Actionable Web Analytics

Jason Burby is Chief Analytics and Optimization Officer for ZAAZ, Inc., a web design and analytics consulting firm. His clients have included eTrade, Ford, Sony, PayPal/eBay, Washington Mutual, Reuters, T-Mobile, Levi Strauss, and Microsoft. Shane Atchison, co-founder and CEO of ZAAZ, Inc., leads its long-term strategic vision of helping companies realize the potential of the Internet and its impact on their business. Among his client list have been Converse, Sony, Ford, Microsoft, and National Geographic.

• Foreword xvIntroduction xxviiPart I The Changing Landscape of Marketing Online 1Chapter 1 The Big Picture 3New Marketing Trends 4The Consumer Revolution 5The Shift from Offline to Online Marketing 8Instant Brand Building (and Destruction) 10Rich Media and Infinite Variety 12The Analysis Mandate 13ROI Marketing 14Innovation 15Some Final Thoughts 16Chapter 2 Performance Marketing 17Data vs. Design 18Web Design Today 18The Web Award Fallacy 19When Visual Design Goes Wrong 19Where Data Goes Wrong 21Performance-Driven Design: Balancing Logic and Creativity 22Case Study: Dealing with Star Power 23Case Study: Forget Marketing at All 24Recap 25Part II Shifting to a Culture of Analysis 27Chapter 3 What “Culture of Analysis” Means 29What Is a Data-Driven Organization? 30Data-Driven Decision Making 31Dynamic Prioritization 32Perking Up Interest in Web Analytics 34Establishing a Web Analytics Steering Committee 34Starting Out Small with a Win 35Empowering Your Employees 36Managing Up 36Impact on Roles beyond the Analytics Team 37Cross-Channel Implications 40Questionnaire: Rating Your Level of Data Drive 41Recap 42Chapter 4 Avoiding Stumbling Points 43Do You Need an Analytics Intervention? 44Analytics Intervention Step 1: Admitting the Problem 44Analytics Intervention Step 2: Admit That You Are the Problem 46Analytics Intervention Step 3: Agree That This Is a Corporate Problem 47The Road to Recovery: Overcoming Real Gaps 48Issue #1: Lack of Established Processes and Methodology 49Issue #2: Failure to Establish Proper KPIs and Metrics 49Issue #3: Data Inaccuracy 50Issue #4: Data Overload 52Issue #5: Inability to Monetize the Impact of Changes 53Issue #6: Inability to Prioritize Opportunities 54Issue #7: Limited Access to Data 54Issue #8: Inadequate Data Integration 55Issue #9: Starting Too Big 56Issue #10: Failure to Tie Goals to KPIs 57Issue #11: No Plan for Acting on Insight 58Issue #12: Lack of Committed Individual and Executive Support 58Recap 59Part III Proven Formula for Success 61Chapter 5 Preparing to Be Data-Driven 63Web Analytics Methodology 64The Four Steps of Web Analytics 65Defining Business Metrics (KPIs) 65Reports 66Analysis 67Optimization and Action 67Results and Starting Again 68Recap 68Chapter 6 Defining Site Goals, KPIs, and Key Metrics 71Defining Overall Business Goals 72Defining Site Goals: The Conversion Funnel 73Awareness 73Interest 73Consideration 74Purchase 74Website Goals and the Marketing Funnel 74Understanding Key Performance Indicators (KPIs) 75Constructing KPIs 76Creating Targets for KPIs 79Common KPIs for Different Site Types 80E-Commerce 80Lead Generation 82Customer Service 83Content Sites 85Branding Sites 87Recap 88Chapter 7 Monetizing Site Behaviors 89The Monetization Challenge 90Case Study: Monetization and Motivation 90Web-Monetization Models 93Top 10 Ways Monetization Models Can Help Your Company 94How to Create Monetization Models 95Assembling a Monetization Model 97Monetization Models for Different Site Types and Behaviors 100E-Commerce Opportunity 100Lead Generation 102Customer Service 104Ad-Supported Content Sites 106Recap 108Chapter 8 Getting the Right Data 109Primary Data Types 110Warning: Avoid Data Smog 110Behavioral Data 111Attitudinal Data 112Balancing Behavioral and Attitudinal Data 112Competitive Data 113Secondary Data Types 116Customer Interaction and Data 116Third-Party Research 117Usability Benchmarking 117Heuristic Evaluation and Expert Reviews 118Community Sourced Data 119Leveraging These Data Types 120Comparing Performance with Others 120What Is a Relative Index? 