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The Shopping Behavior Report is another important aspect of Google Analytics, enabling you to better understand your customers. You can see the access point for the Shopping Behaviour report in the image below:

If you have Enhanced Ecommerce set up in full then you can tell Google Analytics when your users have performed particular shopping behavior. The Shopping Behavior report provides you with a funnel report, and this will enable you to identify where you lose your customers at each step; assuming a sales sequence of visiting the website, viewing product(s), adding product(s) to the cart, checking out, and then finally purchasing.

And the funnel aspect of the report shows you what percentage of people are getting to each stage, and at what point people are leaving.

Funnel Graph

The funnel graph also provides a wealth of valuable data. One such funnel graph is depicted below:

Google analytics shopping behavior report funnel graph

It is important to understand that the blue columns represent the number of sessions that reached that particular activity in the sales funnel, whereas the difference between one blue line and the next shows how many people left at that step. The red arrows pointing to “No Shopping Activity” etc, and percentages in red text, illustrate the absolute and relative abandonment from that step.

Across the top of the graph above each step the absolute numbers and percentages of sessions that reached that step are also shown. While across the bottom of each step this chart shows the absolute numbers and percentages of sessions that did not continue through to the next step. And the percentages in grey arrows are indicative of the relative percentage of sessions from that step that continued through to the next step.

The value of this report is that you can quickly identify the major step in your visitors’ shopping activity that is causing the most abandonment, and then focus on improving that area. If you observe that some areas have a much larger abandonment rate than others then that shows a weakness in your user interface or process.

Ultimately, the average conversion rate for transactions is around 2-3%, so if yours is less, or if you have significant drop-off from any one step, then you have the ability to fix it.

The steps are as follows:

  • All Sessions: visitor comes to the website, leaves or browses the store.
  • Sessions with Product Views: visitor looks at a specific product.
  • Sessions with Add to Cart: visitor adds a product to the shopping cart.
  • Sessions with Check-Out: visitor views the checkout form.
  • Sessions with Transactions: visitor completes the purchase.

One point to note here is that Sessions with Product Views refers to detail product views, and not product impressions. So it is possible that the people who abandoned at step 1 did see a list of products, but then chose not to look at any one of them in more detail.

If you want to use this report, it is important to make sure that your analytics is set up with enhanced eCommerce actions and test them properly. You need to ensure that if someone looks at a product, adds to cart, etc it is actually tracked. If this is not done correctly then you will end up with invalid results, where an entire phase is missing, or the numbers don’t reflect the true behaviour of visitors. I have seen this numerous times; for example, when the eCommerce platform doesn’t send the data for a particular step at the appropriate time.

I would recommend setting a long period of time in the date picker when using this chart, so that you can get enough accuracy, because this report is best for seeing the bigger picture rather than small details.

When you hover over parts of the graph you can quickly create an Ecommerce Segment based on the sessions represented by the particular part of the graph that you’re examining. Having said that, it is better to create segments manually because it gives you more control. We’ll talk more about segments and how to use them in the next section of the course. Refer to a blog post instead.

Criteria Analysis

Finally, criteria analysis is the second part of the Shopping Behaviour Report. This aspect of the report enables you to see the variation in the numbers when you compare different criteria. You can choose to see numbers and percentages that reach each step, or instead the abandonments at each step. This is useful for seeing how different criteria affect your eCommerce results. Keep in mind that the criteria need to be “session” level criteria to be used by this report.

The following is an example comparing sessions between new visitors and returning visitors:

Google analytics shopping behavior report by visitor

And here is an example comparing abandonments between visitors from different countries:

Google analytics shopping behavior report by country

In these reports, you can drill down on the step that you are interested in by sorting by that column. So sort, just click on the column header.

So that’s a detailed step-by-step guide to the Shopping Behavior report, providing you with everything you need to know about this key eCommerce visualisation in Google Analytics. The Shopping Behaviour Report is a really useful one to get right, just because it is so visual and so it tends to gel well with people at different levels in your organisation. The criteria analysis is very powerful and can help you make sense of your eCommerce results.

Sophie Abnett

Here at 1834 Hotels we centralise the marketing for many different hotels. It can be an enormous undertaking to monitor and track the performance of each one, especially when they are all on separate webpages.

Petra helped to resolve this difficulty by creating a central hub in Google Tag Manager and Google Analytics with Cross-domain tracking and Ecommerce as well. She created many valuable analytics dashboards and reports that draw from the centralized source. Now that we have all the analytics tracking working it has improved the accuracy of our reporting and the insights have helped develop better marketing strategies.

Petra’s skills and wealth of knowledge is fantastic. I would recommend Petra without hesitation.

Sophie Abnett Digital Marketing Project Manager at 1834 Hotels Ecommerce / Hotel Google Analytics, Google Tag Manager & Google Data Studio Integration April 23, 2020

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Nadia Rossi Digital Marketing at 1834 Hotels Ecommerce / Hotel Google Ads April 23, 2020

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Michelle Bridger Michelle Bridger, Facebook Advertising Specialist Ecommerce Google Tag Manager, Google Analytics & Google Data Studio Configuration April 23, 2020

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