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Do you ever want to validate the source of the users on your website? It’s easy to track the source of your users through Google Analytics. 

Google Analytics attribution modeling allows us to compare different models to figure out how they would impact our marketing attribution. 

So let’s dive in! 

Attribution Concept

You can validate the source of a user on your website by the process known as attribution. This process breaks down all the different sources that have brought the users to your website. 

On your Google Analytics account, navigate to Acquisition → Overview. 

You can also identify the high-performing sources that have brought you the most sales from the Conversions section. 

However, it is important to understand the process by which Google Analytics decides the source which is attributed. 

Attribution is a set of rules that give credit to the traffic sources for a particular conversion. 

Let’s understand this concept with an example. 

It takes a certain amount of time and a few interactions with various sources until a user becomes a customer. 

As an example, let’s assume these interactions occur in a particular order. 

Following that event, the user also came to our website by searching through a Paid search. 

Finally, the user uses Organic searches to visit our website, and the conversion occurs. 

The Google Analytics attribution method only gives the last source credit for attribution. 

So, in this case, the entire credit for the attribution will go to the Organic sources, and the previous sources will be ignored.

Logically, this is not the best way to assign credit for the conversions. However, this is the way standard attribution models work in Google Analytics. 

As the name suggests, the credit for any attribution is given to the last non-direct source. 

Therefore, it is essential to understand the reports that are affected by this method and whether you should spend time on attribution or not.

🚨 Note: Learning how to track UTM in Google Analytics can also help you improve your marketing skills.

GA Reports by Transaction

On your Google Analytics account, navigate to Acquisition → All Traffic → Source/Medium. 

We’ll be accessing the E-commerce reports for the conversions. 

Under the Transactions column, we can see the conversions made by each source. 

This is probably not the best attribution method, but this is the default attribution method for Google Analytics. 

To completely analyze the attribution, we need to understand the number of times each organic and direct source conversion overlapped with each other. 

We’ll use Multi-Channel Funnels to analyze these overlaps. 

Multi-Channel Funnels Overview

Conversions → Multi-Channel Funnels → Overview reports will show us the overlaps between the traffic channels. 

By default, all the conversions are analyzed in the reports. Select the conversions you want to analyze. 

In our case, we’ll choose Transaction conversations as we’re validating our attributions for conversions. 

The overlaps between the particular channels will be seen in the Multi-Channel Conversion Visualiser. 

For example, the Direct & Referral sources for conversions have a 25.58% overlap in our reports. Let’s name this Overlap A. 

Additionally, we can also notice that the overlap between the Direct & Organic Search is 10.51%. We’ll name this Overlap B. 

More importantly, you can also check the overlap between the Direct & Referral & Organic Search sources. This is 2.72% in our reports. 

Let’s name it Overlap C. 

If we want to find the total number of overlaps between all the channels combined, we can use the following basic math principle. 

The total overlap will be Overlap A + Overlap B – Overlap C, as Overlap C is counted twice. 

Hence, our overall overlap between the three channels will be around 33%. 

We did these calculations for a total of three different traffic sources. But you can choose up to four different sources to generate reports. 

As this model only gives credit to the last traffic source, it is viewed as an accurate report. 

According to our experience, we have realized that if the overlap is less than 20%, then the reports won’t be distorted in most cases. 

However, if the overlap is more than 20%, then we suggest you look at alternative attribution model options available in Google Analytics.

🚨 Note: Tracking funnels can be very useful for your marketing campaigns and you can do so with Google Analytics enhanced eCommerce tracking.

Model Comparison Tool

Navigate to Conversions → Multi-Channel Funnels → Model Comparison Tools. 

This tool helps us to analyze different attribution models in Google Analytics. 

Again, make sure you select only the conversions you want to analyze. In our case, these are Transaction conversions. 

Now, we will show the alternative attribution models and how they attribute conversions and conversion values.

There are seven different attribution models by Google Analytics.

The first model is Last Interaction. As the name suggests, it only gives credit to the last channel, direct or non-direct. 

This is especially useful when you’re optimizing your Google Ads for increasing conversions. 

The fourth one is the First Interaction model. This model is exactly the opposite of the Last Interaction model, as it attributes the conversion to the first interacted source to the user in the conversion path. 

