AI assistants remember past conversations, influencing future recommendations. If a customer frequently mentions a brand in their chats, the LLM is more likely to use it as a reference point in subsequent queries. Encouraging customers to "talk about" your brand to their AI is a new, powerful form of brand building.
Participating in AI commerce isn't just about capturing inbound data. Brands must structure and provide outbound data feeds of their inventory and product details in a format that LLMs can readily access for recommendations and transactions. This represents a significant new technical requirement for marketing teams.
As AI separates brands from the point of purchase, B2C marketers must learn from industries used to intermediaries. They can adopt CPG strategies for being top-of-mind without controlling checkout, and B2B tactics for influencing customers who complete most research before direct engagement.
Customers arriving from AI shopping assistants are high-intent but provide no context on their journey. To fill this 'data black box,' brands must proactively collect zero-party data by asking direct questions through surveys or post-purchase follow-ups to understand the 'why' behind the click.
When customers use AI for product discovery, brands lose visibility into crucial pre-purchase behavior like comparison shopping. This interaction data becomes siloed within the third-party AI platform, creating a new blind spot that makes it difficult to measure marketing impact or understand the customer journey.
How a consumer phrases their query to an LLM dramatically impacts results. A generic search ('leather couch') differs from a brand-informed one ('a couch like X brand'). Brand marketing's new role is to influence consumers to include brand-specific language in their initial prompts, shaping the AI's entire discovery process.