Tunnl has integrated 131 research-backed audience segments into DeepIntent's healthcare programmatic DSP. The segments, which are activated via a data CPM, allow advertisers to target users based on attitudinal data such as care hesitancy and risk perception rather than traditional demographic or condition-based metrics.
This move signals a shift in healthcare ad tech from reactive clinical data to predictive attitudinal modeling. By layering opinion-based segments over its existing Helix marketing cloud, DeepIntent is attempting to solve for patient engagement hurdles that demographics alone cannot address, such as digital healthcare openness. Within the broader ecosystem, this reflects a growing need for privacy-compliant targeting methods that avoid sensitive condition-specific identifiers while maintaining precision. The success of this approach will depend on whether these inferred sentiment scores actually drive higher script lift compared to traditional methods. Watch for the first public case study to see if these 131 segments deliver a measurable improvement in pharmaceutical conversion rates.
DeepIntent has integrated 131 attitudinal audience segments from Tunnl into its healthcare programmatic platform. By utilizing opinion-research models, advertisers can now target users based on care hesitancy and risk perception. This shift from clinical data to predictive modeling aims to improve patient engagement and pharmaceutical conversion rates through more precise targeting.
The integration adds 131 research-backed, attitudinal audience segments to the DeepIntent healthcare DSP, allowing for targeting based on factors like care hesitancy and risk perception.
The audience models are built from large-sample surveys applied to a proprietary U.S. identity graph to infer attitudes across the population.
The segments cover various behavior-based topics, including COVID-19 vaccine intent, support for birth control funding, and confidence in FDA approvals.
Targeting is activated via a data CPM that is billed on top of standard media costs.
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