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

Released October 2009
Publisher(s): Sybex
ISBN: 9780470529393

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Book description

Adeptly address today’s business challenges with this powerful new book from web analytics thought leader Avinash Kaushik. Web Analytics 2.0 presents a new framework that will permanently change how you think about analytics. It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multichannel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers. The book will help your organization become more data driven while you become a super analysis ninja!

Table of contents

  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?"
        1. 2.2.1.1. The Wrong Affordable Tool
        2. 2.2.1.2. The Expensive Tool with the Wrong Staff
        3. 2.2.1.3. The Switch to the Right Tool
      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
        1. 3.1.1.1. Visits
        2. 3.1.1.2. Unique Visitors
          1. 3.1.1.2.1. Month 1 and Week 1 for End of Day 1
          2. 3.1.1.2.2. Month 1 and Week 1 for End of Day 2
          3. 3.1.1.2.3. Month 1 for End of Week 1
          4. 3.1.1.2.4. Month 1 for End of Week 2
          5. 3.1.1.2.5. End of Month 1, for the Whole Month
          6. 3.1.1.2.6. End of Month 2, for the Two Months
      2. 3.1.2. Time on Page and Time on Site
        1. 3.1.2.1. Lesson 1: The Single-Page View Session
        2. 3.1.2.2. Lesson 2: The Case of Tabbed Browsing
    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
        1. 3.6.1.1. Uncomplex
        2. 3.6.1.2. Relevant
        3. 3.6.1.3. Timely
        4. 3.6.1.4. Instantly Useful
      2. 3.6.2. Example of a Great Web Metric
      3. 3.6.3. Three Avinash Life Lessons for Massive Success
        1. 3.6.3.1. Perfection Is...the Enemy of Good Enough
        2. 3.6.3.2. Critical Few, Baby, Critical Few!
        3. 3.6.3.3. The Web Metrics Lifecycle Process Is Your Friend!
    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
        1. 3.7.2.1. Creating Micro-ecosystem Reports
      3. 3.7.3. Starting with Macro Insights
        1. 3.7.3.1. Q1: "How many Visitors are coming to my website?"
        2. 3.7.3.2. Q2: "Where are Visitors coming from?"
        3. 3.7.3.3. Q3: "What do I want Visitors to do on the website?"
        4. 3.7.3.4. Q4: "What are Visitors actually doing?"
  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
        1. 4.3.1.1. Benefits of Segmenting
        2. 4.3.1.2. Creating and Applying Segments
      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
        1. 4.4.1.1. Site Search Usage
        2. 4.4.1.2. Measuring Site Search Quality
        3. 4.4.1.3. Segmenting and Measuring Impact
      2. 4.4.2. Search Engine Optimization (SEO) Analysis
        1. 4.4.2.1. Performance: Traffic from Organic Search
        2. 4.4.2.2. Content Coverage: Indexing by Search Engines
        3. 4.4.2.3. Keyword Performance: Search Engine Results
        4. 4.4.2.4. Outcomes: Goals, Revenue, and ROI
      3. 4.4.3. Pay Per Click/Paid Search Analysis
        1. 4.4.3.1. Performance: Traffic from Paid Search
        2. 4.4.3.2. Measuring the End-to-End View
        3. 4.4.3.3. Analyzing PPC Ad Position
        4. 4.4.3.4. Measuring PPC Customer Behavior
      4. 4.4.4. Direct Traffic Analysis
        1. 4.4.4.1. Reviewing Current Performance
        2. 4.4.4.2. Understanding the Opportunity and Educating Management
        3. 4.4.4.3. Segmenting to Understand Uniqueness
      5. 4.4.5. Email Campaign Analysis
        1. 4.4.5.1. Campaign Response
        2. 4.4.5.2. Website Behavior
        3. 4.4.5.3. Business Outcomes
