An In-Depth Look at Enhanced Ecommerce Reports in Google Analytics

For those of you managing an e-commerce website, the Enhanced Ecommerce reports in Google Analytics (GA) are going to really help you get more insights than you’ve ever gotten about your shop performance from GA before. It requires Universal Analytics and a few additions on top of what you might have implemented before, but with these few customizations, you suddenly get a lot more data, making it very worth your while.

If you haven’t yet upgraded to Universal Analytics, I recommend that you start planning and implementing this when you can in order to start getting the benefit of this report, among the other benefits, as soon as possible.

First let’s start by summarizing the best bits you can expect to find.

New Data

The highlights of Enhanced Ecommerce are the new Shopping Reports, which are much more detailed funnel reports, with the added bonus of segmentation capabilities. The metrics for the ratio of views to purchases and add-to-carts to purchase come a close second, as these can really help you see in the blink of an eye which products are selling well based on the views they receive and which are not getting a good enough level of sales.

My third favorite addition is the ability to track product listings pages to see how well products are performing amongst others on the list. This allows you to see the number of impressions products gained, the click-through rate of each product, and what positions work best for your products. If this doesn’t give everyone an immediate new insight into their product performance I will be surprised. But you will have to get used to this new data and how to take action from it, so let’s dive into the reports and see what we can find to help your store.

Shopping Analysis

This section has two different reports, Shopping Behavior and Checkout Behavior. They are both based on a funnel report, but with a lot more to help you improve performance than ever before.

Shopping Behavior

A funnel showing success metrics for ALL steps in a customer journey, not just through the checkout. This starts with the number of sessions on your website, then how many of these sessions included a product page view, then those that proceeded to checkout steps, then through to a transaction. Rather than just using the old Goal funnel report to see which checkout steps had issues, or having to build something to see where else on site people dropped out, you can now see the data in one easy-to-digest report to know where the critical drop-out points on your website are.

Additionally, you can break this down for different types of user, whether new or returning, mobile or desktop, different countries etc., and you can then start to improve your site’s customer journey for each specific group of users, making the benefits even more pronounced.

shopping-behavior-with-notes

This image shows the visual display you see on this report, which you can immediately tell is more detailed than a goal funnel report. The notes I’ve added explain what all of the numbers are telling you – there are quite a few percentages so it can get confusing as to which one is showing progression, which is showing total reach and which is showing the drop outs (abandonments).

When you get to this report you will also see a table below this visual, which shows the data in numbers with the segmentation option in a drop down so you can choose how to see progression and abandonment. As suggested above, use the drop downs here to find some insights that matter more to your business, whether it’s by country or device type there are likely to be differences for each segment that will help you identify any problems and any particular successes for your site.

Checkout Behavior

The visual in this report follows the same scheme as the Shopping Analysis above, but is limited to the steps you tag as checkout steps on your website. This is particularly good for identifying where users drop out of your checkout – is it that the delivery options do not suit them? Or is there a payment method not working properly?

Again, unlike before, you can now apply segments to this report, and not just the segments in the drop downs on the table. These reports allow you to use advanced segments that you may have built yourself, so if you like, you could compare the journey of people who come from paid traffic sources to those who come through free routes, or those who come from offer sites as they may have issues applying discount codes.

Here’s an example of the table seen in the Checkout Behavior report, in which you will see the columns labeled with the information you have put in to label each of your steps depending what the purpose of each one is. This makes this a highly customized, segmentable representation of how your checkout performs – with the ability to look at sessions or abandonments depending on whether you want to spot success or problems.

checkout-behavior

Product Performance

This is a report that you will be familiar with, however, it now has additional metrics which really help tell you the full picture about a product’s life on site. How many times did you think to yourself, “Yes this might be one of my top sellers, but it should be selling more based on how much traffic I’ve sent it?!” Well now you can see how it’s performed against how many times it was viewed and how many times it was added to cart.

It’s time you can see whether your marketing is working, whether your internal traffic drivers to key products pay off, or if there are some products that are very well received and likely to be purchased but you never thought to promote them.

The columns on the right in this image are the ones you want to review closely; they’re missing any weighted sort functionality but with a few smart filters applied, such as quantity or sales minimums, you should be able to spot the important ones easily enough:

product-performance

In this example you can also see that there is a column available for product refund value, which you can choose to import or send to GA on refund pages. This is a very powerful feature of Universal Analytics and Enhanced Ecommerce, but does take some thinking about to ensure you capture data for this efficiently enough.

Product Performance: Shopping Behavior

The product performance area also hides this gem of a report, which shows you interactions such as views, adds to cart, removals from cart, and checkouts for each product. Again this is detail never seen this clearly before, which is likely to have a massive benefit to your shop as it will allow you to spot those products being added to the cart but not purchased – you got the user halfway there so it’s time to work out how to get them over the final hurdle to make those sales you were to close to!

product-shopping-behavior

Sales Performance

This page is the equivalent to the old Transactions report, showing sales information by order ID. It hasn’t changed much but does now feature a column for refunds, which even if they happen on a different day to the order can be attached to the original order ID, helping you identify sales which you thought were successful but turned out to be less so.

Product List Performance

Another new and valuable report. This one reports on the performance of your product listings pages, and any other areas in which you have shown product snippets to users on site such as related products or recommendation lists.

Having been curious about the implementation of this needing a significant amount of information for every product and some pages being very product heavy, Google Analytics recommends that when worried about this you utilize the product data import feed that is available in Universal Analytics. Using this means you only need to send the product ID or name, position on the list, and list name to GA for each product list impression; the rest of the product information (price, variation, brand, category, etc.) can be stitched together in the backend of GA from the import you give it.

Additionally, my advice in implementation of this feature is that you may not want to be super-specific about the product list, as this will make it harder to spot trends and patterns. Try naming lists relatively generically, such as Category, Brand, and Search Results. Once you have it set up this way you can review how the reports work for you and decide how granular you want to get in future.

The metrics shown here are:

  • Product List Views
  • Product List Clicks
  • Product List CTR
  • Product Adds to Cart
  • Product Checkouts
  • Unique Purchases
  • Product Revenue

Just above the main table you will see links allowing you to break this data down by Position, Product, and Product SKU to give you some additional insights about how products perform and which positions on the list work for you.

Having Position recorded is a great benefit as you can start to accurately work out how many products it is best to display on each list page, which positions your key products need to be in to get noticed, and whether or not this is the reason certain products are not selling. By breaking this down by list type you’ll then see whether your search results are successful and have high click-through rates, or if people are finding relevant products in category pages.

Knowing which of your listings pages are the most successful will help you decide which to use in external marketing activity, such as PPC, as well as whether or not your investment in site search tools and product list ranking algorithm adjustments is paying off.

Time to Dig Into the Reports!

There are more hidden gems in these reports under the Marketing section, but I think that’s enough to be getting on with for now! Best to ensure you can use these new reports to their full extent than trying to spread your resource more thinly across additional reports too early in the day.

I hope you find this guide helpful in uncovering insights about your store from these reports. The team at Google Analytics have really taken e-commerce reporting to the next level and I’m looking forward to seeing what direction this takes us in after such a strong rebuild of this massively important area of analytics.

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