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.
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 195
Mutual 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 243
About the Author :
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.
