Customer 360

In-person Channel Optimization Model To Improve HCP Engagement

An AI/ML Based Engine Is Used To Optimize Call Plans, Sample Drops, And Invites To Speaker Programs, Lunch & Learns

  • Rep Assist
  • Consumption Application For Sales Reps


increase in engagement rates of HCPs

day market share lift predication achieved

view of HCP's insights

“The Rep Assist App developed by D Cube Analytics helped us improve our HCP engagement drastically.

With the help of Rep Assist our sales reps are well equipped with necessary information to speak to a potential target. The best part being that all this information was available right at a click of a button.”


  • A Biopharma company wanted to optimize in-person marketing channels for HCPs (F2F calls, sample drops, Speaker Programs, Lunch & Learns)
  • Initially implement a business rule-based recommendation engine that would eventually be replaced by a more sophisticated AI/ML based engine
  • Enable integration of recommendations and insights with the sales rep platform (Veeva) to suggest next-best-action to reps, along with relevant HCP insights


  • Model predicted the 30 days market share lift post touchpoint to evaluate both channel & content affinity of physicians
  • The model recommended the best time to target the physician
  • Increased collaboration between various teams to maximize market share
  • Reinforcement learning framework (DQN/ DDPG/ Monte Carlo method) evaluated exploration/exploitation strategy


  • One single place to provide 360 views, recommendations and insights against Physician
  • Insights and recommendations contextualized for each target physician
  • Summary KPIs with treatment paradigm deep dives to understand causality and correlations
  • Fostering collaboration with other reps targeting the HCP to fine tune tactics
  • Understand the potential market share from an access perspective and identify physicians with similar behaviors


  • To enable quick rollout, brainstormed and aligned on a business rule-based recommendation engine that was integrated with Veeva
  • Developed a deep reinforcement learning model with ability to adapt to changing HCP behaviors over time to suggest next best action along with best time to approach HCP
  • Developed lookalike model to identify HCP targets for Speaker Programs and Lunch & Learns, based on historical reaction to attendance


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