Interactions between flag rules

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Note

This topic is most helpful if you run experiments and need an in-depth understanding of how your audiences bucket into both experiments and deliveries.

Use rules to refine and target those who see a flag. You can configure the following types of rules:

• Targeted Delivery (flag delivery).
• Experiment (A/B test).
• Multi-armed bandit optimization.

Rulesets and audiences

If you define multiple overlapping audiences in your flag rules, then that user "moves down" multiple rules differently for experiments than for deliveries:

• For experiments, users can still move down to the next rule if they fail traffic bucketing.
• For targeted deliveries, users "fall through" to the Everyone Else rule if they fail traffic bucketing. They move down to the next rule only if they fail audience conditions.

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Note

Multiple experiments per flag are only available for select plans.

For example, in the following screenshot, if user A failed to bucket into the A/B test, they could still bucket into rule #2 if they matched its criteria. However, if user A then failed rule #2, they'd fall through to Everyone Else, even if they would have matched the criteria for rule #3.

The following diagram shows this behavior in detail.

The following table elaborates on the preceding diagrams by showing all possible outcomes for audience and traffic conditions for a combination of one experiment rule and one delivery rule.

Rulesets and total traffic

As an example of how traffic evaluates against multiple rules: if you configure 80% of total traffic to an experiment, and set 50% traffic for a delivery with the same audience, then 80% of traffic goes to the test, and half of the remaining 20%, or 10%, ends up in the delivery.

The following diagrams illustrate some of the ways that traffic can be split across flag rules, depending on the traffic allocations and audiences you set.