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<p>Preface xi</p> <p>Acknowledgments xiii</p> <p>About the Author xv</p> <p><b>Chapter 1 Introduction to Marketing Data Science 1</b></p> <p>1.1 What Is Marketing Data Science? 2</p> <p>1.2 The Role of Data Science in Marketing 4</p> <p>1.3 Marketing Analytics Versus Data Science 5</p> <p>1.4 Key Concepts and Terminology 7</p> <p>1.5 Structure of This Book 9</p> <p>1.6 Practical Example 1: Applying Data Science to Improve Cross-Selling in a Retail Bank Marketing Department 11</p> <p>1.7 Practical Example 2: The Impact of Data Science on a Marketing Campaign 13</p> <p>1.8 Conclusion 15</p> <p>1.9 References 15</p> <p><b>Chapter 2 Data Collection and Preparation 17</b></p> <p>2.1 Introduction 18</p> <p>2.2 Data Sources in Marketing: Evolution and the Emergence of Big Data 19</p> <p>2.3 Data Collection Methods 23</p> <p>2.4 Data Preparation 25</p> <p>2.5 Practical Example: Collecting and Preparing Data for a Customer Churn Analysis 39</p> <p>2.6 Conclusion 41</p> <p>2.7 References 41</p> <p>Exercise 2.1: Data Cleaning and Transformation 43</p> <p>Exercise 2.2: Data Aggregation and Reduction 45</p> <p><b>Chapter 3 Descriptive Analytics in Marketing 49</b></p> <p>3.1 Introduction 50</p> <p>3.2 Overview of Descriptive Analytics 51</p> <p>3.3 Descriptive Statistics for Marketing Data 52</p> <p>3.4 Data Visualization Techniques 56</p> <p>3.5 Exploratory Data Analysis in Marketing 60</p> <p>3.6 Analyzing Marketing Campaign Performance 65</p> <p>3.7 Practical Example: Descriptive Analytics for a Beverage Company’s Social Media Marketing Campaign 68</p> <p>3.8 Conclusion 70</p> <p>3.9 References 71</p> <p>Exercise 3.1: Descriptive Analysis of Marketing Data 72</p> <p>Exercise 3.2: Data Visualization and Interpretation 76</p> <p><b>Chapter 4 Inferential Analytics and Hypothesis Testing 81</b></p> <p>4.1 Introduction 82</p> <p>4.2 Inferential Analytics in Marketing 82</p> <p>4.3 Confidence Intervals 92</p> <p>4.4 A/B Testing in Marketing 95</p> <p>4.5 Hypothesis Testing in Marketing 101</p> <p>4.6 Customer Segmentation and Processing 106</p> <p>4.7 Practical Examples: Inferential Analytics for Customer Segmentation and Hypothesis Testing for Marketing Campaign Performance 115</p> <p>4.8 Conclusion 119</p> <p>4.9 References 120</p> <p>Exercise 4.1: Bayesian Inference for Personalized Marketing 122</p> <p>Exercise 4.2: A/B Testing for Marketing Campaign Evaluation 124</p> <p><b>Chapter 5 Predictive Analytics and Machine Learning 129</b></p> <p>5.1 Introduction 130</p> <p>5.2 Predictive Analytics Techniques 132</p> <p>5.3 Machine Learning Techniques 135</p> <p>5.4 Model Evaluation and Selection 144</p> <p>5.5 Churn Prediction, Customer Lifetime Value, and Propensity Modeling 150</p> <p>5.6 Market Basket Analysis and Recommender Systems 154</p> <p>5.7 Practical Examples: Predictive Analytics and Machine Learning in Marketing 158</p> <p>5.8 Conclusion 164</p> <p>5.9 References 165</p> <p>Exercise 5.1: Churn Prediction Model 167</p> <p>Exercise 5.2: Predict Weekly Sales 170</p> <p><b>Chapter 6 Natural Language Processing in Marketing 173</b></p> <p>6.0 Beginner-Friendly Introduction to Natural Language Processing in Marketing 174</p> <p>6.1 Introduction to Natural Language Processing 174</p> <p>6.2 Text Preprocessing and Feature Extraction in Marketing Natural Language Processing 178</p> <p>6.3 Key Natural Language Processing Techniques for Marketing 182</p> <p>6.4 Chatbots and Voice Assistants in Marketing 188</p> <p>6.5 Practical Examples of Natural Language Processing in Marketing 192</p> <p>6.6 Conclusion 196</p> <p>6.7 References 197</p> <p>Exercise 6.1: Sentiment Analysis 199</p> <p>Exercise 6.2: Text Classification 200                                                      </p> <p><b>Chapter 7 Social Media Analytics and Web Analytics 203</b></p> <p>7.1 Introduction 204</p> <p>7.2 Social Network Analysis 204</p> <p>7.3 Web Analytics Tools and Metrics 212</p> <p>7.4 Social Media Listening and Tracking 221</p> <p>7.5 Conversion Rate Optimization 227</p> <p>7.6 Conclusion 232</p> <p>7.7 References 233</p> <p>Exercise 7.1: Social Network Analysis (SNA) in Marketing 235</p> <p>Exercise 7.2: Web Analytics for Marketing Insights 238</p> <p><b>Chapter 8 Marketing Mix Modeling and Attribution 243</b></p> <p>8.1 Introduction 244</p> <p>8.2 Marketing Mix Modeling Concepts 244</p> <p>8.3 Data-Driven Attribution Models 251</p> <p>8.4 Multi-Touch Attribution 256</p> <p>8.5 Return on Marketing Investment 261</p> <p>8.6 Conclusion 266</p> <p>8.7 References 266</p> <p>Exercise 8.1: Marketing Mix Modeling (MMM) 268</p> <p>Exercise 8.2: Data- Driven Attribution 271</p> <p><b>Chapter 9 Customer Journey Analytics 275</b></p> <p>9.1 Introduction 276</p> <p>9.2 Customer Journey Mapping 276</p> <p>9.3 Touchpoint Analysis 280</p> <p>9.4 Cross-Channel Marketing Optimization 286</p> <p>9.5 Path to Purchase and Attribution Analysis 291</p> <p>9.6 Conclusion 296</p> <p>9.7 References 296</p> <p>Exercise 9.1: Creating a Customer Journey Map 298</p> <p>Exercise 9.2: Touchpoint Effectiveness Analysis 301</p> <p><b>Chapter 10 Experimental Design in Marketing 305</b></p> <p>10.1 Introduction 306</p> <p>10.2 Design of Experiments 306</p> <p>10.3 Fractional Factorial Designs 310</p> <p>10.4 Multi-Armed Bandits 315</p> <p>10.5 Online and Offline Experiments 320</p> <p>10.6 Conclusion 324</p> <p>10.7 References 325</p> <p>Exercise 10.1: Analyzing a Simple A/B Test 327</p> <p>Exercise 10.2: Fractional Factorial Design in Ad Optimization 328</p> <p><b>Chapter 11 Big Data Technologies and Real-Time Analytics 331</b></p> <p>11.1 Introduction 332</p> <p>11.2 Big Data 332</p> <p>11.3 Distributed Computing Frameworks 336</p> <p>11.4 Real-Time Analytics Tools and Techniques 343</p> <p>11.5 Personalization and Real-Time Marketing 348</p> <p>11.6 Conclusion 353</p> <p>11.7 References 354</p> <p><b>Chapter 12 Generative Artificial Intelligence and Its Applications in Marketing 357</b></p> <p>12.1 Introduction 358</p> <p>12.2 Understanding Generative Artificial Intelligence: Basics and Principles 359</p> <p>12.3 Implementing Generative Artificial Intelligence in Content Creation and Personalization 364</p> <p>12.4 Generative Artificial Intelligence in Predictive Analytics and Customer Behavior Modeling 367</p> <p>12.5 Ethical Considerations and Future Prospects of Generative Artificial Intelligence in Marketing 372</p> <p>12.6 Conclusion 375</p> <p>12.7 References 376</p> <p><b>Chapter 13 Ethics, Privacy, and the Future of Marketing Data Science 379</b></p> <p>13.1 Introduction 380</p> <p>13.2 Ethical Considerations in Marketing Data Science 380</p> <p>13.3 Data Privacy Regulations 386</p> <p>13.4 Bias, Fairness, and Transparency 391</p> <p>13.5 Emerging Trends and the Future of Marketing Data Science 395</p> <p>13.6 Conclusion 399</p> <p>13.7 References 400</p> <p>About the Website 403</p> <p>Index 405</p>

