Visualise GA EE data using Data Studio
Are you using Google Analytics Enhanced Ecommerce (EE) for your ecom tracking? Do you want to share EE data using Data Studio (360)? Good. Read on, we’ve found some useful details that can be used to add polish and finesse to your data sharing and visualisation using Data Studio.
Out of the box
You’ve got your EE data looking about right.
Your checkout behaviour has the right steps configured.
You’ve got a funnel looking something like this:
Okay, your labels and values will differ from mine, but anyway, well done. Now you can build your own version of this, tuned to your visualisation requirements in Data Studio.
If you’re not happy with the way the data is presented for whatever reason, change it. Add branding, segments, filters, do maths, include other data – go nuts!
The checkout Behaviour Analysis report includes sessions that join midway. This is often uncomfortable for some users. They don’t get it. They don’t need to see those data points. They only want to see the pure funnel data. Fine. Lets do that:
See how this is cleaner? Okay, it’s a simple example but now we’ll walk through how this was done so if you need to try something more sophisticated, you’ll see how.
Dimensions and Metrics
The start point is quite simple. Connect to your GA data source as you normally would:
If I’ve started too fast having assumed you know how to connect DS to GA, apologies. Maybe take some time to read the docs and try this out before you try the advanced stuff.
If you’re keeping up, great. The example above is a bar chart – pop that on your report, choose the “Sessions” metric and “Shopping Stage” dimension:
The “Shopping Stage” dimension might be new to some of you. This super handy dimension is the main crux of what we’re doing here and we’ll discuss it more later but right now the bar chart looks a little messy and unhelpful:
If you only want to see the checkout stages, filter appropriately:
See, we’re getting there but it needs more work. The bars are the total session on the stage. They include sessions that came from the stage prior and sessions that started on that checkout stage which is why the shopping stage order is wrong. Not quite what we want so we need to get a bit MacGyver-ish.
To see the required session count on CHECKOUT_2 we need to subtract the CHECKOUT_2_WITHOUT_CHECKOUT_1 sessions from the CHECKOUT_2 sessions. This data is quite easy to lift out of BigQuery using a simple enough query but what if you’re not an Analytics 360 customer? No BQ. No problem! I want this post to cover techniques that non-A360 customers can use too so we’ll use sheets instead – we all have access to that.
Open a new Google Sheet. Get the GA add on for Google Sheets and build a query on your EE data in your chosen GA view to pull ga:sessions by ga:shoppingStage ordered by ga:shoppingStage for the last 7 days:
Now you’ve got a query that returns data you can manipulate quite happily in sheets:
See all the different values of the Shopping Stage dimension? Gold! You can get a lot of value from this data.
Before we go any further, configure the sheet to refresh every hour…
Now create a new sheet with formulae added that apply the right maths:
Connect your DS bar chart to the sheet with the maths on it and you’ve got the data you wanted.
Further more, you can extract the abandonment data, run more calculations on it and slot in some score cards at the right places to show the abandonment data.
Finally, you can manipulate your query to include segments (not currently supported in DS). Get segment IDs from the Query Explorer.
Easily visualised, segmented, filtered, custom data in Data Studio refreshed every hour: