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Unstructured data analytics : how to improve customer acquisition, customer retention, and fraud detection and prevention /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Isson, Jean Paul, |d 1971- | |
| 245 | 1 | 0 | |a Unstructured data analytics : |b how to improve customer acquisition, customer retention, and fraud detection and prevention / |c Jean Paul Isson. |
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c 2018. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 347 | |a data file | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed March 12, 2018). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover; Title Page; Copyright; Contents; Foreword; Preface; Acknowledgments; Chapter 1: The Age of Advanced Business Analytics; Introduction; Why the Analytics Hype Today?; 1. Costs to Store and Process Information Have Reduced; 2. Interactive Devices and Censors Have Increased; 3. Data Analytics Infrastructures and Software Have Increased; 4. User-Friendly and Invisible Data Analytics Tools Have Emerged; 5. Data Analytics Is Becoming Mainstream, and It Means a Lot to Our Economy and World; 6. Major Leading Tech Companies Have Pioneered the Data Economy. | |
| 505 | 8 | |a 7. Big Data Analytics Has Become a Big Market Opportunity8. The Number of Data Science University Programs and MOOCs Has Intensified; A Short History of Data Analytics; Early Adopters: Insurance and Finance; What is the Analytics Age?; Interview with Wayne Thompson, Chief Data Scientist at SAS Institute; Key Takeaways; Notes; Further Reading; Chapter 2: Unstructured Data Analytics: The Next Frontier of Analytics Innovation; Introduction; What Is UDA?; Why UDA Today?; Big Data as a Catalyst; Artificial Intelligence (AI); Machine Learning; Deep Learning. | |
| 505 | 8 | |a Representation Learning or Feature LearningNatural Language Processing; Cognitive Computing/Analytics; Neural Network; The UDA Industry; Uses of UDA; How UDA Works; Why UDA Is the Next Analytical Frontier?; Interview with Seth Grimes on Analytics as the Next Business Frontier; UDA Success Stories; Amazon.com; Spotify; Facebook; ITA Software; Internet Search Engines: Bing.com, Google.com, and the Like; Monster Worldwide; The Golden Age of UDA; Key Takeaways; Notes; Further Reading; Chapter 3: The Framework to Put UDA to Work; Introduction; Why Have a Framework to Analyze Unstructured Data? | |
| 505 | 8 | |a The IMPACT Cycle Applied to Unstructured DataFocusing on the IMPACT; Identify Business Questions; Master the Data; Text Parsing Example; The T3; Technique; Tools; Interview with Cindy Forbes, Chief Analytics Officer and Executive Vice President at Manulife Financial; Case Study; Key Takeaways; Notes; Further Reading; Chapter 4: How to Increase Customer Acquisition and Retention with UDA; The Voice of the Customer: A Goldmine for Understanding Customers; Why Should You Care about UDA for Customer Acquisition and Retention?; The Voice of the Customer; Predictive Models and Online Marketing. | |
| 505 | 8 | |a Predictive ModelsUDA and Online Marketing: Optimizing Your Acquisition and Customer Response Models; How Does UDA Applied to Customer Acquisition Work?; The Power of UDA for E-mail Response and Ad Optimization; How to Drive More Conversion and Engagement with UDA Applied to Content; How UDA Applied to Customer Retention (Churn) Works; What Is UDA Applied to Customer Acquisition?; Consumer/Customer Decision Journey; Lessons from McKinsey's Consumer Decision Journey; What Is UDA Applied to Customer Retention (Churn)?; The Power of UDA Powered by Virtual Agent. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | 8 | |a Annotation |b 'Unstructured Data Analytics' provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear example of both traditional business applications and newer, more innovative practices. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Industrial management |x Statistical methods. | |
| 650 | 0 | |a Business planning. | |
| 650 | 6 | |a Gestion d'entreprise |x Méthodes statistiques. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Industrial Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management Science. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Organizational Behavior. |2 bisacsh | |
| 650 | 7 | |a Business planning. |2 fast |0 (OCoLC)fst00842819 | |
| 650 | 7 | |a Industrial management |x Statistical methods. |2 fast |0 (OCoLC)fst00971330 | |
| 776 | 0 | 8 | |i Print version: |a Isson, Jean Paul, 1971- |t Unstructured data analytics. |d Hoboken, New Jersey : Wiley, 2018 |w (DLC) 2017278101 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781119129752/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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