“Data-driven” is a bedrock principle of today’s pharmaceutical marketing. But how well are we keeping up with the tremendous amount of data available to us?
It’s estimated that the average hospital produces an average of 137 terabytes of data per day. That’s the equivalent of 68 million books, 34 million songs, or 27,000 high-definition movies. In short, it’s a massive amount of information – even before considering other valuable data to marketers, such as audience media engagement, search activity, and other consumer activities. But despite this abundance, most pharma marketers never come close to benefiting from the full scope of data generated. In part, that’s because a notable portion of the data is unstructured, difficult to use, or irrelevant to a marketing use case. However, it’s also due to the way many pharma brands use data to guide campaign deployment in direct-to-consumer marketing.
Most pharma marketers build audience lists using commercially available claims data, working within a variety of methodologies to maintain patient privacy while also targeting individuals that could be a match to brand eligibility criteria – e.g., diabetes patients currently on Metformin. In one popular approach, pharma brands (or more typically their marketing agencies) link de-identified claims data to local area codes, look for high concentrations of patients, then focus brand marketing on those locations. However, once they have this geography-based target audience, the focus often shifts to optimizing media placements within those geographies, not to revisiting the audience itself. And therein lies the issue.
The Ripple Effect of Static Audiences
Static audience lists (location-based or otherwise) built using single, point-in-time data fail to reflect the accelerating pace of healthcare and data change. What’s more, given the time lag often seen in claims data, audience lists may be outdated before they even go into use. This creates an interesting paradox – where data unlocks “right time, right place, right person” marketing, but stale data actively detracts from that goal. Here's just a few examples of how static data leads brands off course:
- 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 directed toward the wrong audience at the wrong time, reducing impact and conversion.
- 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 the treatment may have already moved on to other therapies or the communication may come too late – when they are no longer eligible for life-changing treatment.
- Competitive Loss: With multiple pharma brands often vying for the same pool of patients, failing to find and reach the right patients in a timely way can cost brands the chance to convert patients in immediate need of treatment, and open the door for competitors to step in.
- Reduced Commercial Impact: Mistimed or misdirected marketing doesn’t just risk lower prescribing rates, but also can result in fewer prescriptions attributed to marketing activity. While outsiders could be forgiven for thinking 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.
Dynamic Makes a Difference
Being data-driven isn’t enough; pharma marketers need to be dynamically data-driven. That means not settling for one-time, historic audiences, but instead looking for solutions that use claims and other data to make predictions about future patient needs, milestones, and care visits. Rather than basing marketing around patients that have exhibiting brand eligibility signals in the past, it’s critical that pharma marketers find the patients expected to become brand-eligible in the future to build their consumer audiences.
This forward-looking approach is no longer hypothetical. Artificial intelligence has made it a reality, allowing pharma brands and their agency partners to create dynamic, privacy-safe audiences that constantly refresh based on brand eligibility signals and upcoming care visits. That means brands can optimize marketing based on conversion opportunities – consistently reprioritizing geographies and channel mixes based on current / future patient concentrations and media preferences. The result? 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.
Curious to see how your brand could benefit from a dynamic approach to direct-to-consumer marketing? Learn more about OptimizeRx’s AI-guided approach.