You might have noticed some errors in your Google Analytics data.

There are many signs that your data may be in trouble.

That’s perfectly normal.

Google Analytics is one the most used, if not the most well-known, platforms to monitor site performance.

Many SEOs consider it an essential tool.

It’s not perfect.

Most site owners will encounter a discrepancy in the end.

There are several reasons why this is so.

These are two main categories.

• Issues you can fix
• Issues you can’t fix

You can’t control all the problems that may arise on your site.

There are many issues you can fix. You must fix any problems you find on your website.

Your data can give you valuable insights and help improve your website and overall business.

However, this is only true if your decisions are based on accurate information.

You might be guessing if your data is full of discrepancies or incorrect data.

Before we dive into the potential issues causing these issues, it is important to understand dark traffic.

What is dark traffic?

You’re likely to be familiar with “Direct” traffic if you have spent a lot of time in Analytics.

This is a visitor who types your URL into their browser and then navigates directly to your site.

However, this is not always true.

It makes sense, for example, for some users to go directly to your homepage. Your domain name is likely short and easy to remember, making it easy for users to enter and visit your website.

However, if you go to Acquisition > All Traffic > Channels and then select “Direct”, you will likely see that most of your “direct traffic” is landing on pages other than the homepage.

This is especially true for pages with short URLs.

However, most visitors won’t remember your site and will not return to it. How many people, for example, would you expect to remember a URL such as this one from Groupon?

There aren’t many, right?

This is the only logical solution.

Groupon discovered that a lot of their traffic to pages such as this was being attributed to direct searches.

They decided to temporarily de-index their website to find out why. This is not something I would recommend.

The idea is that temporarily removing their website from search would result in a drop in organic traffic. If they noticed a decrease in direct traffic, it is possible to assume that some of the traffic came from searches.

Is that really what happened?

The following graphs will show you the results.

The site’s organic and direct traffic in the week before the test is represented by the purple lines. However, the site was fully indexed.

The orange lines show how each type of traffic performed during the test period.

As expected, their organic traffic dropped to almost nothing.

However, the site’s direct traffic also dropped by 60%.

If all the traffic to a site during a week was just users typing the URL into their browsers, then the traffic level should have remained stable even after it was deindexed.

What’s the conclusion?

Groupon had 60% of its “direct” traffic coming from organic searches.

This is a significant discrepancy, but it’s not uncommon. Another example: The Atlantic found that 25% was misclassified as a direct.

How did they get to that number? And how can you achieve the same?

To isolate dark traffic from your Google Analytics report is the best way to go. This segment will reveal traffic that is being reported as direct but landing on deep pages on your site. You can then logically conclude that it’s not.

Navigate to the Audience Overview report. Then, click “Add Segment.”

Next, select “Traffic Sources” and then choose “[direct].

You can add a filter here to block pages users may be navigating to.

Click on “Conditions”, then choose “Landing Page”. Next, choose “is not one” and then enter a forward slash (“/”) in the field.

Your segment will not include users who visit pages other than your homepage.

You may want to add filters to certain pages of your website that you expect visitors will navigate to.

For example, in Groupon’s case, they often include the URL “groupon.com/getaways” in campaigns that focus on their vacation deals.

It makes sense, therefore, that users will visit those pages.

So if you use easy-to-remember page names in your offline advertising campaigns (like “yourdomain.com/radio” or “yourdomain.com/conference”), you’ll also want to filter out those visits.

These visits can be attributed to campaigns or channels, so they aren’t dark traffic.

You’ll then see, to your right, a summary of how many users match the criteria that you have set for your segment. This will give you an idea of the amount of dark traffic in Analytics reports.

Save your segment and you will see a report.

This report will give you an indication of how dark your site traffic is, and how many conversions can be falsely attributed directly to traffic.

Although you cannot retroactively identify where these visitors and traffic came from, it is possible to get a better understanding of how many digital marketing in Las Vegas strategies and campaigns aren’t getting enough credit for driving conversions and results.

