Innovative Machine Learning Methods for Proactive and Precision Targeting
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Description
Organizations are transforming their sales functions from being reactive to proactive, and from intuition-driven to insight-driven. The emergence of vast amounts of sales and patient level data from multiple sources and platforms has provided companies with more information than they’ve ever had before. Machine learning is a branch of artificial intelligence that enables computers to recognize patterns in existing data, update with new patterns from incoming data and continuously optimize recommendations. Innovative machine learning methods, together with clinical insights and continuous model enhancements, provides superior proactive and precision targeting results. It allows sales functions to improve their sales performance and effectiveness.
In this webinar, we will discuss the following aspects:
- Business objectives and benefits of proactive sales strategy
- Proactively identify sales potentials among targeted universe
- Help sales functions update/prioritize their target and call focus with patient insights
- Improve sales performance
- Outline of machine learning methodology and process
- Data collection, cohort define and feature calculation
- Model build and selection
- Model validation & application
- Model enhancements and validation on precision targeting
- Model introduction: Logistic, Random Forest, XBG, Deep learning
- Enhancement: Stacking, Ensemble, etc (flexibility of using models)
- Demonstrate how proactive and precision targeting optimizes sales performance
- Illustrate how to validate model accuracy on a timely manner
- Describe case study objective
- How selected model is implemented
- Validation on precision on a timely manner
- Other insight of targeting in a timely manner
- Patient profile
- Doctor insight
Presenters:
- Li Zhou, Sr. Principal, Advanced Analytics Organization, Global, IQVIA
- Zhang Zhang, Manager, Advanced Analytics Organization, IQVIA
- Lynn Lu, Senior Principal, Advanced Analytics Organization, IQVIA