A new feature, Enhanced E-Commerce, allows Universal Analytics users to dig deeper into their e-commerce data in order to better their marketing efforts.
For Google Analytics users that manage or interact with e-commerce sites, e-commerce tracking isn't really a new concept. It's a great method of attributing revenue to marketing campaigns, and providing directional indicators that aid in the optimization of these campaigns, keywords, etc. However, beyond the basic data of which products were purchased most, or what the return on ad spend of a specific paid keyword in Bing is, it's not all that easy for marketers to get deeper insights into optimizing their site's e-commerce experience. Cue Enhanced E-Commerce.
Enhanced E-Commerce (EE) reporting is a newly available feature to Universal Analytics users. It gives marketers the ability to not just perform return on ad spend calculations and optimize bids/ads accordingly, but to gain a deeper look into the e-commerce conversion journey that users experience on the site. EE provides a more robust, fuller understanding user experiences when trying to make a purchase. And for that, EE is a great tool for marketers to have, for free.
So in hopes of sparking your interest enough to further research the full features and abilities of Enhanced E-Commerce, here are five items that I find extremely helpful:
When users are presented a product on your site, you can now send this event to Google Analytics and use it to calculate click-through rates of products, and what are your best selling products.
Is your top-selling product in fact your most effective product, or does it just happen to be what users are purchasing the most of? At first glance, these two questions seem the same, but they're actually quite different. Top-selling products are pretty straightforward; they're what you sell the most of. But your most effective selling products are different because you need to look at how many users were exposed to each product, compared to how many ended up viewing a detail page, or better yet purchasing them. You may have what appear to be mediocre products sitting below the fold, which if featured could really change your bottom line.
An item that comes up quite often in implementations for e-commerce sites is how to calculate the performance of an internal campaign or promotion, while keeping the marketing channel campaign information (source, medium, etc.) intact as well. And up to this point in Google Analytics, there hasn't been a great way to calculate this outside the use of events. However, these calculations can quickly get confusing, and proper attribution of channels/campaigns tends to get murky as well.
With EE, marketers can now record the number of impressions of internal promotional banners, how many users clicked on these promotions, their click-through rate, and then how many ultimately purchased after seeing or clicking the promotion. This allows marketers to calculate the influence of their internal campaigns on revenue, while also making sure to credit what channel/campaign ultimately brought the user to the site.
There have been a couple ways to hack around adding refunds to transaction data in Google Analytics, but none have been very elegant or supported refunding partial transactions, i.e. one shirt was returned, but I kept two pairs of jeans.
New within EE is a refund action that lets you upload refund data specific to transactions and either remove the entire transactions, or just specific products from a multiple product transaction.
Product lists are specific sections on your site where products are grouped together based off of a common theme/structure. For example, if products were shown to users from within a catalog search result, or a "users also bought" section, it would make sense for the product lists of these groupings to be "catalog search" and "related products," respectively.
Using EE, you can now see what products are receiving impressions within the different product lists, which are getting clicks, and their click-through rate. This level of detail gives you tremendous insights into which product combinations/matching make sense, and which may need some tweaking.
Shopping Behavior Analysis provides detailed insights into the purchase journey, from product impression through purchase. You can see what steps users are abandoning the process the most and where they are entering back into the process in a later session.
A use case for this information is e-commerce sites that send follow-up emails or remarket coupon codes to users that leave products in their cart. For example, my wife occasionally shops on Old Navy's site, but she never purchases during her first visit because she knows if she adds a few products to her cart and waits a few days, Old Navy will email her a pretty nice coupon code. Using advanced segments to look at users that use a specific coupon code for purchasing, such as an abandoned cart email code, marketers can determine if users are doing this multiple times (like my wife).
And while the newly available data via EE is truly great on its own, where this really gets even more valuable to marketers is when it's combined with other data or Google Analytics features, such as Remarketing Lists. For example, if you wanted to remarket to users based off of a specific product/group of products purchased or added to cart, where the user is in the conversion journey (view cart, billing details, submit, etc.), if a specific coupon code is applied to their order, and on and on and on... you can do that, MARKETERS ARE DOING THAT! (context for the non Georgia Tech fans out there).
If you help manage, optimize, or run marketing campaigns for an e-commerce site, I strongly encourage you to take a look through all the new features and what's possible within Enhanced E-Commerce. It really is pretty amazing for all of this to be freely available. You are required to be using Universal Analytics, but I'm sure you're already working on that upgrade already. If, however, you need some help with either item, check out the Google Analytics Certified Partner Program, where you can find digital analytics consultancies that have been vetted by Google to provide superb support (I recommend Search Discovery, since I'm lucky enough to work there and all).
Have questions? Curious if something is possible? Let me know in the comments and I'll be happy to help out.
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Robert Miller is a Senior Analyst at Search Discovery. He is actively involved in industry organizations, such as the Analysis Exchange and the Digital Analytics Association.
With the Analysis Exchange, he helps non-profits capitalize on their website data, and educates aspiring digital analysts about the foundation of digital analytics, from the implementation of a digital analytics tool to performing analysis and making data-driven recommendations for organizations.
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