Preface xv
Foreword xvii
Introduction xxi
Chapter 1 Growth Engineering 1
The Role of Engineers in Product Growth 2
Key Growth Strategies 3
Habit Formation 3
Freemium Model 4
Experimentation 4
Data-Driven Growth 5
Chapter 2 Observability 7
Instrumentation 9
How to Know What to Instrument 10
Legal and Compliance Checklist 11
A Practical Example of Instrumentation 13
Telemetry 14
Logs 16
Metrics 17
Traces 19
Implementing Observability in Practice 20
Defining the Signals 21
Understanding the Flow 21
Using Observability to Act 22
Making It a Habit 22
Observability Anti-Patterns 22
Tracking Everything Without Purpose 22
Logging Without Context 23
Relying Only on Logs 23
Instrumenting Too Late 23
No Clear Ownership 24
Tools for Observability 24
What This Chapter Covered 27
Key Questions for Reflection 27
Exercise 27
Chapter 3 Data Pipelines 29
What Is a Data Pipeline and Why Does It Matter? 29
Components of a Data Pipeline 31
Ingestion 31
Batch Ingestion 31
Streaming Ingestion 32
Transportation 33
Message Brokers or Queues 34
Streaming Platforms or Distributed Logs 34
Telemetry Forwarders or Data Shippers 34
Processing 35
Keep It Simple at First 36
Validate Early 37
Make It Observable 37
Use Version Control for Logic 38
Storage 39
Data Warehouses 39
Data Lakes 39
When to Use What 40
Visualization 40
Tools and Interfaces 41
Types of Visualizations and When to Use Them 42
Building a Growth Pipeline with Large Language Models 46
Step 1: Define the Role or Persona 47
Step 2: Define What You Want to Measure 48
Step 3: Instrumentation Strategy 48
Step 4: Generate Mock Data 49
Step 5: Process Data 50
Step 6: Store Data 52
Step 7: Visualize Data 53
What This Chapter Covered 53
Key Questions for Reflection 54
Exercise 54
Chapter 4 Data Modeling 55
OLTP vs. OLAP 57
Oltp 57
Olap 57
Modeling for OLTP 58
How to Create an ER Diagram 58
Understanding Cardinality 60
One-to-One (1:1) 60
One-to-Many (1:N) 61
Many-to-Many (N:M) 61
Building an ER Diagram for a Growth Use Case 63
Step 1: Identify Your Entities 63
Step 2: Define the Relationships 63
Step 3: Add Attributes 64
Step 4: Diagram It Out 65
Step 5: Think Through Growth Questions 65
Step 6: Avoid Modeling Pitfalls 66
Step 7: Get Ready for the Next Layer 67
Normalization 67
What Is a Relation? 68
Keys: Primary, Foreign, and Composite 69
Functional Dependencies 70
Normalization 71
Modeling for OLAP 76
Facts and Dimensions 76
Denormalization 78
Star and Snowflake Schemas 79
Star Schema 79
Snowflake Schema 79
Choosing Between Them 80
What This Chapter Covered 80
Key Questions for Reflection 81
Exercise 81
Chapter 5 What Are Experiments? 83
The Philosophy of Experimentation 84
Humility in Product Development 85
Experimentation as a Team Sport 85
Experimentation Protects Users 86
The Anatomy of an Experiment 86
Hypothesis Formation 87
Control and Treatment Groups 88
Randomization 89
Metrics and Scorecards 89
Duration and Sample Size 91
Why Experiments Matter in Growth Engineering 91
Common Misconceptions About Experimentation 93
âExperimentation Slows Us Downâ 93
âExperiments Are Only for Small UI Tweaksâ 94
âOnly Data Scientists Should Run Experimentsâ 95
âWe Can Just Measure After Launch Insteadâ 95
What This Chapter Covered 96
Key Questions for Reflection 97
Exercises 97
Chapter 6 Types of Product Experiments 99
Design Types 100
A/A Test 100
A/B Test 101
A/B/n Test 102
Multivariate Test 103
Holdout Groups 104
Switchback Test 105
Application Types 107
UI/UX Experiments 107
Onboarding Experiments 108
Notification Experiments 109
Pricing Experiments 109
Fake Door Experiments 110
Reverse Experiments 111
What This Chapter Covered 112
Key Questions for Reflection 113
Exercises 113
Chapter 7 Introduction to A/B Testing 115
What Makes a Fair Comparison 116
Triggering 117
Types of Triggering 118
Exposure-Based Triggering 118
Action-Based Triggering 118
Hybrid Triggering 119
Choosing the Right Trigger 119
Example: The Pro-Tip Onboarding Card 119
Randomization 120
Sample Ratio Mismatch 122
Statistical Significance 124
Power and Sample Size 126
Common Mistakes in A/B Testing 127
Stopping Too Soon 127
Running Overlapping Experiments 127
Ignoring Guardrail Metrics 128
Focusing on Significance over Impact 128
Skipping A/A Tests 128
Overlooking Novelty and Learning Effects 128
What This Chapter Covered 129
Key Questions for Reflection 130
Exercises 130
Chapter 8 Building a Growth Engineering Team 133
What Makes a Growth Engineering Team Unique 133
Team Composition and Roles 134
Growth Engineers 134
Product Managers 135
Data Scientists 135
Growth Designers 136
User Experience Researchers 136
Team Structure 137
Centralized Model 137
Embedded Model 138
Hybrid Model 138
Cultural Foundations 139
Experiment over Opinion 139
Shared Metrics and Transparency 140
Learning Loops and Post-Mortems 140
Building Trust for Growth 141
Hiring and Upskilling for Growth 141
The Growth Engineerâs Career Path 143
The Cadence of Growth Teams 144
Weekly Growth Review 144
Hypothesis Review 144
Scorecard Syncs 145
Sharing Learnings 145
What This Chapter Covered 145
Key Questions for Reflection 146
Exercises 146
Chapter 9 The Future of Growth Engineering 149
AI and the Future of Experimentation 150
Designing Experiments 151
AI-Assisted Development 151
Autonomous Experiment Execution 152
AI-Assisted Analysis and Insight Generation 153
How AI Changes the Role of the Growth Engineering Team 155
Growth Engineer 155
Product Manager 156
Data Scientist 158
Designers and UX Researchers 160
Ethics, Privacy, and Responsible Growth in an AI-Driven Era 161
What This Chapter Covered 163
Key Questions for Reflection 164
Exercises 164
Chapter 10 The Growth Engineerâs Workflow 165
Standup 166
Product Alignment 167
Engineering Design 168
Implementation 169
Bug Bash 171
Rollout 172
Scorecard Review 173
Retrospective 174
Communicating Impact 175
What This Chapter Covered 177
Key Questions for Reflection 177
Exercises 178
Index 179
