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In the rapidly evolving landscape of marketing, the fusion of analytics and creativity has heralded a new era of data-driven strategies. Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers stands at the forefront of this revolution, offering a comprehensive guide that bridges the gap between complex data science techniques and practical marketing applications. Authored by Dr. Iain Brown, a renowned expert in data science, this book distills over a decade of professional experience and academic insight into a pivotal resource for the modern marketer.
The journey begins with an exploration of the foundational elements of marketing data science, setting the stage for a deep dive into the methodologies that have transformed the marketing industry. From the intricacies of data collection and preparation to the advanced realms of predictive analytics, natural language processing, and beyond, Dr. Brown elucidates the core principles that underpin effective marketing strategies in the digital age. Each chapter is meticulously designed to not only impart theoretical knowledge but also to offer practical examples and exercises that enable readers to apply these insights in real-world scenarios.
As we traverse the landscape of marketing data science, the book unveils the latest advancements in the field, including cutting-edge discussions on generative AI and its implications for content creation, customer engagement, and ethical marketing practices. Dr. Brown's narrative is both enlightening and empowering, urging readers to leverage the power of data science to innovate, optimize, and excel in their marketing endeavors.
Mastering Marketing Data Science is not just a book; it's a manifesto for the future of marketing. It challenges readers to rethink traditional paradigms and embrace the potential of data science to drive growth, engagement, and value in an increasingly competitive marketplace. Whether you're a student, a marketing professional, or a data scientist looking to specialize in marketing, this book is your gateway to mastering the art and science of marketing in the digital era.
The journey begins with an exploration of the foundational elements of marketing data science, setting the stage for a deep dive into the methodologies that have transformed the marketing industry. From the intricacies of data collection and preparation to the advanced realms of predictive analytics, natural language processing, and beyond, Dr. Brown elucidates the core principles that underpin effective marketing strategies in the digital age. Each chapter is meticulously designed to not only impart theoretical knowledge but also to offer practical examples and exercises that enable readers to apply these insights in real-world scenarios.
As we traverse the landscape of marketing data science, the book unveils the latest advancements in the field, including cutting-edge discussions on generative AI and its implications for content creation, customer engagement, and ethical marketing practices. Dr. Brown's narrative is both enlightening and empowering, urging readers to leverage the power of data science to innovate, optimize, and excel in their marketing endeavors.
Mastering Marketing Data Science is not just a book; it's a manifesto for the future of marketing. It challenges readers to rethink traditional paradigms and embrace the potential of data science to drive growth, engagement, and value in an increasingly competitive marketplace. Whether you're a student, a marketing professional, or a data scientist looking to specialize in marketing, this book is your gateway to mastering the art and science of marketing in the digital era.
In the rapidly evolving landscape of marketing, the fusion of analytics and creativity has heralded a new era of data-driven strategies. Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers stands at the forefront of this revolution, offering a comprehensive guide that bridges the gap between complex data science techniques and practical marketing applications. Authored by Dr. Iain Brown, a renowned expert in data science, this book distills over a decade of professional experience and academic insight into a pivotal resource for the modern marketer.
The journey begins with an exploration of the foundational elements of marketing data science, setting the stage for a deep dive into the methodologies that have transformed the marketing industry. From the intricacies of data collection and preparation to the advanced realms of predictive analytics, natural language processing, and beyond, Dr. Brown elucidates the core principles that underpin effective marketing strategies in the digital age. Each chapter is meticulously designed to not only impart theoretical knowledge but also to offer practical examples and exercises that enable readers to apply these insights in real-world scenarios.
As we traverse the landscape of marketing data science, the book unveils the latest advancements in the field, including cutting-edge discussions on generative AI and its implications for content creation, customer engagement, and ethical marketing practices. Dr. Brown's narrative is both enlightening and empowering, urging readers to leverage the power of data science to innovate, optimize, and excel in their marketing endeavors.
Mastering Marketing Data Science is not just a book; it's a manifesto for the future of marketing. It challenges readers to rethink traditional paradigms and embrace the potential of data science to drive growth, engagement, and value in an increasingly competitive marketplace. Whether you're a student, a marketing professional, or a data scientist looking to specialize in marketing, this book is your gateway to mastering the art and science of marketing in the digital era.
The journey begins with an exploration of the foundational elements of marketing data science, setting the stage for a deep dive into the methodologies that have transformed the marketing industry. From the intricacies of data collection and preparation to the advanced realms of predictive analytics, natural language processing, and beyond, Dr. Brown elucidates the core principles that underpin effective marketing strategies in the digital age. Each chapter is meticulously designed to not only impart theoretical knowledge but also to offer practical examples and exercises that enable readers to apply these insights in real-world scenarios.
As we traverse the landscape of marketing data science, the book unveils the latest advancements in the field, including cutting-edge discussions on generative AI and its implications for content creation, customer engagement, and ethical marketing practices. Dr. Brown's narrative is both enlightening and empowering, urging readers to leverage the power of data science to innovate, optimize, and excel in their marketing endeavors.
Mastering Marketing Data Science is not just a book; it's a manifesto for the future of marketing. It challenges readers to rethink traditional paradigms and embrace the potential of data science to drive growth, engagement, and value in an increasingly competitive marketplace. Whether you're a student, a marketing professional, or a data scientist looking to specialize in marketing, this book is your gateway to mastering the art and science of marketing in the digital era.
Über den Autor
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.
Inhaltsverzeichnis
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
Details
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 432 |
| Inhalt: | Preface xiAcknowledgments xiiiAbout the Author xvChapter 1 Introduction to Marketing Data Science 11.1 What Is Marketing Data Science? 21.2 The Role of Data Science in Marketing 41.3 Marketing Analytics Versus Data Science 51.4 Key Concepts and Terminolo |
| ISBN-13: | 9781394258710 |
| ISBN-10: | 1394258712 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Brown, Iain |
| Hersteller: | Wiley |
| Maße: | 256 x 178 x 26 mm |
| Von/Mit: | Iain Brown |
| Erscheinungsdatum: | 12.06.2024 |
| Gewicht: | 0,771 kg |
Über den Autor
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.
Inhaltsverzeichnis
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
Details
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 432 |
| Inhalt: | Preface xiAcknowledgments xiiiAbout the Author xvChapter 1 Introduction to Marketing Data Science 11.1 What Is Marketing Data Science? 21.2 The Role of Data Science in Marketing 41.3 Marketing Analytics Versus Data Science 51.4 Key Concepts and Terminolo |
| ISBN-13: | 9781394258710 |
| ISBN-10: | 1394258712 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Brown, Iain |
| Hersteller: | Wiley |
| Maße: | 256 x 178 x 26 mm |
| Von/Mit: | Iain Brown |
| Erscheinungsdatum: | 12.06.2024 |
| Gewicht: | 0,771 kg |
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