All these four models give the credit for the conversion to one source only. However, the below three models are comparatively fairer. 

The fifth one is the Linear model. This model evenly distributes the conversion and the conversion values among all the channels in its conversion path. 

This means, that if there are two traffic sources, this model will give 50% credit to each source. Similarly, if there are four different traffic sources, this model gives 25% credit to each source. 

Moving on, the sixth model is called Time Decay. This model is almost the same as the linear model, but it gives more credit to the sources that are towards the end of the conversion path. 

Finally, the last attribution model is Position-Based. This model gives 40% credit each to the first and the last channels, and the rest of the 20% credit is distributed evenly among the other channels in the path. 

So that’s how Google Analytics calculates conversions and conversion values using the different attribution models. 

It is vital to understand the situations and scenarios for which any particular model fits the best.

Comparing Attribution Models

It is important to understand that there is no universal attribution model that works for every website. 

Using correct attribution models can be beneficial to you, as switching your marketing budget among traffic channels can be significantly high. 

Switch the primary dimension to Source/Medium. This gives us a more granular view. 

For our reports, we will choose to analyze Conversion & Value, and % change in Conversions.

The first traffic source is the direct source. 

This suggests a rise of about 26% in the values. 

The difference indicates a decrease of about 20%. 

Let’s see how these models can affect business decisions. 

Hence, we’ll use the First Interaction model in such cases. 

In our case, we’ll filter our channel as google / cpc. Set the Secondary Dimension as Campaign. 

Let’s compare our First Interaction model with the Last Interaction model. 

You can see a significant percentage of change in the conversion distribution. 

The changes range from -14% up to -50%. 

As some of the conversions are at the very beginning of the conversion path, and some of them are near the very end of the conversion path, there is a significant difference in the values. 

So, if you want to bring new customers to the website, you can tag the acquisition campaigns. 

You can also compare the campaigns that are towards the end of the conversion path. Following this, you can choose the attribution model that fits the best. 

You can use the First Interaction model to validate the acquisition campaigns, and the Last Interaction model to evaluate the campaigns at the ending of the conversion path. 

💡 Top Tip: While comparing, never mix the acquisition conversions and the conversions towards the end of the funnel. 

Next, you can compare the first interaction of the path and the last interaction of the conversion path. However, don’t compare all the interactions. 

These are the basic attribution model comparisons that you can use to compare the first four attribution models. 

Advanced Attribution Model

On the other hand, if you compare the Linear and the Time Decay models, you won’t see much difference. 

According to our reports, almost none of the sources have a difference higher than 10%.

This means that the results generated by the Linear and the Time Decay models are almost the same. 

In the next step, let’s also compare a third model, the Position Based model with the Linear and Time Decay models. 

You’ll notice that the difference in the conversion volumes isn’t significant.

So, if you want to use attribution models that don’t give credit to only one of the sources, you can use any of the three, either the Linear, Time Decay, or Position Based model. 

You can compare all three models for your campaigns. If you find that the results are almost the same with any of the campaigns, we suggest you use those models. 

Any change in your campaigns will also affect your business goals. Hence, we suggest you take sufficient time in understanding the models before finally switching to the most suited model for your needs.

🚨 Note: Since Google decided to sunset Universal Analytics 1 year from now, we recommend taking a look at our handy guide on how to upgrade to Google Analytics 4.

FAQ How can I validate the source of users on my website using Google Analytics?

To validate the source of users on your website, you can use the attribution concept in Google Analytics. By navigating to Acquisition → Overview in your Google Analytics account, you can identify the different sources that have brought users to your website.

How can I analyze overlaps between traffic channels in Google Analytics?

You can use the Multi-Channel Funnels Overview reports in Google Analytics to analyze overlaps between traffic channels. By navigating to Conversions → Multi-Channel Funnels → Overview, you can view the overlaps and understand how different channels contribute to conversions.

How can I choose the best attribution model for my campaigns? Summary

We can now see how to compare and choose the best attribution model that fits your business demands. 

Attribution models will choose a particular source to credit for the given conversion by using the user data and conversion path they have followed. 

However, if you want to manipulate how Google Analytics tracks the user path for conversions, then you need to create custom UTM parameters through Google Tag Manager. 

