Bot traffic in GA4 has proven to be an entirely different beast as compared to it’s predecessor Universal Analytics. GA4 touts machine-learning algorithms combined with manual input from Google’s engineering team as their one-size-fits-all bot filter.

While this is a powerful tool as a foundation and may be improved upon in the future, there have been many analysts and data scientists coming out of the woodwork to prove that the default filter is far from perfect out-of-the-box. 

Below, we’re going to be looking at bot filtering in GA4 using the format of the scientific method. We’ll get to:

• Understand the problem
• Ask the right question
• Test our hypothesis (through a case study we’ve done here at DOM) 

This is where we should put a spoiler alert, because we’re all here for a reason, right?

What Is Bot Traffic In Google Analytics 4?

Bot traffic is a data-analytics-specific problem. You may have heard that most bot traffic is not actually malicious in nature and you may even have members of your data team that use bots to scrape the web for useful information.

While it’s true that these bots don’t pose a threat to your cybersecurity, the problem is that these bots will severely skew and bloat your website data, pollute your analytics, and ruin otherwise-useful metrics.

Bots and GA4

Before getting into the most common types of bot traffic, it’s important to understand the three key components of bots.

1. Bots are scripts
2. Scripts are a set of programmable instructions
3. Scripts are most easily run on Virtual Machines

With these key components being taken into consideration, we can begin to look at the types of bots most often encountered in analytics and get to the question we all ideally would like to answer…

Is there a way to identify and filter out bot traffic to a statistically insignificant percentage of sessions?

Common Google Analytics Bot Traffic Type #1: Web Scrapers

Bot is a very open-ended term. Most traffic noise comes from web scrapers, which provide an automated, scalable solution for access to structured web data. You can think of web scrapers like the programming version of RegEx.

Any structured data on the web can be pulled into a spreadsheet at the speed of light. These bots are incredibly useful for lead generation, brand monitoring, company data, market analysis, and basically anywhere that publicly available data is useful.

Web Scrapers & GA4

According to the 9th Annual Bad Bot Report from Imperva, Selenium is the most popular tool used for web scraping. Imperva estimates that web scrapers like Selenium may be responsible for as much as 40-60% of website traffic depending on your industry. This might account for why some websites have such high traffic and yet lackluster conversion rate metrics.

If you’ve ever run into the issue where you’re getting valid-looking leads generated from your form submissions, only to follow up with them and they say they’ve never heard of you and never filled anything out, this bot is the reason. 

Bot Scripts In GA4

Want some more fun facts about bot scripts?

1. Web scrapers can have as little as three lines of code.
2. Most web scrapers save web pages and their data to a file, which takes anywhere from 3-8 seconds.

These facts might lead you to believe that all bots should fall into an average session duration of fewer than two seconds. However most web scrapers are more complex than just looking through a website.

Common Google Analytics Bot Traffic Type #2: Form Fillers

Form Filler Bots And GA4

Form Fillers do as the name implies, they fill out the forms on your website in the hopes their spam tricks people into scams. If you’ve ever run into the issue where you’re getting valid-looking leads generated from your form submissions, only to follow up with them and they say they’ve never heard of you and never filled anything out, this bot is the reason. 

Form fillers are similar to email phishing scams. These bots fill forms automatically using a series of logic statements. They often use stolen user data to appear legitimate. Even though they have people’s real information, they should be treated like all other bot traffic and be filtered out, at the very least, like a non-qualified lead.

Form fillers use a combination of Python – the most popular programming language for making bots – and JavaScript to execute predetermined scripts. While times vary, some form fillers can take less than 2 seconds to fill out a form.

With such a wide variation on the main metrics of web scrapers and form fillers, we need to look at other variables that we can combine average session duration with in order to identify bot traffic and filter it out of GA4. 

What Is A Virtual Machine?

A virtual machine (VM) is software that allows for the emulation of a whole computer or device.

VMs are usable on local disk or on the cloud. They can run as a desktop program or as a mobile app.

VMs are the best way to run scripts regularly. Saying that, there are several limitations to virtual machines. After all, but your computer was only designed to run one computer, your own. That means that limitation have to be set in order to successfully run Virtual Machines. including:

1. Running a whole device. Most devices are built to run only its local, intended software. Emulating other devices, from phones to computers, in one device means that limitations have to be set in order to successfully run VMs.
2. Operating System Versions. VMs use old OS versions because they take up less computing power and take time to be cracked and emulateable.
3. Screen Resolution. Screen resolutions are pre-set and minimally adjustable to ensure consistency of emulation. Common default and maximum sizes include 800×600, 1360×768, 1140×900, and 1600×1200.

What Is Screen Resolution In Google Analytics (GA4)?

On a typical computer or device, this is the resolution of the monitor or built-in screen. This dimension is based on hardware. On a typical computer or device, like the ones you’re using now, the resolution of the monitor or built-in screen will be captured by GA4’s Screen Resolution metric.

If the majority of bots are using Virtual Machines, and Screen Resolution measures the resolution of your physical device, what happens when the device is software?

This leads us to our hypothesis (thank you for your patience).

Our Hypothesis For Bot Filtering In Google Analytics (GA4)

Bots & Screen Resolution In GA4

Bots can be identified through their Screen Resolution because bots use virtual machines that have pre-set and/or strange screen resolutions that stand out in analytics reports.

These suspect users are verifiable through Average Engagement time, OS Version, and various other dimensions due to the fast executable run time of scripts and the technical limitations of Virtual Machines.

But that isn’t enough to definitively say that we can filter out these users from our data reports. 

So… these suspect users are verifiable through:

• Average Engagement Time
• OS Version
• Various other dimensions

Due to the fast executable run time of scripts and the technical limitations of Virtual Machines. 

Results and Supporting Dimensions

To test our hypothesis, we ran a case study on a website that uses GA4 out-of-the-box to see what we could find.

All of our data came from a three-month period.

Traffic Using Virtual Machine Pre-Sets

Firstly, we found that 30% of their traffic belonged to screen resolutions that are common Virtual Machine pre-sets that are also not typically found as real-world device resolutions. 

Next, we’ll look at User Interaction.

User Interactions In Google Analytics

In the top right corner there, you’ll see that the average user with those screen resolutions was on the page for zero seconds but did about 4 things.

If you know a person that can do 4 things in literally no time flat, you let us know. 

They’re probably a world record holder, or they would be… except there were 4,000 “people” across these two screen resolutions alone that could do it, too. 

The only other thing that I’ll point out is the black-color-coded cell over on the left. This screen resolution, a resolution that doesn’t exist as a real physical screen, represented 40% of all form fills for this website.

40% of their leads were useless because they didn’t have a robust bot filter and they just used GA4 out of the box. 

Even though we aren’t going over all the details, this data certainly supports our hypothesis.

• 0% of screen resolutions under 8 seconds of average engagement time were a current OS version
• Mobile users make up 16% of users and 59.5% of [Screen Resolutions + OS] under 1 second
• Devices listed don’t match a real-life screen resolution
• Many devices aren’t listed because JavaScript isn’t able to automatically pull the information
• 98.96% of users under 8 seconds have no device set/detected

How To Set Up Bot Filtering In Google Analytics 4 (With Confidence)

How to Exclude Bot Traffic In GA4

In order to confidently filter bots in GA4, you need to build a layered logic sequence. To spell it out plainly, your bot filter needs to audit traffic with a series of tests. If one statement turns out to be true, then it needs to pass on to the next statement, and so on.

How To Filter Bot Traffic In GA4?

Here’s a brief overview of the 5-step process for filtering bots in GA4.  

1. Begin bot filtering in Google Analytics 4 by pulling suspect screen resolutions
2. Cross reference secondary dimensions – does the screen resolution combine with an old operating system?
3. Find average event time that is less than 8 seconds
4. Test for final, definitive proof before botting using different metrics found in GA4
5. Filter out all users matching criteria

These combinations can be saved as custom metrics in GA4. In addition to that, they are such a powerful tool if you need a reason to compile a great argument for a custom GA4 migration.

Any other questions about bot filtering in GA4? Don’t hesitate to contact us.  

The post Why You Should Set Up Bot Filtering In Google Analytics 4 appeared first on Direct Online Marketing.

Learn more about bounce rate and engagement rate in GA4 in this blog post – Where Did My Bounce Rate Go in GA4? Enter Engaged Sessions.

In analytics, we often find clarity. Unless, of course, those analytics are inserting bounce rates into their equations. In which case we find total confusion from bounce rate in Google Analytics.

Because bounce rate intermingles with a number of other analytic metrics, the stat can either be an illuminating testament to a site’s health (or lack thereof) or an incredibly deceiving distraction.

Some marketers jump the gun on bounce rate statistics.

Bounce rate deception creeps in when we don’t understand the nuances of Google Analytics. Specifically, when we fail to grasp the Google Analytics definition of bounce rate, we set ourselves up to make poor decisions.

What Is Bounce Rate In Google Analytics 4?