      6. 4.4.6. Rich Experience Analysis: Flash, Video, and Widgets
        1. 4.4.6.1. Event Tracking
    5. 4.5. Reality Check: Perspectives on Key Web Analytics Challenges
      1. 4.5.1. Visitor Tracking Cookies
        1. 4.5.1.1. Transient vs. Persistent
        2. 4.5.1.2. First Party vs. Third Party
        3. 4.5.1.3. Cookie Choice and Data Storage
        4. 4.5.1.4. Cookie Deletion Rates
        5. 4.5.1.5. Without Cookies, Life Continues
      2. 4.5.2. Data Sampling 411
        1. 4.5.2.1. Code Red: Sampling Web Pages on Your Site
        2. 4.5.2.2. Code Orange: Sampling Data Collected from Each Page
        3. 4.5.2.3. Code Green: Sampling Data Processed When You Run the Query/Report
      3. 4.5.3. The Value of Historical Data
        1. 4.5.3.1. Data Decay
        2. 4.5.3.2. The Goodness of Not Worrying About History
        3. 4.5.3.3. Saving What Matters
      4. 4.5.4. The Usefulness of Video Playback of Customer Experience
        1. 4.5.4.1. Scale
        2. 4.5.4.2. Interpretation
        3. 4.5.4.3. "You Can Track Sara, Akio, and Roxana"
        4. 4.5.4.4. Cost vs. Benefit
        5. 4.5.4.5. Hope for the Future
      5. 4.5.5. The Ultimate Data Reconciliation Checklist
        1. 4.5.5.1. #1: Don't Compare Data Collected from Web Logs vs. JavaScript Tags
        2. 4.5.5.2. #2: The Gift That Keeps Giving: First- and Third-Party Cookies
        3. 4.5.5.3. #3: Imprecise Website Tagging
        4. 4.5.5.4. #4: Torture Your Vendor: Check Definitions of Key Metrics
        5. 4.5.5.5. #5: A Tough Nut: Sessionization
        6. 4.5.5.6. #6: The Permanent Tripwire: URL Parameter Configuration
        7. 4.5.5.7. #7: The Problem of the Big: Campaign Parameter Configuration
        8. 4.5.5.8. #8: The Hidden Angel: Data Sampling
        9. 4.5.5.9. #9: Order of Tags
  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
        1. 5.4.1.1. Photo-Publishing and Sharing Website
        2. 5.4.1.2. Technical Support Website
        3. 5.4.1.3. Multipurpose Ecommerce Website
    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
        1. 6.1.2.1. Preparing
        2. 6.1.2.2. Conducting the Test
        3. 6.1.2.3. Analyzing the Data
        4. 6.1.2.4. Following Up, Retesting, and Measuring Success
      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
        1. 6.2.1.1. Live Recruiting and Remote Studies
        2. 6.2.1.2. Outsourced Online Usability
    3. 6.3. Surveys: Truly Scalable Listening
      1. 6.3.1. Types of Surveys
        1. 6.3.1.1. Page-Level Surveys
        2. 6.3.1.2. Site-Level Surveys
      2. 6.3.2. The Single Biggest Surveying Mistake
      3. 6.3.3. Three Greatest Survey Questions Ever
        1. 6.3.3.1. Q1: What is the purpose of your visit to our website today?
        2. 6.3.3.2. Q2: Were you able to complete your task?
        3. 6.3.3.3. Q3: If you were not able to complete your task today, why not?
        4. 6.3.3.4. Launching Your Customer-Centric Strategy
      4. 6.3.4. Eight Tips for Choosing an Online Survey Provider
        1. 6.3.4.1. Tip 1: Mathematical Rigor
        2. 6.3.4.2. Tip 2: On-the-Fly Segmentation Capabilities
        3. 6.3.4.3. Tip 3: Benchmarks and Indices
        4. 6.3.4.4. Tip 4: Open Text Categorization
        5. 6.3.4.5. Tip 5: Type of Survey Invitation
        6. 6.3.4.6. Tip 6: Cookie Sophistication
        7. 6.3.4.7. Tip 7: Integration with Clickstream Data
        8. 6.3.4.8. Tip 8: Pilot Friendly
    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
        1. 7.1.1.1. Pros of A/B Testing
        2. 7.1.1.2. Cons of A/B Testing
      2. 7.1.2. Multivariate Testing
        1. 7.1.2.1. Common MVT Techniques
