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Cover of Marketing Analytics: A Comprehensive Guide v1.0
Published: 
February 2022
Page Count: 
386
ISBN (Digital): 
978-1-4533-9894-4

Marketing Analytics: A Comprehensive Guide

Version 1.0
By Christina J. Inge

Included Supplements

Key Features

  • Comprehensive and Practical. Efficiently covers general topics such as
    • Roles of analytics and data
    • Appropriate analytical tools and how to use them
    • How to accurately measure and evaluate results of marketing activities
    • Clear explanations of data models
    • The minimum level of math necessary to accomplish key analytic goals
  • Real-World Focus. Reveals how working marketers actually do their jobs. Models the application of analytics to specific marketing channels and roles. Each chapter opens with a compelling case study to engage students and set the stage for the chapter to come. Stories based on real-life experiences are peppered throughout to bring data to life.
  • Organized by Marketing Channel. Students learn exactly how to measure activity and results in core marketing channels according to their distinct attributes. For example, Chapter 6 presents a comprehensive breakdown of all the social media measurements that matter. It explains engagement really is, why it should be measured, and then exactly how to measure it. In a similar way, each core chapter (Chapters 3–11) tackles a different area of marketing, from email to advertising, goes through all of the specific data related to that area of chapter focus, and details how it’s best measured.
  • Supportive Learning Features
    • “Learning Objectives” at the beginning of every main section preview the material to come.
    • “A Closer Look” is a short, engaging accounts of real-world situations sprinkled throughout the chapter that bring data to life.
    • “Do the Math” guides students to “crunch the numbers” based on just the amount of mathematics necessary to complete the operation. Scenarios are based on an examples or cases introduced within the same chapter.
    • “Conclusion” at the end of every chapter mirrors the chapter’s learning objectives and summarizes key points.
    • “Questions for Further Study” present real-world cases that prompt students to think critically about information the encountered in the preceding chapter.
    • Embedded video links throughout support visual learning, highlight key analytics operations, and bring concepts to life.
  • Superior Coverage of qualitative and quantitative information (Chapter 1), introduction of marketing and non-marketing data (Chapter 2), Google Search (Chapter 3), data brokers (Chapter 3), data sources (Chapter 5), benchmarking data (Chapter 5), social listening and sentiment analysis (Chapter 6), search data and search marketing (Chapter 6), A/B testing (Chapter 7), email marketing (Chapter 7), flowcharts (Chapter 7), metrics categories (Chapter 8), Google Analytics (Chapter 8), using data for decision making (Chapter 9), conversion paths (Chapter 9), shopping behavior (Chapter 9), types of survey questions (Chapter 10), marketing research (Chapter 10), video advertising and video content (Chapter 11), conversion marketing (Chapter 11), data visualization techniques (Chapter 12).

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Marketing Analytics: A Comprehensive Guide is appropriate for the Marketing Analytics course that may also be called Business Analytics, Business Intelligence, Marketing Data, Marketing Measurement, Digital Marketing, Data-Driven Marketing, SEO/SEM, Social Media Marketing, Advertising or similar taught at the undergraduate level at two- and four-year colleges and universities. May also be appropriate for MBA-level courses and as either a primary or secondary text.

Marketing Analytics: A Comprehensive Guide carefully explains how to successfully implement marketing analytics in any organization. Coverage is built around data-driven business cases and the everyday opportunities and challenges faced by marketing professionals. This book’s practical “show me what works” perspective then walks learners through, channel by channel, the specific tools, data, and skills they need to be proficient in marketing analytics. As a result, students develop a deeper understanding of how to use analytics to make better business decisions.

More than a cookbook of strategies, Marketing Analytics: A Comprehensive Guide exposes students to the “why” behind which tools and strategies to use. Metrics are considered in context, not just presented as formulas and how to calculate them. Its comprehensive scope also embraces a cross-section of marketing roles, including social media, marketing automation, email, advertising, content marketing, design, and search engine marketing and automation. Consequently, it is the first book of its kind to lay out in detail how to analyze marketing data based on marketing job functions. In doing so, this book fully prepares current and future marketers to insightfully measure digital marketing activities and meaningfully tie data analytics back to real-world business or creative goals that drive organizational success.

  • About the Author
  • Acknowledgments
  • Preface

    • Chapter 1: What Is Marketing Analytics?

