Artificial intelligence (AI) in healthcare advertising should be seen as no threat to creative professionals. The goal of AI is simply to distill countless data points in order to connect the right group to the right messaging—something that has been done for years as targeting platforms test multiple creatives’ effectiveness within an ad campaign.

What has changed with the advancement of AI is the level to which marketers can create modeled lookalike segments. For instance, where you might historically have had one big segment of potential patients based off of correlative demographic data, advancements in AI allow you to have several very distinct segments: Likely existing patients; likely patients with high threat of noncompliance; likely prediagnosed patients; patients likely to be on second- or third-line therapy; and so on. This level of data analytics and machine learning (ML)/AI allows for creatives that are much more relevant to a more specific, explicit patient experience—it allows for the creative equivalent of personalized medicine.

The Value of AI for Creatives

If anything, AI/ML advancements in the healthcare space should increase the demand for creative professionals as campaigns get more and more complex. Machines can determine what works at levels of complexity incomprehensible to the human mind, but they cannot intuitively craft creative campaigns. More granular targeting segments creates a need for more specific creative campaigns. A campaign that once required five or six creative options can now become multiple campaigns, each with its own very specific segment and each requiring its own five or six creative options, totally separate from the creative used in other sub-campaigns.

Additionally, AI/ML techniques are enabling marketers to identify segments in need of direct communication that would otherwise be unknown. For example, Swoop is currently working with a client to identify segments that look like undiagnosed rare disease patients. The value of this, as with so much of AI’s use, comes in finding patterns among countless data points that have traditionally been impossible to discern in order to model what an undiagnosed rare disease patient looks like. And by building these models, companies can better determine who they’re marketing to, giving the creative team a persona of sorts to build assets for.

AI/ML continues to evolve and offer the pharmaceutical industry the ability to target better-defined patient personas. The granularity of targeting should be seen as a benefit to creative professionals—more and better-defined segments leads to a demand for more and more varied creative, an art and science that cannot be done by machines.

  • Daniel Ruby

    Daniel Ruby is VP of Marketing at Swoop. Daniel specializes in data-driven digital marketing and executing trackable ROI-centric campaigns to drive sales. At Swoop, he works to create compelling, useful, research-backed thought leadership content for the digital health marketing space.

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