Data is at the heart of pharma marketing—and the key to unlocking “right time, right place, right patient” strategies. In today’s highly competitive consumer landscape, it’s not enough to rely on broad demographics, lookalikes, cookies, or other generalized audience approaches. That’s why pharma brands and their agency partners look to real-world medical claims and other available data to build their audience lists for DTC campaigns based on brand eligibility criteria, past prescriptions, and more.
But many marketers overlook (or have come to accept) a key limitation: the inherent data lag in many common sources. For example, real-world claims data can take up to three months to become available. That means your audience segmentations and priorities may not reflect a true picture of the patient landscape at campaign kickoff. This opens the door to four hidden risks that could derail your campaign success.
Risk 1: Patient Journey Misalignment
Pharma companies often design their marketing strategies to reach patients during specific stages of their healthcare journey—whether it’s raising awareness at the onset of symptoms, targeting those who are newly diagnosed, or providing options as patients seek new treatments for chronic conditions. When audience data is stale, marketing efforts are most likely directed toward the wrong audience at the wrong time, reducing impact and conversion.
Risk 2: Missed Eligibility Windows
In fast-moving therapeutic areas like oncology, cardiology, or rare diseases, delayed outreach leads to missed opportunities to engage with patients when they are actively seeking or are receptive to treatment options. Patients who could benefit from earlier intervention may have already moved on to other therapies or, worse, the communication may come too late—when they may no longer be eligible for a potentially life-changing treatment.
Risk 3: Competitive Loss
With multiple pharma brands often vying for the same pool of patients, failing to find and reach qualified patients in a timely way can cost brands the chance to convert patients in immediate need of treatment, and opens the door for competitors to step in.
Risk 4: Reduced Commercial Impact
Mistimed or misdirected marketing doesn’t just lower NRx rates, but also can result in fewer prescriptions attributed to marketing activity. While outside perception is that pharma marketing budgets are unlimited, the reality is that media spending continues to be heavily scrutinized, and less-than-efficient marketing can threaten future budget allocations.
And these risks aren’t just to your marketing metrics. Failing to connect with your eligible patients, when they have the opportunity to convert to your brand, means marketers miss the chance to educate and empower patients as they navigate an increasingly complex healthcare landscape. And it bears repeating, it also means that patients may miss out on potentially life-changing therapies. But thanks to a combination of human ingenuity, a passion for better health, and yes, AI, there’s a safe and better way to target DTC programs. It’s called an adaptive audience.
What Are Adaptive Audiences?
Adaptive DTC audiences are audiences that automatically prioritize consumer segments throughout a campaign, based on the current volumes of brand-eligible patients. That means they stay fresh in a way that conventional approaches can’t. Instead of “dated” data, adaptive audiences use artificial intelligence, predictive analytics, and human guidance to anticipate when patients are approaching brand eligibility and upcoming care visits.
As a result, media is always optimized to reach the segments with the greatest opportunity for brand conversion. It’s a dynamic approach that reflects patients’ evolving care needs. Here’s how adaptive audiences work:
- Defining the Ideal Patient Profile: Like standard audience approaches, the first step in an adaptive audience is defining the target population— their conditions and comorbidities, current line of therapy, recent tests and lab values, their treating care team/specialties, and other clinical factors. But alongside standard features like specific ICD10 and NDC codes, it’s also critical to expand your viewpoint to include often-overlooked socioeconomic, behavioral, and media consumption data. After all, despite a common diagnosis, medication history, or clinical profile, every patient is an individual, shaped by their unique experiences.
- Building a Predictive AI Model: Most conventional audiences stop after the first step – using their patient profile to “find” qualified individuals at campaign start, then prioritizing the segments with the greatest number of patients. Adaptive audiences take a different approach, turning the patient profile into a predictive model. By drawing on many of the same data resources, today’s AI-driven models can accurately predict patients’ future care milestones and upcoming HCP visits.
- Linking Brand Signals to Hyper-Local Geographies: Once the predictive model is live, it constantly looks for brand eligibility signals, and links those signals to hyper-local geographies comprised of the 35M+ available zip-9s. Based on the desired refresh cadence, marketers can then prioritize the zip-9s with the greatest concentration of signals throughout the course of the campaign. This means media is always focused on the populations with the greatest opportunity for brand conversion—and the greatest treatment need. Because all data is de-identified, and the technology provides just the right amount of strategic “noise” in and around the zip-9, it’s a privacy-safe, compliant approach that doesn’t compromise precision.
- Personalizing the Media Mix: Every consumer has their own media consumption habits. Rather than relying on demographic generalizations, adaptive audiences look at the preferred channel mix within each “activated” zip-9, then automatically select the most effective tactics from the available options. Again, AI plays a key role—allowing for channel selection to be optimized at scale, while personalized for consumer habits.
- Ongoing Optimization: Because adaptive audiences are built with embedded AI and machine learning technology, they automatically grow “smarter” over time: refining the predictive model, signal identification, and media deployment based on the data generated from the campaign.
How Does 6x Script Lift Sound?
Taking an adaptive approach offers DTC marketers significant benefits, including increased media efficiency, greater audience penetration, and reduced impression waste. One under-diagnosed neurology brand saw a 75% boost in the number of qualified patients reached and a 92% jump in patient engagement.
But marketing impact is measured in new prescriptions (NRx), and it’s here that the difference is clear. Adaptive audiences drive an average of six times higher script lift than conventional, fixed audience segmentations. By automatically optimizing every media touchpoint for brand conversion, adaptive audiences help brands and consumers thrive in the ever-evolving treatment landscape. More patients receive care-relevant information aligned with their treatment needs, and pharma marketers can demonstrate greater commercial and revenue impact. That’s what we call a win-win, and the right way to be data-driven.
Learn more about OptimizeRx’s adaptive DTC audiences, powered by our Dynamic Audience Activation Platform and Micro-Neighborhood® Targeting technology.
