This article investigates the challenges linked to driving value from a Google Analytics 4 implementation for Google Analytics 4 users and analysts.
Google Analytics 4 and the implementation geeks
Entire libraries could be filled with documentation, guides and how-to’s on how to set up and orchestrate your Google Analytics 4 property. Another library entirely could be dedicated to documented debates on which setup methodology would be the best. I know, as I am one of the people contributing to the piles of documentation, articles and debates.
That being said, we've also come to the realization that we, implementation experts, are putting way too much emphasis on the implementation phase of Google Analytics 4 and nearly not enough on making sure that the end-users, the clients, are able to drive value from the product.
If we would take a look at the (non-exhaustive but for the sake of the argument simplified) stakeholder mapping of who is involved in a Google Analytics 4 project a number of key stakeholders can be identified:
Developers - A major shift in the transition from Universal Analytics to Google Analytics 4 was the introduction of streams. Google Analytics is now capable of combining data streams from multiple sources and devices into a single data set and interface. Next to that the entire data model switched to an event-based measurement model. Even though Google Analytics 4 is able to auto-capture an impressive amount of information the dataLayers and SDKs need to be adapted in order to manually enrich the Google Analytics 4 implementation using an event-based measurement model.
Google Analytics 4 implementation experts - This stakeholder is responsible for the Google Analytics 4 configuration through a Tag Management System (such as Google Tag Manager), quality control, testing and the actual configuration of Google Analytics 4 itself. In most cases, this person also takes care of the functional and technical requirements that are then translated into documentation for the developers.
Google Analytics 4 analyst - This is the stakeholder that will actually drive the value for the business. The analyst turns the data captured and confined in Google Analytics 4 into actionable insights that will propel the business forward. Note that even though I’m using the term analyst it could be anyone ranging from a person in the data team to an e-commerce manager or digital marketer.
Forgetting to empower the Google Analytics 4 analyst
The state of the industry today foresees well in the needs of the first two stakeholders. When going through a Google Analytics 4 implementation the focus is mostly put on ensuring that the tracking plan is well thought through and that documentation is foreseen and contextualised for each developer responsible for a specific stream (website or app).
This means that the stakeholders that are involved in the beginning of the value chain are well equipped when it comes to documentation. The analyst operating Google Analytics 4 and responsible for turning data into insights on the other hand is not.
From Google’s side this is due to the fact that Google currently focuses on what they call the “dual-setup phase”. This means that customers are being asked to deploy Google Analytics 4 in parallel to their existing Universal Analytics infrastructure for the purpose of data collection. This ensures that a backlog/history of data is being collected which will serve the customer going forward. Slowly but surely the focus will be shifting from implementation documentation to more communication around the usage of Google Analytics 4.
Driving value from a Google Analytics 4 implementation starts, next to a correct implementation, with understanding which data is actually collected in the platform. Analysts are not always aware of what is stored in Google Analytics’s data repositories which makes analysis in a world of event-based and property rich data-points very hard. To answer detailed questions the analyst is required to know which event to select and which property to use for breakdown or drill-down purposes. To tackle this challenge the analyst can decide to read through the tracking plan and developer oriented documentation. This documentation is however not adapted to the analyst’s skill level nor context. Answering questions that seem simple can become daunting very quickly for non-technical analysts as a result. Conclusion - The analyst’s documentation needs are usually forgotten. The analyst is often left confused and not in power to maximise the potential that a tool such as Google Analytics 4 can bring to the business.
Empowering the Google Analytics 4 analyst with the proper documentation to drive value
At Human37 we want to ensure that a Google Analytics 4 project, next to being implemented correctly, is also driving value for the business. In order to do so we created a simple piece of documentation specifically for the analyst. Something we call “the analyst matrix”.
The analyst matrix simply consists of a mapping of every event-property combination that resides in your current Google Analytics 4 implementation. As Google Analytics 4 comes with a native Bigquery integration we render the analyst matrix from Bigquery directly.
The analyst matrix consists 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-cheat of which information is available in the analytics platform. Both events and properties are ordered alphabetically in order to give the analyst an easy overview when looking for specific values to include in the analysis.
Building your own matrix
We’ve created a guide for you to render your very own last-mile documentation in the form of a Google Analytics 4 analyst matrix.
Ensure your Google Analytics 4 data is being sent to Bigquery:
Go to https://console.cloud.google.com/bigquery and select your data set Note that we’re sharing screenshots with public data sets so any reader that does not have Google Analytics 4 data in Bigquery can try running an actual Google Analytics 4 analyst matrix.
Select “query table”
Run the following SQL query (also available on Github):
DECLARE props STRING; SET props = ( SELECT CONCAT('("', STRING_AGG( key, '", "'), '")'), FROM ( SELECT DISTINCT key FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`, UNNEST(event_params) ORDER BY key ASC) ); EXECUTE IMMEDIATE FORMAT(""" SELECT * FROM ( SELECT case when count( event_timestamp ) is not null then "X" else "" end as property, event_name, key FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`, unnest(event_params) GROUP BY event_name, key ) PIVOT ( max(property) FOR key in %s ) order by event_name """, props);
Select “view results”
The output is an analyst matrix as indicated in the previous chapter. This can be saved as a CSV and opened in Google Sheets.
The future spells Google Analytics 4
We hope you find the concept of the analyst matrix useful. Our objective is to create awareness that it’s not only about implementation, but as well about adoption. A product or implementation that is not adopted does not create any value. Even though Google Analytics 4 still has a long way to go, it’s definitely the future.
If you haven’t started with Google Analytics 4 we recommend you to start deploying as soon as possible as it's the new baseline of measurement in Google’s world.
Not sure how to start? Reach out and we can help you further.