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End experiment

Experiment-end

Making Data-Driven Decisions​

  1. Identifying the Winning Variant:

    • After analyzing the results, identify the winning variant—the one that shows the best performance across key metrics such as conversion rates, engagement, and statistical significance.
    • Once you've determined the winning variant, decide if it should replace the control group and be deployed to a larger user base.
    • Action: For example, if Variant A significantly outperforms the control, you may choose to implement it as the new default experience for a specific user segment or all users.

  2. Rolling Out the Winning Variant:

    • Once you've identified the winning variant, update your code to implement the changes associated with the winning variant. Deploy these changes across your platform before ending the experiment.
    • Important: Do not end the experiment before deploying the changes, as this can cause users to fall back into the control group or an undefined state, leading to an inconsistent experience.

  3. Iterating on Your Findings:

    • A/B testing is an ongoing process. Use the insights from each experiment to inform future tests, regardless of whether your variant won or lost.
    • Continuously refine your hypotheses, make necessary adjustments, and run new experiments to further optimize your product.
    • Tip: Regular iteration and analysis will drive continuous improvement in user experience and business outcomes.

Example Walkthrough​

Let’s walk through an example:

  • You run an experiment comparing a new onboarding flow (Variant A) against the existing flow (Control).
  • The experiment shows that Variant A improves conversion rates by 15% with a 98% statistical significance, especially among new users.
  • Before ending the experiment, you implement the new flow in your code and deploy it to ensure a smooth transition for all users.
  • Finally, you end the experiment, ensuring all users experience the improved onboarding flow without falling back into the control group.

By following this approach, you'll ensure a smooth rollout of winning variants, preventing disruptions, and making data-driven decisions that improve both user experience and business results.