Mastering Marketing Data Science
A Comprehensive Guide for Today's Marketers
1st Edition
Will be published approx. on 2. May 2024
Book
Hardback
432 pages
978-1-394-25871-0 (ISBN)
Description
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.
Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science.
Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns.
Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science.
Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape.
Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative.
Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable.
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.
Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science.
Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns.
Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science.
Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape.
Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative.
Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 256 mm
Width: 178 mm
Thickness: 26 mm
Weight
771 gr
ISBN-13
978-1-394-25871-0 (9781394258710)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
04/2024
1st Edition
Wiley-ISTE
€90.99
Available for download
E-Book
04/2024
1st Edition
Wiley-ISTE
€90.99
Available for download
Person
DR IAIN BROWN, is the Head of Data Science for Northern Europe at SAS Institute Inc. and Adjunct Professor of Marketing Data Science at the University of Southampton. With over a decade of experience spanning various sectors, he is a thought leader in Data Science, Marketing, AI, and Machine Learning.
His work has not only contributed to significant projects and innovations but also enriched the academic and professional communities through publications in prestigious journals and presentations at internationally renowned conferences.
His work has not only contributed to significant projects and innovations but also enriched the academic and professional communities through publications in prestigious journals and presentations at internationally renowned conferences.
Content
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
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