Personalized Recommendations: Influencing the AI Agent's Choice

Personalized recommendations are a cornerstone of modern e-commerce, designed to increase conversion and average order value by showing human shoppers products they are likely to be interested in. But how do personalized recommendations function when the "customer" is a personal AI shopping agent? This hub explores whether personalization engines can (or should) personalize recommendations for the AI agent, based on its patterns or the likely preferences of the human user it represents, to drive conversion.

AI already plays a critical role in enabling personalized experiences at scale for human customers. AI algorithms analyze customer data to generate personalized recommendations and tailor marketing strategies. By analyzing customer behavior, preferences, and demographic data, AI can segment customers and tailor content, products, and offers to them.

When an AI agent is doing the shopping, the challenge is influencing a system designed to follow potentially rigid instructions from its human user. However, the AI agent is acting on behalf of a human, implying that the human's preferences are the underlying goal. Therefore, personalizing recommendations for the AI agent essentially means personalizing them based on the profile and likely desires of the human user the agent represents.

This could involve:

Leveraging Human User Data:
Using data related to the human user's past purchases, browsing history, stated preferences, and even psychographics (where available and permissible) to inform the recommendations presented. AI's ability to analyze behavior and preferences allows for highly targeted customer segments and detailed customer personas.

Interpreting Agent Behavior:
While an AI agent's behavior might differ from a human's (e.g., faster navigation, programmatic queries), patterns in its interactions could still provide clues about the human user's intent or preferences. For instance, if an agent is consistently searching for products with specific attributes, recommendations could highlight other relevant items with those attributes.

Optimizing for Agent Goals:
Understanding that AI agents are often programmed to achieve specific goals, such as finding the lowest price or locating a product that meets precise specifications. Personalized recommendations might need to align with these goals, perhaps by highlighting discounted complementary products or premium alternatives that meet the agent's criteria.

Adapting Recommendation Engines:
Personalization engines may need to evolve to recognize AI agent traffic and tailor their approach. This could involve presenting data in formats that are easier for agents to parse or adjusting the type of recommendations shown (e.g., prioritizing products with detailed, structured specifications). Recombee, for example, offers AI-powered real-time recommendations and personalized search.

The goal is to increase sales and conversion, even when the direct interaction is with an AI. Personalized recommendations aim to convert human shoppers, and by providing relevant suggestions to the AI agent, the business increases the likelihood that the agent will select a product from their site to recommend or purchase for the human user. AI-driven insights can help personalize campaigns and make data-driven decisions to increase accuracy and efficiency. AI can predict which marketing messages are likely to convert based on past behavior.

In the agentic era, personalized recommendations become a strategy to influence the AI proxy, ultimately reaching the human decision-maker. It requires a sophisticated understanding of both the underlying human customer and the operational logic of their AI agent.