Post-experiment steps

Web experiments in Amplitude Experiment help you test hypotheses, validate ideas, and make data-informed product decisions. After you identify a winning variant, Amplitude recommends moving that variant to your production code base instead of keeping the experiment live at 100% traffic allocation.

When the experiment concludes

When you're ready to end the experiment and select a winner:

1. Analyze the results and confirm a winner. Confirm the experiment reached statistical significance and that the winning variant aligns with business goals.
2. Implement the winner in code. Work with your engineering team to replicate the winning experience in your production code base. For more information, go to Benefits to migrating your winning variant.
3. Deactivate or archive the experiment in Amplitude. Disable the experiment to remove unnecessary logic and prevent accidental reactivation or analysis confusion.
4. Document the outcome. Capture experiment details like the goal, key learning, decision made, and implementation follow-up in an internal knowledge base.

Activate a feature flag

If your change needs rollback capability or an incremental rollout, use a feature flag. Feature flags enable ongoing control without the overhead of experiment logic and metadata.

Benefits to migrating your winning variant

Moving your winning variant to your production code base provides the following benefits:

• Performance and user experience: Running web experiments at 100% adds avoidable client-side overhead to your pages. The overhead increases page load execution time and can negatively impact performance, especially at scale. For more information about how Amplitude optimizes for performance, go to Web Experiment Performance.
• Technical debt: Long-running experiments add clutter to dashboards and experiment environments. Leaving experiments active after a decision causes unnecessary configuration overhead and increases the risk of user-facing errors.
• Platform cost and impression volume: Each experiment evaluation counts toward your monthly impression volume in Experiment. When you run a test at 100% after it no longer provides learning, the experiment still evaluates on each page load. Over time, these evaluations increase your costs and create budgeting inefficiencies.