The complexity of the modern web user experience makes it critical for marketers to track users together across mobile and desktop environments. Since 2013, when Google released the third version of Google Analytics 4, also known as Universal Analytics, it has delivered excellent results for users. But the limitations have become apparent, especially in the last two or three years.
Introducing Google Analytics 4, the highly anticipated Google Analytics product update, announced in October 2020.
With the proliferation of new platforms such as mobile apps and IoT devices, a huge influx of new data sources has emerged; and as powerful as Universal Analytics was, it simply wasn’t up to the job. That’s why Google has returned to the drawing board.
Universal Analytics x Google Analytics 4
When Universal Analytics (UA) was released in 2013, mobile apps weren’t as productive as they are today, so it wasn’t as important for RA to provide marketers with a 360-degree view of the customer journey across sites like the programs.
However, as more and more people start using mobile devices, marketers are looking for a way to analyze data across platforms so they can take a more holistic view of their audience and behavior. After all, the same person can log in to a website on a phone, visit it later on a laptop, and log in the next day on a tablet. With UA, it was difficult to combine these datasets and see customer value across devices.
Modeling capabilities built into Google Analytics 4
The GA4’s increased ability to visualize user data across devices provides a much richer dataset for using built-in machine learning models. These models can help marketers better predict what actions their customers might take.
In the past, if you wanted to use Universal Analytics data models, you needed a data scientist to create the models and a data engineer to re-enter the data so you could work on it. A small project required a lot of technical and expensive work from several teams.
Why user-centric reporting and data collection have become indispensable
Google Analytics 4 is less about creating standard reports and more about analyzing data to find answers to specific questions. With Universal Analytics you can put custom data at the top of the standard report, but with GA4 you can focus on analyzing and searching the data. By reporting users across devices, GA4 can draw a clearer picture of their behavior throughout the customer journey.
Rather than pages or sessions, GA4 focuses on user-based insights gained from events and interactions. This gives you a clearer picture of what your users are doing. This is called “intent-based analytics”: you learn not only what users click or watch, but also what they are trying to do.
Smarter information to improve decision making and ROII
Google Analytics 4’s key differentiators – user-centric reporting and integrated modeling – are powerful tools for marketers to understand the customer journey.
You still need more advanced models from your data team, but another benefit of GA4 is that it also simplifies data extraction. This makes it easy to capture GA4 data and combine it with other data from your customer relationship management system, order history, or other sources. Then your team can spend their time building deeper, richer models that provide a more complete picture of your customers’ behavior.
Using predictive analytics, marketers can differentiate actions between prompts and customers, leading to increased sales and retention without increasing costs. Customers can be ranked based on who is likely to buy and who is least likely to buy. Both classifications are a boon to sales teams because they provide additional insight into the individual buyer’s journey.
Google Analytics 4 and you
After a year like 2020, there will be a natural resistance to further changes. Google Analytics 4 will never be perfect – it’s constantly evolving – but those who use it will reap the rewards as one of the first to adopt new ways of looking at the market.
Take the opportunity to see what others are missing by looking at your analysis differently.