Groupon’s example shows that a significant portion of this traffic could come from your SEO campaigns.

However, if you have advertising campaigns on other platforms, your traffic could be mistakenly mixed in with your direct traffic.

This is an easily fixable problem. It’s the first issue that can be fixed. We’ll discuss it in the next section.

Let’s now look at seven possible problems that could be causing inaccurate data in your Google Analytics reports and what you can do about them.

Google Analytics errors that you can fix

Let’s begin with what you can do.

1. Failure to tag campaigns

A lack of information about your visitors’ origins is one of the leading causes of inaccuracies in Google Analytics.

While some of this is inevitable, you can fix a lot of the problem by adding tracking information at URLs used in online advertising campaigns.

You are missing out on tons of useful data in your Analytics reports if you don’t tag your campaigns.

It is much simpler than you might think to track your campaigns.

It’s as easy as adding UTM parameters to your URLs.

UTM parameters are bits that provide Analytics with data about where exactly users click on a URL.

You can track this information using a variety of parameters:

• Medium: This is where the channel is coming from. Standard channel names can be used, such as Social, Paid, and Email or Referral.

• Source: This site is a part of a channel like Facebook.

• Campaign: You can include the name and description of a particular ad campaign.

• Content: This parameter allows you to track multiple ads in the same campaign.

• Term: Originally, this was a parameter to track keywords in paid search campaigns. But, with AdWords integration and Analytics, it’s rarely needed.

To create a trackable URL for a campaign you launch, use Google’s URL Builder

First, type your domain in the URL box. Next, create a channel name.

The rest of the fields can be left blank, but it is common to include at least one medium.

Let’s take, for example, the possibility of tracking visits to your website from users who click your email signature. You can’t track clicks from email, so they are likely to make up at most a small portion of your dark traffic.

You would add “email” to track clicks and “signature” as the source.

The builder will then provide a URL that you can copy and paste where relevant.

This link would be used in your email signature. Analytics could track the exact way a user arrived at your site by clicking that link.

It is a smart idea to create URLs for every new digital marketing agency in Miami campaign that you launch.

You can also eliminate dark traffic from every URL you track, allowing you to be more confident about the validity of your data.

2. Incorrect tracking code

I have so far focused on the issues that could prevent you from gathering accurate referral data about traffic.

It is important to also consider missing tracking codes, which can hinder you from collecting any data.

Google Analytics uses a JavaScript tag on every page of your website to track visits. You likely copied and pasted the following snippet into your website’s code if you created your account.

Analytics uses this code to register traffic and gather data about users. This code must be on every page of your website. 

Analytics won’t show any data for pages that don’t include it.

This error could be caused by several factors. If you notice a drop after making major code changes to your site, it could be due to an issue in your tags.

Untagged pages won’t register data so it will appear you’ve experienced a significant drop in traffic.

Incorrect tagging could also lead to self-referrals.

Self-referral refers to when your domain is listed as a source for referral traffic to your website. These will be if they are an issue for your site.

This is not what you want.

Analytics tracks user movement on correctly-tagged pages and gives you greater insight into traffic flows.

However, if a user navigates from a page with no tracking code to one that does, the first page becomes a source of referral traffic.

This makes it impossible to access valuable information about how visitors navigate your website.

It also means that your traffic numbers may be inaccurate.

This is an easy issue to solve. All you need is to add your tracking code to every page.

First, identify the pages that are causing the problem.

You can check the script presence manually if you have only a five-page website. It’s not difficult to verify each page individually if there are hundreds of pages.

There are tools that you can use to check if your pages have been tagged and to find those that are not.

If you suspect that your pages may be missing a tracking code but aren’t sure what, this tutorial will show you how to use Screaming Frog’s SEO spider tool to search each page for your Google Analytics code.

You can then fix the problem by correctly implementing your tracking code to the pages that are missing.

This is best done by including the code in your site’s header. 

Site owners often make the error of adding their code to every page individually. This is done to speed up page loading times.