You're reading Google Analytics Attribution Model Explained

Google Analytics 4 Rolls Out New Conversion Attribution Settings

Google Analytics 4 administrators now have greater flexibility in how web conversions are attributed to marketing channels.

Google recently added an “Attribution Settings” option to choose whether conversion credit is assigned to only paid Google Ads campaigns or both paid and organic channels.

Those interested in the combined effect of paid and organic marketing efforts can continue to use the default option, which provides conversion credit to both channel types.

With Google Ads and GA4 becoming increasingly integrated, the option to tailor conversion attribution to match marketing priorities is a valuable tool for data-driven businesses.

Why This Matters For Marketers

This new option offers more control over how you measure the impact of Google Ads campaigns.

This may provide a more accurate assessment of your Google Ads ROI.

On the other hand, allowing credit for both paid and organic channels provides a complete view of the customer journey and how different marketing efforts work together to drive conversions.

This more inclusive option could be a better choice for focusing on the overall impact of your digital marketing strategy.

How to Make the Change

To update your GA4 attribution settings, follow these steps:

Log in to your Google Analytics 4 account

Under “Which channels are you able to assign credit for your web conversions imported into Google Ads?” select either “Google Paid Channels” or “Paid and Organic Channels.”

Hat tip to Himanshu Sharma, who shared this accompanying visual on Twitter:

Note that the changes can take 2-3 days to fully reflect in your Google Ads account as campaign data is reprocessed.

Be sure to check your Google Ads conversions and ROI metrics after the full implementation to see the impact of your selection.

You can switch between options anytime by updating the Attribution Settings and allowing a few days for reprocessing.

For more details, see Google’s support page.

Featured Image: Michael Vi/Shutterstock

How To Link Google Ads To Google Analytics Step

🚨 Note: All standard Universal Analytics properties will stop processing new hits on July 1, 2023. 360 Universal Analytics properties will stop processing new hits on October 1, 2023. That’s why it is recommended to do the GA4 migration. We’ve also created a GA4 version of this post.

Google Ads and Google Analytics are both powerful marketing tools on their own—but what if you could get the best of both worlds by connecting them?

In this guide, you’ll learn why you should link Google Ads to Google Analytics, how to do it, and how to make sense of the collected data. 

An overview of what we’ll cover: 

So let’s start!

Why Connect Your Google Ads and Google Analytics Accounts?

Linking your Google Analytics account to your Google Ads account has two major benefits that you wouldn’t be able to leverage from these tools separately. 

Observe the Behavior of Google Ad Traffic

Firstly, you’ll be able to track the behavior of the users that visit your website from a Google Ad.

For example, did the user visits other pages on the website? Or did they leave immediately? Are they more likely to convert than users who arrived from other sources?

You can answer all of these questions by importing Google Analytics metrics like Bounce Rate, Pages/Session, and Average Session Duration into your Google Ads account.

Thus, linking these two accounts extends your ability to track traffic and user behavior. It also tells you about the quality of traffic that you’re buying with Google Ads.

Google Analytics Retargeting Audience

Secondly, you can retarget an audience from your Google Analytics account using Google Ads. 

Depending on your requirements, you can create different types of audiences in Google Analytics and target them using Google Ads.

Apart from this, you can also import Analytics goals and Ecommerce transactions into your Google Ads account for better goal tracking. Similarly, you can import cross-device conversions into your Google Ads account when you activate Google signals.

So let’s see how to connect these accounts!

Log In with the Same Email Address on Both Accounts

We’ll start by logging into both of our accounts.

🚨 Note: Make sure you are logged in with the same email address on your Google Ads account that you are logged in with your Google Analytics account.

First, find your Google Ads email address at the top right-hand side of the screen.

Your Google Analytics email address will be found under your account name.

Next, we’ll need to check whether we have the correct account permissions set for connecting. 

Check That You Have the Right Account Permissions

One major thing we need to take care of is to grant correct permissions. 

Let’s see how!

Google Ads Permissions

Then, check your access under Access level. You need to have Admin access level set up with your email address.

Google Analytics Permissions

Go over to the Admin section at the lower left-hand side of the platform.

Under User Management, you need to have edit access to the account.

