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Business and Management
Marketing and Sales
Digital Marketing
Marketing Analytics
1. Introduction to Marketing Analytics
2. Foundational Concepts and Metrics
3. The Marketing Analytics Process
4. Data Sources and Collection Methods
5. Analytical Frameworks and Models
6. Customer Analytics and Segmentation
7. Channel-Specific Analytics
8. Advanced Analytical Techniques
9. Tools and Technology Stack
10. Reporting and Data Visualization
11. Strategy and Optimization
12. Ethics and Privacy in Marketing Analytics
Customer Analytics and Segmentation
Customer Segmentation Strategies
Segmentation Fundamentals
Segmentation Criteria
Segment Evaluation
Actionable Segmentation
Demographic Segmentation
Age-Based Segmentation
Gender-Based Segmentation
Income-Based Segmentation
Education-Based Segmentation
Life Stage Segmentation
Geographic Segmentation
Country and Region
Urban vs Rural
Climate-Based Segmentation
Cultural Segmentation
Psychographic Segmentation
Lifestyle Segmentation
Values-Based Segmentation
Personality-Based Segmentation
Interest-Based Segmentation
Behavioral Segmentation
Purchase Behavior
Usage Patterns
Brand Loyalty
Benefits Sought
Occasion-Based Segmentation
Advanced Segmentation Techniques
Statistical Segmentation Methods
Cluster Analysis
K-Means Clustering
Hierarchical Clustering
Decision Tree Segmentation
Machine Learning Segmentation
Unsupervised Learning Approaches
Supervised Learning for Segmentation
Deep Learning Segmentation
Dynamic Segmentation
Real-Time Segmentation
Behavioral Trigger Segmentation
Predictive Segmentation
Customer Lifetime Value Analysis
CLV Calculation Methods
Historical CLV
Predictive CLV
Cohort-Based CLV
Individual vs Aggregate CLV
CLV Modeling Approaches
RFM-Based CLV
Regression-Based CLV
Machine Learning CLV Models
Probabilistic CLV Models
CLV Applications
Customer Acquisition Strategy
Retention Investment Decisions
Pricing Strategy
Resource Allocation
Churn Analysis and Prevention
Churn Definition and Measurement
Voluntary vs Involuntary Churn
Churn Rate Calculation
Churn by Segment
Churn Timing Analysis
Churn Prediction Modeling
Feature Engineering for Churn
Logistic Regression Models
Machine Learning Approaches
Model Evaluation Metrics
Churn Prevention Strategies
Early Warning Systems
Retention Campaigns
Win-Back Campaigns
Customer Success Programs
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5. Analytical Frameworks and Models
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7. Channel-Specific Analytics