Learning Google Analytics: Creating Business Impact and Driving Insights

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

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.