Marketing Measurement and Analytics: An Introduction

Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgments
About the Author
Part 1: Aligning Measurement to Business Goals
	Chapter 1: Exploring the Terminology
		Data
			Historical Origins of the Word “Data”
			The Types of Data
			Limitations of Relying on Data on Its Own
		Metrics
			Examples of Metrics
			Limitations of Metrics on Their Own
		KPIs
			Why KPIs Are Important
			Benefits of KPIs
			Case Study: PB Shoes Need to Set KPIs
		Knowing When to Focus on Metrics or KPIs
		Conclusion
		Endnotes
	Chapter 2: The Hierarchy of Goals and Measurements
		Business Goals
			Why They Are Important
			How to Know Whether They Are Well Defined
			Common Misconceptions
			Examples of Business Goals
		Business Key Performance Indicators
			Why They Are Important
			How to Know Whether They Are Well Defined
			Common Misconceptions
			Examples of Business KPIs
		Marketing KPIs
			Why They Are Important
			How to Know Whether They Are Well Defined
			Common Misconceptions
			Examples of Marketing KPIs
		Marketing Activities
			Why They Are Important
			How to Know Whether They Are Well Defined
			Common Misconceptions
			Examples of Marketing Activities
		Marketing Metrics
			Why They Are Important
			How to Know Whether They Are Well Defined
			Common Misconceptions
			Examples of Common Marketing Metrics
		Conclusion
		Endnotes
	Chapter 3: Distinguishing Between Business and Marketing KPIs
		Case Study: PB Shoes’ Marketing KPIs
		Playing to Their Strengths
		Aligning Marketing KPIs and Business KPIs
		Tracking the Impact of Marketing Initiatives on Overall Business Success
		Enhancing the Credibility of Marketing as a Strategic Function
		Making Data-Driven Decisions
			Case Study: PB Shoes’ Alignment of Business and Marketing KPIs
		Building a Culture of Accountability
			Case Study: PB Shoes’ Culture of Accountability
		Map Marketing KPIs to Stakeholders
			Think Outside Marketing: What Is Best for the Business at Large?
			Identify Stakeholder Priorities
			Consider Your Competitive Landscape
			Choose a Meaningful Measurement Cadence
			Keep It Straightforward
		Conclusion
	Chapter 4: Choosing the Right Marketing Metrics
		Checklist Before Starting
			Understand Your Business and Marketing Goals and KPIs
			Consider Your Audience(s)
			Understand the Available Data
			Evaluate Your Marketing Activities
		Choosing Your Metrics
			Map Marketing Metrics to Your Marketing KPIs
			Choose Metrics That Tell a Story
			Identify Where AI Can Provide Value
			Continuously Improve
		Case Study: Determining Marketing Metrics for a New Shoe Line
			Defining the Business and Marketing Objectives
			Understanding the Target Audience
			Choosing the Right Marketing Metrics
			Leveraging Available Data and Tools
			Results and Insights
			Continuous Improvement
			Final Assessment
		Conclusion
	Chapter 5: Single-Channel vs Multi-Channel Measurement
		Single-Channel Marketing Measurement
			When to Use Single-Channel Measurement
			When Not to Use Single-Channel Measurement
		Multi-Channel Marketing Measurement
			When to Use Multi-Channel Measurement
			When Not to Use Multi-Channel Measurement
		Differences Between Single-Channel and Multi-Channel Measurement
		Considerations for Online Versus Offline Measurement
		Conclusion
	Chapter 6: A Brief Overview of Statistics for Marketers
		Choosing the Right Types of Data to Analyze
			Measures of Central Tendency
			Measures of Variability
		Inferential Statistics
			Populations vs Samples
			Hypothesis Testing
			Confidence Intervals
		Correlation and Causation
			The Importance of Understanding the Difference
			Establishing Causality
		Probability
			Probability Distributions
			Common Challenges with Probability and Marketing
		Conclusion
	Chapter 7: Measurement of AI Implementation and AI Model Quality
		Understanding AI in Marketing
		Key Performance Indicators for AI
			Beyond the Initial Bump
		AI Model Usage
			Evaluating AI Model Accuracy
		Assessing AI’s Contribution to ROI
			Monitoring AI for Continuous Improvement
		Case Study: PB Shoes’ AI Journey
			Personalized Product Recommendations
			Measuring AI’s Impact on Engagement and Conversions
			Continuous Learning and Model Refinement
			Measuring the Effectiveness of Their AI Model
			Creating Transparency to Evaluate Bias
		Conclusion
Part 1 Recap Quiz
Part 2: A Marketing Measurement Framework
	Chapter 8: Investing in a Marketing Measurement Framework
		Alignment of Metrics and KPIs to the Strategy and Goals of the Business
		Consistency in Collecting, Measuring, Testing, and Analyzing Marketing Efforts
		Flexibility to Be Applied to Many Different Marketing Channels
		Increased Organizational Accountability
		Conclusion
	Chapter 9: Components of the Marketing Measurement Framework
		Step 1: Business KPI and Strategy
		Step 2: Marketing Goals and Actions
		Step 3: Metrics and Measurement
		Step 4: Results and Analysis
		Case Study: PB Shoes’ Usage of a Marketing Measurement Framework
		Analysis
		The Framework in Action: PB Shoes
		Conclusion
	Chapter 10: Incorporating AI-Based Tools and Methods
		Aligning AI Tools with Your Marketing Measurement Framework
			Define Objectives
			Select Relevant AI Tools
			Integrate and Implement
			Measure and Optimize
		Case Study: PB Shoes
			Goal Definition
			Tool Selection
			Results
		Conclusion
Part 2 Recap Quiz
Part 3: Data Collection
	Chapter 11: Determining What Data Is Needed
		The Types of Data
			Customer Data
			Business Data
		Determining What Data Is Needed
			The Data Collection Infrastructure
			Collecting vs Reporting
		Conclusion
	Chapter 12: Single- and Multi-Channel Data Collection
		Single-Channel Marketing Data Collection
			Challenges of Single-Channel Data Collection
		Data Collection in a Multi-Channel World
			Understand Each Channel’s Unique Contribution to the Customer Experience
			Understand Each Channel’s Contribution to a Conversion
			Understand Which Channels Are Most Critical to Marketing Efforts and Which Are Not
		Conclusion
	Chapter 13: Creating a Sustainable Data Collection Plan
		The Changing Data Privacy Landscape
			Increasing Consumer Data Privacy Laws
			Third-Party Cookie Deprecation
			Creating a First-Party Data Strategy
		Internal Challenges and Opportunities
			Future-Proofing Your Data Collection Approach
			Data Requests: Working Well with Data and Technology Teams
		Conclusion
		Endnotes
	Chapter 14: Collecting Data in an AI-Driven Marketing Environment
		Understanding AI and ML Data Requirements
			The Four Vs of Big Data
		Real-Time Data and Its Relevance to AI
			Capturing and Processing Data in Real Time
		Leveraging AI for Enhanced Data Collection
			Optimizing Data Collection Strategies for ML
		Conclusion
Part 3 Recap Quiz
Part 4: Measurement and Testing
	Chapter 15: Creating a Marketing Dashboard
		Determining Which Channel(s) to Measure
		Getting Started with Your Dashboard
			Choosing the Right Charts and Graphs
			Common Dashboard Design Mistakes
		Confirm You Are Capturing the Metrics You’ve Defined
		Create the Dashboard
			Connect to Your Data Source
			Design the Layout
			Choose the Metrics
			Choose and Build the Charts
			Test and Style
			Preview and Refine
		Conclusion
	Chapter 16: Beginning with a Strong Hypothesis
		Null and Alternative Hypotheses
		Start with a Question or Problem Statement
			Some Example Questions and Problem Statements
		Be Specific
			Examples of Specificity
			Back to our example
			Back to our example
		Consider the Variables
			Some Examples of Considering Variables
			Back to our example
		Make It Falsifiable
			Some Examples of Falsifiable Hypotheses
			Back to our example
		Test and Learn
		Some More Hypothesis Examples
		Conclusion
	Chapter 17: AI-Based Approaches to Prediction and Hypothesis Development
		The Basics of AI in Prediction
			Case Study: Predicting the Next Big Trend in Pickleball Shoes
		From Historical Data to Predictive Insights
			Data Preprocessing
			Feature Selection and Engineering
			Model Training and Validation
		Predictive Analytics
			Case Study: How PB Shoes Uses Past Sales Data to Anticipate Future Demand Spikes
		Developing Hypotheses with AI
			Case Study: Hypothesis Development at PB Shoes: Using AI to Test New Market Entry Strategies
		AI and Market Segmentation
			Adaptability Is Key
			Case Study: PB Shoes’ Approach to AI-Powered Audience Segmentation
		Testing Hypotheses with AI-Enhanced Tools
			Rapid Analysis Increases the Pace of Optimization
			Case Study: PB Shoes’ AI-Assisted A/B Testing on Digital Ad Effectiveness
		Overcoming Biases and Limitations
			Getting to the Source
			Ensuring Timeliness
			Case Study: How PB Shoes Ensures Unbiased AI Applications
		Conclusion
	Chapter 18: Statistical Considerations for Testing
		Principles of Statistical Testing
			Fundamental Statistical Concepts
		Examples: Using Statistical Principles in Marketing Scenarios
			Example 1: Evaluating Campaign Effectiveness
			Example 2: Product Pricing Strategy
			Example 3: Customer Satisfaction Analysis
		Choosing the Right Statistical Test
		Some Common Statistical Tests That Marketers Use
			T-Test
			Chi-Square Test
			Analysis of Variance (ANOVA)
			Correlation Coefficient
			Logistic Regression
		How to Select the Right Test
			Criteria to Use to Select the Right Test
			How to Confirm the Test Is a Good Fit
			Ways to Tell Whether You’ve Chosen the Wrong Test
		Case Study: PB Shoes’ Marketing Campaign Test
			Background and Objective
			Hypothesis Formulation
			Experiment Design
			Statistical Testing and Results
			Analysis and Next Steps
		Some Common Testing and Analysis Pitfalls (and How to Avoid Them)
			Preventing Common Mistakes
		Using Software Tools for Statistical Testing
		Conclusion
	Chapter 19: Constructing and Running a Single-Channel Test
		Constructing Your Test
			Identify Your Test and Your Controls
			Back to our example
			A/B or Multivariate Test?
