Using Google Analytics to up your eCommerce game
We’ve given you some basic guidelines on the use of Google Analytics for eCommerce before. This article is a deeper dive into what’s possible with Analytics, and how you can use it to get the most out of your eCommerce efforts.
Using UTM parameters
One of the most useful features of Google Analytics is the ability to set your own source and medium for traffic, allowing you to categorise incoming traffic in ways that match your internal business goals and eLearning targets. This is done via the use of ‘UTM parameters’, which are bits of extra information attached to a URL. Google even provides a tool to let you set these up easily: The Campaign URL Builder. As well as the source and medium, you enter values for a campaign name, a term, and content, which gives you five different variables to work with. That’s enough granularity for almost any tracking you might want to apply. UTM parameters feed directly into Google Analytics, so you don’t need to do anything once you’ve created the URL.
Tracking what works
UTM parameters enable you to find the elements that work best; the essential goal of analysis. Somewhere in your traffic profile there is almost certainly a website, social medium, or paid source that works better than anything else, but if it doesn’t provide a lot of traffic, it can get lost in the noise. Segmenting your traffic in different ways – by source, by medium, by referrer, by geographical location, and so forth – and looking at the conversion rates, pages per session visits, and other quality metrics can help you identify what’s working. Once you’ve identified this, then you can work on optimising your [marketing mix] a little better – by posting more on specific social media, contacting a website owner to provide more information or just some encouragement, or by providing more of a specific kind of content for an audience that isn’t otherwise on your own website. And even if you can’t increase the traffic from these high-quality sources, you can look for other sources that are like them – almost no website is completely unique, so you can contact similar sites and offer links or partnerships.
Analytics would be much simpler if people visiting the website either signed up there and then, or visited once and never returned. However, most people will visit a website at least twice before clicking the ‘buy’ button, and might even go away again between that and the actual completion of a sale. Sometimes people come back dozens of times before actually converting. The usual way in which Google Analytics assigns credit for a sale is called ‘last click’ – the source of traffic by which the customer arrived on the site for the visit during which they made their purchase gets the credit. There are other models too, such as ‘first click’ (how the visitor originally found your website), and shared credit, where the purchase is attributed in part to each of the sources across multiple visits. The different models and ways of evaluating traffic sources have several issues: first, they make it hard to know which of your marketing efforts are working, and second, they make your conversion rate look much lower than it really is in terms of actual visitors.
There is no one immediate answer for any of these issues, but you can get more information by looking at some of the more obscure parts of Google Analytics – these ones are under “Conversions” in the left-hand menu, and then “Multi-channel Funnels”. They depend on people allowing cookies, not using ad-blockers (which sometimes block Analytics as well), and using the same browser on the same device. Since none of these are always true, bear in mind that even in the most powerful parts of Google Analytics, you’re never quite seeing the complete story – but you can see enough to make better, more informed decisions.
“Assisted Conversions” shows you how many of your goals were contributed to by other sources of traffic than the last click – that is, what previous visits people who converted had made to your site, and where they arrived from for those visits. You can choose how long a window of time to look at for this, from 1 day to 90. Because people may have visited several times, the ‘assisted conversions’ figure may be higher than the actual number of conversions – but it does give you some guidance on which marketing channels are giving you an extra effect.
Another view of this can be seen under “Top Conversion Paths”, which shows you the number of visits, with their sources, that particular website users made before they converted. These can be as short as two visits, and there’s no real limit to how long they can be. On the LearnUpon website, we routinely see visitors who came back 30 and 40 times from different sources before they decide to sign up. This is because selecting an LMS is a significant decision and customers want to ensure they settle on an LMS partner they can trust. When you’re selling online courses, your traffic will likely look very different if the customer is purchasing a $1,000 course compared to when they’re choosing a $10 course.
Another use of UTM parameters is to do simple A/B testing with your traffic sources. For instance, you might know that some of your eLearning email list are people at C-level positions, and that others are not, and have a theory that the people at C-level convert better. To test this, you can send the same email to both segments, but setting different utm_campaign parameters, such as “c-level” and “non-c-level”. It’s then very easy to see which group converted better; it’s captured directly in Google Analytics.
Similarly, you can de-segment some sources of traffic to make analysis easier. If you’re giving a link to a large number of websites, analysing the incoming traffic from all the individual sources added together can be very difficult. If, however, you set the utm_campaign parameter to be the same for all of them, they can be grouped together automatically by Analytics, making your job in analysing them significantly easier. And since they still retain their original source and medium, if you need to analyse them separately in the future, that’s still entirely possible.
These are just some aspects of Google Analytics’ deeper features. Which other features do you use for your eLearning eCommerce analysis, and how do you use Analytics data to sell more courses?