Google Analytics 4: GA4 for Everyone

translating analytics to strategy

Every couple of years, Google Analytics makes a major update to their website analytics platform used by nearly every website out there to monitor performance. The newest version of analytics, GA4 is available now and Google plans to sunset Universal Analytics (UA) in July of 2023 so now’s the time to start tracking your website performance with GA4 in parallel with your existing UA tracking.

Website analytics

Let me take a step back for those of you not currently using website analytics of any type. For the rest of you, simply skip ahead to the next section.

Website analytics help you assess the performance of your digital marketing strategy so you have information to make data-driven decisions that help optimize your digital performance. Google Analytics is a free platform providing website data including:

  • metrics related to SEO (search engine optimization) such as bounce rate (the number of visits resulting in the visitor leaving the site after viewing a single page), time on site, and number of pages visited
  • who visits your site in terms of age and gender, language, and interests, if enabled. You can also view information regarding where they’re from (by country, city, region, etc), limited information on the device they used during the visit (ie. tablet versus mobile versus desktop), the operating system they used, screen size, and browser
  • where visitors came from, including organic search traffic (visitors who found your site by clicking on a link that showed on the results page based on their query), social media, paid search or display advertising, referrals from other websites, and direct traffic (which happens when a visitor enters your URL into the search engine, as well as being a catchall for miscellaneous traffic)
  • how visitors navigated your website, including pages viewed, exit pages, and landing page performance
  • did visitors convert and which channels generated conversions

Analyzing performance metrics

The value of having this data comes only once you analyze your performance based on the metrics provided by the platform, whether that’s UA or GA4. Raw metrics provide little information to guide better decision-making by themselves. Plus, the raw numbers aren’t completely accurate, thus you can’t be sure that 4633 visitors is meaningfully different from 4635 visitors. Part of the reasoning behind this is that not all visitors allow tracking, part is due to subtle mistakes in implementing tracking on your website.

So, you might ask, why bother with Google Analytics in the first place?

One answer is that it’s the best you can do if you want to track website performance and improve performance, and who doesn’t want to improve their website? Web scrapers and bots just don’t do as good a job of capturing visit data as the Google Analytics platform.

The second part of the answer is that the data is very meaningful if you evaluate performance the right way. That means you compare the performance over time, meaning the data are off by about the same amount each time so the relationship between performance today and a year ago provides useful and accurate data. If your visits increased by 10% year over year, that provides useful information to guide decisions.

The third part of the answer is that, when you segment your data (for instance based on the age or geographic region generating conversion or based on the channel involved in the conversion), you have great insights for improving your conversion or any other goal you set for your website.

What you should measure

One of the biggest problems faced by those unfamiliar with building insights from GA4 or any prior version of Google Analytics is the vast amount of data available, which leads to analysis paralysis.

analysis paralysis
Image courtesy of AIHR

Instead of feeling you must analyze every metric provided by Google Analytics, I recommend the following set of analyses:

  1. Goals
    1. goal 1
    2. goal 2
    3. goal 3
  2. Traffic
    1. # of users
    2. trends over time such as year to year or month to month
    3. by dimension ie. gender, age, location, device, and channel. You may find additional segmentation variables are important for your specific decisions. For instance, if you produce an app, the operating system used by visitors is an important variable.
    4. You might also look at the engagement of visitors, new versus repeat visitors, and other metrics related to your visitors
    5. insights — what recommendations would you make to optimize these metrics. Remember, insights go beyond summarizing the results to build insights based on applying the summary to your business situation. For instance, if you notice that traffic from age group 1 is much lower than other age groups (and age group 1 is an important target market for your firm), you might apply this information to suggest you create better ads and other content that appeals to age group 1.
  3. Channels
    1. visits by channel
    2. trends
    3. segment your channel visits based on the dimensions identified above
    4. insights — what recommendations would you make to optimize these metrics.
  4. Content
    1. top pages by # visits, time on page, exit (fewest exits), and bounce rates
    2. trends
    3. assess the funnel (see below) ie. which content is likely the first visited page, which page do visitors go to next, and so onbig data analytics
    4. segment your channel visits based on the dimensions identified above as well as by channel
    5. insights — what recommendations would you make to optimize these metrics.
  5. Cart Abandonment (put in cart versus conversion)
    1. segment your channel visits based on the dimensions identified above as well as by content funnel leading to abandonment
    2. exit pages at the point where the visitor abandoned their cart
    3. insights — what recommendations would you make to optimize these metrics.
  6. Conversion
    1. funnels
    2. Google Ads metrics (this requires linking your Google Ads account to your Google Analytics account)
    3. segment your channel visits based on the dimensions identified above plus by content
    4. insights — what recommendations would you make to optimize these metrics.
    5. multi-channel attribution modeling, which involves a recognition that customers often visit your site multiple times before making a purchase and each channel contributing a visit to the ultimate conversion should get partial credit for the sale.
    6. ROAS (return on advertising spend), which should include the costs of your strategy on each marketing channel, not just advertising spend
    7. insights from multi-channel attribution modeling
  7. AOV (average order value)
    1. segment your channel visits based on the dimensions identified above as well as by channel
    2. insights — what recommendations would you make to optimize these metrics.
  8. Achievements toward reaching your 3 goals
  9. Overall insights based on all elements discussed above

