Predictive Analytics for Journey-Based Insights & Targeting

A top-10 pharma company needed to accelerate uptake of a therapy by identifying undiagnosed or switching patients and engaging the right healthcare providers (HCPs). Existing segmentation approaches were fragmented, lacked precision, and made it difficult to align medical and commercial teams on a consistent patient definition.

• Defined patient cohorts through collaboration with brand and clinical teams, aligning on demographics, diagnoses, insurance markers, and lab values.
• Developed AI/ML predictive models to identify likely switchers and initiators using graph ML and advanced statistical techniques, refining accuracy through iterative tuning.
• Created predictive patient personas capturing key behavioral, clinical, and payer insights to inform engagement strategies.
• Generated an HCP target list at the NPI level to focus marketing and sales activities on the providers most likely to prescribe post-launch.

• Delivered predictive models with up to 98% accuracy in identifying at-risk or likely-to-switch patient cohorts.
• Produced a prioritized HCP target list, enabling our client to concentrate resources on high-value prescribers most likely to drive adoption.
• Equipped commercial teams with journey-based analytics and consistent measurement frameworks, strengthening alignment between medical, market access, and brand stakeholders.
• Improved operational efficiency by reducing wasted effort on low-value targets and sharpening focus on the highest-yield opportunities.