI shopping does exactly one of three jobs, and confusing them wastes most feed budgets.<\/strong><\/p>\n