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Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention

Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention

Autorzy
Wydawnictwo Wiley & Sons
Data wydania
Liczba stron 432
Forma publikacji książka w twardej oprawie
Język angielski
ISBN 9781119129752
Kategorie Prawdopodobieństwo i statystyka
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Opis książki

Turn unstructured data into valuable business insightUnstructured 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 examples of both traditional business applications and newer, more innovative practices.Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work.Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence.You will learn:* How to increase Customer Acquisition and Customer Retention with UDA* The Power of UDA for Fraud Detection and Prevention* The Power of UDA in Human Capital Management & Human Resource* The Power of UDA in Health Care and Medical Research* The Power of UDA in National Security* The Power of UDA in Legal Services* The Power of UDA for product development* The Power of UDA in Sports* The future of UDAFrom small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.

Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention

Spis treści

Foreword xiiiPreface xvAcknowledgments xixChapter 1 The Age of Advanced Business Analytics 1Introduction 1Why the Analytics Hype Today? 5A Short History of Data Analytics 15What Is the Analytics Age? 22Interview with Wayne Thompson, Chief Data Scientist atSAS Institute 23Key Takeaways 28Notes 29Further Reading 30Chapter 2 Unstructured Data Analytics: The Next Frontier of Analytics Innovation 33Introduction 33What Is UDA? 35Why UDA Today? 39The UDA Industry 48Uses of UDA 51How UDA Works 52Why UDA Is the Next Analytical Frontier? 54Interview with Seth Grimes on Analytics as the NextBusiness Frontier 58UDA Success Stories 60The Golden Age of UDA 64Key Takeaways 65Notes 66Further Reading 67Chapter 3 The Framework to Put UDA to Work 69Introduction 69Why Have a Framework to Analyze Unstructured Data? 70The IMPACT Cycle Applied to Unstructured Data 72Text Parsing Example 81Interview with Cindy Forbes, Chief Analytics Officer and Executive Vice President at Manulife Financial 84Case Study 90Key Takeaways 106Notes 107Further Reading 108Chapter 4 How to Increase Customer Acquisition and Retention with UDA 109The Voice of the Customer: A Goldmine forUnderstanding Customers 109Why Should You Care about UDA for CustomerAcquisition and Retention? 111Predictive Models and Online Marketing 117How Does UDA Applied to Customer Acquisition Work? 118The Power of UDA for E-mail Response and Ad Optimization 124How to Drive More Conversion and Engagement with UDA Applied to Content 124How UDA Applied to Customer Retention (Churn) Works 125What Is UDA Applied to Customer Acquisition? 129What Is UDA Applied to Customer Retention (Churn)? 135The Power of UDA Powered by Virtual Agent 136Benefits of a Virtual Agent or AI Assistant for Customer Experience 138Benefits and Case Studies 139Applying UDA to Your Social Media Presence and Native Ads to Increase Acquisitions 151Key Takeaways 153Notes 154Chapter 5 The Power of UDA to Improve Fraud Detection and Prevention 157Introduction 157Why Should You Care about UDA for Fraud Detection and Prevention? 159Benefits of UDA 163What Is UDA for Fraud? 168How UDA Works in Fraud Detection and Prevention 170UDA Framework for Fraud Detection and Prevention:Insurance 173Major Fraud Detection and Prevention Techniques 176Best Practices Using UDA for Fraud Detection and Prevention 179Interview with Vishwa Kolla, Assistant Vice President Advanced Analytics at John Hancock Financial Services 182Interview with Diane Deperrois, General Manager South-East and Overseas Region, AXA 184Key Takeaways 187Notes 189Further Reading 189Chapter 6 The Power of UDA in Human Capital Management 191Why Should You Care about UDA in Human Resources? 191What Is UDA in HR? 193What Is UDA in HR Really About? 195The Power of UDA in Online Recruitment: Supply and Demand Equation 196The Power of UDA in Talent Sourcing Analytics 197The Power of UDA in Talent Acquisition Analytics 205Artificial Intelligence as a Hiring Assistant 206The Power of UDA in Talent Retention 207Interview with Arun Chidambaram, Director of Global workforce intelligence, Pfizer 208Employee Performance Appraisal Data Review Feedback 210How UDA Works 211Benefits of UDA in HR 212Case Studies 213Interview with Stephani Kingsmill, Executive Vice President and Chief Human Resource Officer, Manulife 213Key Takeaways 216Further Reading 217Chapter 7 The Power of UDA in the Legal Industry 219Why Should You Care about UDA in Legal Services? 219What Is UDA Applied to Legal Services? 224How Does It Work? 224Benefits and Challenges 231Key Takeaways 234Notes 235Further Reading 235Chapter 8 The Power of UDA in Healthcare and Medical Research 237Why Should You Care about UDA in Healthcare? 237What's UDA in Healthcare? 245How UDA Works 250Benefits 255Interview with Mr. François Laviolette, Professor of Computer Science/Director of Big Data Research Centre at Laval University (QC) Canada 257Interview with Paul Zikopolous, Vice President Big Data Cognitive System at IBM 258Case Study 262Key Takeaways 263Notes 264Further Reading 265Chapter 9 The Power of UDA in Product and Service Development 267Why Should You Care about UDA for Product and Service Development? 267UDA and Big Data Analytics 268Interview with Fiona McNeill, Global Product Marketing Manager at SAS Institute 283What Is UDA Applied to Product Development? 297How Is UDA Applied to Product Development? 300How UDA Applied to Product Development Works 301Key Takeaways 303Notes 304Chapter 10 The Power of UDA in National Security 307National Security: Playground for UDA or Civil Liberty Threat? 307What Is UDA for National Security? 310Data Sources of the NSA 310Why UDA for National Security? 314Case Studies 320How UDA Works 322Key Takeaways 323Notes 324Further Reading 325Chapter 11 The Power of UDA in Sports 327The Short History of Sports Analytics: Moneyball 328Why Should You Care about UDA in Sports? 333What Is UDA in Sports? 338How It Works 342Interview with Winston Lin, Director of Strategy and Analytics for the Houston Rockets 343Key Takeaways 347Notes 347Further Reading 348Chapter 12 The Future of Analytics 349Harnessing These Evolving Technologies Will Generate Benefits 350Data Becomes Less Valuable and Analytics Becomes Mainstream 353Predictive Analytics, AI, Machine Learning, and Deep Learning Become the New Standard 355People Analytics Becomes a Standard Department in Businesses 358UDA Becomes More Prevalent in Corporations and Businesses 359Cognitive Analytics Expansion 359The Internet of Things Evolves to the Analytics of Things 360MOOCs and Open Source Software and Applications Will Continue to Explode 361Blockchain and Analytics Will Solve Social Problems 362Human-Centered Computing Will Be Normalized 364Data Governance and Data Security Will Remain the Number-One Risk and Threat 365Key Takeaways 366Notes 367Further Reading 367Appendix A Tech Corner Details 369Singular Value Decomposition (SVD) Algorithm and Applications 370Principal Component Analysis (PCA) and Applications 382PCA Application to Facial Recognition: EigenFaces 392QR Factorization Algorithm and Applications 394Note 399Further Reading 399About The Author 401Index 403

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