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Ecommerce Analytics: Analyze and Improve the Impact of Your Digital Strategy

Book Description

Today's Complete, Focused, Up-to-Date Guide to Analytics for Ecommerce

  • Profit from analytics throughout the entire customer experience and lifecycle

  • Make the most of all the fast-changing data sources now available to you

  • For all ecommerce executives, strategists, entrepreneurs, marketers, analysts, and data scientists

  • Ecommerce Analytics is the only complete single-source guide to analytics for your ecommerce business. It brings together all the knowledge and skills you need to solve your unique problems, and transform your data into better decisions and customer experiences.

    Judah Phillips shows how to use analysis to improve ecommerce marketing and advertising, understand customer behavior, increase conversion rates, strengthen loyalty, optimize merchandising and product mix, streamline transactions, optimize product mix, and accurately attribute sales.

    Drawing on extensive experience leading large-scale analytics programs, he also offers expert guidance on building successful analytical teams; surfacing high-value insights via dashboards and visualization; and managing data governance, security, and privacy.

    Here are the answers you need to make the most of analytics in ecommerce: throughout your organization, across your entire customer lifecycle.

    Table of Contents

    1. About This E-Book
    2. Title Page
    3. Copyright Page
    4. Praise for Ecommerce Analytics
    5. Dedication Page
    6. Contents at a Glance
    7. Contents
    8. Foreword
    9. Acknowledgments
    10. About the Author
    11. 1. Ecommerce Analytics Creates Business Value and Drives Business Growth
    12. 2. The Ecommerce Analytics Value Chain
      1. Identifying and Prioritizing Demand
      2. Developing an Analytical Plan
      3. Activating the Ecommerce Analytics Environment
        1. Elements of an Ecommerce Analytics Environment
        2. Collecting and Governing Data and Metadata
      4. Preparing and Wrangling Data
      5. Analyzing, Predicting, Optimizing, and Automating with Data
      6. Socializing Analytics
      7. Communicating the Economic Impact of Analytics
    13. 3. Methods and Techniques for Ecommerce Analysis
      1. Understanding the Calendar for Ecommerce Analysis
      2. Storytelling Is Important for Ecommerce Analysis
      3. Tukey’s Exploratory Data Analysis Is an Important Concept in Ecommerce Analytics
      4. Types of Data: Simplified
      5. Looking at Data: Shapes of Data
        1. Understanding Basic Stats: Mean, Median, Standard Deviation, and Variance
        2. Plotting Ecommerce Data
        3. Four Plots and Six Plots
        4. Histograms (Regular, Clustered, and Stacked)
        5. Pie Charts
        6. Line Charts
        7. Flow Visualizations
      6. Analyzing Ecommerce Data Using Statistics and Machine Learning
        1. Correlating Data
        2. Regressing Data: Linear, Logistic, and More
        3. Probability and Distributions
        4. Experimenting and Sampling Data
      7. Using Key Performance Indicators for Ecommerce
        1. KPI Metric Example: Page or Screen Views
        2. KPI Metric Example: Visits or Sessions
        3. KPI Metric Example: Returns
        4. KPI Metric Example: Total Revenue and Revenue by N
        5. KPI Metric Example: Gross Margin
        6. KPI Metric Example: Lifetime Value
        7. KPI Metric Example: Repeat Visitors/Users/Customers
        8. KPI Rate Example: Conversion Rate
        9. KPI Rate Example: Step Completion Rate
        10. KPI Rate Example: Abandoned Cart Rate
        11. KPI Average Example: Average Order Value
        12. KPI Derivative Example: Bounce Rate
        13. KPI Derivative Example: Percentage of Orders with Promotions or Discounts
        14. KPI Derivative Example: Inventory Turnover
        15. KPI Derivative Example: Return on Investment
        16. KPI Derivative Example: Loyalty—Time Since Last Visit (Recency)
        17. KPI Derivative Example: Retention—Time Between Visits (Frequency)
        18. KPI Percentage Example: Percentage of X from Source N