Link Your Accounts Together

Check the compatibility of your Google Ads IDs.

Choose and input an account name in the Link group title field. This way, if you have multiple accounts that you connect to your Google Ads account, you can determine where this is coming from. 

Choose where you want to pull data from. You are allowed to choose multiple views. 

Enable auto-tagging to automatically pull data from your Google Ads account into Google Analytics. 

You may also want to leave auto-tagging settings as they are, especially if you are utilizing UTM tags and you want to avoid mixing it up with the auto-tagging feature.

You may also want to try to link Google Ads and Google Analytics through Google Ads’ linking wizard.

So let’s go ahead and see how the data will look once the two accounts are linked! 

Looking at Live Data

Open the homepage of your Google Analytics account. You’ll be able to see all the campaigns and reports under Acquisition → Google Ads → Campaigns. 

On the top of the screen, you’ll see the sales charts. It will show the number of Users vs. Transactions report of a particular timeframe for your campaign.

Going further down on the Campaigns page, you’ll see the different metrics of your campaigns. 

For example, you’ll find the Cost and Revenue in this report. You’ll also see the Ecommerce Conversion Rate, Bounce Rate, Sessions, etc. for your campaigns. 

Similarly, you can analyze and compare the results of different campaigns to increase their effectiveness. 

For example, the bounce rate of a smart campaign can be considered good even if it’s around 80%, but the bounce rate of a shopping campaign will be considered good only if it’s really low.

You can definitely obtain revenue-related information from your Google Ads account. But when you analyze the reports with your Google Analytics account, you can make more informed decisions as you have a holistic view of data. 

FAQ What account permissions do I need to connect my accounts? What data can I see once my accounts are linked?

After linking your accounts, you’ll be able to see more data in both Google Ads and Google Analytics. In Google Analytics, go to Acquisition → Google Ads → Campaigns to view campaigns and reports. You’ll see sales charts, metrics like Cost, Revenue, Ecommerce Conversion Rate, Bounce Rate, and Sessions. You can analyze and compare the results of different campaigns to optimize their effectiveness.

How does linking Google Ads and Google Analytics help with decision-making?

Linking the two accounts provides a holistic view of data, allowing for more informed decision-making. While revenue-related information can be obtained from Google Ads, analyzing reports in Google Analytics provides additional insights and a comprehensive understanding of user behavior, enabling better decision-making for ad campaigns.


So that’s all you need to know about linking your Google Analytics account with your Google Ads account. 

Have you started doing keyword research for your Google Ads campaign? Check out our handy guide on how to use Google Keyword Planner for SEO keyword research.

Is Your Google Analytics Data Gdpr Ready?

Google Analytics has not only made it easier for data-driven marketers to work in compliance with GDPR, but has also introduced a new feature to improve sales and marketing integration

In an era where every marketer is aspiring to be “data-driven”, you, yes you, need to make sure that your marketing decisions are informed by well-rounded data. When we speak about data analytics, Google Analytics is first to make an appearance in any marketer’s mind. Yet another Google tool that has provided revolutionary changes, if we may call it that, in the process of digital data collection, insight and analytics.

Google Analytics is widely used by marketers to generate better business results. Hence, Google is always looking for ways to improve this tool and add new, more relevant features to it. As you may know already, GDPR is around the corner – the new regulation agreed by the European Union, which seeks to improve transparency and effectiveness of data protection activities, is all set to come into play on 25th May 2023.

Leading up to the data protection wave, Google Analytics has introduced data retention control that can be adjusted by the admins. The new “Google Analytics Data Retention controls give [you] the ability to set the amount of time before user-level and event-level data stored by Google Analytics is automatically deleted from Analytics’ servers”, announced Google. The settings will also take effect 25th May 2023, the day GDPR comes into play.

It’s important to note that by default, GA’s Data Retention settings have set its retention levels at 26 months and the “reset on new activity” option is turned ON. If you’re an admin on your Google Analytics account, you will have the option to a data retention of: 14 months; 26 months; 38 months; 50 months; or Do not automatically expire. There will be no implications on the aggregate data if certain user-data is deleted. However, if you don’t wish to reset data retention for a user, you can turn this option off.

