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

Description

• Copyright 2016
• Dimensions: 6" x 9"
• Pages: 368
• Edition: 1st

• Book
• ISBN-10: 0-13-417728-2
• ISBN-13: 978-0-13-417728-1

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.



Sample Content

Sample Pages

Download the sample pages (includes Chapter 1 and Index)

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



Updates

Submit Errata