        19. KPI Percentage Example: Percentage of New Customers (or N Metric)
        20. KPI “Per” Example: Cost and/or Revenue per Visitor
        21. KPI “Per” Example: Revenue per Customer
        22. KPI “Per” Example: Cost per Customer Acquisition
    14. 4. Visualizing, Dashboarding, and Reporting Ecommerce Data and Analysis
      1. Understanding Reporting
      2. Explaining the RASTA Approach to Reporting
      3. Understanding Dashboarding
      4. Explaining the LIVEN Approach to Dashboarding
      5. What Data Should I Start With in an Ecommerce Dashboard?
      6. Understanding Data Visualization
        1. The Process for Data Visualization
        2. Maximizing Impact with Data Visualization: The SCREEN Approach and More
        3. Why Use Data Visualizations?
        4. Types of Data Visualization
    15. 5. Ecommerce Analytics Data Model and Technology
      1. Understanding the Ecommerce Analytics Data Model: Facts and Dimensions
      2. Explaining a Sample Ecommerce Data Model
      3. Understanding the Inventory Fact
      4. Understanding the Product Fact
      5. Understanding the Order Fact
      6. Understanding the Order Item Fact
      7. Understanding the Customers Fact
      8. Understanding the Customer Order Fact
      9. Reviewing Common Dimensions and Measures in Ecommerce
    16. 6. Marketing and Advertising Analytics in Ecommerce
      1. Understanding the Shared Goals of Marketing and Advertising Analysis
      2. Reviewing the Marketing Lifecycle
      3. Understanding Types of Ecommerce Marketing
      4. Analyzing Marketing and Advertising for Ecommerce
      5. What Marketing Data Could You Begin to Analyze?
    17. 7. Analyzing Behavioral Data
      1. Answering Business Questions with Behavioral Analytics
      2. Understanding Metrics and Key Performance Indicators for Behavioral Analysis
      3. Reviewing Types of Ecommerce Behavioral Analysis
        1. Behavioral Flow Analysis
        2. Shopping Behavior Analysis
        3. Content Analysis
        4. In-Page or On-Screen Behavior Analysis
    18. 8. Optimizing for Ecommerce Conversion and User Experience
      1. The Importance of the Value Proposition in Conversion Optimization
      2. The Basics of Conversion Optimization: Persuasion, Psychology, Information Architecture, and Copywriting
      3. The Conversion Optimization Process: Ideation to Hypothesis to Post-Optimization Analysis
      4. The Data for Conversion Optimization: Analytics, Visualization, Research, Usability, Customer, and Technical Data
      5. The Science Behind Conversion Optimization
      6. Succeeding with Conversion Optimization
    19. 9. Analyzing Ecommerce Customers
      1. What Does a Customer Record Look Like in Ecommerce?
      2. What Customer Data Could I Start to Analyze?
      3. Questioning Customer Data with Analytical Thought
      4. Understanding the Ecommerce Customer Analytics Lifecycle
      5. Defining the Types of Customers
      6. Reviewing Types of Customer Analytics
      7. Segmenting Customers
      8. Performing Cohort Analysis
      9. Calculating Customer Lifetime Value
      10. Determining the Cost of Customer Acquisition
      11. Analyzing Customer Churn
      12. Understanding Voice-of-the-Customer Analytics
      13. Doing Recency, Frequency, and Monetary Analysis
      14. Determining Share of Wallet
      15. Scoring Customers
      16. Predicting Customer Behavior
      17. Clustering Customers
      18. Predicting Customer Propensities
      19. Personalizing Customer Experiences
    20. 10. Analyzing Products and Orders in Ecommerce
      1. What Are Ecommerce Orders?
      2. What Order Data Should I Begin to Analyze?
      3. What Metrics and Key Performance Indicators Are Relevant for Ecommerce Orders?
      4. Approaches to Analyzing Orders and Products
        1. Doing Financial Analysis on Orders
        2. Doing Product and Item Analysis on Orders
        3. Doing Promotional Analysis on Orders
        4. Doing Category and Brand Analysis on Orders
        5. Doing Event and Goal Analysis on Orders
        6. Doing Path-to-Purchase Analysis on Orders
        7. Doing Funnel Analysis on Orders
        8. Doing Cluster Analysis on Orders
        9. Doing Up-Sell and Cross-Sell Analysis on Orders
