Keyword research for the sake of keyword research is not useful. What you get is a spreadsheet with 1000’s of rows of words.
What keyword research needs to do is tell a story and give narrative. You need to be able to understand the search landscape for a vertical with actionable insights in totality that’s easily digestible for humans.
In order to do to this you need to be able to quantify all those queries into digestible clusters or categories.
This is also useful for reporting for when key stakeholders ask how rankings are going. You can pull out keyword categories which create great anecdotes for the business.
You do not have to manually tag each keyword, with Python, machine learning or software can do this automatically.
Here’s various resource on how to do this:
- JR Oakes – Using the Apriori algorithm and BERT embeddings to visualize change in search console rankings
- Andy Chadwick – How to use machine learning (if you can’t code) to help your keyword research
- Andreas Voniatis – How To Automate SEO Keyword Clustering By Search Intent With Python
- Lee Foot – Python Script: Automatically Cluster Keywords In Bulk For Actionable Insights V2
- Andrea Volpini – Keyword Clustering for SEO using Embeddings
- Charlie Jackson – Categorising keywords at scale using Python,Pandas and NLTK
Let me know how you approach keyword research in the comments 👇🏻
