What retailers get wrong when setting up Google Analytics

Woman shopping online

Practicology’s Head of Analytics, Lee Howard, looks at the mistakes that retailers make when implementing Google Analytics Enhanced Ecommerce and discusses how to fix it.

With the launch of Enhanced Ecommerce, Google Analytics introduced the concept of the ‘product’. 

Products can now become the starting point of the analysis. At the top level we can look at products and understand where they are most often seen and how people interacted with them in differing contexts/locations. We can see how frequently people add or remove specific products from their cart, as well as how many people buy them.

All of this product functionality is in standard Google Analytics (GA) reporting and designed specifically for ecommerce. However, retailers’ implementations of this key GA functionality are often left with questionable results.

In a nutshell what is Google Analytics Enhanced Ecommerce?

Essentially it sheds light on customer interactions with products on your website. Enhanced Ecommerce is an add-on to the GA tag library.

Not having product information leaves a huge hole in the funnel, since only a small number of sessions end in an order. Now retailers can answer the question: What happens in those other sessions?

  • The journeys where people interact with your products, but don’t buy.
  • The journeys where people add-to-cart but never complete the purchase.

Enhanced Ecommerce’s additional library of tags facilitates the product-related insight. To use these tags, retailers’ developers must provide additional information for the extended tag library, typically through the data layer.

Converse legs - Stats edit.jpg

Analytics delivers tangible results

Obtaining consistent product-driven insight from reliable data can highlight actionable problems with your

  • product availability
  • imagery and copy
  • translation
  • pricing
  • categorisation
  • or indeed, specific segments of your website traffic.

Once identified and fixed the results are improved conversion and an optimal use of your paid traffic.

The potential impact of this is evident in the results of Practicology’s retail clients. After optimising the category list for one client we reported a reduced onsite search usage of 50%. The improved category list meant users began to find products more quickly without struggle and subsequently stopped resorting to onsite search. As a result, we reported reduced exit rates of 5.76%, increased add to cart rates of 4.43% and a 4% increase in revenue from this optimised category.

On an individual product basis, actions taken from the enhanced ecommerce data drove increased product views +200% and revenue +80%.

Practicology consultant, Alicia Watkinson, also leveraged analytics data to deliver tangible results during her time at ASOS:

"I found that merchandised pages (where product was methodically and analytically placed) had an average conversion of 4.6% compared to 3.9% on a non-merchandised page. Building on these findings, I tested merchandising specifically for Australian customers (instead of adopting the UK merchandising structure) and results showed an increase in revenue of over 20% during the 3-month test period. These results were achieved by approaching product merchandising in a targeted way rather than with a blanket approach and customers were shown relevant products as a priority across email marketing, homepage calls to action and site search."

Hello Australia - ASOS.png

So, what goes wrong?

Typically, due to time pressures, or lack of consistency, developers don’t code the data layer as rigorously as required, this leads to unreliable results. Tags don’t receive consistent information along the purchase funnel (perhaps omitting price or category attributes), and the usability of resulting insight suffers.

Proper testing of the implementation can help. However, due to a lack of analytic expertise or time, the testing stage is often skimped on, or skipped completely. Poor implementations leave retailers with a lack of confidence in the data, undermining the value of any associated KPIs or insight.

How does Practicology resolve these challenges?

Practicology’s standard Google Analytics health-check rapidly identifies best practice across your website including analysis of the critical sales funnel. Where discrepancies occur, we identify the corrections needed. We also check the developer’s work to ensure the implementation fixes the gaps. Providing clear next steps to help you gain confidence in your data.

Accessible data

Practicology can help you interpret and visualise your product performance with our data studio interactive dashboards.

Product performance metrics

  • Cart to Detail – Propensity for a product view to turn into a populated bag
  • Buy to Detail – Propensity for a product view to turn into an order
  • Product Checkouts – Propensity for a product to be taken into checkout
  • Product Add – How often a product is added to bag and importantly were from; PLP, Search Results, Collections or another defined list.
  • Product Removes – How often a product is removed from bag before a purchase is completed.

Online checkout image.png

What else do retailers use in Google Analytics Enhanced Ecommerce?

Measuring the success of internal website promotional activity is supported in Enhanced Ecommerce. This allows teams focused on promoting offers, services and products within your website (e.g., creating banners and other on-site promotions) to understand what’s working and establish KPIs for success referencing promotional product add to basket activity and sales.

If you would like to find out more about Enhanced Ecommerce or would like Practicology to run a standard Google Analytics health-check for your business, please contact us today.

Contact : [Please] get in touch