Google Merchant Center Introduces Conversational Attributes for AI-Powered Shopping

The Evolution of Product Discovery in the AI Era

As the landscape of online shopping shifts toward artificial intelligence, Google is fundamentally changing how product data is interpreted. During the recent Google Marketing Live event, the company unveiled a significant update to the Google Merchant Center: the introduction of Conversational Attributes. These new data fields are designed to bridge the gap between traditional product specifications and the natural, conversational way modern consumers interact with AI agents.

For years, product feeds have relied on static attributes like price, SKU, and color. However, with the rise of generative AI, users are no longer just searching for “running shoes”; they are asking complex questions like, “Which of these running shoes are best for flat feet and offer the most arch support?” Conversational attributes provide the structured data necessary for AI to answer these nuanced queries accurately.

What Exactly are Conversational Attributes?

According to Google’s official documentation, conversational attributes are optional data extensions that complement the primary Merchant Center product data specification. Their primary goal is to help AI systems and conversational agents—such as Gemini, AI Mode in Search, and specialized Business Agents—better understand the specific nuances of a product.

By integrating these attributes, retailers can ensure that their products are not just indexed, but are “understandable” to an AI, allowing the AI to describe products in a more human-like, helpful manner that mirrors a real-life shopping assistant.

Breaking Down the Key Conversational Attributes

Google has rolled out dozens of new attributes. Some of the most impactful include:

• Question and Answer [question_and_answer]: Allows merchants to provide direct answers to common customer queries, which can be surfaced directly in a chat interface.
• Document Link [document_link]: Provides a direct path to manuals, guides, or detailed specification sheets, giving the AI a source of truth for technical details.
• Related Product [related_product]: Helps the AI suggest alternatives or complementary items, mimicking the “upselling” process of a physical store clerk.
• Item Group Title [item_group_title]: Organizes products into logical collections, helping AI understand the relationship between different variants.
• Variant Option [variant_option]: Offers deeper granularity on product variations beyond basic size or color.
• Popularity Rank [popularity_rank]: Gives the AI social proof data, enabling it to recommend “trending” or “most popular” items when asked.

Impact on AI-Driven Surfaces

These attributes are specifically tailored for what Google calls “AI-Driven Surfaces.” While traditional Google Shopping ads will still benefit, the real impact will be seen in:

• Gemini: Google’s multimodal AI can now pull specific conversational data to provide more curated recommendations.
• AI Mode in Search: The search experience is evolving from a list of links to a curated conversation; these attributes fuel that transformation.
• Business Agents: Automating customer service and sales via AI agents becomes more effective when the agent has access to conversational-ready data.

Strategizing for the Future of E-commerce

While these attributes are currently optional, early adoption offers a competitive advantage. Merchants who invest in structuring their data for AI today will likely see higher visibility as Google continues to integrate generative AI into the core shopping experience. The shift is clear: the future of e-commerce is not just about being found, but about being the most helpful answer to a customer’s conversational inquiry.