What actually goes in Google Merchant Center conversational attributes

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Conversational attributes are Merchant Center fields Google introduced in 2026 that let a product record answer the questions a shopper would ask a salesperson (what it works with, what to buy instead, what a product question's answer is) rather than only stating its specifications. They exist because AI Mode and the Business Agent need a product record that can hold a conversation, and the standard attribute set was written for a keyword-matching era. Populating them well requires shopper-question language, not catalog language.

Google Merchant Center conversational attributes are new, and most coverage stops at the announcement. This page is about the values.

When did Google add conversational attributes, and why?

Google shipped this in two waves. On 11 January 2026 it announced the Universal Commerce Protocol (UCP), the Business Agent (a brand-voice sales associate inside Search) and Direct Offers in AI Mode: the protocol and the surface. At Google Marketing Live on 20 May 2026 it shipped the fields those surfaces read, six new Merchant Center conversational attributes, rolling out globally. Together they let a shopper have a sales conversation with your catalog instead of scrolling a grid.

The Business Agent has to say something when a shopper asks whether the chair fits under a 29-inch counter, and it can only say what the record says. Google built the surface, the protocol and the fields, not the sentences: the gap under every surface a catalog feeds.

How is a conversational attribute different from the product description?

A description is one block of prose for a human already on your page, read whole. A conversational attribute is a discrete, question-scoped value a machine can retrieve and quote without summarizing a paragraph and guessing right.

The difference is mechanical, not stylistic. Answering "is this rug safe over underfloor heating?" from a description requires inference, and inference is where invented product claims come from: a returns problem before a marketing one. Conversational attributes are the opposite trade: narrow, literal, quotable. They do not replace the standard attribute set, covered field by field in product attributes.

Which shopper questions do Google Merchant Center conversational attributes answer?

Every conversational attribute answers a question type. What makes a value quotable is a number, a condition or a named limitation; marketing adjectives answer nothing.

The question a shopper asks, the question type it belongs to, and what separates a quotable value from an unusable one. The question types below are our grouping, not a published Google taxonomy.

One more question belongs under the table rather than in it, because it is not a question type at all. "What's the catch?" is a posture, and most records leave it unwritten. The quotable answer reads like "sheds for four to six weeks; vacuum without a beater bar." Omitting it is the default choice and the expensive one.

What does a good record look like before and after?

Two categories where the gap is widest. Before is what a well-run catalog looks like today.

Apparel: a linen midi dress

Before. Color, size run, material, care, and 140 words about effortless summer dressing. Feed status green.

After, in the conversational layer.

: Answer to a product question: "Unlined. Sheer in direct sunlight; most customers wear a slip."

: Occasion suitability: "Daytime outdoor events and garden weddings. Not warm below 15°C."

: Substitutes: "For cooler months, the heavier washed-linen weave."

Home: an 8x10 hand-loomed wool rug

Before. Dimensions, pile height, 100% wool, color family, weave type, 160 words about artisanal craft.

After, in the conversational layer.

: Answer to a product question: "Sheds for four to six weeks; vacuum weekly, never on a beater-bar setting."

: Compatibility: "Safe over underfloor heating to the floor manufacturer's limit. Use a low-profile felt pad, not rubber-backed."

: Use-case suitability: "Dining rooms: chairs slide with some resistance at this pile height."

That last line costs a few dining-room sales. It also makes your record the one the assistant trusts on the next question.

Where do you find the answers Google now wants?

You already have them, written by customers, in four places nobody in merchandising reads. Mining them is a five-step job, not a content project.

1. 1.Twelve months of pre-purchase support tickets and chat transcripts. Group by category, not SKU; a small number of questions per category cover most of the volume.
2. 2.Free-text return reasons. Coded reasons ("wrong size") are useless. The typed box is where "I didn't realize it was unlined" lives, and each one is a missing attribute value.
3. 3.Reviews and product-page Q&A filtered to a question mark or "wish I'd known." That filter surfaces the honest-caveat values.
4. 4.Onsite searches that returned results but got no clicks. Questions your catalog answered badly rather than missed.
5. 5.Write once at category level, vary at SKU level. Wool rugs shed: written once, inherited. Pad size and substitute are the SKU-level variance.

Steps 1 to 4 are analyst work, not an engineering project. Step 5 is where it scales to 40,000 SKUs or stalls at 200 hero products, because a one-off pass decays as the assortment turns over.

Why is an empty conversational attribute a silent loss rather than an error?

