Advanced Analytics

Trinity Advanced Analytics leverages a global team of data scientists, therapeutic area experts and experienced life science commercialization advisors across the analytics process.  Clients are able to access:

• Deep expertise in end-to-end design and deployment of AIML solutions
• Efficiencies that accelerate projects by leveraging existing code snippets, business rules and market definitions
• Strong subject matter expertise across therapeutic areas in the analytics process
• Extensive experience across datasets that support varied analytical needs throughout the product lifecycle

Case Studies

• Hyper-Personalized Recommendations for HCPs

  Project Summary

  Objective: Increase market share of target prescribers through an AI-powered Next Best Action model

  Results:

  • Developed hyper-personalized recommendations for HCPs with relevant content, channel and sequence
  • Created a parameterized, brand-agnostic model with incremental training enabled, which adapts to changing prescriber behavior and provides optimal recommendations

  Building the Solution

  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

  Outcome

  This project resulted in a 1.5% increase in engagement rate.  Deliverables included:

  • 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 will be updated based on recent engagements

  “We are happy with the way D Cube Analytics has built this…it’s a 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.”

   

• Site Alert Predictions at the HCP Level

  Project Summary

  Objective: Develop an advanced machine learning algorithm to predict potential site alerts for HCPs in the MCM target universe

  Results: Built an efficient recommendation model to help plan the HCP engagement journey, featuring alerts forecasted out 4 weeks

  Building the Solution

  Besides sales rep deployment, the client used these alerts to direct their non-personal promotion like email and digital advertising.

  The project was done in 2 phases.

  • Phase 1:
    • Data coverage of claims data, EMR data and other sources for incoming site alerts to expand the existing HCP list.
    • Business rule creation to identify incoming site alerts data from additional HCP lists to ensure better coverage and make the existing list more comprehensive
    • Key driver identification for prescribing behavior of HCPs
    • Regression model development to estimate the future number of alerts from HCPs
  • Phase 2 leveraged Phase 1 output:
    • Creation of a channel and content recommendation algorithm for the updated HCP universe
    • Data sources such as MCM activity, sales data and call activity were sent across various channels for effective targeting
    • High-value HCP identification, prioritization and collation

  Outcome

  This project resulted in a 5% increase in site alerts coverage, ~20% improvement in site alerts address rate and ~1.5% lift in engagement.

  • Enrichment of HCP universe and site alerts predictions helped in improving the coverage of total alerts
  • Site alerts forecasting supported informed decisions, HCP targeting and tactical brand share management
  • Increased utilization of sales reps & channel communications
    • Proactive engagement with high-value HCPs was promoted by sending site alerts communications through the right channels
    • Call activity planning was realigned to help sales reps optimize their efforts

  “D Cube Analytics supported [us] greatly in setting up and measuring the impact of the site alerts strategy. Their innovative approach to predict site alerts has helped our teams to intervene proactively to help make treatment decisions.

  This work puts us in an advantageous position by enabling our sales and marketing teams to reach out to HCPs more effectively with well-timed and tailored messaging.”