IES Management College And Research Centre

Chapter 1 Introduction

1.1 Marketing Analytics

1.2 Data for Marketing Analytics

1.3 What Are Business Intelligence, Analytics, and Data Science?

1.4 Analysis

1.5 Exploratory Data Analysis

1.6 Descriptive Analysis

1.7 Predictive Analytics

1.8 Prescriptive Analytics

1.9 Organization of the Book



Chapter 2 Segmentation

2.1 Introduction

2.2 Benefits of Customer Analytics

2.3 Factors Essential for Obtaining Benefits from Customer Analytics

2.4 Segmentation Analytics

2.5 Cluster Analysis



Chapter 3 Positioning

3.1 Introduction

3.2 Perceptual Mapping

3.3 White Spaces

3.4 Umbrella Brands

3.5 Multidimensional Scaling



Chapter 4 Product Analytics

4.1 Introduction

4.2 Analyzing Digital Products

4.3 Analyzing Non-Digital Products



Chapter 5 Pricing

5.1 Introduction

5.2 Goals of Pricing

5.3 Bundling

5.4 Skimming

5.5 Revenue Management

5.6 Promotions

5.7 Discounting

5.8 Price Elasticity of a Beverage Brand



Chapter 6 Marketing Mix

6.1 Introduction

6.2 Market Mix Modeling

6.3 Variables in Market Mix Modeling

6.4 Techniques of Market Mix Modeling



Chapter 7 Customer Journey

7.1 Introduction

7.2 Importance of Customer Journey

7.3 What is Customer Journey Mapping?

7.4 Customer Journey Mapping and Use of Analytics

7.5 How to Map a Customer’s Journey?

7.6 What Does Analytics with Customer Journeys Involve?

7.7 Customer Journey Use Case for a Beverage Brand

7.8 Journey of a Loyal Customer

7.9 Principal Component Analysis

7.10 Applying Principal Components to Brand



Chapter 8 Nurturing Customers

8.1 Introduction

8.2 Metrics for Tracking Customer Experience

8.3 Upgrading Customers: Use Case of Upselling

8.4 Logistic Regression Analysis

8.5 Use of Logistic Regression as a Classification Technique



Chapter 9 Customer Analytics

9.1 Introduction

9.2 Customer Lifetime Value

9.3 Churn Analytics



Chapter 10 Digital Analytics: Metrics and Measurement

10.1 Introduction

10.2 Important Web Metrics

10.3 Attribution Challenge and Shapley Regression

10.4 Test and Control or A/B Testing

10.5 Example Use Case: Webpage Design with A/B Testing

10.6 Search Engine Marketing

10.7 Search Engine Optimization

10.8 SEM or SEO: Which Is the Optimal Choice?

10.9 Social Media Analytics

10.10 App Marketing Metrics



Chapter 11 Artificial Intelligence and Machine Learning

11.1 Introduction

11.2 Importance of AI in Marketing

11.3 Key Applications of AI in Marketing

11.4 Common Terminologies – AI, ML, and DL

11.5 Important Concepts of ML

11.6 Random Forests

11.7 Model Evaluation Using ROC, AUC, and Confusion Matrix

11.8 Boosting Trees

11.9 Variable Importance

11.10 Simple Feed-Forward Network

11.11 Deep Neural Network

11.12 Image Recognition

11.13 Working with Textual Data

11.14 Recommendation Systems

11.15 Challenges Involved with AI



Chapter 12 Data Visualization

12.1 Introduction

12.2 Necessity of Data Visualization

12.3 Charts

12.4 Visualizations Useful with Common Data Science Techniques

12.5 Conclusion



Summary

Key Terms

Discussion Questions

Project



Appendix 1: Installing and Using R

Appendix 2: Installing Python

Endnotes

Index

Marketing Analytics offers marketing students, teachers, and professionals a practical guide to marketing decision models and marketing metrics. The book offers unified reference for various marketing analytics use cases across industries and diverse businesses, such as consumer packaged goods marketers, restaurants and hospitality, e-commerce, entertainment, etc. It provides nuances and trade-offs in using statistical/machine learning methods for various marketing decisions. It explains key marketing metrics and their use with an analytics technique. It offers common best practices of the industry with choice of methods for various decision problems.