Step-by-step guide: how to create a funnel in Looker Studio.

Funnel visualizations show, sequentially, the user journey towards conversion.

They help identify friction points, measure and compare performance, and facilitate the understanding of user behavior, leading to better data-driven decision-making.

Before starting, the first step is to identify which events we want to measure, as the setup will differ if the conversion is filling out a form or making a purchase.

Step 1 - Connect GA4 to Looker Studio

The first step is to set up the data source connection. In this case, we will connect Looker Studio with GA4. If you have already set up the connection correctly, you can skip to step 2. If not, you can follow the steps described in this article.

Step 2 - Create the Custom Dimension

Next, we will create a custom dimension to rename the events, where we will use the ones we defined earlier. In this case, we want to visualize the following: begin_checkout, add_shipping_info, add_payment_info, and purchase.

To create the custom dimension, go to "Manage Data Sources" and enter the edit section. Once inside, click on "Add a Field," and define the custom dimension in the following format:

 

Step 3 - Create the Filter to Include Only the Relevant Events

The filter we will create next allows us to include only the events we want to display in the chart.

We will use the following regular expression to ensure that only the events begin_checkout, add_shipping_info, add_payment_info, and purchase are displayed:

^begin_checkout$|^add_shipping_info$|^add_payment_info$|^purchase$

Step 4 - Import the Funnel Visualization

Once everything is set up, it's time to import the funnel visualization.

To do this, go to the top menu and select the "Community Visualizations" option, located to the right of "Add a Chart."

Captura de pantalla 2023-11-06 a les 15.22.57

Once inside "Build Your Own Visualization," scroll down and enter the following link: "gs://true-metrics-funnelgraph/graphviz."

Captura de pantalla 2023-11-06 a les 15.23.37

Select the only option that appears and accept the permissions. Once done, you can place the visualization in your report and proceed to the final step.

 

Step 5 - Configure and Customize the Funnel

Finally, it's time to configure the funnel to visualize the events we've chosen. The steps to follow are as follows:

  1. The first step is to select the custom dimension we created in step 2.
  2. Next, select the metric we want to analyze, such as total users.
  3. Now, add the filter to include only the events we want to analyze.
  4. The last step is to customize the funnel to your liking.

Remember, depending on the journey and conversion goal, you can adjust the funnel configuration according to your needs.

That’s all for today. If you liked this article, don’t forget to visit our blog, where you’ll find more related articles.

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Group your data like a pro: clustering with K-Means and BigQuery ML

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:

  1. You choose how many groups you want (the well-known “K”).

  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:

  • Train a K-Means model on your data

  • Retrieve the generated centroids

  • Classify each row with its corresponding cluster

This opens up a wide range of possibilities to enrich your dashboards and marketing analysis:

  • Group pages by performance (visits, conversions, revenue)

  • Detect behaviors of returning, new, or inactive users

  • Identify products often bought together or with similar buyer profiles

  • Spot keywords with unusual performance

How many clusters do I need?

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

  • Business knowledge: If you already know you have 3 customer types or 4 product categories, start there.

  • Elbow Method: Run models with different K values and watch for the point where segmentation no longer improves significantly.

  • Iterate thoughtfully: Test, review, and adjust based on how your data behaves.

Real-world examples

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

  • What types of users visit my site, and how do they differ?

  • Which pages show similar performance trends?

  • Which campaigns are generating outlier results?

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

Conclusion

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.

Start simple: take your top-performing pages, group them by sessions and conversions, and see what patterns emerge. You might uncover insights that completely shift how you approach your digital strategy.

 

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