Actionable Web Analytics: Using Data To Make Smart Business Decisions

Actionable Web Analytics: Using Data To Make Smart Business Decisions

by Jason Burby, Shane Atchison, Jim Sterne
Released May 2007
Publisher(s): Sybex
ISBN: 9780470124741

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Book description

Knowing everything you can about each click to your Web site can help you make strategic decisions regarding your business. This book is about the why, not just the how, of web analytics and the rules for developing a "culture of analysis" inside your organization. Why you should collect various types of data. Why you need a strategy. Why it must remain flexible. Why your data must generate meaningful action. The authors answer these critical questions—and many more—using their decade of experience in Web analytics.

Table of contents

  1. Copyright
    1. Dedication
  2. Praise for Actionable Web Analytics: Using Data To Make Smart Business Decisions
  3. Dear Reader
  4. Acknowledgments
  5. About the Authors
    1. ZAAZ
  6. Foreword
    1. Website Optimization
    2. Marketing Optimization
    3. Business Optimization
    4. Asking the Right Questions
  7. Introduction
    1. What’s Inside
  8. I. The Changing Landscape of Marketing Online
    1. 1. The Big Picture
      1. New Marketing Trends
        1. The Consumer Revolution
          1. Consumer-Driven Choice
          2. What Time Does the Store Open?
        2. The Shift from Offline to Online Marketing
          1. The Winning Site
        3. Instant Brand Building (and Destruction)
        4. Rich Media and Infinite Variety
      2. The Analysis Mandate
        1. ROI Marketing
        2. Innovation
      3. Some Final Thoughts
    2. 2. Performance Marketing
      1. Data vs. Design
        1. Web Design Today
      2. The Web Award Fallacy
        1. When Visual Design Goes Wrong
        2. Where Data Goes Wrong
      3. Performance-Driven Design: Balancing Logic and Creativity
        1. Case Study: Dealing with Star Power
        2. Case Study: Forget Marketing at All
      4. Recap
  9. II. Shifting to a Culture of Analysis
    1. 3. What “Culture of Analysis” Means
      1. What Is a Data-Driven Organization?
        1. Data-Driven Decision Making
        2. Dynamic Prioritization
          1. Prioritizing Based on Business Impact
          2. Optimizing Resource Planning
          3. Accelerating the Release Cycle
          4. Holding Initiatives Financially Accountable
      2. Perking Up Interest in Web Analytics
        1. Establishing a Web Analytics Steering Committee
          1. Steering Committee Members
          2. Steering Committee Mandate
        2. Starting Out Small with a Win
        3. Empowering Your Employees
        4. Managing Up
      3. Impact on Roles beyond the Analytics Team
      4. Cross-Channel Implications
      5. Questionnaire: Rating Your Level of Data Drive
      6. Recap
    2. 4. Avoiding Stumbling Points
      1. Do You Need an Analytics Intervention?
        1. Analytics Intervention Step 1: Admitting the Problem
          1. Perceived Gap: Inadequate Analytics Tools
          2. Perceived Gap: Inability to Track a Site
        2. Analytics Intervention Step 2: Admit That You Are the Problem
        3. Analytics Intervention Step 3: Agree That This Is a Corporate Problem
      2. The Road to Recovery: Overcoming Real Gaps
        1. Issue #1: Lack of Established Processes and Methodology
        2. Issue #2: Failure to Establish Proper KPIs and Metrics
        3. Issue #3: Data Inaccuracy
        4. Issue #4: Data Overload
        5. Issue #5: Inability to Monetize the Impact of Changes
        6. Issue #6: Inability to Prioritize Opportunities
        7. Issue #7: Limited Access to Data
        8. Issue #8: Inadequate Data Integration
        9. Issue #9: Starting Too Big
        10. Issue #10: Failure to Tie Goals to KPIs
        11. Issue #11: No Plan for Acting on Insight
        12. Issue #12: Lack of Committed Individual and Executive Support
      3. Recap
  10. III. Proven Formula for Success
    1. 5. Preparing to Be Data-Driven
      1. Web Analytics Methodology
      2. The Four Steps of Web Analytics
        1. Defining Business Metrics (KPIs)
        2. Reports
        3. Analysis
        4. Optimization and Action
      3. Results and Starting Again
      4. Recap
    2. 6. Defining Site Goals, KPIs, and Key Metrics
      1. Defining Overall Business Goals
      2. Defining Site Goals: The Conversion Funnel
        1. Awareness
        2. Interest
        3. Consideration
        4. Purchase
        5. Website Goals and the Marketing Funnel
      3. Understanding Key Performance Indicators (KPIs)
        1. Constructing KPIs
        2. Creating Targets for KPIs
