Google Analytics (4): The story behind the evolution and why it makes sense
To understand why Google Analytics 4 (GA4) got developed and why it is a solution that better fits the need of the current times, we need to ask ourselves why Google Analytics got created in the first place.
It’s November 2005. Google finalises the acquisition of Urchin and renames it Google Analytics. Initially Google Analytics was created two solve the challenges of mainly two people in the organisation (1) the webmasters and (2) the marketer. Singular, because when web analytics saw the light of day, these were the most digitally advanced people in any organisation. The unicorns of that time. At that moment in time webmasters and marketers were interested in a simple way of measurement. The model in use, session based measurement, was everything they needed. Session based measurement allowed hits to be rolled up in sessions and assigned to users. Cross device wasn’t a thing as it would take more than a year before mobile would slowly start a new revolution with the coming of the iphone in 2007. Having a model based on hits that were linked to users and aggregated into sessions made complete sense given the needs of our two peronas.
When creating and maintaining a website was still the job of webmasters, Google Analytics was providing them with plenty of interesting information: most used browsers, average load time, and the list goes on. How organisations were conquering the world wide web and how fast it was expressed in session and users. Next to purely reporting, these metrics were helping them identify how to improve their website. CRO was born. On the other hand, you had the marketers. It is important to keep in mind a large part of Google’s revenue stream came (and still does till today) from media investments on their platform(s). Google Analytics was the business oriented solution that could help marketers identify which sources, mediums or campaigns were performing best for a set of KPIs such as: bounce rate, average time on site, transaction rate, and so on. It was also helping marketers discuss and grasp attribution challenges.
The tipping point
So what happened? Among others, two main trends are worth mentioning: (1) customer journeys have changed and gotten much more complex and (2) more individuals (within organizations) started consuming data.
The user’s journey complexification is certainly not new to you. The rise of mobile, social networks and in general opportunities for users to connect with brands through offline or online touchpoints are among others the reason why we have moved from a relatively linear single-platform model to a much more complex version of it.
On the other side everyone started talking about becoming data-driven and how important it is to use data to make informed business decisions: improving the experience offered on a website or application, being aware of a customer’s profile and past behavior for customer support, etc. Quickly, product owners, customer care teams, IT and data teams were added to the list of analytics data consumers. Together with those new data consumers came new challenges. Indeed, their needs were different: knowing which pages had a high bounce rate or that users were landing or exiting on certain pages of the website was not enough anymore. They wanted to have a complete view of all interactions a user had with an application or a website: which button was clicked, content consumed, where it was clicked and consumed. They also wanted to have a unified view of users across the board. Being able to create a single record of every action and interactions a user had taken with any asset of the brand and enrich it with details and context
This is where the hit/user/pageview model hit its own limitations. This is where GA4 is finding its positioning with its user based and event based tracking model. It provides much more depth to the information a brand can capture, it allows almost infinite enrichment of every interaction a user is having with an application or website. It also allows to easily unify website and application tracking which was until now not possible with the previous version of Google Analytics. Web and application data now reside in a single interface and are enriched with context, details and metadata in the form of parameters. Finally it allows linking every action transformed into an event with users to create this single action record.
Changing the way we track to leverage GA4's full potential
Alongside its new tracking model, GA4 is reinforcing the need for well documented and maintained data tracking plan and lexicon. Indeed, with the previous version of Google Analytics (Universal Analytics or any predecessor) the implementation was relatively straightforward. A limited tracking plan was assembled based on a set of predefined events that simply needed to be tailored to the brand’s specific context. There wasn’t much more than pageviews, ecommerce events and a couple of custom events which only could hold 4 elements (category, action, label and value). This documentation was then used by the developers for the implementation. Later this was deployed through a Tag Management System (Google providing Google Tag Manager) which would piggy-back on a dataLayer.
With GA4, every data consumer (marketer, product, customer care, IT, etc.) now needs to be involved to define what information they want to surface and how it will be surface. This leads to the creation of a data tracking plan and lexicon that are translating the business needs into events, properties and ensure the context around the tracked data is accessible and understandable for every data consumer. This data plan and lexicon becomes the unique source of truth for the data consumers when it comes to understanding which data is tracked, where it is tracked and what it means. As a consequence, and in order to ease analysis and reporting purposes, we are now introducing the concept of analyst matrix. They consist of events as rows and properties as columns. Whenever a particular event-property combination is available it is marked with an “x”. This piece of documentation allows any analyst, technical or non-technical, to have a quick overview/cheat-sheet of which information is available in the analytics platform. If you want to know more about the analyst matrix, check out the article we wrote on the topic.
In our opinion, GA4 is an underestimated and misunderstood solution. The reason behind it is simple, GA4 gives the opportunity to solve the needs and challenges of many more people than the one its ancestor could tackle. This brings confusion for marketers who are not finding back their usual and beloved report. On the other hand, most of those new data consumers don’t yet realize the tool’s full potential.
At Human37 it is one of our missions to ensure every stakeholder properly understands the tool’s value and how it should be used. You want to know more about how we have helped dozens of brands grasping the full potential of GA4? Reach out!