Because nothing turns red. An unpopulated optional attribute triggers no disapproval, no feed error and no drop in item count, so Monday's dashboard looks as healthy as before.

The loss happens one layer down, in retrieval. A keyword surface degrades gracefully: a thin record ranks lower but still ranks. A question-scoped surface does not. When the shopper asks the underfloor-heating question, records that cannot answer are not ranked lower, they are not candidates. There is no report of a conversation you were not in, which is why feed health reporting cannot manage this surface at all.

What are the failure modes?

Four, in the order they happen. Each produces a populated field worse than an empty one.

: Marketing language in a factual field. "Effortlessly versatile" occupies the slot and answers nothing.

: Generating values from your own description. If the source never said whether the dress is lined, no model can produce that fact; it produces a plausible sentence, which is a misrepresentation risk.

: Copy-paste across a category. "Requires a rug pad" is true for the 8x10 and wrong for the 2x3 runner.

: Populating once. The assortment turns over continuously, so a set of answers written once describes a catalog you have stopped selling.

What is still not documented, and what we will not guess

Google announced these fields in May 2026, and several implementation specifics are not things we can state with confidence. We would rather say so than fill the gap.

: Exact field names, cardinality and character limits are still maturing. Build against the current product data specification in Google's Merchant Center documentation, not a blog post, including this one.

: Availability by country and category is not uniform, and we have seen no complete published matrix.

: Attribute-level reporting. We are not aware of any. If Google ships it, this page gets a dated changelog entry.

: How the Business Agent weights these fields against crawled page content when they disagree. Unknown; keep them consistent and watch Google's Shopping announcements.

How do you know it worked when the surface has no reporting?

You run a SKU-level holdout, because the surface will not tell you. Split a category into two cohorts matched on revenue, margin and traffic, populate the conversational layer on cohort A only, leave cohort B untouched for 28 days, and read difference-in-differences on revenue and product-page entrances, not impressions.

That design cuts both ways, including against us. In a matched-spend A/B test with a 28-day holdout, rewriting the product-language input produced a 28% increase in Google Shopping revenue against the untouched control. That covers the product-language record as a whole, not this layer, so we will not call it a conversational-attributes benchmark until the cohort test finishes. Agentic checkout is still a rounding error next to the rest of ecommerce, so this is a cheap position on a surface still being built, while the same values sharpen the Shopping feed that pays this quarter.

Where to start

Pull last quarter's free-text return reasons for your top category, group them into the handful of questions that repeat, and count how many your records can answer. That baseline takes an afternoon.

Then judge any approach, internal or bought, on whether it fixes the product-language input, runs continuously as catalogs churn, covers the surfaces that pay this quarter, and proves lift against a control. Would it survive a finance review? Start from Google's Merchant Center documentation.

Frequently asked questions

What are Google Merchant Center conversational attributes?

Conversational attributes are Merchant Center fields Google introduced in May 2026 that let a product record answer what a shopper would ask a salesperson: what it works with, what to buy instead, and a product question's answer. They give AI Mode and the Business Agent something quotable, because the standard attribute set was built for keyword matching.

When did Google add conversational attributes to the Merchant Center?

Google announced the six Merchant Center conversational attributes at Google Marketing Live on 20 May 2026, four months after the Universal Commerce Protocol, the Business Agent in Search and Direct Offers in AI Mode arrived in January. They are one system: a protocol, a conversational surface, and the fields it reads.

Are conversational attributes required, or optional?

They are additional fields, not a replacement for the required product data specification, so leaving one empty produces no feed error and no disapproval. The cost is invisible rather than absent: the record is not a candidate when a shopper asks the question that field would have answered.

How is a conversational attribute different from a product description?

A description is one block of prose read as a whole, which forces an assistant to infer an answer and risk getting it wrong. A conversational attribute is a discrete, question-scoped value that can be retrieved and quoted directly, which is why literal values outperform marketing language here.

Where do I get the content for conversational attributes?

The answers are already written by customers in support tickets, free-text return reasons, product-page question threads, and reviews containing "wish I'd known." Mine those four at category level for the handful of questions that repeat, then vary the values at SKU level.

Do I need conversational attributes if I do not sell through an agentic checkout?

Yes, because these fields describe a product for conversational discovery rather than transaction, and most shoppers who research in an AI tool still buy on the retailer's own site. The same values sharpen onsite search and the Shopping feed, so the work pays on surfaces that already earn.

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