A significant challenge in modern drug marketing is the inability to predict why a patient might prefer a telehealth consultation over an traditional in-person doctor visit. This fundamental gap in understanding human motivation has long forced pharmaceutical companies to rely on blunt instruments like age or zip code to reach their audience. However, the announcement of a strategic partnership between DeepIntent, a leading healthcare-specific programmatic advertising platform, and Tunnl, a Virginia-based audience intelligence firm, signals a transformative shift toward a more nuanced approach. By integrating 131 specialized, survey-based audience segments directly into DeepIntent’s Demand-Side Platform and Audience Marketplace, the collaboration moves beyond the surface-level what of a medical diagnosis. Instead, it dives into the why of patient behavior, leveraging sophisticated modeling to capture attitudes on trust, risk, and care preferences. This integration allows advertisers to tailor messages to specific psychological profiles, ensuring that a patient hesitant to visit a doctor receives a different creative approach than one who is eager for digital-first health solutions. As the industry continues to evolve in 2026, this move reflects a broader trend of bridging the gap between high-level opinion research and real-time programmatic execution. Marketers are no longer limited to demographic clusters but can now engage with the underlying beliefs that drive treatment adherence and brand loyalty.
Methodology: Bridging the Gap Between Opinion and Action
The core of this collaboration lies in the introduction of 131 distinct audience segments that are now available for immediate activation by healthcare advertisers. These segments are not constructed using traditional metadata or simple browsing history; instead, they are grounded in Tunnl’s proprietary opinion polling and behavioral modeling. The catalog includes highly specific categories such as vaccine intent for influenza and COVID-19, levels of confidence in the FDA’s medication approval process, and public sentiment regarding healthcare funding for various initiatives. This level of granularity allows pharmaceutical brands to address the psychological barriers that often prevent patients from seeking treatment or complying with a prescribed regimen. By understanding whether a patient is more likely to be swayed by a message of safety or a message of convenience, brands can move away from one-size-fits-all creative strategies and toward a model of hyper-personalization that respects the individual’s mindset.
To produce these insights, Tunnl employs a rigorous “closed-loop” methodology that connects the world of survey research with the high-speed environment of programmatic advertising. The process begins with large-sample surveys that capture the stated opinions and behaviors of a representative cross-section of the American population. These raw survey responses are then ingested by advanced AI models that assign predictive scores to individuals across a comprehensive U.S. identity graph. It is important to note that while the majority of individuals within a segment were not directly surveyed, their attitudes are statistically inferred through modeling. This approach provides the scale necessary for large-scale digital campaigns while maintaining a high degree of predictive accuracy. For advertisers, this means that the segments represent more than just a list of names; they represent a collection of probabilities that a specific individual will react favorably to a certain type of messaging based on their underlying values and perceptions of the healthcare system.
Strategic Integration: Building a Multi-Dimensional Targeting Stack
Operationally, these 131 segments are integrated as “off-the-shelf” solutions within the DeepIntent environment, allowing for a seamless transition from strategy to execution. The pricing for these segments follows a standard data model in the industry, where advertisers are billed a data fee on a “per-thousand-impressions” basis. This fee is added to the base media cost, meaning that marketers can purchase these high-value audiences with the same ease as they would any other demographic or geographic target. This structural integration is designed to reduce the friction often associated with using premium third-party data, enabling campaign managers to activate complex behavioral strategies during the planning phase without needing to manage separate contracts or complicated data transfers. This efficiency is a key component of the partnership, as it allows brands to be more agile in responding to changing market conditions or public health trends that may emerge throughout 2026.
This deal is part of a broader, aggressive expansion by DeepIntent throughout 2025 and 2026, as the company has sought to build the most comprehensive marketing infrastructure in the healthcare space. Over the past year, DeepIntent has added premium supply sources such as streaming packages, live sports inventory, and point-of-care placements in electronic health record systems. By combining these expansive media options with the clinical data from its “Helix” marketing cloud—which covers over 240 million patient lives—and the new behavioral insights from Tunnl, DeepIntent has created a multi-dimensional targeting stack. This combination allows a marketer to not only find a patient with a specific condition but also to understand that patient’s likely preferred channel of communication and their psychological readiness to start a new therapy. The addition of conversational AI tools like “Cora” further streamlines this process, allowing planners to navigate these vast data sets using natural language commands, significantly reducing the time required to launch a sophisticated multi-channel campaign.
