Our client, a top 5 global pharmaceutical company, sought to build and deploy an AI-powered multi-channel outreach engine to coordinate provider engagement across multiple brand portfolios. Their manual engagement process lacked the ability to personalize outreach at the individual provider level, relying instead on aggregate customer segments. This approach created substantial inefficiencies, limited scalability, and missed opportunities to tailor interactions based on individual provider traits and behaviors.
OneSix built a custom AI solution leveraging enterprise reinforcement learning technology to automate and optimize provider engagement. Using real-time data on provider traits, behaviors, and patient populations, the solution integrates multi-brand and multi-channel outreach into a single system. The platform, powered by Strong RL, Apache Spark, and Tensorflow, is designed to scale within the client’s on-premise infrastructure to accommodate vast data volumes and diverse brand requirements.
With this AI-driven outreach engine, our client achieved a highly efficient, data-driven provider engagement strategy, enabling coordinated, personalized interactions across brands and channels. The system improved engagement outcomes by predicting provider response probabilities and suggesting targeted interventions. As a result, the pharmaceutical company can engage providers in a more tailored, impactful way, leveraging individual insights at scale and significantly reducing manual efforts.
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