Examples of personalization pipelines built on Databricks and Snowplow include:

• Product recommendation engines: Snowplow collects real-time behavioral data, which is processed in Databricks to power personalized recommendations using collaborative filtering and machine learning models
• Content personalization: Use behavioral data to personalize website content, email campaigns, and app experiences based on user preferences and engagement patterns
• Dynamic pricing: Use real-time data from Snowplow and machine learning models in Databricks to offer dynamic pricing based on customer behavior, demand patterns, and price sensitivity

These pipelines can be enhanced with Snowplow Signals, which provides pre-computed user attributes and real-time customer intelligence that can immediately inform personalization decisions without complex infrastructure management.