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.