Notes
Applied Business Analytics for Marketing Decision-Making
Business Analytics and Data Visualization
Table of Contents
Version 1.0 · July 2026
Contents
This guide is organized in three parts, thirteen chapters, and five appendices. Chapters 1–13 are assigned one per week and follow the session sequence in the course syllabus; the reading for each session is the working material for that session.
Front Matter
Copyright
Dedication
Preface
Who This Guide Is For
How This Guide Is Organized
How Each Chapter Works
Companion Site and Course Files
The AI-Augmented Analytics Workflow
Responsible Use of AI
Required Tools
What Students Are Expected to Learn
How to Read This Guide
A Note on Code
A Note on Visualization
A Note on Managerial Recommendations
Acknowledgments
Part I: Foundations of Marketing Analytics
Chapter 1. Becoming an AI-Augmented Marketing Analyst
Chapter 2. Marketing Analytics as Decision Support
Chapter 3. Data, Measurement, and Marketing Variables
Chapter 4. Data Preparation and Exploratory Analysis
Chapter 5. Descriptive Analytics and Customer Insight
Part II: Predictive Marketing Analytics
Chapter 6. Segmentation and Targeting Analytics
Chapter 7. Relationships, Drivers, and Regression
Chapter 8. Predictive Modeling for Marketing Decisions
Chapter 9. Classification, Propensity, and Churn Models
Chapter 10. Forecasting Demand, Sales, and Campaign Performance
Chapter 11. Experiments, A/B Testing, and Causal Evidence
Part III: Data Visualization and Analytics Communication
Chapter 12. Data Visualization for Analysis
Chapter 13. Data Visualization for Communication and Decision-Making
Appendices
Appendix A. Python and Google Colab Quick Reference
Appendix B. Common Marketing Metrics
Appendix C. AI Prompting Templates for Analytics
Appendix D. AI-Use Documentation Template
Appendix E. Data Visualization Checklist
Back Matter
Glossary
References