Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Rather than simply reporting what has happened, GA4's new cloud integrations enable more data activation, linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations.

Author Mark Edmondson, Google developer expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get the guidance you need to implement them.

You'll learn:

• How Google Cloud integrates with GA4
• The potential use cases that GA4 integrations can enable
• Skills and resources needed to create GA4 integrations
• How much GA4 data capture is necessary to enable use cases
• The process of designing dataflows from strategy through data storage, modeling, and activation
• How to adapt the use cases to fit your business needs

GA4 is the biggest evolution yet of the most popular digital marketing tool used on the web, Google Analytics. BuiltWith.com estimates that around 72% of the top 10,000 websites use Google Analytics, and all those websites will be looking at upgrading from the legacy Universal Analytics to GA4 in the next couple of years. Due to GA4’s new data model, the latest iteration of Google Analytics will not be compatible with its predecessors, unlike past upgrades such as from Urchin to Universal Analytics. The older systems will eventually be sunsetted, so it’s realistic to say that in a few years’ time GA4 could become the most popular analytics solution on the planet.

GA4 offers a new digital marketing paradigm: moving analytics tools beyond reporting what has happened toward influencing what will happen via data activation. Data activation is about making a positive effect on your website so you can see real business impact with your analytics. The trend for digital marketing over the last few years has been toward making faster decisions to help justify the cost of your website, app, or social media activity. As ecommerce booms, digital analytics have become more critical to ensure budgets are allocated correctly in a highly competitive arena.

These new opportunities require learning skills that may be unfamiliar to traditional digital marketers, so this book aims to help bring you up to speed and help your GA4 implementation fulfill its potential. We will demonstrate common use cases for GA4 data activation and provide step-by-step instructions on how to implement them, as well as introduce ideas and concepts to help you build your own bespoke applications.

I hope to give inspiration for those wishing to create their own data activation projects. Code examples will be included to help provide some templates, as well as introductions to various cloud components such as data storage, data modeling, APIs, and serverless functions to help you assess what technologies you may want to enable.

By the end of this book, you will be able to understand the following:

• What use cases GA4 integrations can enable
• What skills and resources are needed
• What capabilities third-party technology needs to fulfill
• How the Google Cloud integrates with GA4
• What data capture is necessary by GA4 to enable use cases
• The process of designing data flows from strategy to data storage, modeling, and activation
• How to respect user privacy choices and why it’s important to do so

I think this is the most exciting era for working in digital analytics, simply because the potential of what you can do now is almost limitless. The cloud has made possible what was impossible for individuals or smaller companies to do even 10 years ago, and that revolution means I truly feel you’re limited only by how much ambition you have. If this book can help inspire even one person to realize that ambition, it will have been a worthwhile venture.

Who This Book Is For

If you’re reading this book, you’re likely a digital marketer with some digital analytics background. Perhaps you’re working in an agency or within a digital marketing department, such as for an ecommerce brand or a web publisher. You may be looking to justify upgrading to GA4 from Universal Analytics or have already made the switch and are now looking to make use of its advanced features. This book aims to both inspire nontechnical readers with what is possible and give enough practical information that technically minded readers can implement the use cases within the book and use the building blocks to create their own bespoke integrations.

The book aims to educate you on the features of GA4’s integrations beyond the basics that you’ve picked up with your one to two years’ experience in digital marketing. You’re probably comfortable with implementing tags on the website and/or reading basic GA reports. More technical users may be using Google APIs and have some JavaScript/Python/R/SQL knowledge as well as some cloud experience.

This book is not an exhaustive roundup of GA4 features. Instead, this book focuses on what you can do today to extract business value out of your GA4 implementation, using the Google Cloud Platform to facilitate it.

About the Author

Mark Edmondson is Senior Data Scientist at IIH Nordic A/S. He is also a Google Developer Expert for Google Analytics and Google Cloud, and has consulted for over 15 years helping global brands with their digital marketing strategy. He contributes to the digital marketing community through his blog and open source contributions, focusing on data science and engineering applications with digital marketing data.

Mark has published several data activation proof of concepts over the years that have been popular within the digital marketing industry, including a web app to measure if certain events affected your data in a statistically significant way; using markov chains with Google Analytics data to predict where users will go so as to prefetch web resources and improve page load speed experience; and using streaming web analytics data to BigQuery to create real-time digital analytics dashboards.