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Using Google Analytics to reduce Checkout Abandonment

Updated: Oct 23, 2020

Once all data collection/tracking issues have been resolved and at least one month of clean data has been collected, the next step that a web analyst/marketer should take is to conduct an analysis of the website’s checkout funnel.

This is because finding & fixing problems with a website’s checkout funnel is one of the quickest and most effective ways to improve a website’s sales since all you are doing is helping the website to convert more visitors into customers with the traffic flow that it is already generating.

One of the best ways of conducting a conversion funnel analysis is to identify the biggest drop-offs from one stage of the funnel to the next and determine the likely causes of these drop-offs.

If you use Google Analytics, you can conduct a checkout funnel analysis with the below reports;

Funnel Visualization Report

Goal Flow Report

Checkout Behaviour Analysis Report

Stages of a Website Checkout Funnel

The stages of a typical website checkout funnel are:

  • Billing and Shipping

  • Payment Details

  • Order Review Page

  • Order Confirmation Page (Sessions with Transactions)

Below is an example of an eCommerce checkout funnel looks in the Checkout Behaviour Analysis Report:

A Checkout Behaviour Analysis Report

In this example, we can conclude that the stage in which the biggest drop-off is the Payment stage with a drop-off rate of 98.11%.

In the scenario, the best hypothesis to come up with would be:

“If we can reduce the drop-off rate of the Payment stage, the business would make more sales”

We then need to carry out the necessary work to test & validate this hypothesis.

Other Possible Hypothesis

The most suitable hypothesis would always vary depending on the stage of the checkout funnel that has the biggest drop-off rate.

For example,

If the Billing and Shipping stage had the biggest drop-off, the following hypothesis would apply:

“If we can reduce the drop-off rate of the Billing & Shipping Page(s), the business would make more sales.”

And so on…

Identifying the reason for drop-offs in a Checkout Funnel

The best way to identify the major reason causing these drop-offs is to use customer-focused analysis techniques such as:

Heatmap Analysis

Session Recordings

Usability Testing

A/B Testing

Online and Offline Surveys

Online and Offline market research

Most of the time, the major reason causing the drop-offs is either a technical issue or a non-technical issue.

Examples of technical issues could be:

Website Errors

Data Collection Issues

Data Integration Issues

Cross-Browser Compatibility Issues

Cross-Device Compatibility Issues

Examples of non-technical issues could be:

Cost of shipping

Unexpected additional costs

Complicated return process

Lack of payment options

Security concerns

According to Statista, the biggest non-technical cause of checkout abandonment is “Cost of shipping”.

Being upfront with the shipping costs, having an easy returns process and utilizing remarketing campaigns are all things you can implement to help reduce the checkout abandonment rate of a website.

Segmenting your Checkout Funnel Analysis

It is important to segment the data in your checkout funnel analysis report in order to gain a deeper understanding of the customers that are going through the checkout funnel and use that deeper understanding to form a stronger hypothesis.

In the earlier example, we established that the Payment stage has the biggest leak in the checkout funnel with a drop-off rate of 98.11%. However, this information alone can’t give us much of an insight because there are a lot more questions we still need to find answers to.

Which users initiated the checkout in the first place?

Why did they initiate the checkout?

Why did they visit the website?

Where did they come from?

How did they enter the checkout?

What were they doing before they entered the checkout?

What device/browser were they using?

Why did they abandon the checkout?

Without segmenting your data, you won’t be able to fully understand the core problem that is causing the drop-offs and how that problem can be resolved/eliminated.

You can segment the data in your checkout funnel analysis by using the advanced segments and filtered views features provided by your web analytics tool.

I would recommend using some or all of the below segments:

  • Organic Search Traffic

  • Tablet and Desktop Traffic

  • Paid Search Traffic

  • Social Media Traffic

  • Site Search Traffic

  • Referral Traffic

  • Email Traffic

  • Mobile Traffic

  • Product Categories

  • Products

  • Location

  • Browsing Day/Time


Optimizing a website’s checkout funnel is one the most effective ways if not the most effective way to produce significant results for your business or client's business in a short span of time.

Start off by identifying the checkout stage that has the biggest drop-off and then segment the data to gain a deeper understanding of the customers that are dropping off the most. You can then use customer-focused analysis techniques such as heatmaps, session recordings, surveys, etc to gain an understanding as to why they are abandoning your checkout.

Use this information to form various hypotheses and validate those hypotheses through usability testing and/or A/B testing.

If you have any questions or would like us to assist in optimizing your website’s checkout funnel – feel free to send us an email at and we'll happy to help.

Thanks for reading!

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