The calculation for bounce rate in Google Analytics 4 is the percentage of sessions that were not engaged sessions. This calculation is different from Google Analytics Universal Analytics (UA) where that calculation is the percentage of all sessions on your site in which users viewed only one page (and, thus, sent one request to the server).

At this point, you might be asking yourself: “what’s an engaged session?” Well, we will get into that next, but here’s a handy post we wrote that goes into more detail: Where Did My Bounce Rate Go in GA4? Enter Engaged Sessions.

What Are Engaged Sessions & Non-Engaged Sessions in Google Analytics 4?

An engaged session (used to calculate engagement rate) is a session that spent more than 10 seconds on the website, had 2 or more pageviews, or had a conversion event occur. On the flip side, a non-engaged session (used to calculate the bounce rate in GA4), spent less than 10 seconds on the website, did not view any other page, and did not perform a conversion event.

In essence, bounce rate in GA4 is the inverse of engagement rate, and these metrics are both valuable in understanding if and how your users are engaging with your website.

How Is Bounce Rate Calculated In GA4?

Simply put, bounce rate in GA4 is the inverse of engagement rate, and these metrics are both valuable in understanding if and how your users are engaging with your website.

How Do You Find Bounce Rate In Google Analytics 4?

Here is how bounce rate is calculated:

(non-engaged sessions) / (sessions) – bounce rate %

For instance:

(9 non-engaged sessions) / (50 sessions) = 18% bounce rate

Here is how engagement rate is calculated:

(engaged sessions) / (sessions) – engagement rate %

For instance:

(21 engaged sessions) / (100 sessions) = 21% engagement rate

The calculations are simple, right? Now that we’ve got the definitions and calculations down, let’s talk about what a good bounce rate is, and how you can use this metric to evaluate and improve your website.

What Is A Good Bounce Rate In Google Analytics?

When it comes to bounce rates, almost every site owner wants this question answered in short order. In fact, it is possible you skipped to this part of the article. That’s OK, don’t feel bad, but you aren’t off the learning hook.

Bounce rate stats are unique to the site and, more specifically, site page experience and goals.

Some people will tell you that a bounce rate over 70% is bad. In fact, a writer for Search Engine Journal explicitly states that a bounce rate north of 70% means “something is probably broken.”

Such a statement seems to imply that a consumer can’t be both happy with the page of your site they arrive on and want to view only that page.

Imagine yourself in Whole Foods. You see a can of tuna on the shelf. You want to know whether eating tuna daily is bad for your health. So you Google it and land on a blog post that argues that tuna should be eaten no more than three times a week. What is the likelihood you will view another page on that website? Are you happy?

You got the information you wanted. You decide that information is trustworthy, so you place three cans of tuna in your cart. You need to finish shopping, so you do not view any other page on the site.

You happily bounce.

The point is this: a high bounce rate should not necessarily be interpreted as a sign of user dissatisfaction. You mustn’t paint with a broad brush when making statements about bounce rate.

There are plenty of sites that recommend bounce rates to shoot for, but we question such an approach.

A better path lies in considering bounce rate as one criterion among many of site health.

Let’s do that now.

Why High Bounce Rates In Google Analytics Occur

Writers tend to turn the “why is bounce rate high” section into a dreary experience. It assumes the position that a high bounce rate is bad. This section assumes that a high bounce rate occurs for both good and bad reasons. Most marketing writers treat the why-is-bounce-rate-so-high section like the postmortem of a professional pitfall. Which is to blindly assume that a high bounce rate is bad for business.

At DOM, we take a more nuanced view. We recognize that evaluating bounce rate requires careful analysis of the particular website, company, and goal in question.

So why might your bounce rate be high?

Your Content Satisfies

Remember our Whole Foods example? It’s OK if you skipped to this section and missed that; I’ll explain again.

If your page solves a problem, your bounce rate might be higher. The fact that your page solves a problem is a good thing.

How can we distinguish a page that satisfies user intent from one that doesn’t, when both may have a high bounce rate?

1. Read the content. Apply common sense.
   A page about fishing in Yellowstone that discusses the best fly fishing fly to use solves a problem. If the person that lands on that page immediately scroll to the section called “USE THIS FLY IN YELLOWSTONE,” the person may decide to bounce after viewing the solution to their problem.A page with a glossary of terms probably satisfies searchers looking for a single definition. DOM’s Internet Marketing Glossary isn’t something someone reads in the way they read Faulkner. They are there to find the definition of a term they don’t understand.
   

   That’s it. No sound of furious further clicks should be expected.

2. View Time On Site (TOS) in Google Analytics.
   What if a person spends five minutes reading a page on your site, but exits without viewing any other page? That does not sound awful. Sure, in certain cases, long and involved content can negatively eat up a reader’s time.However, on the other hand, someone who spends ample time reading your content—and then exits without viewing another post—doesn’t necessarily hate your page. It is important to remember that pages that solve a problem with a quick, straightforward answer may experience both low TOS and high bounce rate.

A Deeper Perspective On Content That Satisfies

Let’s say that you’ve determined your high bounce rate is in line with a positive consumer relationship. Does this mean you should just move on? Not completely.

The high bounce rate led you to investigate a page of content. You determined that the high bounce rate was a reasonable result of your page’s problem-solving content. But now that you are here, you could opt to go ahead and lower the bounce rate for your own benefit.

For example, you could set up a call to action that encourages your readers to check out another related article. You could attempt to sell a service you offer via a CTA button. You could set up an email collection form and collect leads.

Your Site Is Slow

Slow sites are frustrating. You are trying to get information, but some lumpy image builds out at the speed of a turtle. So you back out and find another site with similar information.

You bounce.

It is always important to check your site speed, not only on your devices, but with site speed checkers such as Google PageSpeed.

Fixing site speed can be a tough task. This article is not going to dive in too deeply, but let’s look at some simple things that sometimes help.

• WordPress Plugins
  These can be the devil. If you have too many, they may be overloading your site. Just a single plugin can wreak havoc on your site speed. And testing your site speed right after installing a plugin is unreliable. Many plugins build up a cache, or database, that begins to hobble the site’s loading time months down the road.To resolve such issues, deactivate one element at a time and test, test, test!
• Use Cloudflare.
  Cloudflare is a free cloud network solution. Yes, the free version suits most site owners.Cloudflare downloads the most recent copy of your website, thereby cutting out the host for site loading. It is worth looking into if your poor site speed is causing high bounce rate.We just spent 11 sentences worth of this post explaining simple reasons why site speed on WordPress can get bogged down and cause high bounce rates. That said, Cloudflare is one of our favorite WordPress plugins out there, and we have it included in our exclusive list of the best WordPress plugins for SEO.
• WordPress Themes
  These can be site speed killers. A theme may look pretty and function well when you view the demo, but they can be heavy and sluggish when put into action.Always review themes for their site speed before buying.

Your Page Is A Poor Experience

Look at your page. That big image shoving all the content down only vanishes once the user scrolls into a huge ad. Scroll a bit more, a pop up takes over the user’s screen.

That sounds horrendous. It sounds like a page that most of us would bounce from.

Make sure your site’s user experience is on point if you want people to stick around.

Another reason for poor user experience on a page can be the way it’s delivered. For example, is your page content a lump of text?

If so, break it up with useful headers and, potentially, images.

Your Mobile Experience Is Bad

People access the web via mobile more and more every year. The stats don’t lie.

When your mobile experience is bad, a slow loading and poor user experience scenario is amplified. People using their phones to search want fast solutions.

If your site’s navigation complicates the solution-finding experience, you are likely to lose the consumer. In other words, the consumer may bounce.

Always test your site’s mobile experience. Make sure you test your site on a service like Google PageSpeed and pay special attention to the mobile results section.

Your Content Title Misleads

In order to attract links, your content’s headline needs to attract clicks. A properly written, enticing headline can create success for content marketing campaigns.

However, some content marketers and ad copywriters take too many liberties with headlines. Their overwhelming desire for clicks blinds them to the pitfalls of clickbait.

Yes, clickbait: the act of misleading a person with a headline. Clickbait comes in many flavors and levels of outrageousness. Clearly, you aim to entice people to click, and that in and of itself requires a shiny headline. But you need to be conscientious of when a headline goes from taking a few liberties to downright inaccuracy. When that happens, people bounce.

And it gets worse.

Many people will remember brands that clickbait them and just stop viewing all of their content, even content with accurate headlines. Proper article headlines help nurture relationships with readers as an added benefit.

A Website Linking To Your Site Is Misleading

We discussed inaccurate headlines. Well, if a website is inaccurately linking to a page on your website, you might be compiling bounces because your content doesn’t meet the reader’s expectations.

Unlike inaccurate headlines, issues with exterior sites’ misleading people in the direction of your content is trickier to resolve.

The best way to resolve such a matter is to contact the site owner of the misleading link and request removal or a change in copy.

You could opt to alter your own content, but that isn’t likely to work in most cases.

You’re Running A PPC Landing Page

Pay-per-click landing page designs certainly vary in terms of look, style, and paging. Some pay-per-click landers use single pages with a goal to garner leads. This can result in higher bounce rate statistics. In this case, a marketer will most likely consider the conversion rate over bounce rate statistics.