  • 1.1 Marketing Analytics: More Than Just Crunching Numbers
  • 1.2 A Closer Look: LuxeUnique
  • 1.3 Analytics and the 4Ps
  • 1.4 How to Use This Book
  • 1.5 Core Concepts in Marketing Analytics
  • 1.6 A Closer Look: Optimizing for Our Top Customers
  • 1.7 From Data to Strategy: Using Data Sources
  • 1.8 Case Study: Impact Hub Boston
  • 1.9 Do the Math: ROAS—An Example of How Marketing Metrics Works
  • 1.10 Conclusion
  • 1.11 Questions for Further Study

    • Chapter 2: Internal Data: Many Sources, One Goal

  • 2.1 Types of Internal Data
  • 2.2 A Closer Look: Hannah’s Hardware
  • 2.3 Conducting an Internal Data Audit
  • 2.4 Marketing Attribution, Customer Lifetime Value, and Average Order Value
  • 2.5 Do the Math: Web Analytics—Acquisition Data and Conversion Rate by Channel
  • 2.6 Case Study: Credit Union Association
  • 2.7 Conclusion
  • 2.8 Questions for Further Study

    • Chapter 3: From Direct Mail to Ad Platforms: Supplementing Internal Data with External Databases and Research

  • 3.1 External Data and Marketing
  • 3.2 A Closer Look: Finding the Right Markets
  • 3.3 Types of External Data
  • 3.4 Integrating External and Internal Data
  • 3.5 A Closer Look: Entering a New Market
  • 3.6 Do the Math: Estimating Market Size
  • 3.7 Conclusion
  • 3.8 Questions for Further Study

    • Chapter 4: The Data Engine of Search

  • 4.1 The Data Engine of Search
  • 4.2 A Closer Look: AccountCo
  • 4.3 Core Types of Search Data
  • 4.4 Strategic Paid Search Data
  • 4.5 Using Search Data to Optimize Search Marketing
  • 4.6 Using Search Data for Marketing Strategy
  • 4.7 A Closer Look: Keywords and Product Optimization
  • 4.8 Do the Math: The SEO Audit
  • 4.9 Case Study: Winning the Local Search Rankings
  • 4.10 Conclusion
  • 4.11 Questions for Further Study

    • Chapter 5: Understanding Your Landscape

  • 5.1 Traditional Sources of Competitive Data
  • 5.2 A Closer Look: Building a Brand
  • 5.3 Digital Sources of Competitive Data
  • 5.4 A Closer Look: Launching a New Product
  • 5.5 Industry Data
  • 5.6 Benchmarking
  • 5.7 Do the Math: Conjoint Analysis
  • 5.8 Conclusion
  • 5.9 Questions for Further Study

    • Chapter 6: Measuring the Social Web

  • 6.1 Basic Metrics
  • 6.2 A Closer Look: Post or Profile Metrics?
  • 6.3 Social Listening
  • 6.4 Paid Social Media Metrics
  • 6.5 Advanced Social Media Metrics
  • 6.6 Growth and Conversion Metrics
  • 6.7 Sentiment Analysis
  • 6.8 A Closer Look: Turning Sentiment Analysis into Consumer Insights
  • 6.9 Do the Math: Sentiment by Reach
  • 6.10 Case Study: Tweets for Seats
  • 6.11 Conclusion
  • 6.12 Questions for Further Study

    • Chapter 7: Over 100 Billion Emails a Day?

  • 7.1 Basic Email and Marketing Automation Techniques
  • 7.2 A Closer Look: Building a Loyal Base
  • 7.3 List Health Metrics
  • 7.4 A Closer Look: Shopping Cart Abandonment Campaign
  • 7.5 Advanced Email Metrics
  • 7.6 Marketing Automation Data
  • 7.7 Message Testing and Optimization
  • 7.8 Do the Math: Revenue by Subscriber
  • 7.9 Conclusion
  • 7.10 Questions for Further Study

    • Chapter 8: Measuring the Web’s Data

  • 8.1 What Is Web Analytics?
  • 8.2 A Closer Look: Hats Off to a Great Launch
  • 8.3 Basic Web Metrics
  • 8.4 User Behavior and Marketing Effectiveness Data
  • 8.5 Conversion Data
  • 8.6 Content Data
  • 8.7 A Closer Look: What Do They Want to See?
  • 8.8 Do the Math: Page Conversions
  • 8.9 Conclusion
  • 8.10 Questions for Further Study

