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The ever-expanding realm of Big Data poses a formidable challenge for academic scholars and professionals due to the sheer magnitude and diversity of data types, along with the continuous influx of information from various sources. Extracting valuable insights from this vast and complex dataset is crucial for organizations to uncover market intelligence and make informed decisions. However, without the proper guidance and understanding of Big Data analytics techniques and methodologies, scholars may struggle to navigate this landscape and maximize the potential benefits of their research. In response to this pressing need, Professor Dina Darwish presents Big Data Analytics Techniques for Market Intelligence, a groundbreaking book that addresses the specific challenges faced by scholars and professionals in the field. Through a comprehensive exploration of various techniques and methodologies, this book offers a solution to the hurdles encountered in extracting meaningful information from Big Data. Covering the entire lifecycle of Big Data analytics, including preprocessing, analysis, visualization, and utilization of results, the book equips readers with the knowledge and tools necessary to unlock the power of Big Data and generate valuable market intelligence. With real-world case studies and a focus on practical guidance, scholars and professionals can effectively leverage Big Data analytics to drive strategic decision-making and stay at the forefront of this rapidly evolving field.
Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows. With this book, you'll learn: How DP guarantees privacy when other data anonymization methods don't What preserving individual privacy in a dataset entails How to apply DP in several real-world scenarios and datasets Potential privacy attack methods, including what it means to perform a reidentification attack How to use the OpenDP library in privacy-preserving data releases How to interpret guarantees provided by specific DP data releases
The surge in technological advancements, coupled with the exponential growth of data, has left marketers grappling with the need for a paradigm shift. The once-established methods of consumer engagement are now overshadowed by the complexities of the digital age, demanding a profound understanding of artificial intelligence (AI) and data analytics. The gap between academic knowledge and practical applications in the field of marketing has widened, leaving industry professionals, educators, and students seeking a comprehensive resource to navigate the intricacies of this transformative era. AI-Driven Marketing Research and Data Analytics is a groundbreaking book that serves as a beacon for marketers, educators, and industry leaders alike. With a keen focus on the symbiotic relationship between AI, data analytics, and marketing research, this book bridges the gap between theory and practice. It not only explores the historical evolution of marketing but also provides an innovative examination of how AI and data analytics are reshaping the landscape. Through real-time case studies, ethical considerations, and in-depth insights, the book offers a holistic solution to the challenges faced by marketing professionals in the digital age.
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.
**Winner of the 2021 Leonard L. Berry Marketing Book Award from the American Marketing Association.** Firms are collecting and analyzing customer data at an ever increasing rate in response to evidence that data analytics (precision targeting, improved selling) generates a positive return. Yet efforts often ignore customers’ privacy concerns and feelings of vulnerability with long-term effects on customers’ trust, relationships, and ultimately financial performance. Big data, privacy, and cybersecurity often is relegated to IT and legal teams with minimal regard for customer relationships. This book fills the void by taking a customer-centric approach to privacy. It offers both defensive and offensive marketing-based privacy strategies that strongly position firms in today’s data-intensive landscape. The book also helps managers anticipate future consumer and legislative trends. Drawing from the authors’ own work and extant research, this book offers a compelling guide for building and implementing big data- and privacy-informed business strategies. Specifically, the book: · -Describes the consumer psychology of privacy · -Deconstructs relevant legal and regulatory issues · - Offers defensive privacy strategies · - Describes offensive privacy strategies · Provides an executive summary with the Six Tenets for Effective Privacy Marketing This book will be useful to managers, students, or the casual reader who is interested in how and why big data and consumer privacy are transforming business. Moving beyond summary privacy insights, the book also offers a detailed and compelling action plan for improving performance by protecting against privacy threats as well as developing and implementing offensive privacy strategy. In the future, many firms will be competing through an integrated, customer-centric big data privacy strategy and this book will guide managers in this journey.
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies. This book: • Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data. • Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making. • Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context. • Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions. • Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing.
Firms are collecting and analyzing customer data at an ever increasing rate in response to evidence that data analytics (precision targeting, improved selling) generates a positive return. Yet efforts often ignore customers’ privacy concerns and feelings of vulnerability with long-term effects on customers’ trust, relationships, and ultimately financial performance. Big data, privacy, and cybersecurity often is relegated to IT and legal teams with minimal regard for customer relationships. This book fills the void by taking a customer-centric approach to privacy. It offers both defensive and offensive marketing-based privacy strategies that strongly position firms in today’s data-intensive landscape. The book also helps managers anticipate future consumer and legislative trends. Drawing from the authors’ own work and extant research, this book offers a compelling guide for building and implementing big data- and privacy-informed business strategies. Specifically, the book: · -Describes the consumer psychology of privacy · -Deconstructs relevant legal and regulatory issues · - Offers defensive privacy strategies · - Describes offensive privacy strategies · Provides an executive summary with the Six Tenets for Effective Privacy Marketing This book will be useful to managers, students, or the casual reader who is interested in how and why big data and consumer privacy are transforming business. Moving beyond summary privacy insights, the book also offers a detailed and compelling action plan for improving performance by protecting against privacy threats as well as developing and implementing offensive privacy strategy. In the future, many firms will be competing through an integrated, customer-centric big data privacy strategy and this book will guide managers in this journey.
In recent years, technological advances have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Privacy and Security Policies in Big Data is a pivotal reference source for the latest research on innovative concepts on the management of security and privacy analytics within big data. Featuring extensive coverage on relevant areas such as kinetic knowledge, cognitive analytics, and parallel computing, this publication is an ideal resource for professionals, researchers, academicians, advanced-level students, and technology developers in the field of big data.