When the goal isn’t aimed at clicking through a site and is instead to fill out a lead generation form, bounce rates may be higher. When the goal isn’t to drive click-throughs, nor to have visitors fill out a lead generation form, bounce rates may be higher.

Bounce Rate Tracking On Subdomains

For some sites where people move between subdomains, bounce rate statistics can be tougher to interpret in a meaningful way. The first step in getting a full picture is to have proper analytics tracking set up. Read our Google Analytics Subdomain Tracking guide to make sure your site is set up properly.

Erroneously Low Bounce Rates In Google Analytics

You updated your site months ago. Suddenly, the bounce rate went from 80% to 8%. That new template must really be winning for you, right?

Eh, not so fast.

If your bounce rate is excessively low, there is probably something amiss with your metrics in general. Analytics errors, from a general standpoint, need to be fixed because you can’t know they aren’t adversely affecting other metrics.

Check that the code is installed properly throughout your site, including making sure you don’t have multiple instances installed.

Bounce Rates In GA4 And SEO

Does bounce rate influence SEO?

The answer is yes (and no).

The reason that bounce rates fire up SEO enthusiasts is that no one truly knows whether Google’s search algorithm leverages Google Analytics stats. In a logical world, it would sure seem like Google might rely on easily digestible information to benefit its search results.

Throughout his tenure with Google, former head of Webspam, Matt Cutts, consistently denied that search algorithms base SERPs on Google’s famed analytics program.

While some felt Cutts was covering for a Google conspiracy, others viewed his stance on the matter as quite reasonable.

Let’s think about things for a moment.

Google, using its own analytics program (which is installed all over the web on millions of websites), could open its algorithm up for trickery. This would put a part of the Google search algorithm into the hands of those who most seek to upend it.

Additionally, what would happen to sites with incorrectly installed Google Analytics? All their great content would be banished to the SERP cellar? That just doesn’t make sense.

But Bounce Rate Does Matter To SEO

Google doesn’t need to leverage Google Analytics data in its SERP algorithm formula. Instead, Google tracks the time someone spends on a search results link.

So Google does indeed track bounce rate, just not likely through its own analytics program.

The Final Bounce

If you’ve made it this far, you now have to decide: would you like to view more of DOM’s digital marketing content, or bounce?

The example above illustrates the glaring point of this entire article: a page can be great, while still registering bounces.

Bounce rate, when fully understood, offers marketers a way to gauge content health. When a marketer determines that bounce rate statistics negatively reflect on the page, fixing the issue isn’t always overly difficult.

To get more information on this topic, contact us today for a free consultation or learn more about our status as a Google Premier Partner before you reach out.

The post Bounce Rate In Google Analytics 4 (Everything You Should Know) appeared first on Direct Online Marketing.

GA4 offers a unique opportunity to gain deeper insights into what drives your customers, as well as to fine-tune your marketing strategies accordingly. 

Do you want to know how GA4 can help your company grow with confidence? Get your popcorn ready. We’re about to embark on an exciting journey into the world of analytics, where rich data awaits to unlock the potential of your brand.

We’ve tapped into the wisdom of our own digital marketing analysts to bring you the most frequently asked questions and answers marketers like you have about GA4. 

With GA4, you can track user interactions across all their devices, gain greater insights into user journeys, and supercharge your digital marketing strategies. Get ready to discover the full scope of GA4’s powerful capabilities and how they can revolutionize your business. Let’s begin the journey towards uncovering the full potential of this powerful tool.

What are the key differences between Google Analytics 4 (GA4) and Universal Analytics?

There have been countless changes – mostly for the better- between GA4 and Universal Analytics. Here are some of the biggest, most impactful differences that users need to be made aware of. 

Different Data Models

At a fundamental level, GA4 primarily uses an event-based data model, which adds great flexibility and granularity to your tracking capability. In comparison, Universal Analytics was more heavily reliant on sessions and page views and offered manual configuration of events with hard limits to event label, event category, and event action to describe the nature of the event. 

Biggest Takeaway: Traffic and sessions still matter greatly in GA4! But the fundamental way that GA4 handles session is different with the introduction of the session_start event and the concept of engaged sessions. This means that comparing GA4 session data to Universal Analytics session data is an apples to orange comparison that will not match up correctly. Your traffic numbers will be off from each other by design. As a rule of thumb, it’s infinitely better to stick to one data model and observe trends that occur relative to that singular data platform rather than comparing data between different platforms that are using different data models. 

Greater Customization

Universal Analytics comes out of the box with predefined reports that did a reasonably good job at a one-size fits all approach web analytics. However, the true power in GA4 is that, in the hands of an experienced analyst, it can give you powerful insights that are tailored towards your website. GA4 introduces custom definitions (parameters) that replace custom dimensions and metrics in Universal Analytics, providing more flexibility in data collection. 

Biggest Takeaway: While you can focus on migrating and replicating your Universal Analytics setup in GA4, that is missing the larger overall opportunity at hand. Your analytics should mirror your business needs and priorities and with GA4, you are able to tailor your analytics setup to meet those needs. Unfortunately, GA4 is lackluster out-of-the-box and it’s not entirely intuitive within the platform about what you need to do to get the most out of it. If you want help with your GA4 analytics, then reach out today for a free strategic consultation.  

BigQuery integration

GA4 offers free integration with BigQuery for advanced analysis and reporting, while Universal Analytics requires a premium (360) subscription for the same.

Biggest Takeaway: The native integration with BigQuery allows for more advanced analysis, but also should make you raise the question of who really owns your data. Google may not store your Universal Analytics historical data in perpetuity. Or if you wanted to leverage customized GA4 dashboards in Google Looker Studio or other visualization platform, you are at the mercy of the very limited GA4 API quota. Luckily, this integration offers a cost-effective alternative for storing GA4 data. 

AI-powered insights

GA4 leverages machine learning to provide automated insights and predictive metrics, helping digital marketing managers make more informed decisions and builds predictive audiences that can be used within Google Ads. 

Biggest Takeaway: The ability to leverage GA4 audiences into Google Ads offers quality training data based off of your website’s actual user audience behavior to optimize Google Ad campaigns. With Google ads becoming more reliant on smart automated bidding strategies, the quality training data is even more important to maximizing return on your investment.

How can digital marketing managers benefit from the improved data integration and cross-platform tracking in GA4?

For businesses that leverage mobile apps in addition to websites, the enhanced cross-platform tracking allows for a more seamless experience comparing data streams. Because Google Analytics Firebase and GA4 are both events-based models, they are leveraging similar data schema that allows for cross-platform comparison of data. 

In terms of integrations, GA4 integrates well with the core Google stack via:

• Google Ads
• BigQuery
• Firebase
• Looker Studio

This means a greater ability to take advantage of GA4’s default data-driven attribution modeling, improved cross-device tracking, and great insight into user behavior on your website.

What new insights can GA4 provide for digital marketing campaigns?

Enhanced audience segmentation

GA4 enables digital marketing managers to create dynamic audience lists based on a wide range of user attributes, behaviors, and events. This enhanced segmentation can reveal new insights into how different audience segments interact with marketing campaigns, allowing for more precise targeting and personalization.

Customizable funnels

GA4 allows digital marketing managers to create customizable funnels with multiple touchpoints and conversion events, providing a deeper understanding of the user journey. By analyzing these funnels, managers can identify bottlenecks and areas of improvement, optimizing their campaigns to drive more conversions.

How does GA4’s machine learning capabilities improve data analysis and prediction for digital marketing managers?

GA4’s predictive metrics, like Purchase Probability and Churn Probability, can provide valuable insights into users’ future actions. Digital marketing managers can use this information to proactively target users who are more likely to convert or re-engage those at risk of churning, leading to more effective and efficient campaigns.

How can digital marketing managers leverage GA4’s enhanced audience segmentation features to target their campaigns more effectively?

Develop dynamic remarketing campaigns

With GA4’s advanced audience segmentation capabilities, digital marketing managers can create dynamic remarketing campaigns that are tailored to each user’s specific interactions with their website or app. For example, they can target users who have viewed a particular product category but haven’t made a purchase yet, encouraging them to complete the transaction with personalized offers or recommendations.

Optimize audience bid adjustments

GA4’s audience segmentation features can be used to identify high-value audience segments that generate the most conversions or have the highest lifetime value. Digital marketing managers can then increase bid adjustments for these segments in their Google Ads campaigns, ensuring their ads are more likely to be shown to users who are most likely to convert.

Improve content personalization

By leveraging GA4’s audience segmentation features, digital marketing managers can gain insights into the preferences and interests of their target audience. This information can be used to create personalized content and offers that cater to each segment’s needs and preferences, leading to better user experiences and higher engagement rates.

What are the best practices for setting up GA4 for optimal data collection and reporting?

The best approach we’ve found is to spend time planning what you want to see out of your analytics. This means creating a measurement strategy that mirrors your marketing goals and trying to find ways to mirror those goal KPIs and metrics in your reporting. 