It is placed just above the page’s tag to allow visual elements to load before the code executes. The code is not delayed by the user’s viewing of the elements that are loading.

This is an unnecessary concern. Google Analytics tracking codes load asynchronously. This means that they don’t slow page load times regardless of their location.

Therefore, the best place to put your tracking code is in your header file. This already exists on all of your pages. You don’t need to add it manually when creating new pages.

It will be added to your template so that you can begin collecting data as soon as you upload the page to your website.

3. Multiple Google Analytics tracking codes on a page

Also, a page without a tracking code will cause data loss.

On the flip side, multiple tracking codes can also cause data problems.

Let’s take, for example, updating your website in a way that has a significant impact on a lot of pages. For instance, updating your main navigation bar.

Then you will notice a sudden increase in pageviews that is rough twice the normal rate.

Was your website’s traffic affected by your updates?

Most likely not.

Your tracking code may have been duplicated on one or more of your pages.

Every script on your site records a visit to a new page, regardless of whether they are on the same page. This means that every visitor to a page that has two scripts will record two pageviews.

This could be the reason for a sudden, unexplainable increase in traffic.

Inaccurate bounce rate reporting can also be caused by multiple scripts per page. Analytics registers both scripts in reverse order, so it interprets this as a user moving from one page to the next.

The bounce rate may have dropped suddenly.

Double-check your pages to ensure that they only have one tracking script before you celebrate your increased traffic and low bounce rate.

4. Subdomain tracking is not done correctly

Analytics tracking can be affected if you have subdomains.

For example, if your blog is hosted at “blog.yourdomain.com,” it could be counted as a source of referral traffic — even though it’s part of your site.

You will need to change your tracking code to fix this problem. This step-by-step tutorial will show you how to do cross-domain tracking.

You can also block subdomains appearing as referral sources by setting referee exclusions within your Google Analytics settings.

Open the Admin tab, then click “Tracking Info”, in the Property Column. Next, click on “Referral Exclusions List” and then click “Add Referral Exclusion.”

Enter your subdomain URL here and click “Create”

This will stop your subdomain from appearing as a referral source and Analytics won’t incorrectly register users who move from your subdomain into your main site as referral traffic.

5. Reports show internal traffic 

Google Analytics allows you to track visitors that are likely to be part of your target audience.

You can gain a better understanding of their interactions with your content and make your site more appealing to them so they become customers or clients.

It is important to keep in mind that not all visitors to your website are part of your target audience.

You can, for example.

Most likely, you visit your site regularly for maintenance or to check out the changes made by other members of your staff.

Your visits are not useful for data purposes. Analytics does not need to register them.

This is not a big deal if you are the only one working on your site. Your data won’t be affected by a few more page views.

What if your entire team of developers works on your site regularly?

What if your sales team frequently consults your service pages to verify the features in different plans?

This could have a greater impact.

This is also a simple issue that you can fix. You can filter visits to your office out so they don’t show up in your reports.

Open your Admin tab and select “Filters” under your View. Then click “Add Filter.”

To indicate which location you want to exclude from your reports, give your filter a logical title. You could name your filter “Office” if you have only one office.

However, if you have multiple offices or workspaces, you will need to be more specific about the filter names.

Next, change your filter type from “Exclude” to “Traffic From the IP Addresses” or “That Are Equal To.”

Next, enter the IP address of your office in the IP Address field.

You can search Google for “what’s mine IP address” to find out if you aren’t sure.

Click “Save” to remove traffic from your office from your Analytics reports.

You can then repeat the process for all other IP addresses from which members of your team access your site. You might also want to exclude your home IP address if you work at home.

6. Incorrect goal setting

These tools allow you to determine the frequency with which users take important actions on your website, such as filling out contact forms or making purchases. These reports also give you information about where users are coming from and what content they have accessed before taking any action.

This information will give you a better understanding of the performance of your site and help you to get more visitors to convert.

It is therefore important that your Analytics account accurately tracks every conversion on your website.

