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

  • ID: 3797261
  • Book
  • May 2018
  • Region: Global
  • 432 Pages
  • John Wiley and Sons Ltd
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"Isson′s book is an essential well–written guide for business leaders who want to create value from their unstructured data. I strongly recommend this book to any executive looking to discover insights buried in their Big Data to transform their organization analytics."
MARK STOEVER, CEO, 2020 Inc., former CEO, Monster Worldwide Inc.

"Unstructured Data Analytics (UDA) will impact all of us, and this book provides a well–written summary of what you need to start your journey to stay ahead of your competition. JP Isson captures the essence of UDA in an easy to digest way that I highly recommend to the entire organization."
JOANIE COURTNEY, President and Chief Operating Officer at EmployBridge, Professional Division, Employment Industry Executive, and Speaker

"If you are not analyzing your unstructured data you are probably missing more than 80 percent of insights from data. In this book JP Isson provides an easy to implement framework packed with real–world applications to make your company smarter by fully harnessing its data: structured and unstructured. A masterpiece to stay ahead of the competition."
BRUNO AZIZA, Chief Marketing Officer (CMO), AtScale

"Putting aside hype and academic abstraction, this book brings us tangible case studies from real Unstructured Data Analytics implementations in marketing, fraud detection, insurance, HR, legal, security, sports, medical, and other application areas. As in Isson′s prior books, the author brings this practical grounding together into a solid strategic vision. This is vital reading for managers and practitioners who are venturing into the real world of living data beyond rows and columns."
PASHA ROBERTS, Chief Scientist, Cofounder, Talent Analytics

"Isson continues educating the masses on big data with a book that addresses the next frontier in this ever–evolving field: how to deal with unstructured data."
STEPHANE BRUTUS, RBC Professor in Motivation and Employee Performance, Concordia University

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Foreword xiii

Preface xv

Acknowledgments xix

Chapter 1 The Age of Advanced Business Analytics 1

Introduction 1

Why the Analytics Hype Today? 5

A Short History of Data Analytics 15

What Is the Analytics Age? 22

Interview with Wayne Thompson, Chief Data Scientist at

SAS Institute 23

Key Takeaways 28

Notes 29

Further Reading 30

Chapter 2 Unstructured Data Analytics: The Next Frontier of Analytics Innovation 33

Introduction 33

What Is UDA? 35

Why UDA Today? 39

The UDA Industry 48

Uses of UDA 51

How UDA Works 52

Why UDA Is the Next Analytical Frontier? 54

Interview with Seth Grimes on Analytics as the Next

Business Frontier 58

UDA Success Stories 60

The Golden Age of UDA 64

Key Takeaways 65

Notes 66

Further Reading 67

Chapter 3 The Framework to Put UDA to Work 69

Introduction 69

Why Have a Framework to Analyze Unstructured Data? 70

The IMPACT Cycle Applied to Unstructured Data 72

Text Parsing Example 81

Interview with Cindy Forbes, Chief Analytics Officer and Executive Vice President at Manulife Financial 84

Case Study 90

Key Takeaways 106

Notes 107

Further Reading 108

Chapter 4 How to Increase Customer Acquisition and Retention with UDA 109

The Voice of the Customer: A Goldmine for

Understanding Customers 109

Why Should You Care about UDA for Customer

Acquisition and Retention? 111

Predictive Models and Online Marketing 117

How Does UDA Applied to Customer Acquisition Work? 118

The Power of UDA for E–mail Response and Ad Optimization 124

How to Drive More Conversion and Engagement with UDA Applied to Content 124

How UDA Applied to Customer Retention (Churn) Works 125

What Is UDA Applied to Customer Acquisition? 129

What Is UDA Applied to Customer Retention (Churn)? 135

The Power of UDA Powered by Virtual Agent 136

Benefits of a Virtual Agent or AI Assistant for Customer Experience 138

Benefits and Case Studies 139

Applying UDA to Your Social Media Presence and Native Ads to Increase Acquisitions 151

Key Takeaways 153

Notes 154

Chapter 5 The Power of UDA to Improve Fraud Detection and Prevention 157

Introduction 157

Why Should You Care about UDA for Fraud Detection and Prevention? 159

Benefits of UDA 163

What Is UDA for Fraud? 168

How UDA Works in Fraud Detection and Prevention 170

UDA Framework for Fraud Detection and Prevention:

