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.

“In the last two years, the platforms have gotten really good at machine learning for brand safety,” said Andrew Serby, Zefr’s vp of marketing. “All the content that’s incredibly problematic in terms of the obvious stuff —whether that’s violence or hate speech or crime, they’ve done a really good job.”

What they aren’t good at is nuance.

Efforts to keep brand-safety snafus at bay have seen legitimate news sites and niche publishers fall onto blacklists for publishing kosher content with nonkosher keywords. According to Serby, this is the reason why Zefr is a company that’s trying to shift the advertising zeitgeist from conversations of the black-and-white world of brand safety and into the shades of grey of “brand suitability”—case-by-case definitions of what a particular brand is comfortable advertising against.

Unsurprisingly, humans are better at picking apart these nuances than their machine counterparts, which is why Zefr’s tech works on a brand-by-brand basis, taking their inputs about the kind of content they’re comfortable appearing alongside and building machine learning models off of that.

“Instead of targeting what you don’t want to be around, it’s about being proactive,” Serby explained. “It’s about deciding the kind of content you do want to be around, and finding the content that complements your brand’s message and drives better results.”

For the study, Zefr worked with three brands in three verticals: Nationwide, Ubisoft and ScottsMiracle-Gro, and targeted YouTube ads from these companies against more than 3,000 YouTube viewers who were targeted in one of four ways. The first reflected the typical buy, targeting popular channels with an eye toward targeting a certain demographic. The second was a channel-by-channel buy and the third was based on keywords that were brand-relevant. The fourth used Zefr’s human-centric algorithm, using “signals” from each of the three brands to determine which kinds of content would be suitable—and safe—for each.

“This is content that you might not get in trouble for advertising against, but it’s not the content that an advertiser would be excited to promote,” Serby said. “It’s a much more nuanced discussion.”

That nuance pays off.

When it comes to reaching in-market consumers, Zefr’s proprietary methods were found to convert roughly 11% of the time. While that might not sound like a large number, keyword targeting resulted in only 6% of those same conversions, and channel- or demographic-level targeting barely scraped 1% each. All three of those methods reached an in-market less than Zefr’s proprietary tech, as well, at 75% of the time, rather than upwards of 80%, as Zefr found.

“When brands determine the signals used to identify content that makes the most sense for them, misalignment between content and ad is curbed, and each ad works to its full potential,” Zefr wrote in a statement.

Aside from the relevancy of an ad in the content it’s playing alongside, there’s also the question of the “quality” of content a brand is willing to appear alongside.

“Typically, a brand will have some definition for quality content, in terms of the premium nature of that content—it’s studio-produced; it’s official, TV-like content,” said Serby. Because Zefr focuses more on brand-led signals, rather than quality, it expands that definition. And the more advertisers expand that idea, “the more they can capture what a consumer thinks is quality on platforms that don’t necessarily look like television,” he said.