Keyword Clustering

What is Keyword Clustering used for?

NeuralText Keyword Clustering tool is a grouping tool that does the dirty job for you, speeding up the keyword research phase.

This tool analyses SERP for each keyword you provide, and is able to cluster together keywords based on the semantic similarity and ranked URLs.

As a result, you will obtain an output file with main keyword and variations of this keyword, from which you can create just one article to rank them all. This algorithm is based on the strong assumption that similar keywords that produce the same ranking for your competitors are more likely to be ranked with just one article.

The purpose of keyword grouping tool is to generate clusters of keywords that are more likely to rank together with just one article.

Example: if you submit 3000 keywords, and you obtain, let's say, 150 groups (clusters) of keywords, then you are supposed to write 150 articles, with each article corresponding to a cluster, and in every article you should use all the keywords of the cluster in order to increase the likelihood of that one single article to be ranked for all the keywords corresponding to its cluster. Every cluster has a main keyword, which is the keyword with the highest volume amongst the keywords that are present in the cluster.

So, overall, the keyword clustering tool helps you making an editorial plan.

Create a pivot table from Cluster Report

Google Sheets

1. Download your cluster report

Go to "Cluster Reports" from the left menu
Download the report

2. Import cluster report in Google Sheets

3. Create a pivot table

4. Shape your pivot table

Use the Pivot Table editor to group the terms using the main keyword.

Set the pivot using the rows and the values as in the image below.

5. Use the report

Once you have the pivot, you can start outlining your next article.

Use the keyword in the variations column as section headings or as synonyms for the main keyword.

Tip: You don't need to include all the variations in your article! Use the common sense and don't do keyword stuffing.