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

<p><b>JEAN PAUL ISSON</b> 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. <p><b>JESSE S. HARRIOTT, P<small>H</small>D,</b> 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.

<p>Today's companies operate on a global scale and are inundated with an overwhelming volume of data from a number of multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third-party vendor data, and macroeconomic data, among others. While some businesses don't even know where to start, others are still struggling to move beyond basic reporting. In some instances, management and executives don't have a clear understanding of businessintelligence and don't see the value of analytics. <p>With invaluable insights from authors Jean Paul Isson and Jesse Harriott—renowned businessintelligence (BI) leaders—<i>Win with Advanced Business Analytics: Creating Business Value from Your Data</i> provides CFOs, chief marketing officers, directors of marketing, and business managers with a new way of looking at integrating the multiple types of intelligence into their business practice. <p>Written clearly for the nontechnical professional, this definitive guide shows you how to gain the most opportunity and value from <i>every</i> type of business intelligence, with essential guidance on: <ul> <li>The challenge of business analytics</li> <li>The BASP framework: pillars of business analytics success</li> <li>Big and little data—different types of intelligence</li> <li>Leveraging digital analytics effectively</li> <li>Winning with predictive analytics</li> <li>Social media analytics</li> <li>Mobile analytics</li> <li>The future of analytics</li> </ul> <p>Use data more effectively. Take your analytics to the next level. Unleash the potential buried in your company's data. Do it all with the visionary guidance found in <i>Win with Advanced Business Analytics</i>.

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