Jean-Paul Isson, Jesse Harriott

Win with Advanced Business Analytics (eBook, PDF)

Creating Business Value from Your Data

• Format: PDF

Produktbeschreibung

Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. * Provides the essential concept and framework to implement business analytics * Written clearly for a nontechnical audience * Filled with case studies across a variety of industries * Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 416
• Erscheinungstermin: 17. September 2012
• Englisch
• ISBN-13: 9781118420515
• Artikelnr.: 37341410

Autorenporträt

JEAN PAUL ISSON is an internationally recognized speaker and an expert in advanced business analytics. He is Global Vice President of BI and predictive analytics at Monster Worldwide, Inc., where he has built his team from the ground up and successfully conceived and implemented advanced analytics and web mining solutions. Prior to joining Monster, Isson led the global customer behavior modeling team at Rogers Wireless, implementing churn models and pioneering the Customer Lifetime Value segmentation to optimize services marketing and sales activities. JESSE S. HARRIOTT, PHD, is Chief Analytics Officer for Constant Contact. Previously, Jesse was Chief Knowledge Officer at Monster Worldwide where he helped drive annual revenue from $300 million to over $1.3 billion. Harriott started an international analytics division at Monster and created the Monster Employment Index, now tracked in the United States, Europe, and Asia by millions of people. He also led web analytics, business intelligence, competitive intelligence, data governance, marketing research, and sales analytics departments for Monster. Jesse has taught at the University of Chicago and was named one of Boston's Top 40 Under 40.

Inhaltsangabe

Preface xv
Acknowledgments xvii
Chapter 1 The Challenge of Business Analytics 1
The Challenge from Outside 5
The Challenge from Within 9
Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15
Business Challenges Pillar 18
Data Foundation Pillar 20
Analytics Implementation Pillar 22
Insight Pillar 26
Execution and Measurement Pillar 29
Distributed Knowledge Pillar 31
Innovation Pillar 32
Conclusion 33
Chapter 3 Aligning Key Business Challenges across the Enterprise 35
Mission Statement 36
Business Challenge 38
Identifying Business Challenges as a Consultative Process 39
Identify and Prioritize Business Challenges 41
Analytics Solutions for Business Challenges 45
Chapter 4 Big and Little Data: Different Types of Intelligence 51
Big Data 57
Little Data 61
Laying the Data Foundation: Data Quality 62
Data Sources and Locations 65
Data Definition and Governance 69
Data Dictionary and Data Key Users 72
Sanity Check and Data Visualization 72
Customer Data Integration and Data Management 73
Data Privacy 74
Chapter 5 Who Cares about Data? How to Uncover Insights 77
The IMPACT Cycle 79
Curiosity Can Kill the Cat 82
Master the Data 86
A Fact in Search of Meaning 87
Actions Speak Louder Than Data 88
"Eat Like a Bird, Poop Like an Elephant" 89
Track Your Outcomes 91
The IMPACT Cycle in Action: The Monster Employment Index 92
Chapter 6 Data Visualization: Presenting Information Clearly: The CONVINCE
Framework 95
Convey Meaning 97
Objectivity: Be True to Your Data 99
Necessity: Don't Boil the Ocean 101
Visual Honesty: Size Matters 103
Imagine the Audience 104
Nimble: No Death by 1,000 Graphs 107
Context 107
Encourage Interaction 109
Conclusion 109
Chapter 7 Analytics Implementation: What Works and What Does Not 113
Analytics Implementation Model 117
Vision and Mandate 118
Strategy 119
Organizational Collaboration 121
Human Capital 122
Metrics and Measurement 123
Integrated Processes 124
Customer Experience 125
Technology and Tools 125
Change Management 126
Chapter 8 Voice-of-the-Customer Analytics and Insights 131
By Abhilasha Mehta, PhD
Customer Feedback is Invaluable 132
The Makings of an Effective Voice-of-the-Customer Program 137
Strategy and Elements of the VOC System 152
Common VOC Program Pitfalls 162
Chapter 9 Leveraging Digital Analytics Effectively 165
By Judah Phillips
Strategic and Tactical Use of Digital Analytics 173
Understanding Digital Analytics Concepts 174
Digital Analytics Team: People are Most Important for Analytical Success
184
Digital Analytics Tools 187
Advanced Digital Analytics 191
Digital Analytics and Voice of the Customer 192
Analytics of Site and Landing Page Optimization 194
Call to Action: Unify Traditional and Digital Analytics 195
Chapter 10 Effective Predictive Analytics: What Works and What Does Not 199
What is Predictive Analytics? 201
Unlocking Stage 203
Prediction Stage 206
Optimization Stage 210
Diverse Applications for Diverse Business Problems 213
Financial Service Industries as Pioneers 214
Chapter 11 Predictive Analytics Applied to Human Resources 223
By Jac Fitz-enz, PhD
Staff Roles 225
Assessment: Beyond People 226
Planning Shift 229
Competency versus Capability 229
Production 230
HR Process Management 231
HR Analysis and Predictability 232
Elevate HR with Analytics 233
Value Hierarchy 235
HR Reporting 237
HR Success through Analytics 238
Chapter 12 Social Media Analytics 247
By Judah Phillips
Social Media is Multidimensional 249
Understanding Social Media Analytics: Useful Concepts 251
Is Social Media about Brand or Direct Response? 254
Social Media "Brand" and "Direct Response" Analytics 255
Social Media Tools 259
Social Media Analytical Techniques 262
Social Media Analytics and Privacy 265
Chapter 13 The Competitive Intelligence Mandate 271
Competitive Intelligence Defined 273
Principles for CI Success 275
Chapter 14 Mobile Analytics 285
By Judah Phillips
Understanding Mobile Analytics Concepts 290
How is Mobile Analytics Different from Site Analytics? 291
Importance of Measuring Mobile Analytics 295
Mobile Analytics Tools 296
Business Optimization with Mobile Analytics 298
Chapter 15 Effective Analytics Communication Strategies 301
Communication: The Gap between Analysts and Executives 303
An Effective Analytics Communication Strategy 305
Analytics Communication Tips 314
Communicating through Mobile Business Intelligence 316
Chapter 16 Business Performance Tracking: Execution and Measurement 321
Analytics' Fundamental Questions 324
Analytics Execution 325
Business Performance Tracking 332
Analytics and Marketing 336
Chapter 17 Analytics and Innovation 343
What is Innovation? 344
What is the Promise of Advanced Analytics? 347
What Makes Up Innovation in Analytics? 348
Intersection between Analytics and Innovation 352
Chapter 18 Unstructured Data Analytics: The Next Frontier 359
What is Unstructured Data Analytics? 360
The Unstructured Data Analytics Industry 363
Uses of Unstructured Data Analytics 364
How Unstructured Data Analytics Works 365
Why Unstructured Data is the Next Analytical Frontier 366
Unstructured Analytics Success Stories 372
Chapter 19 The Future of Analytics 377
Data Become Less Valuable 379
Predictive Becomes the New Standard 380
Social Information Processing and Distributed Computing 381
Advances in Machine Learning 382
Traditional Data Models Evolve 383
Analytics Becomes More Accessible to the Nonanalyst 384
Data Science Becomes a Specialized Department 385
Human-Centered Computing 386
Analytics to Solve Social Problems 387
Location-Based Data Explosion 388
Data Privacy Backlash 388
About the Authors 391
Index 393