It’s safe to say that this new feature introduced by Google will help marketers to effectively work in compliance with GDPR, as far as data retention on GA is concerned. However, as mentioned above, Google Analytics is widely used by marketers to generate better business results. But what if you could generate even better results, with its newest Calendly integration feature? GDPR help is not the only thing that GA is offering this month!

For those of you who use Calendly to organise your meeting times with prospects or other team members, the platform has introduced an integration with Google Analytics to allow you to measure campaign funnels and conversions.

As Calendly states, “with [this] new integration, you can track each step of the scheduling process for your events in Google Analytics, including when an invitee:

Lands on your Calendly scheduling page

Chooses an event

Selects a day

Selects a time

Confirms a meeting”

For those of you who have admin access, you can now create a goal Google Analytics to record scheduled meetings as conversions and effectively measure campaign performance. Through this new integration, you can see which campaigns are driving the most meetings and thereby, make insightful improvements to your campaigns!

Google Analytics 4 Faqs: Stay Calm & Keep Tracking

On March 16th, 2023, Google Analytics shocked the marketing industry by announcing that Universal Analytics would stop processing hits in July 2023.

This didn’t go over so well.

Some marketers are unhappy with the user interface; others are frustrated that GA4 does not have key features.

Many are still in the denial phase – besides, isn’t it still in beta?

Let’s take a step back and answer the burning questions here:

Why is this happening?

What do these changes mean?

What do I need to do right now?

Why Universal Analytics Is Updating To Google Analytics 4

Many marketers have built business processes around Universal Analytics and want to know why this change is happening.

So, I asked former Googler Krista Seiden, who helped build GA4 and is also the founder of KS Digital, “Why is this GA4 update happening?”

Seiden explained that GA4 has actually been in development for many years.

Originally, it came out as a public beta called App+Web, and in October 2023, it dropped the beta label and was rebranded as GA4.

“GA4 isn’t so much an update, but an entirely new way of doing analytics – set up to scale for the future, work in a cookieless world, and be a lot more privacy-conscious,” Seiden explained.

Google’s announcement blog was entitled,“Prepare for the future with Google Analytics 4.”

… for the future.

We keep hearing this; what does “for the future” mean?

When I read Google documentation and chatted with analytics experts, I noticed three main themes or ways that GA4 prepares your business for the future:

updated data model,

works in a cookieless world,

and privacy implications.

Let’s unpack each of these.

Data Model

A data model tells Google Analytics what to do with the site visitor information it collects.

Universal Analytics is built on a session-based data model that is 15 years old.

This was before internet devices like smartphones were widely used.

UA measurement was built for independent sessions (group of user interactions within a given time frame) on a desktop device and user activities were tracked with cookies.

Fun fact, I learned from the head of innovation at Adswerve, Charles Farina, that you can actually still implement GA javascript code from 15 years ago.

Yes, I’m talking about the original tracking code (Urchin).

And it still works today.

In the past few years, this old measurement methodology has become obsolete.

As much as we love Google Analytics, there are many examples of how it just does not work with the way users interact with our websites today.

Farina shared an example with conversions.

In Universal Analytics, goals are session-based. You cannot measure goals by user.

If a user watches four videos in one session, it can only count as one conversion.

In GA4, conversions (or goals) are event-based.

Cookieless World

Google Analytics works by setting cookies on a user’s browser when visiting your website.

Cookies allow a website to “remember” information about a visitor.

That information can be as simple as “this user has visited before” or more detailed, like how a user interacted with the site previously.

Cookies are widely used on the web. And they can be helpful for things like remembering what items you put in a cart.

However, cookies also pose a privacy risk because they share data with third parties.

As the world becomes more aware of privacy issues, users increasingly want to opt out of sharing their data.

And because more people opt out of sharing their data, Google Analytics cannot report on all the people who visit a website.

There is a growing gap in the data collected.

Google Analytics had to adapt to remain useful to website owners.

And they did.

GA4 is designed to fill in the gaps using machine learning and other protocols to create reports.

This is called “blended data.”

In the blog post about this change, Google explains.

“Because the technology landscape continues to evolve, the new Analytics is designed to adapt to a future with or without cookies or identifiers.

It uses a flexible approach to measurement, and in the future, will include modeling to fill in the gaps where the data may be incomplete.

