Proof
/ Pharma & Life SciencesMachine Learning Sales Propensity Model for a Top 5 Global Pharma Company
Business situation
A top 5 global pharmaceutical company seeking to increase sales for a rare disease medication with regulatory and legal prescription requirements.
The challenge
Low prescriber base with limited ability to identify physicians who could and should prescribe the product, constrained by regulatory complexity and lack of analytical targeting capability.
Why it matters
Quantum Rise developed a machine learning model to map physicians most likely to prescribe the product, using therapeutic class data, specialty correlations, and campaign performance signals.
What Quantum Rise delivered
The QR/OS™ approach in practice
- • Machine learning prescriber propensity model • Mapping of potential prescribers within the full physician universe • Physician clustering by prescription volume • Inputs: main therapeutic classes, specialties with greatest product affinity, campaign and content signals
Measurable outcome
+15% increase in orders from doctors with more than 60% probability to prescribe. 25% increase in the number of doctors that can potentially prescribe the product.
Expanded the addressable prescriber base and enabled targeted sales and marketing actions against high-propensity physicians.
What became repeatable
A reusable ML prescriber propensity model architecture applicable to any pharmaceutical product launch with a fragmented or underserved prescriber base.