Goodbye Universal Analytics, hello Google Analytics 4

The complexity of the modern web user experience has made it critical for marketers to co-track users across mobile and desktop environments.

Since 2013 when Google released the third version of Google Analytics, AKA Universal Analytics, Google has been delivering great results for users. But its limits have become particularly evident in the last two or three years.

Enter Google Analytics 4, the highly anticipated update to the Google Analytics product that was announced in October 2020.

With the proliferation of new platforms such as mobile apps and IoT devices, a massive influx of new data sources has arisen. and as powerful as Universal Analytics was, it just wasn't up to the task. So Google went back to the drawing board.

This article explains the differences between Universal Analytics and Google Analytics 4, why user-centric reports and data collection are preserved, and how advertisers can get more innovative insights to improve their marketing decisions and ROI.

Universal Analytics vs. Google Analytics 4

When Universal Analytics (UA) was introduced in 2013, mobile apps weren't as productive as they are today. It was therefore not so important for UA to give marketing professionals a 360-degree overview of the customer journey – that is, across websites as apps.

However, as more and more people started using mobile devices, marketers looked for a way to analyze data across platforms to get a more holistic picture of their audiences and behavior. After all, the same person can view a website on a phone, revisit it later on a laptop, and log in on a tablet the next day. With UA, it was a challenge to combine these data sets and look at the customer lifetime value on all devices.

Google was looking for technical changes it could make to the platform to help companies conduct cross-platform analytics. The result was Google Analytics 4 (GA4).

Regardless of what device a person is using, GA4 can capture this data and provide a comprehensive view of how that person is interacting. The product's ability to combine data should be a game changer and will undoubtedly be one of the main selling points of GA4.

Integrated modeling functions in Google Analytics 4

GA4's improved ability to display user data on all devices provides a much larger dataset to run built-in machine learning models. These models can therefore help marketers better predict their customers' actions.

In the past, if you wanted to run models on Universal Analytics data, you needed a data scientist to build the models and data engineers to put the data back in so you could work on it. A small project required a lot of technical and expensive work in several teams.

GA4 offers three new integrated modeling functions that enable you to use your company's resources more efficiently. The three models provide basic but essential predictive metrics:

  • Purchase probability helps predict the likelihood that users who visited your website or app in the last 28 days will make purchases in the next 7 days.
  • Churn probability predicts the likelihood that recently active users will not visit your website or app for the next 7 days.
  • The revenue forecast predicts the revenue expected from all active conversions within the last 28 days by an active user in the last 28 days.

Because these models are directly available in the user interface, your data science team can work on more complex models and solve more complex problems.

Why user-centric reporting and data collection stays here

Google Analytics 4 is less about creating standard reports and more about analyzing data to find answers to specific questions. Universal Analytics allowed you to overlay custom data on top of your standard report. However, with GA4 you can focus on analyzing and exploring the data. By reporting users on all devices, GA4 can paint a clearer picture of their behavior along the customer journey.

Instead of page views or sessions, GA4 focuses on user-based insights gained from events and interactions. As a result, you will get a clearer idea of ​​what your users are doing. This is called "intent-based analysis": it tells you not only what users click or view, but also what they are trying to do.

This shift from "what do people see?" to "What are people doing?" may introduce a whole new direction for analysis: one that focuses on user interaction.

Marketers need to know their customers in order to build relationships with them, but demographics and psychography don't help predict a customer's next action. Behavioral data about users is much better at predicting things like purchases, upgrades, and churn.

What your customers do paints a better picture of their next action than who they are. Hence, the key to understanding customers is knowing their actions across channels and showing them what they expect or need in each moment – the ability to answer before they ask!

Smarter insights to improve decision making and ROI

The key differentiators of Google Analytics 4 – user-centric reporting and built-in modeling – are powerful tools that help marketers understand their customers' journey.

More advanced models still require a lot of work from your data team. Another benefit of GA4 is that it also simplifies data extraction. This makes it easier for you to take your data from GA4 and combine it with other data from your customer relationship management system, the order history or other sources. Then your team can spend their time developing deeper, richer models that give a more complete picture of your customers' behavior.

With the help of predictive analytics, marketers can differentiate actions between prospects and customers, resulting in higher sales and customer loyalty without increasing costs. Customers can be classified according to buyers who are likely to buy and those who are less likely to buy. Both rankings are a boon for sales teams, as they gain additional insights into the individual buyer journey.

Using this ranking insights, sales teams can adjust their strategies to optimize sales. It also gives marketers the ability to help sales teams more accurately forecast their sales pipelines. And which sales team doesn't want a more specific sales pipeline?

Google Analytics 4 and you

After a year like 2020 there will be natural resistance to further change. Google Analytics 4 will never be perfect – it will constantly evolve – but those who do use it will take advantage of an early adopter of new ways of looking at the market.

Take this opportunity to see what others are missing by looking at your analysis differently.

More resources in Google Analytics

How to marry offline and online attribution data for a 360-degree view in Google Analytics

The Small Business Guide to Google Analytics (Infographic)

Marketing ROI: How To Get Actionable Insights From Google Analytics

Comments are closed.