Any eCommerce website should be collecting online sales data, giving insight into the number of transactions, total revenue and conversion rate. Trended over time, this data is extremely useful.
But quite often, eCommerce data being collected isn’t used to its full potential. Google Analytics’ Enhanced eCommerce tracking allows for comprehensive data collection related to online shopping, and there is a lot that can be done with this data. Assuming that the tracking has been configured correctly, here are some best practice approaches to get the most value from your eCommerce data.
A key element of any Enhanced eCommerce implementation is the ability to measure the digital shopping journey at a specific product level. From the moment that the product appears in a search result list, to the detail view, add to cart, checkout and the eventually completed purchase.
Rolled up, this provides a holistic view of how well your shopping journey is functioning.
What we learn from this data depends on where in the funnel users are dropping off. A large number of sessions but view product views signals a targeting and traffic quality problem. Drop-offs after product views could be an indication that users are researching online but purchasing in store, in which case you might want to perform an analysis of in-store sales against online browser patterns. On the other hand, abandonment between the add to cart or checkout stages could be indicative of usability issues.
This can be taken a step further by breaking this funnel into a specific product or category view. This provides valuable insight into which products or categories are more likely to be added to the cart and purchased.
By surfacing these potentially high-revenue products and categories, we are better able to optimise how these are displayed on the website in order to improve performance.
A similar feature of Google Analytics’ Enhanced eCommerce is measuring the steps of the checkout process in a funnel. This view shows how users are moving through the checkout funnel, and where they are dropping off.
What you want to use this for is to identify potential user experience problems with your checkout process. Look out for large drop-offs between the steps to isolate problem areas.
A large drop-off on the payment page might indicate that your payment options are insufficient, while users exiting on the shipment page could be due to poorly communicating delivery costs.
Quite often this would only be the first step in improving user experience. Making use of heatmaps, popup surveys or user testing should provide further insight into how to improve user experience on the problematic step of the journey.
Arguably one of the most important things you can do with your data, a channel analysis is crucial to the success of any eCommerce website.
This goes beyond just a revenue and transaction volume comparison, which is more often linked to the volume of traffic from the channel rather than the quality.
What we want to get out of this is a normalised comparison of the relative quality of each channel, campaign or ad, irrespective of traffic volume.
We would normally suggest you start with comparing conversion rates and average order values.
Begin by comparing the broader channels before diving into a more granular view. As an example, we might find that Facebook carousel ads, while driving less traffic, have a much higher conversion rate than traffic ads. Shifting budget between the ad formats could potentially yield more revenue.
Naturally, there would be other contributing factors, but this provides a good base to start running tests and optimisation efforts.
The purpose of this analysis is to determine whether marketing activities have been weighted correctly, allowing us to make more efficient use of the budget in order to maximise the return on investment.
Margin and Profitability Analysis
Linked to the channel analysis, by introducing product margin information to our data we can get a much more accurate reflection of what is driving the actual bottom line.
Google Analytics has a great, but seldom used feature – Data Import. By uploading offline data, we are able to further augment our data to include margins and profitability.
This has huge implications for a number of our data points. With product margin data we are able to identify which products, categories, channels or campaigns are having the biggest impact on revenue and optimise accordingly.
Do you understand the relationship between your different products or categories? Basket analysis uses historical eCommerce data to determine the likelihood that a product combination is purchased together.
The most common implementation of this is the “Customers Who Bought This Item Also Bought” cross-selling functionality on many eCommerce websites.
However, this data has further use. Analysis of the relationships between different products or categories could inform specific campaigns, promotions or bundles, or inform how site navigation should be laid out in order to maximise revenue.
For further examples of what you can do with your eCommerce data, have a look at one of our sample e-commerce dashboards.
Author : Derik Nieman