This means that you can rely on Google Analytics to help you measure your marketing results and meet customer needs now as you navigate the recovery and as you face uncertainty in the future.”

Data Privacy

Data privacy is a big topic that deserves its own article in length. To oversimplify it, people want more control over their data and its use.

Laws such as GDPR and the California Consumer Privacy Act are enforcing this desire.

Google Analytics says that GA4 is designed with privacy at its core – but what does that mean?

All UA privacy settings will carry over, and we are getting new features.

For example, Google Analytics 4 does not store IP addresses and GA4 relies on first-party cookies, which supposedly keep them compliant with privacy laws.

I encourage you to use this time to consider your data strategy and set the tone for your company’s data privacy policy, assess your digital footprint and consent management, and ensure compliance.

What Do These Changes Mean For My Business?

The second thing marketers want to know is, “How is GA4 different?”

Or really, “How will these changes affect my business?”

Don’t get too caught up in comparing Universal Analytics and GA4.

The numbers won’t match.

It’s a rabbit hole with no actionable or otherwise helpful outcome.

As Seiden pointed out, this is not just a platform upgrade.

It’s a completely new version of Google Analytics.

GA4 is a new data model and a new user interface.

Keep reading for a summary of key differences between UA and GA4 data and how they affect your business.

Changes in Data Modeling

The most important change is the way data is collected.

Universal Analytics uses a session-based data model (collection of user interactions within a given time frame) and collects data as various hit (user interaction) types within these sessions.

This is why watching four videos in one session only counts as one conversion in UA.

Google Analytics 4 is user-based and collects data in the form of events.

Each event has a unique name (event_name parameter) used to identify the event, with additional parameters to describe the event.

For more on the differences between the two data models, see UA versus GA4 data in the Google help docs.

Spam Detection

Have you ever seen a giant spike in traffic in Universal Analytics or a bunch of random traffic sources that you couldn’t explain?

Spammers could send fake data to people’s Google Analytics accounts by using the Measurement Protocol.

As you can imagine, this created a big problem with inaccurate data.

Google has fixed this problem by only allowing hits with a secret key to send data to a GA4 property. This key is visible in your GA4 data stream settings but is not available publicly.

Data Retention

Data retention refers to how long Google Analytics keeps disaggregated data. At the end of the retention period, the data is deleted automatically.

The default setting for data retention in Universal Analytics is 26 months. But you could choose a different time interval, from 14 months to “do not automatically expire.”

In GA4, you can choose to retain data for two months or 14 months.

At the end of the retention period, you keep the aggregated data in standard reports, but the disaggregated data used in Explore reports are no longer available.

What is aggregated versus unaggregated data?

Think of aggregated data as a summary used to look at website visitors as a whole.

And disaggregated data is dissected or broken down into smaller subsections, such as a specific audience or segment.

Shorter retention periods are not really a big deal.

You can still accomplish the same use cases while doing more to respect user data privacy.

You can still run (aggregated) standard reports to show how well you are doing compared to past performance.

And data from the most recent months is the most useful if you want to make predictions and take action.

User Interface: Reporting

GA4 reporting comes with a learning curve.

With Universal Analytics, there was an emphasis on pre-built reports. It was fairly easy and quick to navigate “done-for-you” reports.

Google Analytics 4 is oriented toward taking greater ownership of our data. With that comes the flexibility of custom reporting templates.

Because the data model has changed and the platform is more privacy-conscious, replicating some of the tasks you performed in Universal Analytics may not be possible.

As an agency or freelancer, you have an additional responsibility to communicate wins and opportunities to your accounts.

And they’re going to need time to learn GA4 or, more likely, rely on you to learn GA4.

To visualize the data in a more familiar way to your clients, I highly recommend Data Studio.

What Do I Need To Do Right Now?

There is no need to panic.

You have time to implement GA4 configuration, time to update business processes, and time to learn new reports.

With that said, GA4 needs to take priority on your roadmap.

Audit your existing analytics setup and create a GA4 configuration plan.

Setting up GA4 before July 2023 is mission-critical.

Start building historical data so that you can do a year-over-year analysis next year.

Once GA4 events are collected, get your team up to speed and update your processes.

