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Actionable Web Analytics: Using Data to Make Smart Business Decisions [Anglais] [Broché]

Jason Burby , Shane Atchison

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Table des matières

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

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