I’ve been hearing a lot of aversion about the upcoming migration from Universal Analytics to Google Analytics 4 and I don’t believe it’s justified.
As you’ll read below, GA4 is bringing some incredible upgrades to [most people’s] favorite analytics product.
Revamping and modernizing a legacy tool requires significant change.
And change is hard, I get it. This is quite similar to the changes happening in the Ads ecosystem with Performance Max and Advantage+ campaigns.
changing evolving at once, and I think it’s in every advertiser’s best interest to pay close attention and keep up with the trends.
Most of the change is driven by the tightening requirements for sharing data — limiting the data available for personalized marketing which have impeded the ability to observe certain cohorts of users.
So please take the few hours it’ll take to set up and design your GA4 instance. Your data from Universal Analytics will not transfer over to GA4 so each day that goes by is one where you won’t be able to perform historical analysis with.
Ok, my spiel about why GA4 is so incredible starts back in 2005 when:
Google acquired Urchin Software (the U in ‘UTM’), a web analytics software that was developed in 1995 and turned it into what we now know as Google Analytics. They also made it free for everyone 😊.
Screenshot of Urchin Software from 2002
Some things in Google Analytics are still reminiscent of Urchin today. Even the naming convention of analytics properties which begin with ‘UA’, is short for Urchin Analytics.
Yes, the tracking mechanisms that many of us rely on today were built more than 20 years ago. We even still have documentation available on our site announcing the “new” interface and how to set up tracking tags THAT YOU CAN STILL USE 20 YEARS LATER.
So I hope you can start to see where I’m going with this… there hasn’t been a major update to accommodate for all of the changes that have hit the digital marketing industry.
Yes, the UI changed. The default reporting views aren’t great.
And it may take a few hours to configure properly.
But what you get out of this implementation time investment is unparalleled.
Here’s a short list of some of the challenges with the current iteration of Google Analytics:
- It’s using last-click attribution.
- It doesn’t track cross-devices or between web and app.
- It doesn’t use machine learning.
- It can’t capture offline events.
- It doesn’t deduplicate users across devices and platforms.
- It doesn’t integrate with media buying.
These seem like pretty meaty issues for the modern day advertiser.
How GA4 solves these problems:
Attribution: GA4 upgrades from last-click attribution and uses data-driven attribution (the multi-touch attribution methodology that is now the default in Google Ads) across all of your marketing touchpoints and assigns credit based on how the addition of each ad interaction to the path changes the estimated conversion probability.
Imagine… a free, best-in-class, multi-touch attribution tool that you can use to make decisions about your entire marketing mix.
The model incorporates factors such as time from conversion, device type, number of ad interactions, the order of ad exposure, and the type of creative assets. Using a counterfactual approach, the model contrasts what happened with what could have occurred to determine which touchpoints are most likely to drive conversions. The model attributes conversion credit to these touchpoints based on this likelihood.
Cross-device, cross-platform, and offline tracking: GA4 upgrades from duplicated cross-device users and actions to now helping you measure customer journeys that span devices, platforms, and into the offline, allowing for a full picture of how your users interact with your content, based both on Google data and a flexible event collection and data model
For instance, when you enable User ID, Analytics can measure journeys that include a visit to your mobile website and a conversion in your mobile app, helping you answer questions such as: “are customers who start on my App or Web more valuable from an LTV perspective?”
Is this not what data dreams are made of?
Fully configurable and customizable event tracking: Perhaps the biggest change in this evolution from UA to GA4 can be seen with Event tracking.
Universal Analytics collects data based on web sessions and hits. These actions are powered by cookies (which are going away) and are not that configurable.
Each event in UA had to be set up individually using Google Tag Manager and could only associate four specific attributes to each event (category, action, label, and value).
Events were also limited to tracking actions that took place within a page which meant that you were out of luck if you wanted to collect custom information that didn’t match one of the predefined hit types.
And lastly, when looking to track event counts in UA, all of your events were consolidated into one report where it was tough to deep dive and find actionable insights.
In GA4, events are highly configurable and customizable in both setup and reporting capabilities. You can use events to track any action or piece of information you’d like.
From the pages people view (which are automatically tracked) to button clicks or even information you’ve collected in another platform (like your email marketing platform or CRM). You can send the data to Google Analytics using events.
For example, you can use an event to measure when someone loads a page, clicks a link, or completes a purchase, or to measure system behavior, such as when an app crashes or an impression is served.
You can even fire an event when someone returns a product.
You can even create custom user properties specific to your business. Think ‘high LTV customers’ who reach a certain spend threshold or ‘loyal customers’ who interact with your business frequently:
Now, because of the flexibility in Event creation, this is going to change the way that you’re used to reporting on conversions. This seems to be a major complaint.
A conversion is more than a purchase now, it’s any interaction or occurrence that's valuable to your business.
For example, a user purchasing from your store or subscribing to your newsletter are examples of common conversions. Any time you want to record a conversion, you simply mark an event that measures the interaction or occurrence as a conversion.
So to report on these like you used to, get familiar with the filters in the reporting modules as you can swap conversions in/out as needed. Browse the template library for more commonly used reports and save them to your instance.
Increase access to insights with faster reporting, embedded machine learning, automated insights and new editable reporting tools and metrics:
Once you get a hang of the new interface, you’ll see the magic. Here are some of my favorite upgrades:
- Flexible drag-and-drop interface to visualize your data through multiple techniques.
- Use advanced analytics techniques such as Cohort exploration, Funnel exploration and Pathing exploration.
- Access new User lifetime metrics to gain deeper insight into the lifetime activity of customers across platforms.
- New predictive metrics can be added to reports.
- Create segments and audiences from your analysis, or share and export your analysis data for use in other tools.
And some ways where machine learning can help you:
- Anticipate new growth opportunities and predict future user behavior through Google’s AI
- Identify insights and reports with natural language search (e.g., ask questions about your analytics data)
- Automatically surface analytics insights on major data changes or emerging trends in your data
- Detect anomalies within your data for metrics or within a segment at a point in time
That’s all for this week. Please consult your Google team who is available to help you get your shiny new GA4 instance setup and ready for you to customize.