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Building a Digital Analytics Organization: Create Value by Integrating Analytical Processes, Technology, and People into Business Operations

Book Description

Drive maximum business value from digital analytics, web analytics, site analytics, and business intelligence! In Building a Digital Analytics Organization, pioneering expert Judah Phillips thoroughly explains digital analytics to business practitioners, and presents best practices for using it to reduce costs and increase profitable revenue throughout the business. Phillips covers everything from making the business case through defining and executing strategy, and shows how to successfully integrate analytical processes, technology, and people in all aspects of operations. This unbiased and product-independent guide is replete with examples, many based on the author’s own extensive experience. Coverage includes: key concepts; focusing initiatives and strategy on business value, not technology; building an effective analytics organization; choosing the right tools (and understanding their limitations); creating processes and managing data; analyzing paid, owned, and earned digital media; performing competitive and qualitative analyses; optimizing and testing sites; implementing integrated multichannel digital analytics; targeting consumers; automating marketing processes; and preparing for the revolutionary “analytical economy.” For all business practitioners interested in analytics and business intelligence in all areas of the organization.

Table of Contents

  1. About This eBook
  2. Title Page
  3. Copyright Page
  4. Praise for Building a Digital Analytics Organization
  5. Dedication Page
  6. Table of Contents
  7. Foreword
  8. Acknowledgments
  9. About the Author
  10. 1. Using Digital Analytics to Create Business Value
    1. Big Data and Data Science Requires Digital Analytics
    2. Defining Digital Analytics
  11. 2. Analytics Value Chain and the P’s of Digital Analytics
    1. Explaining the P’s of Digital Analytics
    2. The Analytics Value Chain: Process for Tactical and Strategic Success
    3. The Analytics Work Request Process
  12. 3. Building an Analytics Organization
    1. Justifying Investment in the Analytics Team
    2. Creating the Analytics Team Business Justification and Investment Consideration
    3. Reporting to Executives and Other Teams about Analytics Value Generation
    4. Creating Analytics Team Roles and Responsibilities
    5. Enhancing Your Career: Methods for Success
  13. 4. What Are Analytics Tools?
    1. To Build or to Buy?
    2. Balancing Management of Analytics Technology: Should “the Business” or IT Run It?
    3. Selecting an Analytics Tool
    4. Succeeding with Tool Deployment
    5. Business Concerns: Maintenance
    6. Why Do Digital Analytics Tools and Data Decay?
  14. 5. Methods and Techniques for Digital Analysis
    1. Storytelling Is Important for Analysis
    2. Tukey’s Exploratory Data Analysis Is an Important Concept in Digital Analytics
    3. Types of Data: Simplified
    4. Looking at Data: Shapes of Data
    5. Analyzing Digital Data Using Statistics and Machine Learning
  15. 6. Defining, Planning, Collecting, and Governing Data in Digital Analytics
    1. Defining Digital Data: How to Do It
    2. What Are Business Definitions for Digital Data?
    3. What Are Operational Definitions for Digital Data?
    4. What Are Technical Definitions for Digital Data?
    5. Creating and Maintaining Data Definitions
    6. Planning for Digital Data: What Should You Do?
    7. Collecting Digital Data: What You Need to Know
    8. Governing Digital Data: The Data Governance Function
    9. The Data Governance Team: What Do They Do?
    10. The Process for Data Governance Across Programs, Projects, and Teams
    11. The Difficulty of Testing and Verifying Data
  16. 7. Reporting Data and Using Key Performance Indicators
    1. What Is Reporting and How Does It Happen?
    2. The Five Elements of Excellent Reporting: RASTA
    3. The Difference Between Reporting and Dashboarding
    4. What Is Dashboarding and How Does It Happen?
    5. The Five Elements of Excellent Dashboarding: LIVES
    6. Understanding Key Performance Indicators (KPIs)
    7. Where Does Reporting and Dashboarding Fit in the Analytics Value Chain?
    8. Example KPIs: Averages, Percentages, Rates/Ratios, “Per X,” and Derivatives
    9. Real-Time Versus Timely Data: A Practitioner Perspective
  17. 8. Optimization and Testing with Digital Analytics: Test, Don’t Guess
    1. Reviewing the AB Test: Start Here
    2. Expanding to Multivariate Testing
    3. Creating a Testing and Optimization Plan
    4. The Process of AB and Multivariate Testing
    5. Technologies and Methods for Measuring, Analyzing, and Reporting Results of AB and Multivariate Testing
    6. Types of Optimization Enabled Through Testing
    7. Setting Up a Digital Optimization Program
    8. Developing Controlled Experiments and Digital Data Science
    9. Tips for Testing and Optimizing Digital Experiences
  18. 9. Qualitative and Voice of Customer Data and Digital Analytics
    1. Listening to Your Customer Is More Important Today Than Ever Before
    2. Tools of the Trade: Market Research and Qualitative Data Collection Methods and Techniques
    3. Creating Customer Feedback Systems Such as Call Centers and Online Feedback Forms
    4. What Does a Qualitative Data Team Do and How Does It Work with Digital Analytics?
    5. Integrating Digital Behavioral Data with Qualitative Data
    6. Working Successfully Together and with the Business: Qualitative and Quantitative Data, Research, and Analytics Teams
  19. 10. Competitive Intelligence and Digital Analytics
    1. Competitive Intelligence Versus Digital Intelligence
    2. Types of Digital Competitive Intelligence: Real-World Examples
    3. Digital Competitive Intelligence Tools and Methods
    4. The Process for Digital Competitive Intelligence
    5. Integrating Digital Behavioral Data with Competitive Intelligence
  20. 11. Targeting and Automation with Digital Analytics
    1. Types of Targeting
    2. Where in Digital Does Targeting Occur?
    3. What Is Retargeting?
    4. Types of Retargeting
    5. How Can the Digital Analytics Team Assist the Process of Targeting and Retargeting?
    6. Suggestions When Targeting and Retargeting: Lessons Learned
  21. 12. Converging Omnichannels and Integrating Data for Understanding Customers, Audiences, and Media
    1. Types of Omnichannel Data
    2. Omnichannel Data Metrics
    3. Defining Customer Analytics: Enabled by Omnichannel Data Integration
    4. Questioning Customers Using Their Data and Your Analytics
    5. The Unified Customer Life Cycle
    6. Work Activities in Customer Analytics via Omnichannel Data Integration
    7. Challenges to Customer Analytics
    8. What’s Required for the Digital Analytics Team to Do Customer Analytics via Omnichannel Integration?
  22. 13. Future of Digital Analytics
    1. Predictive Personalization
    2. Closed-Loop Behavioral Feedback Systems
    3. Real-Time, Addressable, Relevant Content and Advertising Delivered Unified Across Multiscreens
    4. Sensing and Responding
    5. Interacting and Alerting
    6. Geo-Specific Relevance and Intent Targeting
    7. Automated Services and Product Delivery
    8. Data-Interactive Shopper and Customer Experiences
    9. The Future of Analytics Requires Privacy and Ethics
  23. Works Cited
  24. Index