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How to Track Cart Abandonment Rates in Google Analytics

How to Track Cart Abandonment Rates in Google Analytics


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In this post, we’ll look at how to track cart abandonment using Google Analytics.

On average, around 77% of customers who add items to a shopping basket will leave the site without completing a purchase.

This number varies between types of sites and can depend on factors such as good checkout design and the length and complexity of the purchase process.

This is our latest cart abandonment data from 2018, and we can see that some sectors, like fashion, have lower rates, while sectors like finance and travel are above the average rate.

A certain level of cart abandonment is a fact of life, and customers have various reasons to abandon purchases. However, it’s important to work at keeping abandonment rates as low as possible, as well as attempting to recover any abandonment carts.

For this, it’s important to track cart abandonment, looking for any changes and investigating the differences between different customer segments, devices, regions and more.

Tracking cart abandonment in Google Analytics is fairly simple. There’s a manual route to do this, setting up a custom funnel, but from last year Google has made this much easier.

How to Track Cart Abandonment

Simply login to your Google Analytics account and head to Conversions > Shopping Behavior on the left. (I’m using Google’s demo account here)

You’ll now see a report like the one below showing different stages of the on-site customer journey for the time period you selected.

The basket (or cart) abandonment figure, as well as the subsequent checkout abandonment figure, showing those visitors that entered but didn’t complete checkout.

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

From a retailer’s perspective, splitting the two stats allows them to monitor trends in checkout abandonment. For example, if the percentage abandoning during checkout is more than normal, that might alert them to possible checkout usability issues.

From this overall data, the interesting thing is to look at it in different ways by using segments.

You can already see the differences between new and returning visitors under the chart, but there’s more to look at.

If you hover over any of the red arrows on the chart, you can create a segment.

These segments can be applied to other reports. For example, you can see if abandonments are influence by the device used.

You could apply the same segments to traffic sources, regions, demographics, browser used and more.

The ultimate aim of this is to understand customer behavior on your site and make improvements where you identify possible issues.

For example, if higher than usual numbers are abandoning at the payment stage, is there an issue here? Perhaps a confusing form field, or customers aren’t seeing the payment option they want?

If this is the case you can carry out user tests to identify issues and test new versions of the page to reduce drop off.

There are many possible reasons for higher abandonment rates, or spikes where they are higher. It could be that your site doesn’t work so well in a specific browser, or customers from a particular location drop out because shipping costs are high.

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

It’s also important to combine analytics with other CRO methods. Analytics can help you to identify problem areas, but once these areas of friction have been identified, it’s then a case of understanding the ‘why’.

Analytics can help here, but it’s likely to take a mixture of CRO methods to really identify the causes of abandonment; user surveys and feedback, and observed user testing are some of the most effective.

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Graham Charlton

Graham Charlton is Editor in Chief at SaleCycle. He's been covering ecommerce and digital marketing for more than a decade, having previously written reports and articles for Econsultancy. ClickZ, Search Engine Watch and more.