Google Analytics Audits: Common Issues Affecting Data Accuracy

Google Analytics Audits: Common Issues Affecting Data Accuracy

Few things can be as frustrating as finding out that you have a weak implementation of your Google Analytics installation. You work hard on your website for about a year, only to find that you cannot rely on any of the data that you have gathered. This alone makes any SEO forecasting you do worthless. You can’t make accurate SEO strategy changes because you can’t trust your data. Unfortunately, this is an all-too-common scenario that I have run into with website owners.

If you do any sort of digital marketing, you are aware how important accurate data can be. From analyzing the performance of your current website, to creating adjustments in your overall SEO strategy. When you experience dips in traffic, you can’t always assume it’s a penalty. Sometimes, it’s due to links sending you traffic that went away. Other times, it’s due to a faulty analytics implementation.

Many times, site owners suspect issues with their data long before they finally pull the trigger on an audit. It’s never too late for the audit, but most are surprised at the depths of the issues they face. From being under penalty to finding critical code placement errors,

But, how do you know? One of the first things you can do is perform a Google Analytics audit. From the ground up, these audits examine, with a deep dive, your analytics implementation for issues, critical maintenance items, and other issues affecting the trustworthiness of your data. Data trustworthiness is not a worthless goal. For if you cannot trust your data, your SEO decisions could all be for naught.

Tracking Code Placement

Nothing is worse for a Google Analytics implementations than incorrect code placement, affecting the amount of data that is registered by the platform. Did you know that placement of your GA tracking code can affect when the tag fires, which means the difference between overreporting and underreporting?

While both introduce inaccuracies in the reporting of your data, overreporting results in relying on data where the GA tag fires multiple times, creating inaccuracies of people visiting the site. In this example, your site could be reporting 50,000 visits when you mean to report 32,000 visits. In underreporting, it doesn’t fire all the time. In this example, GA could report 18,000 visits when you mean to report 25,000.

Both can cause issues when attempting to make changes in SEO strategy direction, and they can cause issues when you are reporting data to clients. This is why it always pays to be aware where your tracking code is placed, and that you follow directions in GA on how to install the tracking code.

Beware, though, bad coding can also cause issues with tracking accuracy as well. Which all feeds into why it’s important to ensure that your GA implementation is accurate. Another angle for this is: you can use GA to tell a story about anything, assuming you know what you’re doing. If you don’t know what you’re doing, your overall story is going to be so inaccurate, you cannot rely on it.

Sending the Wrong Information

Your GA code placement can also send the wrong information. This information can have disastrous impact on any future conversion rate optimization decisions you make, leading to disastrous SEO results. If bad information corrupts the data, you have already lost. This data corruption can affect things like goals in GA, which need accurate tags on the elements you want to track.

Say, for example, you wanted to find out what happens when people click on certain buttons in GA. You could setup goals attached to each button, but if they are not implemented with the correct tagging system, your goals could be sending the wrong information, resulting in incorrect goals being reported.

When this happens, the wrong information can kill your data’s accuracy.

Setting Up the Filters Incorrectly

To say this is problematic is an understatement. Without the proper oversight, setting up filters incorrectly can lead to conflicting data. For example, include and exclude filters, when using them at the same time, and lead to conflicting data that in turn leads to inaccuracy in your decisions. Being unfamiliar with how filters work is disastrous in and of itself.

Did you know you can set an ISP domain filter? If you set this up incorrectly, you could lose visits from other people. Working with specific IP addresses is a better solution for this tracking, especially if you want to filter out your own behavior.

You can setup a filter to exclude visits from your own IP, so that you don’t track your own visits to the website when working on it (beware that GA does not have this filter implemented by default).

Filter Order Can Affect Data Both Ways

Say, for example a company had many email campaigns running at once. These are all incorrectly tagged with different filters. Instead of installing them correctly under one name, multiple filters were created to resolve the issue. Say one campaign showed up in a separate medium – ContactCampaign instead of the original medium: Contact-Campaign.

If an include filter is fully processed before Contact-Campaign is corrected to ContactCampaign, you may not see any data actually measured in Google Analytics.

The fix for this specific issue is easy, but nonetheless can be challenging to spot for those who are less savvy in Google Analytics. That’s why this is important to understand how filter processing works before you setup a ton of them for data tracking use.

Data Sampling and How It Affects GA

Did you know that Google Analytics data is often sampled? This can get in the way of the reporting of accurate data. Data sampling is more of an estimate rather than counting the individual visits.

Here’s an example. Say you have GA, and it’s sampling based on sessions, rather than users. In GA, users really means “browsers”. While there have been improvements in data attribution in the recent past, it is most likely that if you visit a site from more than one device, you will be counted as two users rather than one user.

This is why it pays to be mindful of how GA is processing the traffic that your website actually receives.

Custom Campaigns Tracked Incorrectly

There are a number of reasons why campaigns may be tracked incorrectly. The usual issue is that they were configured incorrectly, or the parameters were manually added with errors in their values.

Several ways this can happen include the following:

Say you love implementing projects with custom coding. Even if you’re familiar with the ins and outs of using UTM parameters, it would be a mistake to create them without the URL builder tool. By using this tool every time you create a campaign, you can track URLs without major issues affecting data integrity.

Some social media advertising can show up in GA as a possible referral source. This can complicate matters because it may not actually be a referral source at all. This issue occurs when you don’t add parameters to the specific campaign URL before the creation of your ads. The best way to fix this issue? Always make sure to run  your URL through Google’s URL builder.

Being in a hurry and creating the wrong landing page for your custom URL can cause even bigger issues than you might expect. While this may be repetitive, using Google’s URL builder tool can ensure that any URLs you create from this point forward will be correct each and every time. Plus, be sure to minimize distractions in the future so you don’t create incorrect URLs again.

You would be surprised how handy Google’s URL Builder can be in a tight spot.

Conflicts with Existing Scripts

Believe it or not (to your peril), other JS code can conflict with Google Analytics, making data accuracy an impossible task. Existing code variables which are the same as those that GA uses are a common issue. If you don’t make sure that your scripts use code variables that are not the same as the ones Google Analytics uses, you can cause issues.

These conflicts can cause major errors with one or both scripts that are being executed on that page. One example:

If you have GA installed along with an application like a form script or other registration page loading at the same time, these can conflict with each other.

And, what’s even worse is it may not be entirely obvious to you at the outset. Cookies in one visitor’s browser could be changed by other scripts, causing unrecorded traffic within your GA implementation.

Seemingly Small Issues Can Cause Big Errors

As you can see, when subtle issues in your Google Analytics implementation are incorrect, these can cause big problems when it comes to the kind of data that is reported by GA. Combing through your GA installation manually with a GA audit is the only way to find and repair these issues before they cause major problems with your SEO strategy later.

Building a comprehensive auditing process from the beginning is the only way to make sure that these issues don’t rear their ugly head when you least expect it. Creating a robust GA implementation is the only way to ensuring the accuracy of your data. From the position of your GA tracking code, to regex coding, and proper implementation of filters, all of these can cause data issues if you are not careful.

By ensuring that the proper oversight exists and is observed every time you install GA on your client accounts, you can avoid having unnecessary issues appear later and surprise you.

Google Analytics Audits: Common Issues Affecting Data Accuracy