Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention
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| Format: | Buch |
| Sprache: | English |
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Wiley
[2018]
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| 003 | DE-604 | ||
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| 008 | 180419s2018 a||| |||| 00||| eng d | ||
| 020 | |a 9781119129752 |9 978-1-119-12975-2 | ||
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| 035 | |a (DE-599)BVBBV044912890 | ||
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| 100 | 1 | |a Isson, Jean-Paul |d 1971- |e Verfasser |0 (DE-588)1027729894 |4 aut | |
| 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 New York |b Wiley |c [2018] | |
| 300 | |a xxiii, 408 Seiten |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Unternehmen |0 (DE-588)4061963-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Big Data |0 (DE-588)4802620-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Big data | |
| 653 | 0 | |a Data mining | |
| 653 | 0 | |a Electronic data processing | |
| 653 | 0 | |a Business intelligence | |
| 653 | 0 | |a Customer relations | |
| 689 | 0 | 0 | |a Unternehmen |0 (DE-588)4061963-1 |D s |
| 689 | 0 | 1 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 978-1-119-32549-9 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030306369&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030306369 | ||
Datensatz im Suchindex
| _version_ | 1804178476289228800 |
|---|---|
| adam_text | Foreword xiii
Preface xv
Acknowledgments xix
Chapter t The Age of Advanced Business Analytics 1
Introduction I
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 UD A? 35
Why UD A Today? 3 9
The UD A Industry 48
UsesofUDA 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
vu
viii ► CONTENTS
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? Ill
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
CONTENTS iX
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 Kolia, 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 23 5
X ^CONTENTS
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 NS A 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: Money ball 328
Why Should You Care about UDA in Sports? 333
CONTENTS ◄ Xi
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 3 50
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 3 58
UDA Becomes More Prevalent in Corporations and
Businesses 359
Cognitive Analytics Expansion 3 59
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
PC A 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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| id | DE-604.BV044912890 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:04:34Z |
| institution | BVB |
| isbn | 9781119129752 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030306369 |
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| physical | xxiii, 408 Seiten Illustrationen |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Wiley |
| record_format | marc |
| spelling | Isson, Jean-Paul 1971- Verfasser (DE-588)1027729894 aut Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention Jean Paul Isson New York Wiley [2018] xxiii, 408 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Unternehmen (DE-588)4061963-1 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Big data Data mining Electronic data processing Business intelligence Customer relations Unternehmen (DE-588)4061963-1 s Datenanalyse (DE-588)4123037-1 s Big Data (DE-588)4802620-7 s b DE-604 Erscheint auch als Online-Ausgabe, PDF 978-1-119-32550-5 Erscheint auch als Online-Ausgabe, EPUB 978-1-119-32549-9 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030306369&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Isson, Jean-Paul 1971- Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention Unternehmen (DE-588)4061963-1 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4061963-1 (DE-588)4802620-7 (DE-588)4123037-1 |
| title | Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention |
| title_auth | Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention |
| title_exact_search | Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention |
| title_full | Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention Jean Paul Isson |
| title_fullStr | Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention Jean Paul Isson |
| title_full_unstemmed | Unstructured data analytics how to improve customer acquisition, customer retention, and fraud detection and prevention Jean Paul Isson |
| title_short | Unstructured data analytics |
| title_sort | unstructured data analytics how to improve customer acquisition customer retention and fraud detection and prevention |
| title_sub | how to improve customer acquisition, customer retention, and fraud detection and prevention |
| topic | Unternehmen (DE-588)4061963-1 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Unternehmen Big Data Datenanalyse |
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