TY  - BOOK
AU  - Brown, Iain
TI  - Mastering marketing data science: a comprehensive guide for today's marketers
SN  - 9781394258710
U1  - 658.8 
PY  - 2025///
CY  - Hoboken
PB  - John Wiley Sons, Ltd.
KW  - Marketing
KW  - Data science
N1  - Table of contents:
Preface xi

Acknowledgments xiii

About the Author xv

Chapter 1 Introduction to Marketing Data Science 1

1.1 What Is Marketing Data Science? 2

1.2 The Role of Data Science in Marketing 4

1.3 Marketing Analytics Versus Data Science 5

1.4 Key Concepts and Terminology 7

1.5 Structure of This Book 9

1.6 Practical Example 1: Applying Data Science to Improve Cross-Selling in a Retail Bank Marketing Department 11

1.7 Practical Example 2: The Impact of Data Science on a Marketing Campaign 13

1.8 Conclusion 15

1.9 References 15

Chapter 2 Data Collection and Preparation 17

2.1 Introduction 18

2.2 Data Sources in Marketing: Evolution and the Emergence of Big Data 19

2.3 Data Collection Methods 23

2.4 Data Preparation 25

2.5 Practical Example: Collecting and Preparing Data for a Customer Churn Analysis 39

2.6 Conclusion 41

2.7 References 41

Exercise 2.1: Data Cleaning and Transformation 43

Exercise 2.2: Data Aggregation and Reduction 45

Chapter 3 Descriptive Analytics in Marketing 49

3.1 Introduction 50

3.2 Overview of Descriptive Analytics 51

3.3 Descriptive Statistics for Marketing Data 52

3.4 Data Visualization Techniques 56

3.5 Exploratory Data Analysis in Marketing 60

3.6 Analyzing Marketing Campaign Performance 65

3.7 Practical Example: Descriptive Analytics for a Beverage Company’s Social Media Marketing Campaign 68

3.8 Conclusion 70

3.9 References 71

Exercise 3.1: Descriptive Analysis of Marketing Data 72

Exercise 3.2: Data Visualization and Interpretation 76

Chapter 4 Inferential Analytics and Hypothesis Testing 81

4.1 Introduction 82

4.2 Inferential Analytics in Marketing 82

4.3 Confidence Intervals 92

4.4 A/B Testing in Marketing 95

4.5 Hypothesis Testing in Marketing 101

4.6 Customer Segmentation and Processing 106

4.7 Practical Examples: Inferential Analytics for Customer Segmentation and Hypothesis Testing for Marketing Campaign Performance 115

4.8 Conclusion 119

4.9 References 120

Exercise 4.1: Bayesian Inference for Personalized Marketing 122

Exercise 4.2: A/B Testing for Marketing Campaign Evaluation 124

Chapter 5 Predictive Analytics and Machine Learning 129

5.1 Introduction 130

5.2 Predictive Analytics Techniques 132

5.3 Machine Learning Techniques 135

5.4 Model Evaluation and Selection 144

5.5 Churn Prediction, Customer Lifetime Value, and Propensity Modeling 150

5.6 Market Basket Analysis and Recommender Systems 154

5.7 Practical Examples: Predictive Analytics and Machine Learning in Marketing 158

5.8 Conclusion 164

5.9 References 165

Exercise 5.1: Churn Prediction Model 167

Exercise 5.2: Predict Weekly Sales 170

Chapter 6 Natural Language Processing in Marketing 173

6.0 Beginner-Friendly Introduction to Natural Language Processing in Marketing 174

6.1 Introduction to Natural Language Processing 174

6.2 Text Preprocessing and Feature Extraction in Marketing Natural Language Processing 178

6.3 Key Natural Language Processing Techniques for Marketing 182

6.4 Chatbots and Voice Assistants in Marketing 188

6.5 Practical Examples of Natural Language Processing in Marketing 192

6.6 Conclusion 196

6.7 References 197

Exercise 6.1: Sentiment Analysis 199

Exercise 6.2: Text Classification 200                                                      

Chapter 7 Social Media Analytics and Web Analytics 203

7.1 Introduction 204

7.2 Social Network Analysis 204

7.3 Web Analytics Tools and Metrics 212

7.4 Social Media Listening and Tracking 221

7.5 Conversion Rate Optimization 227

7.6 Conclusion 232

7.7 References 233

Exercise 7.1: Social Network Analysis (SNA) in Marketing 235

Exercise 7.2: Web Analytics for Marketing Insights 238

Chapter 8 Marketing Mix Modeling and Attribution 243

8.1 Introduction 244

8.2 Marketing Mix Modeling Concepts 244

8.3 Data-Driven Attribution Models 251

8.4 Multi-Touch Attribution 256

8.5 Return on Marketing Investment 261

8.6 Conclusion 266

8.7 References 266

Exercise 8.1: Marketing Mix Modeling (MMM) 268

Exercise 8.2: Data- Driven Attribution 271

Chapter 9 Customer Journey Analytics 275

9.1 Introduction 276

9.2 Customer Journey Mapping 276

9.3 Touchpoint Analysis 280

9.4 Cross-Channel Marketing Optimization 286

9.5 Path to Purchase and Attribution Analysis 291

9.6 Conclusion 296

9.7 References 296

Exercise 9.1: Creating a Customer Journey Map 298

Exercise 9.2: Touchpoint Effectiveness Analysis 301

Chapter 10 Experimental Design in Marketing 305

10.1 Introduction 306

10.2 Design of Experiments 306

10.3 Fractional Factorial Designs 310

10.4 Multi-Armed Bandits 315

10.5 Online and Offline Experiments 320

10.6 Conclusion 324

10.7 References 325

Exercise 10.1: Analyzing a Simple A/B Test 327

Exercise 10.2: Fractional Factorial Design in Ad Optimization 328

Chapter 11 Big Data Technologies and Real-Time Analytics 331

11.1 Introduction 332

11.2 Big Data 332

11.3 Distributed Computing Frameworks 336

11.4 Real-Time Analytics Tools and Techniques 343

11.5 Personalization and Real-Time Marketing 348

11.6 Conclusion 353

11.7 References 354

Chapter 12 Generative Artificial Intelligence and Its Applications in Marketing 357

12.1 Introduction 358

12.2 Understanding Generative Artificial Intelligence: Basics and Principles 359

12.3 Implementing Generative Artificial Intelligence in Content Creation and Personalization 364

12.4 Generative Artificial Intelligence in Predictive Analytics and Customer Behavior Modeling 367

12.5 Ethical Considerations and Future Prospects of Generative Artificial Intelligence in Marketing 372

12.6 Conclusion 375

12.7 References 376

Chapter 13 Ethics, Privacy, and the Future of Marketing Data Science 379

13.1 Introduction 380

13.2 Ethical Considerations in Marketing Data Science 380

13.3 Data Privacy Regulations 386

13.4 Bias, Fairness, and Transparency 391

13.5 Emerging Trends and the Future of Marketing Data Science 395

13.6 Conclusion 399

13.7 References 400

About the Website 403

Index 405
[https://www.wiley.com/en-us/Mastering+Marketing+Data+Science%3A+A+Comprehensive+Guide+for+Today's+Marketers-p-9781394258710#tableofcontents-section]
N2  - Unlock the Power of Data: Transform Your Marketing Strategies with Data Science

In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively.
(https://www.wiley.com/en-us/Mastering+Marketing+Data+Science%3A+A+Comprehensive+Guide+for+Today's+Marketers-p-9781394258710#description-section)
ER  -