By scanning all the keywords, we gather data from SERPs for all keywords, compare their positions, and group them according to the similarity of their results - Clustering Level that can be set up by users (from 3 to 7 similar keywords). 

Note: Keyword credits are required to run this feature.

You can choose two options: Cluster All or Cluster Selected

We suggest you should go with the filtering first to get a decent & low-hanging fruit keyword list before clustering, which can help save keyword credits as well as strengthen your keyword strategy. For further reference about the clustering step in our WriterZen recommended workflow, check out this tutorial

Clustering generally requires more computing power, so you can take a break and enjoy your coffee while the data is being processed. The report table will pop up in the form of a project in Keyword Importer.

Here are the results showing up:

There are 4 main pieces of information you need to notice: 

- Total Keywords: The number of keywords is clustered in this project.

- Cluster Level: The number of similar results ranking in the top 10 of SERPS placement, based on your earlier setup.

- Clustered Groups: This shows you how many clustered groups there are in the results.

- Non-in-any-Cluster keywords: This shows you the number of single keywords which are not in any clusters.

Let us explain the file you will get after exporting:

Once you export the file in the cluster function, you will be able to see 3 tabs as above:

- Project Information: This tab will show all detailed information related to the project, with the data of 4 main pieces of information explained above.

- Clustering Keywords: This tab will show all the keyword cluster groups in detail. 

- Non-in-any-Cluster keywords: These keywords for which no cluster exists (which do not belong to any cluster group).

For more information about how to use clustering functions in WriterZen, please review this blog post: 

Topic Cluster & Use cases in Content Strategy: 

Content strategy: how to create topic clusters and use cases