How sure are you about the reliability of your Google Analytics (GA) data on a scale of 1 to 10?

Although Google Analytics allows you to collect detailed information about your visitors, the custom settings and filters may collect inaccurate data.

There could be many reasons behind incorrect data collection in GA. In this article, we look at some common mistakes and how to solve them:

Applying filters to the GA properties

Applying filters to your GA property without proper testing may cause issues. You may lose your website data or corrupt your analytics results permanently.

If you don’t have one yet, we recommend having a ‘Testing View’ in your Google Analytics account. This allows you to test filters before applying them to the ‘Master View’. Of course, you should have a “raw” view too which is always unfiltered. Read more about this below:

The new GA version (GA4) has a different type of filter structure compared to the old version (Universal Analytics). However, the same principle applies to GA4. The recommended way to configure your filters is to test and review the data before applying it to the ‘Master View’.

Applying a filter without testing or reviewing it may cause your GA account to filter out valuable insights. This may impact your data collection and throw your metrics completely out of line.

Double tracking. Having duplicate analytics or multiple tags on your website

In some cases, more than one GA page view tag could be accidently implemented on your website. For example, a separate page view tag could be available on the campaign pages with another one for tracking other activities.

This may cause Google Analytics to record multiple page views. This will contaminate your data! It will also affect your conversions and KPIs. That’s bad for tracking the effectiveness of your website.

To prevent this use Google's Tag Assistant Chrome Extension or the Google Tag Manager (GTM) Preview feature to review the tags. Doing this means you can check the page view tags on your website and remove any additional tags.

Not filtering out internal traffic and bots

Internal traffic is basically the internal website traffic generated by you and/or your team when your website is visited by you.

Most of the time, even for testing purposes, you or your team may visit your website and trigger an event. This will impact your Google Analytics data! It will show an inflated number of events, conversions, and page views.

Avoid this by filtering out internal traffic from the ‘Master View’ and create a separate view for tracking the internal traffic. If you’re using GA4, you can create a filter for internal traffic. Do this by selecting the ‘Data Filters’ option from the ‘Data Settings’ section.

We recommend testing the internal traffic filter in the ‘Testing View’ before adding the filter to the ‘Master View’. It’s important to check the accuracy of the data periodically. This ensures the filter is running as expected and is only filtering internal traffic.

Additionally, you should eliminate bot traffic as this will contaminate your results. This can be done in three simple steps. Find out more about bot filtering here.

Check and review your tags before publishing

The best way to make sure your events or conversions are working is to test them. Do this in Google’s Tag Assistant Google Chrome Extension or in GTM ‘Preview Mode’.

By using the GTM ‘Preview’ mode, you can make sure where your events are being triggered. This ensures you're recording the right events.

We recommend checking all tags on your website before publishing. Make sure you’re not saving data multiple times or on the wrong page or location.

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Missing or incorrectly setting up the cross-domain tracking

Some websites may direct their users to a different domain for checkout, purchase, downloads or other actions. This creates a gap in your data collection if you don't have a cross-domain tracking setup for all of your domains and subdomains.

As a result of not having a cross domain tracking configuration, you may not have information about the entire user journey of your visitors (from the actual domain to the referring domain) and where they’re coming from and their actions.

Implementing cross-domain tracking provides you with better insights on the entire user journey for all domains or subdomains.

Using an old version of GA/GTM tracking code

If you’ve been using GA for a while, an old version of containers could be on your website.

Your website should have the newest GA or GTM tracking code implemented. This will enable you to track visitors' activities accurately and have in-depth insights related to your visitors’ actions. By using the newest version of GA or GTM tracking, you can get the most from Google Analytics data.

You can check if the GA tracking code on your site is up to date. Use the Google Tag Manager Tag Assistant Chrome extension to create a recording of your website behaviour. If there’s no warning in the Tag Assistant Chrome Extension report, then the container on your website is up-to-date. It means your website is using the version that provides the most detailed insights about your visitors.

Incorrect setup of GTM/GA - Non-standard implementation

You can add Google Analytics to your site by using GTM. You can also inject the GA code directly into your website's pages with the help of web developers.

For a correct implementation, the GA header and body codes must be added to all pages on your website. In some cases, these codes could be implemented incorrectly or could have missing or incorrect scripts.

Having an incorrect GTM or GA implementation on your website may affect your data collection. You can check the implementation of tags on your website by creating a recording of your page behaviour using the Google Tag Assistant Chrome Extension.

Being unaware of duplicate events/conversions

Google Analytics allows you to enable event tracking on your website via GTM, or by default, with Google Analytics 4.

The event tracking function allows you to track specific actions on your website.

For example, by using an event or conversion tracking in GA, you have the opportunity to track actions like button clicks.

If you haven't tested your events before implementing them, or if it's been a while since you added them, it's a good idea to test them. You can often spot potential event tracking issues by looking at the difference between unique conversions and total conversions in your Google Analytics reports.

If you use different analytics tools like Matomo Analytics, you can identify potential issues by comparing your GA metrics with whichever tools you use.

Not having an unfiltered view

An unfiltered view is a Google Analytics view that doesn’t have any filters. It only contains your raw traffic data.

Although filters can help you segment and filter some parts of your data, they may cause issues if configured incorrectly. For this reason, having “Unfiltered - Raw Data View” can help you recover your data and view unfiltered results. Google suggests having a 'Master View', 'Testing View' and 'All Website Raw Data View'.

Not using UTM parameters for your campaigns

GA groups any non-direct traffic under Referral Traffic. If you’re running a PPC, social media or email campaign, GA considers the traffic as referral traffic.

Work with your digital marketing team to specify some tracking variables related to your campaign in your campaign URLs. This enables Google Analytics to provide more detailed metrics about your campaigns, including campaign IDs and names in your GA results.

Specifying these details in your campaign URL will help you easily view your data in the campaign reports section of GA. You can use Google's Campaign URL Builder to create custom tracking variables for your campaigns.

Not excluding URL query parameters

Some third party platforms may use various query parameters such as Visitor ID, and Session ID in the URL. This distinguishes users, user sessions and clicks.

These parameters may appear as a different page visit even if the users have visited the same pages on your website. For example, you'll see the following pages as two separate URLs in the GA reports. The tool adds the 'sessionid' data to the URL as a parameter (despite it being the same page):

  • https//www.domain.com/apples?sessionid=7832932
  • https//www.domain.com/apples

Talk to Codehouse

We’ve covered most of the Google Analytics mistakes that cause improper and incorrect data collection in this article. Are you still unsure about your data collection despite checking and auditing every possible setting in Google Analytics?

At Codehouse our analytics experts can help you to review your data and correct possible configuration issues on your account. To get most from your analytics and to make sure your website is tracking your business KPIs correctly, get in touch.