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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.
Judah Phillips helps companies create value with analytics and data science by improving business performance. Judah has led analytics and data science teams for Fortune 500 companies and has improved their financial performance through the applied analysis of data, the management of analytical and technical resources, and the alignment and optimization of analytics strategy against short-term roadmaps and long-term strategic visions. Judah strongly believes that cutting-edge technology is critical and necessary but often becomes technical overhead unless strategy is aligned with excellence in organizational development, operational management, and delivery execution that is solidly tied to impacting material financial goals. Judah has worked for or been hired as a consultant by Internet companies, media companies, consumer product companies, financial services firms, and various types of agencies.
• He is the sole author of three books on analytics, including Ecommerce Analytics, Building a Digital Analytics Organization, and Digital Analytics Primer. Judah has also authored chapters, edited, or contributed to the development of other books: Measuring the Digital World, Advanced Business Analytics, Sales and Marketing Analytics, Digital Is Changing Everything, The Complete Guide to B2B Marketing, and Multichannel Marketing Metrics. • He served on various boards of or advised established and start-up technology companies, including global leaders in digital analytics, mobile analytics, ecommerce, mobile apps, and advertising technology. • He is an Adjunct Professor at Babson College and has guest lectured on analytics and data science at the business schools for New York University, Boston College, Northeastern University, and others. • He is the former V.I.P. at Harvard Innovation Lab, where he advised Harvard start-ups about analytics and data science. • He has spoken at more than 70 technology and industry conferences since 2006.
Judah holds a master of science in finance and a master of business administration from Northeastern University and a B.A. from the University of Massachusetts Amherst.
Table of Contents
Chapter 1 Ecommerce Analytics Creates Business Value and Drives Business Growth 1 Chapter 2 The Ecommerce Analytics Value Chain 9 Identifying and Prioritizing Demand 11 Developing an Analytical Plan 14 Activating the Ecommerce Analytics Environment 16 Preparing and Wrangling Data 20 Analyzing, Predicting, Optimizing, and Automating with Data 22 Socializing Analytics 23 Communicating the Economic Impact of Analytics 24 Chapter 3 Methods and Techniques for Ecommerce Analysis 27 Understanding the Calendar for Ecommerce Analysis 28 Storytelling Is Important for Ecommerce Analysis 29 Tukey’s Exploratory Data Analysis Is an Important Concept in Ecommerce Analytics 31 Types of Data: Simplified 34 Looking at Data: Shapes of Data 36 Analyzing Ecommerce Data Using Statistics and Machine Learning 47 Using Key Performance Indicators for Ecommerce 58 Chapter 4 Visualizing, Dashboarding, and Reporting Ecommerce Data and Analysis 71 Understanding Reporting 75 Explaining the RASTA Approach to Reporting 77 Understanding Dashboarding 77 Explaining the LIVEN Approach to Dashboarding 80 What Data Should I Start With in an Ecommerce Dashboard? 81 Understanding Data Visualization 81 Chapter 5 Ecommerce Analytics Data Model and Technology 91 Understanding the Ecommerce Analytics Data Model: Facts and Dimensions 93 Explaining a Sample Ecommerce Data Model 96 Understanding the Inventory Fact 97 Understanding the Product Fact 98 Understanding the Order Fact 98 Understanding the Order Item Fact 99 Understanding the Customers Fact 99 Understanding the Customer Order Fact 100 Reviewing Common Dimensions and Measures in Ecommerce 100 Chapter 6 Marketing and Advertising Analytics in Ecommerce 103 Understanding the Shared Goals of Marketing and Advertising Analysis 105 Reviewing the Marketing Lifecycle 108 Understanding Types of Ecommerce Marketing 111 Analyzing Marketing and Advertising for Ecommerce 112 What Marketing Data Could You Begin to Analyze? 