Case Study
Large sportswear retailer outpaces return on advertising spend and revenue goals with machine learning campaign split test
One of the largest sportswear retailers in the world looked to increase their direct- to-consumer sales in a cost-effective way. WMM specialists worked with the client to create a head-to-head test to evaluate the performance of Google’s machine learning against existing methods.
WMM team members strategized to use Google-powered machine learning solutions to get the job done. Using Google campaign drafts and experiments, a split test used Target ROAS to ensure data consistency.
During the draft and experiment split test, the machine learning campaigns outperformed the existing shopping campaign, generating an increase of 26% in ROAS and 24% in revenue compared to the control group. Based on this success, we worked with the heavyweight retailer to introduce a plan to further test the rollout across all Google Ads campaigns.
“Giving away control was a challenge, but after careful testing we are convinced it can boost revenue growth.”
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