Actionable Web Analytics

Actionable Web Analytics : Using Data to Make Smart Business Decisions

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Knowing everything you can about each click to your Web site can help you make strategic decisions regarding your business. This book is about the why, not just the how, of web analytics and the rules for developing a "culture of analysis" inside your organization. Why you should collect various types of data. Why you need a strategy. Why it must remain flexible. Why your data must generate meaningful action. The authors answer these critical questions--and many more--using their decade of experience in Web analytics.show more

Product details

  • Electronic book text | 288 pages
  • John Wiley & Sons Inc
  • Sybex Inc.,U.S.
  • IN, United States
  • 047062485X
  • 9780470624852

Table of contents

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

About Jason Burby

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.show more

Rating details

66 ratings
3.46 out of 5 stars
5 14% (9)
4 35% (23)
3 41% (27)
2 6% (4)
1 5% (3)
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