Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World [Pehme köide]

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

Experimentation Slows Us Down 93

Experiments Are Only for Small UI Tweaks 94

Only Data Scientists Should Run Experiments 95

We Can Just Measure After Launch Instead 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 Engineers 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 Engineers 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

RITA OKONKWO is a software engineer specializing in growth systems. She works in Microsofts Experiences + Devices organization, where she focuses on experimentation, intelligence, and scalable growth infrastructure. She combines academic insight with real-world engineering experience to bridge the gap between technical systems and product strategy.