Conversational Attributes in AI Search

One of the most significant developments is the rise of AI-powered search. Rather than relying on short keyword-based searches, users are increasingly asking more natural, conversational questions when researching products, comparing options and seeking recommendations. Instead of simply displaying a list of links, AI-powered search can understand context, answer follow-up questions and recommend products that best match a customer's specific requirements.

For ecommerce teams, this represents another shift in how product data should be viewed. Product feeds are no longer just about titles, prices, images and availability, but now they need to answer the kinds of questions customers naturally ask throughout the shopping journey.

For retailers, this means product information needs to go beyond the basic attributes traditionally used for ecommerce. While titles, descriptions and specifications remain essential, AI-driven search benefits from richer contextual information that helps explain how products differ, who they are best suited for, and what makes them unique.

This is where conversational attributes come in.

Google Merchant Center's conversational attributes are optional fields that compliment the existing Merchant Center product data specification. They are designed to help AI systems and conversational shopping experiences build a deeper understanding of individual products, making it easier for customers to discover relevant products through AI Mode in Google Search and other Google shopping experiences.

Importantly, conversational attributes are not intended to replace or duplicate your existing product data. If key information is already provided through fields such as product descriptions or product details, retailers should instead use conversational attributes to add context where it will genuinely help customers better understand a product.

When these attributes are submitted, they can help customers discover deeper, quicker and often more relevant information about products, while also enhancing traditional search experiences.

Some examples of conversational attributes available within Google Merchant Center include:

• Question and answer (question_and_answer)
• Document link (document_link)
• Related product (related_product)
• Item group title (item_group_title)
• Variant option (variant_option)
• Popularity rank (popularity_rank)

The table below provides a few further examples of available conversational attributes, explaining how each can be used and providing example values.

These attributes can be added alongside an existing product feed and are designed to provide additional descriptive information about products, including variant information and supporting documentation, helping Google better understand the relationships and context within a retailer's catalogue.

Which retailers will benefit most?

Conversational attributes are particularly valuable for retailers with:

• Large product catalogues where many products are visually or technically similar.
• Products available in multiple sizes, colours or configurations.
• Technical or specialist products where customers often have detailed questions before purchasing.
• Product ranges where supporting documents, FAQs or buying guides can help customers make informed decisions.

For example, a furniture retailer selling multiple sofa ranges could use conversational attributes to distinguish between fabrics, seating capacities, comfort levels and matching products. Similarly, an electronics retailer could link specification documents or FAQs to help AI better answer customer questions about compatibility or product features.

How retailers can get started

As conversational attributes are optional, businesses don't need to overhaul their entire product catalogue immediately. A good starting point is to review the existing product feed and identify where additional context would genuinely help customers understand the products.

Consider focusing on products that:

• Have complex variants or multiple configurations.
• Generate frequent customer service enquiries before purchase.
• Experience higher return rates due to customer misunderstanding.
• Require buying guides, specification sheets, manuals or FAQs to support purchasing decisions.
• Have complimentary or related products that customers commonly purchase together.

Starting with a small number of high-value or high-complexity product ranges allows retailers to assess the potential benefits before expanding conversational attributes across their wider catalogue.

While conversational attributes are unlikely to transform performance overnight, they represent another step towards richer product discovery. As AI-driven shopping experiences become more prominent, retailers that provide clearer, more structured product information will be better positioned to surface relevant recommendations and help customers make more confident purchasing decisions.

Rather than viewing product feeds solely as a requirement for Shopping listings, retailers should increasingly see them as a strategic asset that helps AI systems understand, compare and recommend their products across Google's evolving search experiences.