How Zefr Uses Humans to Help YouTube's Less-Than-Perfect Targeting

According to new research out today co-produced by L.A.-based contextual data company Zefr and the advertising analytics outfit Magna, media buyers could get better bang for their buck by including humans in their content-review process, rather than relying on preset white- or blacklists. Zefr—which patented its “human in the loop” brand-suitability algorithm that guides machine learning models using human review—found that the resulting ads turned out to be more relevant, reached consumers that were more likely to be in-market and were more likely to convert as a result.

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