> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dqlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use Cases

> Real-world Behavioral metric examples in Prizm — transaction volume trend monitoring and ML feature drift detection.

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## Example use cases

**Transaction volume trend monitoring:** An e-commerce platform processes \~50,000 orders on weekdays and \~30,000 on weekends. A behavioral metric learns the weekly pattern and alerts when volume drops more than 20% below the expected level for that day of the week — something a static threshold would miss on Sundays when lower volume is normal.

**ML feature drift detection:** A `propensity_score` feature historically ranges between 0.3–0.7 with a stable mean. After a code change to the scoring logic, the mean drifts to 0.15. A behavioral metric on the Mean of `propensity_score` alerts on the trend break before the degraded feature reaches production model inference.

<Tip>
  Behavioral metrics are most effective for signals with predictable seasonal or cyclical patterns — volumes that peak on certain days, batch loads that always complete within a window, or revenue that follows a monthly growth trend. Use **Independent** mode for single-segment monitoring and **Correlated** mode when dimensions interact and isolated checks would miss localized anomalies.
</Tip>