Hyper-personalized HCP Recommendations

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Client Situation

The objective was to increase market share of target prescribers through an AI-powered Next Best Action model

Trinity’s Role

We were able to deploy this solution in 3 weeks. The project included:

  • Discussions with the client team to finalize standardized business rules for brand-agnostic data and KPI definitions
  • Master dataset development through applying business rules and automated monthly PySpark code refreshes
  • State-of-the-art machine learning algorithms like reinforcement learning, deep neural networks with dynamic recalibration and automated hyperparameter tuning for enriching HCP recommendations
  • Automated data engineering pipelines to append the recent data to master data set at regular intervals
  • Sequential Content – channel combination is recommended for each HCP at timely intervals without any manual intervention

Project Outcomes

This project resulted in a 9% increase in engagement rate. Deliverables included:

  • Hyper-personalized recommendations for HCPs with relevant content, channel and sequence
  • A parameterized, brand-agnostic model with incremental training enabled, which adapts to changing prescriber behavior and provides optimal recommendations
    • Seamless data flow to and from channel partners
    • Re-usable and modularized codes to help in quick enhancements, easy refreshes and reduction of additional effort
    • Self-learning enabled in final recommendations – results are updated based on recent engagements

“We are happy with this ‘Re-configurable White Box Solution.’ Several man-hours that usually go in other projects are avoided here due to parameterization and config creations. Most of all, being able to change the rules without help from the development team at any point is very helpful with quick changes in requirements and ad hoc analysis.”


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