See how Snowplow Signals enables dynamic in-session personalization by capturing user behavior in real-time. This technical demo walks through a travel booking site that adapts content, recommendations, and AI agent responses based on live user interactions.
What you'll see:
- Real-time UI updates based on user filtering and browsing behavior
- Dynamic content adaptation (imagery, copy, and search results)
- Context-aware AI agent that leverages behavioral signals for personalized recommendations
- Profile updates that instantly reflect changing user intent (budget vs. luxury travel preferences)
- Behavioral data feeding both traditional product personalization and agentic experiences
The demo shows how capturing in-session user activity (filtering preferences, article engagement, pricing), paired with historical user context, enables products to deliver truly personalized experiences without manual segmentation. Perfect for product and engineering teams exploring in-session product personalization or contextual AI implementations.