You are previewing Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity.

Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity

  1. Copyright
  2. Dear Reader,
  3. Acknowledgments
  4. About the Author
  5. Introduction
    1. The Awesome World of Data-Driven Decision Making
    2. What's Inside the Book?
    3. Valuable Multimedia Content on the CD
    4. Request for Feedback
    5. The Beginning
  6. 1. The Bold New World of Web Analytics 2.0
    1. 1.1. State of the Analytics Union
    2. 1.2. State of the Industry
    3. 1.3. Rethinking Web Analytics: Meet Web Analytics 2.0
      1. 1.3.1. The What: Clickstream
      2. 1.3.2. The How Much: Multiple Outcomes Analysis
      3. 1.3.3. The Why: Experimentation and Testing
      4. 1.3.4. The Why: Voice of Customer
      5. 1.3.5. The What Else: Competitive Intelligence
    4. 1.4. Change: Yes We Can!
      1. 1.4.1. The Strategic Imperative
      2. 1.4.2. The Tactical Shift
      3. 1.4.3. Bonus Analytics
  7. 2. The Optimal Strategy for Choosing Your Web Analytics Soul Mate
    1. 2.1. Predetermining Your Future Success
    2. 2.2. Step 1: Three Critical Questions to Ask Yourself Before You Seek an Analytics Soul Mate!
      1. 2.2.1. Q1: "Do I want reporting or analysis?"
      2. 2.2.2. Q2: "Do I have IT strength, business strength, or both?"
      3. 2.2.3. Q3: "Am I solving just for Clickstream or for Web Analytics 2.0?"
    3. 2.3. Step 2: Ten Questions to Ask Vendors Before You Marry Them
      1. 2.3.1. Q1: "What is the difference between your tool/solution and free tools from Yahoo! and Google?"
      2. 2.3.2. Q2: "Are you 100 percent ASP, or do you offer a software version? Are you planning a software version?"
      3. 2.3.3. Q3: "What data capture mechanisms do you use?"
      4. 2.3.4. Q4: "Can you calculate the total cost of ownership for your tool?"
      5. 2.3.5. Q5: "What kind of support do you offer? What do you include for free, and what costs more? Is it free 24/7?"
      6. 2.3.6. Q6: "What features in your tool allow me to segment the data?"
      7. 2.3.7. Q7: "What options do I have for exporting data from your system into our company's system?"
      8. 2.3.8. Q8: "What features do you provide for me to integrate data from other sources into your tool?"
      9. 2.3.9. Q9: "Can you name two new features/tools/acquisitions your company is cooking up to stay ahead of your competition for the next three years?"
      10. 2.3.10. Q10: "Why did the last two clients you lost cancel their contracts? Who are they using now? May we call one of these former clients?"
    4. 2.4. Comparing Web Analytics Vendors: Diversify and Conquer
      1. 2.4.1. The Three-Bucket Strategy
    5. 2.5. Step 3: Identifying Your Web Analytics Soul Mate (How to Run an Effective Tool Pilot)
    6. 2.6. Step 4: Negotiating the Prenuptials: Check SLAs for Your Web Analytics Vendor Contract
  8. 3. The Awesome World of Clickstream Analysis: Metrics
    1. 3.1. Standard Metrics Revisited: Eight Critical Web Metrics
      1. 3.1.1. Visits and Visitors
      2. 3.1.2. Time on Page and Time on Site
    2. 3.2. Bounce Rate
    3. 3.3. Exit Rate
    4. 3.4. Conversion Rate
    5. 3.5. Engagement
    6. 3.6. Web Metrics Demystified
      1. 3.6.1. Four Attributes of Great Metrics
      2. 3.6.2. Example of a Great Web Metric
      3. 3.6.3. Three Avinash Life Lessons for Massive Success
    7. 3.7. Strategically Aligned Tactics for Impactful Web Metrics
      1. 3.7.1. Diagnosing the Root Cause of a Metric's Performance—Conversion
      2. 3.7.2. Leveraging Custom Reporting
      3. 3.7.3. Starting with Macro Insights
  9. 4. The Awesome World of Clickstream Analysis: Practical Solutions
    1. 4.1. A Web Analytics Primer
      1. 4.1.1. Getting Primitive Indicators Out of the Way
