| customer data platform case studies: Customer Data Platforms Martin Kihn, Christopher B. O'Hara, 2020-11-05 Master the hottest technology around to drive marketing success Marketers are faced with a stark and challenging dilemma: customers demand deep personalization, but they are increasingly leery of offering the type of personal data required to make it happen. As a solution to this problem, Customer Data Platforms have come to the fore, offering companies a way to capture, unify, activate, and analyze customer data. CDPs are the hottest marketing technology around today, but are they worthy of the hype? Customer Data Platforms takes a deep dive into everything CDP so you can learn how to steer your firm toward the future of personalization. Over the years, many of us have built byzantine “stacks” of various marketing and advertising technology in an attempt to deliver the fabled “right person, right message, right time” experience. This can lead to siloed systems, disconnected processes, and legacy technical debt. CDPs offer a way to simplify the stack and deliver a balanced and engaging customer experience. Customer Data Platforms breaks down the fundamentals, including how to: Understand the problems of managing customer data Understand what CDPs are and what they do (and don't do) Organize and harmonize customer data for use in marketing Build a safe, compliant first-party data asset that your brand can use as fuel Create a data-driven culture that puts customers at the center of everything you do Understand how to use AI and machine learning to drive the future of personalization Orchestrate modern customer journeys that react to customers in real-time Power analytics with customer data to get closer to true attribution In this book, you’ll discover how to build 1:1 engagement that scales at the speed of today’s customers. |
| customer data platform case studies: Customer Data Platforms Martin Kihn, Christopher B. O'Hara, 2020-12-15 Master the hottest technology around to drive marketing success Marketers are faced with a stark and challenging dilemma: customers demand deep personalization, but they are increasingly leery of offering the type of personal data required to make it happen. As a solution to this problem, Customer Data Platforms have come to the fore, offering companies a way to capture, unify, activate, and analyze customer data. CDPs are the hottest marketing technology around today, but are they worthy of the hype? Customer Data Platforms takes a deep dive into everything CDP so you can learn how to steer your firm toward the future of personalization. Over the years, many of us have built byzantine “stacks” of various marketing and advertising technology in an attempt to deliver the fabled “right person, right message, right time” experience. This can lead to siloed systems, disconnected processes, and legacy technical debt. CDPs offer a way to simplify the stack and deliver a balanced and engaging customer experience. Customer Data Platforms breaks down the fundamentals, including how to: Understand the problems of managing customer data Understand what CDPs are and what they do (and don't do) Organize and harmonize customer data for use in marketing Build a safe, compliant first-party data asset that your brand can use as fuel Create a data-driven culture that puts customers at the center of everything you do Understand how to use AI and machine learning to drive the future of personalization Orchestrate modern customer journeys that react to customers in real-time Power analytics with customer data to get closer to true attribution In this book, you’ll discover how to build 1:1 engagement that scales at the speed of today’s customers. |
| customer data platform case studies: Introduction to Data Platforms Anthony David Giordano, 2022-11-03 Digital, cloud, and artificial intelligence (AI) have disrupted how we use data. This disruption has changed the way we need to provision, curate, and publish data for the multiple use cases in today's technology-driven environment. This text will cover how to design, develop, and evolve a data platform for all the uses of enterprise data needed in today's digital organization. This book focuses on explaining what a data platform is, what value it provides, how is it engineered, and how to deploy a data platform and support organization. In this context, Introduction to Data Platforms reviews the current requirements for data in the digital age and quantifies the use cases; discusses the evolution of data over the past twenty years, which is a core driver of the modern data platform; defines what a data platform is and defines the architectural components and layers of a data platform; provides the architectural layers or capabilities of a data platform; reviews cloud- and commercial-software vendors that populate the data-platform space; provides a step-by-step approach to engineering, deploying, supporting, and evolving a data-platform environment; provides a step-by-step approach to migrating legacy data warehouses, data marts, and data lakes/sandboxes to a data platform; and reviews organizational structures for managing data platform environments. |