116 Chapter 7 Analyzing Behavioral Data 119 Answering Business Questions with Behavioral Analytics 123 Understanding Metrics and Key Performance Indicators for Behavioral Analysis 124 Reviewing Types of Ecommerce Behavioral Analysis 126 Chapter 8 Optimizing for Ecommerce Conversion and User Experience 133 The Importance of the Value Proposition in Conversion Optimization 137 The Basics of Conversion Optimization: Persuasion, Psychology, Information Architecture, and Copywriting 138 The Conversion Optimization Process: Ideation to Hypothesis to Post-Optimization Analysis 141 The Data for Conversion Optimization: Analytics, Visualization, Research, Usability, Customer, and Technical Data 145 The Science Behind Conversion Optimization 147 Succeeding with Conversion Optimization 151 Chapter 9 Analyzing Ecommerce Customers 155 What Does a Customer Record Look Like in Ecommerce? 156 What Customer Data Could I Start to Analyze? 157 Questioning Customer Data with Analytical Thought 158 Understanding the Ecommerce Customer Analytics Lifecycle 159 Defining the Types of Customers 161 Reviewing Types of Customer Analytics 162 Segmenting Customers 163 Performing Cohort Analysis 165 Calculating Customer Lifetime Value 166 Determining the Cost of Customer Acquisition 168 Analyzing Customer Churn 169 Understanding Voice-of-the-Customer Analytics 170 Doing Recency, Frequency, and Monetary Analysis 171 Determining Share of Wallet 172 Scoring Customers 173 Predicting Customer Behavior 174 Clustering Customers 175 Predicting Customer Propensities 176 Personalizing Customer Experiences 178 Chapter 10 Analyzing Products and Orders in Ecommerce 179 What Are Ecommerce Orders? 181 What Order Data Should I Begin to Analyze? 183 What Metrics and Key Performance Indicators Are Relevant for Ecommerce Orders? 184 Approaches to Analyzing Orders and Products 186 Analyzing Products in Ecommerce 193 Analyzing Merchandising in Ecommerce 198 What Merchandising Data Should I Start Analyzing First? 210 Chapter 11 Attribution in Ecommerce Analytics 213 Attributing Sources of Buyers, Conversion, Revenue, and Profit 217 Understanding Engagement Mapping and the Types of Attribution 220 The Difference between Top-Down and Bottom-Up Approaches to Attribution 224 A Framework for Assessing Attribution Software 225 Chapter 12 What Is an Ecommerce Platform? 229 Understanding the Core Components of an Ecommerce Platform 232 Understanding the Business Functions Supported by an Ecommerce Platform 235 Determining an Analytical Approach to Analyzing the Ecommerce Platform 239 Chapter 13 Integrating Data and Analysis to Drive Your Ecommerce Strategy 241 Defining the Types of Data, Single-Channel to Omnichannel 243 Integrating Data from a Technical Perspective 246 Integrating Analytics Applications 259 Integrating Data from a Business Perspective 261 Chapter 14 Governing Data and Ensuring Privacy and Security 263 Applying Data Governance in Ecommerce 268 Applying Data Privacy and Security in Ecommerce 272 Governance, Privacy, and Security Are Part of the Analyst’s Job 276 Chapter 15 Building Analytics Organizations and Socializing Successful Analytics 279 Suggesting a Universal Approach for Building Successful Analytics Organizations 280 Determine and Justify the Need for an Analytics Team 283 Gain Support for Hiring or Appointing a Leader for Analytics 285 Hire the Analytics Leader 287 Gather Business Requirements 288 Create the Mission and Vision for the Analytics Team 289 Create an Organizational Model 289 Hire Staff 291 Assess the Current State Capabilities and Determine the Future State Capabilities 291 Assess the Current State Technology Architecture and Determine the Future State Architecture 292 Begin Building an Analytics Road Map 294 Train Staff 294 Map Current Processes, Interactions, and Workflows 295 Build Templates and Artifacts to Support the Analytics Process 296 Create a Supply-and-Demand Management Model 296 Create an Operating Model for Working with Stakeholders 297 Use, Deploy, or Upgrade Existing or New Technology 298 Collect or Acquire New Data 298 Implement a Data Catalog, Master Data Management, and Data Governance 299 Meet with Stakeholders and Participate in Business Processes, and Then Socialize Analysis on a Regular Cadence and Periodicity 300 Do Analysis and Data Science and Deliver It 300 Lead or Assist with New Work Resulting from Analytical Processes 302 Document and Socialize the Financial Impact and Business Outcomes Resulting from Analysis 303 Continue to Do Analysis, Socialize It, and Manage Technology While Emphasizing the Business Impact Ad Infinitum 303 Manage Change and Support Stakeholders 304 Chapter 16 The Future of Ecommerce Analytics 307 The Future of Data Collection and Preparation 311 The Future Is Data Experiences 313 Future Analytics and Technology Capabilities 314 Bibliography 319 Index 329