		Elements to Test
			Audience Segmentation
			Elements of the Creative
			Call to Action (CTA)
			Offers and Discounts
			Other Personalized Elements
		Running the Test
			Back to our example
			Plan and Execute the Test
			Analyze the Data
			Promote the Winners
			Implement the Changes
			Monitor and Optimize
		Conclusion
	Chapter 20: Single-Channel Tests in a Multi-Channel World
		Understand Where the Channel Fits Within the Customer Journey
		Select the Appropriate Metrics for the Channel
		Understand the Channel’s Contribution to the Goal
		Attribution Models
			First-Click Attribution
			Last-Click Attribution
			Linear Attribution
			Time Decay Attribution
			Position-Based Attribution
		Conclusion
	Chapter 21: Multi-Channel Measurement
		Multi-Touch Attribution (MTA)
			Why MTA Is Valuable
			How to Implement MTA Successfully
			Caveats and Limitations
		Media Mix Modeling
			Why MMM Is Valuable
			How to Implement MMM Effectively
			Caveats and Limitations
		Alternatives to MTA and MMM
		When to Use Each Method
		Conclusion
Part 4 Recap Quiz
Part 5: Refining and Improving Your Results
	Chapter 22: Introduction to Analysis and Improvement
		Analyze the Results
			Context
			Sample Size
			Data Quality
			Confounding Variables
		The Next Stages in the Process
			Interpreting the Results
			Experiment, Refine, and Continuously Improve
		Conclusion
	Chapter 23: Analyzing Your Results
		Questions to Ask to Gain a Deeper Understanding of Your Marketing Results
			What Are Our Goals and Did We Achieve Them?
			Back to our example
			What Are the Primary Reasons We Succeeded (or Failed)?
			Are There Particular Areas We Succeeded in More Than Others?
			What Are the Industry Benchmarks and Did We Exceed Them?
			Are the Numbers Too Good (or Bad) and Why?
			What Could We Have Done Better?
		Common Misconceptions
			Misconception 1: More Data Equals Better Insights
			Misconception 2: Correlation Equals Causation
			Misconception 3: Anecdotal vs Statistical Significance
			Misconception 4: Data Analysis Is Always Objective
			Misconception 5: Data Speaks for Itself
			Misconception 6: Predictive Analytics Is Always Precise
		Conclusion
	Chapter 24: Using Generative AI for Analysis
		Generative AI and Marketing Data Analysis
			Comparing Generative AI Analysis with Traditional Data Analysis Methods
		Evaluating the Effectiveness of Marketing Campaigns with AI
			Measuring Campaign Success Against KPIs
			Uncovering Patterns and Predicting Future Campaign Performance
		Case Study: Optimizing PB Shoes’ Marketing Mix
			Analyzing Marketing Channel Effectiveness with Generative AI
			Insights Gained
			Actions Taken
			Outcomes
		Enhancing Marketing ROI with Generative AI
			Leveraging AI Analysis to Optimize Budget Allocation and Resource Investment
			Incorporating AI Insights into Financial Decisions in Marketing
		Limitations and Challenges in AI-Powered Analysis
			Some Ways to Mitigate These Challenges
		Conclusion
	Chapter 25: Interpreting Results
		Tell a Story with the Results
			The Art of Simplification and Connection
			Case Study: PB Shoes’ New Ad Campaign
			Engaging and Persuasive Communication
		When in Doubt, Test and Validate Your Assumptions
			Re-Evaluating Data with a Critical Eye
			Using Statistical Tools for Deeper Insights
			Collaboration and Critical Review
			Case Study: PB Shoes’ Quarterly Performance Report
		Use Attribution Modeling to Measure the Impact of Different Touchpoints
			Case Study: PB Shoes’ Multi-Channel Marketing Campaign Analysis
		Connect Marketing Metrics to Revenue and Established Business KPIs
		Watch out for Bias
		Communicate Results Effectively
		Conclusion
	Chapter 26: Experimenting, Refining, and Continuous Improvement
		Benefits of Experimentation
			Understand Your Customers’ Behavior and Preferences
			Stay Ahead of Your Competitors
			Optimize Your Budget Allocation
			Measure the Impact of Your Marketing Efforts Toward Business Goals
			Improve Your Customer Experience
			The Critical Role of Experimentation
		Continuous Improvement
			The Benefits of Continuous Improvement
			Applying Continuous Improvement to Your Strategy
			Common Continuous Improvement Tools and Techniques
			Measuring the Success of Continuous Improvement
		Conclusion
Part 5 Recap Quiz
Epilogue
Appendix A: Glossary of Select Marketing Measurements and Formulas
Appendix B: Recap Quiz Answers
Index