Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity
Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Chapter 1 The Bold New World of Web Analytics 2.0 1
State of the Analytics Union 2
State of the Industry 3
Rethinking Web Analytics: Meet Web Analytics 2.0 4
Chapter 2 The Optimal Strategy for Choosing Your Web Analytics Soul Mate 15
Predetermining Your Future Success 16
Step 1: Three Critical Questions to Ask Yourself Before You Seek an Analytics Soul Mate! 17
Step 2: Ten Questions to Ask Vendors Before You Marry Them 21
Comparing Web Analytics Vendors: Diversify and Conquer 28
Step 3: Identifying Your Web Analytics Soul Mate (How to Run an Effective Tool Pilot) 29
Step 4: Negotiating the Prenuptials: Check SLAs for Your Web Analytics Vendor Contract 32
Chapter 3 The Awesome World of Clickstream Analysis: Metrics 35
Standard Metrics Revisited: Eight Critical Web Metrics 36
Bounce Rate 51
Exit Rate 53
Conversion Rate 55
Web Metrics Demystified 59
Strategically-aligned Tactics for Impactful Web Metrics 64
Chapter 4 The Awesome World of Clickstream Analysis: Practical Solutions 75
A Web Analytics Primer 76
The Best Web Analytics Report 85
Foundational Analytical Strategies 87
Everyday Clickstream Analyses Made Actionable 94
Reality Check: Perspectives on Key Web Analytics Challenges 126
Chapter 5 The Key to Glory: Measuring Success 145
Focus on the “Critical Few” 147
Five Examples of Actionable Outcome KPIs 149
Moving Beyond Conversion Rates 151
Measuring Macro and Micro Conversions 156
Quantifying Economic Value 159
Measuring Success for a Non-ecommerce Website 162
Measuring B2B Websites 166
Chapter 6 Solving the “Why” Puzzle: Leveraging Qualitative Data 169
Lab Usability Studies: What, Why, and How Much? 170
Usability Alternatives: Remote and Online Outsourced 175
Surveys: Truly Scalable Listening 179
Web-Enabled Emerging User Research Options 190
Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation 195
A Primer on Testing Options: A/B and MVT 197
Actionable Testing Ideas 202
Controlled Experiments: Step Up Your Analytics Game! 205
Creating and Nurturing a Testing Culture 209
Chapter 8 Competitive Intelligence Analysis 213
CI Data Sources, Types, and Secrets 214
Website Traffic Analysis 221
Search and Keyword Analysis 225
Audience Identification and Segmentation Analysis 235
Chapter 9 Emerging Analytics: Social, Mobile, and Video 241
Measuring the New Social Web: The Data Challenge 242
Analyzing Offline Customer Experiences (Applications) 248
Analyzing Mobile Customer Experiences 250
Measuring the Success of Blogs 257
Quantifying the Impact of Twitter 266
Analyzing Performance of Videos 273
Chapter 10 Optimal Solutions for Hidden Web Analytics Traps 283
Accuracy or Precision? 284
A Six-Step Process for Dealing with Data Quality 286
Building the Action Dashboard 288
Nonline Marketing Opportunity and Multichannel Measurement 294
The Promise and Challenge of Behavior Targeting 298
Online Data Mining and Predictive Analytics: Challenges 302
Path to Nirvana: Steps Toward Intelligent Analytics Evolution 306
Chapter 11 Guiding Principles for Becoming an Analysis Ninja 313
Context Is Queen 314
Comparing KPI Trends Over Time 321
Beyond the Top 10: What’s Changed 324
True Value: Measuring Latent Conversions and Visitor Behavior 327
Four Inactionable KPI Measurement Techniques 330
Search: Achieving the Optimal Long-Tail Strategy 338
Search: Measuring the Value of Upper Funnel Keywords 346
Search: Advanced Pay-per-Click Analyses 348
Chapter 12 Advanced Principles for Becoming an Analysis Ninja 357
Multitouch Campaign Attribution Analysis 358
Multichannel Analytics: Measurement Tips for a Nonline World 368
Chapter 13 The Web Analytics Career 385
Planning a Web Analytics Career: Options, Salary Prospects, and Growth 386
Cultivating Skills for a Successful Career in Web Analysis 393
An Optimal Day in the Life of an Analysis Ninja 401
Hiring the Best: Advice for Analytics Managers and Directors 403
Chapter 14 HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture 407
Transforming Company Culture: How to Excite People About Analytics 408
Changing Metric Definitions to Change Cultures: Brand Evangelists Index 415
Slay the Data Quality Dragon: Shift from Questioning to Using Data 420
Five Rules for Creating a Data-Driven Boss 426
Need Budget? Strategies for Embarrassing Your Organization 429
Strategies to Break Down Barriers to Web Measurement 432
Who Owns Web Analytics? 440
Appendix About the Companion CD 443
Do you think you've discovered an error in this book? Please check the list of errata below to see if we've already addressed the error. If not, please submit the error via our Errata Form. We will attempt to verify your error; if you're right, we will post a correction below.
|Short urls in the 1st printing no longer redirect to the correct web pages
The author used a website called sn.im to provide short urls for all the web pages to which he refers in the book. That site is now defunct and so those urls do not redirect properly as printed in the book. The short urls are now being handled through another domain, called "zqi.me". To go to any of the webpages, you need to replace the domain in the url, "sn.im" with the domain "zqi.me". The shortcut portion of the short urls remains unchanged.
In addition, there is an Excel spreadsheet included on the CD that lists all the short urls and the web pages to which they point. Go to the Resources and Downloads tab to download a corrected version of that file that lists the new short urls with the zqi.me domain.
|4||99||Error in discussion of data shown in Figure 4.22
The first sentence on the page, "You can quickly see that Visitors refine their query for the word blogger only 1.8 percent of the time, a likely indication that they find what they are looking for right away."
You can quickly see that Visitors refine their query for the word cross domain only 3.23 percent of the time, a likely indication that they find what they are looking for right away.
|4||111||Reference to incorrect figures
In the first paragraph, the reference to Figure 4.28 and Figure 4.29 is incorrect. It should read "Figure 4.27 and Figure 4.28".
|5||160||Text correction: Typographical error in calculation
In the sixth paragraph, a comma has been substituted for a divide by character: "/".
"1,900 , 30 = $63" should read "1,900 / 30 = $63"
In the sixth paragraph, replace "I can take the average of that value over the past three months and compute that each click on the site is worth $25. Total monthly value? 338 × 35 = $8,450." with the following text:
"I can follow that same process for rest of the conferences and to compute the value of each click on that page I can, as an example, take the average of that value. Final economic value created? I received 338 clicks on that page and my average value was $25 so the total was 338 x 25 = $8450."
|9||various||Text correction: Mobile analytics provider Bango Analytics incorrectly called "Bongo"
|6/11/10||1st and 2nd|
The fourth sentence under "Analyzing Visual Impression Share and Lost Revenue" contains an error.
"Variables that go into that decision include the big prices, quality score..." should read
"Variables that go into that decision include the bids, quality score..."
The third sentence in the third paragraph contains an error.
"You likely expect exact match keywords that are precise and that carefully chosen targets will perform better than precise and certainly broad match." should read:
"You likely expect exact match keywords that are precise and that carefully chosen targets will perform better than phrase match and certainly broad match."
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