Day 1: Foundations of Advanced Marketing Analytics

• Recap: Role of Marketing Analytics in the Modern Business Landscape
• Data Ecosystem in Marketing: First-Party, Second-Party, and Third-Party Data
• Data Quality, Governance, and Integration Challenges
• Review of Key Marketing Metrics and KPIs
• Introduction to Analytical Frameworks (Descriptive, Predictive, Prescriptive)

Day 2: Customer Segmentation and Targeting

• Advanced Segmentation Techniques: Behavioural, Psychographic, Value-Based
• Cluster Analysis (K-Means, Hierarchical) for Customer Grouping
• Factor Analysis for Dimension Reduction and Customer Profiling
• Applying Segmentation Insights to Targeting Strategies
• Personalization at Scale

Day 3: Customer Lifetime Value (CLV) and Churn Analysis

• Components and Calculation of CLV (Historical, Predictive)
• RFM (Recency, Frequency, Monetary) Analysis
• Predictive Models for Churn (Logistic Regression, Decision Trees)
• Strategies for Increasing CLV and Reducing Churn

Day 4: Experimental Design and A/B Testing

• Principles of Causal Inference in Marketing
• Designing Effective A/B Tests (Hypothesis, Sample Size, Duration)
• Multivariate Testing and MVT Tools
• Interpreting A/B Test Results and Statistical Significance
• Common Pitfalls and Best Practices in Experimentation

Day 5: Marketing Attribution Modelling

• Challenges of Multi-Channel Marketing and Attribution
• Rule-Based Attribution Models (First-Click, Last-Click, Linear, Time Decay, Position-Based)
• Data-Driven Attribution Models (Markov Chains, Shapley Value)
• Implementing and Evaluating Attribution Models
• Attribution in the Cookie less Future