| customer data platform case studies: Dependable Computing Ravishankar K. Iyer, Zbigniew T. Kalbarczyk, Nithin M. Nakka, 2024-04-18 Dependable Computing Covering dependability from software and hardware perspectives Dependable Computing: Design and Assessment looks at both the software and hardware aspects of dependability. This book: Provides an in-depth examination of dependability/fault tolerance topics Describes dependability taxonomy, and briefly contrasts classical techniques with their modern counterparts or extensions Walks up the system stack from the hardware logic via operating systems up to software applications with respect to how they are hardened for dependability Describes the use of measurement-based analysis of computing systems Illustrates technology through real-life applications Discusses security attacks and unique dependability requirements for emerging applications, e.g., smart electric power grids and cloud computing Finally, using critical societal applications such as autonomous vehicles, large-scale clouds, and engineering solutions for healthcare, the book illustrates the emerging challenges faced in making artificial intelligence (AI) and its applications dependable and trustworthy. This book is suitable for those studying in the fields of computer engineering and computer science. Professionals who are working within the new reality to ensure dependable computing will find helpful information to support their efforts. With the support of practical case studies and use cases from both academia and real-world deployments, the book provides a journey of developments that include the impact of artificial intelligence and machine learning on this ever-growing field. This book offers a single compendium that spans the myriad areas in which dependability has been applied, providing theoretical concepts and applied knowledge with content that will excite a beginner, and rigor that will satisfy an expert. Accompanying the book is an online repository of problem sets and solutions, as well as slides for instructors, that span the chapters of the book. |
| customer data platform case studies: Winning with Data in the Business of Sports Fiona Green, 2021-03-17 New technologies mean that sports clubs and governing bodies are generating more data than ever to help manage their relationship with fans, their performance, and their income streams. This new edition of Winning with Data in the Business of Sports explains how to acquire, store, maintain, and use data in the most effective ways. The key developments are three-fold: new technology, new understanding of how to apply that technology, and the new laws informing and controlling the data that can be generated from the technology. Important developments that have occurred since the publication of the first edition include the General Data Protection Regulations (GDPR) and the COVID-19 pandemic. With a focus on these unique challenges coupled with the opportunities the use of data creates, this book is essential reading for professionals within the sports industry. This second edition includes: - An introduction to new technologies, the data they generate, and the supporting processes we need to have in place to use them. - Brand new case studies with recent examples of creative applications from clubs, teams, leagues, and governing bodies, including Arsenal, AS Roma, ICC Cricket World Cup, LA Kings, Portland Trail Blazers, and UEFA. - The sports industry’s response to tighter data legislation introduced primarily though the GDPR. - The role of data and direct engagement during the COVID-19 pandemic. The book provides clear guidance and knowledge that sports industry professionals need to understand the role of data for the business side of sports. It is essential reading for sports clubs, governing bodies and those working in sports marketing, media and communications, sponsorship, merchandise, ticketing, events, and participation development. The book will also be of interest to students of sports management. |
| customer data platform case studies: Navigating Usability and User Experience in a Multi-Platform World Correia, Anacleto, 2024-11-01 The combined discipline of usability and user experience (UX) design emphasizes crafting solutions that prioritize the needs, preferences, and expectations of diverse users. As digital platforms and services continue to proliferate, the demand for seamless, user-centered experiences across various devices and interfaces has grown. Designers must now be well-versed in cross-platform design principles, ensuring that users encounter consistent, intuitive interactions regardless of the platform. Navigating Usability and User Experience in a Multi-Platform World delves into the ever-evolving field of usability and UX design, with a particular emphasis on multi-platform solutions. It approaches usability and UX design, exploring the discipline, its core principles, and its significance in the modern digital landscape. Covering topics such as agriculture, human resource development (HRD), and wearable devices, this book is an excellent resource for teachers, instructional designers, curriculum developers, program developers, administrators, educational software developers, policymakers, researchers, education professionals, training professionals, privacy practitioners, government officials, academicians, and more. |
