Dstillery Adds New Custom Patient Targeting to its ID-free Solutions Suite

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Dstillery, the custom audience solutions company, announced the release of Custom Patient Targeting, a new privacy-safe predictive behavioral targeting solution designed for healthcare brands.

Healthcare brands want to achieve more precise direct-to-consumer (DTC) ad targeting. However, they face strict data requirements from HIPAA, NAI guidelines, rules from demand-side platforms (DSPs), first-party data limitations and demographic conditions. Until now, these requirements have limited healthcare brands’ targeting options across the programmatic web, resulting in campaign waste and suboptimal performance.

Powered by Dstillery’s patented ID-free technology, Custom Patient Targeting uses AI-powered predictive modeling to learn how, when and from where de-identified patients browse the web. When combined with a seed data set representing a desired patient outcome, Custom Patient Targeting builds a just-for-your-condition model that targets only the impressions most likely to drive the desired patient outcomes – without user tracking.

“Healthcare advertisers face a unique challenge with DTC targeting. They want less wasteful targeting, but strict data requirements have constrained their ability to get the precision they desire,” said Taejin In, SVP of Product at Dstillery. “Using our ID-free technology, we can offer healthcare brands the precision and customization to drive optimal patient outcomes without sacrificing privacy or compliance.”

In sum, Custom Patient Targeting is precise, custom and compliant:

  • Every potential impression on the ad-supported internet is scored and ranked based on its likelihood of reaching a brand’s patient. Only the impressions most likely to convert are bid on – a level of granular targeting unheard of in healthcare.

  • Every custom model is built using a seed that defines the patient the brand is trying to reach, including first-party data, demographic/behavioral attributes and search keywords (i.e., drug names, symptoms, comorbidities). The ability to use ICD-10 codes as a seed signal is in development and will be available soon.

  • Custom Patient Targeting doesn’t use IDs or rely on user-based targeting, ensuring 100% compliance with all laws, policies and guidelines from HIPAA, NAI and DSPs.

Agencies and their clients are already proving how Custom Patient Targeting has positively impacted patient outcomes and the KPIs brands want to drive. A pharmaceutical brand tested Custom Patient Targeting to lower their cost per action (CPA). The campaign reduced the brand’s CPA by 84% while delivering 3x more impressions than the contextual targeting solution.

“We’re excited to see this level of campaign performance, particularly in an early test. The key differentiator with Custom Patient Targeting compared to traditional solutions is this: Our AI can predict behavior, not simply infer it from context or demographics, without user tracking,” said In.