Behavioral Intelligence: Understanding the Modern Patient Journey
The strategic rationale for this partnership is based on the insight that demographics alone are often poor predictors of actual healthcare behavior. For example, two individuals with the same demographic profile and the same medical diagnosis may navigate the healthcare system in completely different ways. One might be a “digital-first” patient who prioritizes time-saving services and is comfortable with telehealth, while another might be a “hesitant” patient who requires significant reassurance and detailed safety data before engaging with a new brand. A third profile might be “caregiver-dependent,” where the primary decision-maker is not the patient themselves but a family member who manages their health needs. By categorizing individuals into these specific segments, advertisers can ensure that their creative assets are aligned with the recipient’s decision-making style. This alignment is intended to drive higher engagement and, ultimately, more meaningful interactions between patients and healthcare providers, as the messaging addresses the specific anxieties or motivations of the audience.
Furthermore, this behavioral approach allows brands to identify “leading indicators” of patient action. Traditional clinical data is often a “lagging indicator,” showing what has already happened—such as a diagnosis or a filled prescription. Attitudinal data, however, provides insight into what is likely to happen next. If a segment shows a high level of hesitancy toward seeking care, a brand can intervene with educational content that addresses those specific fears before the patient decides to forgo an appointment. This proactive stance is particularly valuable in disease states where early intervention is critical for long-term outcomes. By leveraging Tunnl’s modeling to understand these precursors to action, pharmaceutical companies can play a more active role in the patient journey, providing the right information at the right time to help patients overcome the psychological hurdles that stand between them and better health. This represents a shift from purely commercial advertising toward a model of health advocacy and education.
Privacy and Precision: Navigating the Evolving Regulatory Landscape
As global and domestic regulations surrounding data privacy continue to tighten, the focus on modeled behavioral data offers a more sustainable path forward for the healthcare industry. DeepIntent and Tunnl have prioritized a “privacy-safe” approach that avoids the use of sensitive, identifiable clinical records in the targeting process. Instead, the use of survey-based modeling allows for precision without the same level of risk associated with 1:1 patient tracking. This is increasingly important as regulators examine how “hashed” identifiers are used to link disparate data sets. By relying on inferred attitudes and broad statistical modeling, the partnership provides a layer of anonymity that is consistent with modern privacy standards. This approach allows pharmaceutical brands to maintain their sophisticated targeting capabilities while mitigating the potential for regulatory pushback, a balance that is becoming more difficult to achieve as the definitions of personal data and anonymity are redefined by authorities in the United States and abroad.
The partnership also highlights a fascinating convergence between political strategy and healthcare marketing. Tunnl was founded by veterans of political data science who spent years refining the art of swaying voters by understanding their core values and perceived risks. These same techniques are now being applied to the healthcare sector, where the “choice” of a medication or a provider is often driven by similar psychological factors. This crossover suggests that the future of pharmaceutical advertising will be less about the clinical features of a product and more about how that product fits into the patient’s worldview. By segmenting audiences based on trust, risk tolerance, and social advocacy, brands can speak to patients as citizens and individuals rather than just as cases in a medical database. This holistic view of the consumer is a hallmark of the new data ecosystem that DeepIntent and Tunnl are building, reflecting a sophisticated understanding of how modern identity is constructed through a combination of personal beliefs and health needs.
Future Considerations: Actionable Insights for Pharmaceutical Marketers
The integration of Tunnl’s behavioral segments into the DeepIntent platform marked a significant milestone in the evolution of healthcare media buying. This collaboration successfully bridged the gap between theoretical audience research and practical programmatic application, providing marketers with a toolset that was previously reserved for political campaigns or high-end consumer brands. By moving beyond demographic data, the partnership allowed for a more empathetic approach to advertising that accounted for the psychological diversity of the patient population. The market’s reception of these segments proved that there was a substantial appetite for data that could explain the motivations behind patient actions. Advertisers who adopted these tools early found that they could achieve higher resonance with their target audiences, as the messaging felt more relevant and less intrusive. This success underscored the importance of behavioral intelligence as a core component of the pharmaceutical marketing stack, setting the stage for further innovations in modeled audience data.
Looking ahead, the next step for pharmaceutical marketers is to rigorously validate these behavioral segments against real-world health outcomes. While the initial engagement metrics were promising, the ultimate measure of success remains “script lift”—the verifiable increase in prescriptions resulting from a specific campaign intervention. Marketers should focus on designing observational studies and A/B tests that compare the performance of attitudinal targeting against traditional demographic or condition-based methods. Additionally, brands should look to integrate these insights further up the funnel, using behavioral data to inform everything from product development to physician outreach strategies. As the industry moves closer to a fully integrated, data-driven model of care, the ability to understand and predict patient mindsets will become a primary competitive advantage. The focus should remain on maintaining privacy while increasing the depth of understanding, ensuring that every ad serves as a helpful guide for the patient rather than just another digital distraction.
