In life science brands’ quest for highly targeted, clinically-relevant HCP communications, EHR “trigger” data is an increasingly popular choice. Its benefits include accuracy and control over message deployment, but many brands don’t realize that this precision comes at a cost – decreased share of voice with the physicians who matter.
To be clear, EHR-based programs that use 1-2 data points (ICD10, NDC, etc.) to align brand information with a specific patient profile can be a cost-effective way to reach the right HCPs. However, when programs are built with 3+ combined data triggers to find complex patient profiles, the number of “triggered” situations declines. That means that brands are delivering fewer overall impressions, achieving a smaller share of voice and reaching a lower number of physicians with their key messages.
While many brands looking for a hard-to-find audience may think “quality over quantity”, it’s not just audience size that’s a barrier to program success. Other significant drawbacks include:
For brands that need more precision beyond 1-2 key NDC or IDC10 criteria, there’s a better way to build a data-driven targeting model: predictive patient finding.
Today’s AI-driven targeting models are capable of identifying complex patient profiles with even more accuracy than traditional triggers, and without sacrificing physician reach. They do this by shifting from a reactive “trigger” approach to a proactive, predictive patient flow model. Here’s how it works:
The result? Up to 50% increase in brand share-of-voice without sacrificing targeting precision, plus greater cost-efficiency from less wasted impressions and better cross-channel alignment.
If you’re struggling to balance targeting precision and HCP reach for your life science brand, we’d welcome the chance to show you how a predictive patient approach maximizes program impact. Connect with us today!