Fortunately, keyword research is an area that we can automate much of to make our work easier and data-driven.
For example, Python can help us determine which keywords match each other so that we can cluster them. I’ll explain exactly how this works and at the end of the article. I’ll give you the code in a Google Colab so you can use it yourself.
Keyword clustering, a major time killer
After drawing up a longlist, the largest part of keyword research Turkey Phone Number into play: merging these keywords to ensure that it is clear which keywords you can combine to build the most complete page possible. But clustering also remains an essential part of preventing cannibalism.
You can hardly get it right
However, manually going through this list takes a lot of time, and you are often forced to do this based on your own expertise. End result: keywords clustered based on your own expertise,
without knowing whether this really matches people’s search behavior in search engines.
If you do check the latter, you will have your client or manager on your roof, because it took an enormous amount of time.
Other colleagues and I have encountered this problem several times in practice. This got me thinking. What if you can determine on the basis of a database which keywords do or do not belong together and combine this in a bulk?
based on SERP similarity
The similarity of the search results
page gives a strong signal when two keywords have Belgium Phone Number same search intent.
Google’s algorithm has been developed in such a way that when users search for the keyword ‘bicycle’, they are shown a search results page with products and we can conclude that there is a purchasing-oriented search intention behind this.