      4. Common KPIs for Different Site Types
        1. E-Commerce
        2. Lead Generation
        3. Customer Service
        4. Content Sites
          1. Advertising-Based Content Site KPIs
          2. Subscription-Based Content Site KPIs
        5. Branding Sites
      5. Recap
    3. 7. Monetizing Site Behaviors
      1. The Monetization Challenge
        1. Case Study: Monetization and Motivation
          1. Conversation A: An Unmonetized Argument
          2. Conversation B: Monetization Changes Everything
      2. Web-Monetization Models
        1. Top 10 Ways Monetization Models Can Help Your Company
        2. How to Create Monetization Models
          1. Direct Behaviors
            1. Sales
            2. Lead Generation
            3. Customer Service
          2. Indirect Behaviors
            1. Referrals
            2. Offline Sales
            3. Customer Satisfaction
        3. Assembling a Monetization Model
      3. Monetization Models for Different Site Types and Behaviors
        1. E-Commerce Opportunity
          1. The Monetization Model
        2. Lead Generation
        3. Customer Service
          1. A Warning About Conversion Rates and Support
        4. Ad-Supported Content Sites
      4. Recap
    4. 8. Getting the Right Data
      1. Primary Data Types
        1. Warning: Avoid Data Smog
        2. Behavioral Data
        3. Attitudinal Data
        4. Balancing Behavioral and Attitudinal Data
        5. Competitive Data
          1. Types of Competitive Data
            1. Third-Party Private Networks
            2. User-Centric Networks (Comscore, Nielsen//Netratings)
            3. Network-Centric (Hitwise)
          2. Leveraging Competitive Data
          3. Getting Started with Competitive Data
      2. Secondary Data Types
        1. Customer Interaction and Data
        2. Third-Party Research
        3. Usability Benchmarking
        4. Heuristic Evaluation and Expert Reviews
        5. Community Sourced Data
        6. Leveraging These Data Types
      3. Comparing Performance with Others
      4. What Is a Relative Index?
        1. Examples of Relative Indices
      5. Customer Engagement
        1. Methodology: Leveraging Indices across Your Organization
          1. Step 1: Define the Key Index
          2. Step 2: Determine the Frequency of Reporting
          3. Step 3: Cultivate a Culture of Analysis
          4. Step 4: Make Budget Allocations
          5. Step 5: Benchmark Marketing Initiatives
          6. Step 6: Benchmark Site-Behavior Types
          7. Step 7: Prioritize Resources
          8. Step 8: Learn
      6. Case Study: Leveraging Different Data Types to Improve Site Performance
      7. Recap
    5. 9. Analyzing Site Performance
      1. Analysis vs. Reporting
        1. Don’t Blame Your Tools
          1. Where Did We Go Wrong?
      2. Examples of Analysis
        1. Analyzing Purchasing Processes to Find Opportunities
          1. Handholding
          2. Commitment and Acceleration
          3. Helping the Decision
          4. Fine-Tuning for Your Audience
        2. Analyzing Lead Processes to Find Opportunities
        3. Understanding What Onsite Search Is Telling You
          1. Understanding the Terms Visitors Search For
          2. Micro–Case Study: An Apparel Store
        4. Evaluating the Effectiveness of Your Home Page
        5. Evaluating the Effectiveness of Branding Content: Branding Metrics
        6. Evaluating the Effectiveness of Campaign Landing Pages
      3. Segmenting Traffic to Identify Behavioral Differences
        1. Segmenting Your Audience
        2. Case Study: Segmenting for a Financial Services Provider
      4. Analyzing Drivers to Offline Conversion
        1. Tracking Online Partner Handoffs and Brick-And-Mortar Referrals
        2. Tracking Offline Handoffs to Sales Reps
        3. Tracking Visitors to a Call Center
      5. Delayed Conversion
        1. Tracking Delayed Conversion
        2. Reporting in a Timely Manner
      6. Recap
    6. 10. Prioritizing
      1. How We Prioritize
        1. The Principles of Dynamic Prioritization
        2. Traditional Resource Prioritization
          1. Web Steering Committees
      2. Dynamic Prioritization
        1. Prioritization Based on Business Impact
        2. Optimized Resource Planning
        3. Accelerating the Release Cycle
        4. Holding Initiatives Financially Accountable
        5. Dynamic Prioritization Scorecard
        6. Dynamic Prioritization in Action
          1. Lead Generation
          2. Online Commerce
          3. Customer Service
      3. Forecasting Potential Impact
        1. Comparing Opportunities
        2. Moving Your Company Toward Dynamic Prioritization
        3. Overcoming Common Excuses
        4. Conclusion
