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by Gemma Holloway on 19th December 2013
Google Analytics (GA) is a great tool for businesses to track their website data; It’s relatively easy to implement, it’s simple to use and best of all it’s free! There’s a whole wealth of advanced functionality that can be implemented, but even for someone with little knowledge of Google Analytics, it’s perfect as standard, or at least it should be.
I have come across many situations where simple mistakes have been made, purely due to a lack of knowledge. So, I have put together this blog post to cover the most common errors with standard Google Analytics usage and how to fix them.
Whilst the implementation of Google Analytics should be a simple process, there are still lots of instances where errors can occur. So let’s have a look at some of them now;
I’ve uploaded my GA tracking code, but no data is being collected
You GA tracking code should be copy and pasted directly from the GA admin panel. Copying it to another program first, such as Microsoft Word or Outlook, can insert white space characters that when uploaded into the HTML of your website can stop the tracking code from working.
A great tip is to upload your GA tracking code and then navigate to the real time report in GA. Visit your website and ensure a visit is registered within the overview real time report.
Most of my pages are tracking data, but some aren’t
I’ve come across many instances where data is being tracked for the majority of a website, but a certain section has no data, for example, the blog. This is because the GA tracking code isn’t present on all pages of the website; therefore, only those containing the code are being tracked.
After implementing the code, run your site through Screaming Frog by configuring a custom filter to pick up any pages which do not contain your code. If you are frequently adding pages to your website, it is beneficial to carry out this check on a regular basis.
I have multiple profile filters but only one is working
Now there are lots of errors revolving around profile filters, but the situation I am referring to here is when a user wants to collect data from an ‘OR’ category in a separate profile. For example, visits from New Zealand OR France; Visits from Organic OR Paid Search etc. Profile filters execute sequentially therefore ‘OR’ categories should be set up as filters using a pipe (|) to split the items you are looking to include.
In several cases, I have seen it where two filters have been set up, for example, one to include traffic from New Zealand and another for traffic from France. Only New Zealand traffic passes through the first filter, which means there is no French traffic to be filtered once it reaches the second stage of filtration.
The correct implementation is below.
I am using UTM parameters to track my internal campaign, but it’s skewing my data
UTM parameters are used to track additional campaign data. You can learn more about them here. One important thing to remember, if you do choose to use them, is that UTM parameters should only ever be used on external links. When loaded, a URL with a UTM parameter attached records a new visit in GA meaning that if used internally you are artificially inflating the number of visits recorded within your account.
My Average Time On Page is long and therefore, my pages are fine
Average time on page is calculated when a user navigates to another page on your website. It takes the timestamp from the new page and takes away the time stamp from the first page. Basically what I am saying is that if a user leaves your site from that page then their time on page is not recorded. Therefore if your average time on page is high for a specific webpage, but your bounce rate/% exit are high, then the time on page data is based on an insignificant amount of data.
It is important to take into consideration the % exit and bounce rate of a page when analysing the average time on page to determine whether or not the page’s content meets the needs of the user.
For example, the blog post detailed below should take around 10 minutes to read, therefore, the 00:11:05 average time on page would suggest that on average users are reading the entire blog post. But when we look at the bounce rate and % exit, both values are particularly high, meaning that the 00:11:05 is based on a smaller amount of data than first thought.
The best way of getting round this is to set up a goal which will fire after a certain amount of time on page. You can then view this data in conjunction with your pages and determine the percentage of visitors remaining on site for the desired amount of time. You then need to optimise those with the lowest percentage.
My bounce rate is very low, users must love my site
On several occasions I have come across businesses that have redesigned a section of their website and have seen their bounce rate decrease by a significant amount. For example, from 50% to 5%. Whilst this may appear that you have done something extremely good on site to keep users there, it is worth considering other possible causes of this. A bounce rate under 15% is highly suspicious.
The likely cause of this is that your Google Analytics tracking code has been duplicated on some of your pages. Therefore, when one of these pages is landed on, the code is fired twice and therefore, if a user leaves straight away the bounce is not registered.
My site has a high bounce rate, therefore, I need to do something quick
It is always a good idea to try and keep users on your site, but sometimes a high bounce rate isn’t an indicator of something being wrong. For example, the purpose of a blog post is to provide information, therefore, if a user finds the information they require on the first page of your website then there is no need for them to stay on site. In this case it is actually an indicator that your website is doing what it should.
To make your analysis more accurate, I would suggest segmenting your pages by category before analysing metrics such as bounce rate. Consider the purpose of a page and group pages which similar purposes together.
These are just some of the most common errors I have come across whilst working with Google Analytics but I’m sure you’ve probably seen others in your time. Let me know what common ones you’ve encountered in the comments.
Person climbing over the word mistake via BigStock