        10. Doing Next-Best-Action Analysis on Orders
      5. Analyzing Products in Ecommerce
        1. Understanding Useful Types of Product Analysis for Ecommerce
        2. Product Brand Analysis
        3. Product Category Analysis
        4. Customer Service Analysis
        5. Product Returns Analysis
        6. Social Media Product Analysis
      6. Analyzing Merchandising in Ecommerce
        1. Testing Merchandising Creative
        2. Performing Inventory Analysis
        3. Analyzing Product Offers
        4. Determining the Optimal Price via Pricing Analysis
        5. Understanding the Sales Impact of Merchandising
        6. Analyzing Suppliers and the Supply Chain
        7. Determining Effective and Profitable Markdowns, Promotions, and Discounts
      7. What Merchandising Data Should I Start Analyzing First?
    21. 11. Attribution in Ecommerce Analytics
      1. Attributing Sources of Buyers, Conversion, Revenue, and Profit
      2. Understanding Engagement Mapping and the Types of Attribution
      3. The Difference between Top-Down and Bottom-Up Approaches to Attribution
      4. A Framework for Assessing Attribution Software
    22. 12. What Is an Ecommerce Platform?
      1. Understanding the Core Components of an Ecommerce Platform
      2. Understanding the Business Functions Supported by an Ecommerce Platform
      3. Determining an Analytical Approach to Analyzing the Ecommerce Platform
    23. 13. Integrating Data and Analysis to Drive Your Ecommerce Strategy
      1. Defining the Types of Data, Single-Channel to Omnichannel
      2. Integrating Data from a Technical Perspective
        1. Agile Versus Waterfall Delivery
        2. Integration with Operational Data Stores
        3. Integration with On-Premises Enterprise Data Warehouses
        4. Integration with Cloud Data Sources
        5. Integration with Data Lakes
        6. Integration with Data Federation
        7. Integration with Data Virtualization
      3. Integrating Analytics Applications
      4. Integrating Data from a Business Perspective
    24. 14. Governing Data and Ensuring Privacy and Security
      1. Applying Data Governance in Ecommerce
      2. Applying Data Privacy and Security in Ecommerce
      3. Governance, Privacy, and Security Are Part of the Analyst’s Job
    25. 15. Building Analytics Organizations and Socializing Successful Analytics
      1. Suggesting a Universal Approach for Building Successful Analytics Organizations
      2. Determine and Justify the Need for an Analytics Team
      3. Gain Support for Hiring or Appointing a Leader for Analytics
      4. Hire the Analytics Leader
      5. Gather Business Requirements
      6. Create the Mission and Vision for the Analytics Team
      7. Create an Organizational Model
      8. Hire Staff
      9. Assess the Current State Capabilities and Determine the Future State Capabilities
      10. Assess the Current State Technology Architecture and Determine the Future State Architecture
      11. Begin Building an Analytics Road Map
      12. Train Staff
      13. Map Current Processes, Interactions, and Workflows
      14. Build Templates and Artifacts to Support the Analytics Process
      15. Create a Supply-and-Demand Management Model
      16. Create an Operating Model for Working with Stakeholders
      17. Use, Deploy, or Upgrade Existing or New Technology
      18. Collect or Acquire New Data
      19. Implement a Data Catalog, Master Data Management, and Data Governance
      20. Meet with Stakeholders and Participate in Business Processes, and Then Socialize Analysis on a Regular Cadence and Periodicity
      21. Do Analysis and Data Science and Deliver It
      22. Lead or Assist with New Work Resulting from Analytical Processes
      23. Document and Socialize the Financial Impact and Business Outcomes Resulting from Analysis
      24. Continue to Do Analysis, Socialize It, and Manage Technology While Emphasizing the Business Impact Ad Infinitum
      25. Manage Change and Support Stakeholders
    26. 16. The Future of Ecommerce Analytics
      1. The Future of Data Collection and Preparation
      2. The Future Is Data Experiences
      3. Future Analytics and Technology Capabilities
    27. Bibliography
    28. Index