      4. Recap
    7. 11. Moving from Analysis to Site Optimization
      1. Testing Methodologies and Tools
        1. A/B Testing
        2. A/B/n Testing
        3. Multivariate Tests
        4. How to Choose a Test Type
        5. Testing Tools
      2. What to Test
        1. Prioritizing Tests
        2. Creating a Successful Test
        3. Understanding Post-Test Analysis
      3. Optimizing Segment Performance
        1. Example One: Behavior-Based Testing
        2. Example Two: Day-of-the-Week Testing
      4. Planning for Optimization
        1. Budgeting for Optimization
        2. Skills Needed for a Successful Optimization Team
      5. Overcoming IT Doubts
        1. IT Doesn’t Understand the Process
        2. Testing Prioritization
        3. Lack of Executive Support
      6. Learning from Your Successes and Mistakes
        1. Learning from the Good and the Bad
        2. A Quick Way Up the Learning Curve
        3. Spreading the Word
      7. Test Examples
        1. Price
        2. Promotional
        3. Message
        4. Page Layout
        5. New Site Launches or New Functionality
        6. Site Navigation and Taxonomy
      8. Recap
    8. 12. Agencies
      1. Why Use an Agency at All?
      2. Finding an Agency
      3. Creating an RFP
        1. Introduction and Company Background
          1. Example 1: RobotWear
          2. Example 2: Snappy Scissors
          3. Recap: Company/Brand Background
        2. Scope of Work and Business Goals
          1. Example 1: RobotWear
          2. Example 2: Snappy Scissors
        3. Timelines
          1. RobotWear Example
        4. Financials
        5. The Rest of the RFP: Asking the Right Questions
      4. Mutual Objective: Success
        1. Doing the Work
        2. The Secret Agency Sauce
      5. Recap
    9. 13. The Creative Brief
      1. What Is a Creative Brief?
        1. The Brief
        2. Components of a Data-Driven Brief
        3. Creative Brief Metrics
      2. Analytics and Creativity
        1. The Iterative Design Cycle
      3. A Sample Creative Brief
        1. Creative Brief: Robotwear.Com
          1. Purpose of the brief
          2. What is the overarching strategy?
          3. Who are we targeting?
          4. What do we want to accomplish?
            1. Brand Goals
            2. Business Goals
          5. What is the core message that we want to communicate with this campaign?
          6. What is the campaign strategy? How does it support the larger strategy for the RobotWear web channel?
          7. What is the campaign concept?
          8. What are the campaign offers?
          9. What are the main campaign components?
            1. Robotwear.Com
            2. Marketing
          10. What are the key milestones for the project?
      4. Recap
    10. 14. Staffing and Tuning Your Web Team
      1. Skills That Make a Great Web Analyst
        1. Technical vs. Interpretive Expertise
        2. Key Web Analyst Skills
        3. The Roles of the Web Analyst
      2. Building Your Web-Analytics Team: Internal and External Teams
        1. Estimating Your Cost
        2. Key Analytics Positions
        3. Expanding the Circle of Influence
        4. Internal vs. External Teams
      3. Education and Training for Web Analysts
        1. Web Analytics Association
        2. Conferences
        3. University of British Columbia Courses
        4. Message Boards
        5. ClickZ and Other Online Media
        6. Blogs
        7. Web Analytics Wednesdays
        8. Vendor Training
        9. Agency Partners
        10. Hands-on Experience
      4. Recap
    11. 15. Partners
      1. When to Choose an Analytics Tool Vendor
      2. Methodology for Selecting a Tool
        1. Selecting a Review Committee
        2. Establishing a Timeline
      3. Criteria to Review and Select Vendors
        1. Technical and Implementation Questions
        2. Reporting and Analysis
        3. Account Service and Support
      4. 10 Questions to Ask Web Analytics Vendors
        1. Comparing to Free Tools
        2. ASP or Software Version
        3. Data Capture
        4. Total Cost of Ownership
        5. Support
        6. Data Segmentation
        7. Data Export and Options
        8. Data Integration
        9. The Future
        10. References
      5. Recap
      6. Conclusion
    12. A. Web Analytics “Big Three” Definitions
      1. How We Define Terms
        1. Definition Framework Overview
      2. Term: Unique Visitors
      3. Term: Visits/Sessions
      4. Term: Page Views

Product information

  • Title: Actionable Web Analytics: Using Data To Make Smart Business Decisions
  • Author(s): Jason Burby, Shane Atchison, Jim Sterne
  • Release date: May 2007
  • Publisher(s): Sybex
  • ISBN: 9780470124741