The Segmentation course provides a comprehensive end-to-end framework for physician and patient segmentation in pharmaceutical and healthcare contexts. From data foundations and analytics techniques to business activation and ROI measurement, learners gain the skills to design, validate, and deploy segmentation models that drive real commercial impact across sales, marketing, and market access functions.
Master physician and patient segmentation frameworks used across sales, marketing, market access, and patient outcomes functions in pharma.
Apply clustering, machine learning, and statistical techniques to real healthcare datasets to build, validate, and profile actionable segments.
Learn how to activate segments through omnichannel personalization, message mapping, and sales call planning strategies that link directly to business KPIs.
Table of Contents
Segmentation:
Module 1: Introduction to Segmentation in Healthcare
1. What is Segmentation?
2. Why Segmentation Matters in Pharma & Healthcare
3. When Segmentation in not required
4. Differences Between Consumer, Physician & Patient Segmentation
5. Data collection in Segmentation
6. Evolution of Segmentation in Healthcare
7. Business Impact – Sales, Marketing, Market Access & Patient Outcomes
Module 2: Data Foundations for Segmentation
2.1 Internal Data Sources
• Sales data
• CRM interactions
• Prescription / Claims data
• Field force activity
2.2 External Data Sources
• Market research
• EMR / EHR datasets
• Digital behaviour data
• Public health & epidemiology data
2.3 Data Privacy, Ethics & Compliance (HIPAA / GDPR / Local Regulations)
Module 3: Physician Segmentation Framework
3.1 Objectives of Physician Segmentation
3.2 Key Segmentation Dimensions
• Prescribing behaviour
• Specialty & patient mix
• Practice type & setting
• Adoption mindset
• Influence network
• Digital engagement
3.3 Common Physician Segmentation Models
• Decile & volume-based segmentation
• Behavioural segmentation
• Attitudinal segmentation
• Network-based segmentation
• Digital maturity segmentation
Module 4: Patient Segmentation Framework
4.1 Why Patient Segmentation is Critical Today
4.2 Key Segmentation Variables
• Demographics & socio-economics
• Disease stage & severity
• Treatment adherence behaviour
• Access & affordability
• Psychographic & lifestyle factors
• Digital health adoption
• Treatment Adherence
4.3 Common Patient Segmentation Models
• Risk-based segmentation
• Journey-based segmentation
• Needs-based segmentation
• Engagement-based segmentation
Module 5: Analytics Techniques for Segmentation
5.1 Exploratory Data Analysis for Segmentation
5.2 Statistical Techniques
• Clustering (K-means, Hierarchical, DBSCAN)
• Factor analysis
• Decision trees
5.3 Machine Learning for Segmentation
• Supervised vs Unsupervised methods
• Feature engineering
• Model validation
5.4 Segment Profiling & Interpretation
Module 6: Validation & Business Alignment
6.1 Segment Stability & Refresh Cycles
6.2 Field Force Validation Workshops
6.3 Linking Segments to Business Strategy
6.4 KPI Mapping for Each Segment
Module 7: Activation – Turning Segments into Action
7.1 Segment-specific Engagement Strategies
7.2 Omnichannel Personalization
7.3 Message Mapping by Segment
7.4 Sales Call Planning Using Segments
7.5 Patient Support Programs by Segment
Module 8: Technology Enablement
8.1 CRM & Marketing Automation Integration
8.2 Data Platforms & Analytics Tools
8.3 AI-powered Next Best Action Systems
8.4 Dashboards & Reporting Framework
Module 9: Measuring Impact & ROI
9.1 Commercial KPIs
9.2 Patient Outcome Metrics
9.3 Campaign Effectiveness Analysis
9.4 Continuous Improvement Framework
Module 10: Case Studies & Capstone Project
10.1 Global Pharma Case Studies
10.2 India-specific Market Scenarios
10.3 End-to-End Segmentation Project
• Dataset analysis
• Model build
• Segment interpretation
• Go-to-market strategy