If you have any problems with your goal tracking, you should immediately address them.

It’s usually easy to identify when your goal reporting isn’t working properly because your Analytics account doesn’t contain all the conversion-related information.

If a user submits contact forms, for example, their information will show up either in your inbox or in a third-party management tool such as Nutshell and Salesforce.

While you won’t likely spend much time tallying up all the submissions received, it is easy to see if they are being reported accurately in Analytics.

If you have been receiving form submissions but not in your conversion report, it’s clear that there’s a problem with your goal setup.

This is a sign that you need to dig into your Goal setup to find out what is happening.

Navigate to your View and open your Admin tab. Select “Goals”

To identify the problem and fix it, you can use Google’s goal troubleshooting article.

7. Referral spam

A significant increase in referral traffic could be a sign of a good sign.

However, before you get too excited about it, make sure that you are ensuring that it is legitimate.

In many cases, sudden spikes can be a sign of referral spam. This is not valuable and can lead to inaccurate reporting.

Referral spam is essentially fake traffic generated by spam bots and crawlers. These pageviews don’t come from real visitors.

Referral spam can result in artificially high pageviews and unusable referral data.

Open your referral report to check if any unusual domains are sending traffic to your site.

Look at the bounce rate for any domains that sound spammy. You can assume that referral spam is occurring if it’s close to 100%.

Take a look at two examples of spam domains. 

These two domains’ “traffic” is not real and doesn’t add value to the Analytics reports.

There are several ways to deal with referral spam if it is a problem for your site.

You can first remove spam from existing reports by creating a custom segment that excludes data related to domains that you have identified as spam.

To prevent future “traffic” coming from these sites, follow this tutorial about filtering referral spam.

Google Analytics errors that you cannot control

After I have covered the problems you can fix, let me now tell you about the ones that you cannot.

These discrepancies can’t be eliminated, but knowing them can help you understand your data better.

Let’s now look at seven issues that could be affecting your Analytics data.

1. JavaScript is sometimes disabled in some browsers

Analytics uses a JavaScript tag to track your traffic, as I have already mentioned.

However, if JavaScript is disabled in a browser’s browser, it won’t allow you to register any data.

This is a very minor issue.

Yahoo reported in 2010 that less than 2% of US internet users had JavaScript disabled.

Blackberry also reported that only 0.2% of pageviews came from browsers without JavaScript disabled in 2016.

Although it is beneficial to know this possibility, it won’t likely have much impact on your Analytics data.

2. Some visitors won’t accept cookies

Google Analytics collects data via cookies in addition to your tracking code.

Analytics uses cookies to identify visitors and then aggregate their behavior over multiple visits. Cookies are crucial.

Let’s take, for instance, a user who clicks on a Facebook ad to arrive at your site. They visit a few pages and then they leave. 

They type their brand name into Google and click on your site in the search results. Then they make a purchase.

Analytics uses cookie data to determine if these visits come from the same person. Although they will register as distinct sessions, Analytics can tell you from your attribution reports if the user arrived first as a result of a Facebook ad.

This would not be possible without cookie data. It wouldn’t be possible to see how users behave across multiple visits to your website.

Analytics may not be able to collect cookie data. This can cause reports to be distorted.

This can be caused by a few things.

1. The browser the visitor doesn’t support cookies.
2. The firewall of the visitor blocks or deletes cookies
3. Cookies can be deleted by the visitor manually.

Yell found that 0.2% of users do not allow cookies in a single study.

This doesn’t include cookie data that is erased when a user clears their browsing histories.

It’s safe to assume, however, that cookies-avoiding users have very little impact on Analytics data.

3. Cookies timeout

We have already established that users who refuse to accept cookies are unlikely to have an impact on your reports.

It’s important to remember that cookies can be timed out.

Google Analytics uses two types of cookies to track visitor’s movements:

A persistent cookie. The persistent cookie is stored on the user’s computer when they first visit. It stays on a user’s device for 2 years or until it is removed, reinstalled, or manually deleted. 

