Where did my customer go? (How to uncover real user journeys using BigQuery)

For a long time, GA4 was where marketing teams would go to gain insights from their data, and GA4 still has its benefits. However, when it comes for user journey reporting, GA4 path explorations and standard reports carry limitations that keep the true user journeys hidden. 

• GA4 path explorations force you to build paths forwards or backwards from a single chosen page or event. This is great to give you a snapshot of a particular journey, but prevents site-wide, end-to-end user journey reporting. 
• Visual representations of thousands of unique user journeys can often be cluttered and unclear making analysis difficult. 
• GA4 evaluates user journeys in isolated session blocks, not giving information about how returning buyers navigate across multiple visits prior to purchase.
• Standard GA4 exploration reports frequently apply sampling and thresholding which could hide some high-converting user journeys.

By exporting raw, unsampled GA4 event data into BigQuery, you gain the ability to stitch every page view and every part of the user journey into a single, readable, chronological string, even when it spans several sessions and channels. As Katie reassured the room, marketers don’t need to build this themselves. The setup can be handed to a data team or developers, ready for analysts or marketing teams to review in their reporting platforms. 

The process works in four steps:

1. Pull raw event data from GA4 into BigQuery.
2. Use event timestamps to put each user’s activity in chronological order and stitch it into a journey.
3. Group identical journeys together, with users and revenue/conversions for each.
4. Pull the table into your reporting platform to sort, filter and analyse.

The result is an interactive table where each row reads like a story: 

Category page ➔ Product page ➔ Product page ➔ Cart ➔ Checkout ➔ Purchase

The table also contains revenue or conversion data so you can see which user journeys are commercially important. 

Session breaks, traffic channels, and repeat interactions are embedded directly into the string. You can immediately see when someone left the site, how they returned, and where they experienced navigation friction.

Once the BigQuery tables are connected to your reporting platform, the real insights can be uncovered. In her talk, Katie walked through five ways to analyse your BigQuery user journey data to answer the following key questions:

• Which user journeys generate the most revenue/conversions?
• Which pages are causing friction in user journeys?
• What is causing users to leave the checkout process?
• How do different channels contribute to conversion throughout the user journey?
• Which pages positively and negatively impact conversion rate when included in user journeys?

Katie also demonstrated how categorising pages into buckets (e.g. Brand, Product, Trust, Purchase) can help make your data set more manageable and allow you to identify broader user journey trends.

The BigQuery journey tables transform raw event logs into actionable navigation insights, allowing marketing teams to diagnose site friction, replicate high-performing paths, and use insights to improve conversion rates. 

• GA4 reports don’t provide full user journey data, BigQuery allows us to see the whole picture. 
• BigQuery stitches multi-session interactions and attribution channels into a digestible, timeline user journey strings in an easy-to-analyse format.
• Analysing your BigQuery data can help you answer key questions about how users move through your site. 
• Grouping pages into categories can help to spot broader user journey patterns. 

Want help uncovering how users really move through your site? Contact the team at Varn to talk about how user journey analysis could work for your site.