MK practical web analytics for user experience aug 2013

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Library of Congress Cataloging-in-Publication Data
Beasley, Michael, 1980–
Practical web analytics for user experience: how analytics can help you understand
your users / Michael Beasley.
pages cm
Includes bibliographical references and index.
1. Web usage mining. 2. Internet users—Attitudes. 3. Web sites—Development. I. Title.
ZA4235.B43 2013
006.3—dc23
2013010542
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
ISBN: 978-0-12-404619-1
Printed in the United States of America
13 14 15 16 17 10 9 8 7 6 5 4 3 2 1
For information on all Morgan Kaufmann publications visit our website at www.mkp.com
v
Practical Web Analytics for User Experience . DOI:
© 2014 Elsevier Inc. All rights reserved.2011
http://dx.doi.org/10.1016/B978-0-12-404619-1.00019-8
Contents
ACKNOWLEDGMENTS xiii
ABOUT THE AUTHOR xv
CHAPTER 1 Introduction 1
What Is Web Analytics? 2
User Experience and Web Analytics Questions 3
Web Analytics and User Experience: A Perfect Fit 3
About This Book 4
Part 1: Introduction to Web Analytics 4
Part 2: Learning About Users through Web Analytics 4
Part 3: Advanced Topics 5
Google Analytics 6
Part 1 Introduction to Web Analytics 9
CHAPTER 2 Web Analytics Approach 11
Introduction 11
Get to Know Your Website 11
A Model of Analysis 14
Pose the Question 15
Gather Data 16
Transform Data 16
Analyze 16
Answer the Question 17
Balancing Time and the Need for Certainty 17
Showing Your Work 18
Context Matters 18
Data Over Time 19
Proportion is Key 20
Sometimes the Data Contradict You 22
Sometimes the Answer is “No” 22
Contentsvi
Make Your Findings Repeatable 22
Key Takeaways 23
CHAPTER 3 How Web Analytics Works 25
Introduction 25
Log File Analysis 25
Page Tagging 26
Cookies 27
Accuracy 28
Accounts and Profiles 29
Click Analytics 30
Metrics and Dimensions 31
Visits 32
Unique Visitors (Metric) 32
Pageviews (Metric) 34
Pages/Visit (Metric) 35
Average Visit Duration 35
Bounce Rate (Metric) 36
% New Visits (Metric) 36
Using These Metrics 37
Interacting With Data In Google Analytics 37
Plot Rows 39
Secondary Dimension 39
Sort Type 39
Search 41
Beyond Tables 43
Key Takeaways 47
CHAPTER 4 Goals 49
Introduction 49
What are Goals and Conversions? 49
Unfortunate Colliding Terms 51
All Websites Should Have Goals 51
Why Do Goals Matter for User Experience? 51
Conversion Rate 52
Goal Reports in Google Analytics 53
Goal URLs 58
Reverse Goal Path 58
Funnel Visualization Report 60
Goal Flow 61
E-commerce 62
Multichannel Funnels 62
Contents
vii
When Do You Use These Reports? 63
Finding the Right Things to Measure as Key
Performance Indicators 63
What Should You Measure? 64
Do Users Want To Do These Things? 69
What Can You Measure on a Website that Can
Constitute a Goal? 69
Reaching a Specific Page 70
On-Page Action 71
Engagement 72
Going Beyond the Website 72
Tying It Together 73
Key Takeaways 74
Part 2 Learning about Users through Web
Analytics 75
CHAPTER 5 Learning about Users 77
Introduction 77
Visitor Analysis 78
Demographics—Location 78
Behavior—New vs. Returning 79
Behavior—Frequency & Recency 79
Behavior—Engagement 80
Technology—Browser & OS 81
Mobile—Overview 81
Custom (As in Custom Variables) 81
Key Takeaways 82
CHAPTER 6 Traffic Analysis: Learning How Users Got to
Your Website 83
Introduction 83
Source and Medium (Dimensions) 83
Organic Search 85
Why Analyze Keywords? 87
Search Query Analysis 89
Exporting the Data 90
Create Candidate Categories 92
Processing the Data 93
Analyzing the Data Again. 96
Basic Keyword Analysis 98
Export the Data 98
Contentsviii
Categorize the Keywords 98
Compare Metrics 99
Referral Traffic 99
Direct Traffic 102
Paid Search Keywords 103
Key Takeaways 104
CHAPTER 7 Analyzing How People Use Your Content 105
Introduction 105
Website Content Reports 105
High Pageviews/Low Pageviews 107
Pageviews are Much Higher than Unique Pageviews 109
Low Time on Page 110
High Time on Page 112
High Entrances to Unique Pageviews Ratio 112
High Bounce Rate 113
High % Exit 114
Page Value 114
Comparing Page Metrics to Similar Pages 115
More Reports 116
Key Takeaways 120
CHAPTER 8 Click-Path Analysis 121
Introduction 121
Focus on Relationships between Pages 122
Navigation Summary 123
“Visitors Flow” Report 126
