Have you ever run Google Analytics reports only to discover that the data doesn't match your server logs?
If so, you've come to the right place!
Accessing highly accurate, reliable data helps businesses make informed decisions.
However, rather than seeking perfect data, knowing what caused some of the errors and finding ways to fix them can assist you in organizing your data more efficiently.
Google Analytics is the most popular tool for tracking website performance. So many websites rely on it to measure all their stats, from page views to conversion rates.
Almost certainly, you're also using GA to track your visitors' activities and your website's performance.
Yet, you might have figured out by now that it's not without its flaws!
Like any other Google product, Google Analytics also suffers from some bugs and errors.
Even though they are not critical, they still are not accurate and cause fluctuations in data.
In this guide, I'm going to run down 6 of the most common Google Analytics errors.
This way, by learning about these issues and how to fix them, you can ensure your analytics data is as accurate as possible.
There are a number of reasons why your Google Analytics might show incorrect data.
First, you need to make sure you are measuring the right metrics and that your tracking setup is working!
Another reason for the lack of accuracy is that Google Analytics cannot identify users who are behaving differently on one or more devices.
One reason might be due to the fact that users have installed ad/tracking blockers across multiple devices since the last time they logged in.
As long as a user disables their ad blocker in order to access a particular website, GA might attribute their subsequent interactions to this user, even if they did not intend to do so.
The following are some other reasons for Google Analytics inaccuracy:
Can you trust your analytics data? The short answer is Yes!
Google Analytics is a reliable tool that gives accurate results in most cases.
However, it’s not 100% accurate because of some glitches and problems.
The following are six common errors that may cause the data from GA not to be accurate and some suggestions for fixing them. Let's get started!
If you’ve ever run an offline campaign such as a magazine or newspaper advert, you’ll know how difficult it can be to track its results. But does that mean you shouldn't try? 🧐
There is no doubt that sending someone to your website from a billboard or newspaper ad is different from sending them from a landing page or a banner on a website.
Certainly, offline advertising can be tricky to monitor, and many marketers make the mistake of believing that their offline strategy is untraceable.
However, it is not entirely impossible to follow the results of offline campaigns!
When you neglect your offline campaigns,you end up with holes in your analytics bucket, resulting in much confusion; So it's better to be addressed as soon as possible!
Since the campaign runs offline, you need to find a way to tag the element with your specific campaign’s details. And can be done in one of several ways:
By tracking your offline marketing, you are not only able to analyze and fine-tune your GA data in a more accurate manner, but you are also able to improve your campaigns at an accelerated rate.
Google Analytics allows you to track visitors' activities on domains and subdomains separately, but by default, it tracks sessions using a single domain across all subdomains.⚠️
Tracking users who come to your site from a link across domains can cause problems, such as attributing higher traffic numbers.
This is because cross-domain tracking in Google Analytics, by default, has no way to distinguish between traffic that came from the link and users who came directly to the new domain through a search engine.
This can impact your capacity to understand your visitors' behavior significantly.
Additionally, it can make it seem like your marketing campaigns are performing better than they actually are! (AKA inaccurate data).😵💫
You can either use Google Analytics or Google Tag Manager to set up cross-domain tracking.
Google Analytics offers instructions on adding the necessary parameters but, on the other hand, strongly suggests using the official Google Linker plugin instead of trying to implement cross-domain tracking yourself.
In any case, it may be more convenient to implement Cross Domain Tracking using Google Tag Manager, which offers two options.
You can take many steps to improve the clarity of your data, and one way to do that is by addressing duplicate data.
There is one common way for duplicate data to appear. That's when someone enters your URL in uppercase letters into their browser.
You might face this quite frequently!
The following is an example of the duplicate error I mentioned above:
However, even though users will reach the right page when this happens, the accuracy of your Google Analytics data may be compromised.
It's because every URI (end of the URL) creates a new line in Analytics reports.
Duplicate data cannot be removed from reports once it's been inserted. Fortunately, this can be avoided if you take preventative measures.
Here are the steps you should take to prevent data duplication in Google Analytics:
Go to your Analytics admin menu and select "Filters" from the "All Web Site Data" section.
Choose "New Filter" and then check the "Create New Filter" option.
Enter "Force Lowercase" into the Filter Name field.
Set the Filter Type to "Custom".
Check the "Lowercase" option.
Select "Request URI" from the dropdown menu.
Create the filter by clicking "Save."
When you create or view a report in Google Analytics, it includes numerical data for your website’s visitors, traffic sources, and your visitors' behavior on your website.
But this raw data is more valuable than you might think!
The raw data returned by Google Analytics is found in the All Website Data view, the default view when creating a new property.
Never mess with your raw data cause you can't go back!
If you filter your primary data but do not have the View for the same site in another Property, you will not be able to retrieve it.
It will not be possible to apply analytics retroactively to filtered data.
This data view is the answer to all your data-analysis problems, whether you are looking at a small filtered segment or an entire data set. So, take good care of it!
When someone refers to their own site using a keyword, it’s called a ‘self-referral.’
Self-referrals can affect acquisition, behavior, and conversion data in Google Analytics, compromising your data integrity.
When you set up your Analytics account structure with a single Property for multiple domains and subdomains, you will have self-referrals. You can avoid this by using filters.
You can find the setting under Google Analytics >> Admin >> Account >> Property >> Tracking Info >> Referral Exclusion List.
Add domains that should not be reflected in your traffic reports.
Some other causes of self-referrals in Google Analytics are:
So your bounce rate is abnormally low, and you’re wondering what’s going on with your Google Analytics account?
It's likely that you just discovered a problem with how you collect data. Any analytical service should interpret the information it contains, so it's essential to understand the different metrics and figures to know what they mean.
The typical bounce rate for a site depends on the type of content, but it’s not unusual to see something around 60% or higher.
You should check the following issues if your bounce rate suddenly drops below normal:
For instance, if there is an Analytics plugin with the same property ID as an analytics code in “Theme Setting,” you have a problem.
It can happen when the same property identifier has been used multiple times within separate pages or posts, causing them both to show up under a different property identifier (PID) when viewing reports for each type!
You'll need to remove either/or modify whichever does not work properly until it does match what's being displayed within the Google Analytics account; otherwise, everything would look mixed together - not a good thing at all.
Google Tag Manager is the new kid on the block that you’ll want to keep an eye on if you care about Google Analytics. 👀
Make sure you don't run Google Tag Manager alongside a standalone copy of the GA code.
In this case, there will be two triggers per site; you must choose which one you want to use and then remove the other.
Of course, you have a popup or live chat window on your website!
Sometimes, these features send triggers to Analytics, but don't worry, you can set them as "non-interaction events" so they don’t affect your bounce rate.
Yes, just like that, you can fix the problem!
Additionally, popup builders like Popupsmart can help you balance your bounce rate while improving market growth and conversion rates.
Google Analytics usually takes up to 24 to 48 hours to show your website traffic after you first set it up. On the other hand, if you work with real-time reports, you can determine if Analytics is working correctly.
The short answer is No! As with any other analytics tool, Google also suffers from inaccuracies due to errors I have tried to list in this article. However, a lack of information about where your visitors come from is one of the biggest causes of inaccuracy in Google Analytics.
As with all analysis solutions, there will always be some level of inaccuracy.
In fact, the better you know your analytics software (i.e., Google Analytics), the smaller this margin of error becomes.
Take a look at the list above and see if you are experiencing any of these issues. If you are, don’t be afraid cause you are not alone.
Don't just rely on outdated information/solutions; look at recent updates and look for the best ones.✅
What other data errors did you encounter, and how did you handle them? Let us know in the comments.👇🏻
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