      2. 4.1.2. Understanding Visitor Acquisition Strengths
      3. 4.1.3. Fixing Stuff and Saving Money
      4. 4.1.4. Click Density Analysis
      5. 4.1.5. Measuring Visits to Purchase
    2. 4.2. The Best Web Analytics Report
      1. 4.2.1. Sources of Traffic
      2. 4.2.2. Outcomes
    3. 4.3. Foundational Analytical Strategies
      1. 4.3.1. Segment or Go Home
      2. 4.3.2. Focus on Customer Behavior, Not Aggregates
    4. 4.4. Everyday Clickstream Analyses Made Actionable
      1. 4.4.1. Internal Site Search Analysis
      2. 4.4.2. Search Engine Optimization (SEO) Analysis
      3. 4.4.3. Pay Per Click/Paid Search Analysis
      4. 4.4.4. Direct Traffic Analysis
      5. 4.4.5. Email Campaign Analysis
      6. 4.4.6. Rich Experience Analysis: Flash, Video, and Widgets
    5. 4.5. Reality Check: Perspectives on Key Web Analytics Challenges
      1. 4.5.1. Visitor Tracking Cookies
      2. 4.5.2. Data Sampling 411
      3. 4.5.3. The Value of Historical Data
      4. 4.5.4. The Usefulness of Video Playback of Customer Experience
      5. 4.5.5. The Ultimate Data Reconciliation Checklist
  10. 5. The Key to Glory: Measuring Success
    1. 5.1. Focus on the "Critical Few"
    2. 5.2. Five Examples of Actionable Outcome KPIs
      1. 5.2.1. Task Completion Rate
      2. 5.2.2. Share of Search
      3. 5.2.3. Visitor Loyalty and Recency
      4. 5.2.4. RSS/Feed Subscribers
      5. 5.2.5. % of Valuable Exits
    3. 5.3. Moving Beyond Conversion Rates
      1. 5.3.1. Cart and Checkout Abandonment
      2. 5.3.2. Days and Visits to Purchase
      3. 5.3.3. Average Order Value
      4. 5.3.4. Primary Purpose (Identify the Convertible)
    4. 5.4. Measuring Macro and Micro Conversions
      1. 5.4.1. Examples of Macro and Micro Conversions
    5. 5.5. Quantifying Economic Value
    6. 5.6. Measuring Success for a Non-ecommerce Website
      1. 5.6.1. Visitor Loyalty
      2. 5.6.2. Visitor Recency
      3. 5.6.3. Length of Visit
      4. 5.6.4. Depth of Visit
    7. 5.7. Measuring B2B Websites
  11. 6. Solving the "Why" Puzzle—Leveraging Qualitative Data
    1. 6.1. Lab Usability Studies: What, Why, and How Much?
      1. 6.1.1. What Is Lab Usability?
      2. 6.1.2. How to Conduct a Test
      3. 6.1.3. Best Practices for Lab Usability Studies
      4. 6.1.4. Benefits of Lab Usability Studies
      5. 6.1.5. Areas of Caution
    2. 6.2. Usability Alternatives: Remote and Online Outsourced
      1. 6.2.1. Live Recruiting and Remote User Research
    3. 6.3. Surveys: Truly Scalable Listening
      1. 6.3.1. Types of Surveys
      2. 6.3.2. The Single Biggest Surveying Mistake
      3. 6.3.3. Three Greatest Survey Questions Ever
      4. 6.3.4. Eight Tips for Choosing an Online Survey Provider
    4. 6.4. Web-Enabled Emerging User Research Options
      1. 6.4.1. Competitive Benchmarking Studies
      2. 6.4.2. Rapid Usability Tests
      3. 6.4.3. Online Card-Sorting Studies
      4. 6.4.4. Artificially Intelligent Visual Heat Maps
  12. 7. Failing Faster: Unleashing the Power of Testing and Experimentation
    1. 7.1. A Primer on Testing Options: A/B and MVT
      1. 7.1.1. A/B Testing
      2. 7.1.2. Multivariate Testing
    2. 7.2. Actionable Testing Ideas
      1. 7.2.1. Fix the Big Losers—Landing Pages
      2. 7.2.2. Focus on Checkout, Registration, and Lead Submission Pages
      3. 7.2.3. Optimize the Number and Layout of Ads
      4. 7.2.4. Test Different Prices and Selling Tactics
      5. 7.2.5. Test Box Layouts, DVD Covers, and Offline Stuff
      6. 7.2.6. Optimize Your Outbound Marketing Efforts
    3. 7.3. Controlled Experiments: Step Up Your Analytics Game!
      1. 7.3.1. Measuring Paid Search Impact on Brand Keywords and Cannibalization
      2. 7.3.2. Examples of Controlled Experiments
