How to calculate the Conversion Rate for Ecommerce in GA4

As many of you may have noticed, the Ecommerce Conversion Rate metric no longer appears in Google Analytics and we miss it in our GA4, Looker Studio... 

reports.

An alternative metric would be Session Key Event Rate, which measures the number of sessions in which at least one conversion (configured as an event) occurs among the total number of sessions. But if we do a scan in Google Analytics 4 to see the conversion of purchases and we add the Purchases, Sessions and Session key event rate metrics, it will not show us the conversion of the Purchase event, in principle it will give the conversion of all the events considered Key events. For the conversion of the Purchase event, we should add the following filter:

Image 1-1

The actual calculation of the conversion of the Purchase event will not show us the conversion of the Purchase event.

The actual conversion calculation should be the following: Key events/Sessions=8/1.712=0.47%.

Although as we can see sometimes this calculation fails:

Image 2-1

In addition, we have observed strange behaviors in the figures in some properties in which the % is very high or when we add dimensions such as the Session Default channel group in which the figures differ from the real ones.

In short, we need another solution to replace our beloved Ecommerce Conversion Rate. And luckily, this solution is simple, fast and effective.

We simply need to create a calculated metric following these steps:

1.  Go to Admin.

1.

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2.  Select Custom definitions inside the Data display.

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3.  And then go to the Calculated metrics section and select Create calculated metric.

 

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4.  Now we can create the metric with the characteristics we want in the following drop-down.

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5.  In the name section, we can title the metric, for example: ecommerce cr.The API name section will be filled automatically, but we can modify it manually if we wish.  We can also write a description, although it is not a mandatory field.

In the formula section, we must select the event {E-commerce purchases} and divide it by sessions and then multiply it by one hundred: 

{{E-commerce purchases} / {Sessions}*100
And the unit of measurement to select will be Standard.

 

Image 7-1

 

6.  Finally, we save this metric and it will already appear in the platform and retroactively. To check it, we will do the same scan again as we did before, but with the metric created instead of the Session key event rate and without the need to add the filter. 


Image 8-1

Now, yes, the calculation with our calculated metric is correct: 0.47%

As we can see, the only small drawback is that we can't show that it is a percentage, but surely sooner or later we will be able to solve this unknown. So even though GA4 has removed the "Ecommerce Conversion Rate" metric, the solution using calculated metrics offers a quick and accurate alternative. With this simple adjustment, you can continue to measure the conversion of your transactions and effectively optimize your business decisions.

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Working with large volumes of marketing data—whether it’s web traffic, keywords, users, or campaigns—can feel overwhelming. These data sets often aren’t organized or categorized in a useful way, and facing them can feel like trying to understand a conversation in an unfamiliar language.

But what if you could automatically discover patterns and create data groups—without manual rules, endless scripts, or leaving your BigQuery analysis environment?

That’s exactly what K-Means with BigQuery ML allows you to do.

What is K-Means and why should you care?

K-Means is a clustering algorithm—a technique for grouping similar items. Imagine you have a table with thousands of URLs, users, or products. Instead of going through each one manually, K-Means can automatically find groups with common patterns: pages with similar performance, campaigns with similar outcomes, or users with shared behaviors.

And the best part? With BigQuery ML, you can apply K-Means using plain SQL—no need for Python scripts or external tools.

How does it actually work?

The process behind K-Means is surprisingly simple:

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  2. The algorithm picks initial points called centroids.

  3. Each row in your data is assigned to the nearest centroid.

  4. The centroids are recalculated using the assigned data.

  5. This process repeats until the groups stabilize.

The result? Every row in your table is tagged with the cluster it belongs to. Now you can analyze the patterns of each group and make better-informed decisions.

How to apply it in BigQuery ML

BigQuery ML simplifies the entire process. With just a few lines of SQL, you can:

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How many clusters do I need?

Choosing the right number of clusters (“K”) is critical. Here are a few strategies:

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Real-world examples

With K-Means in BigQuery, you can answer questions like:

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Grouping data this way not only saves time—it reveals opportunities and issues that might otherwise go unnoticed.

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If you're handling large data sets and need to identify patterns fast, clustering with K-Means and BigQuery ML can be a game-changer. You don’t need to be a data scientist or build complex solutions from scratch. You just need to understand your business and ask the right questions—BigQuery can handle the rest.

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