Transitioning to Smarter Social Bidding Without Killing Short-Term Performance

The Challenge

johnnie-O is an apparel brand whose style is best described as “East Coast classic meets West Coast casual.” They pride themselves in making high-quality, versatile pieces for boys and men, and effective social media advertising is a vital part of their direct-to-consumer growth strategy.

When ADM linked with johnnie-O to guide their paid social efforts, they weren’t getting the most bang for their buck. Previously, their campaigns were set up for ad set budget optimization (ABO), as opposed to campaign budget optimization (CBO). ABO is a manual daily spend limit per ad set, so it can’t take advantage of Meta’s advanced algorithms to push spend towards the highest-yielding targets the way that CBO does. 

Transitioning to CBO was a necessity for johnnie-O’s ROAS goals, but fundamental changes to entire accounts must be executed with care and intent.

Our Solution

Switching from ABO to CBO would have an immediate impact on results and would set johnnie-O’s social ads up for sustained success in the future. But changing to an automated campaign bidding structure will send ad sets into a learning phase, which may slow an account’s momentum before the algorithms find the right balance of audience, ad set, and spend. If this were executed all-at-once across an account, it could seriously hinder short-term performance. 

To avoid widespread dropoff in johnnie-O’s Meta advertising performance, ADM had to find the right time to transition each of the brand’s campaigns individually. We decided to pair these transitions with other major changes to the campaigns—like budget, ad creative, or messaging. This would allow the algorithm to tailor delivery based on the new materials as it learned, and would minimize negative impact by condensing two major changes into a single learning period.