      3. 7.3.3. Challenges and Benefits
    4. 7.4. Creating and Nurturing a Testing Culture
      1. 7.4.1. Tip 1: Your First Test is "Do or Die"
      2. 7.4.2. Tip 2: Don't Get Caught in the Tool/Consultant Hype
      3. 7.4.3. Tip 3: "Open the Kimono"—Get Over Yourself
      4. 7.4.4. Tip 4: Start with a Hypothesis
      5. 7.4.5. Tip 5: Make Goals Evaluation Criteria and Up-Front Decisions
      6. 7.4.6. Tip 6: Test For and Measure Multiple Outcomes
      7. 7.4.7. Tip 7: Source Your Tests in Customer Pain
      8. 7.4.8. Tip 8: Analyze Data and Communicate Learnings
      9. 7.4.9. Tip 9: Two Must-Haves: Evangelism and Expertise
  13. 8. Competitive Intelligence Analysis
    1. 8.1. CI Data Sources, Types, and Secrets
      1. 8.1.1. Toolbar Data
      2. 8.1.2. Panel Data
      3. 8.1.3. ISP (Network) Data
      4. 8.1.4. Search Engine Data
      5. 8.1.5. Benchmarks from Web Analytics Vendors
      6. 8.1.6. Self-reported Data
      7. 8.1.7. Hybrid Data
    2. 8.2. Website Traffic Analysis
      1. 8.2.1. Comparing Long-Term Traffic Trends
      2. 8.2.2. Analyzing Competitive Sites Overlap and Opportunities
      3. 8.2.3. Analyzing Referrals and Destinations
    3. 8.3. Search and Keyword Analysis
      1. 8.3.1. Top Keywords Performance Trend
      2. 8.3.2. Geographic Interest and Opportunity Analysis
      3. 8.3.3. Related and Fast-Rising Searches
      4. 8.3.4. Share-of-Shelf Analysis
      5. 8.3.5. Competitive Keyword Advantage Analysis
      6. 8.3.6. Keyword Expansion Analysis
    4. 8.4. Audience Identification and Segmentation Analysis
      1. 8.4.1. Demographic Segmentation Analysis
      2. 8.4.2. Psychographic Segmentation Analysis
      3. 8.4.3. Search Behavior and Audience Segmentation Analysis
  14. 9. Emerging Analytics: Social, Mobile, and Video
    1. 9.1. Measuring the New Social Web: The Data Challenge
      1. 9.1.1. The Content Democracy Evolution
      2. 9.1.2. The Twitter Revolution
    2. 9.2. Analyzing Offline Customer Experiences (Applications)
    3. 9.3. Analyzing Mobile Customer Experiences
      1. 9.3.1. Mobile Data Collection: Options
      2. 9.3.2. Mobile Reporting and Analysis
    4. 9.4. Measuring the Success of Blogs
      1. 9.4.1. Raw Author Contribution
      2. 9.4.2. Holistic Audience Growth
      3. 9.4.3. Citations and Ripple Index
      4. 9.4.4. Cost of Blogging
      5. 9.4.5. Benefit (ROI) from Blogging
    5. 9.5. Quantifying the Impact of Twitter
      1. 9.5.1. Growth in Number of Followers
      2. 9.5.2. Message Amplification
      3. 9.5.3. Click-Through Rates and Conversions
      4. 9.5.4. Conversation Rate
      5. 9.5.5. Emerging Twitter Metrics
    6. 9.6. Analyzing Performance of Videos
      1. 9.6.1. Data Collection for Videos
      2. 9.6.2. Key Video Metrics and Analysis
      3. 9.6.3. Advanced Video Analysis
  15. 10. Optimal Solutions for Hidden Web Analytics Traps
    1. 10.1. Accuracy or Precision?
    2. 10.2. A Six-Step Process for Dealing with Data Quality
    3. 10.3. Building the Action Dashboard
      1. 10.3.1. Creating Awesome Dashboards
      2. 10.3.2. The Consolidated Dashboard
      3. 10.3.3. Five Rules for High-Impact Dashboards
    4. 10.4. Nonline Marketing Opportunity and Multichannel Measurement
      1. 10.4.1. Shifting to the Nonline Marketing Model
      2. 10.4.2. Multichannel Analytics
    5. 10.5. The Promise and Challenge of Behavior Targeting
      1. 10.5.1. The Promise of Behavior Targeting
      2. 10.5.2. Overcoming Fundamental Analytics Challenges
      3. 10.5.3. Two Prerequisites for Behavior Targeting
    6. 10.6. Online Data Mining and Predictive Analytics: Challenges
      1. 10.6.1. Type of Data
      2. 10.6.2. Number of Variables
      3. 10.6.3. Multiple Primary Purposes
