Why should I connect the RRSS to Marfeel?

Tips to take advantage of this functionality

Social networks have become one of the main sources of information. More and more users are consuming content through Facebook, Twitter (X), Instagram, or even YouTube. These platforms facilitate access to information, and make its consumption quicker and more immediate.

Analyzing the traffic coming from these platforms is a must for many digital newspapers, and Marfeel facilitates this task thanks to its multiple integrations.

Next we will see some of the most relevant functionalities:

Content Filtering in Marfeel Compass


One of the first advantages of connecting social networks with Marfeel is the ability to filter and segment traffic coming from these platforms. Through Marfeel Compass, users can customize filters to monitor social traffic.

For example, it is possible to add a "Social network" filter that allows visualizing and analyzing traffic from specific networks such as TikTok, Twitter or Facebook, making it easier to understand how these contribute to the overall site performance.

Filters can be added to the Marfeel Compass.


 

Impact of Publications


Another interesting functionality is the impact of publications in different social networks on a given news or article. Marfeel offers a very visual analysis when we select a publication from Compass.

Impact of Publications.






 



 

When we open the publication, the icons of the different social networks appear above the time series. This way we see quickly and easily which posts bring more traffic to the article, which networks generate more volume and the impact over time.

New Metrics and Dimensions in Marfeel Insights


The connection to social networks introduces new metrics and dimensions in Marfeel Insights. Elements such as "social post text" allow cross-referencing information about published content with social interaction metrics, such as comments and shares.

Another very noteworthy functionality is the ability to analyze content performance according to content-related dimensions, such as sections, subsections or authors.


Configuring the report as seen above, we get a table like the one shown below.


 

This table allows us to quickly visualize which authors, type of content, sections, etc. generate more comments, interactions or likes regarding the publications that are made. This way we see what type of content is performing better in each of the social networks.

Another interesting dimension is that the content is performing better in each social network.

Another interesting dimension is social post text, which allows us to see the content of the post directly on the Marfeel interface.

Content Planning and Scheduling


The last functionality we are going to see today is the planning and publication of content in RRSS through the social planner.

In the side menu we access the planner section. Once inside we will see a summary of all posts, both those published and those that are scheduled.



Social planner.


By clicking the button at the top right we can create a post and share it instantly or schedule it for later. By default, when posting content instantly, there is a 1 minute window to edit or delete the content.

Currently, it allows you to schedule content on Twitter, Facebook and Pinterest, but other networks are expected to be added soon.

This is the latest news from Marfeel in the field of social networks. If you found this post useful and you want to keep up to date with the latest news about this tool, do not hesitate to subscribe and follow us.

 

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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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