Additionally, GA4 has more robust platform reports, but requires detailed understanding of your setup to maximize its usage. Not everyone in your organization will feasibly want to get to this level of understanding, which means the better approach can be customized dashboards that answer “What questions do I want answered from my web data?”. 

In terms of GA4 configuration best practices, here is a handy GA4 checklist that is comprehensive and easy-to-setup.

How does GA4’s event-based tracking differ from Universal Analytics, and how can digital marketing managers benefit from it?

For lead generation sites with lots of forms and gated content assets, you may be able to set up detailed event parameters such as form_name, content_type, form_id to be able to differentiate how different content and different forms are performing on your site overall. You also can analyze audience segments to see if there are patterns of behavior existing on a website. A practical use case would be being able to identify what events converting users are taking on your website versus events non-converting users as a whole are taking. Is there content that answers questions for an engaged user that leads to conversion? These are interesting insights to uncover that previously with Universal Analytics were harder to come by. 

GA4’s enhanced ecommerce model and funnel reports are also significantly more robust than it was for Universal analytics. For a multi-step checkout process, you are able to clearly identify when users add items to cart, view their cart, begin checkout, and complete a purchase. This allows for greater visibility of conversion drop off points.

What are the key data visualization features in GA4 that can help digital marketing managers make more informed decisions?

While it requires intimate knowledge of your GA4 configuration, the free form or blank report allows you to set up a custom report to compare events to custom dimensions and metrics. 

In terms of other report types, the funnel reporting and path exploration reports are significantly upgraded in GA4 versus Universal Analytics. That being said, they’re not intuitive to use and require a baseline knowledge of GA4 and the events tracked in your setup.

There are limitations to comparing session-scoped and event data that Google introduced in December 2022. Unfortunately, blending this type of data together would require BigQuery or another alternative.

How do GA4’s privacy and consent features help digital marketing managers comply with data protection regulations like GDPR and CCPA?

Privacy and consent features

GA4 offers better privacy controls, including consent mode and data deletion capabilities, helping marketers comply with data protection regulations like GDPR and CCPA.

I used the auto-migration tool in Universal Analytics to upgrade to Google Analytics 4. Was this a good idea?

Unfortunately, businesses have found out the hard way that the auto-migration tool was woefully insufficient at porting over users from UA to GA4. Far from an easy button, it does not migrate the setup in a way that makes sense for most businesses. In some cases, we’d recommend that you start with a net new setup because the quality of data is unusable for those using this migration tool.

What are the top GA4 integrations that digital marketing managers should consider for their marketing tech stack?

Google Ads: Link your GA4 property with your Google Ads account to analyze ad performance and optimize your campaigns.

Google Search Console: Integrate GA4 with Google Search Console to monitor organic search performance and identify optimization opportunities.

Google Data Studio: Connect GA4 with Google Data Studio to create custom data visualizations and interactive reports.

BigQuery: Integrate GA4 with BigQuery for advanced data analysis and reporting capabilities.

Firebase: Connect GA4 with Firebase to track app performance and user engagement.

Most other software platforms are leveraging the GA4 API, but are adapting to changes to provide solutions including Shopify, WordPress, Salesforce, Marketo, Hubspot, etc.

How can digital marketing managers use GA4’s conversion modeling to measure the effectiveness of their campaigns more accurately?

GA4 Conversion modeling refers to how GA4 estimates some conversions that it doesn’t observe directly based on the modeling of data. This doesn’t mean that the data is less trustworthy necessarily, but it is a clear difference versus Universal Analytics. There are a couple of main implications that a digital marketing manager should be aware of, which are that recent conversion data may be subject to change. The modeling may result in shifting of recent conversion data, so understand that numbers may change slightly over time. The other major implication is that Google Analytics is best used when thinking about patterns and trended data versus stressing over exact numbers. As it is, with Apple’s Intelligent tracking protection and increasing changes to data privacy, there is less guarantee of completely accurate data, but overall trends and sample sizes should still inform strategic decision-making.

What are the recommended GA4 training resources for digital marketing managers to get up to speed quickly?

Of course the DOM blog is a great resource! But beyond that:

• Analyticsmania.com
• Measureschool.com
• Simoahova.com

How can GA4’s enhanced ecommerce tracking features help digital marketing managers optimize their online store performance?

GA4 provides very clear pathways for evaluating checkout funnels and overall product performance. Understanding how different traffic channels are performing in terms of conversion events is critical and should help inform how conversion optimization efforts are taken on site. GA4 is not the answer to why problems happen – behavioral analytics tools such as heat-mapping or A/B testing help better with this – but GA4 can help pinpoint where conversion drop off points are happening as well as provide clues for what problems may exist.

What are the top GA4 custom reports and dashboards that digital marketing managers should create for better campaign monitoring?

The most powerful visualizations would be connecting GA4 to other visualization platforms such as Google Looker Studio, Tableau, or other visualization alternative. Ideally, to avoid the GA4 quota issue, these SEO, PPC, and general marketing dashboards are generated with integration from BigQuery or other data warehouse/database system.

How can GA4’s predictive metrics help digital marketing managers optimize their budget allocation and campaign targeting?

GA4 predictive audiences are the most likely application where it uses machine learning to predict custom audiences such as those likely to convert or those likely to churn. Keep in mind, your website needs enough of a data set to be able to have these insights generated. 

I am not getting the same kind of insights I want out of my GA4. How do I fix this?

1. Generate a measurement strategy that identifies the business goals, marketing goals,  KPIs, and metrics that are important for a business. 
2. Install GA4 through GTM. The biggest challenge is likely in this step, as there are a lot of custom CMS websites that don’t have a proper data layer setup to capture the right data in GTM. 
3. Integrate GA4 with the Google stack.
4. Generate a custom dashboard that answers the most important questions you want answered out of your data. 

DOM offers GA4 analytics setup and training if you need help with the above.

How can digital marketing managers use GA4’s advanced analysis techniques to gain deeper insights into user behavior and campaign performance?

Integrating GA4 and Google Ads data with BigQuery unleashes the ability to blend data and evaluate performance through deeper data analysis. Within platform, there are powerful insights, but to be able to manipulate and blend data together that is only possible with external solutions will further untap its potential.

What is the future of GA4, and how can digital marketing managers stay updated on the latest developments and features?

GA4 is continually evolving and is most powerful when digital marketing managers spend the time to understand its nuances. Hopefully, GA4 will become more feature rich and adjustments to the UI happen to lower the learning curve for brand new users. 

GA4 is meant to be integrated with Google Tag Manager and it would be very difficult and time-consuming to be maintained through hard-coded gtag. 

The data landscape will also likely be changing with the popularity and dynamic changes resulting from AI and data privacy laws. It is up to digital marketing managers to stay current as things are rapidly developing.

You Made It To The End?

If you’ve made it to the bottom of this post, you just uncovered some crucial insights into optimizing your analytics strategies with GA4. 

With answers to 20 vital questions, you’ve gained the foundation to build a solid data-driven approach for your brand. 

Have a question you didn’t see answered above? Contact us and let us know – we’re always eager to help you grow online with confidence. 

Thank you for diving in with us, and let us know if we can support you with our top-rated marketing analytics services as you complete your GA4 migration journey.

The post 19 Popular GA4 Questions (With The Right Answers From Our Expert Analysts) appeared first on Direct Online Marketing.

Have you been seeing the acronym GA4 littering your LinkedIn feed and wondered “what the heck is that?” Likely followed by “should I care?”

The answer to the first question: GA4 = Google Analytics 4.  The answer to the second?  Absolutely (assuming getting insights into how your marketing efforts are impacting sales).

GA4 was actually first released in beta as “Google App + Web” in 2019 and then reintroduced as GA4 in October 2020. Earlier this year, Google announced it’s sunsetting the ubiquitous current version of its analytics platform – Universal Analytics, or GA3 – in 2023.

This change set the stage for 2023 to be the year that GA4 becomes the only analytics solution for any organization currently using Google Analytics. In other words…

You Have No Choice, So Just Flipping Do It!

Otherwise you’ll lose even basic reporting on how your website performs. Forget about advanced insights; you won’t even see how many people came to your site or how they got there.

On July 1, 2023 Google snaps their fingers and your Universal Analytics reporting goes away completely.  That means the 28,000,000+ websites currently using the un-upgraded version of Google Analytics have some work to do.

If your website ranks among those 28 million, here’s what the next several months are going to look like for your analytics account:

• Until July 1: all of your data will be collected in Universal Analytics.
• After July 1: you won’t collect any new data.
• After December 31: you won’t be able to access your historical Universal Analytics data in the Google Analytics platform.

(Note: if your Google Analytics property was created after October 2020, you may already be using GA4. Breathe a sigh of relief.)