    • Chapter 9: Going Beyond the Basics

  • 9.1 A Closer Look: Is Our Site Really Optimized?
  • 9.2 Advanced Conversion Metrics
  • 9.3 E-Commerce Data
  • 9.4 Technology Information
  • 9.5 Behavior Data
  • 9.6 Demographic and Psychographic Data
  • 9.7 A Closer Look: Bounce Rate and Event Tracking
  • 9.8 Conclusion
  • 9.9 Questions for Further Study

    • Chapter 10: Offline Research Matters, Even in an Online World

  • 10.1 Surveying Consumers to Learn Their Preferences
  • 10.2 A Closer Look: Finding Our Market
  • 10.3 Types of Research
  • 10.4 Survey Design and Deployment
  • 10.5 Questionnaire Design
  • 10.6 Qualitative, In-Depth Research
  • 10.7 Analyzing Market Research Data
  • 10.8 A Closer Look: Creating the Right Interface
  • 10.9 Do the Math: Standard Deviation
  • 10.10 Conclusion
  • 10.11 Questions for Further Study

    • Chapter 11: The Engagement Economy

  • 11.1 Understanding Video Metrics
  • 11.2 Video Content Metrics
  • 11.3 Video Ad Metrics
  • 11.4 A Closer Look: Forget the Stage Fright—Go Live
  • 11.5 Mobile App Metrics
  • 11.6 A Closer Look: Do We Want to Keep Them Entertained or Loyal?
  • 11.7 Do the Math: Measuring Net Engagement Growth
  • 11.8 Conclusion
  • 11.9 Questions for Further Study

    • Chapter 12: Predictive Analytics, Data Visualizations, and Dashboards

  • 12.1 Data Mining in Marketing Analytics
  • 12.2 A Closer Look: The Next Must-Have Product?
  • 12.3 Data Mining and Predictive Analytics in Marketing
  • 12.4 How Does Data Mining and Predictive Analytics Work?
  • 12.5 Basic Concepts of Predictive Analytics: A Primer for Marketers
  • 12.6 Natural Language Processing for Data Mining
  • 12.7 Using Predictive Analytics
  • 12.8 Data Visiualization
  • 12.9 Making Data Actionable: Creating Dashboards
  • 12.10 Tools of Dashboard Management and Data Visualization
  • 12.11 A Closer Look: Can We Trust the Numbers?
  • 12.12 Do the Math: Bag-of-Words
  • 12.13 Conclusion
  • 12.14 Questions for Further Study
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    Christina J. Inge Harvard University Division of Continuing Education

    Christina J. Inge (MS University of Wyoming) Instructor, Harvard University Division of Continuing Education and Instructor and Curriculum Consultant, Northeastern University is an expert marketing practitioner with over 20 years’ experience in digital media. She currently runs Thoughtlight, a digital marketing practice in Boston she founded. Christina is a frequent presenter who has spoken at the MTech Summit, eMetrics (now the Marketing Analytics Summit), BlogHer, and Content Marketing Conference. She had led webinars on analytics for MarketingProfs and writes extensively on digital marketing.

    Christina developed the marketing analytics courses for the Harvard Division of Continuing Education master’s degree management concentration. She also developed the New England College of Business bachelor’s in digital marketing, the first digital marketing BS program for adults in the region. She has designed over 20 courses in digital marketing for accredited colleges and universities, focusing on analytics, social media, SEO, and mobile technologies. Christina also served on the board of the American Marketing Association Boston Chapter as Vice President of Social Media, was Vice Chair of the IEEE Boston Entrepreneurs Network, and was Innovator-in-Residence at the Wentworth Institute of Technology. Christina has worked in both digital marketing and web development with organizations ranging from Pega Systems and Nissan to the Smithsonian and a variety of technology startups. She specializes in translating business requirements into a cohesive vision for technology implementation, across the web, mobile, customer relationship management (CRM), and data analytics.

    Additions & Errata

    2/23/22: Duplicate text deleted from Preface.

    9/9/22

    • 3.6 Added definitions for volume per customer and penetration rate
    • 3.6 Do the Math: 78 updated to 780
    • 7.7 A/B Multivariate testing video updated to: https://youtu.be/fp6UWlZm1jk
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