When figuring out if ranking drops result from a change on Google’s side or your own, it’s helpful to know if anything changed on the page that lost rankings.
Sometimes, these changes aren’t well documented, especially if multiple stakeholders can edit pages. Or there may not be a changelog system that enables you to find what was changed. It may even be that the changelog system doesn’t show the complete picture and doesn’t include broader changes that impact internal linking or alterations within the HTML head or anything outside the main content area. Although I think it’s best to work towards creating an internal system that accounts for all of this, it’s not feasible for every organization or SEO professional. What do you do?
ChatGPT and generative AI has changed the way we do many things, and SEO is no exception.
With OpenAI’s recent announcement, individuals and businesses now have the opportunity to create their own custom versions of ChatGPT, known as “GPTs”, through a user-friendly GPT builder.
This customization allows for a more targeted application of AI, catering to specific needs and preferences, permitting the inclusion of PDFs and other knowledge stores to create a RAG-like experience.
And with the inclusion of “Actions” within these GPTs, we can augment the output of them to be even more useful for SEO purposes—including real data without the need for a custom langchain Python application, and avoiding hallucinated data in the output (previously a real flaw with using ChatGPT for SEO purposes).
I had the opportunity to present at MozCon this year.
My presentation, on the topic of “redefining technical SEO” was littered with various Python scripts. In an attempt to keep them all in one place, I’ve embedded them into the following post.
I will update this later with greater explanations about what each one does, and how you might use them.
In this day in age, you can’t find a modern SEO guide that doesn’t recommend using the schema as part of your overall search strategy, and for a good reason. Schema markup, which has been supported by all the major search engines (Google, Bing, Yahoo, DuckDuckGo, etc.) for the better half of seven years now, has abysmal adoption rates. Seen across all ends of the spectrum, from intimidating to superfluous, most of the time talking with the SEO community its use is simply avoided due to the time it takes to implement. Like writing meta titles/descriptions, schema markup data is a very contextual, backend aspect of SEO which isn’t an inheritance ranking factor. So, the returns of adopting schema are outweighed by the time it takes to implement across hundreds to thousands of URLs. But, what if there was a way to automate the process?
Do you know the pain of tagging keywords? Maybe you delegate that task to the intern and try to forget it.
Either way, keyword tagging can be a long and tiresome process.
Before we get into how to hack the process, let’s address the question: “Why do we even need to tag our keywords?”