So does this mean you should wait like the grasshopper until June 30th to switch?  I would urge you in the strongest terms not to wait for the following reasons:

1. Universal Analytics and GA4 pull data differently, so they aren’t apples-to-apples comparison. You’ll want to run both versions side-by-side so you can compare the two.
2. Some of the metrics – like engagement rate – are brand new, as is the user interface. Give yourself time to get educated and used to everything new.
3. Custom implementations – if you want to go that route – can take 6-8 weeks or more.  And that’s from the start date.  If you’re using an outside agency, realize you may have to wait weeks or months to even kick off.

Want more information on the end of life for Google Universal Analytics properties? Click here.

Don’t Just Settle: Build a Whole Measurement Strategy

No matter where you are in the process of setting up GA4 for your organization, you should take this time to take a step back and really evaluate your measurement strategy.

Most marketers we speak with don’t have confidence in the insights they currently receive from Google Analytics – and often aren’t even sure all the data is correct.

Every business and website is unique. Because of that, it’s highly likely that your GA4 implementation will need to be customized as well. Make sure you seize this opportunity upfront to think through a true measurement strategy before you migrate.

That way, you can take this new platform and tap into real, actionable insights into how your target audience is using your website.

Plus, Google has baked in some really cool new features in GA4. It’s more flexible and scalable than Universal Analytics and boasts some predictive features.

In other words, Google will be able to start predicting how some audiences will behave, including what kind of sales and lead conversions you will receive.  Keep in mind Google’s crystal ball isn’t perfect, although we expect it to improve other time.

Just be aware that the cost of creating all these new features is that GA4 isn’t plug-and-play like Universal Analytics.  You’ll need to spend time strategizing what kind of insights you want, then configuring from there.

Not Sure Where To Start With A GA4 Implementation? We’re Here To Help.

In order to adopt this platform correctly, it’s going to require your business to undergo a complete shift in how you measure campaign performance.

Once you get started with customized GA4 reporting, you’ll be able to make better data-driven decisions with your digital properties and marketing investments.

If you have yet to get started with your migration or have run into roadblocks, our upcoming webinar will be a great place for you to gather your team and get started.

“Is Google Analytics 4 Better? (A: It’s All About The Implementation)” launches December 15th at 1PM. Click here to get registered.

Hope to see you there!

Have questions about setting up GA4 or about the webinar itself? Reach out to us and our team will be happy to help with no obligation.

Supporting tech companies with their GA4 implementation is one of the biggest responsibilities our agency is going to have in the coming year.

Good luck in all your marketing efforts in 2023 and, as always, be generous with giving and receiving help.

The post Still Putting Off GA4 Implementation? Here’s Why You Can’t Wait. appeared first on Direct Online Marketing.

If yes, then you likely have been experiencing a lot of issues with data presenting in your dashboards.

A few weeks back, our team noticed some GA4 issues with data loading into Looker Studio.

And by issues, well, we mean none of the data was loading.

Instead of seeing your pretty reports in Looker Studio, are you toggling between your dashboards and receiving error messages like these?

• The underlying data quota limit was exceeded. Please try again later.
• Failed to fetch the data from the underlying dataset.
• Quota Error: the data set has been accessed too many times.
• Exhausted concurrent request Quota, please send fewer requests concurrently.
• Sorry we have encountered an error and were unable to complete your request.

If so, then you have errors in your dashboard due to the fact that Looker Studio was unable to load data because your GA4 API has reached its quota limit.

Learn everything you want to know about these Looker Studio and GA4 issues and what options we see as potential long-term and short-term solutions for you below.

So What Caused These GA4 + Looker Studio Errors To Happen?

We know the biggest question you probably have right now – how can you get your Looker Studio reports that use GA4 data working again?

Sadly, the answer is not as simple as “change this to that and you’ll get your reports back.”

The week of November 14 – 18, 2022, Looker Studio users noticed they were receiving error messages in their reports that use GA4 data. Specifically, users began receiving data set configuration errors indicating that Looker Studio users could not connect to their data sets.

Coupled with the upcoming transition for websites to use GA4 as the default Google Analytics platform, this has left many of you (and basically all of us in the analytics industry) looking for solutions with that looming hard deadline coming up in July 2023.

Why Are These GA4 + Looker Studio Errors Happening?

Many analysts first thought the errors were caused by Looker Studio. However, with some more time to reflect on this, the source is actually related to the GA4 API.

More specifically, the cause of these errors is that Google started enforcing their Google Analytics Data API (GA4) quota limits without prior notice. This means that the errors are not unique to Looker Studio, but are applicable to any product using the GA4 API – even Google Sheets.

While the issues apply to other tools, they are exacerbated by how Looker Studio processes requests.

Every element (that’s right – every single chart, table, and scorecard) in the report represent one separate API request. These requests come through every time the data has to be refreshed.

If you (or anyone with access to the dashboards on your team) engage in normal user behaviors such as:

• Changing report date ranges
• Toggling filters on and off
• Refreshing your report throughout the day

You are very likely to burn through your allotted API Calls in quick order.

GA4 Quota Limits

For the purposes of this addressing the errors you are likely facing with your reports, we will only be covering quota limits for standard GA4 properties. Google’s GA4 API quota documentation can be found here.

The quota limit can be broken down into two main categories:

• Concurrent requests
• Tokens used per time period (hour / day).

GA4 Concurrent Request Quota Errors

Errors related to concurrent requests are the most common quota errors. They are measured by the number of requests being simultaneously executed, which is limited to 10. Since every element in Looker Studio represents a separate API request, having 10 different elements on a report page can be enough to trigger a concurrent request error.

GA4 Tokens Used Per Time Period Errors

The other category of quota errors is based on the number of tokens used per hour or per day.

Every API query costs a certain number of tokens based on factors such as the complexity of the queries or the size of the source data table. The more data Google has to process in your query, the higher the expense to your API quota.

There are token limits at the property and project level and have both hourly and daily limits. These limits are compounded by the fact that API requests are processed every time data has to be refreshed.

So if you have several robust Looker Studio reports being used by many users, you can hit these limits very quickly.

What Google Is Saying About These GA4 + Looker Studio Errors

On 11/23, Google provided an update on its Looker Studio community forum to help address issues related to the quota limits.

Their plan has two components related to how Looker Studio retries queries to mitigate the impact of concurrent requests on the quota limits and showing users the amount of quota tokens consumed by different report components.

They have also provided a troubleshooting guide.

Short Term Solution To Your GA4 + Looker Studio Errors: Intermediary Storage

Unfortunately, analysts are currently limited on steps they can take to eliminate errors related to quota limits as Looker Studio is currently configured. The steps Google outlined above can potentially help mitigate the impact of the query limits on your reporting, but the underlying issue – the large number of queries Looker Studio makes to the GA4 API – still remains.

If you wish to continue to use the GA4 API for your reporting, the best scalable way to circumvent the quota issue is to leverage an intermediary storage solution to limit the number of API queries. This method reduces the number of queries by essentially creating a static snapshot of the data which you will then use as the data source for your reporting.

This can be accomplished using Google Sheets or Looker Studio’s Extract Data native connector.

This solution has two major drawbacks: date range limitations and user duplication.

Date Range Limitations

Both Google Sheets and the Extract Data connector have limits related to the size of the data (Google Sheets is limited to 10M cells, Extract Data can only have 100MB of data).

While these limits may be manageable for basic reporting, they are insufficient for the robust reporting that users have come to expect from GA4.

User Duplication

The other, arguably more troublesome, drawback to using an intermediary storage solution is that static data sources will not dedupe users across days.

For example, if you visit a site three days in a reporting period (from the same device & browser, assuming cookies persist), you should be counted as one (1) user. But in a static data source, you will be counted as three (3) users.

The only way to solve this? Count the unique number of User IDs across those days. However, this compounds the previously mentioned issues related to the size of the data source and also introduces issues with cardinality.

Because of these limitations, if you are looking to utilize an intermediary storage solution, we recommend exploring more robust third-party solutions such as Analytics Canvas.

Long Term Solution: Ditch The API And Integrate GA4 With BigQuery

This change to the GA4 API signals to a broader audience what many analysts have already realized:

Google BigQuery must be leveraged in order to get the most out of your GA4 implementation.

While the landscape of digital analytics is rapidly changing, it is clear that analysts, agencies, and businesses as a whole will need to come up with a viable solution for data storage and data analysis that isn’t dependent only on GA4.

How Can You Get Your Reports Working Again?

As documented above, you really only have two viable options for avoiding these GA4 errors in your Looker Studio reports.

1. Intermediary storage
2. BigQuery integration

If you already have GA4 setup – have no fear, it is still collecting data and can provide you with insights. The challenge you are facing right now is related to extraction of the data and visualization of the data in Looker Studio.

Saying all of that, if you are involved in any element of web analytics at a company that wants to make actionable, data-driven decisions using GA4 and Looker Studio – all you likely have right now are errors in your reports.

To get granular insights from your large, complex datasets, your list of options really boils down to one option: get your data into BigQuery.

While the short term solutions may work for now, it is probably best to get connected with an analytics team that knows how to handle the challenges you are currently facing.

Learn more about our UA to GA4 migration support services here.

The post Why You Have GA4 Connector Errors In Your Looker Studio Reports appeared first on Direct Online Marketing.