| customer data platform case studies: Case Studies in the Wine Industry Cristina Santini, Alessio Cavicchi, 2018-11-27 Case Studies in the Wine Industry aims to close the gap between academic researchers and industry professionals through real world scenarios and field-based research. The book explores how consumer and sensory science has been implemented in the wine industry to achieve certain goals, including the rejuvenation of product image, the shaping of new market places, the achievement of market differentiation and geographical diffusion, the achievement of customer loyalty, and the promotion of traditional features of the product. There is an emerging demand from wine industry professionals and undergraduate and postgraduate students who attend business and agricultural studies courses who want to gain practical information through real cases and field-based research. - Bridges the gap between scholars and practitioners in understanding consumers of wine - Allows scientists and professionals to make the most of R&D outcomes - Advances consumer science research to address business problems in the wine industry |
| customer data platform case studies: Developing a Path to Data Dominance Arthur Langer, Arka Mukherjee, 2023-04-25 Most existing companies struggle currently because they lack the tools and strategies to move product departments into independent platforms that can be retrofitted to form dynamic new products based on consumer demands. This book provides managers and professionals with the necessary approaches for designing software and hardware architectures to support data platform organizations. Specifically, it demonstrates how to automate the decomposition of existing platforms into smaller parts that can be reused to form new variations. This task requires significant analysis and design methodologies and procedures to create an infrastructure based on data as opposed to products. These new knowledge bases allow data-centric professionals to pursue actions that can better predict and respond to the unexpected. Featuring case examples from companies such as Lego, FedEx, General Electric (GE), Pfizer, P&G and more, this book is appropriate for C-level executives engaged in the digital transformation of their firms; entrepreneurs of digital platform companies; and senior software engineers that need to design Internet of Things (IoT) devices and integrate them with block chain and multi-cloud architectures. In addition, this book is also useful for graduate-level coursework in data science. |
| customer data platform case studies: AWS Certified Database Specialty (DBS-C01) Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| customer data platform case studies: Data Integrity and Quality Santhosh Kumar Balan, 2021-06-23 Data integrity is the quality, reliability, trustworthiness, and completeness of a data set, providing accuracy, consistency, and context. Data quality refers to the state of qualitative or quantitative pieces of information. Over five sections, this book discusses data integrity and data quality as well as their applications in various fields. |
| customer data platform case studies: Data-Driven Customer Engagement Ralf Strauss, |
| customer data platform case studies: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-11-04 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website. |
| customer data platform case studies: Winning With Data Fiona Green, 2018-08-06 For many years, sports rights owners have had an ‘if you build it, they will come’ attitude, suggesting they take their fans for granted. Combined with advances in broadcasting quality, digital marketing, and social media, this has resulted in diminishing attendances and participation levels. The use of CRM (Customer Relationship Management), BI (Business Intelligence) and Data Analytics has therefore become integral to doing business in sports, emulating the approach used by brands such as Amazon, Netflix, and Spotify. Technology has made the world a smaller place; clubs and teams can now connect with their fans anywhere in the world, allowing them to grow their marketplace, but they operate in an ‘attention economy’ where there’s too much choice and engagement is key. This book sets out to share the processes and principles the sports industry uses to capitalise on the natural loyalty it creates. Case studies and commentary from around the world are used to demonstrate some of the practices implemented by the world’s leading sports brands including clubs Arsenal and the San Antonio Spurs. the governing bodies of UEFA and Special Olympics International, and the MLS and NHL. With a focus on our unique challenges coupled with the opportunities the use of data creates, this book is essential reading for professionals within the sports industry. |
| customer data platform case studies: Python Machine Learning Case Studies Danish Haroon, 2017-10-27 Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems. What You Will Learn Gain insights into machine learning concepts Work on real-world applications of machine learning Learn concepts of model selection and optimization Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts. |
