Even products with millions of daily active users can face decline if engagement metrics fail to capture true user value, not just frequency. Product teams diligently track active users, but often miss deeper insights that predict churn and drive sustainable growth. This oversight blinds product managers to underlying user value. Without a comprehensive approach to engagement metrics—including value derivation and feature adoption—product managers risk misinterpreting product health, leading to preventable churn and stalled growth. As Paddle notes, customers using a product frequently but not deriving significant value are at increased churn risk. Mere activity does not equate to value; deeper user interaction understanding is crucial.

1. Product Engagement Score (PES)

Best for: Holistic product health assessment.

The Product Engagement Score (PES) combines stickiness, feature adoption, and retention, according to Paddle. This multi-dimensional metric offers a comprehensive view of user interaction and value derivation. Its strength lies in identifying improvement areas, though careful definition is needed to avoid masking individual weaknesses. The implication is that PES moves beyond surface-level metrics to reveal the true health of user engagement, making it a powerful predictor of long-term product success.

2. Customer Retention/Churn

Best for: Long-term product viability and user loyalty.

Retention tracks users remaining active after a specific period, often three months, according to Paddle. High churn directly indicates a lack of sustained user value. While a critical measure of loyalty and sustainable growth, its lagging nature means it only signals a problem after it occurs, necessitating deeper analysis to understand why users leave.

3. Stickiness

Best for: Measuring habitual product use.

Stickiness calculates the percentage of users returning daily or weekly, according to Paddle. While it reveals habitual usage and can indicate perceived value, high stickiness alone does not guarantee deep value extraction. A user might frequently open an app out of habit, but rarely complete core tasks, implying a need to pair stickiness with other metrics for true insight.