Triggering in-product experiences based on real-time events requires infrastructure that can capture user behavior, compute context, and deliver decisions to applications within milliseconds.

Technical Requirements:

• Real-time event streaming: Capture behavioral events (clicks, page views, feature usage) with sub-second latency.
• User attribute computation: Calculate both in-session signals (current page, cart value) and historical context (purchase history, lifetime value).
• Low-latency serving layer: APIs that deliver user context to applications fast enough for real-time personalization (typically <100ms).
• Trigger logic: Rules or ML models that determine which experience to show based on user context.

In-Product Experience Examples:

• Dynamic pricing adjustments when users show hesitation
• Personalized product recommendations during browsing
• Proactive support chat when users struggle with checkout
• Adaptive UI that simplifies navigation for frequent users
• Smart nudges to prevent cart abandonment

With Snowplow Signals, product and engineering teams get real-time customer intelligence infrastructure designed for these exact use cases:

• Profiles Store: Low-latency API (45ms p50) serving real-time and historical user attributes
• Streaming Engine: Calculates in-session attributes from live event streams
• Interventions: Push-based engine for triggering personalized actions based on rules or ML
• SDKs: Python and TypeScript tools for defining and retrieving user attributes

Snowplow Signals helps teams ship real-time personalization in weeks instead of years of custom infrastructure development.