122Examples of Relative Indices 122Customer Engagement 123Methodology: Leveraging Indices across Your Organization 124Case Study: Leveraging Different Data Types to Improve Site Performance 126Recap 128Chapter 9 Analyzing Site Performance 129Analysis vs. Reporting 130Don’t Blame Your Tools 131Examples of Analysis 132Analyzing Purchasing Processes to Find Opportunities 132Analyzing Lead Processes to Find Opportunities 135Understanding What Onsite Search Is Telling You 136Evaluating the Effectiveness of Your Home Page 138Evaluating the Effectiveness of Branding Content: Branding Metrics 138Evaluating the Effectiveness of Campaign Landing Pages 140Segmenting Traffic to Identify Behavioral Differences 142Segmenting Your Audience 142Case Study: Segmenting for a Financial Services Provider 143Analyzing Drivers to Offline Conversion 144Tracking Online Partner Handoffs and Brick-And-Mortar Referrals 144Tracking Offline Handoffs to Sales Reps 144Tracking Visitors to a Call Center 145Delayed Conversion 146Tracking Delayed Conversion 146Reporting in a Timely Manner 147Recap 147Chapter 10 Prioritizing 149How We Prioritize 150The Principles of Dynamic Prioritization 150Traditional Resource Prioritization 151Dynamic Prioritization 152Dynamic Prioritization Scorecard 154Dynamic Prioritization in Action 154Forecasting Potential Impact 155Comparing Opportunities 157Moving Your Company Toward Dynamic Prioritization 157Overcoming Common Excuses 158Conclusion 159Recap 160Chapter 11 Moving from Analysis to Site Optimization 161Testing Methodologies and Tools 162A/B Testing 162A/B/n Testing 162Multivariate Tests 162How to Choose a Test Type 163Testing Tools 164What to Test 164Prioritizing Tests 166Creating a Successful Test 167Understanding Post-Test Analysis 168Optimizing Segment Performance 168Example One: Behavior-Based Testing 169Example Two: Day-of-the-Week Testing 169Planning for Optimization 169Budgeting for Optimization 170Skills Needed for a Successful Optimization Team 171Overcoming IT Doubts 173IT Doesn’t Understand the Process 174Testing Prioritization 174Lack of Executive Support 174Learning from Your Successes and Mistakes 175Learning from the Good and the Bad 175A Quick Way Up the Learning Curve 176Spreading the Word 176Test Examples 176Price 177Promotional 178Message 179Page Layout 180New Site Launches or New Functionality 180Site Navigation and Taxonomy 181Recap 182Chapter 12 Agencies 185Why Use an Agency at All? 186Finding an Agency 187Creating an RFP 188Introduction and Company Background 189Scope of Work and Business Goals 191Timelines 193Financials 194The Rest of the RFP: Asking the Right Questions 195Mutual Objective: Success 196Doing the Work 198The Secret Agency Sauce 199Recap 200Chapter 13 The Creative Brief 201What Is a Creative Brief? 202The Brief 202Components of a Data-Driven Brief 203Creative Brief Metrics 203Analytics and Creativity 205The Iterative Design Cycle 206A Sample Creative Brief 206Creative Brief: Robotwear.Com 206Recap 210Chapter 14 Staffing and Tuning Your Web Team 211Skills That Make a Great Web Analyst 212Technical vs. Interpretive Expertise 212Key Web Analyst Skills 213The Roles of the Web Analyst 214Building Your Web-Analytics Team: Internal and External Teams 215Estimating Your Cost 215Key Analytics Positions 216Expanding the Circle of Influence 217Internal vs. External Teams 217Education and Training for Web Analysts 219Web Analytics Association 219Conferences 219University of British Columbia Courses 220Message Boards 220ClickZ and Other Online Media 220Blogs 220Web Analytics Wednesdays 220Vendor Training 221Agency Partners 221Hands-on Experience 221Recap 221Chapter 15 Partners 223When to Choose an Analytics Tool Vendor 224Methodology for Selecting a Tool 225Selecting a Review Committee 225Establishing a Timeline 226Criteria to Review and Select Vendors 22610 Questions to Ask Web Analytics Vendors 228Comparing to Free Tools 229ASP or Software Version 229Data Capture 230Total Cost of Ownership 230Support 231Data Segmentation 232Data Export and Options 232Data Integration 233The Future 233References 234Recap 234Conclusion 235Appendix:Web Analytics “Big Three” Definitions 237How We Define Terms 238Definition Framework Overview 239Term: Unique Visitors 239Term: Visits/Sessions 240Term: Page Views 240Index 243