To use Snowplow behavioral data in Databricks for churn prediction:

• Collect detailed event-level behavioral data from Snowplow, such as user interactions, product views, engagement metrics, and session patterns
• Stream the data into Databricks for real-time processing and feature engineering using Apache Spark and MLflow
• Train machine learning models (survival analysis, XGBoost, or ensemble methods) on this data to identify patterns associated with customer churn
• Use the churn prediction model to take proactive actions, such as personalized retention offers or targeted outreach campaigns

Snowplow Signals can enhance churn prediction by providing real-time customer intelligence through computed attributes like engagement scores, satisfaction levels, and behavioral risk indicators, enabling more immediate and targeted retention interventions.