Growth Engineering : How to Build Systems That Drive Product Success in an AI-Driven World.

Format:

Book

Author/Creator:

Okonkwo, Rita.

Language:

English

Subjects (All):

Artificial intelligence.

Product management.

Physical Description:

1 online resource (211 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

Build software that users actually use with proven growth-oriented software development strategies In Growth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World, experienced software engineer with the Microsoft Experiences + Devices Growth team, Rita Okonkwo, delivers a strategic guide for anyone interested in.

Contents:

Cover

Title Page

Copyright

About the Author

Acknowledgments

Contents

Preface

Foreword

Introduction

Why Now

Why This Book

Chapter 1 Growth Engineering

The Role of Engineers in Product Growth

Key Growth Strategies

Habit Formation

Freemium Model

Experimentation

Data-Driven Growth

Chapter 2 Observability

Instrumentation

How to Know What to Instrument

Legal and Compliance Checklist

A Practical Example of Instrumentation

Telemetry

Logs

Metrics

Traces

Implementing Observability in Practice

Defining the Signals

Understanding the Flow

Using Observability to Act

Making It a Habit

Observability Anti-Patterns

Tracking Everything Without Purpose

Logging Without Context

Relying Only on Logs

Instrumenting Too Late

No Clear Ownership

Tools for Observability

What This Chapter Covered

Key Questions for Reflection

Exercise

Chapter 3 Data Pipelines

What Is a Data Pipeline and Why Does It Matter?

Components of a Data Pipeline

Ingestion

Batch Ingestion

Streaming Ingestion

Transportation

Message Brokers or Queues

Streaming Platforms or Distributed Logs

Telemetry Forwarders or Data Shippers

Processing

Keep It Simple at First

Validate Early

Make It Observable

Use Version Control for Logic

Storage

Data Warehouses

Data Lakes

When to Use What

Visualization

Tools and Interfaces

Types of Visualizations and When to Use Them

Building a Growth Pipeline with Large Language Models

Step 1: Define the Role or Persona

Step 2: Define What You Want to Measure

Step 3: Instrumentation Strategy

Step 4: Generate Mock Data

Step 5: Process Data

Step 6: Store Data

Step 7: Visualize Data

Exercise.

Chapter 4 Data Modeling

OLTP vs. OLAP

OLTP

OLAP

Modeling for OLTP

How to Create an ER Diagram

Understanding Cardinality

One-to-One(1:1)

One-to-Many(1:N)

Many-to-Many(N:M)

Building an ER Diagram for a Growth Use Case

Step 1: Identify Your Entities

Step 2: Define the Relationships

Step 3: Add Attributes

Step 4: Diagram It Out

Step 5: Think Through Growth Questions

Step 6: Avoid Modeling Pitfalls

Step 7: Get Ready for the Next Layer

Normalization

What Is a Relation?

Keys: Primary, Foreign, and Composite

Functional Dependencies

Modeling for OLAP

Facts and Dimensions

Denormalization

Star and Snowflake Schemas

Star Schema

Snowflake Schema

Choosing Between Them

Chapter 5 What Are Experiments?

The Philosophy of Experimentation

Humility in Product Development

Experimentation as a Team Sport

Experimentation Protects Users

The Anatomy of an Experiment

Hypothesis Formation

Control and Treatment Groups

Randomization

Metrics and Scorecards

Duration and Sample Size

Why Experiments Matter in Growth Engineering

Common Misconceptions About Experimentation

"Experimentation Slows Us Down"

"Experiments Are Only for Small UI Tweaks"

"Only Data Scientists Should Run Experiments"

"We Can Just Measure After Launch Instead"

Exercises

Chapter 6 Types of Product Experiments

Design Types

A/A Test

A/B Test

A/B/n Test

Multivariate Test

Holdout Groups

Switchback Test

Application Types

UI/UX Experiments

Onboarding Experiments

Notification Experiments

Pricing Experiments

Fake Door Experiments

Reverse Experiments

What This Chapter Covered.

Key Questions for Reflection

Chapter 7 Introduction to A/B Testing

What Makes a Fair Comparison

Triggering

Types of Triggering

Exposure-BasedTriggering

Action-BasedTriggering

Hybrid Triggering

Choosing the Right Trigger

Example: The Pro-Tip Onboarding Card

Sample Ratio Mismatch

Statistical Significance

Power and Sample Size

Common Mistakes in A/B Testing

Stopping Too Soon

Running Overlapping Experiments

Ignoring Guardrail Metrics

Focusing on Significance over Impact

Skipping A/A Tests

Overlooking Novelty and Learning Effects

Chapter 8 Building a Growth Engineering Team

What Makes a Growth Engineering Team Unique

Team Composition and Roles

Growth Engineers

Product Managers

Data Scientists

Growth Designers

User Experience Researchers

Team Structure

Centralized Model

Embedded Model

Hybrid Model

Cultural Foundations

Experiment over Opinion

Shared Metrics and Transparency

Learning Loops and Post-Mortems

Building Trust for Growth

Hiring and Upskilling for Growth

The Growth Engineer's Career Path

The Cadence of Growth Teams

Weekly Growth Review

Hypothesis Review

Scorecard Syncs

Sharing Learnings

Chapter 9 The Future of Growth Engineering

AI and the Future of Experimentation

Designing Experiments

AI-Assisted Development

Autonomous Experiment Execution

AI-Assisted Analysis and Insight Generation

How AI Changes the Role of the Growth Engineering Team

Growth Engineer

Product Manager

Data Scientist

Designers and UX Researchers

Ethics, Privacy, and Responsible Growth in an AI-Driven Era

Chapter 10 The Growth Engineer's Workflow

Standup

Product Alignment

Engineering Design

Implementation

Bug Bash

Rollout

Scorecard Review

Retrospective

Communicating Impact

Index

EULA.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-394-40647-9

1-394-37847-5

9781394378470

OCLC:

1577547046