Since launching in 2005, Google Analytics has provided countless businesses with website insights to drive strategic business decisions.
While there is a wealth of information available on the platform, Google Analytics users that are not overly familiar with the platform can easily misinterpret the metrics. Relying on inaccurate insights can result in poor business decisions, particularly for SMEs where small anomalies can have a large weighted impact on outputted data.
The following is a list of common data anomalies and how to fix them.
1. Goals aren’t measuring conversions
Setting up goals on Google Analytics is an efficient way of measuring the quality of traffic that arrives on your site, but can often be a ‘set and forget’ activity. Unless you are checking in daily or weekly to see how your website is performing, broken goals can go unnoticed for months.
It’s not uncommon for web developers to update buttons, URLs or entire pages on your website. Even small changes to your website — for example, changing a URL from www.example.com.au/thank-you to www.example.com.au/thankyou — can make your Google Analytics rules redundant.
Custom Events in Google Analytics is a neat tool that allows you to keeping abreast of sudden changes in user activity. You can set up daily, weekly or monthly alerts based on rules that you create. You can then set up email alerts to make sure that you’re notified when the alert gets triggered. Below is an example of a customised event that triggers an alert if a goal called ‘Sign Up’ drops below 1 in value on any given day.
2. Weird spikes in traffic
When comparing site traffic data between periods of time — e.g. comparing data to the previous month or the previous year — spikes in traffic normally indicate stronger performance or effective campaign execution; however, this is not always the case.
Some websites may receive spikes in traffic as a result of spam bots targeting the site, which inflates traffic volume and also negatively affects site metrics. Below is an example of a website that saw significant increase in referral traffic over the period of a year. By drilling into where this traffic came from, we can see that 63% consisted of automated spam bots crawling the site. Not only does this inflate traffic, but it also negatively impacts your on-site engagement metrics, such as Avg. Time on Page, Pages/Session and % Exit Rates.
Implementing spam filters is the best way to ensure that the insights you derive from data are accurate. While sifting through referrals for relevant spam bots can be a long and arduous process, implementing a hostname filter will take care of a bulk of your spam issues. This excellent Moz article explains spam in greater detail and offers a simple method for cleaning up your spam data.
3. Strong traffic arriving directly to your site when your URL isn’t obvious
If a lot of your traffic comes from the Source / Medium: (direct) / none, this generally means that a lot of people are visiting your site by typing your URL directly into the web browser. Generally, this means that you have strong brand awareness and marketing, but this is not always the case.
A potential red flag is if you have an obscure URL that is not obvious for your brand. For example, while you may have heard of the popular digital education website General Assembly, they do not have a common .com or .com.au website; in fact their URL is http://generalassemb.ly.
Google defines a Source / Medium based on the website that you have arrived from. This means that if you’ve arrived from an app, or a website that does not have Google Analytics tracking on it, Google cannot identify where that traffic comes from. As a result, Google then categorises this traffic as (direct) / (none).
A common example of this is if you are receiving a lot of link clicks from Facebook app users. As Google does not attribute this traffic to a website, it will arrive at your site as (direct) / (none).
Building URLs with custom tagged parameters allows you to define which Source / Mediums will appear on Google Analytics when you click a link. Yep, you can call it whatever you want, even Source: Batman, Medium: Gotham City. But keep in mind that your best practice is to have your Google Analytics reports easily comprehensible not just to yourself, but to any marketer that goes into the account.
Google has a really great, and easy to use URL builder that also explains what the different parameters are and examples of how to use them.
4. Excellent bounce rates
A bounce rate is defined as a user that visits a website, does not interact with the website and then leaves. The following is an example of a website with a really great bounce rate that hovers between 1-10%. At first look, this is very impressive, but once you dig in deeper you can see a different story.
Bounce rates were actually around 50% in the preceding months and dropped off significantly at some point in late July.
In this instance, two installations of the Google Analytics code exist. While Google Analytics is smart enough not to double count the number of site visitors in this case, it is not smart enough to self-adjust the bounce rate.
Multiple installations of the Google Analytics code generally occur in two instances:
- Website developers who are not familiar with Google Analytics installation accidentally install the tracking code twice.
- Users that are new to Google Tag Manager may push the Google Analytics code through the container while the code is also hardcoded on the actual site.
Removing the second instance of the Google Analytics code will fix this problem. If you are not able to do this yourself, speak to your web developer.
These four examples of data anomalies within Google Analytics indicate just how important it is for businesses to have a deep understanding of their digital data, in order to be able to interpret it accurately.
At Found Digital, we perform a full Google Analytics clean-up as part of our on-boarding process for clients that sign up for our digital services. Get in touch with us to find out more.