Rule Execution Timing (Wait X Days Safeguard)
Optimization rules include a Wait for X Days safeguard that controls how frequently a rule can execute.
This mechanism prevents the system from applying the same rule repeatedly within a short time period. Without this safeguard, automated rules could trigger too frequently and cause unstable campaign behavior.
By introducing a waiting period between rule executions, EVA ensures that campaigns have enough time to generate new performance data before additional adjustments are made.
Why rule timing control is necessary
Advertising performance metrics change gradually over time. If optimization rules were allowed to run continuously without a delay, the system might repeatedly apply the same adjustment before the previous change has had time to affect campaign performance.
For example, if a bid reduction rule runs multiple times within a short time frame, the bid could decrease too quickly and negatively impact campaign visibility.
The Wait for X Days safeguard prevents this by enforcing a defined pause between rule executions.
How the wait period works
When a rule triggers and performs its action, the system records the execution.
The rule will not run again until the defined waiting period has passed. During this time, EVA continues collecting performance data and evaluating campaign behavior, but the rule remains inactive until the waiting period expires.
Once the waiting period ends, the rule becomes eligible to run again if the defined conditions are still satisfied.
Example rule with execution timing

Example configuration:
IF ACoS (last 30 days) > 30%
THEN Decrease Bid by 10%
WAIT 7 days
In this example:
- The system evaluates the ACoS condition.
- If the condition is met, the bid is reduced.
- After the adjustment, the rule enters a 7-day waiting period.
- During this time, the rule cannot execute again.
- After the waiting period ends, the system can evaluate the rule again.
Benefits of execution timing safeguards
The Wait for X Days setting helps maintain stable and controlled optimization behavior.
It ensures that:
- campaign adjustments are not applied too frequently
- performance changes have time to materialize
- optimization rules do not conflict with each other
- bid changes remain gradual and predictable
By spacing rule executions over time, EVA allows automated optimizations to improve campaign performance without causing rapid or disruptive changes.