| customer data platform case studies: AI-Powered Productivity Dr. Asma Asfour, 2024-07-29 This book, AI-Powered Productivity, aims to provide a guide to understanding, utilizing AI and generative tools in various professional settings. The primary purpose of this book is to offer readers a deep dive into the concepts, tools, and practices that define the current AI landscape. From foundational principles to advanced applications, this book is structured to cater to both beginners and professionals looking to enhance their knowledge and skills in AI. This book is divided into nine chapters, each focusing on a specific aspect of AI and its practical applications: Chapter 1 introduces the basic concepts of AI, its impact on various sectors, and key factors driving its rapid advancement, along with an overview of generative AI tools. Chapter 2 delves into large language models like ChatGPT, Google Gemini, Claude, Microsoft's Turing NLG, and Facebook's BlenderBot, exploring their integration with multimodal technologies and their effects on professional productivity. Chapter 3 offers a practical guide to mastering LLM prompting and customization, including tutorials on crafting effective prompts and advanced techniques, as well as real-world examples of AI applications. Chapter 4 examines how AI can enhance individual productivity, focusing on professional and personal benefits, ethical use, and future trends. Chapter 5 addresses data-driven decision- making, covering data analysis techniques, AI in trend identification, consumer behavior analysis, strategic planning, and product development. Chapter 6 discusses strategic and ethical considerations of AI, including AI feasibility, tool selection, multimodal workflows, and best practices for ethical AI development and deployment. Chapter 7 highlights the role of AI in transforming training and professional development, covering structured training programs, continuous learning initiatives, and fostering a culture of innovation and experimentation. Chapter 8 provides a guide to successfully implementing AI in organizations, discussing team composition, collaborative approaches, iterative development processes, and strategic alignment for AI initiatives. Finally, Chapter 9 looks ahead to the future of work, preparing readers for the AI revolution by addressing training and education, career paths, common fears, and future trends in the workforce. The primary audience for the book is professionals seeking to enhance productivity and organizations or businesses. For professionals, the book targets individuals from various industries, reflecting its aim to reach a broad audience across different professional fields. It is designed for employees at all levels, offering valuable insights to both newcomers to AI and seasoned professionals. Covering a range of topics from foundational concepts to advanced applications, the book is particularly relevant for those interested in improving efficiency, with a strong emphasis on practical applications and productivity tools to optimize work processes. For organizations and businesses, the book serves as a valuable resource for decision-makers and managers, especially with chapters on data-driven decision-making, strategic considerations, and AI implementation. HR and training professionals will find the focus on AI in training and development beneficial for talent management, while IT and technology teams will appreciate the information on AI tools and concepts. |
| customer data platform case studies: AWS Certified SAP on AWS Specialty (PAS-C01) Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| customer data platform case studies: Data Warehousing in the Age of Big Data Krish Krishnan, 2013-05-02 Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. - Learn how to leverage Big Data by effectively integrating it into your data warehouse. - Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies - Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements |
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| customer data platform case studies: Exploring the Intricacies of Data Analytics Nishant Baxi, 2023-09-04 Definition of Data Analytics Data analytics is a broad term used to refer to the process of examining, cleansing, transforming, and modeling data sets to discover useful information and support decision-making. This complex discipline encompasses a variety of techniques and theories drawn from many fields within the broad context of mathematics, statistics, computer science, and information science (Suthaharan, 2016). Data analytics techniques are generally divided into four types: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics involves analyzing historical data to identify patterns and trends; this is often used in business intelligence. Diagnostic analytics seeks to identify the causes of a particular outcome. |
| customer data platform case studies: Emerging Technologies and Security in Cloud Computing Lakshmi, D., Tyagi, Amit Kumar, 2024-02-14 In today's digital age, the exponential growth of cloud computing services has brought significant opportunities for businesses and individuals alike. However, this surge in cloud adoption has also ushered in a host of critical concerns, with the paramount issues being data privacy and security. The goal of protecting sensitive information from cyber threats and ensuring confidentiality has become increasingly challenging for organizations across industries. Emerging Technologies and Security in Cloud Computing is a comprehensive guide designed to tackle these pressing concerns head-on. This authoritative book provides a robust framework for understanding and addressing the multifaceted issues surrounding data privacy and security in the cloud. It serves as a beacon of knowledge for academic scholars, researchers, and IT professionals seeking practical solutions to safeguard sensitive data. |