To make analysis easier, I recommend adding all metrics to a dashboard (Universal Analytics, see below, provides the ability to create custom dashboards while GA4 works best by using the Google Data Studio to create your dashboards).

google analytics for web

Assessing conversion

Conversion (which UA calls goals) is critical because that’s what makes your business a success or failure. Hence, it rates a discussion all its own. Conversion actually involves a process with multiple steps that looks like this:

Every step in the process is important, not just the actual conversion. Without all the steps leading up to conversion, you lose the sale.

Appropriate for our discussion today, Google Analytics doesn’t do a great job of assessing performance at the top of this funnel. With GA4, however, you don’t have to set up goal funnels as a separate task, as they’re already available on the platform natively. This provides more accurate assessments of the top-of-funnel behaviors. You can also set events to assess shopping cart abandonment and assess which stage or stages result in the highest levels of abandonment.

Making the GA4 switch

If you’re currently using Universal Analytics, you should continue collecting data with your existing tracking code (for more on setting up Google Analytics, including installing the tracking code, see this post). If you’re new to Google Analytics, you follow the same process outlined in this post to create a property. Either way, you want to start tracking GA4 analytics by creating and installing the new tracking code on your website. You want to run GA4 and Universal Analytics in parallel probably until Google retires Universal Analytics in 2023. The rationale for parallel applications is that GA4 isn’t fully-featured right now, so you won’t have all the metrics you’re used to from Universal Analytics. For instance, as of this date, GA4 isn’t reporting demographic data on visitors. Some features might make it into GA4 in the future, but some aren’t possible because of the way GA4 tracks visits.

With Universal Analytics, website reporting was fueled by cookies. When a visitor landed on any page of your website, Google captured their visit information on a cookie that was sent to the analytics platform. With the elimination of 3rd party cookies in an effort to provide additional privacy protection to users, Google needed a new way to capture visit data. GA4 uses machine learning and 1st party cookies to capture data from visits on mobile apps, as well as your website so it’s in compliance with GDPR and California privacy laws.

GA4 relies on event tracking, so key metrics are available that you only got in Universal Analytics by using Google Tag Manager. For instance, you can collect data related to:

  • session start, which was collected in UA as a session based on time
  • actions taken on a page such as watching a video or downloading a PDF
  • page view, such as viewing a certain percentage of the page content
  • events from apps and mobile are integrated versus requiring separate tools for tracking

In contrast to UA, GA4 offers fewer reports (using the report tab versus individual tabs for visits, content, etc), while offering better customization to create the reports needed for your specific needs. Reports are divided into life cycle, which includes subreports on acquisition, engagement (including events and content), retention, and monetization (including purchases through both apps and websites), and user, which includes demographics (currently only language and geography are available) and tech.

Explorations are also part of the new platform. Users create explorations to look at how user groups interact with your website to develop insights. You can perform explorations on an ad hoc basis or save these deep reports for use in the future.

Under the advertising tab, you can look at how channels impact conversion; identifying those channels with the greatest (or least) impact on conversion. This is also where attribution modeling lives to compare different model types.

Finally, the configure tab allows you to assess the performance or add new events. You can also designate whether these events represent a conversion to develop better conversion insights.


GA4 is coming whether you want it to or not. For those of you using UA, you want to run this new platform in parallel to assess how well it does relative to GA4. Also, some metrics aren’t currently available on the new platform, so you still need UA.

If you haven’t started assessing website performance, now’s the perfect time to start. If you don’t access your results daily, as you should, now’s a great time to get in the habit of looking at your reports and using the information to help your website performance improve.

GA4 looks a little different from what you’re used to in UA but, if you take the time to learn how to get the most from the new platform, I think you’ll find it a great tool for monitoring and optimizing your digital marketing efforts.

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