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When you run multiple experiments, features, and personalizations concurrently, visitors often encounter more than one campaign at the same time. Their behavior reflects the combined effect of every campaign they encountered, not just your experiment. Without a way to separate these effects, you cannot tell whether your results reflect your variation’s true performance or interference from another campaign. Cross-Campaign Analysis lets you filter or break down your experiment’s results based on which other campaigns visitors encountered. Use it to isolate your experiment from outside influence, detect interference between campaigns, and measure synergies when campaigns work well together.

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Filter experiment results

  1. On the Results page of your experiment, open the Filter audience dropdown in the Audience tab (located in the right-hand side panel).
  2. Click Add filter.
  3. Select an option to filter your results:
    • Exposed experiments
    • Exposed personalizations
    • Exposed features
  4. Choose whether to Include (only show visitors exposed to these) or Exclude (only show visitors not exposed to these).
  5. Select the specific items you want to include or exclude:
    • Experiments: Click the checkbox next to the experiment’s name to include the experiment and all its variations. Alternatively, click the checkboxes for specific variations if you only want to include a subset.
    • Personalizations: Select the specific personalizations.
    • Features: Select the specific features.

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Break down experiment results

Breakdowns segment your experiment’s results into groups based on visitors’ exposure to other campaigns, so you can compare performance within your experiment based on external exposure.
  1. On the Results page of your experiment, open the Breakdown audience dropdown in the Audience tab (located in the right-hand side panel).
  2. Click Add breakdown.
  3. Select an option to break down your results:
    • Cross experiments
    • Cross features
    • Cross personalizations
  4. Select which experiments, features, or personalizations you want to use to segment your results.

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Interpretation and benefits

Use Cross-Campaign Analysis to answer critical questions about your platform’s holistic performance:
  • Identify interference: Determine whether a low-performing personalization running simultaneously is negatively affecting a high-performing experiment’s results.
  • Validate feature interactions: See how a new feature flag affects the conversion rate of an unrelated experiment to confirm your releases are stable.
  • Measure synergies: Confirm whether two specific variations or campaigns deliver a greater uplift when combined than when run separately.