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| customer data platform case studies: Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation Christophe Danjou, |
| customer data platform case studies: What's Your Digital Business Model? Peter Weill, Stephanie Woerner, 2018-04-17 Digital transformation is not about technology--it's about change. In the rapidly changing digital economy, you can't succeed by merely tweaking management practices that led to past success. And yet, while many leaders and managers recognize the threat from digital--and the potential opportunity--they lack a common language and compelling framework to help them assess it and guide them in responding. They don't know how to think about their digital business model. In this concise, practical book, MIT digital research leaders Peter Weill and Stephanie Woerner provide a powerful yet straightforward framework that has been field-tested globally with dozens of senior management teams. Based on years of study at the MIT Center for Information Systems Research (CISR), the authors find that digitization is moving companies' business models on two dimensions: from value chains to digital ecosystems, and from a fuzzy understanding of the needs of end customers to a sharper one. Looking at these dimensions in combination results in four distinct business models, each with different capabilities. The book then sets out six driving questions, in separate chapters, that help managers and executives clarify where they are currently in an increasingly digital business landscape and highlight what's needed to move toward a higher-value digital business model. Filled with straightforward self-assessments, motivating examples, and sharp financial analyses of where profits are made, this smart book will help you tackle the threats, leverage the opportunities, and create winning digital strategies. |
| customer data platform case studies: Marketing and Sales Automation Uwe Hannig, Uwe Seebacher, 2023-05-02 This book clarifies based on latest findings and research what one needs to know about marketing and sales automation, how to manage projects to implement them, select and implement tools, and what results can be achieved. It also outlines what can be expected in the future such as the automation of corporate communication and Human Resources. The range of topics spans from the creation of a valid data base in the context of applied AI for realizing predictive intelligence and the effects of data regulations such as the European General Data Protection Regulation (GDPR) when addressing customers and prospects to recommendations for selecting and implementing the necessary IT systems. Experts also report on their experiences in regard to Conversion-rate-optimization (CRO) and provide tips and assistance on how to optimize and ensure the highest RoI for marketing and sales automation. A special focus will be placed on the dovetailing of marketing and sales and the management of the customer journey as well as the improvement of the customer experience. |
| customer data platform case studies: Business Modelling in the Dynamic Digital Space Omar A El Sawy, Francis Pereira, 2012-08-14 This Springer Briefs volume guides the reader in a comprehensive form to design new digital business models. The book provides strategic roadmaps for enterprises in the digital world, and a comprehensive framework to assess new business models. It aligns both, research and a practical perspective through real case study examples. Even extreme scenarios are employed to ensure that innovative approaches are being considered adequately. |
| customer data platform case studies: The role of data for digital markets contestability Jan Krämer, Daniel Schnurr, Sally Broughton Micova, 2020-09-09 This report analyses the processes that turn data into economic value for online search, e-commerce and media platforms. It concludes that forcing data sharing through policy intervention would not prevent dominant incumbents to continue to benefit economically from greater access to data over new entrants. Instead, policy makers should focus on enabling niche entry, niche growth and a level playing field for competitors in new and emerging markets. Data play a central role in the business models that shape competition and innovation in digital markets. As dominant providers of online services collect ever more user data they generate data-driven network effects. They can then improve their services faster, and venture faster into related markets than competitors with less data, thereby raising entry barriers for innovative start-ups. The authors, Sally Broughton Micova (CERRE & University of East Anglia), Jan Krämer (CERRE & University of Passau) and Daniel Schnurr (University of Passau), have analysed processes that transform data into