If you are responsible for one of the roughly 28 million-plus websites that still uses Google Universal Analytics, then you’ve probably wondered this to yourself since Google announced it was sunsetting the UA platform earlier this year.

Maybe you’ve heard that GA4 is going to require a total shift in how you are going to measure web performance. We get it, that can be a pretty scary thought. We understand that you would prefer to keep using the analytics platform you’ve come to know and love over the last decade plus.

We also know you can’t stop thinking THIS to yourself…

Is Google Analytics 4 Really Better
Than Universal Analytics?

If you’re tired of this question running a marathon in your mind, we invite you to come and get the answer for yourself in our upcoming webinar on December 15, 2022.

Will You Actually Get The Answer By Attending This Webinar?

Great question. You’ve probably signed up for a bunch of webinars over your career where the only feedback you received to your questions were…

“Our service/product is the only answer! Buy it now.”
“We know the answer… but we’ll only tell you if you pay us.”
“Your question isn’t the right question. Here’s an answer to something our sales team wants us to talk to you about.”

When you register for this webinar, you can fully expect to learn if GA4 is better than UA. In fact, when we were reviewing this with our Senior Digital Analysts recently, they actually gave us the answer… you ready?

Here it comes.

It depends…

On how you implement it!

So, while we hate answering questions with questions here at DOM, we have to do it. How do you plan on implementing GA4?

In addition to getting the answer to that loaded question, you can also get answers to the following questions:

• How does GA4 compare to what we normally get in UA (aka GA3)?
• What comes “out of the box” in GA4?
• What will we miss out on if we use “out of the box” GA4 after UA/GA3 is sunset?
• Why does GA4 need to be a custom installation?
• What does a “finished” implementation of GA4 look like?

If you would like to be one of the first to save your seat for a webinar about GA4 that is actually informative, click here to register.

PS – don’t see the actual question you have listed above? Send it to us when you register for the event and our team will make sure it gets addressed during the webinar!

Still Waiting To Get Started?

Totally respect your patience. That’s a virtue (at least that’s what we’ve been told).

But just so you know, on July 1, 2023, Google is going to sunset the analytics platform you’ve relied on for years.

When that happens, all you will be left with to track your site performance (from Google, anyway) is GA4.

This move to GA4 is much more than a simple upgrade from UA – it’s a complete shift in how you are going to measure web performance. Your migration is the first step towards shifting how your company measures web performance.

Need an introduction or refresher to this game-changing analytics platform before saving your seat for this webinar? Check out some of our other resources below.

Find yourself feeling like you’ve got a handle on everything included in those resources? No worries. Check out some more of our content to understand some of the differences between GA4 and UA.

• Bounce Rate In Google Analytics 4 (Everything You Should Know)
• Welcome Engaged Sessions Into Your List Of Meaningful Metrics
• Google Analytics 4 vs Universal Analytics
• 4 Easy Steps To Set Up Cross Domain Tracking With GA4

The Experts Answering “Is Google Analytics 4 Better?”:

Steve D’Angelo
Senior Digital Analyst

Steve D’Angelo is certified in multiple disciplines of marketing from Google (including Google Analytics) and HubSpot. He has spent his career helping growing and enterprise companies in data-driven industries like healthcare, technology, and insurance create actionable measurement strategies so they can fully utilize complex analytics setups.

Dana Schumacher
Senior Digital Analyst

Dana Schumacher is a Google Analytics Qualified Individual who was one of the first hires at DOM dedicated to campaign analysis. Prior to joining our team, Dana spent a decade-plus as an analyst for publicly traded enterprises, eCommerce companies, and software development firms.

Host: Justin Seibert
President

20-year digital marketing veteran Justin Seibert started in the field in 2001 as a one-man digital marketing department for an LA-based firm. In 2006, he founded DOM and has grown it into an Inc. 5000 company regarded as one of Google’s Top 3% Premier Partners.

Ready To Sign Up
After Seeing The Speaker Lineup?
Click Here To Get Registered.

The post Is GA4 Better Than UA? Find Out In Our Next Webinar! appeared first on Direct Online Marketing.

So what changed?  Don’t fall behind on your company’s analytics, take a look at our exclusive GA4 setup checklist we have released.

Compiled by two of our Senior Digital Analysts (learn more about them below), our GA4 Setup Checklist is an effective starter guide for companies of all sizes that need to learn if:

• They are making the setup process too complicated
• Their implementation follows best practices
• They can confidently understand GA4
• They want to make sure they avoid any future “Oh $#!+” moments with their marketing analytics

Our company has conversations daily with growing companies about digital marketing and campaign measurement. From our agency President down to the newest member of the account management team, we understand how important it can be to not only understand GA4 but also the ever-changing landscape of digital analytics.  Has your company had these questions recently:

• Is our website properly set up in GA4?
• What more do we need out of our current setup?
• How difficult is this platform to actually make useful?

Setting up GA4 is more than a simple change of tracking scripts and fields in Google Tag Manager. Instead, every GA4 migration plan should serve as the first step towards shifting how your company is going to measure web performance.

Our GA4 setup guide is designed to serve as that initial planning document for you and your company.

What’s Included In Our Guide To Set Up Google Analytics 4?

Glad you asked. Our Google Analytics 4 setup guide includes:

• What you need to plan and prepare before getting started
• 15 settings to review as you review or reset GA4 properties, data streams, and data collections
• Tips for setting up event and conversion tracking
• Applicable integrations to connect
• 3 best practices hand-selected by our team of digital analysts

Do any of these sound like areas of concern for you as you dive into GA4? Are you ready to invest the time to get meaningful insights from the data GA4 provides?

If you answered yes to any of the questions above, click here to download our GA4 implementation checklist.

Who Is Our Guide To Set Up Google Analytics 4 Intended For?

Whether you have already gone through the GA4 setup assistant or not, this checklist will be helpful to confirm a completed migration will provide your marketing teams with meaningful insights.

However, we do think it’s fair to tell you upfront – this is a checklist for the preparation and initial setup phases of a GA4 implementation.

If you are a developer or analyst looking for a very specific fix to a problem you have encountered during a migration, you may not find our checklist to be the solution you seek. Sorry!

That said, this checklist should be perfect for you if you are a:

• Seasoned marketing leader making data-driven decisions
• Growth hacker on the lookout for the next channel to scale
• Emerging marketer who lives in Universal GA to track KPIs

If this sounds like you and your company haven’t made it too far along the GA4 migration path, get the GA4 checklist here.

GA4 is a daunting task – if you or your company feel lost or overwhelmed at the process, contact us – we’d love to work with you in setting up your GA4 and analytics so you can track your company’s successes.

Want to learn more about the analysts who created this checklist? Meet them below!

Meet Our Senior Digital Analysts Who Created The Checklist

Senior Digital Advertising Analyst Steve D’Angelo

Steve D’Angelo is certified in multiple disciplines of digital marketing and advertising from Google (including Google Analytics) and HubSpot. He has spent his entire career helping growing and enterprise companies in data-driven industries like healthcare, technology, and insurance create actionable measurement strategies so they can fully utilize their complex analytics setups.

Click here to get connected with him on LinkedIn.

Senior Digital Analyst Dana Schumacher

Dana Schumacher is a Google Analytics Qualified Individual who was one of the first hires at DOM specifically dedicated to campaign analysis. Prior to joining our team to help our clients grow their SEO and social media campaigns with greater confidence, Dana spent over a decade serving as an in-house analyst for publicly traded enterprises,  eCommerce companies, and software development firms.

Click here to get connected with her on LinkedIn.

Want To Get More About GA4?

These helpful links below are great places to get started!

• GA4 Is Inevitable… Are You Ready To Leverage It’s Insights? (Webinar)
• Is Google Analytics 4 Better? (A: It’s All About The Implementation) (Webinar)
• Why You Have GA4 Connector Errors In Your Looker Studio Reports
• How to Track Users Across Subdomains (Updated For GA4)
• Bounce Rate In Google Analytics 4 (Everything You Should Know)
• What Are Engaged Sessions In GA4? (Updated August 2022)
• Begin Your Google Analytics 4 Training In Our Upcoming Webinar
• 4 Easy Steps To Set Up Cross Domain Tracking With GA4
• Google Analytics 4 vs Universal Analytics

The post Unsure How To Set Up Google Analytics 4? There’s A Checklist For That! appeared first on Direct Online Marketing.

Well, they must have heard us all the way over here in Pittsburgh, because on July 11, 2022, Google announced that it’s bringing back its bounce rate metric as a way of measuring user engagement.

So, what is bounce rate vs. engagement rate? How are these things calculated? Let’s talk about how these metrics are defined in GA4.

What Is Bounce Rate in GA4? Is It The Same as Bounce Rate in Universal Analytics?

Bounce rate in GA4 is calculated a little differently than it is in Universal Analytics. In GA4, the bounce rate metric is a percentage of sessions that were not engaged sessions. For instance, if a user visits your website and leaves in under 10 seconds without triggering an event, then that session will count as a bounce. So, take that session number and divide it by the total sessions, and you get the bounce rate.

