Consumers use AI tools like ChatGPT for product discovery, receiving relevant brand recommendations they were previously unaware of. This lengthens the consideration phase, creating a new battleground for marketers in the middle of the funnel.
Traditional website optimization focused on human experience and SEO for search bots. A third pillar is now essential: optimizing for AI advisory tools and recommendation engines through structured data like product feeds and APIs.
The concept of AI agents autonomously making purchases is largely hype. The real, current opportunity is in the underappreciated role AI plays in the discovery and consideration phase, where consumers use it for low-risk tasks like product research and recommendations.
Instead of relying on user data or cookies, Large Language Models (LLMs) can analyze the content of publisher web pages to infer purchase intent. This allows marketers to target audiences based on the context of what they are reading, a fully privacy-compliant approach.
Unlike traditional machine learning that only learns from ad clicks, deep learning analyzes the entire user population (both exposed and not exposed to ads). This comparison reveals true incremental performance, moving beyond simple conversion attribution.
Deep learning models can process vast, unstructured datasets directly, unlike traditional machine learning which requires data scientists to pre-select and summarize variables ('features'). This automates a key data science task, freeing up teams for higher-value work.