In our latest SaleCycle Academy video, Marc Davison explains how to track cart abandonment with Google Analytics. 


Video Transcription

We’ve all heard the stat that around 75% of customers who add items to their shopping basket will leave the website without completing their purchase.

However, this can vary between different sites, and it’s important to track your own cart abandonment rates so you can monitor any trends and changes and fix any potential problems that exist on your checkout process.

It’s relatively simple to track cart abandonment rates in Google Analytics, so let’s step into the lab…And find out how.

Simply login to your Google Analytics account and head to Conversions and Shopping Behavior on the left hand side.

You should see a report for the different stages of your customer’s on-site journey for the time period you selected.

As you view the chart from left to right, you should see how many visitors dropped out at various stages of the customer journey.

Most cart abandonment data, ours included, counts the total number of baskets abandoned, whether at the cart page or during the checkout process.

Google Analytics shows cart and checkout abandonment, which can help retailers to monitor trends in the checkout process.

For example, if the percentage of users leaving the website during the checkout is higher than normal, that might alert them to any problems that exist in usability during the checkout process.

Once you have this data, it can be useful to look at it in different ways by using segments.

For example, are there different abandonment rates between different browsers or devices?

If so then this may suggest a problem with how your site works for these different types of users.

There are many possible reasons for higher abandonment rates.

It could be that your website doesn’t work well in a particular browser, or that customers in a particular location drop out because shipping costs are too high.

The key is to use your data to understand your abandonments, and to be aware when any unusual patterns appear.