          1. 7.1.2.1.1. Pros of Multivariate Testing
          2. 7.1.2.1.2. Cons of 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
        1. 9.3.1.1. Logs-Based Solutions
        2. 9.3.1.2. Packet-Sniffing-Based Solutions
        3. 9.3.1.3. Tag-Based Solutions—JavaScript or Image
      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
        1. 9.4.2.1. Conversation Rate
      3. 9.4.3. Citations and Ripple Index
      4. 9.4.4. Cost of Blogging
      5. 9.4.5. Benefit (ROI) from Blogging
        1. 9.4.5.1. Comparative Value
        2. 9.4.5.2. Direct Value
        3. 9.4.5.3. Nontraditional Value
        4. 9.4.5.4. Unquantifiable Value
    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
        1. 9.5.3.1. Average Shared Links CTR
        2. 9.5.3.2. Conversion Rate (Outcomes)
      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
        1. 9.6.2.1. Baseline Performance Metrics
          1. 9.6.2.1.1. Tracking Attention or Audience Engagement
        2. 9.6.2.2. Reporting on Social Engagement
        3. 9.6.2.3. Tracking Viralness
        4. 9.6.2.4. Segment, Segment, Segment
      3. 9.6.3. Advanced Video Analysis
        1. 9.6.3.1. Computing Contextual Influence
        2. 9.6.3.2. Actively Collecting Voice of Customer (VOC)
        3. 9.6.3.3. Testing to Measure Actual Customer Behavior
  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
        1. 10.3.3.1. Benchmark and Segment
        2. 10.3.3.2. Isolate Your Critical Few Metrics
        3. 10.3.3.3. Don't Stop at Metrics—Include Insights
        4. 10.3.3.4. The Power of a Single Page
        5. 10.3.3.5. Churn and Stay Relevant
    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
        1. 10.5.3.1. Invest in Solid Customer Listening Posts
        2. 10.5.3.2. Do A/B or Multivariate Testing First
    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
        1. 10.6.5.1. Pace of Change on the Web
    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
        1. 10.7.2.1. Configuring Reporting
        2. 10.7.2.2. Configuring Goals
      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
        1. 11.5.1.1. Segmenting Average Data
        2. 11.5.1.2. Using Distributions
      2. 11.5.2. Percentages
        1. 11.5.2.1. Show Relevant Raw Numbers
        2. 11.5.2.2. Segment the Data
        3. 11.5.2.3. User Statistical Significance
      3. 11.5.3. Ratios
        1. 11.5.3.1. Use Custom Reports to Put the Ratio in Context
        2. 11.5.3.2. Resist Setting "Golden" Rules
      4. 11.5.4. Compound or Calculated Metrics
        1. 11.5.4.1. Don't Use Compound/Calculated Metrics
        2. 11.5.4.2. Degrade to Critical Few Components
        3. 11.5.4.3. Revisit and Revalidate
    6. 11.6. Search: Achieving the Optimal Long-Tail Strategy
      1. 11.6.1. Compute Your Head and Tail
        1. 11.6.1.1. Insights You Can Find
        2. 11.6.1.2. Actions to Take
      2. 11.6.2. Understanding Your Brand and Category Terms
        1. 11.6.2.1. Why Should You Care?
      3. 11.6.3. The Optimal Search Marketing Strategy
        1. 11.6.3.1. The SEM Fix
        2. 11.6.3.2. Insights You Can Find
        3. 11.6.3.3. Actions to Take
      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
        1. 12.1.3.1. Last-Click Credit
        2. 12.1.3.2. First-Click Credit
        3. 12.1.3.3. Even-Click Credit
        4. 12.1.3.4. Split Credit
        5. 12.1.3.5. Custom Credit
      4. 12.1.4. Core Challenge with Attribution Analysis in the Real World
      5. 12.1.5. Promising Alternatives to Attribution Analysis
        1. 12.1.5.1. Media Mix Modeling
          1. 12.1.5.1.1. Challenges of Online Media Mix Modeling
        2. 12.1.5.2. Marginal Attribution Analysis
          1. 12.1.5.2.1. Marginal Attribution Analysis Challenges
      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
        1. 12.2.1.1. Vanity URLs
        2. 12.2.1.2. Unique Redeemable Coupons and Offer Codes
        3. 12.2.1.3. Online Surveys and Market Research
        4. 12.2.1.4. Correlating Traffic Patterns and Offline Ad Schedules
        5. 12.2.1.5. Leveraging the Power of Controlled Experiments
      2. 12.2.2. Tracking the Offline Impact of Online Campaigns
        1. 12.2.2.1. Measuring Offline Calls to Action
        2. 12.2.2.2. Tracking Phone Calls and Live Chat
        3. 12.2.2.3. Using Unique Coupon Codes and Offers
        4. 12.2.2.4. Marrying and Mining Online and Offline Data
        5. 12.2.2.5. Using Surveys to Predict Offline Impact
        6. 12.2.2.6. Conducting Controlled Experiments
        7. 12.2.2.7. Leveraging Primary Research
  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
        1. 13.1.1.1. Career Prospects
        2. 13.1.1.2. Salary Prospects
        3. 13.1.1.3. Long-Term Job Title Growth (Promotions)
      2. 13.1.2. Business Individual Contributor
        1. 13.1.2.1. Career Prospects
        2. 13.1.2.2. Salary Prospects
        3. 13.1.2.3. Long-Term Job Title Growth
      3. 13.1.3. Technical Team Leader
        1. 13.1.3.1. Career Prospects
        2. 13.1.3.2. Salary Prospects
        3. 13.1.3.3. Long-Term Job Title Growth
      4. 13.1.4. Business Team Leader
        1. 13.1.4.1. Career Prospects
        2. 13.1.4.2. Salary Prospects
        3. 13.1.4.3. Long-Term Job Title Growth
    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
        1. 13.2.3.1. Web Analytics Education
        2. 13.2.3.2. Beyond a Web Analytics Education
        3. 13.2.3.3. Online Advertising Education
        4. 13.2.3.4. Social Media Analytics Education
        5. 13.2.3.5. The Bottom Line on Online Marketing and Analytics Education
      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
        1. 14.1.1.1. Start with Outcomes and Impact, Not Visits
        2. 14.1.1.2. Create Heroes and Role Models
        3. 14.1.1.3. If You Want Excitement, Make It Fun!
        4. 14.1.1.4. Hold Contests
        5. 14.1.1.5. Hold Internal Conferences
        6. 14.1.1.6. Hold Office Hours
    2. 14.2. Deliver Reports and Analyses That Drive Action
      1. 14.2.1.
        1. 14.2.1.1. The "What's Your Point?" Filter
        2. 14.2.1.2. The Hard Tie to Business Outcomes
        3. 14.2.1.3. Present Multiple Perspectives: True Web Analytics 2.0
      2. 14.2.2. The Unböring Filter
      3. 14.2.3. Connecting Insights with Actual Data
        1. 14.2.3.1. The Expectations of Scale Filter
        2. 14.2.3.2. Having Something Unique to Say
    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
        1. 14.4.1.1. Educate Your Organization About the "Perfect" Source
      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
        1. 14.4.7.1. Illogical Customer Behavior and Inaccurate Benchmarks
      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.
        1. 14.6.1.1. Implement an Experimentation and Testing Program
      2. 14.6.2. Capture Voice of Customer
        1. 14.6.2.1. Deploy the Benchmarks
        2. 14.6.2.2. Competitive Intelligence: Your New Best Friend
      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
        1. 14.7.7.1. Difficulty Reconciling Data
      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

Product information

  • Title: Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity
  • Author(s):
  • Release date: October 2009
  • Publisher(s): Sybex
  • ISBN: 9780470529393