When creating a campaign experiment, which is a best practice?

Choose two or three metrics that can be used to reliably gauge campaign performance and determine the winner of a particular test.

After the end of an experiment, assess performance over a period of time that includes ramp-up.

Isolate one variable to focus on for each test and use different tests when assessing the impact of more than one change.

As a means of expediting experiments, new ads aren't subject to the same approval process during time of the experiments.


Choose an option to see if it’s correct. Check the explanation below. Learn Smarter, not Harder.

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Explanation: When creating a campaign experiment, which is a best practice?


Explanation: When creating a campaign experiment, the best practice is to **isolate one variable to focus on for each test and use different tests when assessing the impact of more than one change**. This approach ensures that the experiment remains controlled, allowing for clear and accurate analysis of the impact of the specific variable being tested. By isolating one variable, such as changing ad copy or adjusting bidding strategies, marketers can accurately attribute any changes in performance to that specific modification, avoiding confounding factors that could muddy the results. Using different tests for each variable change prevents overlap and interference between tests, maintaining the integrity of the experiment. This methodological rigor is essential for drawing meaningful insights and making informed decisions based on experiment outcomes, ultimately leading to more effective campaign optimization and overall performance improvement.
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