      4. 10.6.4. Multiple Visit Behaviors
      5. 10.6.5. Missing Primary Keys and Data Sets
    7. 10.7. Path to Nirvana: Steps Toward Intelligent Analytics Evolution
      1. 10.7.1. Step 1: Tag, Baby, Tag!
      2. 10.7.2. Step 2: Configuring Web Analytics Tool Settings
      3. 10.7.3. Step 3: Campaign/Acquisition Tracking
      4. 10.7.4. Step 4: Revenue and Uber-intelligence
      5. 10.7.5. Step 5: Rich-Media Tracking (Flash, Widgets, Video)
  16. 11. Guiding Principles for Becoming an Analysis Ninja
    1. 11.1. Context Is Queen
      1. 11.1.1. Comparing Key Metrics Performance for Different Time Periods
      2. 11.1.2. Providing Context Through Segmenting
      3. 11.1.3. Comparing Key Metrics and Segments Against Site Average
      4. 11.1.4. Joining PALM (People Against Lonely Metrics)
      5. 11.1.5. Leveraging Industry Benchmarks and Competitive Data
      6. 11.1.6. Tapping into Tribal Knowledge
    2. 11.2. Comparing KPI Trends Over Time
      1. 11.2.1. Presenting Tribal Knowledge
      2. 11.2.2. Segmenting to the Rescue!
    3. 11.3. Beyond the Top 10: What's Changed
    4. 11.4. True Value: Measuring Latent Conversions and Visitor Behavior
      1. 11.4.1. Latent Visitor Behavior
      2. 11.4.2. Latent Conversions
    5. 11.5. Four Inactionable KPI Measurement Techniques
      1. 11.5.1. Averages
      2. 11.5.2. Percentages
      3. 11.5.3. Ratios
      4. 11.5.4. Compound or Calculated Metrics
    6. 11.6. Search: Achieving the Optimal Long-Tail Strategy
      1. 11.6.1. Compute Your Head and Tail
      2. 11.6.2. Understanding Your Brand and Category Terms
      3. 11.6.3. The Optimal Search Marketing Strategy
      4. 11.6.4. Executing the Optimal Long-Tail Strategy
    7. 11.7. Search: Measuring the Value of Upper Funnel Keywords
    8. 11.8. Search: Advanced Pay-per-Click Analyses
      1. 11.8.1. Identifying Keyword Arbitrage Opportunities
      2. 11.8.2. Focusing on "What's Changed"
      3. 11.8.3. Analyzing Visual Impression Share and Lost Revenue
      4. 11.8.4. Embracing the ROI Distribution Report
      5. 11.8.5. Zeroing In on the User Search Query and Match Types
  17. 12. Advanced Principles for Becoming an Analysis Ninja
    1. 12.1. Multitouch Campaign Attribution Analysis
      1. 12.1.1. What Is All This Multitouch?
      2. 12.1.2. Do You Have an Attribution Problem?
      3. 12.1.3. Attribution Models
      4. 12.1.4. Core Challenge with Attribution Analysis in the Real World
      5. 12.1.5. Promising Alternatives to Attribution Analysis
      6. 12.1.6. Parting Thoughts About Multitouch
    2. 12.2. Multichannel Analytics: Measurement Tips for a Nonline World
      1. 12.2.1. Tracking Online Impact of Offline Campaigns
      2. 12.2.2. Tracking the Offline Impact of Online Campaigns
  18. 13. The Web Analytics Career
    1. 13.1. Planning a Web Analytics Career: Options, Salary Prospects, and Growth
      1. 13.1.1. Technical Individual Contributor
      2. 13.1.2. Business Individual Contributor
      3. 13.1.3. Technical Team Leader
      4. 13.1.4. Business Team Leader
    2. 13.2. Cultivating Skills for a Successful Career in Web Analysis
      1. 13.2.1. Do It: Use the Data
      2. 13.2.2. Get Experience with Multiple Tools
      3. 13.2.3. Play in the Real World
      4. 13.2.4. Become a Data Capture Detective
      5. 13.2.5. Rock Math: Learn Basic Statistics
      6. 13.2.6. Ask Good Questions
      7. 13.2.7. Work Closely with Business Teams
      8. 13.2.8. Learn Effective Data Visualization and Presentation
      9. 13.2.9. Stay Current: Attend Free Webinars
      10. 13.2.10. Stay Current: Read Blogs
    3. 13.3. An Optimal Day in the Life of an Analysis Ninja
    4. 13.4. Hiring the Best: Advice for Analytics Managers and Directors
      1. 13.4.1. Key Attributes of Great Analytics Professionals