In Universal Analytics, it’s calculated a little differently. In UA, the bounce rate is a percentage of single page sessions in which there was no interaction with the page. These interactions would include link clicks or triggering tracked events. Also, the duration spent on the page isn’t a factor in UA. Even if the user spends several minutes on the page, if they don’t interact with the page, the session will count as a bounce.

Understanding Engagement Rate in GA4

The big problem with bounce rates is that they don’t give any insight into what is and is not working on your site. They simply show that something is causing a certain percentage of visitors to leave before engaging with anything on the page they visited.

Ultimately, this doesn’t tell you what to fix or how to fix it. It basically just makes you feel bad about yourself.

Google Analytics 4, provides a metric that can offer more value in terms of how users interact with your website. This is called “engaged sessions.”

Essentially, GA4 has inverted the way it measures users’ interactions on your site. Instead of measuring how many people are only providing single-page sessions, Google Analytics now measures the engaged sessions.

An engaged session is recorded when a user remains on your site or app for more than 10 seconds, views one or more pages, or triggers a conversion event. Conversion events are clicking on CTA, using your site’s search bar, signing up for a newsletter, among other things.

It’s because of this new way of measuring engagements that bounce rate doesn’t even make sense for tracking purposes in GA4.

Want to go in depth on bounce rate in Google Analytics? Everything you have ever wanted to know about bounce rate can be found here.

The Value of Bounce Rate & Engagement Rate

Simply put, bounce rate is the inverse of engagement rate in GA4. Bounce rate calculates the sessions that were not engaged, while engagement rate provides insight into the sessions that were engaged.

Both metrics are valuable in their own way, and can provide very useful insight into your website’s performance. While these metrics can provide value at a high level, looking at the total for your website, they provide the most valuable insight when you analyze them from a smaller, more segmented perspective.

Some of the ways bounce rate & Engagement Rate Provide Value:

• Opportunities for content optimization for pages that have high bounce rate
• CRO improvements to strengthen the paths to conversion on your website
• Better understanding of what your traffic is engaging with, and where they are engaging
• The relevancy of your content to the audience you are driving to your website
• Identifying user experience/website design areas for improvement

How to Find Engagement & Bounce Rates in GA4

To find your engagement rates in GA4, all you have to do is look at your engagement reports. This is done by clicking Reports > Engagement on the left navigation.

This report centralizes and summarizes the data across your detailed reports and gives you a look into user engagement across all of your connected channels.

The metrics you’ll find are as follows:

• Engagement Rate
• Engagement Sessions
• Engaged Sessions Per User
• Average Engagement Time

When it comes to bounce rate, currently, this metric can only be applied to exploration reports, and cannot be found within the reporting interface of GA4. To view and create explorations within your GA4 account, navigate to “Explore” within the main interface and view or create an exploration of your choice.

From there, you will need to add the bounce rate metric to any exploration report you create.

We Can Help You With Analytics

We get it, not everybody is as excited about Google Analytics 4 as we are. In fact, you might not be super interested in analytics at all. At Direct Online Marketing, we are equipped to help you with your Google Analytics 4 implementation and overall analytics strategy.

Learn more about how the new platform compares to the old platform in our blog post, “Google Anlaytics 4 vs Univsersal Analytics,” or contact us today for a free measurement strategy session for implementing GA4.

The post Where Did My Bounce Rate Go in GA4? Enter Engaged Sessions (Updated August 2022) appeared first on Direct Online Marketing.

What you are about to read focuses on Google Analytics tracking across multiple domains.

Looking for information regarding tracking for multiple subdomains? Google Analytics 4 handles that automatically. You DO NOT need to do any sort of cross-domain tracking for those situations.

Why Are Multiple Domains a Cause for Concern in Google Analytics?

Google Analytics tracks users with first-party cookies, by default. This means that when you visit a website, Google stores a unique and random identifier in your browser (a cookie). When you navigate across the website, Google checks the value of that cookie and can identify you as the same person/user.

Google Analytics understands that you are the same user and session.

This can cause problems because that cookie cannot persist across multiple domains. So, if your company has multiple domains, and you want to track the same user across those domains, a new cookie will be created, resulting in too many users in reporting. Every visit across the domain by the same user, will actually count as separate users in Google Analytics reporting.

This issue poses other data integrity issues such as self-referrals, but we won’t get into that here.

Resolving these issues can get complicated. Cross-domain tracking in Universal Analytics is dependent upon code changes and/or Google Tag Manager configurations. Implementing and configuring cross-domain tracking in UA often left marketers and analysts confused about exactly what to do.

Fortunately, Google Analytics 4 makes this critical data piece more streamlined and consistent.

How Does Google Analytics 4 Simplify Cross Domain Tracking?

Simply put, Google Analytics 4 passes a special parameter across domains so that the same user can be defined as….the same user. This special parameter allows Google Analytics 4 to understand that this visitor is the same person navigating across two different websites.

So what do you need to do to ensure proper cross-domain configuration in Google Analytics 4? Glad you asked!

Configuring Cross Domain Tracking in Google Analytics 4

There are a couple requirements before you move ahead with configuring cross domain tracking in GA4.

1. For the solution to work properly, all websites MUST use the same GA4 measurement ID/implementation.
2. Make sure you have a list of all websites that need to be included in your cross domain configuration.

Step 1: In GA4, navigate to Admin. Click on Data Streams and click on your stream

Step 2: navigate to More Tagging Settings > Configure Your Domains

Step 3: Enter all domains, one by one, that should be included in the cross domain tracking set-up. Hit save.

Testing Your Cross Domain Configuration in Google Analytics 4

Once you’ve added your websites in the domain configuration area of your GA4 property, you’ll want to test your set-up to ensure the cross-domain tracking is working correctly. There may be a delay with the configuration, so give it a few minutes or so before you proceed with testing.

When you’re ready to test, go to your first website and find an outbound link that goes to one of the domains on your list. Click the link, and once you land on the second domain, take a look at the URL. It should contain the parameter “_gl=” along with a string of numbers, letters and symbols.

It will look something like this:

If you find that gl parameter in your URL – congrats! This is working as expected.

If you want to take your testing a step deeper, you can use the DebugView in Google Analytics 4. In the DebugView, you should find your device and see page_view events that will contain the page_location with the domains listed.

Looking for further details on this? We suggest you check out this YouTube clip from the Google Analytics team.

If you have multiple domains, setting up cross domain tracking in GA4 is a must. By completing this setup, you can rest assured that you have a complete view of customer behavior across all of your domains.

Need to set up cross domain tracking in GA4, but want an experienced team to work alongside you to make sure you are getting the most enhanced measurement of your customer behavior? Contact us for a free analytics strategy session today before you get started setting up GA for your business.

The post 4 Easy Steps To Set Up Cross Domain Tracking With GA4 appeared first on Direct Online Marketing.

Recently, the majority of this chatter came because of a Google announcement heard around the digital world… that they planned to sunset Universal Analytics by July 2023.

The initial reaction to the news among marketers and website owners seemed to range from dread and apprehension to begrudging acceptance of a new way of life.

While Google had largely made it known that the transition to GA4 would likely happen in the not-so-distant future, presenting a hard deadline and conveying to users that historical data would not be maintained in Universal Analytics past a certain date represented a harsh wake-up call to reality for many. The debate about Google Analytics 4 vs. Universal Analytics has suddenly moved from a theoretical conversation to grudging acceptance.

Having to migrate a custom installation from Universal Analytics or setting up a brand new configuration represents an investment of time, money, and an acknowledgment of sunk costs into a system that is on its way to being deprecated.

But the switch to GA4 isn’t a negative change – far from it – as it represents a badly needed overhaul and presents a unique opportunity for businesses to gain a competitive advantage against direct competitors who are slow to adapt to GA4.

Why the Shift to GA4?

Universal Analytics is an amazingly popular tool that is used by just under 30 million websites. Despite this popularity, Universal Analytics (UA) is based on a data model that is over 20 years old and is in need of a hard reset.

New challenges such as privacy concerns over cookie usage, or maintaining accuracy of cross-device tracking have presented obstacles that have necessitated a data paradigm shift that can adapt with an evolving landscape. As a result, GA4 is not simply an enhancement to UA, but an entirely new product using a new data model.

Comparing Data Models In Google Analytics 4 vs Universal Analytics

GA4 uses an event-based data model, while Universal Analytics is a session-based model.

Arguably the biggest differences between UA and GA4 are the changes in the data model, which is the foundation that every previously deployed Google Analytics is built upon. Universal Analytics is a session-based data model. Within a session, UA collects and stores various interactions (called hits) that are grouped within these sessions. These sessions are the foundation of Universal Analytics reporting. 

Some examples of Universal Analytics hit types are: 

• Pageview
• Screenview
• Event
• Transaction
• Item
• Social
• Exception
• Timing

GA4’s event-based model processes each interaction/hit as a standalone event. Put simply, everything is now an event. Similar to how hits are grouped under Sessions in UA, event parameters are grouped under events in GA4 and serve as additional pieces of metadata to add context to the event data. 