economic value for online search, e-commerce and media platforms. They find that in each case, more data, especially on user behaviour, gradually improves the quality of the service, thereby generating high economic benefits for the firm. The authors find that data-driven network effects can nevertheless be a source of efficiency which can ultimately benefit consumers. Even if some data is shared through policy intervention, dominant incumbents will continue to benefit economically and competitively from greater access to data over new entrants. “We conclude that it is neither realistic nor desirable to try to break data-driven network effects through policy intervention. Instead, we would strongly encourage policy makers to focus on enabling niche entry and niche growth. To do so, they should facilitate the sharing of behavioural user data gathered by the dominant firm with other firms.” The authors provide policy recommendations for data access remedies to safeguard competition, innovation and the openness of the digital ecosystem: 1. Remedies that achieve a more level playing field in the digital economy by breaking the data-driven network effects of data-rich incumbents should be entertained as a last resort and only under specific conditions. 2. Policy makers should foster data sharing on two levels to strike a balance between consumers’ privacy, competition and innovation. They should require the sharing of aggregated and anonymised raw user data in bulk, after a careful review and on a case-by-case basis. They should also facilitate the sharing of detailed raw user data through improved data portability, based on individual users’ consent. Bulk sharing of raw user data should be limited to data that was collected as a by-product of the incumbent’s dominant user-facing service, such as search logs, in order to maintain incentives for innovation and data collection. The main challenge will be to balance privacy concerns with maintaining enough detailed data to ensure it is of value to third-parties. 3. Dominant firms should also be obliged to allow consumers to port their raw data to another provider continuously and in real time. Privacy concerns can then be overcome and the shared user profiles can be more detailed than under bulk sharing. In concert with bulk-sharing, data portability can be a valuable source for attaining both detailed and representative data sets. |
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| customer data platform case studies: Microsoft Certified: Identity and Access Administrator Associate (SC-300) , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| customer data platform case studies: Inside Sales Exam Essentials Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| customer data platform case studies: Writing AI Prompts For Dummies Stephanie Diamond, Jeffrey Allan, 2024-04-02 Learn the art of writing effective AI prompts and break into an exciting new career field Unlock the full power of generative AI with Writing AI Prompts For Dummies, a comprehensive guide that will teach you how to confidentially write effective AI prompts. Whether it's text, images, or even videos and music you're aiming to create, this book provides the foundational knowledge and practical strategies needed to produce impressive results. Embark on a journey of discovery with Writing AI Prompts For Dummies and learn how to: Craft AI prompts that produce the most powerful results. Navigate the complexities of different AI platforms with ease. Generate a diverse range of content, from compelling narratives to stunning visuals. Refine AI-generated output to perfection and integrate that output effectively into your business or project. This resource is brimming with expert guidance and will help you write AI prompts that achieve your objectives. Whether you're a marketer, educator, artist, or entrepreneur, Writing AI Prompts For Dummies is your indispensable guide for leveraging AI to its fullest potential. Get ready to harness the power of artificial intelligence and spark a revolution in your creative and professional efforts. |
| customer data platform case studies: Data as a Service Pushpak Sarkar, 2015-08-24 Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions |
| customer data platform case studies: Data Analytics for Business Fenio Annansingh, Joseph Bon Sesay, 2022-04-20 Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor’s manual are also provided as online supplements. This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas. |
