Publicis Health Media and Meta Find Legacy Interest-based Targeting Less Effective in Healthcare

Meta threw what could be a potential wrench in healthcare marketing plans when it announced in November 2021 that it was deprecating detailed targeting. Starting January 19, 2022, marketers could no longer target based on peoples' interests around certain health causes such as “lung cancer awareness,” “world diabetes day,” or “chemotherapy,” in an effort to protect digital privacy. A new white paper from both Meta and Publicis Health Media tests the best viable targeting alternatives for healthcare marketers. Surprisingly, the study found that other techniques, particularly lookalike audiences, outperform the now legacy interest-based targeting segments. “We didn’t need that health targeting in the end,” said Patty Ryan, gvp, paid social media at PHM. The report, one of the first and only performance analyses since the removal of the targeting segments, provides preliminary hope for marketers in the fast-approaching cookie-free programmatic landscape. As platforms like Meta deprecate the tools digital marketers used to target in the name of privacy, they will have to find new techniques to drive the same results. The report suggests interest-based targeting solutions may not have even been the most effective anyway. Looking over a three-month period from January to March 2022, PHM conducted 14 studies across brands and therapeutic areas. In 86% of studies, lookalike audiences were the most effective targeting technique, which was measured as the lowest cost per result based on the client’s desired objective. The lookalike audience targeting solutions outperformed the legacy interest-based targeting solutions that have since been deprecated, the report found. Meta allowed advertisers who were already using legacy interest-based targeting in campaigns to continue doing so until March (January 19 was the cutoff for new campaigns). This allowed PHM to test the efficacy of the since deprecated targeting attributes. Lookalike audiences are created by Meta’s technology, extrapolating from seed audiences provided by the advertiser. PHM created these seed audiences from signals, such as people who have previously been on the brand’s website or watched the brand’s video. “We use modeling to identify people who have similar likes and characteristics as the current audience that you had been using,” said Jenny Streets, head of health at Meta. Finding new ways to reach healthcare customers The study underscores the power of lookalike audiences as an alternative and effective targeting technique. These can be as low as 1% different than the seed audiences they're based on, though such precise groupings lack scale, or can be as much as 10% different. The study found a 5% difference between seed and lookalike audiences is optimal for driving the lowest cost per result, which was a larger difference than PHM has previously used to target. “There is an opportunity within our strategies to scale more than we had in the past,” Ryan said. “Bigger audiences and allowing the platform and the machine learning to do a lot of that work from a delivery and machine targeting perspective [worked].” While the lookalike audiences achieved the lowest cost per desired outcome, third-party audiences using data from firms like Epsilon could sometimes generate stronger outcomes, but at a higher price. In this instance, more bespoke approaches for the client would be the best option, according to Ryan and Streets. Keeping Publishers at the Center of the ID Conversation More research is welcome for healthcare marketers, who already tend to adopt a cautious lens before adopting any targeting technique. “The [targeting] vetting process goes through several rounds of review before getting enacted. That’s just how strict that it needs to be,” said Britt Travis, director of video investment at healthcare-focused agency Eversana Intouch. “There is so many people involved with the backend."