A year from now, they will need to be comfortable using Google Analytics 4 to make marketing decisions.

Start planning team training sessions. SEJ rounded up the top educational guides and GA4 resources here.

Last but not least, make plans to extract historical data in Universal Analytics before July 2023.  BigQuery doesn’t cost anything aside from the low storage fees.

Final Thoughts

You’re not just getting an upgrade when you switch to Google Analytics 4. You’re getting an entirely new way of analytics.

This solution is necessary to respect user data privacy and get actionable insights in a cookie-less world.

At the heart of this change is a new data model that makes GA4 different from what we have used in the past decade.

Right now, it’s important to configure GA4 and conversion events for year-over-year data when UA is sunset in July 2023.

After embracing the change, you might enjoy the flexibility and user insights with GA4.

Happy tracking!

More resources:

Featured Image: Paulo Bobita/Search Engine Journal

How To Filter Out Referral Spam In Google Analytics

Referral spammers have been making their way into our Google Analytics (GA) data without ever actually visiting our websites since around 2013.

Referral spam may show up to administrators as either a fake traffic referral, a search term, or a direct visit.

Referral spambots hijack the referrer that displays in your GA referral traffic, indicating a page visit from their preferred site even though a user has not viewed the page.

The problem is that marketers have to manually decipher and filter this type of traffic out of their GA data to make proper sense of it.

Since we rely on GA to make major ongoing marketing decisions, clean data means everything to us.

Without knowing about referral spam and how to filter it, marketers could be making weighted conclusions based on bogus bot traffic.

In this column, marketers will learn how to clean their Google Analytics data by filtering referral spam.

If you’ve recently migrated to Google Analytics 4, we’ve got a section in here for you too.

For the Love of Filters, What Is Referral Spam?

Referral spam, also known as referrer spam or ghost spam, is created by spam bots that are made to visit websites and artificially trigger a page view.

It sounds sketchy, but bots are just pieces of programmed script that are designed to complete a task automatically online.

It’s estimated that 37% of website activity is created by bots, and less than half of this bot activity is legit.

Desirable bots include:

Search crawlers creating search engine results pages.

Checkers monitoring the health of your website.

Feed fetchers converting content to a mobile format.

The other half of bots aren’t so noble.

Some are designed specifically to spam our referral reports by sending false HTTP requests to our websites with the ability to create non-human traffic otherwise known as bot traffic.

You Cannot Weigh Your Gold with Garbage on the Scale

Referral spam artificially inflates your Google Analytics data.

The level of artificial inflation depends on the amount of referral spam your website is getting, which can vary depending on your industry.

Similarly, the threat this traffic poses to the integrity of your data is directly proportional to the amount of legitimate traffic your website receives on a normal day.

For example, if you receive thousands or even tens of thousands of visits every month, your data won’t be significantly skewed by a couple of hundred spam referral sessions.

However, if you only receive 50-100 visits every month, a couple of hundred spam referrals would throw off your GA data completely, effectively suffocating legitimate traffic.

If you aren’t aware of this problem, it can be very dangerous to your marketing strategy.

How to Filter Referral Spam in Universal Analytics

It’s a nuisance to have bots spamming our websites.

The good news is that it has historically been pretty straightforward to filter this type of traffic.

However, the plot thickened in October 2023, when Google launched Google Analytics 4.

We’ll discuss referral spam in this new version of GA in the next section.

For now, let’s see how to achieve this important task inside your Universal Analytics account.

Make sure that you have the necessary permissions to make changes in your Google Analytics account at the Admin level and then navigate there.

To get started, first create a new view.

It’s a best practice in GA to test new configurations like filters in a new view, instead of in your default raw data view since changes can be permanent and mistakes can be made along the way.

Select the type of view you are creating, either Website or Mobile app.

Then give it a name, and select the same regions and time zone as your main view to make sure you’re comparing apples to apples:

Google will do the bulk of the referral spam filtering work for you automatically.

Navigate to your test view View Settings and ensure that the option to Exclude all hits from known bots and spiders is selected:

By checking this off, you’ll automatically and easily be able to filter out about 75-80% of bot traffic.

Another best practice is to add an annotation to mark the date you started filtering bot traffic.

