You've automatically created homogeneous groups .

[jahid12]

So, all this system does when creating the model is canada phone number list define the average metric values ​​for each centroid. These centroids will be the core of each cluster . From your data, the distance to all centroids will be calculated (for each row of data in your table), so the closest centroid belongs to the cluster.

In the end, what you get is a label for each of your data indicating which cluster the data belongs to (the closest centroid).

Magic!

And what purpose does all this serve in our day-to-day marketing and analytics? Well, for a lot of things. I've already told you some of them, but let's continue...

Create audiences of users with similar behavior : Grouping them by their usage data, revisits, goals met, etc.
Identify high/low performing pages : by taking performance metrics in the discipline you are researching (in SEO you could look at impressions, clicks, and conversions)
Group products by interest and purchasing patterns ,
Detect anomalies in any metrics system you measure…
–
In short, K-means clusters are used to simplify the complexity of large volumes of data (GA, GSC, Purchasing, stocks, any large database) and find hidden patterns that help us better understand what is happening.

The First Challenge: How Many Clusters Should You Create? (Deciding on the 'K')
Here comes one of the crucial (and sometimes somewhat subjective) parts of K-Means: choosing the number of clusters, the famous 'K'. The algorithm needs you to tell it how many groups you want to form. But what's the right number? Two? Five? Twenty?

Choosing the right 'K' is crucial, as it directly affects the quality and usefulness of your results:

If you choose a 'K' that's too low: You risk creating very large and heterogeneous clusters. It's like making only two piles in a Lego box: "red pieces" and "non-red pieces." Within "non-red pieces," you'd have everything (blue, yellow, wheels, shapes, etc.), and that group wouldn't give you much useful information. In your data, you could be mixing pages with very different performance in the same cluster