Dashboard's in Looker Studio interactive on your website

As many of you may know, Looker Studio is a tool that allows you to create Dashboard's to visualize data from different sources.

In our LinkedIn and Blog you will find different tricks, updates and features about the platform and today we bring you a couple of somewhat special Looker tricks.

Link a url in an image

Looker has a feature that allows us to link to a url when we click on an image. And to set it up, we simply need to follow these 3 steps:

1- Inside a Dashboard, Go to "Insert/Insert" and at the bottom select "Image/Image."

1-Image

2- Select where we want to paste the image, although later we can move and/or resize it. And select the image we want to insert in "Select a file/Selecciona un archivo."

 

 

2-Select a file

3- And finally, we go to "Insert link/Insert link" and add the url we want:


3-Insert link

4

As shown in the screenshot, we can choose whether we want the link to open in a new tab or in the current tab.

And there is also the option to open the link in a new tab.

In addition, there is also the possibility that instead of selecting a url, we can select a Page from the Dashboard itself, clicking on the hole we have to fill the link and selecting the page we want to direct.

5

Link the url's of a table

Another way for users to access the web from our Dashboard's is by creating a new field as follows:

1-We will create a table with dimension "Landing page" and the metric we want, in this case we will use "Views". 

67

2- As our goal is to be able to access the landings of the created table, we will create a new field in "Add dimension/Add dimension" and select "Add field/Add field".

8 add dimension 9- add field

3- Once there we will write the name of the field we want and add the following formula: HYPERLINK(CONCAT(Hostname,Landing page),Landing page). We'll hit "Apply" and we'll have our field created.

10-formula

 

4- As we can see, to the right of the initial dimension we now have the same dimension of "Landing page" but with the possibility to redirect to the landings of our website. 

11

5-If we want, we can remove the initial dimension and thus only have the new field. In fact, it is not necessary to include from the beginning that first dimension and only creating a new field with the formula will already serve us.

 

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And so far, this is the first step of the article.

And this is the end of today's article, thank you very much for reading our blog and we hope you found it useful.

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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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