Tackling shopping cart abandonment with data analytics

Tackling shopping cart abandonment is one of the key challenges of online retailers today. Luckily we’ve got one very effective tool on our side: Data analytics.

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Today, the average shopping cart abandonment rate in online retail hovers at a disheartening 69.57%. To put that into an even more ominous perspective, that’s $18bn lost every single year. All because customers simply didn’t want to check out. What’s going on?

There are dozens of reasons why you could be experiencing a high shopping cart abandonment rate. Yet understanding the exact reason isn’t an easy task. Luckily, when it comes to the war on shopping cart abandonment, we have one very efficient ally: Data analytics.

In this article, we take a look at how to tackle the problem of shopping cart abandonment by conducting a thorough analysis of your ecommerce data.


Top reasons people abandon shopping carts

If you’re trying to figure out why you’ve got such a high shopping cart abandonment rate, it’s worth first understanding the main reasons why people abandon baskets today.

With so much competition, consumers are more fickle than ever. Add to this the fact that ‘the Amazon effect’ has given people expectations of a completely frictionless online shopping experience. And so, according to analysis by Statista, these are the top 6 reasons people abandon shopping carts:

  1. Unexpected costs like extra shipping or taxes (56% of consumers report this as a reason for abandoning cart)
  2. User was just browsing (37% of consumers)
  3. Found a better price elsewhere (36% of consumers)
  4. Navigation too complicated (25% of consumers)
  5. The process took too long (21% of consumers)
  6. Security concerns (17% of consumers)

To understand the exact reasons why users are abandoning their carts on your website, however, you’ll have to conduct a thorough analysis of your ecommerce data. In the next section, we’ll break down how.


How to track and analyse shopping cart abandonment with Google Analytics

Of course, there’s plenty of tools out there that help you analyse shopping cart abandonment. But today we’ll look at how to do it Google Analytics, since it’s a favourite among ecommerce teams.


How to see where users dropped off on Google Analytics

If you want to see where users are dropping off from your website, simply head to your Google Analytics account and click on Conversions>Ecommerce>Shopping behaviour. The platform will then bring up a chart that shows exactly what percentage of visitors dropped out there. Your graph will look something like this.

This data visualization graph breaks down exactly when people left your online store, and how many abandoned carts when they did. The chart below breaks down the demographics of visitors in whatever dimension you wish to view so you can drill down a bit deeper into what sort of users left when.

Most users will drop off before the point of checkout because of consumers’ tendency to browse multiple sites. These are largely unavoidable losses (although there are a few things you can do to try and improve your ecommerce sales). However, this graph also tells you how many are abandoning carts because of issues at the checkout, which can help you pinpoint if your checkout process is where the issue lies.

As you can see, on this client’s account there was a whopping 93.6% checkout abandonment rate (we’ll explore why in why in just a moment). However this section of the platform doesn’t provide you with any details into what happened on the checkout to cause this abandonment rate. You’ll have to look at the checkout behaviour to find out more.


How to see where users dropped out on the checkout on Google Analytics

To drill down deeper into checkout behaviour on your ecommerce website, you need to click on Conversions>Ecommerce>Checkout behavior (just below Shopping behaviour). You should then see a graph that looks a bit like this.

This graph breaks down exactly where the users who started checking out began to drop off. The chart below allows you to break down the demographics of visitors into whatever dimension you wish to view so you can drill down a bit deeper into what kind of user left, and when.

On this client’s graph, you can see that the drop offs occurred at the payment and review level, suggesting that the issue was the price of this company’s products. If you’re experiencing high levels of drop off at these stages, you could consider tweaking your target market since your products seem to be out of their price range. You might also want to rethink your pricing and perhaps run some discounts to persuade more people to checkout.

On the other hand, if you were experiencing more drop offs at the billing and shipping section, then this would indicate that the billing process was too difficult or clunky, or that your shipping costs were too high.



Technical issues on checkout pages

Of course, there could be technical issues behind these drop offs too. You might want to also check for the following (especially if you’ve already taken measures to prevent drop offs):

  • A broken page link
  • Slow page loading times
  • Poor or confusing navigation
  • Cross-browser compatibility issues
  • Cross-device compatibility issues

Checking for all of this can be an incredibly lengthy process. Learn how automating your ecommerce analytics can alert you to only the insights you need to know, saving you time and money.


Segmenting checkout behaviour

Now you know exactly where users are dropping off on the checkout funnel. But you still don’t have the exact answers some key user behaviour questions, such as:

  • Were they from organic search, paid search, social media, email or somewhere else?
  • Did they use a desktop, mobile or tablet?
  • Why did they visit the site?
  • Why exactly did they abandon the checkout?

On Google Analytics, you can segment your ecommerce audience to try and understand the answers to these questions. You can choose to segment the audience by:

  • Organic search traffic
  • Paid search traffic
  • Social media traffic
  • Tablet and desktop traffic
  • Mobile traffic
  • Direct traffic
  • Site search traffic
  • Referral traffic
  • Email traffic
  • Products
  • Products categories

Learn how to set up enhanced ecommerce segments in Google Analytics’ guide.


Alternative ways of understanding high checkout abandonment rate

There are other data analytics-driven ways of understanding high checkout abandonment rates, of course. Here’s a few of the best alternative methods:

Heatmaps and session recordings

Heatmaps and session recordings can be very handy at helping you understand how good the UX of your site is. They allow you to investigate how your visitor’s purchasing behaviour, the journeys they took on your website, what CTAs aren’t converting and where you need to simplify the checkout process.


A/B testing

A/B testing is another method of finding ways to optimise your website and checkout pages. You can test various elements to see what your customers respond to. Try experimenting with value propositions and special offers, the number of steps in the checkout process and different CTA buttons.


Online and offline surveys

Of course, if you really want to know why your audience is abandoning carts, you could always ask them. Try sending out surveys to your customers to ask them what parts of the shopping process they found difficult and how you could improve. Offering rewards for taking part, such as discounts or the chance to win a prize, will increase the chances of customers taking the survey.


Automated KPI analysis

In an ideal world, you’d be able to analyse every customer journey and the performance of all your website pages 24/7. This would mean that you’d be able to see exactly where issues are arising and address them before they have an impact on shopping cart abandonment rates – and ultimately revenue. But this isn’t a feasible or scalable solution when you’re manually tracking and analysing KPIs. Humans aren’t made to number crunch, day in, day out, and we’re always going to make mistakes and miss out on important things.

AI, however, is perfect for this task. It’s trained to identify trends and patterns in data that are invisible to the human eye, so it can pick up on issues in your ecommerce data in real time. For example, if a checkout page is taking too long to load and dropping payments as a result, an AI, like the one that powers our automated KPI analysis platform Millimetric, will identify this straight away.

Automated KPI analysis platform, Millimetric, in action

With AI combing through all your website’s data 24/7 and alerting you to unusual behaviour in your ecommerce data, you can prevent issues on your website and checkout before they happen. Learn more about how automated KPI analysis helps drive better customer experiences, conversions and ROI.





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