In most cases, Google Analytics 4 projects start as a technical request. A marketing team, a client or the speaker requests the implementation of monitoring. Someone has access to the property and assumes the data will eventually tell a story worth listening to.
Maybe sometimes it is. But most of the time it produces a collection of numbers that seem precise but answer questions that no one has actually asked.
The measurement framework must take precedence. Everything else should flow from there. Because it gives a purpose to the technical setup before anyone starts measuring.
The main problem is that there is too much data and not enough action. And this is the gap that a measurement framework is meant to fill.
Supervise the work before monitoring the work
1. First define success in simple language
Before creating an event required to answer the question, define success. Success needs to be described in a way that people can recognize it when they see it.
If your goal is to generate leads, does success mean more total requests or better qualified requests?
Focus on content performance? Then, define success metrics like more organic traffic, returning visitors, more visits to business pages, or achieving more assisted conversions.
If you want to grow e-commerce, does success mean more purchases, higher average order value, fewer checkout abandonments, or more loyal customers?
Each answer leads to a different measurement plan.
This is why I don’t think analytical planning can be separated from the business context. The same data can be useful, irrelevant or misleading depending on the company’s objectives.
2. Start with questions, not measurements
This is where I would further delay reporting before getting the answers to the important questions that will define the measurement framework. Survey your business like it’s a crush and get this data! But first, make a clear list of questions and don’t stop until you get the answers.
Well, now you might be wondering: what does the company need to respond with more confidence?
For example:
- Why do users give up before submitting a request?
- Which landing pages generate valuable inquiries?
- How does the behavior of returning visitors differ from the behavior of new visitors?
- Which product or service pages need improvement?
At this point, the goal is to shape the measurement setup. Because a dashboard should help people decide what to do next.
This seems obvious, but this is where many reporting setups get complicated. They include metrics because they are available, not because anyone decided what action they support.
The exercise here is simple but sometimes overlooked: Write down all the questions your management team would ask if they had access to unlimited, squeaky-clean data. Then review this list and identify questions that your current setup could answer.
It is up to your measurement framework to bridge the gap between these two lists. Once these questions are asked, the framework can define what an outcome actually looks like.
3. Determine what might help answer these questions
Once success is defined, the next question is: what can happen on the website? What does the user experience?
A good measurement framework should attempt to identify behaviors that show a person is moving closer to meaningful action.
For example, if the question is “Why do users give up before submitting a request?” “, there are a few things we may need to understand first.
Are people responding to the call to action? Is the drop worse on mobile? Is this happening more from a specific landing page or traffic source?
These are the behaviors behind the question.
The same goes for a question like “Which landing pages generate interesting inquiries?” » The answer probably doesn’t just lie in the number of sessions. You may need to examine what users do after arriving on the page.
Your questions become more useful for measurement when you can relate them to something observable.
4. Don’t treat every metric like a KPI
One of the reasons analytics reports are confusing is that tracked actions are treated as if they all have the same level of importance. This is not the case. For example, a purchase is definitely not the same as viewing a product page.
This is not to say that small actions are useless. But they play a different role.
I like to separate measurements into three layers:
Business results:
These are the results that the business ultimately cares about: revenue, qualified leads, pipeline, purchases, subscriptions, customer retention or acquisition.
Performance indicators:
These allow you to show whether users are moving towards these types of results: demo request rate, payment completion rate, trial registration rate, conversion rate of returning visitors or passage of content to commercial pages.
Diagnostic signals:
These help explain why something might be happening: form abandonment, device-level abandonment, filter usage, internal search behavior, CTA clicks, or engagement with specific page types.
This is important because not all numbers belong to the same report. Stakeholders need results and some performance indicators. Marketing teams may need signals at the channel, landing page, and content levels.
Analysts may need diagnostic data to investigate problems. Developers may need event-level details to validate whether the implementation works.
→ See also: GA4 Metrics Every Advertiser Should Pay Attention to
5. Decide what not to measure
This is perhaps the most overlooked part of planning. However, a good measurement framework should also indicate what does not need to be tracked.
This may seem uncomfortable because analytics tools make a lot of tracking possible. But more tracking doesn’t automatically mean better metrics.
Sometimes that just means more maintenance. Because every event has a cost. Someone has to implement it, test it, document it, explain it, and ultimately decide if it still matters.
If no one is using the data, it probably doesn’t belong in the base configuration.
At this point, a simple test can help. If this number changed, would anyone do things differently? If the answer is no, it may not be worth pursuing at this time.
6. Keep in mind that GA4 is not the entire measurement system
Another important conversation needs to happen before implementation: what should GA4 be trusted and where should another system be treated as the source of truth?
Because GA4 can tell you a lot about digital behavior. It can show where users came from, what pages they visited, what actions they took, and where they went down. But it shouldn’t always be seen as the final answer to everything.
As Rémi Kerhoas supportedchoosing a single source of truth can become an attribution trap because each system has its own model, limitations and blind spots.
Therefore, for example, for e-commerce businesses, the e-commerce platform can be a clearer source of information on orders and revenue. For B2B companies, the CRM system can be the best source of truth about lead quality.
Therefore, my recommendation here is that when considering a measurement framework, you determine which questions GA4 can answer, which questions require another system, and where the data should be compared.
7. Transform the framework into an implementation brief
Once you know what success means to the business, what questions need answering, what behaviors can help answer them, technical work becomes much easier.
This is where GA4 has its place in the process.
The team can now decide:
- What events should be followed.
- Which events should be marked as key events.
- What settings are needed.
- Which audiences or segments are important.
- What reports should be created.
- What data should be compared to CRM, e-commerce, sales or product data.
- Which interactions do not yet need to be tracked.
This is much cleaner than opening GA4 first and trying to make decisions inside the tool.
The framework becomes the brief. The implementation is still technical, but it’s no longer guesswork.
8. Validate before anyone uses the data
Even with a solid framework, the data still needs to be verified. An event appearing in GA4 does not automatically mean that it is reliable.
He can shoot twice. Or it could be triggered too early. This may be affected by consent settings etc.
This is why validation should not be seen as a small final technical task.
The goal is not perfect data. Perfect data rarely exists. The goal is to have data that is clear and reliable enough to support decisions.
The tool comes after reflection
I still find GA4’s capabilities to be genuinely useful. However, the process must begin with defining success and the business questions that need to be answered. When teams determine these first, the analytics setup becomes much more focused.
Events have a purpose, dashboards serve a specific function, and explaining reports becomes easier.
If you skip these first steps, GA4 transforms into an ambiguity repository. This is why the measurement framework comes first.
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