Holdout groups: Think of them as a set of users you intentionally exclude from your experiments. Their main purpose is to serve as a baseline. This way, you can measure the effect of your changes against a group untouched by any experimental modifications.
Why are they important? Holdout groups give you a clear picture of how effective your experiments are. They help you see whether the changes you implement actually make a difference. Without them, you'd struggle to determine if improvements are due to your changes or just natural variations.
Control groups: Holdout groups essentially act as your control group. By comparing the behavior of the holdout group with the experimental group, you can identify any significant differences. This comparison is crucial for understanding the true impact of your interventions.
Holdout groups provide a baseline for comparison. They help you see the true effect of experiments. Without them, you'd struggle to identify real changes. For a detailed explanation, check out What is a holdout?.
Use prerequisite flags to separate groups. Assign a small percentage, like 5%, to the holdout group. This ensures you have a reliable control group for comparison. For more details, see How to use Holdouts and Best Practices.
For practical implementation, refer to Creating your First Holdout.
Compare revenue per user and sign-up rates. These metrics show clear differences. They help identify experiment impacts. Learn more about revenue per user and sign-up rates.
Look for significant differences between groups. Adjust your experiment design based on these findings. This ensures accurate and actionable insights. Learn more about how to create and analyze holdout groups, and explore best practices for holdouts.
For additional resources, visit the documentation and guides.
Use holdout groups to measure impact on sales and user engagement. Compare how different promotions work. Understand what drives customer behavior.
Evaluate sign-up and retention rates. Identify which features keep users subscribed. Adjust strategies based on data-driven insights.
Assess feature usage and user behavior. Determine which updates improve user experience. Optimize app performance by learning from holdout data.