Ecommerce Analytics: Analyze and Improve the Impact of Your Digital Strategy
- ByJudah Phillips
- Published Apr 4, 2016 byPearson FT Press. Part of theFT Press Analyticsseries.
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Description
- Copyright 2016
- Dimensions: 6" x 9"
- Pages: 368
- Edition: 1st
- eBook (Watermarked)
- ISBN-10: 0-13-417794-0
- ISBN-13: 978-0-13-417794-6
Today's Complete, Focused, Up-to-Date Guide to Analytics for Ecommerce
- Profit from analytics throughout the entire customer experience and lifecycle
- 最重要的是快速变化的数据源s now available to you
- For all ecommerce executives, strategists, entrepreneurs, marketers, analysts, and data scientists
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.
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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
分析电子商务的市场营销和广告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
理解电子商务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
与利益相关者和参与业务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
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