Annotations act as a helpful reference to remember significant changes over time and can help teams keep a record of these types of changes.

Next, you’ll have to do a bit of manual work to weed out any remaining spam making it through Google’s filter.

But before you can do that, you need to know which spam sites are getting in.

How Do You Identify Spam Referral Traffic in GA?

If you want to see if the websites that you suspect to be spam in your Referrals report actually are, first check if they’re on this list or this list of known spam websites.

Other indicators are a bounce rate of either 0 or 100%, a session time of 0 seconds (it’s easy to see how data could become skewed with outliers like these), and a hostname referral that’s not set.

With the list of “bad referrers,” you can block them manually.

Head over to your Referrals report, and filter by descending bounce rate.

That number can vary according to your traffic volume.

In the example below 50 was used.

To identify suspected spam referral sites, use the pointers above.

It is important to roll out filtering in your test view account first.

Once these sites are filtered, they’re gone for good (so you better be damn sure that it truly is spam!).

Once you’re sure, create your list in Notepad or Text Editor so you can paste it back into GA.

Cut down all the URLs to their top-level domain (TLD).

For example, chúng tôi is an affiliate of chúng tôi so it’s better to just add chúng tôi to your potential referral exclusion list.

Now create a regular expression with your list of URLs, so it looks like the example below from Moz:

Be careful to separate websites with a pipe bar, and to add a backslash in front of the domain extension.

This will allow for other subdomains belonging to that TLD to be excluded, as well.

Now, you’re finally ready to create your filter!

Give your new filter a descriptive name like Referral Spam for easy identification later on.

Change your Filter Type to Custom, and change the Exclude Filter Field to Campaign Source (not the Referral field).

Finally, paste your pre-made list of referral spam URLs:

Once you start filtering referral spam, you can start to see how much it was and is affecting your traffic.

It could account for a fair portion of your website traffic if left unchecked, so it’s easy to see why search marketers get annoyed by it.

Blocking Referral Spam Using Data Filters in Google Analytics 4

If you’ve recently started using Google Analytics, or actively migrated your Universal Analytics account, you should have a Google Analytics 4 (GA 4) property (which is now the default).

While digital marketers are going to love the new engagement tab, setting up filters for spam referrers looks different now.

Most prominently is the fact that in the new Google Analytics 4 Admin interface, the View column is no longer present.

Instead, GA 4 uses Data Streams, which does not have its own column.

With the new GA 4, marketers can create up to 10 data filters per property.

Internal traffic filters are suggested and somewhat pre-configured.

However, currently, there are only two types of filters available:

Developer Traffic

Internal Traffic

Neither of these seems appropriate for filtering external referral spam.

What’s more, if you turn to Google support for help, you find yourself in an endless loop between Google’s top-drawer banner that tells you to navigate to Google Analytics 4 support and the search bar on that page that takes you back to the Universal Analytics results for filtering referral domains.

We’ve reached out to Google to clarify exactly how to do this in GA 4, and they confirmed that it isn’t yet possible (current at time of publication).

Google said:

“…since GA4 is a new upgraded product in Analytics, thus the feature i.e “Referral Exclusions” are yet to be launched in GA. Different resources have different timelines, so we cannot assure a specific date for the launch. However, I would like to inform you that the feature is being worked upon…”

While we wait for the ability to exclude referral spam in GA4, I recommend creating an old and new version of Google Analytics:

One in your legacy Universal Analytics mode.

And a new one in Google Analytics 4 mode.

Follow the instructions from the previous section in Universal Analytics to filter referral spam from your GA reports for now.

The good news is that this new iteration of Google Analytics has testing built-in, so it won’t be necessary to create new views for implementing new configurations:

The Benefits of Weeding Out Referrer Spam

Clean data is everything when it comes to making meaningful and actionable conclusions based on it.

With these powerful tactics behind you, you’ll be able to filter referral spam so you can make decisions based on facts.

Since referral spam can hit lower-traffic websites even harder than larger sites, it’s important that marketing teams of all sizes stay on top of it.

That means checking for new referral spam websites regularly and adding them to your exclusion list.

Remember to keep your Universal Analytics view alive for now, until we know more about how to exclude referral spam in Google Analytics 4.

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