Insurance 173

Major Fraud Detection and Prevention Techniques 176

Best Practices Using UDA for Fraud Detection and Prevention 179

Interview with Vishwa Kolla, Assistant Vice President Advanced Analytics at John Hancock Financial Services 182

Interview with Diane Deperrois, General Manager South–East and Overseas Region, AXA 184

Key Takeaways 187

Notes 189

Further Reading 189

Chapter 6 The Power of UDA in Human Capital Management 191

Why Should You Care about UDA in Human Resources? 191

What Is UDA in HR? 193

What Is UDA in HR Really About? 195

The Power of UDA in Online Recruitment: Supply and Demand Equation 196

The Power of UDA in Talent Sourcing Analytics 197

The Power of UDA in Talent Acquisition Analytics 205

Artificial Intelligence as a Hiring Assistant 206

The Power of UDA in Talent Retention 207

Interview with Arun Chidambaram, Director of Global workforce intelligence, Pfizer 208

Employee Performance Appraisal Data Review Feedback 210

How UDA Works 211

Benefits of UDA in HR 212

Case Studies 213

Interview with Stephani Kingsmill, Executive Vice President and Chief Human Resource Officer, Manulife 213

Key Takeaways 216

Further Reading 217

Chapter 7 The Power of UDA in the Legal Industry 219

Why Should You Care about UDA in Legal Services? 219

What Is UDA Applied to Legal Services? 224

How Does It Work? 224

Benefits and Challenges 231

Key Takeaways 234

Notes 235

Further Reading 235

Chapter 8 The Power of UDA in Healthcare and Medical Research 237

Why Should You Care about UDA in Healthcare? 237

What s UDA in Healthcare? 245

How UDA Works 250

Benefits 255

Interview with Mr. François Laviolette, Professor of Computer Science/Director of Big Data Research Centre at Laval University (QC) Canada 257

Interview with Paul Zikopolous, Vice President Big Data Cognitive System at IBM 258

Case Study 262

Key Takeaways 263

Notes 264

Further Reading 265

Chapter 9 The Power of UDA in Product and Service Development 267

Why Should You Care about UDA for Product and Service Development? 267

UDA and Big Data Analytics 268

Interview with Fiona McNeill, Global Product Marketing Manager at SAS Institute 283

What Is UDA Applied to Product Development? 297

How Is UDA Applied to Product Development? 300

How UDA Applied to Product Development Works 301

Key Takeaways 303

Notes 304

Chapter 10 The Power of UDA in National Security 307

National Security: Playground for UDA or Civil Liberty Threat? 307

What Is UDA for National Security? 310

Data Sources of the NSA 310

Why UDA for National Security? 314

Case Studies 320

How UDA Works 322

Key Takeaways 323

Notes 324

Further Reading 325

Chapter 11 The Power of UDA in Sports 327

The Short History of Sports Analytics: Moneyball 328

Why Should You Care about UDA in Sports? 333

What Is UDA in Sports? 338

How It Works 342

Interview with Winston Lin, Director of Strategy and Analytics for the Houston Rockets 343

Key Takeaways 347

Notes 347

Further Reading 348

Chapter 12 The Future of Analytics 349

Harnessing These Evolving Technologies Will Generate Benefits 350

Data Becomes Less Valuable and Analytics Becomes Mainstream 353

Predictive Analytics, AI, Machine Learning, and Deep Learning Become the New Standard 355

People Analytics Becomes a Standard Department in Businesses 358

UDA Becomes More Prevalent in Corporations and Businesses 359

Cognitive Analytics Expansion 359

The Internet of Things Evolves to the Analytics of Things 360

MOOCs and Open Source Software and Applications Will Continue to Explode 361

Blockchain and Analytics Will Solve Social Problems 362

Human–Centered Computing Will Be Normalized 364

Data Governance and Data Security Will Remain the Number–One Risk and Threat 365

Key Takeaways 366

Notes 367

Further Reading 367

Appendix A Tech Corner Details 369

Singular Value Decomposition (SVD) Algorithm and Applications 370

Principal Component Analysis (PCA) and Applications 382

PCA Application to Facial Recognition: EigenFaces 392

QR Factorization Algorithm and Applications 394

Note 399

Further Reading 399

About The Author 401

Index 403

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Jean Paul Isson
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