Best practices for patient audience targeting and measurement

Conventional, demographic-driven, one-size-fits-all patient audiences fall short. They fail to consider a brand’s specific needs and core therapeutic advantages, as well as how patients consume healthcare information.

In a recent podcast with Steve Madden, GM and editor-in-chief, MM+M, Swoop cofounder and CTO Simeon Simeonov explained how applying privacy-safe machine learning and artificial intelligence to real world data can help medical marketing professionals build highly targeted audience segments customized for campaigns and activated through the most effective channels.

Targeting with accuracy and speed

Swoop’s goal, as Simeonov explained at the outset, is based on a very simple premise, “You should target what you want to measure and measure what you want to accomplish.”

The issue, as he went on to explain, is that oftentimes “People want to achieve one thing and they measure that thing but target something completely different. The reason for this, historically, is time and money — in the past it took a long time and was expensive to build custom audience segments.”

The fix? Machine learning and artificial intelligence, both of which Swoop leverages to create custom segments tuned to the specific objectives of a campaign, activated via programmatic, social media, site personalization, addressable television, linear television and/or on-demand audio.