Ecommerce Analytics by Phillips, Judah -- Read -- Imperial Library of Trantor

Index

About This E-Book Title Page Copyright Page Praise for Ecommerce Analytics Dedication Page Contents at a Glance Contents Foreword Acknowledgments About the Author 1. Ecommerce Analytics Creates Business Value and Drives Business Growth 2. The Ecommerce Analytics Value Chain

Identifying and Prioritizing Demand Developing an Analytical Plan Activating the Ecommerce Analytics Environment

Elements of an Ecommerce Analytics Environment Collecting and Governing Data and Metadata

Preparing and Wrangling Data Analyzing, Predicting, Optimizing, and Automating with Data Socializing Analytics Communicating the Economic Impact of Analytics

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 Types of Data: Simplified Looking at Data: Shapes of Data

Understanding Basic Stats: Mean, Median, Standard Deviation, and Variance Plotting Ecommerce Data Four Plots and Six Plots Histograms (Regular, Clustered, and Stacked) Pie Charts Line Charts Flow Visualizations

Analyzing Ecommerce Data Using Statistics and Machine Learning

Correlating Data Regressing Data: Linear, Logistic, and More Probability and Distributions Experimenting and Sampling Data

Using Key Performance Indicators for Ecommerce

KPI Metric Example: Page or Screen Views KPI Metric Example: Visits or Sessions KPI Metric Example: Returns KPI Metric Example: Total Revenue and Revenue by N KPI Metric Example: Gross Margin KPI Metric Example: Lifetime Value KPI Metric Example: Repeat Visitors/Users/Customers KPI Rate Example: Conversion Rate KPI Rate Example: Step Completion Rate KPI Rate Example: Abandoned Cart Rate KPI Average Example: Average Order Value KPI Derivative Example: Bounce Rate KPI Derivative Example: Percentage of Orders with Promotions or Discounts KPI Derivative Example: Inventory Turnover KPI Derivative Example: Return on Investment KPI Derivative Example: Loyalty—Time Since Last Visit (Recency) KPI Derivative Example: Retention—Time Between Visits (Frequency) KPI Percentage Example: Percentage of X from Source N KPI Percentage Example: Percentage of New Customers (or N Metric) KPI “Per” Example: Cost and/or Revenue per Visitor KPI “Per” Example: Revenue per Customer KPI “Per” Example: Cost per Customer Acquisition

4. Visualizing, Dashboarding, and Reporting Ecommerce Data and Analysis

Understanding Reporting Explaining the RASTA Approach to Reporting Understanding Dashboarding Explaining the LIVEN Approach to Dashboarding What Data Should I Start With in an Ecommerce Dashboard? Understanding Data Visualization

The Process for Data Visualization Maximizing Impact with Data Visualization: The SCREEN Approach and More Why Use Data Visualizations? Types of Data Visualization

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 Fact Understanding the Order Item Fact Understanding the Customers Fact Understanding the Customer Order Fact Reviewing Common Dimensions and Measures in Ecommerce

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?

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

Behavioral Flow Analysis Shopping Behavior Analysis Content Analysis In-Page or On-Screen Behavior Analysis

8. Optimizing for Ecommerce Conversion and User Experience

The Importance of the Value Proposition in Conversion Optimization The Basics of Conversion Optimization: Persuasion, Psychology, Information Architecture, and Copywriting The Conversion Optimization Process: Ideation to Hypothesis to Post-Optimization Analysis The Data for Conversion Optimization: Analytics, Visualization, Research, Usability, Customer, and Technical Data The Science Behind Conversion Optimization Succeeding with Conversion Optimization

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 Segmenting Customers Performing Cohort Analysis Calculating Customer Lifetime Value Determining the Cost of Customer Acquisition Analyzing Customer Churn Understanding Voice-of-the-Customer Analytics Doing Recency, Frequency, and Monetary Analysis Determining Share of Wallet Scoring Customers Predicting Customer Behavior Clustering Customers Predicting Customer Propensities Personalizing Customer Experiences

10. Analyzing Products and Orders in Ecommerce

What Are Ecommerce Orders? 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

Doing Financial Analysis on Orders Doing Product and Item Analysis on Orders Doing Promotional Analysis on Orders Doing Category and Brand Analysis on Orders Doing Event and Goal Analysis on Orders Doing Path-to-Purchase Analysis on Orders Doing Funnel Analysis on Orders Doing Cluster Analysis on Orders Doing Up-Sell and Cross-Sell Analysis on Orders Doing Next-Best-Action Analysis on Orders

Analyzing Products in Ecommerce

Understanding Useful Types of Product Analysis for Ecommerce Product Brand Analysis Product Category Analysis Customer Service Analysis Product Returns Analysis Social Media Product Analysis

Analyzing Merchandising in Ecommerce

Testing Merchandising Creative Performing Inventory Analysis Analyzing Product Offers Determining the Optimal Price via Pricing Analysis Understanding the Sales Impact of Merchandising Analyzing Suppliers and the Supply Chain Determining Effective and Profitable Markdowns, Promotions, and Discounts

What Merchandising Data Should I Start Analyzing First?

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

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

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

Agile Versus Waterfall Delivery Integration with Operational Data Stores Integration with On-Premises Enterprise Data Warehouses Integration with Cloud Data Sources Integration with Data Lakes Integration with Data Federation Integration with Data Virtualization

Integrating Analytics Applications Integrating Data from a Business Perspective

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

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 Hire the Analytics Leader Gather Business Requirements Create the Mission and Vision for the Analytics Team Create an Organizational Model Hire Staff 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 Train Staff 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 Collect or Acquire New Data Implement a Data Catalog, Master Data Management, and Data Governance Meet with Stakeholders and Participate in Business Processes, and Then Socialize Analysis on a Regular Cadence and Periodicity 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 Continue to Do Analysis, Socialize It, and Manage Technology While Emphasizing the Business Impact Ad Infinitum Manage Change and Support Stakeholders

16. The Future of Ecommerce Analytics

The Future of Data Collection and Preparation The Future Is Data Experiences Future Analytics and Technology Capabilities

Bibliography Index

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