Intent Detection

Intent detection goes beyond understanding what a user is doing to infer why they are doing it. A user browsing product comparison pages has a different intent than one reading beginner guides, even if both are in the same product category. Understanding intent enables the system to serve the right experience at the right stage of the user's decision process.

For growth teams, intent detection is a high-value personalization signal because intent directly predicts what kind of experience will be most effective. AI models detect intent through analysis of search query semantics, page navigation patterns, click sequences, and dwell time distributions. Growth engineers should build intent classification systems that map behavioral patterns to actionable intent categories specific to their product, such as browsing versus buying intent, learning versus evaluating intent, or discovery versus specific-search intent. The classified intent then drives personalization decisions: users with purchase intent see streamlined conversion paths, users with research intent see comparison tools and educational content, and users with discovery intent see curated recommendations. The key challenge is that intent is latent and evolves within a session, requiring models that update intent estimates as new behavioral signals arrive.