Having these additional granular pieces of information can provide real benefits to your analytics. For example, it is very common for organizations to have countless forms, built using different tools, with different offers speaking to different parts of the funnel, scattered across your main site and subdomains.

Let’s say in this specific example, a SaaS client is using Contact Form 7, HubSpot Forms and Gravity Forms across their web properties for demo requests, free trials, newsletter signups and gated whitepaper downloads. We could pass the following custom event parameters for all form submission events (i.e, sign_up and generate_lead).

form_type: cf7, hubspot, gravity.

form_offer: demo, free trial, newsletter, whitepaper

form_id: passes the unique ID of each form so that you can map back to it in the tool it was built in.

This would allow the client to see which form types and/or offers are performing best and easily differentiate between the forms using a unique ID.

How Do You Measure Sessions In Google Analytics 4 vs Universal Analytics?

The concept of a session is a fundamental consideration in how we analyze user behavior. So even though GA4 has moved away from the session-driven model of UA, never fear, sessions themselves are still present and able to be analyzed in reports.

However, the method for how the sessions are calculated has changed.

The session_start automatic event is the new method for how sessions are calculated in GA4. The session_start event initiates when a user opens your website or app in the foreground, or views a page or screen when no session is currently active (e.g. the previous session has timed out).

A session can expire or timeout based on a period of inactivity. The default session timeout in GA4 is 30 minutes (the same as in UA), but can be adjusted up to 7 hours and 55 minutes.

Google measures inactivity based on the user_engagement automatic event. This event acts as a timer periodically sending events to GA4 as long as the site/app is in the foreground. So if a user_engagement event does not fire in the allotted duration set (default 30 minutes), the session times out.

Why The User Engagement Event In GA4 Is A Better Way To Measure Sessions

The user_engagement event further enriches session measurement by adding additional session-related metrics. Session related metrics include:

• Sessions: The number of sessions that began on your site or app (the session_start event was triggered).
• Engaged sessions: The number of sessions that lasted 10 seconds or longer, or had 1 or more conversion events or 2 or more page or screen views.
• Engaged sessions per user: The number of engaged sessions / the number of users
• Engagement rate: The ratio, represented as a percentage, of Engaged sessions relative to total sessions.

So while this shift in how sessions are calculated will certainly take some getting used to for marketers familiar with UA, from our perspective, this new model allows for new, useful, session related metrics that can provide more flexible and insightful behavioral analysis when compared to UA.

How GA4 Is Going To Enable Better Engagement Measurement

One of the biggest frustrations analysts have with Universal Analytics is the inability to measure engagement on a page. Bounce rate has long been the de facto stat for measuring engagement in UA. A bounce is when a user visits a single page and exits without navigating to a second page (bounce rate = bounces ÷ sessions).

However, there are plausible scenarios where users interact by only visiting a single page of your site. Although instead of that visit being a low or no value visit, the single page visit of your site actually  signals that visitors are finding exactly what they need right away. Situations like this could include reading a blog post or finding an answer on a frequently asked question page.

In these scenarios, many casual Universal Analytics users will use time-based metrics such as “Average Session Duration” for users who landed on that page. Unfortunately, Average Session Duration is one of the most misleading and misused metrics in UA, as many do not realize that UA can’t measure the time a user spent looking at the last page of their session.

So what you are actually viewing is the average session duration for sessions where the user visited multiple pages. The new engaged sessions metric helps fill this gap. As we can see, the pain behind a Google Analytics 4 vs. Universal Analytics conversation loses a little bit of its sting with the right implementation.

(Reference: Google’s documentation comparing Session in Google Analytics 4 vs. Universal Analytics: https://support.google.com/analytics/answer/9964640?hl=en#sessions&zippy=%2Cin-this-article
https://support.google.com/analytics/answer/11986666#sessions&zippy=%2Cin-this-article )

How Does Event Measurement Change in Google Analytics 4 vs Universal Analytics?

At this point, we have all grown accustomed to Universal Analytics’ event taxonomy of Event Category, Event Action and Event Label. While the structure is certainly a useful framework through which we have constructed our event naming conventions, it is somewhat rigid and has several drawbacks.

For example, we have a standard naming convention for measuring UA link click events that we use for the purpose of measuring several things.

• Event Category = “All Link Clicks”. The logic is to have a uniform name for all link clicks to keep reports clean and to easily be able to track unique link clicks on a given page.
• Event Action = {{cJS – Click Type}}. This is a custom JavaScript variable created in Google Tag Manager that returns whether the link click was Internal, Outbound, Click to Call, mailTo Click, or a PDF Download Click. This allows us to segment link clicks by user intent and to better understand user behavior.
• Event Label is {{cJS – Lowercase Click Text}}~{{Click URL}}. This combines the text of the link with the URL of the link so that we are able to differentiate between different links.

How GA4 Removes Event Taxonomy

As you can see from our example above, we are trying to implement workarounds to squeeze more information out of the event than the three standard event fields allow by concatenating multiple variables in the event label field. We could use hit-scoped custom dimensions to collect this data, but there is a limit of 20 custom dimensions per Google Analytics property built using UA, so custom dimensions have to be used sparingly in those installations. The ideal solution would be if we could add additional fields to the event as needed to collect the data–that is exactly what new GA4 property installs allow you to do.

GA4 totally removes UA’s event taxonomy. Event Category, Event Action and Event Label are no more, now the only standard field is Event Name. Additional event data can be collected via parameters; which are additional pieces of metadata that add context to events. Some parameters (e.g., language, page_title, page_location, page_referrer & screen_resolution) are sent automatically. In addition to automatically sent parameters, up to 25 custom parameters can be sent for each event.

When you consider the previous workarounds we had to utilize in my UA link click example, you can see one example of how the flexibility of GA4’s event-based model can, if properly configured, unlock more granular insights than its predecessor was ever capable of.

Types of Google Analytics 4 Events

There are four types of events in GA4:

1. Automatically collected events
2. Enhanced measurement events
3. Recommended events
4. Custom events

Automatically collected and Enhanced measurement events are what we would consider out of the box event tracking.

Automatically collected events

As the name suggests, these events are collected automatically when you set up your data stream for GA4 and are triggered by basic interactions with your site or app.

Here is a full list of GA4’s automatically collected events.

Enhanced Measurement events

Enhanced measurement events allow you to measure events right in GA4’s user interface. No Google Tag Manager configuration or coding required! Within the Admin section of GA4, you can enable any (we recommend all ) of the following events:

• Page views (page_view)
• Scrolls (scroll)
• Outbound clicks (click)
• Site searches (view_search_results)
• Video engagement (video_start, video_progress, video_complete)

You can read additional information related to GA4’s Enhanced measurement events here.

While out of the box event tracking (Automatically collected events and Enhanced measurement events) can fulfill basic needs, you must go further to get the most of your GA4 implementation. This can be accomplished via Recommended Events and Custom Events.

Recommended Events

Recommended events are events that you implement that are not easily configured out of the box. Because these events require additional context to be meaningful, they’re not sent automatically.

While the firing/trigger logic of the events need to be customized, they have names and parameters that are predefined by Google for the purpose of unlocking existing and future reporting capabilities. Adding these events are crucial for getting the most value of GA4 reports, helping you better understand user behavior.

An example of a recommended event would be sign_up, set to be triggered when a user signs up for an offer. You can then use GA4 reports to analyze which user sign-up methods are the most popular or convert the best.

Here is a full list of GA4 Recommended events.

Custom Events

Finally, custom events act as a sort of catch-all. Meaning if an event you have identified as valuable is not captured by any of the three other event types, it must require a custom implementation.

Why Google Analytics 4’s Data Model is Superior To Universal Analytics

It is clear that Google designed GA4 to be a more customizable future-proof tool than its predecessor. By moving to the event-based collection, Analytics is more flexible, scalable, and customizable.

The event-based model allows for all hits to be stored as a single data type. This is a welcome shift for analysts who have longed for improved granularity and have used workarounds (via hit-scoped custom dimensions) to segment and analyze data on a hit-by-hit basis. This constant and granular measurement of interactions across web streams provide you with even richer insights from your data.

Implement a Customized Measurement Strategy

Look beyond platform defaults. GA4 out of the box is actually a much more robust solution than UA. As we previously discussed, enhanced measurement allows for default recommended events to be added, while UA requires events to be configured using Google Tag Manager or with the help of a developer. Despite this, in any comparison of Google Analytics 4 vs Universal Analytics, GA4 still does not cater to the unique needs of every website.

Every business and every website are unique, meaning that every analytics implementation must be unique as well. To implement analytics in a way that is tailored to your business’s unique needs, custom events, custom dimensions and custom metrics are a necessity.

Out of the box measurement is a good place to start, but you must invest time in customizing your analytics implementation to get the insights that your business needs.

Contact DOM today for a free measurement strategy session for implementing GA4.

The post Google Analytics 4 vs Universal Analytics appeared first on Direct Online Marketing.