Question: Lucas has launched two LinkedIn ad campaigns simultaneously and is comparing their results without using A/B Testing. Both campaigns target the same audience and are competing for the same impressions in the ad auction. What are the implications of this approach?
- The campaigns are competing against each other in the same auction, introducing statistical bias, which affects the winning rate of each campaign.
- The winning campaign will always be the one with the best content, regardless of auction dynamics.
- Running two campaigns at once guarantees better auction results for both.
- Both campaigns are equally likely to succeed in reaching their target audience without interference.
Explanation
LinkedIn A/B Testing is designed to compare campaign variations without allowing them to interfere with each other in the auction. Running separate campaigns against the same audience can cause both campaigns to compete for the same impressions. That competition can create statistical bias because delivery and win rate are affected by auction dynamics, not only campaign performance. A controlled test provides a cleaner comparison by separating variants through the platform’s testing setup.
Why the other options are incorrect
Best content is incorrect because auction outcomes are also influenced by bids, relevance, audience overlap, and delivery conditions.
Better auction results is incorrect because overlapping campaigns can create internal competition rather than guaranteed improvement.
Equally likely to succeed is incorrect because both campaigns can interfere with each other when targeting the same audience.
Source for verification
https://www.linkedin.com/help/lms/answer/a529753
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