Session cookies. A session cookie is a cookie that is set each time a user visits your site. Each time a user visits your site, they receive a new session cookie.

Is this important?

The following scenario is possible. The following method could be used by a user:

1. Pages visited
2. Two hours of dinner, with the door open.
3. Recovers and begins browsing

Keep in mind, however, that Google Analytics will terminate a visitor session after 30 minutes of inactivity.

A new session cookie is placed in the above scenario when the visitor begins browsing again. Google Analytics would consider it a new session even though the visitor never left the page.

This is not an inaccurate way to register data. It is sensible to treat two browsing sessions, which take 30+ minutes each, as distinct occasions.

It’s still a complex reporting detail that Analytics users need to be aware of.

4. Same user on multiple devices

An example illustrates how switching between devices can affect your Analytics data.

Take a look at this scenario:

1. Jane stands in line at the grocery shop.
2. She uses her iPhone to search for a product from a website.
3. Her iPhone has a cookie.
4. She returns home, unloads her groceries, then purchases the same product on her laptop.

Jane’s behavior as she switches between devices would be recorded and the site would be able to register her for both sessions.

Analytics isn’t so advanced. Analytics can’t track when users switch between devices.

Not yet, at least.

The purchase as it stands will be attributed to a new visit.

This can cause discrepancies in reports due to the high prevalence of cross-device usage.

One survey revealed that 81% of Internet users use multiple devices to browse the Internet, while 67% report shopping from multiple devices.

Unfortunately, you cannot collect more precise data about users who visit your website on multiple devices.

Nonetheless, it is possible to be aware of these possibilities and develop hypotheses about strange user behavior on your website.

5. Google Analytics does not reprocess information

Let’s suppose you have been using Google Analytics to track data for two years.

You then decide to gain greater insight into users’ conversions by creating a funnel to achieve one of your most important goals.

It will be possible to begin collecting data about how users use the funnel. Google Analytics won’t retroactively apply the funnel to data already collected.

The goal funnel will be visible as soon as you create it, but there will not be any data before then.

Fair enough, it would seem unreasonable to expect a service for free that processes the huge amount of data Analytics processes each day.

If you create a filter, don’t assume that all your data will reflect the change. This can give you a better picture of your site’s performance.

To view your historical data you can create segments, just as I explained in the section about dark traffic.

This step is crucial as you will not be able to use data that does not reflect the changes made by your filter.

6. Google Analytics is not in real-time

This doesn’t automatically make your reports incorrect, but it is important to keep in mind that Google Analytics does not display information in “real-time.”

Your Real-Time report lets you see real-time data.

For all other reports, however, it is best to assume that Google Analytics is 24hrs behind.

If you make major changes to your website, please wait at least one day before looking into Analytics.

If this happens, your changes won’t have an impact yet. It’s too early to begin analyzing your results.

7. The platform may be sampling your data

Google Analytics may use data sampling to help you run reports that include a lot of data if you have a website with a lot of activity.

They won’t include all your data in the report.

It is very resource-intensive to include large amounts of data. The platform can help speed up the process by providing a subset.

If you see a yellow box similar to this one at the upper right corner, it means that your data has been sampled.

You don’t have to worry about data sampling if your site isn’t getting a lot of traffic.

However, if this box appears, it is important to realize that the report may not reflect all traffic to your site and could be biased.

Conclusion

Your Analytics reports’ accuracy will depend on many factors.

You have some control over these factors. You can remedy any issues you see in your reports by taking steps to correct them.

You can only be aware of all the other factors that could impact your reports.

Let’s say this: A tape measure measuring afoot at 10 inches is not the best way to build a house.

However, as long as that error is recognized and corrected, you can still construct a solid house.

You can fix the tape measure if you have the ability. If you don’t have control, you should learn to make the best judgments possible, even if the data is flawed.

Analytics can still be a useful tool for site owners, even if there are slight differences. The more you are aware of these inaccuracies the easier it is to improve your site.