| customer data platform case studies: Microsoft Certified: Azure Virtual Desktop Specialty (AZ-140) Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| customer data platform case studies: Agile Processes in Software Engineering and Extreme Programming – Workshops Maria Paasivaara, Philippe Kruchten, 2020-09-23 This open access book constitutes the 6 research workshops, the Agile Education and Training Track, the Doctoral Symposium, as well as a panel presented at XP 2020, the 21st International Conference on Agile Software Development, which was held during June 8-12, 2020. The conference was planned to take place at the IT University of Copenhagen, Denmark. Due to the COVID 19 pandemic, the conference was held online. In 2020, the following six workshops took place: Third International Workshop on Software-Intensive Business Eighth International Workshop on Large-Scale Agile Development Second European Symposium on Serverless Computing and Applications Second International Workshop on Agile Transformation First International Workshop on Agility with Microservices Programming Third International Workshop on Autonomous Agile Teams XP is the premier agile software development conference combining research and practice. It is a unique forum where agile researchers, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. XP conferences provide an informal environment to learn and trigger discussions and welcome both people new to agile and seasoned agile practitioners. The 31 papers presented in this volume were carefully reviewed and selected from overall 79 submissions. In addition to the 26 workshop papers, this volume also includes 2 papers from the Agile Education and Training Track and 3 papers from the Doctoral Symposium. Furthermore, the book contains a summary of a panel discussion with the topic “Covid-19’s Influence on the Future of Agile”. |
| customer data platform case studies: Data Governance Dimitrios Sargiotis, |
| customer data platform case studies: Sustainable Security Practices Using Blockchain, Quantum and Post-Quantum Technologies for Real Time Applications Adarsh Kumar, |
| customer data platform case studies: Data Mining Using SAS Applications George Fernandez, 2010-12-12 Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data. Learn how to convert PC databases to SAS data Discover sampling techniques to create training and validation samples Understand frequency data analysis for categorical data Explore supervised and unsupervised learning Master exploratory graphical techniques Acquire model validation techniques in regression and classification The text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes! |
| customer data platform case studies: Pro SQL Server 2008 Analytics Brian Paulen, Jeff Finken, 2009-11-03 Pro SQL Server 2008 Analytics provides everything you need to know to develop sophisticated and visually appealing sales and marketing dashboards using SQL Server 2008 and to integrate those dashboards with SharePoint, PerformancePoint, and other key Microsoft technologies. The book begins by addressing the many misconceptions that surround the use of key performance indicators (KPIs) and giving a brief overview of the business intelligence (BI) and reporting tools that can be combined on the Microsoft platform to help you generate the results that you need. The focus of the book is to help you implement a successful business intelligence project of your own. The text discusses many of the required project planning components, provides overviews and examples associated with Microsoft's BI tools, and gives detailed examples of successful dashboard implementations. |
consumer、customer、client 有何区别？ - 知乎
对于customer和consumer，我上marketing的课的时候区分过这两个定义。 customer behavior：a broad term that covers individual consumers who buy goods and services for their own use …
Consumer与customer有区别吗？具体作什么区别？ - 知乎
Mar 18, 2014 · 一般把 customer 翻译做 “客户“ 比如你是杜蕾斯的生产商，那么中国总代，上海曼伦商贸有限公司，就是你的customer，然后从曼伦进货的全家就是曼伦的customer，然后隔 …
Windows 10 business 和 consumer 中的专业版有什么不同？ - 知乎
Mar 14, 2020 · Windows10 有business editions 和 consumer editions 版。其中每个都有 专业工作站版，可这2个专业工作…
想问一下大家web of science文献检索点不动 只能用作者检索怎么 …
手机电脑打开都是这样 我想用文献检索 不想用作者检索啊啊啊啊啊
什么是CRM系统？它的作用是什么？ - 知乎
CRM（Customer Relationship Management），即客户关系管理系统.。 是指利用软件、硬件和网络技术，为企业建立一个客户信息收集、管理、分析和利用的信息系统。通俗地讲， CRM就 …
请问金融系统中提到的KYC是做什么用的？ - 知乎
KYC看着高端，其实我们每个人都经历过。例如，当你去银行开户的时候，都必须要提交身份证件，甚至有时候还要提交家庭住址证明。这便是一个最简单的KYC。(也叫做CIP - Customer …
什么是SCRM？为什么企业要做SCRM？ - 知乎
SCRM翻译后的全程是：Social Customer Relationship Management ，可以看到这里的“S”原来是“Social”，也就是“社交”的意思。 尽管只是多了一个S，却将原先CRM呈现的客户管理行为转 …
什么是跨境电商，你们了解多少？ - 知乎
跨境电子商务是指不同国度或地域的买卖双方经过互联网以邮件或者快递等方式通关，将传统贸易中的展现、洽谈和成交环节数字化，完成产品进口的的新型贸易方式，当前主流的跨境电商形 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
新媒体的KOL、KOC是什么? - 知乎
KOC有双重身份，即Customer和Creator，KOC是消费者的同时也是创作者，是对消费者的消费决策起到关键作用的群体。 KOL与KOC在本质上截然不同，是两个群体。前者是推，而KOC是 …
consumer、customer、client 有何区别？ - 知乎
对于customer和consumer，我上marketing的课的时候区分过这两个定义。 customer behavior：a broad term that covers individual consumers who buy goods and services for their own use …
Consumer与customer有区别吗？具体作什么区别？ - 知乎
Mar 18, 2014 · 一般把 customer 翻译做 “客户“ 比如你是杜蕾斯的生产商，那么中国总代，上海曼伦商贸有限公司，就是你的customer，然后从曼伦进货的全家就是曼伦的customer，然后隔壁 …
Windows 10 business 和 consumer 中的专业版有什么不同？ - 知乎
Mar 14, 2020 · Windows10 有business editions 和 consumer editions 版。其中每个都有 专业工作站版，可这2个专业工作…
想问一下大家web of science文献检索点不动 只能用作者检索怎么 …
手机电脑打开都是这样 我想用文献检索 不想用作者检索啊啊啊啊啊
什么是CRM系统？它的作用是什么？ - 知乎
CRM（Customer Relationship Management），即客户关系管理系统.。 是指利用软件、硬件和网络技术，为企业建立一个客户信息收集、管理、分析和利用的信息系统。通俗地讲， CRM就 …
请问金融系统中提到的KYC是做什么用的？ - 知乎
KYC看着高端，其实我们每个人都经历过。例如，当你去银行开户的时候，都必须要提交身份证件，甚至有时候还要提交家庭住址证明。这便是一个最简单的KYC。(也叫做CIP - Customer …
什么是SCRM？为什么企业要做SCRM？ - 知乎
SCRM翻译后的全程是：Social Customer Relationship Management ，可以看到这里的“S”原来是“Social”，也就是“社交”的意思。 尽管只是多了一个S，却将原先CRM呈现的客户管理行为转移 …
什么是跨境电商，你们了解多少？ - 知乎
跨境电子商务是指不同国度或地域的买卖双方经过互联网以邮件或者快递等方式通关，将传统贸易中的展现、洽谈和成交环节数字化，完成产品进口的的新型贸易方式，当前主流的跨境电商形 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
新媒体的KOL、KOC是什么? - 知乎
KOC有双重身份，即Customer和Creator，KOC是消费者的同时也是创作者，是对消费者的消费决策起到关键作用的群体。 KOL与KOC在本质上截然不同，是两个群体。前者是推，而KOC是 …