Analyzing How Users Move from One Page
Type to Another 128
An Example: AwesomePetToys.com 129
Key Takeaways 134
CHAPTER 9 Segmentation 135
Introduction 135
Why Segment Data? 135
How To Segment Data 140
Google Analytics’ Advanced Segments 142
What are the Ways You Can Segment Data? 145
AND, OR, and Sequence of Filters 145
Metrics 145
Dimensions 146
Useful Ways to Segment for UX Questions 147
Segmenting According to a Page 147
Contents
ix
Segmenting According to User Traits 150
Segmenting According to Information Need 150
Whether or Not Users Completed a Goal 152
What Pages Users Landed On 152
What Pages Users Viewed/Didn’t View 153
The Tip of the Iceberg 154
Key Takeaways 154
CHAPTER 10 Pairing Analytics Data with UX Methods 157
Introduction 157
Personas 157
Segmenting Based on Personas 157
Building Better Personas 161
Usability Testing 162
Test Planning 162
Test Analysis 164
Usability Test Reports 165
Usability Inspection 166
Identifying Potential Problems 167
Evidence for Problems 167
Design and Design Objectives 167
How Much Will You Improve a Number? 169
Key Takeaways 169
CHAPTER 11 Measuring the Effects of Changes 171
Introduction 171
Reframe as a Rate 172
Choose What to Measure 172
Choose When to Measure 173
Types of Changes 174
Conversion Rate 174
Redirect Traffic 176
Time on Page and Other Continuous Metrics 179
Changing Many Things at Once 180
Reporting 182
New Designs Don’t Always Work 183
Key Takeaways 183
Part 3 Advanced Topics 185
CHAPTER 12 Measuring Behavior within Pages 187
Introduction 187
Google Analytics In-Page Analytics 187
Contentsx
Click Analytics Tools 189
Making Clicks Measureable in Page Tagging
Analytics Tools 190
Defining Events 191
Putting It Together 193
Analyzing Event Data 194
Pages and Events—What Happened Where? 195
Making Rates 198
Segmentation 198
Virtual Pageviews 199
Key Takeaways 199
CHAPTER 13 A/B Testing 201
Introduction 201
Designing An Experiment 201
Select a Page That You Wish to Improve 201
Determine a Metric for Judging Improvement 202
Design One or More Alternatives 202
Tracking Code 203
Tools 203
Estimating the Length of a Test 205
Monitoring and “Winning” 205
Ending a Test Early 206
Key Takeaways 207
CHAPTER 14 Analytics Profiles 209
Introduction 209
Profiles 209
What are Profile Filters? 210
Making URLs Easier to Read 211
Easier Click-path Analysis by Combining Pages 212
A Profile for UX Data 213
Key Takeaways 213
CHAPTER 15 Regular Reporting and Talking to Stakeholders 215
Introduction 215
Reporting Culture 215
Why You Report Analytics Data 216
Why You Monitor Analytics Data 217
Choosing Metrics to Report 218
Reporting Frequency 220
Keep It Concise 220
Contents
xi
Making the Case for Usability Activities 221
Making the Case for Design Changes 221
Making the Case for User Research 224
Key Takeaways 224
CHAPTER 16 Web Analytics in the Near Future 227
Introduction 227
Mobile Application Analytics 227
Cross-Device Measurement 228
Better Measurement of On-Page Behavior 228
Connecting to Other Data Sources 228
The Continuing Dominance of Google 229
Things Will Keep Changing 229
INDEX 231
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Practical Web Analytics for User Experience . DOI:
© 2014 Elsevier Inc. All rights reserved.2014
http://dx.doi.org/10.1016/B978-0-12-404619-1.00020-4
Acknowledgments
This book exists because of the help of several people. I’d like to thank the
people who have read this book and offered feedback along the way: Daniel
O’Neil, Christina York, and Mark Newman whose technical review made this
book considerably better; Andrew Grohowski and Barbra Wells, who was
also the person who got me thinking I could write this; the people at Pure
Visibility—Dunrie Greiling, Linda Girard, Jeremy Lopatin, Bill Smith, and
more—who pushed me and helped me learn and gave me the space to make
mistakes; awesome clients like Lisa Ocasio and Harmony Faust who asked the
questions that made me dig deeper and find new ways to use data; Veronica
Machak for listening to me complain and taking my first professional photo;
Emily Merchant for being my writing buddy and also listening to me com-
plain; and Melissa Bowen, who supported me and helped me clear the time I
needed to work and, of course, listened to me complain. And thanks to Mom
and Dad for the love and support over the years.