Commercial Effectiveness
Case Study 12 -
Developed an NBA model that recommends the next best actions based on customer behavior
Business Problem
Reps struggled to effectively correlate information from various reports and systems. Unable to adjust messaging as the patients were switching dynamically.
ProcDNA’s Approach
Raw Data From Source
Identified the right data sources (example – Weekly/Monthly Sales, Call activity, Email engagement, Website, Speaker Bureau etc.) for each suggestion and model training.
Constraint File
Incorporated constraints like rep time-off, call/email capping, HCP consent, etc.
Model Training
Trained the deep learning model (TCN & FCN) using the past 6 months data like Sales, Call, Email & HCP affinity.
Predict
An intelligent optimization algorithm – Monte Carlo Tree Search was used to minimize the number of iterations needed to identify the promotion sequence that returns the highest value for an HCP.
Veeva Suggestions
Predicted alerts were sent as suggestions on Veeva app using Veeva API integration.
Client Impact
IVA Call Utilization
IVA call utilization increased from 75% to 90% post suggestions launch
Increased Product Sales
Based on control test analysis, product sales increased by ~4% within the first year of suggestions launch
Meaningful Conversation
Sales reps were able to have meaningful conversations with HCPs
Contact Us
Our Offices
San Francisco
Chicago
Boston
New Jersey
Delhi
Pune
Signup for the Newsletter