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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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