Feature engineering for churn prediction models

Feature engineering involves creating new, meaningful data points from existing information. It's typically done by data scientists or analysts, not product developers. Those features are new ways of looking at existing data to better predict churn.

Key aspects of this process include:

• Time-based insights: Create data points like "days since last login"
• Behavior patterns: Develop metrics that show how engaged users are
• Data summaries: Combine information over weeks or months
• Combined insights: Mix different data types to create new information
• Industry-specific metrics: Create data points unique to your product type

For instance, a music streaming service might create a data point for "percentage of skipped songs" or "listening time trend over the last month."

Good feature creation can make churn prediction models much more accurate.^[1]