Chapter 1 Ecommerce Analytics Creates Business Value and Drives Business Growth
Chapter 2 The Ecommerce Analytics Value Chain
Identifying and Prioritizing Demand
Activating the Ecommerce Analytics Environment
Analyzing, Predicting, Optimizing, and Automating with Data
Communicating the Economic Impact of Analytics
Chapter 3 Methods and Techniques for Ecommerce Analysis
Understanding the Calendar for Ecommerce Analysis
Storytelling Is Important for Ecommerce Analysis
Tukey’s Exploratory Data Analysis Is an Important Concept in Ecommerce Analytics
Looking at Data: Shapes of Data
Analyzing Ecommerce Data Using Statistics and Machine Learning
Using Key Performance Indicators for Ecommerce
Chapter 4 Visualizing, Dashboarding, and Reporting Ecommerce Data and Analysis
Explaining the RASTA Approach to Reporting
Explaining the LIVEN Approach to Dashboarding
What Data Should I Start With in an Ecommerce Dashboard?
Understanding Data Visualization
Chapter 5 Ecommerce Analytics Data Model and Technology
Understanding the Ecommerce Analytics Data Model: Facts and Dimensions
Explaining a Sample Ecommerce Data Model
Understanding the Inventory Fact
Understanding the Product Fact
Understanding the Order Item Fact
Understanding the Customers Fact
Understanding the Customer Order Fact
Reviewing Common Dimensions and Measures in Ecommerce
Chapter 6 Marketing and Advertising Analytics in Ecommerce
Understanding the Shared Goals of Marketing and Advertising Analysis
Reviewing the Marketing Lifecycle
Understanding Types of Ecommerce Marketing
Analyzing Marketing and Advertising for Ecommerce
What Marketing Data Could You Begin to Analyze?
Chapter 7 Analyzing Behavioral Data
Answering Business Questions with Behavioral Analytics
Understanding Metrics and Key Performance Indicators for Behavioral Analysis
Reviewing Types of Ecommerce Behavioral Analysis
Chapter 8 Optimizing for Ecommerce Conversion and User Experience
The Importance of the Value Proposition in Conversion Optimization
The Conversion Optimization Process: Ideation to Hypothesis to Post-Optimization Analysis
The Science Behind Conversion Optimization
Succeeding with Conversion Optimization
Chapter 9 Analyzing Ecommerce Customers
What Does a Customer Record Look Like in Ecommerce?
What Customer Data Could I Start to Analyze?
Questioning Customer Data with Analytical Thought
Understanding the Ecommerce Customer Analytics Lifecycle
Defining the Types of Customers
Reviewing Types of Customer Analytics
Calculating Customer Lifetime Value
Determining the Cost of Customer Acquisition
Understanding Voice-of-the-Customer Analytics
Doing Recency, Frequency, and Monetary Analysis
Predicting Customer Propensities
Personalizing Customer Experiences
Chapter 10 Analyzing Products and Orders in Ecommerce
What Order Data Should I Begin to Analyze?
What Metrics and Key Performance Indicators Are Relevant for Ecommerce Orders?
Approaches to Analyzing Orders and Products
Analyzing Products in Ecommerce
Analyzing Merchandising in Ecommerce
What Merchandising Data Should I Start Analyzing First?
Chapter 11 Attribution in Ecommerce Analytics
Attributing Sources of Buyers, Conversion, Revenue, and Profit
Understanding Engagement Mapping and the Types of Attribution
The Difference between Top-Down and Bottom-Up Approaches to Attribution
A Framework for Assessing Attribution Software
Chapter 12 What Is an Ecommerce Platform?
Understanding the Core Components of an Ecommerce Platform
Understanding the Business Functions Supported by an Ecommerce Platform
Determining an Analytical Approach to Analyzing the Ecommerce Platform
Chapter 13 Integrating Data and Analysis to Drive Your Ecommerce Strategy
Defining the Types of Data, Single-Channel to Omnichannel
Integrating Data from a Technical Perspective
Integrating Analytics Applications
Integrating Data from a Business Perspective
Chapter 14 Governing Data and Ensuring Privacy and Security
Applying Data Governance in Ecommerce
Applying Data Privacy and Security in Ecommerce
Governance, Privacy, and Security Are Part of the Analyst’s Job
Chapter 15 Building Analytics Organizations and Socializing Successful Analytics
Suggesting a Universal Approach for Building Successful Analytics Organizations
Determine and Justify the Need for an Analytics Team
Gain Support for Hiring or Appointing a Leader for Analytics
Create the Mission and Vision for the Analytics Team
Create an Organizational Model
Assess the Current State Capabilities and Determine the Future State Capabilities
Assess the Current State Technology Architecture and Determine the Future State Architecture
Begin Building an Analytics Road Map
Map Current Processes, Interactions, and Workflows
Build Templates and Artifacts to Support the Analytics Process
Create a Supply-and-Demand Management Model
Create an Operating Model for Working with Stakeholders
Use, Deploy, or Upgrade Existing or New Technology
Implement a Data Catalog, Master Data Management, and Data Governance
Do Analysis and Data Science and Deliver It
Lead or Assist with New Work Resulting from Analytical Processes
Document and Socialize the Financial Impact and Business Outcomes Resulting from Analysis
Manage Change and Support Stakeholders
Chapter 16 The Future of Ecommerce Analytics
The Future of Data Collection and Preparation
The Future Is Data Experiences