Understanding User Behavior with GA4 Data Analysis in BigQuery

Introduction

In today’s digital landscape, understanding user behavior is crucial for businesses aiming to enhance their online presence and optimise performance. Google Analytics 4 (GA4) offers powerful insights into user interactions, and by leveraging the capabilities of BigQuery, we can conduct advanced data analysis to gain deeper insights.

Analysing User Sessions

In this article, we’ll explore a SQL script designed to analyse user sessions using GA4 data stored in BigQuery. Let’s break down the script step by step:

Step 1: Identifying Session Start Times

The script begins by identifying session start times using the session_start event. This is achieved by selecting relevant fields such as event name, date, user pseudo ID, and event timestamp. The LEAD() function is utilised to identify the timestamp of the next session start for each user.

Step 2: Adding Session IDs

Next, the script creates session IDs by joining the original event data with the session start times. It calculates a unique session ID for each session using the SHA256 hash function applied to the combination of user pseudo ID and session start time.

Step 3: Session Statistics Calculation

The script then calculates various session statistics, including user engagement, page views, view items, add-to-cart events, begin checkout events and purchases. It also calculates the duration of each session.

Step 4: Aggregating Results

Finally, the script aggregates the results by grouping them based on the event date. It calculates metrics such as the total number of sessions, unique users, engaged sessions, bounce rate, average session duration, page views, and conversion rate.

Conclusion

By executing this SQL script on GA4 data stored in BigQuery, businesses can gain valuable insights into user behavior and session performance. Understanding how users interact with their online platforms enables businesses to make informed decisions to optimise user experience, drive engagement, and ultimately, achieve their goals.

Note: Make sure to replace the dataset and table names in the script with your actual dataset and table names before executing it in BigQuery.