Win with Advanced Business Analytics

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.

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