<p> <B>DR IAIN BROWN, </B> 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. <p>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.

<p> <b>Unveil the future of marketing with data science as your guide, transforming insights into action and theory into practice</b> <p>In an era where data reigns supreme, <i>Mastering Marketing Data Science: A Comprehensive Guide for Today’s Marketers </i>emerges as the quintessential guide for navigating the complex terrain of digital marketing analytics. Authored by Dr. Iain Brown, a leader in the field of data science, this comprehensive tome is meticulously crafted for students, professionals, and data scientists eager to unlock the transformative power of data in marketing. Through a unique blend of theoretical insights and practical applications, Brown demystifies the science behind data-driven marketing strategies that resonate in today’s digital-first world. <p>With chapters that traverse from the fundamentals of marketing data science to the cutting edge of generative AI, this guide is replete with real-world examples, hands-on exercises, and actionable insights. Readers are equipped to master the art of data collection, analysis, and application, paving the way for innovative marketing solutions that captivate and convert. Whether it’s through predictive analytics, NLP, or customer journey analytics, Brown’s expertise illuminates the path to leveraging data for strategic advantage. <p><i>Mastering Marketing Data Science </i>is more than a book; it’s a roadmap to the future of marketing. Dr. Brown invites readers to embark on a journey of discovery and transformation, where data science becomes the cornerstone of marketing excellence. Prepare to be empowered with knowledge that not only enlightens but also enables the application of data science principles to real-world marketing challenges, driving unparalleled business value and innovation. <p><b>Praise for MASTERING MARKETING DATA SCIENCE</b> <p>“<i>This book is an indispensable resource for marketers seeking to harness the power of data science to drive innovation, enhance customer engagement, and achieve competitive advantage in today’s digital landscape. A must-read for both seasoned professionals and those aspiring to transform their marketing strategies through data science.”</i><BR> <b>— Bernard Marr,</b> Bestselling Author and International Keynote Speaker on Business and Technology <p>“<i>This is an outstanding and timely book on </i>Mastering Marketing Data Science <i>as it provides a unique blend of foundational as well as emerging topics</i>.”<BR> <b>— Prof. dr. Bart Baesens,</b> Professor KU Leuven, Lecturer Southampton Business School <p>“Mastering Marketing Data Science <i>redefines the landscape of modern marketing, offering a compelling roadmap for harnessing the power of data science</i>.”<BR> <b>— Professor Ganna Pogrebna,</b> Lead for Behavioural Data Science at the Alan Turing Institute (UK); Executive Director at AI and Cyber Futures Institute and Honorary Professor at the University of Sydney Business School (Australia)

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