Problem
How do we optimize title tags? The traditional approach of optimizing title tags relies on traditional keyword research – essentially firing up a keyword research tools like SEMRush or Ahrefs finding a target keyword with a reasonable volume and sticking in the title tag, H1, meta description, and calling it a day.
How do we make title tags that are more aligned with the language (words) that users search with?
Hypothesis
My thinking is that users are more likely to click on title tags if they feature the words they are using in their searches. By using GSC/Bing query data, we can see the words people are using when they search for our content. By including those words in our title tags, we will see higher CTR to our content.
Caveats
Google is more frequently rewriting title tags to better match what (they believe) users are looking for. It may be that even with our updated title tag, they choose to rewrite it.
Proposed MVP Process
- Break down search queries into unigrams.
- Identify highly-searched for relevant unigrams that are not included in your title tag. This could be queries with high impressions, low CTR etc.
- Add those words to the title tag in a meaningful, non-keyword stuffy way.
Though this is probably a process that could be handled with a nifty Python script, my coding skills mean this would take me too long to figure out. I’m working on an MVP in PowerBI for how this could work.
Measuring
It will be important to benchmark the current clicks or click-through rate and analyze the changes – is this an approach that works and has a discernible impact on user behavior in search results?
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