Home / Intelligence / Case Studies / Early Adopter Identification Engine
Client Situation
In order to plan and efficiently utilize their sales and marketing efforts, a mid-sized biopharma client wanted to identify HCPs likely to adopt their drug in the early stage of launch
Trinity’s Role
- Developed an Early Adopter Identification Engine to predict which prescribers were likely to adopt the drug in the initial launch stage
- Collaborated with internal stakeholders and downstream partners for seamless execution
- Created a customizable dashboard for HCP profiling, targeting prioritization and sales pattern identification
- Identified and segmented early adopters in different segments through and various
classification factors
- Identified and segmented early adopters in different segments through and various
Project Outcomes
- Use of the Early Adopter solution delivered double* the adoption rate compared to the control group
*Lift percentage was obtained using test control methodology implemented during the targeting process
Related Intelligence
Blog
The Next Wave of Global AI Medical Devices: Innovation in Action
A recent bipartisan initiative by Senators Martin Heinrich, Mike Rounds, Marsha Blackburn and Todd Young urged the Centers for Medicare & Medicaid Services (CMS) to establish a formal payment pathway for algorithm-based health care services (ABHS). This move addresses the need for stable Medicare reimbursement for FDA-cleared AI and machine learning (ML) medical devices, which […]
Read More
Webinars
Human Centered Design Approach to GenAI for Life Sciences
Available On Demand
We need to think through not just whether GenAI works, but how we make it usable for people and impactful for our business… The next webinar in our “Making AI Real” webinar series will focus on people, process and technology—three interconnected components that are key to successful GenAI implementation. Join Trinity Life Sciences’ GenAI experts […]
Watch Now
Case Studies
HCP-level Site Alert Predictions
Trinity was able to develop an advanced machine learning algorithm to predict potential site alerts for HCPs in the target universe
Read More