      2. 13.4.2. Experienced or Novice: Making the Right Choice
      3. 13.4.3. The Single Greatest Test in an Interview: Critical Thinking
  19. 14. HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture
    1. 14.1. Transforming Company Culture: How to Excite People About Analytics
      1. 14.1.1. Do Something Surprising: Don't Puke Data
    2. 14.2. Deliver Reports and Analyses That Drive Action
      1. 14.2.1.
      2. 14.2.2. The Unböring Filter
      3. 14.2.3. Connecting Insights with Actual Data
    3. 14.3. Changing Metric Definitions to Change Cultures: Brand Evangelists Index
      1. 14.3.1. The Case and the Analysis
      2. 14.3.2. The Problem
      3. 14.3.3. The Solution
      4. 14.3.4. The Results
      5. 14.3.5. The Outcome
      6. 14.3.6. An Alternative Calculation: Weighted Mean
      7. 14.3.7. The Punch Line
    4. 14.4. Slay the Data Quality Dragon: Shift from Questioning to Using Data
      1. 14.4.1. Pick a Different Boss
      2. 14.4.2. Distract HiPPOs with Actionable Insights
      3. 14.4.3. Dirty Little Secret 1: Head Data Can Be Actionable in the First Week/Month
      4. 14.4.4. Dirty Little Secret 2: Data Precision Improves Lower in the Funnel
      5. 14.4.5. The Solution Is Not to Implement Another Tool!
      6. 14.4.6. Recognize Diminishing Marginal Returns
      7. 14.4.7. Small Site, Bigger Problems
      8. 14.4.8. Fail Faster on the Web
    5. 14.5. Five Rules for Creating a Data-Driven Boss
      1. 14.5.1. Get Over Yourself
      2. 14.5.2. Embrace Incompleteness
      3. 14.5.3. Always Give 10 Percent Extra
      4. 14.5.4. Become a Marketer
      5. 14.5.5. Business in the Service of Data. Not!
      6. 14.5.6. Adopt the Web Analytics 2.0 Mind-Set
    6. 14.6. Need Budget? Strategies for Embarrassing Your Organization
      1. 14.6.1.
      2. 14.6.2. Capture Voice of Customer
      3. 14.6.3. Hijack a Friendly Website
      4. 14.6.4. If All Else Fails...Call Me!
    7. 14.7. Strategies to Break Down Barriers to Web Measurement
      1. 14.7.1. First, a Surprising Insight
      2. 14.7.2. Lack of Budget/Resources
      3. 14.7.3. Lack of Strategy
      4. 14.7.4. Siloed Organization
      5. 14.7.5. Lack of Understanding
      6. 14.7.6. Too Much Data
      7. 14.7.7. Lack of Senior Management Buy-In
      8. 14.7.8. IT Blockages
      9. 14.7.9. Lack of Trust in Analytics
      10. 14.7.10. Finding Staff
      11. 14.7.11. Poor Technology
    8. 14.8. Who Owns Web Analytics?
      1. 14.8.1. To Centralize or Not to Centralize
      2. 14.8.2. Evolution of the Team
  20. A. About the Companion CD
    1. A.1. What You'll Find on the CD
      1. A.1.1. Podcasts
      2. A.1.2. Videos
      3. A.1.3. Resources and Presentations
      4. A.1.4. Adobe Reader
    2. A.2. System Requirements
    3. A.3. Using the CD
    4. A.4. Troubleshooting
      1. A.4.1. Customer Care
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Chapter 10. Optimal Solutions for Hidden Web Analytics Traps

It is time to tackle some of the toughest issues in web analytics and earn your black belt, the last step to becoming an analysis ninja.

This chapter touches on some of the obstacles you'll face on your quest to be a data-driven analyst and marketer. The quest is not about tools; it is about the right mental model and approach and about thinking differently and internalizing the cost of decisions—from the mundane to the strategic.

You'll use the tactics discussed in this chapter nearly every day of your data-driven life, and if you use them right, these approaches will ensure you massive success.

Chapter Contents

Accuracy or Precision?

A Six-Step Process for Dealing with Data Quality

Building ...

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